diff --git a/neuralcvd/preprocessing/ukbb_tabular/0_decode_ukbb.ipynb b/neuralcvd/preprocessing/ukbb_tabular/0_decode_ukbb.ipynb
deleted file mode 100644
index 340b5c3..0000000
--- a/neuralcvd/preprocessing/ukbb_tabular/0_decode_ukbb.ipynb
+++ /dev/null
@@ -1,57 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "library(ukbtools)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "my_ukb_data <- ukb_df(\"decoded\", path = \"/data/project/uk_bb/cvd/data/ukb_downloads/updated_showcase_43098/ukb_data\")\n",
-    "df_field <- ukb_df_field(\"decoded\", path = \"/data/project/uk_bb/cvd/data/ukb_downloads/updated_showcase_43098/ukb_data\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "arrow::write_feather(my_ukb_data, \"/data/analysis/ag-reils/ag-reils-shared/cardioRS/data/1_decoded/ukb_data.feather\")\n",
-    "arrow::write_feather(df_field, \"/data/analysis/ag-reils/ag-reils-shared/cardioRS/data/1_decoded/ukb_data_field.feather\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "R [conda env:python]",
-   "language": "R",
-   "name": "conda-env-python-r"
-  },
-  "language_info": {
-   "codemirror_mode": "r",
-   "file_extension": ".r",
-   "mimetype": "text/x-r-source",
-   "name": "R",
-   "pygments_lexer": "r",
-   "version": "4.0.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/neuralcvd/preprocessing/ukbb_tabular/1_dataportal_exploration.ipynb b/neuralcvd/preprocessing/ukbb_tabular/1_dataportal_exploration.ipynb
deleted file mode 100644
index b048dc7..0000000
--- a/neuralcvd/preprocessing/ukbb_tabular/1_dataportal_exploration.ipynb
+++ /dev/null
@@ -1,6492 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Preprocessing"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "-- \u001b[1mAttaching packages\u001b[22m --------------------------------------- tidyverse 1.3.0 --\n",
-      "\n",
-      "\u001b[32mv\u001b[39m \u001b[34mggplot2\u001b[39m 3.3.2     \u001b[32mv\u001b[39m \u001b[34mpurrr  \u001b[39m 0.3.4\n",
-      "\u001b[32mv\u001b[39m \u001b[34mtibble \u001b[39m 3.0.3     \u001b[32mv\u001b[39m \u001b[34mdplyr  \u001b[39m 1.0.2\n",
-      "\u001b[32mv\u001b[39m \u001b[34mtidyr  \u001b[39m 1.1.2     \u001b[32mv\u001b[39m \u001b[34mstringr\u001b[39m 1.4.0\n",
-      "\u001b[32mv\u001b[39m \u001b[34mreadr  \u001b[39m 1.3.1     \u001b[32mv\u001b[39m \u001b[34mforcats\u001b[39m 0.5.0\n",
-      "\n",
-      "-- \u001b[1mConflicts\u001b[22m ------------------------------------------ tidyverse_conflicts() --\n",
-      "\u001b[31mx\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mfilter()\u001b[39m masks \u001b[34mstats\u001b[39m::filter()\n",
-      "\u001b[31mx\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mlag()\u001b[39m    masks \u001b[34mstats\u001b[39m::lag()\n",
-      "\n",
-      "\n",
-      "Attaching package: 'glue'\n",
-      "\n",
-      "\n",
-      "The following object is masked from 'package:dplyr':\n",
-      "\n",
-      "    collapse\n",
-      "\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "library(tidyverse)\n",
-    "library(glue)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 108,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "base_size = 25\n",
-    "title_size = 35\n",
-    "facet_size = 15\n",
-    "geom_text_size=7\n",
-    "library(ggplot2); \n",
-    "theme_set(theme_classic(base_size = base_size) + \n",
-    "          theme(strip.background = element_blank(), plot.title=element_text(size=title_size, hjust=0.5), \n",
-    "                #axis.title.y = element_blank(), axis.text.y = element_blank(), axis.ticks.y = element_blank(), axis.line.y = element_blank(), \n",
-    "                strip.text.x = element_text(size = facet_size), axis.line = element_line(size = 0.2), axis.ticks=element_line(size=0.2))) "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:31:49.436340Z",
-     "start_time": "2020-11-04T12:31:48.732042Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "dataset_name = \"210212_cvd_gp\"\n",
-    "path = \"/data/analysis/ag-reils/steinfej/code/umbrella/pre/ukbb\"\n",
-    "data_path = \"/data/analysis/ag-reils/ag-reils-shared/cardioRS/data\"\n",
-    "dataset_path = glue(\"{data_path}/2_datasets_pre/{dataset_name}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 133,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Joining, by = \"eid\"\n",
-      "\n",
-      "Joining, by = \"eid\"\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "basics = arrow::read_feather(glue(\"{dataset_path}/temp_basics.feather\"))\n",
-    "measurements = arrow::read_feather(glue(\"{dataset_path}/temp_measurements.feather\"))\n",
-    "labs = arrow::read_feather(glue(\"{dataset_path}/temp_labs.feather\"))\n",
-    "data = basics %>% left_join(measurements, on=\"eid\") %>% left_join(labs, on=\"eid\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 151,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style>\n",
-       ".list-inline {list-style: none; margin:0; padding: 0}\n",
-       ".list-inline>li {display: inline-block}\n",
-       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
-       "</style>\n",
-       "<ol class=list-inline><li>'eid'</li><li>'age_at_recruitment_f21022_0_0'</li><li>'sex_f31_0_0'</li><li>'ethnic_background_f21000_0_0'</li><li>'townsend_deprivation_index_at_recruitment_f189_0_0'</li><li>'date_of_attending_assessment_centre_f53_0_0'</li><li>'uk_biobank_assessment_centre_f54_0_0'</li><li>'birth_date'</li><li>'body_mass_index_bmi_f21001_0_0'</li><li>'weight_f21002_0_0'</li><li>'pulse_wave_arterial_stiffness_index_f21021_0_0'</li><li>'pulse_wave_reflection_index_f4195_0_0'</li><li>'waist_circumference_f48_0_0'</li><li>'hip_circumference_f49_0_0'</li><li>'standing_height_f50_0_0'</li><li>'trunk_fat_percentage_f23127_0_0'</li><li>'body_fat_percentage_f23099_0_0'</li><li>'basal_metabolic_rate_f23105_0_0'</li><li>'forced_vital_capacity_fvc_best_measure_f20151_0_0'</li><li>'forced_expiratory_volume_in_1second_fev1_best_measure_f20150_0_0'</li><li>'fev1_fvc_ratio_zscore_f20258_0_0'</li><li>'peak_expiratory_flow_pef_f3064_0_2'</li><li>'peak_expiratory_flow_pef_f3064_0_1'</li><li>'peak_expiratory_flow_pef_f3064_0_0'</li><li>'systolic_blood_pressure_automated_reading_f4080'</li><li>'diastolic_blood_pressure_automated_reading_f4079'</li><li>'pulse_rate_automated_reading_f102'</li><li>'basophill_count_f30160_0_0'</li><li>'basophill_percentage_f30220_0_0'</li><li>'eosinophill_count_f30150_0_0'</li><li>'eosinophill_percentage_f30210_0_0'</li><li>'haematocrit_percentage_f30030_0_0'</li><li>'haemoglobin_concentration_f30020_0_0'</li><li>'high_light_scatter_reticulocyte_count_f30300_0_0'</li><li>'high_light_scatter_reticulocyte_percentage_f30290_0_0'</li><li>'immature_reticulocyte_fraction_f30280_0_0'</li><li>'lymphocyte_count_f30120_0_0'</li><li>'lymphocyte_percentage_f30180_0_0'</li><li>'mean_corpuscular_haemoglobin_f30050_0_0'</li><li>'mean_corpuscular_haemoglobin_concentration_f30060_0_0'</li><li>'mean_corpuscular_volume_f30040_0_0'</li><li>'mean_platelet_thrombocyte_volume_f30100_0_0'</li><li>'mean_reticulocyte_volume_f30260_0_0'</li><li>'mean_sphered_cell_volume_f30270_0_0'</li><li>'monocyte_count_f30130_0_0'</li><li>'monocyte_percentage_f30190_0_0'</li><li>'neutrophill_count_f30140_0_0'</li><li>'neutrophill_percentage_f30200_0_0'</li><li>'nucleated_red_blood_cell_count_f30170_0_0'</li><li>'nucleated_red_blood_cell_percentage_f30230_0_0'</li><li>'platelet_count_f30080_0_0'</li><li>'platelet_crit_f30090_0_0'</li><li>'platelet_distribution_width_f30110_0_0'</li><li>'red_blood_cell_erythrocyte_count_f30010_0_0'</li><li>'red_blood_cell_erythrocyte_distribution_width_f30070_0_0'</li><li>'reticulocyte_count_f30250_0_0'</li><li>'reticulocyte_percentage_f30240_0_0'</li><li>'white_blood_cell_leukocyte_count_f30000_0_0'</li><li>'alanine_aminotransferase_f30620_0_0'</li><li>'albumin_f30600_0_0'</li><li>'alkaline_phosphatase_f30610_0_0'</li><li>'apolipoprotein_a_f30630_0_0'</li><li>'apolipoprotein_b_f30640_0_0'</li><li>'aspartate_aminotransferase_f30650_0_0'</li><li>'creactive_protein_f30710_0_0'</li><li>'calcium_f30680_0_0'</li><li>'cholesterol_f30690_0_0'</li><li>'creatinine_f30700_0_0'</li><li>'cystatin_c_f30720_0_0'</li><li>'direct_bilirubin_f30660_0_0'</li><li>'gamma_glutamyltransferase_f30730_0_0'</li><li>'glucose_f30740_0_0'</li><li>'glycated_haemoglobin_hba1c_f30750_0_0'</li><li>'hdl_cholesterol_f30760_0_0'</li><li>'igf1_f30770_0_0'</li><li>'ldl_direct_f30780_0_0'</li><li>'lipoprotein_a_f30790_0_0'</li><li>'oestradiol_f30800_0_0'</li><li>'phosphate_f30810_0_0'</li><li>'rheumatoid_factor_f30820_0_0'</li><li>'shbg_f30830_0_0'</li><li>'testosterone_f30850_0_0'</li><li>'total_bilirubin_f30840_0_0'</li><li>'total_protein_f30860_0_0'</li><li>'triglycerides_f30870_0_0'</li><li>'urate_f30880_0_0'</li><li>'urea_f30670_0_0'</li><li>'vitamin_d_f30890_0_0'</li></ol>\n"
-      ],
-      "text/latex": [
-       "\\begin{enumerate*}\n",
-       "\\item 'eid'\n",
-       "\\item 'age\\_at\\_recruitment\\_f21022\\_0\\_0'\n",
-       "\\item 'sex\\_f31\\_0\\_0'\n",
-       "\\item 'ethnic\\_background\\_f21000\\_0\\_0'\n",
-       "\\item 'townsend\\_deprivation\\_index\\_at\\_recruitment\\_f189\\_0\\_0'\n",
-       "\\item 'date\\_of\\_attending\\_assessment\\_centre\\_f53\\_0\\_0'\n",
-       "\\item 'uk\\_biobank\\_assessment\\_centre\\_f54\\_0\\_0'\n",
-       "\\item 'birth\\_date'\n",
-       "\\item 'body\\_mass\\_index\\_bmi\\_f21001\\_0\\_0'\n",
-       "\\item 'weight\\_f21002\\_0\\_0'\n",
-       "\\item 'pulse\\_wave\\_arterial\\_stiffness\\_index\\_f21021\\_0\\_0'\n",
-       "\\item 'pulse\\_wave\\_reflection\\_index\\_f4195\\_0\\_0'\n",
-       "\\item 'waist\\_circumference\\_f48\\_0\\_0'\n",
-       "\\item 'hip\\_circumference\\_f49\\_0\\_0'\n",
-       "\\item 'standing\\_height\\_f50\\_0\\_0'\n",
-       "\\item 'trunk\\_fat\\_percentage\\_f23127\\_0\\_0'\n",
-       "\\item 'body\\_fat\\_percentage\\_f23099\\_0\\_0'\n",
-       "\\item 'basal\\_metabolic\\_rate\\_f23105\\_0\\_0'\n",
-       "\\item 'forced\\_vital\\_capacity\\_fvc\\_best\\_measure\\_f20151\\_0\\_0'\n",
-       "\\item 'forced\\_expiratory\\_volume\\_in\\_1second\\_fev1\\_best\\_measure\\_f20150\\_0\\_0'\n",
-       "\\item 'fev1\\_fvc\\_ratio\\_zscore\\_f20258\\_0\\_0'\n",
-       "\\item 'peak\\_expiratory\\_flow\\_pef\\_f3064\\_0\\_2'\n",
-       "\\item 'peak\\_expiratory\\_flow\\_pef\\_f3064\\_0\\_1'\n",
-       "\\item 'peak\\_expiratory\\_flow\\_pef\\_f3064\\_0\\_0'\n",
-       "\\item 'systolic\\_blood\\_pressure\\_automated\\_reading\\_f4080'\n",
-       "\\item 'diastolic\\_blood\\_pressure\\_automated\\_reading\\_f4079'\n",
-       "\\item 'pulse\\_rate\\_automated\\_reading\\_f102'\n",
-       "\\item 'basophill\\_count\\_f30160\\_0\\_0'\n",
-       "\\item 'basophill\\_percentage\\_f30220\\_0\\_0'\n",
-       "\\item 'eosinophill\\_count\\_f30150\\_0\\_0'\n",
-       "\\item 'eosinophill\\_percentage\\_f30210\\_0\\_0'\n",
-       "\\item 'haematocrit\\_percentage\\_f30030\\_0\\_0'\n",
-       "\\item 'haemoglobin\\_concentration\\_f30020\\_0\\_0'\n",
-       "\\item 'high\\_light\\_scatter\\_reticulocyte\\_count\\_f30300\\_0\\_0'\n",
-       "\\item 'high\\_light\\_scatter\\_reticulocyte\\_percentage\\_f30290\\_0\\_0'\n",
-       "\\item 'immature\\_reticulocyte\\_fraction\\_f30280\\_0\\_0'\n",
-       "\\item 'lymphocyte\\_count\\_f30120\\_0\\_0'\n",
-       "\\item 'lymphocyte\\_percentage\\_f30180\\_0\\_0'\n",
-       "\\item 'mean\\_corpuscular\\_haemoglobin\\_f30050\\_0\\_0'\n",
-       "\\item 'mean\\_corpuscular\\_haemoglobin\\_concentration\\_f30060\\_0\\_0'\n",
-       "\\item 'mean\\_corpuscular\\_volume\\_f30040\\_0\\_0'\n",
-       "\\item 'mean\\_platelet\\_thrombocyte\\_volume\\_f30100\\_0\\_0'\n",
-       "\\item 'mean\\_reticulocyte\\_volume\\_f30260\\_0\\_0'\n",
-       "\\item 'mean\\_sphered\\_cell\\_volume\\_f30270\\_0\\_0'\n",
-       "\\item 'monocyte\\_count\\_f30130\\_0\\_0'\n",
-       "\\item 'monocyte\\_percentage\\_f30190\\_0\\_0'\n",
-       "\\item 'neutrophill\\_count\\_f30140\\_0\\_0'\n",
-       "\\item 'neutrophill\\_percentage\\_f30200\\_0\\_0'\n",
-       "\\item 'nucleated\\_red\\_blood\\_cell\\_count\\_f30170\\_0\\_0'\n",
-       "\\item 'nucleated\\_red\\_blood\\_cell\\_percentage\\_f30230\\_0\\_0'\n",
-       "\\item 'platelet\\_count\\_f30080\\_0\\_0'\n",
-       "\\item 'platelet\\_crit\\_f30090\\_0\\_0'\n",
-       "\\item 'platelet\\_distribution\\_width\\_f30110\\_0\\_0'\n",
-       "\\item 'red\\_blood\\_cell\\_erythrocyte\\_count\\_f30010\\_0\\_0'\n",
-       "\\item 'red\\_blood\\_cell\\_erythrocyte\\_distribution\\_width\\_f30070\\_0\\_0'\n",
-       "\\item 'reticulocyte\\_count\\_f30250\\_0\\_0'\n",
-       "\\item 'reticulocyte\\_percentage\\_f30240\\_0\\_0'\n",
-       "\\item 'white\\_blood\\_cell\\_leukocyte\\_count\\_f30000\\_0\\_0'\n",
-       "\\item 'alanine\\_aminotransferase\\_f30620\\_0\\_0'\n",
-       "\\item 'albumin\\_f30600\\_0\\_0'\n",
-       "\\item 'alkaline\\_phosphatase\\_f30610\\_0\\_0'\n",
-       "\\item 'apolipoprotein\\_a\\_f30630\\_0\\_0'\n",
-       "\\item 'apolipoprotein\\_b\\_f30640\\_0\\_0'\n",
-       "\\item 'aspartate\\_aminotransferase\\_f30650\\_0\\_0'\n",
-       "\\item 'creactive\\_protein\\_f30710\\_0\\_0'\n",
-       "\\item 'calcium\\_f30680\\_0\\_0'\n",
-       "\\item 'cholesterol\\_f30690\\_0\\_0'\n",
-       "\\item 'creatinine\\_f30700\\_0\\_0'\n",
-       "\\item 'cystatin\\_c\\_f30720\\_0\\_0'\n",
-       "\\item 'direct\\_bilirubin\\_f30660\\_0\\_0'\n",
-       "\\item 'gamma\\_glutamyltransferase\\_f30730\\_0\\_0'\n",
-       "\\item 'glucose\\_f30740\\_0\\_0'\n",
-       "\\item 'glycated\\_haemoglobin\\_hba1c\\_f30750\\_0\\_0'\n",
-       "\\item 'hdl\\_cholesterol\\_f30760\\_0\\_0'\n",
-       "\\item 'igf1\\_f30770\\_0\\_0'\n",
-       "\\item 'ldl\\_direct\\_f30780\\_0\\_0'\n",
-       "\\item 'lipoprotein\\_a\\_f30790\\_0\\_0'\n",
-       "\\item 'oestradiol\\_f30800\\_0\\_0'\n",
-       "\\item 'phosphate\\_f30810\\_0\\_0'\n",
-       "\\item 'rheumatoid\\_factor\\_f30820\\_0\\_0'\n",
-       "\\item 'shbg\\_f30830\\_0\\_0'\n",
-       "\\item 'testosterone\\_f30850\\_0\\_0'\n",
-       "\\item 'total\\_bilirubin\\_f30840\\_0\\_0'\n",
-       "\\item 'total\\_protein\\_f30860\\_0\\_0'\n",
-       "\\item 'triglycerides\\_f30870\\_0\\_0'\n",
-       "\\item 'urate\\_f30880\\_0\\_0'\n",
-       "\\item 'urea\\_f30670\\_0\\_0'\n",
-       "\\item 'vitamin\\_d\\_f30890\\_0\\_0'\n",
-       "\\end{enumerate*}\n"
-      ],
-      "text/markdown": [
-       "1. 'eid'\n",
-       "2. 'age_at_recruitment_f21022_0_0'\n",
-       "3. 'sex_f31_0_0'\n",
-       "4. 'ethnic_background_f21000_0_0'\n",
-       "5. 'townsend_deprivation_index_at_recruitment_f189_0_0'\n",
-       "6. 'date_of_attending_assessment_centre_f53_0_0'\n",
-       "7. 'uk_biobank_assessment_centre_f54_0_0'\n",
-       "8. 'birth_date'\n",
-       "9. 'body_mass_index_bmi_f21001_0_0'\n",
-       "10. 'weight_f21002_0_0'\n",
-       "11. 'pulse_wave_arterial_stiffness_index_f21021_0_0'\n",
-       "12. 'pulse_wave_reflection_index_f4195_0_0'\n",
-       "13. 'waist_circumference_f48_0_0'\n",
-       "14. 'hip_circumference_f49_0_0'\n",
-       "15. 'standing_height_f50_0_0'\n",
-       "16. 'trunk_fat_percentage_f23127_0_0'\n",
-       "17. 'body_fat_percentage_f23099_0_0'\n",
-       "18. 'basal_metabolic_rate_f23105_0_0'\n",
-       "19. 'forced_vital_capacity_fvc_best_measure_f20151_0_0'\n",
-       "20. 'forced_expiratory_volume_in_1second_fev1_best_measure_f20150_0_0'\n",
-       "21. 'fev1_fvc_ratio_zscore_f20258_0_0'\n",
-       "22. 'peak_expiratory_flow_pef_f3064_0_2'\n",
-       "23. 'peak_expiratory_flow_pef_f3064_0_1'\n",
-       "24. 'peak_expiratory_flow_pef_f3064_0_0'\n",
-       "25. 'systolic_blood_pressure_automated_reading_f4080'\n",
-       "26. 'diastolic_blood_pressure_automated_reading_f4079'\n",
-       "27. 'pulse_rate_automated_reading_f102'\n",
-       "28. 'basophill_count_f30160_0_0'\n",
-       "29. 'basophill_percentage_f30220_0_0'\n",
-       "30. 'eosinophill_count_f30150_0_0'\n",
-       "31. 'eosinophill_percentage_f30210_0_0'\n",
-       "32. 'haematocrit_percentage_f30030_0_0'\n",
-       "33. 'haemoglobin_concentration_f30020_0_0'\n",
-       "34. 'high_light_scatter_reticulocyte_count_f30300_0_0'\n",
-       "35. 'high_light_scatter_reticulocyte_percentage_f30290_0_0'\n",
-       "36. 'immature_reticulocyte_fraction_f30280_0_0'\n",
-       "37. 'lymphocyte_count_f30120_0_0'\n",
-       "38. 'lymphocyte_percentage_f30180_0_0'\n",
-       "39. 'mean_corpuscular_haemoglobin_f30050_0_0'\n",
-       "40. 'mean_corpuscular_haemoglobin_concentration_f30060_0_0'\n",
-       "41. 'mean_corpuscular_volume_f30040_0_0'\n",
-       "42. 'mean_platelet_thrombocyte_volume_f30100_0_0'\n",
-       "43. 'mean_reticulocyte_volume_f30260_0_0'\n",
-       "44. 'mean_sphered_cell_volume_f30270_0_0'\n",
-       "45. 'monocyte_count_f30130_0_0'\n",
-       "46. 'monocyte_percentage_f30190_0_0'\n",
-       "47. 'neutrophill_count_f30140_0_0'\n",
-       "48. 'neutrophill_percentage_f30200_0_0'\n",
-       "49. 'nucleated_red_blood_cell_count_f30170_0_0'\n",
-       "50. 'nucleated_red_blood_cell_percentage_f30230_0_0'\n",
-       "51. 'platelet_count_f30080_0_0'\n",
-       "52. 'platelet_crit_f30090_0_0'\n",
-       "53. 'platelet_distribution_width_f30110_0_0'\n",
-       "54. 'red_blood_cell_erythrocyte_count_f30010_0_0'\n",
-       "55. 'red_blood_cell_erythrocyte_distribution_width_f30070_0_0'\n",
-       "56. 'reticulocyte_count_f30250_0_0'\n",
-       "57. 'reticulocyte_percentage_f30240_0_0'\n",
-       "58. 'white_blood_cell_leukocyte_count_f30000_0_0'\n",
-       "59. 'alanine_aminotransferase_f30620_0_0'\n",
-       "60. 'albumin_f30600_0_0'\n",
-       "61. 'alkaline_phosphatase_f30610_0_0'\n",
-       "62. 'apolipoprotein_a_f30630_0_0'\n",
-       "63. 'apolipoprotein_b_f30640_0_0'\n",
-       "64. 'aspartate_aminotransferase_f30650_0_0'\n",
-       "65. 'creactive_protein_f30710_0_0'\n",
-       "66. 'calcium_f30680_0_0'\n",
-       "67. 'cholesterol_f30690_0_0'\n",
-       "68. 'creatinine_f30700_0_0'\n",
-       "69. 'cystatin_c_f30720_0_0'\n",
-       "70. 'direct_bilirubin_f30660_0_0'\n",
-       "71. 'gamma_glutamyltransferase_f30730_0_0'\n",
-       "72. 'glucose_f30740_0_0'\n",
-       "73. 'glycated_haemoglobin_hba1c_f30750_0_0'\n",
-       "74. 'hdl_cholesterol_f30760_0_0'\n",
-       "75. 'igf1_f30770_0_0'\n",
-       "76. 'ldl_direct_f30780_0_0'\n",
-       "77. 'lipoprotein_a_f30790_0_0'\n",
-       "78. 'oestradiol_f30800_0_0'\n",
-       "79. 'phosphate_f30810_0_0'\n",
-       "80. 'rheumatoid_factor_f30820_0_0'\n",
-       "81. 'shbg_f30830_0_0'\n",
-       "82. 'testosterone_f30850_0_0'\n",
-       "83. 'total_bilirubin_f30840_0_0'\n",
-       "84. 'total_protein_f30860_0_0'\n",
-       "85. 'triglycerides_f30870_0_0'\n",
-       "86. 'urate_f30880_0_0'\n",
-       "87. 'urea_f30670_0_0'\n",
-       "88. 'vitamin_d_f30890_0_0'\n",
-       "\n",
-       "\n"
-      ],
-      "text/plain": [
-       " [1] \"eid\"                                                             \n",
-       " [2] \"age_at_recruitment_f21022_0_0\"                                   \n",
-       " [3] \"sex_f31_0_0\"                                                     \n",
-       " [4] \"ethnic_background_f21000_0_0\"                                    \n",
-       " [5] \"townsend_deprivation_index_at_recruitment_f189_0_0\"              \n",
-       " [6] \"date_of_attending_assessment_centre_f53_0_0\"                     \n",
-       " [7] \"uk_biobank_assessment_centre_f54_0_0\"                            \n",
-       " [8] \"birth_date\"                                                      \n",
-       " [9] \"body_mass_index_bmi_f21001_0_0\"                                  \n",
-       "[10] \"weight_f21002_0_0\"                                               \n",
-       "[11] \"pulse_wave_arterial_stiffness_index_f21021_0_0\"                  \n",
-       "[12] \"pulse_wave_reflection_index_f4195_0_0\"                           \n",
-       "[13] \"waist_circumference_f48_0_0\"                                     \n",
-       "[14] \"hip_circumference_f49_0_0\"                                       \n",
-       "[15] \"standing_height_f50_0_0\"                                         \n",
-       "[16] \"trunk_fat_percentage_f23127_0_0\"                                 \n",
-       "[17] \"body_fat_percentage_f23099_0_0\"                                  \n",
-       "[18] \"basal_metabolic_rate_f23105_0_0\"                                 \n",
-       "[19] \"forced_vital_capacity_fvc_best_measure_f20151_0_0\"               \n",
-       "[20] \"forced_expiratory_volume_in_1second_fev1_best_measure_f20150_0_0\"\n",
-       "[21] \"fev1_fvc_ratio_zscore_f20258_0_0\"                                \n",
-       "[22] \"peak_expiratory_flow_pef_f3064_0_2\"                              \n",
-       "[23] \"peak_expiratory_flow_pef_f3064_0_1\"                              \n",
-       "[24] \"peak_expiratory_flow_pef_f3064_0_0\"                              \n",
-       "[25] \"systolic_blood_pressure_automated_reading_f4080\"                 \n",
-       "[26] \"diastolic_blood_pressure_automated_reading_f4079\"                \n",
-       "[27] \"pulse_rate_automated_reading_f102\"                               \n",
-       "[28] \"basophill_count_f30160_0_0\"                                      \n",
-       "[29] \"basophill_percentage_f30220_0_0\"                                 \n",
-       "[30] \"eosinophill_count_f30150_0_0\"                                    \n",
-       "[31] \"eosinophill_percentage_f30210_0_0\"                               \n",
-       "[32] \"haematocrit_percentage_f30030_0_0\"                               \n",
-       "[33] \"haemoglobin_concentration_f30020_0_0\"                            \n",
-       "[34] \"high_light_scatter_reticulocyte_count_f30300_0_0\"                \n",
-       "[35] \"high_light_scatter_reticulocyte_percentage_f30290_0_0\"           \n",
-       "[36] \"immature_reticulocyte_fraction_f30280_0_0\"                       \n",
-       "[37] \"lymphocyte_count_f30120_0_0\"                                     \n",
-       "[38] \"lymphocyte_percentage_f30180_0_0\"                                \n",
-       "[39] \"mean_corpuscular_haemoglobin_f30050_0_0\"                         \n",
-       "[40] \"mean_corpuscular_haemoglobin_concentration_f30060_0_0\"           \n",
-       "[41] \"mean_corpuscular_volume_f30040_0_0\"                              \n",
-       "[42] \"mean_platelet_thrombocyte_volume_f30100_0_0\"                     \n",
-       "[43] \"mean_reticulocyte_volume_f30260_0_0\"                             \n",
-       "[44] \"mean_sphered_cell_volume_f30270_0_0\"                             \n",
-       "[45] \"monocyte_count_f30130_0_0\"                                       \n",
-       "[46] \"monocyte_percentage_f30190_0_0\"                                  \n",
-       "[47] \"neutrophill_count_f30140_0_0\"                                    \n",
-       "[48] \"neutrophill_percentage_f30200_0_0\"                               \n",
-       "[49] \"nucleated_red_blood_cell_count_f30170_0_0\"                       \n",
-       "[50] \"nucleated_red_blood_cell_percentage_f30230_0_0\"                  \n",
-       "[51] \"platelet_count_f30080_0_0\"                                       \n",
-       "[52] \"platelet_crit_f30090_0_0\"                                        \n",
-       "[53] \"platelet_distribution_width_f30110_0_0\"                          \n",
-       "[54] \"red_blood_cell_erythrocyte_count_f30010_0_0\"                     \n",
-       "[55] \"red_blood_cell_erythrocyte_distribution_width_f30070_0_0\"        \n",
-       "[56] \"reticulocyte_count_f30250_0_0\"                                   \n",
-       "[57] \"reticulocyte_percentage_f30240_0_0\"                              \n",
-       "[58] \"white_blood_cell_leukocyte_count_f30000_0_0\"                     \n",
-       "[59] \"alanine_aminotransferase_f30620_0_0\"                             \n",
-       "[60] \"albumin_f30600_0_0\"                                              \n",
-       "[61] \"alkaline_phosphatase_f30610_0_0\"                                 \n",
-       "[62] \"apolipoprotein_a_f30630_0_0\"                                     \n",
-       "[63] \"apolipoprotein_b_f30640_0_0\"                                     \n",
-       "[64] \"aspartate_aminotransferase_f30650_0_0\"                           \n",
-       "[65] \"creactive_protein_f30710_0_0\"                                    \n",
-       "[66] \"calcium_f30680_0_0\"                                              \n",
-       "[67] \"cholesterol_f30690_0_0\"                                          \n",
-       "[68] \"creatinine_f30700_0_0\"                                           \n",
-       "[69] \"cystatin_c_f30720_0_0\"                                           \n",
-       "[70] \"direct_bilirubin_f30660_0_0\"                                     \n",
-       "[71] \"gamma_glutamyltransferase_f30730_0_0\"                            \n",
-       "[72] \"glucose_f30740_0_0\"                                              \n",
-       "[73] \"glycated_haemoglobin_hba1c_f30750_0_0\"                           \n",
-       "[74] \"hdl_cholesterol_f30760_0_0\"                                      \n",
-       "[75] \"igf1_f30770_0_0\"                                                 \n",
-       "[76] \"ldl_direct_f30780_0_0\"                                           \n",
-       "[77] \"lipoprotein_a_f30790_0_0\"                                        \n",
-       "[78] \"oestradiol_f30800_0_0\"                                           \n",
-       "[79] \"phosphate_f30810_0_0\"                                            \n",
-       "[80] \"rheumatoid_factor_f30820_0_0\"                                    \n",
-       "[81] \"shbg_f30830_0_0\"                                                 \n",
-       "[82] \"testosterone_f30850_0_0\"                                         \n",
-       "[83] \"total_bilirubin_f30840_0_0\"                                      \n",
-       "[84] \"total_protein_f30860_0_0\"                                        \n",
-       "[85] \"triglycerides_f30870_0_0\"                                        \n",
-       "[86] \"urate_f30880_0_0\"                                                \n",
-       "[87] \"urea_f30670_0_0\"                                                 \n",
-       "[88] \"vitamin_d_f30890_0_0\"                                            "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "colnames(data)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 147,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_long = data %>% pivot_longer(all_of(colnames(data)[9:88]), names_to=\"column\", values_to=\"value\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 163,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<table>\n",
-       "<caption>A tibble: 6 × 5</caption>\n",
-       "<thead>\n",
-       "\t<tr><th scope=col>eid</th><th scope=col>name</th><th scope=col>meaning</th><th scope=col>date</th><th scope=col>value</th></tr>\n",
-       "\t<tr><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;date&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
-       "</thead>\n",
-       "<tbody>\n",
-       "\t<tr><td>1000018</td><td>            </td><td>         </td><td>2009-11-12</td><td> 26.5557</td></tr>\n",
-       "\t<tr><td>1000018</td><td>O/E - weight</td><td>162763007</td><td>2009-11-12</td><td> 63.8000</td></tr>\n",
-       "\t<tr><td>1000018</td><td>            </td><td>         </td><td>2009-11-12</td><td>  7.2770</td></tr>\n",
-       "\t<tr><td>1000018</td><td>            </td><td>         </td><td>2009-11-12</td><td> 80.0000</td></tr>\n",
-       "\t<tr><td>1000018</td><td>            </td><td>         </td><td>2009-11-12</td><td> 85.0000</td></tr>\n",
-       "\t<tr><td>1000018</td><td>            </td><td>         </td><td>2009-11-12</td><td>107.0000</td></tr>\n",
-       "</tbody>\n",
-       "</table>\n"
-      ],
-      "text/latex": [
-       "A tibble: 6 × 5\n",
-       "\\begin{tabular}{lllll}\n",
-       " eid & name & meaning & date & value\\\\\n",
-       " <int> & <chr> & <chr> & <date> & <dbl>\\\\\n",
-       "\\hline\n",
-       "\t 1000018 &              &           & 2009-11-12 &  26.5557\\\\\n",
-       "\t 1000018 & O/E - weight & 162763007 & 2009-11-12 &  63.8000\\\\\n",
-       "\t 1000018 &              &           & 2009-11-12 &   7.2770\\\\\n",
-       "\t 1000018 &              &           & 2009-11-12 &  80.0000\\\\\n",
-       "\t 1000018 &              &           & 2009-11-12 &  85.0000\\\\\n",
-       "\t 1000018 &              &           & 2009-11-12 & 107.0000\\\\\n",
-       "\\end{tabular}\n"
-      ],
-      "text/markdown": [
-       "\n",
-       "A tibble: 6 × 5\n",
-       "\n",
-       "| eid &lt;int&gt; | name &lt;chr&gt; | meaning &lt;chr&gt; | date &lt;date&gt; | value &lt;dbl&gt; |\n",
-       "|---|---|---|---|---|\n",
-       "| 1000018 | <!----> | <!----> | 2009-11-12 |  26.5557 |\n",
-       "| 1000018 | O/E - weight | 162763007 | 2009-11-12 |  63.8000 |\n",
-       "| 1000018 | <!----> | <!----> | 2009-11-12 |   7.2770 |\n",
-       "| 1000018 | <!----> | <!----> | 2009-11-12 |  80.0000 |\n",
-       "| 1000018 | <!----> | <!----> | 2009-11-12 |  85.0000 |\n",
-       "| 1000018 | <!----> | <!----> | 2009-11-12 | 107.0000 |\n",
-       "\n"
-      ],
-      "text/plain": [
-       "  eid     name         meaning   date       value   \n",
-       "1 1000018                        2009-11-12  26.5557\n",
-       "2 1000018 O/E - weight 162763007 2009-11-12  63.8000\n",
-       "3 1000018                        2009-11-12   7.2770\n",
-       "4 1000018                        2009-11-12  80.0000\n",
-       "5 1000018                        2009-11-12  85.0000\n",
-       "6 1000018                        2009-11-12 107.0000"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "data_mean = data_long %>% \n",
-    "    #filter(column %in% c(\"weight_f21002_0_0\", 'systolic_blood_pressure_automated_reading_f4080', \"diastolic_blood_pressure_automated_reading_f4079\", 'cholesterol_f30690_0_0',\n",
-    "     #                  \"hdl_cholesterol_f30760_0_0\", 'ldl_direct_f30780_0_0', \"triglycerides_f30870_0_0\"))\n",
-    "    mutate(name = case_when(column == \"weight_f21002_0_0\" ~ \"O/E - weight\",\n",
-    "                            column == 'systolic_blood_pressure_automated_reading_f4080' ~ \"O/E - Systolic BP reading\",\n",
-    "                            column == \"diastolic_blood_pressure_automated_reading_f4079\" ~ \"O/E - Dystolic BP reading\",\n",
-    "                            column == \"cholesterol_f30690_0_0\" ~ \"Serum cholesterol level\",\n",
-    "                            column == \"triglycerides_f30870_0_0\" ~ \"Serum triglycerides level\",\n",
-    "                            column == \"hdl_cholesterol_f30760_0_0\" ~ \"Serum high density lipoprotein cholesterol level\",\n",
-    "                            column == 'ldl_direct_f30780_0_0' ~ \"Serum low density lipoprotein cholesterol level\",\n",
-    "                            TRUE ~ \"\"),\n",
-    "           meaning = case_when(column == \"weight_f21002_0_0\" ~ \"162763007\",\n",
-    "                               column == 'systolic_blood_pressure_automated_reading_f4080' ~ \"163030003\",\n",
-    "                               column == \"diastolic_blood_pressure_automated_reading_f4079\" ~ \"163031004\",\n",
-    "                               column == \"cholesterol_f30690_0_0\" ~ \"1005671000000105\",\n",
-    "                               column == \"triglycerides_f30870_0_0\" ~ \"1005691000000109\",\n",
-    "                               column == \"hdl_cholesterol_f30760_0_0\" ~ \"1005681000000107\",\n",
-    "                               column == 'ldl_direct_f30780_0_0' ~ \"1022191000000100\",\n",
-    "                               TRUE ~ \"\")) %>% mutate(date = date_of_attending_assessment_centre_f53_0_0) %>% select(eid, name, meaning, date, value)\n",
-    "head(data_mean)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "gp_measurements = arrow::read_feather(glue(\"{data_path}/1_decoded/codes_gp_measurements_210120.feather\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "gp_eids = (gp_measurements %>% select(eid) %>% distinct())$eid"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 169,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "n=10\n",
-    "eids = (data %>% select(eid) %>% filter(eid %in% gp_eids) %>% distinct() %>% sample_n(n))$eid"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 171,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Joining, by = \"eid\"\n",
-      "\n",
-      "`geom_smooth()` using formula 'y ~ x'\n",
-      "\n",
-      "Warning message in qt((1 - level)/2, df):\n",
-      "\"NaNs produced\"\n",
-      "Warning message in qt((1 - level)/2, df):\n",
-      "\"NaNs produced\"\n",
-      "Warning message in qt((1 - level)/2, df):\n",
-      "\"NaNs produced\"\n",
-      "Warning message in qt((1 - level)/2, df):\n",
-      "\"NaNs produced\"\n",
-      "Warning message in qt((1 - level)/2, df):\n",
-      "\"NaNs produced\"\n",
-      "Warning message in qt((1 - level)/2, df):\n",
-      "\"NaNs produced\"\n",
-      "Warning message in qt((1 - level)/2, df):\n",
-      "\"NaNs produced\"\n",
-      "Warning message in max(ids, na.rm = TRUE):\n",
-      "\"no non-missing arguments to max; returning -Inf\"\n",
-      "Warning message in max(ids, na.rm = TRUE):\n",
-      "\"no non-missing arguments to max; returning -Inf\"\n",
-      "Warning message in max(ids, na.rm = TRUE):\n",
-      "\"no non-missing arguments to max; returning -Inf\"\n",
-      "Warning message in max(ids, na.rm = TRUE):\n",
-      "\"no non-missing arguments to max; returning -Inf\"\n",
-      "Warning message in max(ids, na.rm = TRUE):\n",
-      "\"no non-missing arguments to max; returning -Inf\"\n",
-      "Warning message in max(ids, na.rm = TRUE):\n",
-      "\"no non-missing arguments to max; returning -Inf\"\n",
-      "Warning message in max(ids, na.rm = TRUE):\n",
-      "\"no non-missing arguments to max; returning -Inf\"\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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dOmOTs7u7i4VKtvyjRuTk7O9OnTe/TooQQ1Go2Pj49S8/Lly+3t7eW/C8qJSFZW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-      "text/plain": [
-       "plot without title"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 1500,
-       "width": 1200
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "options(repr.plot.width=20, repr.plot.height=25)\n",
-    "library(lubridate)\n",
-    "df = rbind(gp_measurements %>% select(eid, date, meaning, name, value), data_mean) %>% \n",
-    "    filter(eid %in% eids) %>% \n",
-    "    filter(name %in% c(#\"O/E - Systolic BP reading\", \"O/E - Diastolic BP reading\", \n",
-    "                       #\"O/E - weight\", \n",
-    "                        \"Serum cholesterol level\", \"Serum triglycerides level\",\n",
-    "                       \"Serum high density lipoprotein cholesterol level\", \"Serum low density lipoprotein cholesterol level\")) %>%\n",
-    "    filter(as.numeric(value)>0) %>% \n",
-    "    filter(!((name==\"Haemoglobin estimation\")&(value> 50))) %>%\n",
-    "    filter(!((name==\"O/E - Diastolic BP reading\")&(value> 400))) %>%\n",
-    "    filter(!((name==\"O/E - Systolic BP reading\")&(value> 400))) %>%\n",
-    "    filter(!((name==\"O/E - weight\")&(value> 300))) %>%\n",
-    "    filter(!((name==\"Serum cholesterol level\")&(value> 20))) %>%\n",
-    "    filter(!((name==\"Serum creatinine level\")&(value> 1000))) %>%\n",
-    "    filter(!((name==\"Serum high density lipoprotein cholesterol level\")&(value> 100))) %>%\n",
-    "    #filter(!((name==\"Finding of body mass index\")&(value> 50))) %>%\n",
-    "    left_join(data, on=\"eid\") %>% mutate(time = time_length(difftime(date, birth_date), \"years\"))%>% filter(time>0)\n",
-    "\n",
-    "ggplot(df, aes(x=time, y=as.numeric(value)), color=as.factor(name))+geom_point(alpha=0.1)+\n",
-    "    facet_grid(eid~name, scales=\"free\", labeller=label_wrap_gen(width = 10, multi_line = TRUE))+\n",
-    "    geom_smooth(method=\"lm\")+\n",
-    "    geom_vline(aes(xintercept=age_at_recruitment_f21022_0_0), linetype=\"24\", alpha=0.5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 135,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<table>\n",
-       "<caption>A tibble: 216 × 97</caption>\n",
-       "<thead>\n",
-       "\t<tr><th scope=col>eid</th><th scope=col>date</th><th scope=col>code</th><th scope=col>value1</th><th scope=col>value2</th><th scope=col>value3</th><th scope=col>meaning</th><th scope=col>name</th><th scope=col>value</th><th scope=col>age_at_recruitment_f21022_0_0</th><th scope=col>⋯</th><th scope=col>rheumatoid_factor_f30820_0_0</th><th scope=col>shbg_f30830_0_0</th><th scope=col>testosterone_f30850_0_0</th><th scope=col>total_bilirubin_f30840_0_0</th><th scope=col>total_protein_f30860_0_0</th><th scope=col>triglycerides_f30870_0_0</th><th scope=col>urate_f30880_0_0</th><th scope=col>urea_f30670_0_0</th><th scope=col>vitamin_d_f30890_0_0</th><th scope=col>time</th></tr>\n",
-       "\t<tr><th scope=col>&lt;int&gt;</th><th scope=col>&lt;date&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>⋯</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
-       "</thead>\n",
-       "<tbody>\n",
-       "\t<tr><td>1659367</td><td>2009-11-30</td><td>22A..</td><td>83.000 </td><td>NA</td><td>NA</td><td>162763007       </td><td>O/E - weight                                    </td><td> 83.00</td><td>42</td><td>⋯</td><td>NA</td><td>NA</td><td>11.148</td><td> 8.89</td><td>   NA</td><td>1.332</td><td>276.2</td><td>3.72</td><td>43.2</td><td>42.24504</td></tr>\n",
-       "\t<tr><td>1659367</td><td>2009-12-21</td><td>22A..</td><td>86.000 </td><td>NA</td><td>NA</td><td>162763007       </td><td>O/E - weight                                    </td><td> 86.00</td><td>42</td><td>⋯</td><td>NA</td><td>NA</td><td>11.148</td><td> 8.89</td><td>   NA</td><td>1.332</td><td>276.2</td><td>3.72</td><td>43.2</td><td>42.30253</td></tr>\n",
-       "\t<tr><td>1659367</td><td>2015-08-27</td><td>22A..</td><td>96.000 </td><td>NA</td><td>NA</td><td>162763007       </td><td>O/E - weight                                    </td><td> 96.00</td><td>42</td><td>⋯</td><td>NA</td><td>NA</td><td>11.148</td><td> 8.89</td><td>   NA</td><td>1.332</td><td>276.2</td><td>3.72</td><td>43.2</td><td>47.98357</td></tr>\n",
-       "\t<tr><td>1659367</td><td>2015-08-27</td><td>2469.</td><td>122.000</td><td>NA</td><td>NA</td><td>163030003       </td><td>O/E - Systolic BP reading                       </td><td>122.00</td><td>42</td><td>⋯</td><td>NA</td><td>NA</td><td>11.148</td><td> 8.89</td><td>   NA</td><td>1.332</td><td>276.2</td><td>3.72</td><td>43.2</td><td>47.98357</td></tr>\n",
-       "\t<tr><td>1659367</td><td>2015-08-27</td><td>2469.</td><td>135.000</td><td>NA</td><td>NA</td><td>163030003       </td><td>O/E - Systolic BP reading                       </td><td>135.00</td><td>42</td><td>⋯</td><td>NA</td><td>NA</td><td>11.148</td><td> 8.89</td><td>   NA</td><td>1.332</td><td>276.2</td><td>3.72</td><td>43.2</td><td>47.98357</td></tr>\n",
-       "\t<tr><td>1659367</td><td>2015-08-27</td><td>246A.</td><td>77.000 </td><td>NA</td><td>NA</td><td>163031004       </td><td>O/E - Diastolic BP reading                      </td><td> 77.00</td><td>42</td><td>⋯</td><td>NA</td><td>NA</td><td>11.148</td><td> 8.89</td><td>   NA</td><td>1.332</td><td>276.2</td><td>3.72</td><td>43.2</td><td>47.98357</td></tr>\n",
-       "\t<tr><td>1659367</td><td>2015-08-27</td><td>246A.</td><td>79.000 </td><td>NA</td><td>NA</td><td>163031004       </td><td>O/E - Diastolic BP reading                      </td><td> 79.00</td><td>42</td><td>⋯</td><td>NA</td><td>NA</td><td>11.148</td><td> 8.89</td><td>   NA</td><td>1.332</td><td>276.2</td><td>3.72</td><td>43.2</td><td>47.98357</td></tr>\n",
-       "\t<tr><td>1659367</td><td>2015-08-27</td><td>44P5.</td><td>1.100  </td><td>NA</td><td>NA</td><td>1005681000000107</td><td>Serum high density lipoprotein cholesterol level</td><td>  1.10</td><td>42</td><td>⋯</td><td>NA</td><td>NA</td><td>11.148</td><td> 8.89</td><td>   NA</td><td>1.332</td><td>276.2</td><td>3.72</td><td>43.2</td><td>47.98357</td></tr>\n",
-       "\t<tr><td>1659367</td><td>2015-08-27</td><td>XE2eD</td><td>5.200  </td><td>NA</td><td>NA</td><td>1005671000000105</td><td>Serum cholesterol level                         </td><td>  5.20</td><td>42</td><td>⋯</td><td>NA</td><td>NA</td><td>11.148</td><td> 8.89</td><td>   NA</td><td>1.332</td><td>276.2</td><td>3.72</td><td>43.2</td><td>47.98357</td></tr>\n",
-       "\t<tr><td>1659367</td><td>2015-08-27</td><td>XE2q9</td><td>2.000  </td><td>NA</td><td>NA</td><td>1005691000000109</td><td>Serum triglycerides level                       </td><td>  2.00</td><td>42</td><td>⋯</td><td>NA</td><td>NA</td><td>11.148</td><td> 8.89</td><td>   NA</td><td>1.332</td><td>276.2</td><td>3.72</td><td>43.2</td><td>47.98357</td></tr>\n",
-       "\t<tr><td>2091573</td><td>2005-04-22</td><td>2469.</td><td>142.000</td><td>NA</td><td>NA</td><td>163030003       </td><td>O/E - Systolic BP reading                       </td><td>142.00</td><td>49</td><td>⋯</td><td>NA</td><td>51</td><td>10.978</td><td>11.12</td><td>79.19</td><td>2.321</td><td>425.1</td><td>4.52</td><td>81.4</td><td>45.44285</td></tr>\n",
-       "\t<tr><td>2091573</td><td>2005-04-22</td><td>246A.</td><td>82.000 </td><td>NA</td><td>NA</td><td>163031004       </td><td>O/E - Diastolic BP reading                      </td><td> 82.00</td><td>49</td><td>⋯</td><td>NA</td><td>51</td><td>10.978</td><td>11.12</td><td>79.19</td><td>2.321</td><td>425.1</td><td>4.52</td><td>81.4</td><td>45.44285</td></tr>\n",
-       "\t<tr><td>2091573</td><td>2008-01-18</td><td>2469.</td><td>130.000</td><td>NA</td><td>NA</td><td>163030003       </td><td>O/E - Systolic BP reading                       </td><td>130.00</td><td>49</td><td>⋯</td><td>NA</td><td>51</td><td>10.978</td><td>11.12</td><td>79.19</td><td>2.321</td><td>425.1</td><td>4.52</td><td>81.4</td><td>48.18344</td></tr>\n",
-       "\t<tr><td>2091573</td><td>2008-01-18</td><td>246A.</td><td>84.000 </td><td>NA</td><td>NA</td><td>163031004       </td><td>O/E - Diastolic BP reading                      </td><td> 84.00</td><td>49</td><td>⋯</td><td>NA</td><td>51</td><td>10.978</td><td>11.12</td><td>79.19</td><td>2.321</td><td>425.1</td><td>4.52</td><td>81.4</td><td>48.18344</td></tr>\n",
-       "\t<tr><td>2091573</td><td>2011-12-21</td><td>44P5.</td><td>1.200  </td><td>NA</td><td>NA</td><td>1005681000000107</td><td>Serum high density lipoprotein cholesterol level</td><td>  1.20</td><td>49</td><td>⋯</td><td>NA</td><td>51</td><td>10.978</td><td>11.12</td><td>79.19</td><td>2.321</td><td>425.1</td><td>4.52</td><td>81.4</td><td>52.10678</td></tr>\n",
-       "\t<tr><td>2091573</td><td>2011-12-21</td><td>44P6.</td><td>3.700  </td><td>NA</td><td>NA</td><td>1022191000000100</td><td>Serum low density lipoprotein cholesterol level </td><td>  3.70</td><td>49</td><td>⋯</td><td>NA</td><td>51</td><td>10.978</td><td>11.12</td><td>79.19</td><td>2.321</td><td>425.1</td><td>4.52</td><td>81.4</td><td>52.10678</td></tr>\n",
-       "\t<tr><td>2091573</td><td>2011-12-21</td><td>XE2eD</td><td>6.300  </td><td>NA</td><td>NA</td><td>1005671000000105</td><td>Serum cholesterol level                         </td><td>  6.30</td><td>49</td><td>⋯</td><td>NA</td><td>51</td><td>10.978</td><td>11.12</td><td>79.19</td><td>2.321</td><td>425.1</td><td>4.52</td><td>81.4</td><td>52.10678</td></tr>\n",
-       "\t<tr><td>2091573</td><td>2011-12-21</td><td>XE2q9</td><td>3.000  </td><td>NA</td><td>NA</td><td>1005691000000109</td><td>Serum triglycerides level                       </td><td>  3.00</td><td>49</td><td>⋯</td><td>NA</td><td>51</td><td>10.978</td><td>11.12</td><td>79.19</td><td>2.321</td><td>425.1</td><td>4.52</td><td>81.4</td><td>52.10678</td></tr>\n",
-       "\t<tr><td>2091573</td><td>2014-02-11</td><td>22A..</td><td>74.000 </td><td>NA</td><td>NA</td><td>162763007       </td><td>O/E - weight                                    </td><td> 74.00</td><td>49</td><td>⋯</td><td>NA</td><td>51</td><td>10.978</td><td>11.12</td><td>79.19</td><td>2.321</td><td>425.1</td><td>4.52</td><td>81.4</td><td>54.25051</td></tr>\n",
-       "\t<tr><td>2091573</td><td>2014-02-11</td><td>2469.</td><td>115.000</td><td>NA</td><td>NA</td><td>163030003       </td><td>O/E - Systolic BP reading                       </td><td>115.00</td><td>49</td><td>⋯</td><td>NA</td><td>51</td><td>10.978</td><td>11.12</td><td>79.19</td><td>2.321</td><td>425.1</td><td>4.52</td><td>81.4</td><td>54.25051</td></tr>\n",
-       "\t<tr><td>2091573</td><td>2014-02-11</td><td>246A.</td><td>65.000 </td><td>NA</td><td>NA</td><td>163031004       </td><td>O/E - Diastolic BP reading                      </td><td> 65.00</td><td>49</td><td>⋯</td><td>NA</td><td>51</td><td>10.978</td><td>11.12</td><td>79.19</td><td>2.321</td><td>425.1</td><td>4.52</td><td>81.4</td><td>54.25051</td></tr>\n",
-       "\t<tr><td>2091573</td><td>2014-02-11</td><td>44P5.</td><td>0.770  </td><td>NA</td><td>NA</td><td>1005681000000107</td><td>Serum high density lipoprotein cholesterol level</td><td>  0.77</td><td>49</td><td>⋯</td><td>NA</td><td>51</td><td>10.978</td><td>11.12</td><td>79.19</td><td>2.321</td><td>425.1</td><td>4.52</td><td>81.4</td><td>54.25051</td></tr>\n",
-       "\t<tr><td>2091573</td><td>2014-03-27</td><td>22A..</td><td>73.180 </td><td>NA</td><td>NA</td><td>162763007       </td><td>O/E - weight                                    </td><td> 73.18</td><td>49</td><td>⋯</td><td>NA</td><td>51</td><td>10.978</td><td>11.12</td><td>79.19</td><td>2.321</td><td>425.1</td><td>4.52</td><td>81.4</td><td>54.37098</td></tr>\n",
-       "\t<tr><td>2091573</td><td>2014-04-08</td><td>44P5.</td><td>1.100  </td><td>NA</td><td>NA</td><td>1005681000000107</td><td>Serum high density lipoprotein cholesterol level</td><td>  1.10</td><td>49</td><td>⋯</td><td>NA</td><td>51</td><td>10.978</td><td>11.12</td><td>79.19</td><td>2.321</td><td>425.1</td><td>4.52</td><td>81.4</td><td>54.40383</td></tr>\n",
-       "\t<tr><td>2091573</td><td>2014-04-08</td><td>44P6.</td><td>0.800  </td><td>NA</td><td>NA</td><td>1022191000000100</td><td>Serum low density lipoprotein cholesterol level </td><td>  0.80</td><td>49</td><td>⋯</td><td>NA</td><td>51</td><td>10.978</td><td>11.12</td><td>79.19</td><td>2.321</td><td>425.1</td><td>4.52</td><td>81.4</td><td>54.40383</td></tr>\n",
-       "\t<tr><td>2091573</td><td>2014-04-08</td><td>XE2eD</td><td>2.200  </td><td>NA</td><td>NA</td><td>1005671000000105</td><td>Serum cholesterol level                         </td><td>  2.20</td><td>49</td><td>⋯</td><td>NA</td><td>51</td><td>10.978</td><td>11.12</td><td>79.19</td><td>2.321</td><td>425.1</td><td>4.52</td><td>81.4</td><td>54.40383</td></tr>\n",
-       "\t<tr><td>2091573</td><td>2014-04-08</td><td>XE2q9</td><td>0.600  </td><td>NA</td><td>NA</td><td>1005691000000109</td><td>Serum triglycerides level                       </td><td>  0.60</td><td>49</td><td>⋯</td><td>NA</td><td>51</td><td>10.978</td><td>11.12</td><td>79.19</td><td>2.321</td><td>425.1</td><td>4.52</td><td>81.4</td><td>54.40383</td></tr>\n",
-       "\t<tr><td>2091573</td><td>2014-05-12</td><td>22A..</td><td>72.000 </td><td>NA</td><td>NA</td><td>162763007       </td><td>O/E - weight                                    </td><td> 72.00</td><td>49</td><td>⋯</td><td>NA</td><td>51</td><td>10.978</td><td>11.12</td><td>79.19</td><td>2.321</td><td>425.1</td><td>4.52</td><td>81.4</td><td>54.49692</td></tr>\n",
-       "\t<tr><td>2091573</td><td>2014-05-12</td><td>2469.</td><td>116.000</td><td>NA</td><td>NA</td><td>163030003       </td><td>O/E - Systolic BP reading                       </td><td>116.00</td><td>49</td><td>⋯</td><td>NA</td><td>51</td><td>10.978</td><td>11.12</td><td>79.19</td><td>2.321</td><td>425.1</td><td>4.52</td><td>81.4</td><td>54.49692</td></tr>\n",
-       "\t<tr><td>2091573</td><td>2014-05-12</td><td>246A.</td><td>73.000 </td><td>NA</td><td>NA</td><td>163031004       </td><td>O/E - Diastolic BP reading                      </td><td> 73.00</td><td>49</td><td>⋯</td><td>NA</td><td>51</td><td>10.978</td><td>11.12</td><td>79.19</td><td>2.321</td><td>425.1</td><td>4.52</td><td>81.4</td><td>54.49692</td></tr>\n",
-       "\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋱</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
-       "\t<tr><td>5550237</td><td>2015-09-29</td><td>246A.</td><td>75.000 </td><td>NA    </td><td>NA</td><td>163031004       </td><td>O/E - Diastolic BP reading                      </td><td> 75.0</td><td>53</td><td>⋯</td><td>NA</td><td>NA</td><td>1.113</td><td> 7.19</td><td>NA</td><td>2.563</td><td>320.7</td><td>4.10</td><td>50.3</td><td>60.24641</td></tr>\n",
-       "\t<tr><td>5708666</td><td>1998-07-29</td><td>22A..</td><td>62.100 </td><td>NA    </td><td>NA</td><td>162763007       </td><td>O/E - weight                                    </td><td> 62.1</td><td>53</td><td>⋯</td><td>NA</td><td>NA</td><td>0.963</td><td>17.84</td><td>NA</td><td>0.864</td><td>213.6</td><td>2.61</td><td>35.2</td><td>43.46886</td></tr>\n",
-       "\t<tr><td>5708666</td><td>1998-07-29</td><td>246..</td><td>110.000</td><td>70.000</td><td>NA</td><td>163030003       </td><td>O/E - Systolic BP reading                       </td><td>110.0</td><td>53</td><td>⋯</td><td>NA</td><td>NA</td><td>0.963</td><td>17.84</td><td>NA</td><td>0.864</td><td>213.6</td><td>2.61</td><td>35.2</td><td>43.46886</td></tr>\n",
-       "\t<tr><td>5708666</td><td>1998-07-29</td><td>246..</td><td>110.000</td><td>70.000</td><td>NA</td><td>163031004       </td><td>O/E - Diastolic BP reading                      </td><td> 70.0</td><td>53</td><td>⋯</td><td>NA</td><td>NA</td><td>0.963</td><td>17.84</td><td>NA</td><td>0.864</td><td>213.6</td><td>2.61</td><td>35.2</td><td>43.46886</td></tr>\n",
-       "\t<tr><td>5708666</td><td>2003-11-13</td><td>246..</td><td>110.000</td><td>50.000</td><td>NA</td><td>163030003       </td><td>O/E - Systolic BP reading                       </td><td>110.0</td><td>53</td><td>⋯</td><td>NA</td><td>NA</td><td>0.963</td><td>17.84</td><td>NA</td><td>0.864</td><td>213.6</td><td>2.61</td><td>35.2</td><td>48.76112</td></tr>\n",
-       "\t<tr><td>5708666</td><td>2003-11-13</td><td>246..</td><td>110.000</td><td>50.000</td><td>NA</td><td>163031004       </td><td>O/E - Diastolic BP reading                      </td><td> 50.0</td><td>53</td><td>⋯</td><td>NA</td><td>NA</td><td>0.963</td><td>17.84</td><td>NA</td><td>0.864</td><td>213.6</td><td>2.61</td><td>35.2</td><td>48.76112</td></tr>\n",
-       "\t<tr><td>5708666</td><td>2004-06-17</td><td>246..</td><td>122.000</td><td>77.000</td><td>NA</td><td>163030003       </td><td>O/E - Systolic BP reading                       </td><td>122.0</td><td>53</td><td>⋯</td><td>NA</td><td>NA</td><td>0.963</td><td>17.84</td><td>NA</td><td>0.864</td><td>213.6</td><td>2.61</td><td>35.2</td><td>49.35524</td></tr>\n",
-       "\t<tr><td>5708666</td><td>2004-06-17</td><td>246..</td><td>122.000</td><td>77.000</td><td>NA</td><td>163031004       </td><td>O/E - Diastolic BP reading                      </td><td> 77.0</td><td>53</td><td>⋯</td><td>NA</td><td>NA</td><td>0.963</td><td>17.84</td><td>NA</td><td>0.864</td><td>213.6</td><td>2.61</td><td>35.2</td><td>49.35524</td></tr>\n",
-       "\t<tr><td>5708666</td><td>2005-04-06</td><td>246..</td><td>110.000</td><td>70.000</td><td>NA</td><td>163030003       </td><td>O/E - Systolic BP reading                       </td><td>110.0</td><td>53</td><td>⋯</td><td>NA</td><td>NA</td><td>0.963</td><td>17.84</td><td>NA</td><td>0.864</td><td>213.6</td><td>2.61</td><td>35.2</td><td>50.15743</td></tr>\n",
-       "\t<tr><td>5708666</td><td>2005-04-06</td><td>246..</td><td>110.000</td><td>70.000</td><td>NA</td><td>163031004       </td><td>O/E - Diastolic BP reading                      </td><td> 70.0</td><td>53</td><td>⋯</td><td>NA</td><td>NA</td><td>0.963</td><td>17.84</td><td>NA</td><td>0.864</td><td>213.6</td><td>2.61</td><td>35.2</td><td>50.15743</td></tr>\n",
-       "\t<tr><td>5708666</td><td>2005-06-29</td><td>246..</td><td>120.000</td><td>80.000</td><td>NA</td><td>163030003       </td><td>O/E - Systolic BP reading                       </td><td>120.0</td><td>53</td><td>⋯</td><td>NA</td><td>NA</td><td>0.963</td><td>17.84</td><td>NA</td><td>0.864</td><td>213.6</td><td>2.61</td><td>35.2</td><td>50.38741</td></tr>\n",
-       "\t<tr><td>5708666</td><td>2005-06-29</td><td>246..</td><td>120.000</td><td>80.000</td><td>NA</td><td>163031004       </td><td>O/E - Diastolic BP reading                      </td><td> 80.0</td><td>53</td><td>⋯</td><td>NA</td><td>NA</td><td>0.963</td><td>17.84</td><td>NA</td><td>0.864</td><td>213.6</td><td>2.61</td><td>35.2</td><td>50.38741</td></tr>\n",
-       "\t<tr><td>5708666</td><td>2006-01-17</td><td>246..</td><td>110.000</td><td>70.000</td><td>NA</td><td>163030003       </td><td>O/E - Systolic BP reading                       </td><td>110.0</td><td>53</td><td>⋯</td><td>NA</td><td>NA</td><td>0.963</td><td>17.84</td><td>NA</td><td>0.864</td><td>213.6</td><td>2.61</td><td>35.2</td><td>50.94045</td></tr>\n",
-       "\t<tr><td>5708666</td><td>2006-01-17</td><td>246..</td><td>110.000</td><td>70.000</td><td>NA</td><td>163031004       </td><td>O/E - Diastolic BP reading                      </td><td> 70.0</td><td>53</td><td>⋯</td><td>NA</td><td>NA</td><td>0.963</td><td>17.84</td><td>NA</td><td>0.864</td><td>213.6</td><td>2.61</td><td>35.2</td><td>50.94045</td></tr>\n",
-       "\t<tr><td>5708666</td><td>2006-11-08</td><td>22A..</td><td>63.000 </td><td>NA    </td><td>NA</td><td>162763007       </td><td>O/E - weight                                    </td><td> 63.0</td><td>53</td><td>⋯</td><td>NA</td><td>NA</td><td>0.963</td><td>17.84</td><td>NA</td><td>0.864</td><td>213.6</td><td>2.61</td><td>35.2</td><td>51.74812</td></tr>\n",
-       "\t<tr><td>5708666</td><td>2006-11-08</td><td>246..</td><td>122.000</td><td>80.000</td><td>NA</td><td>163030003       </td><td>O/E - Systolic BP reading                       </td><td>122.0</td><td>53</td><td>⋯</td><td>NA</td><td>NA</td><td>0.963</td><td>17.84</td><td>NA</td><td>0.864</td><td>213.6</td><td>2.61</td><td>35.2</td><td>51.74812</td></tr>\n",
-       "\t<tr><td>5708666</td><td>2006-11-08</td><td>246..</td><td>122.000</td><td>80.000</td><td>NA</td><td>163031004       </td><td>O/E - Diastolic BP reading                      </td><td> 80.0</td><td>53</td><td>⋯</td><td>NA</td><td>NA</td><td>0.963</td><td>17.84</td><td>NA</td><td>0.864</td><td>213.6</td><td>2.61</td><td>35.2</td><td>51.74812</td></tr>\n",
-       "\t<tr><td>5708666</td><td>2013-04-04</td><td>246..</td><td>139.000</td><td>87.000</td><td>NA</td><td>163030003       </td><td>O/E - Systolic BP reading                       </td><td>139.0</td><td>53</td><td>⋯</td><td>NA</td><td>NA</td><td>0.963</td><td>17.84</td><td>NA</td><td>0.864</td><td>213.6</td><td>2.61</td><td>35.2</td><td>58.15195</td></tr>\n",
-       "\t<tr><td>5708666</td><td>2013-04-04</td><td>246..</td><td>139.000</td><td>87.000</td><td>NA</td><td>163031004       </td><td>O/E - Diastolic BP reading                      </td><td> 87.0</td><td>53</td><td>⋯</td><td>NA</td><td>NA</td><td>0.963</td><td>17.84</td><td>NA</td><td>0.864</td><td>213.6</td><td>2.61</td><td>35.2</td><td>58.15195</td></tr>\n",
-       "\t<tr><td>5708666</td><td>2015-07-20</td><td>246..</td><td>130.000</td><td>70.000</td><td>NA</td><td>163030003       </td><td>O/E - Systolic BP reading                       </td><td>130.0</td><td>53</td><td>⋯</td><td>NA</td><td>NA</td><td>0.963</td><td>17.84</td><td>NA</td><td>0.864</td><td>213.6</td><td>2.61</td><td>35.2</td><td>60.44353</td></tr>\n",
-       "\t<tr><td>5708666</td><td>2015-07-20</td><td>246..</td><td>130.000</td><td>70.000</td><td>NA</td><td>163031004       </td><td>O/E - Diastolic BP reading                      </td><td> 70.0</td><td>53</td><td>⋯</td><td>NA</td><td>NA</td><td>0.963</td><td>17.84</td><td>NA</td><td>0.864</td><td>213.6</td><td>2.61</td><td>35.2</td><td>60.44353</td></tr>\n",
-       "\t<tr><td>5708666</td><td>2015-08-06</td><td>22A..</td><td>63.200 </td><td>NA    </td><td>NA</td><td>162763007       </td><td>O/E - weight                                    </td><td> 63.2</td><td>53</td><td>⋯</td><td>NA</td><td>NA</td><td>0.963</td><td>17.84</td><td>NA</td><td>0.864</td><td>213.6</td><td>2.61</td><td>35.2</td><td>60.49008</td></tr>\n",
-       "\t<tr><td>5708666</td><td>2015-08-06</td><td>246..</td><td>131.000</td><td>84.000</td><td>NA</td><td>163030003       </td><td>O/E - Systolic BP reading                       </td><td>131.0</td><td>53</td><td>⋯</td><td>NA</td><td>NA</td><td>0.963</td><td>17.84</td><td>NA</td><td>0.864</td><td>213.6</td><td>2.61</td><td>35.2</td><td>60.49008</td></tr>\n",
-       "\t<tr><td>5708666</td><td>2015-08-06</td><td>246..</td><td>131.000</td><td>84.000</td><td>NA</td><td>163031004       </td><td>O/E - Diastolic BP reading                      </td><td> 84.0</td><td>53</td><td>⋯</td><td>NA</td><td>NA</td><td>0.963</td><td>17.84</td><td>NA</td><td>0.864</td><td>213.6</td><td>2.61</td><td>35.2</td><td>60.49008</td></tr>\n",
-       "\t<tr><td>5708666</td><td>2015-12-10</td><td>246..</td><td>110.000</td><td>70.000</td><td>NA</td><td>163030003       </td><td>O/E - Systolic BP reading                       </td><td>110.0</td><td>53</td><td>⋯</td><td>NA</td><td>NA</td><td>0.963</td><td>17.84</td><td>NA</td><td>0.864</td><td>213.6</td><td>2.61</td><td>35.2</td><td>60.83504</td></tr>\n",
-       "\t<tr><td>5708666</td><td>2015-12-10</td><td>246..</td><td>110.000</td><td>70.000</td><td>NA</td><td>163031004       </td><td>O/E - Diastolic BP reading                      </td><td> 70.0</td><td>53</td><td>⋯</td><td>NA</td><td>NA</td><td>0.963</td><td>17.84</td><td>NA</td><td>0.864</td><td>213.6</td><td>2.61</td><td>35.2</td><td>60.83504</td></tr>\n",
-       "\t<tr><td>5708666</td><td>2015-12-14</td><td>XE2eD</td><td>5.600  </td><td>NA    </td><td>NA</td><td>1005671000000105</td><td>Serum cholesterol level                         </td><td>  5.6</td><td>53</td><td>⋯</td><td>NA</td><td>NA</td><td>0.963</td><td>17.84</td><td>NA</td><td>0.864</td><td>213.6</td><td>2.61</td><td>35.2</td><td>60.84600</td></tr>\n",
-       "\t<tr><td>5708666</td><td>2015-12-14</td><td>44P5.</td><td>2.000  </td><td>NA    </td><td>NA</td><td>1005681000000107</td><td>Serum high density lipoprotein cholesterol level</td><td>  2.0</td><td>53</td><td>⋯</td><td>NA</td><td>NA</td><td>0.963</td><td>17.84</td><td>NA</td><td>0.864</td><td>213.6</td><td>2.61</td><td>35.2</td><td>60.84600</td></tr>\n",
-       "\t<tr><td>5708666</td><td>2015-12-14</td><td>44P6.</td><td>3.100  </td><td>NA    </td><td>NA</td><td>1022191000000100</td><td>Serum low density lipoprotein cholesterol level </td><td>  3.1</td><td>53</td><td>⋯</td><td>NA</td><td>NA</td><td>0.963</td><td>17.84</td><td>NA</td><td>0.864</td><td>213.6</td><td>2.61</td><td>35.2</td><td>60.84600</td></tr>\n",
-       "\t<tr><td>5708666</td><td>2015-12-14</td><td>XE2q9</td><td>1.100  </td><td>NA    </td><td>NA</td><td>1005691000000109</td><td>Serum triglycerides level                       </td><td>  1.1</td><td>53</td><td>⋯</td><td>NA</td><td>NA</td><td>0.963</td><td>17.84</td><td>NA</td><td>0.864</td><td>213.6</td><td>2.61</td><td>35.2</td><td>60.84600</td></tr>\n",
-       "</tbody>\n",
-       "</table>\n"
-      ],
-      "text/latex": [
-       "A tibble: 216 × 97\n",
-       "\\begin{tabular}{lllllllllllllllllllll}\n",
-       " eid & date & code & value1 & value2 & value3 & meaning & name & value & age\\_at\\_recruitment\\_f21022\\_0\\_0 & ⋯ & rheumatoid\\_factor\\_f30820\\_0\\_0 & shbg\\_f30830\\_0\\_0 & testosterone\\_f30850\\_0\\_0 & total\\_bilirubin\\_f30840\\_0\\_0 & total\\_protein\\_f30860\\_0\\_0 & triglycerides\\_f30870\\_0\\_0 & urate\\_f30880\\_0\\_0 & urea\\_f30670\\_0\\_0 & vitamin\\_d\\_f30890\\_0\\_0 & time\\\\\n",
-       " <int> & <date> & <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <dbl> & <dbl> & ⋯ & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
-       "\\hline\n",
-       "\t 1659367 & 2009-11-30 & 22A.. & 83.000  & NA & NA & 162763007        & O/E - weight                                     &  83.00 & 42 & ⋯ & NA & NA & 11.148 &  8.89 &    NA & 1.332 & 276.2 & 3.72 & 43.2 & 42.24504\\\\\n",
-       "\t 1659367 & 2009-12-21 & 22A.. & 86.000  & NA & NA & 162763007        & O/E - weight                                     &  86.00 & 42 & ⋯ & NA & NA & 11.148 &  8.89 &    NA & 1.332 & 276.2 & 3.72 & 43.2 & 42.30253\\\\\n",
-       "\t 1659367 & 2015-08-27 & 22A.. & 96.000  & NA & NA & 162763007        & O/E - weight                                     &  96.00 & 42 & ⋯ & NA & NA & 11.148 &  8.89 &    NA & 1.332 & 276.2 & 3.72 & 43.2 & 47.98357\\\\\n",
-       "\t 1659367 & 2015-08-27 & 2469. & 122.000 & NA & NA & 163030003        & O/E - Systolic BP reading                        & 122.00 & 42 & ⋯ & NA & NA & 11.148 &  8.89 &    NA & 1.332 & 276.2 & 3.72 & 43.2 & 47.98357\\\\\n",
-       "\t 1659367 & 2015-08-27 & 2469. & 135.000 & NA & NA & 163030003        & O/E - Systolic BP reading                        & 135.00 & 42 & ⋯ & NA & NA & 11.148 &  8.89 &    NA & 1.332 & 276.2 & 3.72 & 43.2 & 47.98357\\\\\n",
-       "\t 1659367 & 2015-08-27 & 246A. & 77.000  & NA & NA & 163031004        & O/E - Diastolic BP reading                       &  77.00 & 42 & ⋯ & NA & NA & 11.148 &  8.89 &    NA & 1.332 & 276.2 & 3.72 & 43.2 & 47.98357\\\\\n",
-       "\t 1659367 & 2015-08-27 & 246A. & 79.000  & NA & NA & 163031004        & O/E - Diastolic BP reading                       &  79.00 & 42 & ⋯ & NA & NA & 11.148 &  8.89 &    NA & 1.332 & 276.2 & 3.72 & 43.2 & 47.98357\\\\\n",
-       "\t 1659367 & 2015-08-27 & 44P5. & 1.100   & NA & NA & 1005681000000107 & Serum high density lipoprotein cholesterol level &   1.10 & 42 & ⋯ & NA & NA & 11.148 &  8.89 &    NA & 1.332 & 276.2 & 3.72 & 43.2 & 47.98357\\\\\n",
-       "\t 1659367 & 2015-08-27 & XE2eD & 5.200   & NA & NA & 1005671000000105 & Serum cholesterol level                          &   5.20 & 42 & ⋯ & NA & NA & 11.148 &  8.89 &    NA & 1.332 & 276.2 & 3.72 & 43.2 & 47.98357\\\\\n",
-       "\t 1659367 & 2015-08-27 & XE2q9 & 2.000   & NA & NA & 1005691000000109 & Serum triglycerides level                        &   2.00 & 42 & ⋯ & NA & NA & 11.148 &  8.89 &    NA & 1.332 & 276.2 & 3.72 & 43.2 & 47.98357\\\\\n",
-       "\t 2091573 & 2005-04-22 & 2469. & 142.000 & NA & NA & 163030003        & O/E - Systolic BP reading                        & 142.00 & 49 & ⋯ & NA & 51 & 10.978 & 11.12 & 79.19 & 2.321 & 425.1 & 4.52 & 81.4 & 45.44285\\\\\n",
-       "\t 2091573 & 2005-04-22 & 246A. & 82.000  & NA & NA & 163031004        & O/E - Diastolic BP reading                       &  82.00 & 49 & ⋯ & NA & 51 & 10.978 & 11.12 & 79.19 & 2.321 & 425.1 & 4.52 & 81.4 & 45.44285\\\\\n",
-       "\t 2091573 & 2008-01-18 & 2469. & 130.000 & NA & NA & 163030003        & O/E - Systolic BP reading                        & 130.00 & 49 & ⋯ & NA & 51 & 10.978 & 11.12 & 79.19 & 2.321 & 425.1 & 4.52 & 81.4 & 48.18344\\\\\n",
-       "\t 2091573 & 2008-01-18 & 246A. & 84.000  & NA & NA & 163031004        & O/E - Diastolic BP reading                       &  84.00 & 49 & ⋯ & NA & 51 & 10.978 & 11.12 & 79.19 & 2.321 & 425.1 & 4.52 & 81.4 & 48.18344\\\\\n",
-       "\t 2091573 & 2011-12-21 & 44P5. & 1.200   & NA & NA & 1005681000000107 & Serum high density lipoprotein cholesterol level &   1.20 & 49 & ⋯ & NA & 51 & 10.978 & 11.12 & 79.19 & 2.321 & 425.1 & 4.52 & 81.4 & 52.10678\\\\\n",
-       "\t 2091573 & 2011-12-21 & 44P6. & 3.700   & NA & NA & 1022191000000100 & Serum low density lipoprotein cholesterol level  &   3.70 & 49 & ⋯ & NA & 51 & 10.978 & 11.12 & 79.19 & 2.321 & 425.1 & 4.52 & 81.4 & 52.10678\\\\\n",
-       "\t 2091573 & 2011-12-21 & XE2eD & 6.300   & NA & NA & 1005671000000105 & Serum cholesterol level                          &   6.30 & 49 & ⋯ & NA & 51 & 10.978 & 11.12 & 79.19 & 2.321 & 425.1 & 4.52 & 81.4 & 52.10678\\\\\n",
-       "\t 2091573 & 2011-12-21 & XE2q9 & 3.000   & NA & NA & 1005691000000109 & Serum triglycerides level                        &   3.00 & 49 & ⋯ & NA & 51 & 10.978 & 11.12 & 79.19 & 2.321 & 425.1 & 4.52 & 81.4 & 52.10678\\\\\n",
-       "\t 2091573 & 2014-02-11 & 22A.. & 74.000  & NA & NA & 162763007        & O/E - weight                                     &  74.00 & 49 & ⋯ & NA & 51 & 10.978 & 11.12 & 79.19 & 2.321 & 425.1 & 4.52 & 81.4 & 54.25051\\\\\n",
-       "\t 2091573 & 2014-02-11 & 2469. & 115.000 & NA & NA & 163030003        & O/E - Systolic BP reading                        & 115.00 & 49 & ⋯ & NA & 51 & 10.978 & 11.12 & 79.19 & 2.321 & 425.1 & 4.52 & 81.4 & 54.25051\\\\\n",
-       "\t 2091573 & 2014-02-11 & 246A. & 65.000  & NA & NA & 163031004        & O/E - Diastolic BP reading                       &  65.00 & 49 & ⋯ & NA & 51 & 10.978 & 11.12 & 79.19 & 2.321 & 425.1 & 4.52 & 81.4 & 54.25051\\\\\n",
-       "\t 2091573 & 2014-02-11 & 44P5. & 0.770   & NA & NA & 1005681000000107 & Serum high density lipoprotein cholesterol level &   0.77 & 49 & ⋯ & NA & 51 & 10.978 & 11.12 & 79.19 & 2.321 & 425.1 & 4.52 & 81.4 & 54.25051\\\\\n",
-       "\t 2091573 & 2014-03-27 & 22A.. & 73.180  & NA & NA & 162763007        & O/E - weight                                     &  73.18 & 49 & ⋯ & NA & 51 & 10.978 & 11.12 & 79.19 & 2.321 & 425.1 & 4.52 & 81.4 & 54.37098\\\\\n",
-       "\t 2091573 & 2014-04-08 & 44P5. & 1.100   & NA & NA & 1005681000000107 & Serum high density lipoprotein cholesterol level &   1.10 & 49 & ⋯ & NA & 51 & 10.978 & 11.12 & 79.19 & 2.321 & 425.1 & 4.52 & 81.4 & 54.40383\\\\\n",
-       "\t 2091573 & 2014-04-08 & 44P6. & 0.800   & NA & NA & 1022191000000100 & Serum low density lipoprotein cholesterol level  &   0.80 & 49 & ⋯ & NA & 51 & 10.978 & 11.12 & 79.19 & 2.321 & 425.1 & 4.52 & 81.4 & 54.40383\\\\\n",
-       "\t 2091573 & 2014-04-08 & XE2eD & 2.200   & NA & NA & 1005671000000105 & Serum cholesterol level                          &   2.20 & 49 & ⋯ & NA & 51 & 10.978 & 11.12 & 79.19 & 2.321 & 425.1 & 4.52 & 81.4 & 54.40383\\\\\n",
-       "\t 2091573 & 2014-04-08 & XE2q9 & 0.600   & NA & NA & 1005691000000109 & Serum triglycerides level                        &   0.60 & 49 & ⋯ & NA & 51 & 10.978 & 11.12 & 79.19 & 2.321 & 425.1 & 4.52 & 81.4 & 54.40383\\\\\n",
-       "\t 2091573 & 2014-05-12 & 22A.. & 72.000  & NA & NA & 162763007        & O/E - weight                                     &  72.00 & 49 & ⋯ & NA & 51 & 10.978 & 11.12 & 79.19 & 2.321 & 425.1 & 4.52 & 81.4 & 54.49692\\\\\n",
-       "\t 2091573 & 2014-05-12 & 2469. & 116.000 & NA & NA & 163030003        & O/E - Systolic BP reading                        & 116.00 & 49 & ⋯ & NA & 51 & 10.978 & 11.12 & 79.19 & 2.321 & 425.1 & 4.52 & 81.4 & 54.49692\\\\\n",
-       "\t 2091573 & 2014-05-12 & 246A. & 73.000  & NA & NA & 163031004        & O/E - Diastolic BP reading                       &  73.00 & 49 & ⋯ & NA & 51 & 10.978 & 11.12 & 79.19 & 2.321 & 425.1 & 4.52 & 81.4 & 54.49692\\\\\n",
-       "\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋱ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
-       "\t 5550237 & 2015-09-29 & 246A. & 75.000  & NA     & NA & 163031004        & O/E - Diastolic BP reading                       &  75.0 & 53 & ⋯ & NA & NA & 1.113 &  7.19 & NA & 2.563 & 320.7 & 4.10 & 50.3 & 60.24641\\\\\n",
-       "\t 5708666 & 1998-07-29 & 22A.. & 62.100  & NA     & NA & 162763007        & O/E - weight                                     &  62.1 & 53 & ⋯ & NA & NA & 0.963 & 17.84 & NA & 0.864 & 213.6 & 2.61 & 35.2 & 43.46886\\\\\n",
-       "\t 5708666 & 1998-07-29 & 246.. & 110.000 & 70.000 & NA & 163030003        & O/E - Systolic BP reading                        & 110.0 & 53 & ⋯ & NA & NA & 0.963 & 17.84 & NA & 0.864 & 213.6 & 2.61 & 35.2 & 43.46886\\\\\n",
-       "\t 5708666 & 1998-07-29 & 246.. & 110.000 & 70.000 & NA & 163031004        & O/E - Diastolic BP reading                       &  70.0 & 53 & ⋯ & NA & NA & 0.963 & 17.84 & NA & 0.864 & 213.6 & 2.61 & 35.2 & 43.46886\\\\\n",
-       "\t 5708666 & 2003-11-13 & 246.. & 110.000 & 50.000 & NA & 163030003        & O/E - Systolic BP reading                        & 110.0 & 53 & ⋯ & NA & NA & 0.963 & 17.84 & NA & 0.864 & 213.6 & 2.61 & 35.2 & 48.76112\\\\\n",
-       "\t 5708666 & 2003-11-13 & 246.. & 110.000 & 50.000 & NA & 163031004        & O/E - Diastolic BP reading                       &  50.0 & 53 & ⋯ & NA & NA & 0.963 & 17.84 & NA & 0.864 & 213.6 & 2.61 & 35.2 & 48.76112\\\\\n",
-       "\t 5708666 & 2004-06-17 & 246.. & 122.000 & 77.000 & NA & 163030003        & O/E - Systolic BP reading                        & 122.0 & 53 & ⋯ & NA & NA & 0.963 & 17.84 & NA & 0.864 & 213.6 & 2.61 & 35.2 & 49.35524\\\\\n",
-       "\t 5708666 & 2004-06-17 & 246.. & 122.000 & 77.000 & NA & 163031004        & O/E - Diastolic BP reading                       &  77.0 & 53 & ⋯ & NA & NA & 0.963 & 17.84 & NA & 0.864 & 213.6 & 2.61 & 35.2 & 49.35524\\\\\n",
-       "\t 5708666 & 2005-04-06 & 246.. & 110.000 & 70.000 & NA & 163030003        & O/E - Systolic BP reading                        & 110.0 & 53 & ⋯ & NA & NA & 0.963 & 17.84 & NA & 0.864 & 213.6 & 2.61 & 35.2 & 50.15743\\\\\n",
-       "\t 5708666 & 2005-04-06 & 246.. & 110.000 & 70.000 & NA & 163031004        & O/E - Diastolic BP reading                       &  70.0 & 53 & ⋯ & NA & NA & 0.963 & 17.84 & NA & 0.864 & 213.6 & 2.61 & 35.2 & 50.15743\\\\\n",
-       "\t 5708666 & 2005-06-29 & 246.. & 120.000 & 80.000 & NA & 163030003        & O/E - Systolic BP reading                        & 120.0 & 53 & ⋯ & NA & NA & 0.963 & 17.84 & NA & 0.864 & 213.6 & 2.61 & 35.2 & 50.38741\\\\\n",
-       "\t 5708666 & 2005-06-29 & 246.. & 120.000 & 80.000 & NA & 163031004        & O/E - Diastolic BP reading                       &  80.0 & 53 & ⋯ & NA & NA & 0.963 & 17.84 & NA & 0.864 & 213.6 & 2.61 & 35.2 & 50.38741\\\\\n",
-       "\t 5708666 & 2006-01-17 & 246.. & 110.000 & 70.000 & NA & 163030003        & O/E - Systolic BP reading                        & 110.0 & 53 & ⋯ & NA & NA & 0.963 & 17.84 & NA & 0.864 & 213.6 & 2.61 & 35.2 & 50.94045\\\\\n",
-       "\t 5708666 & 2006-01-17 & 246.. & 110.000 & 70.000 & NA & 163031004        & O/E - Diastolic BP reading                       &  70.0 & 53 & ⋯ & NA & NA & 0.963 & 17.84 & NA & 0.864 & 213.6 & 2.61 & 35.2 & 50.94045\\\\\n",
-       "\t 5708666 & 2006-11-08 & 22A.. & 63.000  & NA     & NA & 162763007        & O/E - weight                                     &  63.0 & 53 & ⋯ & NA & NA & 0.963 & 17.84 & NA & 0.864 & 213.6 & 2.61 & 35.2 & 51.74812\\\\\n",
-       "\t 5708666 & 2006-11-08 & 246.. & 122.000 & 80.000 & NA & 163030003        & O/E - Systolic BP reading                        & 122.0 & 53 & ⋯ & NA & NA & 0.963 & 17.84 & NA & 0.864 & 213.6 & 2.61 & 35.2 & 51.74812\\\\\n",
-       "\t 5708666 & 2006-11-08 & 246.. & 122.000 & 80.000 & NA & 163031004        & O/E - Diastolic BP reading                       &  80.0 & 53 & ⋯ & NA & NA & 0.963 & 17.84 & NA & 0.864 & 213.6 & 2.61 & 35.2 & 51.74812\\\\\n",
-       "\t 5708666 & 2013-04-04 & 246.. & 139.000 & 87.000 & NA & 163030003        & O/E - Systolic BP reading                        & 139.0 & 53 & ⋯ & NA & NA & 0.963 & 17.84 & NA & 0.864 & 213.6 & 2.61 & 35.2 & 58.15195\\\\\n",
-       "\t 5708666 & 2013-04-04 & 246.. & 139.000 & 87.000 & NA & 163031004        & O/E - Diastolic BP reading                       &  87.0 & 53 & ⋯ & NA & NA & 0.963 & 17.84 & NA & 0.864 & 213.6 & 2.61 & 35.2 & 58.15195\\\\\n",
-       "\t 5708666 & 2015-07-20 & 246.. & 130.000 & 70.000 & NA & 163030003        & O/E - Systolic BP reading                        & 130.0 & 53 & ⋯ & NA & NA & 0.963 & 17.84 & NA & 0.864 & 213.6 & 2.61 & 35.2 & 60.44353\\\\\n",
-       "\t 5708666 & 2015-07-20 & 246.. & 130.000 & 70.000 & NA & 163031004        & O/E - Diastolic BP reading                       &  70.0 & 53 & ⋯ & NA & NA & 0.963 & 17.84 & NA & 0.864 & 213.6 & 2.61 & 35.2 & 60.44353\\\\\n",
-       "\t 5708666 & 2015-08-06 & 22A.. & 63.200  & NA     & NA & 162763007        & O/E - weight                                     &  63.2 & 53 & ⋯ & NA & NA & 0.963 & 17.84 & NA & 0.864 & 213.6 & 2.61 & 35.2 & 60.49008\\\\\n",
-       "\t 5708666 & 2015-08-06 & 246.. & 131.000 & 84.000 & NA & 163030003        & O/E - Systolic BP reading                        & 131.0 & 53 & ⋯ & NA & NA & 0.963 & 17.84 & NA & 0.864 & 213.6 & 2.61 & 35.2 & 60.49008\\\\\n",
-       "\t 5708666 & 2015-08-06 & 246.. & 131.000 & 84.000 & NA & 163031004        & O/E - Diastolic BP reading                       &  84.0 & 53 & ⋯ & NA & NA & 0.963 & 17.84 & NA & 0.864 & 213.6 & 2.61 & 35.2 & 60.49008\\\\\n",
-       "\t 5708666 & 2015-12-10 & 246.. & 110.000 & 70.000 & NA & 163030003        & O/E - Systolic BP reading                        & 110.0 & 53 & ⋯ & NA & NA & 0.963 & 17.84 & NA & 0.864 & 213.6 & 2.61 & 35.2 & 60.83504\\\\\n",
-       "\t 5708666 & 2015-12-10 & 246.. & 110.000 & 70.000 & NA & 163031004        & O/E - Diastolic BP reading                       &  70.0 & 53 & ⋯ & NA & NA & 0.963 & 17.84 & NA & 0.864 & 213.6 & 2.61 & 35.2 & 60.83504\\\\\n",
-       "\t 5708666 & 2015-12-14 & XE2eD & 5.600   & NA     & NA & 1005671000000105 & Serum cholesterol level                          &   5.6 & 53 & ⋯ & NA & NA & 0.963 & 17.84 & NA & 0.864 & 213.6 & 2.61 & 35.2 & 60.84600\\\\\n",
-       "\t 5708666 & 2015-12-14 & 44P5. & 2.000   & NA     & NA & 1005681000000107 & Serum high density lipoprotein cholesterol level &   2.0 & 53 & ⋯ & NA & NA & 0.963 & 17.84 & NA & 0.864 & 213.6 & 2.61 & 35.2 & 60.84600\\\\\n",
-       "\t 5708666 & 2015-12-14 & 44P6. & 3.100   & NA     & NA & 1022191000000100 & Serum low density lipoprotein cholesterol level  &   3.1 & 53 & ⋯ & NA & NA & 0.963 & 17.84 & NA & 0.864 & 213.6 & 2.61 & 35.2 & 60.84600\\\\\n",
-       "\t 5708666 & 2015-12-14 & XE2q9 & 1.100   & NA     & NA & 1005691000000109 & Serum triglycerides level                        &   1.1 & 53 & ⋯ & NA & NA & 0.963 & 17.84 & NA & 0.864 & 213.6 & 2.61 & 35.2 & 60.84600\\\\\n",
-       "\\end{tabular}\n"
-      ],
-      "text/markdown": [
-       "\n",
-       "A tibble: 216 × 97\n",
-       "\n",
-       "| eid &lt;int&gt; | date &lt;date&gt; | code &lt;chr&gt; | value1 &lt;chr&gt; | value2 &lt;chr&gt; | value3 &lt;chr&gt; | meaning &lt;chr&gt; | name &lt;chr&gt; | value &lt;dbl&gt; | age_at_recruitment_f21022_0_0 &lt;dbl&gt; | ⋯ ⋯ | rheumatoid_factor_f30820_0_0 &lt;dbl&gt; | shbg_f30830_0_0 &lt;dbl&gt; | testosterone_f30850_0_0 &lt;dbl&gt; | total_bilirubin_f30840_0_0 &lt;dbl&gt; | total_protein_f30860_0_0 &lt;dbl&gt; | triglycerides_f30870_0_0 &lt;dbl&gt; | urate_f30880_0_0 &lt;dbl&gt; | urea_f30670_0_0 &lt;dbl&gt; | vitamin_d_f30890_0_0 &lt;dbl&gt; | time &lt;dbl&gt; |\n",
-       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
-       "| 1659367 | 2009-11-30 | 22A.. | 83.000  | NA | NA | 162763007        | O/E - weight                                     |  83.00 | 42 | ⋯ | NA | NA | 11.148 |  8.89 |    NA | 1.332 | 276.2 | 3.72 | 43.2 | 42.24504 |\n",
-       "| 1659367 | 2009-12-21 | 22A.. | 86.000  | NA | NA | 162763007        | O/E - weight                                     |  86.00 | 42 | ⋯ | NA | NA | 11.148 |  8.89 |    NA | 1.332 | 276.2 | 3.72 | 43.2 | 42.30253 |\n",
-       "| 1659367 | 2015-08-27 | 22A.. | 96.000  | NA | NA | 162763007        | O/E - weight                                     |  96.00 | 42 | ⋯ | NA | NA | 11.148 |  8.89 |    NA | 1.332 | 276.2 | 3.72 | 43.2 | 47.98357 |\n",
-       "| 1659367 | 2015-08-27 | 2469. | 122.000 | NA | NA | 163030003        | O/E - Systolic BP reading                        | 122.00 | 42 | ⋯ | NA | NA | 11.148 |  8.89 |    NA | 1.332 | 276.2 | 3.72 | 43.2 | 47.98357 |\n",
-       "| 1659367 | 2015-08-27 | 2469. | 135.000 | NA | NA | 163030003        | O/E - Systolic BP reading                        | 135.00 | 42 | ⋯ | NA | NA | 11.148 |  8.89 |    NA | 1.332 | 276.2 | 3.72 | 43.2 | 47.98357 |\n",
-       "| 1659367 | 2015-08-27 | 246A. | 77.000  | NA | NA | 163031004        | O/E - Diastolic BP reading                       |  77.00 | 42 | ⋯ | NA | NA | 11.148 |  8.89 |    NA | 1.332 | 276.2 | 3.72 | 43.2 | 47.98357 |\n",
-       "| 1659367 | 2015-08-27 | 246A. | 79.000  | NA | NA | 163031004        | O/E - Diastolic BP reading                       |  79.00 | 42 | ⋯ | NA | NA | 11.148 |  8.89 |    NA | 1.332 | 276.2 | 3.72 | 43.2 | 47.98357 |\n",
-       "| 1659367 | 2015-08-27 | 44P5. | 1.100   | NA | NA | 1005681000000107 | Serum high density lipoprotein cholesterol level |   1.10 | 42 | ⋯ | NA | NA | 11.148 |  8.89 |    NA | 1.332 | 276.2 | 3.72 | 43.2 | 47.98357 |\n",
-       "| 1659367 | 2015-08-27 | XE2eD | 5.200   | NA | NA | 1005671000000105 | Serum cholesterol level                          |   5.20 | 42 | ⋯ | NA | NA | 11.148 |  8.89 |    NA | 1.332 | 276.2 | 3.72 | 43.2 | 47.98357 |\n",
-       "| 1659367 | 2015-08-27 | XE2q9 | 2.000   | NA | NA | 1005691000000109 | Serum triglycerides level                        |   2.00 | 42 | ⋯ | NA | NA | 11.148 |  8.89 |    NA | 1.332 | 276.2 | 3.72 | 43.2 | 47.98357 |\n",
-       "| 2091573 | 2005-04-22 | 2469. | 142.000 | NA | NA | 163030003        | O/E - Systolic BP reading                        | 142.00 | 49 | ⋯ | NA | 51 | 10.978 | 11.12 | 79.19 | 2.321 | 425.1 | 4.52 | 81.4 | 45.44285 |\n",
-       "| 2091573 | 2005-04-22 | 246A. | 82.000  | NA | NA | 163031004        | O/E - Diastolic BP reading                       |  82.00 | 49 | ⋯ | NA | 51 | 10.978 | 11.12 | 79.19 | 2.321 | 425.1 | 4.52 | 81.4 | 45.44285 |\n",
-       "| 2091573 | 2008-01-18 | 2469. | 130.000 | NA | NA | 163030003        | O/E - Systolic BP reading                        | 130.00 | 49 | ⋯ | NA | 51 | 10.978 | 11.12 | 79.19 | 2.321 | 425.1 | 4.52 | 81.4 | 48.18344 |\n",
-       "| 2091573 | 2008-01-18 | 246A. | 84.000  | NA | NA | 163031004        | O/E - Diastolic BP reading                       |  84.00 | 49 | ⋯ | NA | 51 | 10.978 | 11.12 | 79.19 | 2.321 | 425.1 | 4.52 | 81.4 | 48.18344 |\n",
-       "| 2091573 | 2011-12-21 | 44P5. | 1.200   | NA | NA | 1005681000000107 | Serum high density lipoprotein cholesterol level |   1.20 | 49 | ⋯ | NA | 51 | 10.978 | 11.12 | 79.19 | 2.321 | 425.1 | 4.52 | 81.4 | 52.10678 |\n",
-       "| 2091573 | 2011-12-21 | 44P6. | 3.700   | NA | NA | 1022191000000100 | Serum low density lipoprotein cholesterol level  |   3.70 | 49 | ⋯ | NA | 51 | 10.978 | 11.12 | 79.19 | 2.321 | 425.1 | 4.52 | 81.4 | 52.10678 |\n",
-       "| 2091573 | 2011-12-21 | XE2eD | 6.300   | NA | NA | 1005671000000105 | Serum cholesterol level                          |   6.30 | 49 | ⋯ | NA | 51 | 10.978 | 11.12 | 79.19 | 2.321 | 425.1 | 4.52 | 81.4 | 52.10678 |\n",
-       "| 2091573 | 2011-12-21 | XE2q9 | 3.000   | NA | NA | 1005691000000109 | Serum triglycerides level                        |   3.00 | 49 | ⋯ | NA | 51 | 10.978 | 11.12 | 79.19 | 2.321 | 425.1 | 4.52 | 81.4 | 52.10678 |\n",
-       "| 2091573 | 2014-02-11 | 22A.. | 74.000  | NA | NA | 162763007        | O/E - weight                                     |  74.00 | 49 | ⋯ | NA | 51 | 10.978 | 11.12 | 79.19 | 2.321 | 425.1 | 4.52 | 81.4 | 54.25051 |\n",
-       "| 2091573 | 2014-02-11 | 2469. | 115.000 | NA | NA | 163030003        | O/E - Systolic BP reading                        | 115.00 | 49 | ⋯ | NA | 51 | 10.978 | 11.12 | 79.19 | 2.321 | 425.1 | 4.52 | 81.4 | 54.25051 |\n",
-       "| 2091573 | 2014-02-11 | 246A. | 65.000  | NA | NA | 163031004        | O/E - Diastolic BP reading                       |  65.00 | 49 | ⋯ | NA | 51 | 10.978 | 11.12 | 79.19 | 2.321 | 425.1 | 4.52 | 81.4 | 54.25051 |\n",
-       "| 2091573 | 2014-02-11 | 44P5. | 0.770   | NA | NA | 1005681000000107 | Serum high density lipoprotein cholesterol level |   0.77 | 49 | ⋯ | NA | 51 | 10.978 | 11.12 | 79.19 | 2.321 | 425.1 | 4.52 | 81.4 | 54.25051 |\n",
-       "| 2091573 | 2014-03-27 | 22A.. | 73.180  | NA | NA | 162763007        | O/E - weight                                     |  73.18 | 49 | ⋯ | NA | 51 | 10.978 | 11.12 | 79.19 | 2.321 | 425.1 | 4.52 | 81.4 | 54.37098 |\n",
-       "| 2091573 | 2014-04-08 | 44P5. | 1.100   | NA | NA | 1005681000000107 | Serum high density lipoprotein cholesterol level |   1.10 | 49 | ⋯ | NA | 51 | 10.978 | 11.12 | 79.19 | 2.321 | 425.1 | 4.52 | 81.4 | 54.40383 |\n",
-       "| 2091573 | 2014-04-08 | 44P6. | 0.800   | NA | NA | 1022191000000100 | Serum low density lipoprotein cholesterol level  |   0.80 | 49 | ⋯ | NA | 51 | 10.978 | 11.12 | 79.19 | 2.321 | 425.1 | 4.52 | 81.4 | 54.40383 |\n",
-       "| 2091573 | 2014-04-08 | XE2eD | 2.200   | NA | NA | 1005671000000105 | Serum cholesterol level                          |   2.20 | 49 | ⋯ | NA | 51 | 10.978 | 11.12 | 79.19 | 2.321 | 425.1 | 4.52 | 81.4 | 54.40383 |\n",
-       "| 2091573 | 2014-04-08 | XE2q9 | 0.600   | NA | NA | 1005691000000109 | Serum triglycerides level                        |   0.60 | 49 | ⋯ | NA | 51 | 10.978 | 11.12 | 79.19 | 2.321 | 425.1 | 4.52 | 81.4 | 54.40383 |\n",
-       "| 2091573 | 2014-05-12 | 22A.. | 72.000  | NA | NA | 162763007        | O/E - weight                                     |  72.00 | 49 | ⋯ | NA | 51 | 10.978 | 11.12 | 79.19 | 2.321 | 425.1 | 4.52 | 81.4 | 54.49692 |\n",
-       "| 2091573 | 2014-05-12 | 2469. | 116.000 | NA | NA | 163030003        | O/E - Systolic BP reading                        | 116.00 | 49 | ⋯ | NA | 51 | 10.978 | 11.12 | 79.19 | 2.321 | 425.1 | 4.52 | 81.4 | 54.49692 |\n",
-       "| 2091573 | 2014-05-12 | 246A. | 73.000  | NA | NA | 163031004        | O/E - Diastolic BP reading                       |  73.00 | 49 | ⋯ | NA | 51 | 10.978 | 11.12 | 79.19 | 2.321 | 425.1 | 4.52 | 81.4 | 54.49692 |\n",
-       "| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋱ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |\n",
-       "| 5550237 | 2015-09-29 | 246A. | 75.000  | NA     | NA | 163031004        | O/E - Diastolic BP reading                       |  75.0 | 53 | ⋯ | NA | NA | 1.113 |  7.19 | NA | 2.563 | 320.7 | 4.10 | 50.3 | 60.24641 |\n",
-       "| 5708666 | 1998-07-29 | 22A.. | 62.100  | NA     | NA | 162763007        | O/E - weight                                     |  62.1 | 53 | ⋯ | NA | NA | 0.963 | 17.84 | NA | 0.864 | 213.6 | 2.61 | 35.2 | 43.46886 |\n",
-       "| 5708666 | 1998-07-29 | 246.. | 110.000 | 70.000 | NA | 163030003        | O/E - Systolic BP reading                        | 110.0 | 53 | ⋯ | NA | NA | 0.963 | 17.84 | NA | 0.864 | 213.6 | 2.61 | 35.2 | 43.46886 |\n",
-       "| 5708666 | 1998-07-29 | 246.. | 110.000 | 70.000 | NA | 163031004        | O/E - Diastolic BP reading                       |  70.0 | 53 | ⋯ | NA | NA | 0.963 | 17.84 | NA | 0.864 | 213.6 | 2.61 | 35.2 | 43.46886 |\n",
-       "| 5708666 | 2003-11-13 | 246.. | 110.000 | 50.000 | NA | 163030003        | O/E - Systolic BP reading                        | 110.0 | 53 | ⋯ | NA | NA | 0.963 | 17.84 | NA | 0.864 | 213.6 | 2.61 | 35.2 | 48.76112 |\n",
-       "| 5708666 | 2003-11-13 | 246.. | 110.000 | 50.000 | NA | 163031004        | O/E - Diastolic BP reading                       |  50.0 | 53 | ⋯ | NA | NA | 0.963 | 17.84 | NA | 0.864 | 213.6 | 2.61 | 35.2 | 48.76112 |\n",
-       "| 5708666 | 2004-06-17 | 246.. | 122.000 | 77.000 | NA | 163030003        | O/E - Systolic BP reading                        | 122.0 | 53 | ⋯ | NA | NA | 0.963 | 17.84 | NA | 0.864 | 213.6 | 2.61 | 35.2 | 49.35524 |\n",
-       "| 5708666 | 2004-06-17 | 246.. | 122.000 | 77.000 | NA | 163031004        | O/E - Diastolic BP reading                       |  77.0 | 53 | ⋯ | NA | NA | 0.963 | 17.84 | NA | 0.864 | 213.6 | 2.61 | 35.2 | 49.35524 |\n",
-       "| 5708666 | 2005-04-06 | 246.. | 110.000 | 70.000 | NA | 163030003        | O/E - Systolic BP reading                        | 110.0 | 53 | ⋯ | NA | NA | 0.963 | 17.84 | NA | 0.864 | 213.6 | 2.61 | 35.2 | 50.15743 |\n",
-       "| 5708666 | 2005-04-06 | 246.. | 110.000 | 70.000 | NA | 163031004        | O/E - Diastolic BP reading                       |  70.0 | 53 | ⋯ | NA | NA | 0.963 | 17.84 | NA | 0.864 | 213.6 | 2.61 | 35.2 | 50.15743 |\n",
-       "| 5708666 | 2005-06-29 | 246.. | 120.000 | 80.000 | NA | 163030003        | O/E - Systolic BP reading                        | 120.0 | 53 | ⋯ | NA | NA | 0.963 | 17.84 | NA | 0.864 | 213.6 | 2.61 | 35.2 | 50.38741 |\n",
-       "| 5708666 | 2005-06-29 | 246.. | 120.000 | 80.000 | NA | 163031004        | O/E - Diastolic BP reading                       |  80.0 | 53 | ⋯ | NA | NA | 0.963 | 17.84 | NA | 0.864 | 213.6 | 2.61 | 35.2 | 50.38741 |\n",
-       "| 5708666 | 2006-01-17 | 246.. | 110.000 | 70.000 | NA | 163030003        | O/E - Systolic BP reading                        | 110.0 | 53 | ⋯ | NA | NA | 0.963 | 17.84 | NA | 0.864 | 213.6 | 2.61 | 35.2 | 50.94045 |\n",
-       "| 5708666 | 2006-01-17 | 246.. | 110.000 | 70.000 | NA | 163031004        | O/E - Diastolic BP reading                       |  70.0 | 53 | ⋯ | NA | NA | 0.963 | 17.84 | NA | 0.864 | 213.6 | 2.61 | 35.2 | 50.94045 |\n",
-       "| 5708666 | 2006-11-08 | 22A.. | 63.000  | NA     | NA | 162763007        | O/E - weight                                     |  63.0 | 53 | ⋯ | NA | NA | 0.963 | 17.84 | NA | 0.864 | 213.6 | 2.61 | 35.2 | 51.74812 |\n",
-       "| 5708666 | 2006-11-08 | 246.. | 122.000 | 80.000 | NA | 163030003        | O/E - Systolic BP reading                        | 122.0 | 53 | ⋯ | NA | NA | 0.963 | 17.84 | NA | 0.864 | 213.6 | 2.61 | 35.2 | 51.74812 |\n",
-       "| 5708666 | 2006-11-08 | 246.. | 122.000 | 80.000 | NA | 163031004        | O/E - Diastolic BP reading                       |  80.0 | 53 | ⋯ | NA | NA | 0.963 | 17.84 | NA | 0.864 | 213.6 | 2.61 | 35.2 | 51.74812 |\n",
-       "| 5708666 | 2013-04-04 | 246.. | 139.000 | 87.000 | NA | 163030003        | O/E - Systolic BP reading                        | 139.0 | 53 | ⋯ | NA | NA | 0.963 | 17.84 | NA | 0.864 | 213.6 | 2.61 | 35.2 | 58.15195 |\n",
-       "| 5708666 | 2013-04-04 | 246.. | 139.000 | 87.000 | NA | 163031004        | O/E - Diastolic BP reading                       |  87.0 | 53 | ⋯ | NA | NA | 0.963 | 17.84 | NA | 0.864 | 213.6 | 2.61 | 35.2 | 58.15195 |\n",
-       "| 5708666 | 2015-07-20 | 246.. | 130.000 | 70.000 | NA | 163030003        | O/E - Systolic BP reading                        | 130.0 | 53 | ⋯ | NA | NA | 0.963 | 17.84 | NA | 0.864 | 213.6 | 2.61 | 35.2 | 60.44353 |\n",
-       "| 5708666 | 2015-07-20 | 246.. | 130.000 | 70.000 | NA | 163031004        | O/E - Diastolic BP reading                       |  70.0 | 53 | ⋯ | NA | NA | 0.963 | 17.84 | NA | 0.864 | 213.6 | 2.61 | 35.2 | 60.44353 |\n",
-       "| 5708666 | 2015-08-06 | 22A.. | 63.200  | NA     | NA | 162763007        | O/E - weight                                     |  63.2 | 53 | ⋯ | NA | NA | 0.963 | 17.84 | NA | 0.864 | 213.6 | 2.61 | 35.2 | 60.49008 |\n",
-       "| 5708666 | 2015-08-06 | 246.. | 131.000 | 84.000 | NA | 163030003        | O/E - Systolic BP reading                        | 131.0 | 53 | ⋯ | NA | NA | 0.963 | 17.84 | NA | 0.864 | 213.6 | 2.61 | 35.2 | 60.49008 |\n",
-       "| 5708666 | 2015-08-06 | 246.. | 131.000 | 84.000 | NA | 163031004        | O/E - Diastolic BP reading                       |  84.0 | 53 | ⋯ | NA | NA | 0.963 | 17.84 | NA | 0.864 | 213.6 | 2.61 | 35.2 | 60.49008 |\n",
-       "| 5708666 | 2015-12-10 | 246.. | 110.000 | 70.000 | NA | 163030003        | O/E - Systolic BP reading                        | 110.0 | 53 | ⋯ | NA | NA | 0.963 | 17.84 | NA | 0.864 | 213.6 | 2.61 | 35.2 | 60.83504 |\n",
-       "| 5708666 | 2015-12-10 | 246.. | 110.000 | 70.000 | NA | 163031004        | O/E - Diastolic BP reading                       |  70.0 | 53 | ⋯ | NA | NA | 0.963 | 17.84 | NA | 0.864 | 213.6 | 2.61 | 35.2 | 60.83504 |\n",
-       "| 5708666 | 2015-12-14 | XE2eD | 5.600   | NA     | NA | 1005671000000105 | Serum cholesterol level                          |   5.6 | 53 | ⋯ | NA | NA | 0.963 | 17.84 | NA | 0.864 | 213.6 | 2.61 | 35.2 | 60.84600 |\n",
-       "| 5708666 | 2015-12-14 | 44P5. | 2.000   | NA     | NA | 1005681000000107 | Serum high density lipoprotein cholesterol level |   2.0 | 53 | ⋯ | NA | NA | 0.963 | 17.84 | NA | 0.864 | 213.6 | 2.61 | 35.2 | 60.84600 |\n",
-       "| 5708666 | 2015-12-14 | 44P6. | 3.100   | NA     | NA | 1022191000000100 | Serum low density lipoprotein cholesterol level  |   3.1 | 53 | ⋯ | NA | NA | 0.963 | 17.84 | NA | 0.864 | 213.6 | 2.61 | 35.2 | 60.84600 |\n",
-       "| 5708666 | 2015-12-14 | XE2q9 | 1.100   | NA     | NA | 1005691000000109 | Serum triglycerides level                        |   1.1 | 53 | ⋯ | NA | NA | 0.963 | 17.84 | NA | 0.864 | 213.6 | 2.61 | 35.2 | 60.84600 |\n",
-       "\n"
-      ],
-      "text/plain": [
-       "       eid     date       code   value1  value2 value3 meaning         \n",
-       "1      1659367 2009-11-30 22A..  83.000  NA     NA     162763007       \n",
-       "2      1659367 2009-12-21 22A..  86.000  NA     NA     162763007       \n",
-       "3      1659367 2015-08-27 22A..  96.000  NA     NA     162763007       \n",
-       "4      1659367 2015-08-27 2469.  122.000 NA     NA     163030003       \n",
-       "5      1659367 2015-08-27 2469.  135.000 NA     NA     163030003       \n",
-       "6      1659367 2015-08-27 246A.  77.000  NA     NA     163031004       \n",
-       "7      1659367 2015-08-27 246A.  79.000  NA     NA     163031004       \n",
-       "8      1659367 2015-08-27 44P5.  1.100   NA     NA     1005681000000107\n",
-       "9      1659367 2015-08-27 XE2eD  5.200   NA     NA     1005671000000105\n",
-       "10     1659367 2015-08-27 XE2q9  2.000   NA     NA     1005691000000109\n",
-       "11     2091573 2005-04-22 2469.  142.000 NA     NA     163030003       \n",
-       "12     2091573 2005-04-22 246A.  82.000  NA     NA     163031004       \n",
-       "13     2091573 2008-01-18 2469.  130.000 NA     NA     163030003       \n",
-       "14     2091573 2008-01-18 246A.  84.000  NA     NA     163031004       \n",
-       "15     2091573 2011-12-21 44P5.  1.200   NA     NA     1005681000000107\n",
-       "16     2091573 2011-12-21 44P6.  3.700   NA     NA     1022191000000100\n",
-       "17     2091573 2011-12-21 XE2eD  6.300   NA     NA     1005671000000105\n",
-       "18     2091573 2011-12-21 XE2q9  3.000   NA     NA     1005691000000109\n",
-       "19     2091573 2014-02-11 22A..  74.000  NA     NA     162763007       \n",
-       "20     2091573 2014-02-11 2469.  115.000 NA     NA     163030003       \n",
-       "21     2091573 2014-02-11 246A.  65.000  NA     NA     163031004       \n",
-       "22     2091573 2014-02-11 44P5.  0.770   NA     NA     1005681000000107\n",
-       "23     2091573 2014-03-27 22A..  73.180  NA     NA     162763007       \n",
-       "24     2091573 2014-04-08 44P5.  1.100   NA     NA     1005681000000107\n",
-       "25     2091573 2014-04-08 44P6.  0.800   NA     NA     1022191000000100\n",
-       "26     2091573 2014-04-08 XE2eD  2.200   NA     NA     1005671000000105\n",
-       "27     2091573 2014-04-08 XE2q9  0.600   NA     NA     1005691000000109\n",
-       "28     2091573 2014-05-12 22A..  72.000  NA     NA     162763007       \n",
-       "29     2091573 2014-05-12 2469.  116.000 NA     NA     163030003       \n",
-       "30     2091573 2014-05-12 246A.  73.000  NA     NA     163031004       \n",
-       "<U+22EE> <U+22EE> <U+22EE>   <U+22EE> <U+22EE> <U+22EE> <U+22EE> <U+22EE>        \n",
-       "187    5550237 2015-09-29 246A.  75.000  NA     NA     163031004       \n",
-       "188    5708666 1998-07-29 22A..  62.100  NA     NA     162763007       \n",
-       "189    5708666 1998-07-29 246..  110.000 70.000 NA     163030003       \n",
-       "190    5708666 1998-07-29 246..  110.000 70.000 NA     163031004       \n",
-       "191    5708666 2003-11-13 246..  110.000 50.000 NA     163030003       \n",
-       "192    5708666 2003-11-13 246..  110.000 50.000 NA     163031004       \n",
-       "193    5708666 2004-06-17 246..  122.000 77.000 NA     163030003       \n",
-       "194    5708666 2004-06-17 246..  122.000 77.000 NA     163031004       \n",
-       "195    5708666 2005-04-06 246..  110.000 70.000 NA     163030003       \n",
-       "196    5708666 2005-04-06 246..  110.000 70.000 NA     163031004       \n",
-       "197    5708666 2005-06-29 246..  120.000 80.000 NA     163030003       \n",
-       "198    5708666 2005-06-29 246..  120.000 80.000 NA     163031004       \n",
-       "199    5708666 2006-01-17 246..  110.000 70.000 NA     163030003       \n",
-       "200    5708666 2006-01-17 246..  110.000 70.000 NA     163031004       \n",
-       "201    5708666 2006-11-08 22A..  63.000  NA     NA     162763007       \n",
-       "202    5708666 2006-11-08 246..  122.000 80.000 NA     163030003       \n",
-       "203    5708666 2006-11-08 246..  122.000 80.000 NA     163031004       \n",
-       "204    5708666 2013-04-04 246..  139.000 87.000 NA     163030003       \n",
-       "205    5708666 2013-04-04 246..  139.000 87.000 NA     163031004       \n",
-       "206    5708666 2015-07-20 246..  130.000 70.000 NA     163030003       \n",
-       "207    5708666 2015-07-20 246..  130.000 70.000 NA     163031004       \n",
-       "208    5708666 2015-08-06 22A..  63.200  NA     NA     162763007       \n",
-       "209    5708666 2015-08-06 246..  131.000 84.000 NA     163030003       \n",
-       "210    5708666 2015-08-06 246..  131.000 84.000 NA     163031004       \n",
-       "211    5708666 2015-12-10 246..  110.000 70.000 NA     163030003       \n",
-       "212    5708666 2015-12-10 246..  110.000 70.000 NA     163031004       \n",
-       "213    5708666 2015-12-14 XE2eD  5.600   NA     NA     1005671000000105\n",
-       "214    5708666 2015-12-14 44P5.  2.000   NA     NA     1005681000000107\n",
-       "215    5708666 2015-12-14 44P6.  3.100   NA     NA     1022191000000100\n",
-       "216    5708666 2015-12-14 XE2q9  1.100   NA     NA     1005691000000109\n",
-       "       name                                             value \n",
-       "1      O/E - weight                                      83.00\n",
-       "2      O/E - weight                                      86.00\n",
-       "3      O/E - weight                                      96.00\n",
-       "4      O/E - Systolic BP reading                        122.00\n",
-       "5      O/E - Systolic BP reading                        135.00\n",
-       "6      O/E - Diastolic BP reading                        77.00\n",
-       "7      O/E - Diastolic BP reading                        79.00\n",
-       "8      Serum high density lipoprotein cholesterol level   1.10\n",
-       "9      Serum cholesterol level                            5.20\n",
-       "10     Serum triglycerides level                          2.00\n",
-       "11     O/E - Systolic BP reading                        142.00\n",
-       "12     O/E - Diastolic BP reading                        82.00\n",
-       "13     O/E - Systolic BP reading                        130.00\n",
-       "14     O/E - Diastolic BP reading                        84.00\n",
-       "15     Serum high density lipoprotein cholesterol level   1.20\n",
-       "16     Serum low density lipoprotein cholesterol level    3.70\n",
-       "17     Serum cholesterol level                            6.30\n",
-       "18     Serum triglycerides level                          3.00\n",
-       "19     O/E - weight                                      74.00\n",
-       "20     O/E - Systolic BP reading                        115.00\n",
-       "21     O/E - Diastolic BP reading                        65.00\n",
-       "22     Serum high density lipoprotein cholesterol level   0.77\n",
-       "23     O/E - weight                                      73.18\n",
-       "24     Serum high density lipoprotein cholesterol level   1.10\n",
-       "25     Serum low density lipoprotein cholesterol level    0.80\n",
-       "26     Serum cholesterol level                            2.20\n",
-       "27     Serum triglycerides level                          0.60\n",
-       "28     O/E - weight                                      72.00\n",
-       "29     O/E - Systolic BP reading                        116.00\n",
-       "30     O/E - Diastolic BP reading                        73.00\n",
-       "<U+22EE> <U+22EE>                                         <U+22EE>\n",
-       "187    O/E - Diastolic BP reading                        75.0 \n",
-       "188    O/E - weight                                      62.1 \n",
-       "189    O/E - Systolic BP reading                        110.0 \n",
-       "190    O/E - Diastolic BP reading                        70.0 \n",
-       "191    O/E - Systolic BP reading                        110.0 \n",
-       "192    O/E - Diastolic BP reading                        50.0 \n",
-       "193    O/E - Systolic BP reading                        122.0 \n",
-       "194    O/E - Diastolic BP reading                        77.0 \n",
-       "195    O/E - Systolic BP reading                        110.0 \n",
-       "196    O/E - Diastolic BP reading                        70.0 \n",
-       "197    O/E - Systolic BP reading                        120.0 \n",
-       "198    O/E - Diastolic BP reading                        80.0 \n",
-       "199    O/E - Systolic BP reading                        110.0 \n",
-       "200    O/E - Diastolic BP reading                        70.0 \n",
-       "201    O/E - weight                                      63.0 \n",
-       "202    O/E - Systolic BP reading                        122.0 \n",
-       "203    O/E - Diastolic BP reading                        80.0 \n",
-       "204    O/E - Systolic BP reading                        139.0 \n",
-       "205    O/E - Diastolic BP reading                        87.0 \n",
-       "206    O/E - Systolic BP reading                        130.0 \n",
-       "207    O/E - Diastolic BP reading                        70.0 \n",
-       "208    O/E - weight                                      63.2 \n",
-       "209    O/E - Systolic BP reading                        131.0 \n",
-       "210    O/E - Diastolic BP reading                        84.0 \n",
-       "211    O/E - Systolic BP reading                        110.0 \n",
-       "212    O/E - Diastolic BP reading                        70.0 \n",
-       "213    Serum cholesterol level                            5.6 \n",
-       "214    Serum high density lipoprotein cholesterol level   2.0 \n",
-       "215    Serum low density lipoprotein cholesterol level    3.1 \n",
-       "216    Serum triglycerides level                          1.1 \n",
-       "       age_at_recruitment_f21022_0_0 <U+22EF>   rheumatoid_factor_f30820_0_0\n",
-       "1      42                            <U+22EF> NA                          \n",
-       "2      42                            <U+22EF> NA                          \n",
-       "3      42                            <U+22EF> NA                          \n",
-       "4      42                            <U+22EF> NA                          \n",
-       "5      42                            <U+22EF> NA                          \n",
-       "6      42                            <U+22EF> NA                          \n",
-       "7      42                            <U+22EF> NA                          \n",
-       "8      42                            <U+22EF> NA                          \n",
-       "9      42                            <U+22EF> NA                          \n",
-       "10     42                            <U+22EF> NA                          \n",
-       "11     49                            <U+22EF> NA                          \n",
-       "12     49                            <U+22EF> NA                          \n",
-       "13     49                            <U+22EF> NA                          \n",
-       "14     49                            <U+22EF> NA                          \n",
-       "15     49                            <U+22EF> NA                          \n",
-       "16     49                            <U+22EF> NA                          \n",
-       "17     49                            <U+22EF> NA                          \n",
-       "18     49                            <U+22EF> NA                          \n",
-       "19     49                            <U+22EF> NA                          \n",
-       "20     49                            <U+22EF> NA                          \n",
-       "21     49                            <U+22EF> NA                          \n",
-       "22     49                            <U+22EF> NA                          \n",
-       "23     49                            <U+22EF> NA                          \n",
-       "24     49                            <U+22EF> NA                          \n",
-       "25     49                            <U+22EF> NA                          \n",
-       "26     49                            <U+22EF> NA                          \n",
-       "27     49                            <U+22EF> NA                          \n",
-       "28     49                            <U+22EF> NA                          \n",
-       "29     49                            <U+22EF> NA                          \n",
-       "30     49                            <U+22EF> NA                          \n",
-       "<U+22EE> <U+22EE>                      <U+22F1> <U+22EE>                    \n",
-       "187    53                            <U+22EF> NA                          \n",
-       "188    53                            <U+22EF> NA                          \n",
-       "189    53                            <U+22EF> NA                          \n",
-       "190    53                            <U+22EF> NA                          \n",
-       "191    53                            <U+22EF> NA                          \n",
-       "192    53                            <U+22EF> NA                          \n",
-       "193    53                            <U+22EF> NA                          \n",
-       "194    53                            <U+22EF> NA                          \n",
-       "195    53                            <U+22EF> NA                          \n",
-       "196    53                            <U+22EF> NA                          \n",
-       "197    53                            <U+22EF> NA                          \n",
-       "198    53                            <U+22EF> NA                          \n",
-       "199    53                            <U+22EF> NA                          \n",
-       "200    53                            <U+22EF> NA                          \n",
-       "201    53                            <U+22EF> NA                          \n",
-       "202    53                            <U+22EF> NA                          \n",
-       "203    53                            <U+22EF> NA                          \n",
-       "204    53                            <U+22EF> NA                          \n",
-       "205    53                            <U+22EF> NA                          \n",
-       "206    53                            <U+22EF> NA                          \n",
-       "207    53                            <U+22EF> NA                          \n",
-       "208    53                            <U+22EF> NA                          \n",
-       "209    53                            <U+22EF> NA                          \n",
-       "210    53                            <U+22EF> NA                          \n",
-       "211    53                            <U+22EF> NA                          \n",
-       "212    53                            <U+22EF> NA                          \n",
-       "213    53                            <U+22EF> NA                          \n",
-       "214    53                            <U+22EF> NA                          \n",
-       "215    53                            <U+22EF> NA                          \n",
-       "216    53                            <U+22EF> NA                          \n",
-       "       shbg_f30830_0_0 testosterone_f30850_0_0 total_bilirubin_f30840_0_0\n",
-       "1      NA              11.148                   8.89                     \n",
-       "2      NA              11.148                   8.89                     \n",
-       "3      NA              11.148                   8.89                     \n",
-       "4      NA              11.148                   8.89                     \n",
-       "5      NA              11.148                   8.89                     \n",
-       "6      NA              11.148                   8.89                     \n",
-       "7      NA              11.148                   8.89                     \n",
-       "8      NA              11.148                   8.89                     \n",
-       "9      NA              11.148                   8.89                     \n",
-       "10     NA              11.148                   8.89                     \n",
-       "11     51              10.978                  11.12                     \n",
-       "12     51              10.978                  11.12                     \n",
-       "13     51              10.978                  11.12                     \n",
-       "14     51              10.978                  11.12                     \n",
-       "15     51              10.978                  11.12                     \n",
-       "16     51              10.978                  11.12                     \n",
-       "17     51              10.978                  11.12                     \n",
-       "18     51              10.978                  11.12                     \n",
-       "19     51              10.978                  11.12                     \n",
-       "20     51              10.978                  11.12                     \n",
-       "21     51              10.978                  11.12                     \n",
-       "22     51              10.978                  11.12                     \n",
-       "23     51              10.978                  11.12                     \n",
-       "24     51              10.978                  11.12                     \n",
-       "25     51              10.978                  11.12                     \n",
-       "26     51              10.978                  11.12                     \n",
-       "27     51              10.978                  11.12                     \n",
-       "28     51              10.978                  11.12                     \n",
-       "29     51              10.978                  11.12                     \n",
-       "30     51              10.978                  11.12                     \n",
-       "<U+22EE> <U+22EE>        <U+22EE>                <U+22EE>                  \n",
-       "187    NA              1.113                    7.19                     \n",
-       "188    NA              0.963                   17.84                     \n",
-       "189    NA              0.963                   17.84                     \n",
-       "190    NA              0.963                   17.84                     \n",
-       "191    NA              0.963                   17.84                     \n",
-       "192    NA              0.963                   17.84                     \n",
-       "193    NA              0.963                   17.84                     \n",
-       "194    NA              0.963                   17.84                     \n",
-       "195    NA              0.963                   17.84                     \n",
-       "196    NA              0.963                   17.84                     \n",
-       "197    NA              0.963                   17.84                     \n",
-       "198    NA              0.963                   17.84                     \n",
-       "199    NA              0.963                   17.84                     \n",
-       "200    NA              0.963                   17.84                     \n",
-       "201    NA              0.963                   17.84                     \n",
-       "202    NA              0.963                   17.84                     \n",
-       "203    NA              0.963                   17.84                     \n",
-       "204    NA              0.963                   17.84                     \n",
-       "205    NA              0.963                   17.84                     \n",
-       "206    NA              0.963                   17.84                     \n",
-       "207    NA              0.963                   17.84                     \n",
-       "208    NA              0.963                   17.84                     \n",
-       "209    NA              0.963                   17.84                     \n",
-       "210    NA              0.963                   17.84                     \n",
-       "211    NA              0.963                   17.84                     \n",
-       "212    NA              0.963                   17.84                     \n",
-       "213    NA              0.963                   17.84                     \n",
-       "214    NA              0.963                   17.84                     \n",
-       "215    NA              0.963                   17.84                     \n",
-       "216    NA              0.963                   17.84                     \n",
-       "       total_protein_f30860_0_0 triglycerides_f30870_0_0 urate_f30880_0_0\n",
-       "1         NA                    1.332                    276.2           \n",
-       "2         NA                    1.332                    276.2           \n",
-       "3         NA                    1.332                    276.2           \n",
-       "4         NA                    1.332                    276.2           \n",
-       "5         NA                    1.332                    276.2           \n",
-       "6         NA                    1.332                    276.2           \n",
-       "7         NA                    1.332                    276.2           \n",
-       "8         NA                    1.332                    276.2           \n",
-       "9         NA                    1.332                    276.2           \n",
-       "10        NA                    1.332                    276.2           \n",
-       "11     79.19                    2.321                    425.1           \n",
-       "12     79.19                    2.321                    425.1           \n",
-       "13     79.19                    2.321                    425.1           \n",
-       "14     79.19                    2.321                    425.1           \n",
-       "15     79.19                    2.321                    425.1           \n",
-       "16     79.19                    2.321                    425.1           \n",
-       "17     79.19                    2.321                    425.1           \n",
-       "18     79.19                    2.321                    425.1           \n",
-       "19     79.19                    2.321                    425.1           \n",
-       "20     79.19                    2.321                    425.1           \n",
-       "21     79.19                    2.321                    425.1           \n",
-       "22     79.19                    2.321                    425.1           \n",
-       "23     79.19                    2.321                    425.1           \n",
-       "24     79.19                    2.321                    425.1           \n",
-       "25     79.19                    2.321                    425.1           \n",
-       "26     79.19                    2.321                    425.1           \n",
-       "27     79.19                    2.321                    425.1           \n",
-       "28     79.19                    2.321                    425.1           \n",
-       "29     79.19                    2.321                    425.1           \n",
-       "30     79.19                    2.321                    425.1           \n",
-       "<U+22EE> <U+22EE>                 <U+22EE>                 <U+22EE>        \n",
-       "187    NA                       2.563                    320.7           \n",
-       "188    NA                       0.864                    213.6           \n",
-       "189    NA                       0.864                    213.6           \n",
-       "190    NA                       0.864                    213.6           \n",
-       "191    NA                       0.864                    213.6           \n",
-       "192    NA                       0.864                    213.6           \n",
-       "193    NA                       0.864                    213.6           \n",
-       "194    NA                       0.864                    213.6           \n",
-       "195    NA                       0.864                    213.6           \n",
-       "196    NA                       0.864                    213.6           \n",
-       "197    NA                       0.864                    213.6           \n",
-       "198    NA                       0.864                    213.6           \n",
-       "199    NA                       0.864                    213.6           \n",
-       "200    NA                       0.864                    213.6           \n",
-       "201    NA                       0.864                    213.6           \n",
-       "202    NA                       0.864                    213.6           \n",
-       "203    NA                       0.864                    213.6           \n",
-       "204    NA                       0.864                    213.6           \n",
-       "205    NA                       0.864                    213.6           \n",
-       "206    NA                       0.864                    213.6           \n",
-       "207    NA                       0.864                    213.6           \n",
-       "208    NA                       0.864                    213.6           \n",
-       "209    NA                       0.864                    213.6           \n",
-       "210    NA                       0.864                    213.6           \n",
-       "211    NA                       0.864                    213.6           \n",
-       "212    NA                       0.864                    213.6           \n",
-       "213    NA                       0.864                    213.6           \n",
-       "214    NA                       0.864                    213.6           \n",
-       "215    NA                       0.864                    213.6           \n",
-       "216    NA                       0.864                    213.6           \n",
-       "       urea_f30670_0_0 vitamin_d_f30890_0_0 time    \n",
-       "1      3.72            43.2                 42.24504\n",
-       "2      3.72            43.2                 42.30253\n",
-       "3      3.72            43.2                 47.98357\n",
-       "4      3.72            43.2                 47.98357\n",
-       "5      3.72            43.2                 47.98357\n",
-       "6      3.72            43.2                 47.98357\n",
-       "7      3.72            43.2                 47.98357\n",
-       "8      3.72            43.2                 47.98357\n",
-       "9      3.72            43.2                 47.98357\n",
-       "10     3.72            43.2                 47.98357\n",
-       "11     4.52            81.4                 45.44285\n",
-       "12     4.52            81.4                 45.44285\n",
-       "13     4.52            81.4                 48.18344\n",
-       "14     4.52            81.4                 48.18344\n",
-       "15     4.52            81.4                 52.10678\n",
-       "16     4.52            81.4                 52.10678\n",
-       "17     4.52            81.4                 52.10678\n",
-       "18     4.52            81.4                 52.10678\n",
-       "19     4.52            81.4                 54.25051\n",
-       "20     4.52            81.4                 54.25051\n",
-       "21     4.52            81.4                 54.25051\n",
-       "22     4.52            81.4                 54.25051\n",
-       "23     4.52            81.4                 54.37098\n",
-       "24     4.52            81.4                 54.40383\n",
-       "25     4.52            81.4                 54.40383\n",
-       "26     4.52            81.4                 54.40383\n",
-       "27     4.52            81.4                 54.40383\n",
-       "28     4.52            81.4                 54.49692\n",
-       "29     4.52            81.4                 54.49692\n",
-       "30     4.52            81.4                 54.49692\n",
-       "<U+22EE> <U+22EE>        <U+22EE>             <U+22EE>\n",
-       "187    4.10            50.3                 60.24641\n",
-       "188    2.61            35.2                 43.46886\n",
-       "189    2.61            35.2                 43.46886\n",
-       "190    2.61            35.2                 43.46886\n",
-       "191    2.61            35.2                 48.76112\n",
-       "192    2.61            35.2                 48.76112\n",
-       "193    2.61            35.2                 49.35524\n",
-       "194    2.61            35.2                 49.35524\n",
-       "195    2.61            35.2                 50.15743\n",
-       "196    2.61            35.2                 50.15743\n",
-       "197    2.61            35.2                 50.38741\n",
-       "198    2.61            35.2                 50.38741\n",
-       "199    2.61            35.2                 50.94045\n",
-       "200    2.61            35.2                 50.94045\n",
-       "201    2.61            35.2                 51.74812\n",
-       "202    2.61            35.2                 51.74812\n",
-       "203    2.61            35.2                 51.74812\n",
-       "204    2.61            35.2                 58.15195\n",
-       "205    2.61            35.2                 58.15195\n",
-       "206    2.61            35.2                 60.44353\n",
-       "207    2.61            35.2                 60.44353\n",
-       "208    2.61            35.2                 60.49008\n",
-       "209    2.61            35.2                 60.49008\n",
-       "210    2.61            35.2                 60.49008\n",
-       "211    2.61            35.2                 60.83504\n",
-       "212    2.61            35.2                 60.83504\n",
-       "213    2.61            35.2                 60.84600\n",
-       "214    2.61            35.2                 60.84600\n",
-       "215    2.61            35.2                 60.84600\n",
-       "216    2.61            35.2                 60.84600"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "options(repr.plot.width=20, repr.plot.height=25)\n",
-    "library(lubridate)\n",
-    "df = gp_measurements %>% filter(eid %in% eids) %>% filter(name %in% c(\"O/E - Systolic BP reading\", \"O/E - Diastolic BP reading\", \n",
-    "                                                                      \"Serum creatinine level\", \"Haemoglobin estimation\",\n",
-    "                                                                      \"GFR (glomerular filtration rate) calculated by abbreviated Modification of Diet in Renal Disease Study Group calculation\", \"Serum glucose level\", \t\n",
-    "                                                                      \"Total white cell count\", \"O/E - weight\", \"Serum cholesterol level\", \n",
-    "                                                                      \"Serum high density lipoprotein cholesterol level\", \"Serum low density lipoprotein cholesterol level\",\n",
-    "                                                                      \"Serum triglycerides level\")) %>%\n",
-    "    filter(as.numeric(value1)>0) %>% \n",
-    "    filter(!((name==\"Haemoglobin estimation\")&(value> 50))) %>%\n",
-    "    filter(!((name==\"O/E - Diastolic BP reading\")&(value> 400))) %>%\n",
-    "    filter(!((name==\"O/E - Systolic BP reading\")&(value> 400))) %>%\n",
-    "    filter(!((name==\"O/E - weight\")&(value> 300))) %>%\n",
-    "    filter(!((name==\"Serum cholesterol level\")&(value> 20))) %>%\n",
-    "    filter(!((name==\"Serum creatinine level\")&(value> 1000))) %>%\n",
-    "    filter(!((name==\"Serum high density lipoprotein cholesterol level\")&(value> 100))) %>%\n",
-    "    #filter(!((name==\"Finding of body mass index\")&(value> 50))) %>%\n",
-    "    left_join(data, on=\"eid\") %>% mutate(time = time_length(difftime(date, birth_date), \"years\"))%>% filter(time>0)\n",
-    "\n",
-    "ggplot(df, aes(x=time, y=as.numeric(value)), color=as.factor(name))+geom_point(alpha=0.1)+\n",
-    "    facet_grid(eid~name, scales=\"free_y\", labeller=label_wrap_gen(width = 10, multi_line = TRUE))+\n",
-    "    geom_smooth(method=\"lm\")#+\n",
-    "    #geom_vline(aes(xintercept=age_at_recruitment_f21022_0_0))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 94,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<table>\n",
-       "<caption>A tibble: 16306 × 2</caption>\n",
-       "<thead>\n",
-       "\t<tr><th scope=col>name</th><th scope=col>n</th></tr>\n",
-       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
-       "</thead>\n",
-       "<tbody>\n",
-       "\t<tr><td>O/E - Diastolic BP reading                                                                                              </td><td>4142442</td></tr>\n",
-       "\t<tr><td>O/E - Systolic BP reading                                                                                               </td><td>3963088</td></tr>\n",
-       "\t<tr><td>Serum creatinine level                                                                                                  </td><td>1670214</td></tr>\n",
-       "\t<tr><td>Haemoglobin estimation                                                                                                  </td><td>1662102</td></tr>\n",
-       "\t<tr><td>Total white cell count                                                                                                  </td><td>1630899</td></tr>\n",
-       "\t<tr><td>O/E - weight                                                                                                            </td><td>1588105</td></tr>\n",
-       "\t<tr><td>Serum sodium level                                                                                                      </td><td>1587227</td></tr>\n",
-       "\t<tr><td>Serum potassium level                                                                                                   </td><td>1582109</td></tr>\n",
-       "\t<tr><td>Finding of body mass index                                                                                              </td><td>1371580</td></tr>\n",
-       "\t<tr><td>Serum alanine aminotransferase level                                                                                    </td><td>1364116</td></tr>\n",
-       "\t<tr><td>Serum urea level                                                                                                        </td><td>1349233</td></tr>\n",
-       "\t<tr><td>Serum albumin level                                                                                                     </td><td>1344654</td></tr>\n",
-       "\t<tr><td>Serum alkaline phosphatase level                                                                                        </td><td>1314225</td></tr>\n",
-       "\t<tr><td>Platelet count                                                                                                          </td><td>1264036</td></tr>\n",
-       "\t<tr><td>Serum cholesterol level                                                                                                 </td><td>1248251</td></tr>\n",
-       "\t<tr><td>Haematocrit - packed cell volume                                                                                        </td><td>1247902</td></tr>\n",
-       "\t<tr><td>MCV - Mean corpuscular volume                                                                                           </td><td>1246810</td></tr>\n",
-       "\t<tr><td>Erythrocyte count                                                                                                       </td><td>1238243</td></tr>\n",
-       "\t<tr><td>MCH - Mean corpuscular haemoglobin                                                                                      </td><td>1224630</td></tr>\n",
-       "\t<tr><td>GFR (glomerular filtration rate) calculated by abbreviated Modification of Diet in Renal Disease Study Group calculation</td><td>1212917</td></tr>\n",
-       "\t<tr><td>Granulocyte count                                                                                                       </td><td>1211942</td></tr>\n",
-       "\t<tr><td>Lymphocyte count                                                                                                        </td><td>1206974</td></tr>\n",
-       "\t<tr><td>Monocyte count                                                                                                          </td><td>1205707</td></tr>\n",
-       "\t<tr><td>Eosinophil count                                                                                                        </td><td>1204617</td></tr>\n",
-       "\t<tr><td>Basophil count                                                                                                          </td><td>1165935</td></tr>\n",
-       "\t<tr><td>Serum high density lipoprotein cholesterol level                                                                        </td><td>1059995</td></tr>\n",
-       "\t<tr><td>Serum triglycerides level                                                                                               </td><td>1033506</td></tr>\n",
-       "\t<tr><td>O/E - height                                                                                                            </td><td> 955235</td></tr>\n",
-       "\t<tr><td>Review of medication                                                                                                    </td><td> 955104</td></tr>\n",
-       "\t<tr><td>Serum TSH (thyroid stimulating hormone) level                                                                           </td><td> 862321</td></tr>\n",
-       "\t<tr><td>⋮</td><td>⋮</td></tr>\n",
-       "\t<tr><td>X-ray of tibia/fibula abnormal                                                             </td><td>1</td></tr>\n",
-       "\t<tr><td>X-ray of tibia/fibula normal                                                               </td><td>1</td></tr>\n",
-       "\t<tr><td>X-ray pelvimetry abnormal                                                                  </td><td>1</td></tr>\n",
-       "\t<tr><td>X-ray phalanges of fingers abnormal                                                        </td><td>1</td></tr>\n",
-       "\t<tr><td>Xiphoid cartilage sprain                                                                   </td><td>1</td></tr>\n",
-       "\t<tr><td>Xylose absorption test                                                                     </td><td>1</td></tr>\n",
-       "\t<tr><td>Zenker's diverticulum                                                                      </td><td>1</td></tr>\n",
-       "\t<tr><td>Zygomatic complex of bones                                                                 </td><td>1</td></tr>\n",
-       "\t<tr><td>[D]Abnormal reflex, unspecified                                                            </td><td>1</td></tr>\n",
-       "\t<tr><td>[D]Anorexia                                                                                </td><td>1</td></tr>\n",
-       "\t<tr><td>[D]Bony lytic lesions on X-ray                                                             </td><td>1</td></tr>\n",
-       "\t<tr><td>[D]Local superficial swelling, mass or lump                                                </td><td>1</td></tr>\n",
-       "\t<tr><td>[D]Lump on shin                                                                            </td><td>1</td></tr>\n",
-       "\t<tr><td>[D]Other abdominal or pelvic symptom                                                       </td><td>1</td></tr>\n",
-       "\t<tr><td>[D]Swelling, mass and lump of chest                                                        </td><td>1</td></tr>\n",
-       "\t<tr><td>[M]Tubular adenoma or adenocarcinoma NOS                                                   </td><td>1</td></tr>\n",
-       "\t<tr><td>[SO]Skin of umbilicus                                                                      </td><td>1</td></tr>\n",
-       "\t<tr><td>[V]Assessment for procedure                                                                </td><td>1</td></tr>\n",
-       "\t<tr><td>[V]Bone marrow donor                                                                       </td><td>1</td></tr>\n",
-       "\t<tr><td>[V]Examination of ears and hearing                                                         </td><td>1</td></tr>\n",
-       "\t<tr><td>[V]Fitting or adjustment of hearing aid                                                    </td><td>1</td></tr>\n",
-       "\t<tr><td>[V]Kidney donor                                                                            </td><td>1</td></tr>\n",
-       "\t<tr><td>[V]Parent - child conflict                                                                 </td><td>1</td></tr>\n",
-       "\t<tr><td>[V]Personal history of malignant neoplasm of male genital organ                            </td><td>1</td></tr>\n",
-       "\t<tr><td>[V]Plastic surgery used in aftercare                                                       </td><td>1</td></tr>\n",
-       "\t<tr><td>[X]Otorhinolaryngological drugs and preparations causing adverse effects in therapeutic use</td><td>1</td></tr>\n",
-       "\t<tr><td>[X]Schizophrenia, schizotypal and delusional disorders                                     </td><td>1</td></tr>\n",
-       "\t<tr><td>cups/day                                                                                   </td><td>1</td></tr>\n",
-       "\t<tr><td>von Jaksch's anemia                                                                        </td><td>1</td></tr>\n",
-       "\t<tr><td>von Willebrand disorder                                                                    </td><td>1</td></tr>\n",
-       "</tbody>\n",
-       "</table>\n"
-      ],
-      "text/latex": [
-       "A tibble: 16306 × 2\n",
-       "\\begin{tabular}{ll}\n",
-       " name & n\\\\\n",
-       " <chr> & <int>\\\\\n",
-       "\\hline\n",
-       "\t O/E - Diastolic BP reading                                                                                               & 4142442\\\\\n",
-       "\t O/E - Systolic BP reading                                                                                                & 3963088\\\\\n",
-       "\t Serum creatinine level                                                                                                   & 1670214\\\\\n",
-       "\t Haemoglobin estimation                                                                                                   & 1662102\\\\\n",
-       "\t Total white cell count                                                                                                   & 1630899\\\\\n",
-       "\t O/E - weight                                                                                                             & 1588105\\\\\n",
-       "\t Serum sodium level                                                                                                       & 1587227\\\\\n",
-       "\t Serum potassium level                                                                                                    & 1582109\\\\\n",
-       "\t Finding of body mass index                                                                                               & 1371580\\\\\n",
-       "\t Serum alanine aminotransferase level                                                                                     & 1364116\\\\\n",
-       "\t Serum urea level                                                                                                         & 1349233\\\\\n",
-       "\t Serum albumin level                                                                                                      & 1344654\\\\\n",
-       "\t Serum alkaline phosphatase level                                                                                         & 1314225\\\\\n",
-       "\t Platelet count                                                                                                           & 1264036\\\\\n",
-       "\t Serum cholesterol level                                                                                                  & 1248251\\\\\n",
-       "\t Haematocrit - packed cell volume                                                                                         & 1247902\\\\\n",
-       "\t MCV - Mean corpuscular volume                                                                                            & 1246810\\\\\n",
-       "\t Erythrocyte count                                                                                                        & 1238243\\\\\n",
-       "\t MCH - Mean corpuscular haemoglobin                                                                                       & 1224630\\\\\n",
-       "\t GFR (glomerular filtration rate) calculated by abbreviated Modification of Diet in Renal Disease Study Group calculation & 1212917\\\\\n",
-       "\t Granulocyte count                                                                                                        & 1211942\\\\\n",
-       "\t Lymphocyte count                                                                                                         & 1206974\\\\\n",
-       "\t Monocyte count                                                                                                           & 1205707\\\\\n",
-       "\t Eosinophil count                                                                                                         & 1204617\\\\\n",
-       "\t Basophil count                                                                                                           & 1165935\\\\\n",
-       "\t Serum high density lipoprotein cholesterol level                                                                         & 1059995\\\\\n",
-       "\t Serum triglycerides level                                                                                                & 1033506\\\\\n",
-       "\t O/E - height                                                                                                             &  955235\\\\\n",
-       "\t Review of medication                                                                                                     &  955104\\\\\n",
-       "\t Serum TSH (thyroid stimulating hormone) level                                                                            &  862321\\\\\n",
-       "\t ⋮ & ⋮\\\\\n",
-       "\t X-ray of tibia/fibula abnormal                                                              & 1\\\\\n",
-       "\t X-ray of tibia/fibula normal                                                                & 1\\\\\n",
-       "\t X-ray pelvimetry abnormal                                                                   & 1\\\\\n",
-       "\t X-ray phalanges of fingers abnormal                                                         & 1\\\\\n",
-       "\t Xiphoid cartilage sprain                                                                    & 1\\\\\n",
-       "\t Xylose absorption test                                                                      & 1\\\\\n",
-       "\t Zenker's diverticulum                                                                       & 1\\\\\n",
-       "\t Zygomatic complex of bones                                                                  & 1\\\\\n",
-       "\t {[}D{]}Abnormal reflex, unspecified                                                             & 1\\\\\n",
-       "\t {[}D{]}Anorexia                                                                                 & 1\\\\\n",
-       "\t {[}D{]}Bony lytic lesions on X-ray                                                              & 1\\\\\n",
-       "\t {[}D{]}Local superficial swelling, mass or lump                                                 & 1\\\\\n",
-       "\t {[}D{]}Lump on shin                                                                             & 1\\\\\n",
-       "\t {[}D{]}Other abdominal or pelvic symptom                                                        & 1\\\\\n",
-       "\t {[}D{]}Swelling, mass and lump of chest                                                         & 1\\\\\n",
-       "\t {[}M{]}Tubular adenoma or adenocarcinoma NOS                                                    & 1\\\\\n",
-       "\t {[}SO{]}Skin of umbilicus                                                                       & 1\\\\\n",
-       "\t {[}V{]}Assessment for procedure                                                                 & 1\\\\\n",
-       "\t {[}V{]}Bone marrow donor                                                                        & 1\\\\\n",
-       "\t {[}V{]}Examination of ears and hearing                                                          & 1\\\\\n",
-       "\t {[}V{]}Fitting or adjustment of hearing aid                                                     & 1\\\\\n",
-       "\t {[}V{]}Kidney donor                                                                             & 1\\\\\n",
-       "\t {[}V{]}Parent - child conflict                                                                  & 1\\\\\n",
-       "\t {[}V{]}Personal history of malignant neoplasm of male genital organ                             & 1\\\\\n",
-       "\t {[}V{]}Plastic surgery used in aftercare                                                        & 1\\\\\n",
-       "\t {[}X{]}Otorhinolaryngological drugs and preparations causing adverse effects in therapeutic use & 1\\\\\n",
-       "\t {[}X{]}Schizophrenia, schizotypal and delusional disorders                                      & 1\\\\\n",
-       "\t cups/day                                                                                    & 1\\\\\n",
-       "\t von Jaksch's anemia                                                                         & 1\\\\\n",
-       "\t von Willebrand disorder                                                                     & 1\\\\\n",
-       "\\end{tabular}\n"
-      ],
-      "text/markdown": [
-       "\n",
-       "A tibble: 16306 × 2\n",
-       "\n",
-       "| name &lt;chr&gt; | n &lt;int&gt; |\n",
-       "|---|---|\n",
-       "| O/E - Diastolic BP reading                                                                                               | 4142442 |\n",
-       "| O/E - Systolic BP reading                                                                                                | 3963088 |\n",
-       "| Serum creatinine level                                                                                                   | 1670214 |\n",
-       "| Haemoglobin estimation                                                                                                   | 1662102 |\n",
-       "| Total white cell count                                                                                                   | 1630899 |\n",
-       "| O/E - weight                                                                                                             | 1588105 |\n",
-       "| Serum sodium level                                                                                                       | 1587227 |\n",
-       "| Serum potassium level                                                                                                    | 1582109 |\n",
-       "| Finding of body mass index                                                                                               | 1371580 |\n",
-       "| Serum alanine aminotransferase level                                                                                     | 1364116 |\n",
-       "| Serum urea level                                                                                                         | 1349233 |\n",
-       "| Serum albumin level                                                                                                      | 1344654 |\n",
-       "| Serum alkaline phosphatase level                                                                                         | 1314225 |\n",
-       "| Platelet count                                                                                                           | 1264036 |\n",
-       "| Serum cholesterol level                                                                                                  | 1248251 |\n",
-       "| Haematocrit - packed cell volume                                                                                         | 1247902 |\n",
-       "| MCV - Mean corpuscular volume                                                                                            | 1246810 |\n",
-       "| Erythrocyte count                                                                                                        | 1238243 |\n",
-       "| MCH - Mean corpuscular haemoglobin                                                                                       | 1224630 |\n",
-       "| GFR (glomerular filtration rate) calculated by abbreviated Modification of Diet in Renal Disease Study Group calculation | 1212917 |\n",
-       "| Granulocyte count                                                                                                        | 1211942 |\n",
-       "| Lymphocyte count                                                                                                         | 1206974 |\n",
-       "| Monocyte count                                                                                                           | 1205707 |\n",
-       "| Eosinophil count                                                                                                         | 1204617 |\n",
-       "| Basophil count                                                                                                           | 1165935 |\n",
-       "| Serum high density lipoprotein cholesterol level                                                                         | 1059995 |\n",
-       "| Serum triglycerides level                                                                                                | 1033506 |\n",
-       "| O/E - height                                                                                                             |  955235 |\n",
-       "| Review of medication                                                                                                     |  955104 |\n",
-       "| Serum TSH (thyroid stimulating hormone) level                                                                            |  862321 |\n",
-       "| ⋮ | ⋮ |\n",
-       "| X-ray of tibia/fibula abnormal                                                              | 1 |\n",
-       "| X-ray of tibia/fibula normal                                                                | 1 |\n",
-       "| X-ray pelvimetry abnormal                                                                   | 1 |\n",
-       "| X-ray phalanges of fingers abnormal                                                         | 1 |\n",
-       "| Xiphoid cartilage sprain                                                                    | 1 |\n",
-       "| Xylose absorption test                                                                      | 1 |\n",
-       "| Zenker's diverticulum                                                                       | 1 |\n",
-       "| Zygomatic complex of bones                                                                  | 1 |\n",
-       "| [D]Abnormal reflex, unspecified                                                             | 1 |\n",
-       "| [D]Anorexia                                                                                 | 1 |\n",
-       "| [D]Bony lytic lesions on X-ray                                                              | 1 |\n",
-       "| [D]Local superficial swelling, mass or lump                                                 | 1 |\n",
-       "| [D]Lump on shin                                                                             | 1 |\n",
-       "| [D]Other abdominal or pelvic symptom                                                        | 1 |\n",
-       "| [D]Swelling, mass and lump of chest                                                         | 1 |\n",
-       "| [M]Tubular adenoma or adenocarcinoma NOS                                                    | 1 |\n",
-       "| [SO]Skin of umbilicus                                                                       | 1 |\n",
-       "| [V]Assessment for procedure                                                                 | 1 |\n",
-       "| [V]Bone marrow donor                                                                        | 1 |\n",
-       "| [V]Examination of ears and hearing                                                          | 1 |\n",
-       "| [V]Fitting or adjustment of hearing aid                                                     | 1 |\n",
-       "| [V]Kidney donor                                                                             | 1 |\n",
-       "| [V]Parent - child conflict                                                                  | 1 |\n",
-       "| [V]Personal history of malignant neoplasm of male genital organ                             | 1 |\n",
-       "| [V]Plastic surgery used in aftercare                                                        | 1 |\n",
-       "| [X]Otorhinolaryngological drugs and preparations causing adverse effects in therapeutic use | 1 |\n",
-       "| [X]Schizophrenia, schizotypal and delusional disorders                                      | 1 |\n",
-       "| cups/day                                                                                    | 1 |\n",
-       "| von Jaksch's anemia                                                                         | 1 |\n",
-       "| von Willebrand disorder                                                                     | 1 |\n",
-       "\n"
-      ],
-      "text/plain": [
-       "       name                                                                                                                    \n",
-       "1      O/E - Diastolic BP reading                                                                                              \n",
-       "2      O/E - Systolic BP reading                                                                                               \n",
-       "3      Serum creatinine level                                                                                                  \n",
-       "4      Haemoglobin estimation                                                                                                  \n",
-       "5      Total white cell count                                                                                                  \n",
-       "6      O/E - weight                                                                                                            \n",
-       "7      Serum sodium level                                                                                                      \n",
-       "8      Serum potassium level                                                                                                   \n",
-       "9      Finding of body mass index                                                                                              \n",
-       "10     Serum alanine aminotransferase level                                                                                    \n",
-       "11     Serum urea level                                                                                                        \n",
-       "12     Serum albumin level                                                                                                     \n",
-       "13     Serum alkaline phosphatase level                                                                                        \n",
-       "14     Platelet count                                                                                                          \n",
-       "15     Serum cholesterol level                                                                                                 \n",
-       "16     Haematocrit - packed cell volume                                                                                        \n",
-       "17     MCV - Mean corpuscular volume                                                                                           \n",
-       "18     Erythrocyte count                                                                                                       \n",
-       "19     MCH - Mean corpuscular haemoglobin                                                                                      \n",
-       "20     GFR (glomerular filtration rate) calculated by abbreviated Modification of Diet in Renal Disease Study Group calculation\n",
-       "21     Granulocyte count                                                                                                       \n",
-       "22     Lymphocyte count                                                                                                        \n",
-       "23     Monocyte count                                                                                                          \n",
-       "24     Eosinophil count                                                                                                        \n",
-       "25     Basophil count                                                                                                          \n",
-       "26     Serum high density lipoprotein cholesterol level                                                                        \n",
-       "27     Serum triglycerides level                                                                                               \n",
-       "28     O/E - height                                                                                                            \n",
-       "29     Review of medication                                                                                                    \n",
-       "30     Serum TSH (thyroid stimulating hormone) level                                                                           \n",
-       "<U+22EE> <U+22EE>                                                                                                                \n",
-       "16277  X-ray of tibia/fibula abnormal                                                                                          \n",
-       "16278  X-ray of tibia/fibula normal                                                                                            \n",
-       "16279  X-ray pelvimetry abnormal                                                                                               \n",
-       "16280  X-ray phalanges of fingers abnormal                                                                                     \n",
-       "16281  Xiphoid cartilage sprain                                                                                                \n",
-       "16282  Xylose absorption test                                                                                                  \n",
-       "16283  Zenker's diverticulum                                                                                                   \n",
-       "16284  Zygomatic complex of bones                                                                                              \n",
-       "16285  [D]Abnormal reflex, unspecified                                                                                         \n",
-       "16286  [D]Anorexia                                                                                                             \n",
-       "16287  [D]Bony lytic lesions on X-ray                                                                                          \n",
-       "16288  [D]Local superficial swelling, mass or lump                                                                             \n",
-       "16289  [D]Lump on shin                                                                                                         \n",
-       "16290  [D]Other abdominal or pelvic symptom                                                                                    \n",
-       "16291  [D]Swelling, mass and lump of chest                                                                                     \n",
-       "16292  [M]Tubular adenoma or adenocarcinoma NOS                                                                                \n",
-       "16293  [SO]Skin of umbilicus                                                                                                   \n",
-       "16294  [V]Assessment for procedure                                                                                             \n",
-       "16295  [V]Bone marrow donor                                                                                                    \n",
-       "16296  [V]Examination of ears and hearing                                                                                      \n",
-       "16297  [V]Fitting or adjustment of hearing aid                                                                                 \n",
-       "16298  [V]Kidney donor                                                                                                         \n",
-       "16299  [V]Parent - child conflict                                                                                              \n",
-       "16300  [V]Personal history of malignant neoplasm of male genital organ                                                         \n",
-       "16301  [V]Plastic surgery used in aftercare                                                                                    \n",
-       "16302  [X]Otorhinolaryngological drugs and preparations causing adverse effects in therapeutic use                             \n",
-       "16303  [X]Schizophrenia, schizotypal and delusional disorders                                                                  \n",
-       "16304  cups/day                                                                                                                \n",
-       "16305  von Jaksch's anemia                                                                                                     \n",
-       "16306  von Willebrand disorder                                                                                                 \n",
-       "       n      \n",
-       "1      4142442\n",
-       "2      3963088\n",
-       "3      1670214\n",
-       "4      1662102\n",
-       "5      1630899\n",
-       "6      1588105\n",
-       "7      1587227\n",
-       "8      1582109\n",
-       "9      1371580\n",
-       "10     1364116\n",
-       "11     1349233\n",
-       "12     1344654\n",
-       "13     1314225\n",
-       "14     1264036\n",
-       "15     1248251\n",
-       "16     1247902\n",
-       "17     1246810\n",
-       "18     1238243\n",
-       "19     1224630\n",
-       "20     1212917\n",
-       "21     1211942\n",
-       "22     1206974\n",
-       "23     1205707\n",
-       "24     1204617\n",
-       "25     1165935\n",
-       "26     1059995\n",
-       "27     1033506\n",
-       "28      955235\n",
-       "29      955104\n",
-       "30      862321\n",
-       "<U+22EE> <U+22EE>\n",
-       "16277  1      \n",
-       "16278  1      \n",
-       "16279  1      \n",
-       "16280  1      \n",
-       "16281  1      \n",
-       "16282  1      \n",
-       "16283  1      \n",
-       "16284  1      \n",
-       "16285  1      \n",
-       "16286  1      \n",
-       "16287  1      \n",
-       "16288  1      \n",
-       "16289  1      \n",
-       "16290  1      \n",
-       "16291  1      \n",
-       "16292  1      \n",
-       "16293  1      \n",
-       "16294  1      \n",
-       "16295  1      \n",
-       "16296  1      \n",
-       "16297  1      \n",
-       "16298  1      \n",
-       "16299  1      \n",
-       "16300  1      \n",
-       "16301  1      \n",
-       "16302  1      \n",
-       "16303  1      \n",
-       "16304  1      \n",
-       "16305  1      \n",
-       "16306  1      "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "gp_measurements %>% count(name, sort=TRUE)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 79,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<table>\n",
-       "<caption>A tibble: 3729 × 17</caption>\n",
-       "<thead>\n",
-       "\t<tr><th scope=col>eid</th><th scope=col>date</th><th scope=col>code</th><th scope=col>value1</th><th scope=col>value2</th><th scope=col>value3</th><th scope=col>meaning</th><th scope=col>name</th><th scope=col>value</th><th scope=col>age_at_recruitment_f21022_0_0</th><th scope=col>sex_f31_0_0</th><th scope=col>ethnic_background_f21000_0_0</th><th scope=col>townsend_deprivation_index_at_recruitment_f189_0_0</th><th scope=col>date_of_attending_assessment_centre_f53_0_0</th><th scope=col>uk_biobank_assessment_centre_f54_0_0</th><th scope=col>birth_date</th><th scope=col>time</th></tr>\n",
-       "\t<tr><th scope=col>&lt;int&gt;</th><th scope=col>&lt;date&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;date&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;date&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
-       "</thead>\n",
-       "<tbody>\n",
-       "\t<tr><td>1005194</td><td>1993-06-30</td><td>246..</td><td>120.000</td><td>80.000</td><td>NA</td><td>163030003</td><td>O/E - Systolic BP reading </td><td>120</td><td>65</td><td>Male</td><td>White</td><td>1.80551</td><td>2009-10-23</td><td>Sheffield</td><td>1944-10-23</td><td>48.68446</td></tr>\n",
-       "\t<tr><td>1005194</td><td>1993-06-30</td><td>246..</td><td>120.000</td><td>80.000</td><td>NA</td><td>163031004</td><td>O/E - Diastolic BP reading</td><td> 80</td><td>65</td><td>Male</td><td>White</td><td>1.80551</td><td>2009-10-23</td><td>Sheffield</td><td>1944-10-23</td><td>48.68446</td></tr>\n",
-       "\t<tr><td>1005194</td><td>1997-07-14</td><td>246..</td><td>130.000</td><td>80.000</td><td>NA</td><td>163030003</td><td>O/E - Systolic BP reading </td><td>130</td><td>65</td><td>Male</td><td>White</td><td>1.80551</td><td>2009-10-23</td><td>Sheffield</td><td>1944-10-23</td><td>52.72279</td></tr>\n",
-       "\t<tr><td>1005194</td><td>1997-07-14</td><td>246..</td><td>130.000</td><td>80.000</td><td>NA</td><td>163031004</td><td>O/E - Diastolic BP reading</td><td> 80</td><td>65</td><td>Male</td><td>White</td><td>1.80551</td><td>2009-10-23</td><td>Sheffield</td><td>1944-10-23</td><td>52.72279</td></tr>\n",
-       "\t<tr><td>1005194</td><td>1999-01-21</td><td>246..</td><td>142.000</td><td>94.000</td><td>NA</td><td>163030003</td><td>O/E - Systolic BP reading </td><td>142</td><td>65</td><td>Male</td><td>White</td><td>1.80551</td><td>2009-10-23</td><td>Sheffield</td><td>1944-10-23</td><td>54.24504</td></tr>\n",
-       "\t<tr><td>1005194</td><td>1999-01-21</td><td>246..</td><td>142.000</td><td>94.000</td><td>NA</td><td>163031004</td><td>O/E - Diastolic BP reading</td><td> 94</td><td>65</td><td>Male</td><td>White</td><td>1.80551</td><td>2009-10-23</td><td>Sheffield</td><td>1944-10-23</td><td>54.24504</td></tr>\n",
-       "\t<tr><td>1005194</td><td>1999-01-27</td><td>246..</td><td>142.000</td><td>92.000</td><td>NA</td><td>163030003</td><td>O/E - Systolic BP reading </td><td>142</td><td>65</td><td>Male</td><td>White</td><td>1.80551</td><td>2009-10-23</td><td>Sheffield</td><td>1944-10-23</td><td>54.26146</td></tr>\n",
-       "\t<tr><td>1005194</td><td>1999-01-27</td><td>246..</td><td>142.000</td><td>92.000</td><td>NA</td><td>163031004</td><td>O/E - Diastolic BP reading</td><td> 92</td><td>65</td><td>Male</td><td>White</td><td>1.80551</td><td>2009-10-23</td><td>Sheffield</td><td>1944-10-23</td><td>54.26146</td></tr>\n",
-       "\t<tr><td>1005194</td><td>1999-10-06</td><td>246..</td><td>140.000</td><td>86.000</td><td>NA</td><td>163030003</td><td>O/E - Systolic BP reading </td><td>140</td><td>65</td><td>Male</td><td>White</td><td>1.80551</td><td>2009-10-23</td><td>Sheffield</td><td>1944-10-23</td><td>54.95140</td></tr>\n",
-       "\t<tr><td>1005194</td><td>1999-10-06</td><td>246..</td><td>140.000</td><td>86.000</td><td>NA</td><td>163031004</td><td>O/E - Diastolic BP reading</td><td> 86</td><td>65</td><td>Male</td><td>White</td><td>1.80551</td><td>2009-10-23</td><td>Sheffield</td><td>1944-10-23</td><td>54.95140</td></tr>\n",
-       "\t<tr><td>1005194</td><td>1999-12-08</td><td>246..</td><td>148.000</td><td>82.000</td><td>NA</td><td>163030003</td><td>O/E - Systolic BP reading </td><td>148</td><td>65</td><td>Male</td><td>White</td><td>1.80551</td><td>2009-10-23</td><td>Sheffield</td><td>1944-10-23</td><td>55.12389</td></tr>\n",
-       "\t<tr><td>1005194</td><td>1999-12-08</td><td>246..</td><td>148.000</td><td>82.000</td><td>NA</td><td>163031004</td><td>O/E - Diastolic BP reading</td><td> 82</td><td>65</td><td>Male</td><td>White</td><td>1.80551</td><td>2009-10-23</td><td>Sheffield</td><td>1944-10-23</td><td>55.12389</td></tr>\n",
-       "\t<tr><td>1005194</td><td>2000-06-19</td><td>246..</td><td>120.000</td><td>84.000</td><td>NA</td><td>163030003</td><td>O/E - Systolic BP reading </td><td>120</td><td>65</td><td>Male</td><td>White</td><td>1.80551</td><td>2009-10-23</td><td>Sheffield</td><td>1944-10-23</td><td>55.65503</td></tr>\n",
-       "\t<tr><td>1005194</td><td>2000-06-19</td><td>246..</td><td>120.000</td><td>84.000</td><td>NA</td><td>163031004</td><td>O/E - Diastolic BP reading</td><td> 84</td><td>65</td><td>Male</td><td>White</td><td>1.80551</td><td>2009-10-23</td><td>Sheffield</td><td>1944-10-23</td><td>55.65503</td></tr>\n",
-       "\t<tr><td>1005194</td><td>2001-01-05</td><td>246..</td><td>150.000</td><td>90.000</td><td>NA</td><td>163030003</td><td>O/E - Systolic BP reading </td><td>150</td><td>65</td><td>Male</td><td>White</td><td>1.80551</td><td>2009-10-23</td><td>Sheffield</td><td>1944-10-23</td><td>56.20260</td></tr>\n",
-       "\t<tr><td>1005194</td><td>2001-01-05</td><td>246..</td><td>150.000</td><td>90.000</td><td>NA</td><td>163031004</td><td>O/E - Diastolic BP reading</td><td> 90</td><td>65</td><td>Male</td><td>White</td><td>1.80551</td><td>2009-10-23</td><td>Sheffield</td><td>1944-10-23</td><td>56.20260</td></tr>\n",
-       "\t<tr><td>1005194</td><td>2001-02-02</td><td>246..</td><td>140.000</td><td>90.000</td><td>NA</td><td>163030003</td><td>O/E - Systolic BP reading </td><td>140</td><td>65</td><td>Male</td><td>White</td><td>1.80551</td><td>2009-10-23</td><td>Sheffield</td><td>1944-10-23</td><td>56.27926</td></tr>\n",
-       "\t<tr><td>1005194</td><td>2001-02-02</td><td>246..</td><td>140.000</td><td>90.000</td><td>NA</td><td>163031004</td><td>O/E - Diastolic BP reading</td><td> 90</td><td>65</td><td>Male</td><td>White</td><td>1.80551</td><td>2009-10-23</td><td>Sheffield</td><td>1944-10-23</td><td>56.27926</td></tr>\n",
-       "\t<tr><td>1005194</td><td>2001-07-05</td><td>246..</td><td>130.000</td><td>74.000</td><td>NA</td><td>163030003</td><td>O/E - Systolic BP reading </td><td>130</td><td>65</td><td>Male</td><td>White</td><td>1.80551</td><td>2009-10-23</td><td>Sheffield</td><td>1944-10-23</td><td>56.69815</td></tr>\n",
-       "\t<tr><td>1005194</td><td>2001-07-05</td><td>246..</td><td>130.000</td><td>74.000</td><td>NA</td><td>163031004</td><td>O/E - Diastolic BP reading</td><td> 74</td><td>65</td><td>Male</td><td>White</td><td>1.80551</td><td>2009-10-23</td><td>Sheffield</td><td>1944-10-23</td><td>56.69815</td></tr>\n",
-       "\t<tr><td>1005194</td><td>2002-05-01</td><td>246..</td><td>144.000</td><td>80.000</td><td>NA</td><td>163030003</td><td>O/E - Systolic BP reading </td><td>144</td><td>65</td><td>Male</td><td>White</td><td>1.80551</td><td>2009-10-23</td><td>Sheffield</td><td>1944-10-23</td><td>57.51951</td></tr>\n",
-       "\t<tr><td>1005194</td><td>2002-05-01</td><td>246..</td><td>144.000</td><td>80.000</td><td>NA</td><td>163031004</td><td>O/E - Diastolic BP reading</td><td> 80</td><td>65</td><td>Male</td><td>White</td><td>1.80551</td><td>2009-10-23</td><td>Sheffield</td><td>1944-10-23</td><td>57.51951</td></tr>\n",
-       "\t<tr><td>1005194</td><td>2003-03-03</td><td>246..</td><td>145.000</td><td>81.000</td><td>NA</td><td>163030003</td><td>O/E - Systolic BP reading </td><td>145</td><td>65</td><td>Male</td><td>White</td><td>1.80551</td><td>2009-10-23</td><td>Sheffield</td><td>1944-10-23</td><td>58.35729</td></tr>\n",
-       "\t<tr><td>1005194</td><td>2003-03-03</td><td>246..</td><td>145.000</td><td>81.000</td><td>NA</td><td>163031004</td><td>O/E - Diastolic BP reading</td><td> 81</td><td>65</td><td>Male</td><td>White</td><td>1.80551</td><td>2009-10-23</td><td>Sheffield</td><td>1944-10-23</td><td>58.35729</td></tr>\n",
-       "\t<tr><td>1005194</td><td>2003-03-18</td><td>246..</td><td>137.000</td><td>67.000</td><td>NA</td><td>163030003</td><td>O/E - Systolic BP reading </td><td>137</td><td>65</td><td>Male</td><td>White</td><td>1.80551</td><td>2009-10-23</td><td>Sheffield</td><td>1944-10-23</td><td>58.39836</td></tr>\n",
-       "\t<tr><td>1005194</td><td>2003-03-18</td><td>246..</td><td>137.000</td><td>67.000</td><td>NA</td><td>163031004</td><td>O/E - Diastolic BP reading</td><td> 67</td><td>65</td><td>Male</td><td>White</td><td>1.80551</td><td>2009-10-23</td><td>Sheffield</td><td>1944-10-23</td><td>58.39836</td></tr>\n",
-       "\t<tr><td>1005194</td><td>2003-03-18</td><td>246..</td><td>146.000</td><td>92.000</td><td>NA</td><td>163030003</td><td>O/E - Systolic BP reading </td><td>146</td><td>65</td><td>Male</td><td>White</td><td>1.80551</td><td>2009-10-23</td><td>Sheffield</td><td>1944-10-23</td><td>58.39836</td></tr>\n",
-       "\t<tr><td>1005194</td><td>2003-03-18</td><td>246..</td><td>146.000</td><td>92.000</td><td>NA</td><td>163031004</td><td>O/E - Diastolic BP reading</td><td> 92</td><td>65</td><td>Male</td><td>White</td><td>1.80551</td><td>2009-10-23</td><td>Sheffield</td><td>1944-10-23</td><td>58.39836</td></tr>\n",
-       "\t<tr><td>1005194</td><td>2003-03-19</td><td>246..</td><td>145.000</td><td>80.000</td><td>NA</td><td>163030003</td><td>O/E - Systolic BP reading </td><td>145</td><td>65</td><td>Male</td><td>White</td><td>1.80551</td><td>2009-10-23</td><td>Sheffield</td><td>1944-10-23</td><td>58.40110</td></tr>\n",
-       "\t<tr><td>1005194</td><td>2003-03-19</td><td>246..</td><td>145.000</td><td>80.000</td><td>NA</td><td>163031004</td><td>O/E - Diastolic BP reading</td><td> 80</td><td>65</td><td>Male</td><td>White</td><td>1.80551</td><td>2009-10-23</td><td>Sheffield</td><td>1944-10-23</td><td>58.40110</td></tr>\n",
-       "\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
-       "\t<tr><td>5996198</td><td>2005-09-28</td><td>2469.</td><td>120.000</td><td>NA</td><td>NA</td><td>163030003</td><td>O/E - Systolic BP reading </td><td>120</td><td>41</td><td>Female</td><td>White</td><td>-0.297723</td><td>2009-02-26</td><td>Oxford</td><td>1968-02-26</td><td>37.58795</td></tr>\n",
-       "\t<tr><td>5996198</td><td>2005-09-28</td><td>246A.</td><td>84.000 </td><td>NA</td><td>NA</td><td>163031004</td><td>O/E - Diastolic BP reading</td><td> 84</td><td>41</td><td>Female</td><td>White</td><td>-0.297723</td><td>2009-02-26</td><td>Oxford</td><td>1968-02-26</td><td>37.58795</td></tr>\n",
-       "\t<tr><td>5996198</td><td>2006-03-10</td><td>2469.</td><td>129.000</td><td>NA</td><td>NA</td><td>163030003</td><td>O/E - Systolic BP reading </td><td>129</td><td>41</td><td>Female</td><td>White</td><td>-0.297723</td><td>2009-02-26</td><td>Oxford</td><td>1968-02-26</td><td>38.03422</td></tr>\n",
-       "\t<tr><td>5996198</td><td>2006-03-10</td><td>246A.</td><td>89.000 </td><td>NA</td><td>NA</td><td>163031004</td><td>O/E - Diastolic BP reading</td><td> 89</td><td>41</td><td>Female</td><td>White</td><td>-0.297723</td><td>2009-02-26</td><td>Oxford</td><td>1968-02-26</td><td>38.03422</td></tr>\n",
-       "\t<tr><td>5996198</td><td>2007-09-19</td><td>2469.</td><td>126.000</td><td>NA</td><td>NA</td><td>163030003</td><td>O/E - Systolic BP reading </td><td>126</td><td>41</td><td>Female</td><td>White</td><td>-0.297723</td><td>2009-02-26</td><td>Oxford</td><td>1968-02-26</td><td>39.56194</td></tr>\n",
-       "\t<tr><td>5996198</td><td>2007-09-19</td><td>246A.</td><td>80.000 </td><td>NA</td><td>NA</td><td>163031004</td><td>O/E - Diastolic BP reading</td><td> 80</td><td>41</td><td>Female</td><td>White</td><td>-0.297723</td><td>2009-02-26</td><td>Oxford</td><td>1968-02-26</td><td>39.56194</td></tr>\n",
-       "\t<tr><td>5996198</td><td>2012-08-09</td><td>2469.</td><td>132.000</td><td>NA</td><td>NA</td><td>163030003</td><td>O/E - Systolic BP reading </td><td>132</td><td>41</td><td>Female</td><td>White</td><td>-0.297723</td><td>2009-02-26</td><td>Oxford</td><td>1968-02-26</td><td>44.45175</td></tr>\n",
-       "\t<tr><td>5996198</td><td>2012-08-09</td><td>246A.</td><td>86.000 </td><td>NA</td><td>NA</td><td>163031004</td><td>O/E - Diastolic BP reading</td><td> 86</td><td>41</td><td>Female</td><td>White</td><td>-0.297723</td><td>2009-02-26</td><td>Oxford</td><td>1968-02-26</td><td>44.45175</td></tr>\n",
-       "\t<tr><td>5996198</td><td>2012-11-07</td><td>2469.</td><td>140.000</td><td>NA</td><td>NA</td><td>163030003</td><td>O/E - Systolic BP reading </td><td>140</td><td>41</td><td>Female</td><td>White</td><td>-0.297723</td><td>2009-02-26</td><td>Oxford</td><td>1968-02-26</td><td>44.69815</td></tr>\n",
-       "\t<tr><td>5996198</td><td>2012-11-07</td><td>246A.</td><td>68.000 </td><td>NA</td><td>NA</td><td>163031004</td><td>O/E - Diastolic BP reading</td><td> 68</td><td>41</td><td>Female</td><td>White</td><td>-0.297723</td><td>2009-02-26</td><td>Oxford</td><td>1968-02-26</td><td>44.69815</td></tr>\n",
-       "\t<tr><td>5996198</td><td>2013-03-06</td><td>2469.</td><td>139.000</td><td>NA</td><td>NA</td><td>163030003</td><td>O/E - Systolic BP reading </td><td>139</td><td>41</td><td>Female</td><td>White</td><td>-0.297723</td><td>2009-02-26</td><td>Oxford</td><td>1968-02-26</td><td>45.02396</td></tr>\n",
-       "\t<tr><td>5996198</td><td>2013-03-06</td><td>246A.</td><td>88.000 </td><td>NA</td><td>NA</td><td>163031004</td><td>O/E - Diastolic BP reading</td><td> 88</td><td>41</td><td>Female</td><td>White</td><td>-0.297723</td><td>2009-02-26</td><td>Oxford</td><td>1968-02-26</td><td>45.02396</td></tr>\n",
-       "\t<tr><td>5996198</td><td>2014-01-17</td><td>2469.</td><td>148.000</td><td>NA</td><td>NA</td><td>163030003</td><td>O/E - Systolic BP reading </td><td>148</td><td>41</td><td>Female</td><td>White</td><td>-0.297723</td><td>2009-02-26</td><td>Oxford</td><td>1968-02-26</td><td>45.89185</td></tr>\n",
-       "\t<tr><td>5996198</td><td>2014-01-17</td><td>246A.</td><td>72.000 </td><td>NA</td><td>NA</td><td>163031004</td><td>O/E - Diastolic BP reading</td><td> 72</td><td>41</td><td>Female</td><td>White</td><td>-0.297723</td><td>2009-02-26</td><td>Oxford</td><td>1968-02-26</td><td>45.89185</td></tr>\n",
-       "\t<tr><td>5996198</td><td>2014-02-03</td><td>2469.</td><td>145.000</td><td>NA</td><td>NA</td><td>163030003</td><td>O/E - Systolic BP reading </td><td>145</td><td>41</td><td>Female</td><td>White</td><td>-0.297723</td><td>2009-02-26</td><td>Oxford</td><td>1968-02-26</td><td>45.93840</td></tr>\n",
-       "\t<tr><td>5996198</td><td>2014-02-03</td><td>246A.</td><td>98.000 </td><td>NA</td><td>NA</td><td>163031004</td><td>O/E - Diastolic BP reading</td><td> 98</td><td>41</td><td>Female</td><td>White</td><td>-0.297723</td><td>2009-02-26</td><td>Oxford</td><td>1968-02-26</td><td>45.93840</td></tr>\n",
-       "\t<tr><td>5996198</td><td>2014-02-07</td><td>2469.</td><td>137.000</td><td>NA</td><td>NA</td><td>163030003</td><td>O/E - Systolic BP reading </td><td>137</td><td>41</td><td>Female</td><td>White</td><td>-0.297723</td><td>2009-02-26</td><td>Oxford</td><td>1968-02-26</td><td>45.94935</td></tr>\n",
-       "\t<tr><td>5996198</td><td>2014-02-07</td><td>246A.</td><td>85.000 </td><td>NA</td><td>NA</td><td>163031004</td><td>O/E - Diastolic BP reading</td><td> 85</td><td>41</td><td>Female</td><td>White</td><td>-0.297723</td><td>2009-02-26</td><td>Oxford</td><td>1968-02-26</td><td>45.94935</td></tr>\n",
-       "\t<tr><td>5996198</td><td>2014-03-18</td><td>2469.</td><td>145.000</td><td>NA</td><td>NA</td><td>163030003</td><td>O/E - Systolic BP reading </td><td>145</td><td>41</td><td>Female</td><td>White</td><td>-0.297723</td><td>2009-02-26</td><td>Oxford</td><td>1968-02-26</td><td>46.05613</td></tr>\n",
-       "\t<tr><td>5996198</td><td>2014-03-18</td><td>246A.</td><td>98.000 </td><td>NA</td><td>NA</td><td>163031004</td><td>O/E - Diastolic BP reading</td><td> 98</td><td>41</td><td>Female</td><td>White</td><td>-0.297723</td><td>2009-02-26</td><td>Oxford</td><td>1968-02-26</td><td>46.05613</td></tr>\n",
-       "\t<tr><td>5996198</td><td>2014-04-11</td><td>2469.</td><td>130.000</td><td>NA</td><td>NA</td><td>163030003</td><td>O/E - Systolic BP reading </td><td>130</td><td>41</td><td>Female</td><td>White</td><td>-0.297723</td><td>2009-02-26</td><td>Oxford</td><td>1968-02-26</td><td>46.12183</td></tr>\n",
-       "\t<tr><td>5996198</td><td>2014-04-11</td><td>246A.</td><td>60.000 </td><td>NA</td><td>NA</td><td>163031004</td><td>O/E - Diastolic BP reading</td><td> 60</td><td>41</td><td>Female</td><td>White</td><td>-0.297723</td><td>2009-02-26</td><td>Oxford</td><td>1968-02-26</td><td>46.12183</td></tr>\n",
-       "\t<tr><td>5996198</td><td>2014-05-14</td><td>2469.</td><td>132.000</td><td>NA</td><td>NA</td><td>163030003</td><td>O/E - Systolic BP reading </td><td>132</td><td>41</td><td>Female</td><td>White</td><td>-0.297723</td><td>2009-02-26</td><td>Oxford</td><td>1968-02-26</td><td>46.21218</td></tr>\n",
-       "\t<tr><td>5996198</td><td>2014-05-14</td><td>246A.</td><td>84.000 </td><td>NA</td><td>NA</td><td>163031004</td><td>O/E - Diastolic BP reading</td><td> 84</td><td>41</td><td>Female</td><td>White</td><td>-0.297723</td><td>2009-02-26</td><td>Oxford</td><td>1968-02-26</td><td>46.21218</td></tr>\n",
-       "\t<tr><td>5996198</td><td>2015-09-22</td><td>2469.</td><td>124.000</td><td>NA</td><td>NA</td><td>163030003</td><td>O/E - Systolic BP reading </td><td>124</td><td>41</td><td>Female</td><td>White</td><td>-0.297723</td><td>2009-02-26</td><td>Oxford</td><td>1968-02-26</td><td>47.57016</td></tr>\n",
-       "\t<tr><td>5996198</td><td>2015-09-22</td><td>246A.</td><td>88.000 </td><td>NA</td><td>NA</td><td>163031004</td><td>O/E - Diastolic BP reading</td><td> 88</td><td>41</td><td>Female</td><td>White</td><td>-0.297723</td><td>2009-02-26</td><td>Oxford</td><td>1968-02-26</td><td>47.57016</td></tr>\n",
-       "\t<tr><td>5996198</td><td>2015-10-01</td><td>2469.</td><td>133.000</td><td>NA</td><td>NA</td><td>163030003</td><td>O/E - Systolic BP reading </td><td>133</td><td>41</td><td>Female</td><td>White</td><td>-0.297723</td><td>2009-02-26</td><td>Oxford</td><td>1968-02-26</td><td>47.59480</td></tr>\n",
-       "\t<tr><td>5996198</td><td>2015-10-01</td><td>246A.</td><td>95.000 </td><td>NA</td><td>NA</td><td>163031004</td><td>O/E - Diastolic BP reading</td><td> 95</td><td>41</td><td>Female</td><td>White</td><td>-0.297723</td><td>2009-02-26</td><td>Oxford</td><td>1968-02-26</td><td>47.59480</td></tr>\n",
-       "\t<tr><td>5996198</td><td>2015-11-12</td><td>2469.</td><td>134.000</td><td>NA</td><td>NA</td><td>163030003</td><td>O/E - Systolic BP reading </td><td>134</td><td>41</td><td>Female</td><td>White</td><td>-0.297723</td><td>2009-02-26</td><td>Oxford</td><td>1968-02-26</td><td>47.70979</td></tr>\n",
-       "\t<tr><td>5996198</td><td>2015-11-12</td><td>246A.</td><td>82.000 </td><td>NA</td><td>NA</td><td>163031004</td><td>O/E - Diastolic BP reading</td><td> 82</td><td>41</td><td>Female</td><td>White</td><td>-0.297723</td><td>2009-02-26</td><td>Oxford</td><td>1968-02-26</td><td>47.70979</td></tr>\n",
-       "</tbody>\n",
-       "</table>\n"
-      ],
-      "text/latex": [
-       "A tibble: 3729 × 17\n",
-       "\\begin{tabular}{lllllllllllllllll}\n",
-       " eid & date & code & value1 & value2 & value3 & meaning & name & value & age\\_at\\_recruitment\\_f21022\\_0\\_0 & sex\\_f31\\_0\\_0 & ethnic\\_background\\_f21000\\_0\\_0 & townsend\\_deprivation\\_index\\_at\\_recruitment\\_f189\\_0\\_0 & date\\_of\\_attending\\_assessment\\_centre\\_f53\\_0\\_0 & uk\\_biobank\\_assessment\\_centre\\_f54\\_0\\_0 & birth\\_date & time\\\\\n",
-       " <int> & <date> & <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <dbl> & <dbl> & <fct> & <fct> & <dbl> & <date> & <chr> & <date> & <dbl>\\\\\n",
-       "\\hline\n",
-       "\t 1005194 & 1993-06-30 & 246.. & 120.000 & 80.000 & NA & 163030003 & O/E - Systolic BP reading  & 120 & 65 & Male & White & 1.80551 & 2009-10-23 & Sheffield & 1944-10-23 & 48.68446\\\\\n",
-       "\t 1005194 & 1993-06-30 & 246.. & 120.000 & 80.000 & NA & 163031004 & O/E - Diastolic BP reading &  80 & 65 & Male & White & 1.80551 & 2009-10-23 & Sheffield & 1944-10-23 & 48.68446\\\\\n",
-       "\t 1005194 & 1997-07-14 & 246.. & 130.000 & 80.000 & NA & 163030003 & O/E - Systolic BP reading  & 130 & 65 & Male & White & 1.80551 & 2009-10-23 & Sheffield & 1944-10-23 & 52.72279\\\\\n",
-       "\t 1005194 & 1997-07-14 & 246.. & 130.000 & 80.000 & NA & 163031004 & O/E - Diastolic BP reading &  80 & 65 & Male & White & 1.80551 & 2009-10-23 & Sheffield & 1944-10-23 & 52.72279\\\\\n",
-       "\t 1005194 & 1999-01-21 & 246.. & 142.000 & 94.000 & NA & 163030003 & O/E - Systolic BP reading  & 142 & 65 & Male & White & 1.80551 & 2009-10-23 & Sheffield & 1944-10-23 & 54.24504\\\\\n",
-       "\t 1005194 & 1999-01-21 & 246.. & 142.000 & 94.000 & NA & 163031004 & O/E - Diastolic BP reading &  94 & 65 & Male & White & 1.80551 & 2009-10-23 & Sheffield & 1944-10-23 & 54.24504\\\\\n",
-       "\t 1005194 & 1999-01-27 & 246.. & 142.000 & 92.000 & NA & 163030003 & O/E - Systolic BP reading  & 142 & 65 & Male & White & 1.80551 & 2009-10-23 & Sheffield & 1944-10-23 & 54.26146\\\\\n",
-       "\t 1005194 & 1999-01-27 & 246.. & 142.000 & 92.000 & NA & 163031004 & O/E - Diastolic BP reading &  92 & 65 & Male & White & 1.80551 & 2009-10-23 & Sheffield & 1944-10-23 & 54.26146\\\\\n",
-       "\t 1005194 & 1999-10-06 & 246.. & 140.000 & 86.000 & NA & 163030003 & O/E - Systolic BP reading  & 140 & 65 & Male & White & 1.80551 & 2009-10-23 & Sheffield & 1944-10-23 & 54.95140\\\\\n",
-       "\t 1005194 & 1999-10-06 & 246.. & 140.000 & 86.000 & NA & 163031004 & O/E - Diastolic BP reading &  86 & 65 & Male & White & 1.80551 & 2009-10-23 & Sheffield & 1944-10-23 & 54.95140\\\\\n",
-       "\t 1005194 & 1999-12-08 & 246.. & 148.000 & 82.000 & NA & 163030003 & O/E - Systolic BP reading  & 148 & 65 & Male & White & 1.80551 & 2009-10-23 & Sheffield & 1944-10-23 & 55.12389\\\\\n",
-       "\t 1005194 & 1999-12-08 & 246.. & 148.000 & 82.000 & NA & 163031004 & O/E - Diastolic BP reading &  82 & 65 & Male & White & 1.80551 & 2009-10-23 & Sheffield & 1944-10-23 & 55.12389\\\\\n",
-       "\t 1005194 & 2000-06-19 & 246.. & 120.000 & 84.000 & NA & 163030003 & O/E - Systolic BP reading  & 120 & 65 & Male & White & 1.80551 & 2009-10-23 & Sheffield & 1944-10-23 & 55.65503\\\\\n",
-       "\t 1005194 & 2000-06-19 & 246.. & 120.000 & 84.000 & NA & 163031004 & O/E - Diastolic BP reading &  84 & 65 & Male & White & 1.80551 & 2009-10-23 & Sheffield & 1944-10-23 & 55.65503\\\\\n",
-       "\t 1005194 & 2001-01-05 & 246.. & 150.000 & 90.000 & NA & 163030003 & O/E - Systolic BP reading  & 150 & 65 & Male & White & 1.80551 & 2009-10-23 & Sheffield & 1944-10-23 & 56.20260\\\\\n",
-       "\t 1005194 & 2001-01-05 & 246.. & 150.000 & 90.000 & NA & 163031004 & O/E - Diastolic BP reading &  90 & 65 & Male & White & 1.80551 & 2009-10-23 & Sheffield & 1944-10-23 & 56.20260\\\\\n",
-       "\t 1005194 & 2001-02-02 & 246.. & 140.000 & 90.000 & NA & 163030003 & O/E - Systolic BP reading  & 140 & 65 & Male & White & 1.80551 & 2009-10-23 & Sheffield & 1944-10-23 & 56.27926\\\\\n",
-       "\t 1005194 & 2001-02-02 & 246.. & 140.000 & 90.000 & NA & 163031004 & O/E - Diastolic BP reading &  90 & 65 & Male & White & 1.80551 & 2009-10-23 & Sheffield & 1944-10-23 & 56.27926\\\\\n",
-       "\t 1005194 & 2001-07-05 & 246.. & 130.000 & 74.000 & NA & 163030003 & O/E - Systolic BP reading  & 130 & 65 & Male & White & 1.80551 & 2009-10-23 & Sheffield & 1944-10-23 & 56.69815\\\\\n",
-       "\t 1005194 & 2001-07-05 & 246.. & 130.000 & 74.000 & NA & 163031004 & O/E - Diastolic BP reading &  74 & 65 & Male & White & 1.80551 & 2009-10-23 & Sheffield & 1944-10-23 & 56.69815\\\\\n",
-       "\t 1005194 & 2002-05-01 & 246.. & 144.000 & 80.000 & NA & 163030003 & O/E - Systolic BP reading  & 144 & 65 & Male & White & 1.80551 & 2009-10-23 & Sheffield & 1944-10-23 & 57.51951\\\\\n",
-       "\t 1005194 & 2002-05-01 & 246.. & 144.000 & 80.000 & NA & 163031004 & O/E - Diastolic BP reading &  80 & 65 & Male & White & 1.80551 & 2009-10-23 & Sheffield & 1944-10-23 & 57.51951\\\\\n",
-       "\t 1005194 & 2003-03-03 & 246.. & 145.000 & 81.000 & NA & 163030003 & O/E - Systolic BP reading  & 145 & 65 & Male & White & 1.80551 & 2009-10-23 & Sheffield & 1944-10-23 & 58.35729\\\\\n",
-       "\t 1005194 & 2003-03-03 & 246.. & 145.000 & 81.000 & NA & 163031004 & O/E - Diastolic BP reading &  81 & 65 & Male & White & 1.80551 & 2009-10-23 & Sheffield & 1944-10-23 & 58.35729\\\\\n",
-       "\t 1005194 & 2003-03-18 & 246.. & 137.000 & 67.000 & NA & 163030003 & O/E - Systolic BP reading  & 137 & 65 & Male & White & 1.80551 & 2009-10-23 & Sheffield & 1944-10-23 & 58.39836\\\\\n",
-       "\t 1005194 & 2003-03-18 & 246.. & 137.000 & 67.000 & NA & 163031004 & O/E - Diastolic BP reading &  67 & 65 & Male & White & 1.80551 & 2009-10-23 & Sheffield & 1944-10-23 & 58.39836\\\\\n",
-       "\t 1005194 & 2003-03-18 & 246.. & 146.000 & 92.000 & NA & 163030003 & O/E - Systolic BP reading  & 146 & 65 & Male & White & 1.80551 & 2009-10-23 & Sheffield & 1944-10-23 & 58.39836\\\\\n",
-       "\t 1005194 & 2003-03-18 & 246.. & 146.000 & 92.000 & NA & 163031004 & O/E - Diastolic BP reading &  92 & 65 & Male & White & 1.80551 & 2009-10-23 & Sheffield & 1944-10-23 & 58.39836\\\\\n",
-       "\t 1005194 & 2003-03-19 & 246.. & 145.000 & 80.000 & NA & 163030003 & O/E - Systolic BP reading  & 145 & 65 & Male & White & 1.80551 & 2009-10-23 & Sheffield & 1944-10-23 & 58.40110\\\\\n",
-       "\t 1005194 & 2003-03-19 & 246.. & 145.000 & 80.000 & NA & 163031004 & O/E - Diastolic BP reading &  80 & 65 & Male & White & 1.80551 & 2009-10-23 & Sheffield & 1944-10-23 & 58.40110\\\\\n",
-       "\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
-       "\t 5996198 & 2005-09-28 & 2469. & 120.000 & NA & NA & 163030003 & O/E - Systolic BP reading  & 120 & 41 & Female & White & -0.297723 & 2009-02-26 & Oxford & 1968-02-26 & 37.58795\\\\\n",
-       "\t 5996198 & 2005-09-28 & 246A. & 84.000  & NA & NA & 163031004 & O/E - Diastolic BP reading &  84 & 41 & Female & White & -0.297723 & 2009-02-26 & Oxford & 1968-02-26 & 37.58795\\\\\n",
-       "\t 5996198 & 2006-03-10 & 2469. & 129.000 & NA & NA & 163030003 & O/E - Systolic BP reading  & 129 & 41 & Female & White & -0.297723 & 2009-02-26 & Oxford & 1968-02-26 & 38.03422\\\\\n",
-       "\t 5996198 & 2006-03-10 & 246A. & 89.000  & NA & NA & 163031004 & O/E - Diastolic BP reading &  89 & 41 & Female & White & -0.297723 & 2009-02-26 & Oxford & 1968-02-26 & 38.03422\\\\\n",
-       "\t 5996198 & 2007-09-19 & 2469. & 126.000 & NA & NA & 163030003 & O/E - Systolic BP reading  & 126 & 41 & Female & White & -0.297723 & 2009-02-26 & Oxford & 1968-02-26 & 39.56194\\\\\n",
-       "\t 5996198 & 2007-09-19 & 246A. & 80.000  & NA & NA & 163031004 & O/E - Diastolic BP reading &  80 & 41 & Female & White & -0.297723 & 2009-02-26 & Oxford & 1968-02-26 & 39.56194\\\\\n",
-       "\t 5996198 & 2012-08-09 & 2469. & 132.000 & NA & NA & 163030003 & O/E - Systolic BP reading  & 132 & 41 & Female & White & -0.297723 & 2009-02-26 & Oxford & 1968-02-26 & 44.45175\\\\\n",
-       "\t 5996198 & 2012-08-09 & 246A. & 86.000  & NA & NA & 163031004 & O/E - Diastolic BP reading &  86 & 41 & Female & White & -0.297723 & 2009-02-26 & Oxford & 1968-02-26 & 44.45175\\\\\n",
-       "\t 5996198 & 2012-11-07 & 2469. & 140.000 & NA & NA & 163030003 & O/E - Systolic BP reading  & 140 & 41 & Female & White & -0.297723 & 2009-02-26 & Oxford & 1968-02-26 & 44.69815\\\\\n",
-       "\t 5996198 & 2012-11-07 & 246A. & 68.000  & NA & NA & 163031004 & O/E - Diastolic BP reading &  68 & 41 & Female & White & -0.297723 & 2009-02-26 & Oxford & 1968-02-26 & 44.69815\\\\\n",
-       "\t 5996198 & 2013-03-06 & 2469. & 139.000 & NA & NA & 163030003 & O/E - Systolic BP reading  & 139 & 41 & Female & White & -0.297723 & 2009-02-26 & Oxford & 1968-02-26 & 45.02396\\\\\n",
-       "\t 5996198 & 2013-03-06 & 246A. & 88.000  & NA & NA & 163031004 & O/E - Diastolic BP reading &  88 & 41 & Female & White & -0.297723 & 2009-02-26 & Oxford & 1968-02-26 & 45.02396\\\\\n",
-       "\t 5996198 & 2014-01-17 & 2469. & 148.000 & NA & NA & 163030003 & O/E - Systolic BP reading  & 148 & 41 & Female & White & -0.297723 & 2009-02-26 & Oxford & 1968-02-26 & 45.89185\\\\\n",
-       "\t 5996198 & 2014-01-17 & 246A. & 72.000  & NA & NA & 163031004 & O/E - Diastolic BP reading &  72 & 41 & Female & White & -0.297723 & 2009-02-26 & Oxford & 1968-02-26 & 45.89185\\\\\n",
-       "\t 5996198 & 2014-02-03 & 2469. & 145.000 & NA & NA & 163030003 & O/E - Systolic BP reading  & 145 & 41 & Female & White & -0.297723 & 2009-02-26 & Oxford & 1968-02-26 & 45.93840\\\\\n",
-       "\t 5996198 & 2014-02-03 & 246A. & 98.000  & NA & NA & 163031004 & O/E - Diastolic BP reading &  98 & 41 & Female & White & -0.297723 & 2009-02-26 & Oxford & 1968-02-26 & 45.93840\\\\\n",
-       "\t 5996198 & 2014-02-07 & 2469. & 137.000 & NA & NA & 163030003 & O/E - Systolic BP reading  & 137 & 41 & Female & White & -0.297723 & 2009-02-26 & Oxford & 1968-02-26 & 45.94935\\\\\n",
-       "\t 5996198 & 2014-02-07 & 246A. & 85.000  & NA & NA & 163031004 & O/E - Diastolic BP reading &  85 & 41 & Female & White & -0.297723 & 2009-02-26 & Oxford & 1968-02-26 & 45.94935\\\\\n",
-       "\t 5996198 & 2014-03-18 & 2469. & 145.000 & NA & NA & 163030003 & O/E - Systolic BP reading  & 145 & 41 & Female & White & -0.297723 & 2009-02-26 & Oxford & 1968-02-26 & 46.05613\\\\\n",
-       "\t 5996198 & 2014-03-18 & 246A. & 98.000  & NA & NA & 163031004 & O/E - Diastolic BP reading &  98 & 41 & Female & White & -0.297723 & 2009-02-26 & Oxford & 1968-02-26 & 46.05613\\\\\n",
-       "\t 5996198 & 2014-04-11 & 2469. & 130.000 & NA & NA & 163030003 & O/E - Systolic BP reading  & 130 & 41 & Female & White & -0.297723 & 2009-02-26 & Oxford & 1968-02-26 & 46.12183\\\\\n",
-       "\t 5996198 & 2014-04-11 & 246A. & 60.000  & NA & NA & 163031004 & O/E - Diastolic BP reading &  60 & 41 & Female & White & -0.297723 & 2009-02-26 & Oxford & 1968-02-26 & 46.12183\\\\\n",
-       "\t 5996198 & 2014-05-14 & 2469. & 132.000 & NA & NA & 163030003 & O/E - Systolic BP reading  & 132 & 41 & Female & White & -0.297723 & 2009-02-26 & Oxford & 1968-02-26 & 46.21218\\\\\n",
-       "\t 5996198 & 2014-05-14 & 246A. & 84.000  & NA & NA & 163031004 & O/E - Diastolic BP reading &  84 & 41 & Female & White & -0.297723 & 2009-02-26 & Oxford & 1968-02-26 & 46.21218\\\\\n",
-       "\t 5996198 & 2015-09-22 & 2469. & 124.000 & NA & NA & 163030003 & O/E - Systolic BP reading  & 124 & 41 & Female & White & -0.297723 & 2009-02-26 & Oxford & 1968-02-26 & 47.57016\\\\\n",
-       "\t 5996198 & 2015-09-22 & 246A. & 88.000  & NA & NA & 163031004 & O/E - Diastolic BP reading &  88 & 41 & Female & White & -0.297723 & 2009-02-26 & Oxford & 1968-02-26 & 47.57016\\\\\n",
-       "\t 5996198 & 2015-10-01 & 2469. & 133.000 & NA & NA & 163030003 & O/E - Systolic BP reading  & 133 & 41 & Female & White & -0.297723 & 2009-02-26 & Oxford & 1968-02-26 & 47.59480\\\\\n",
-       "\t 5996198 & 2015-10-01 & 246A. & 95.000  & NA & NA & 163031004 & O/E - Diastolic BP reading &  95 & 41 & Female & White & -0.297723 & 2009-02-26 & Oxford & 1968-02-26 & 47.59480\\\\\n",
-       "\t 5996198 & 2015-11-12 & 2469. & 134.000 & NA & NA & 163030003 & O/E - Systolic BP reading  & 134 & 41 & Female & White & -0.297723 & 2009-02-26 & Oxford & 1968-02-26 & 47.70979\\\\\n",
-       "\t 5996198 & 2015-11-12 & 246A. & 82.000  & NA & NA & 163031004 & O/E - Diastolic BP reading &  82 & 41 & Female & White & -0.297723 & 2009-02-26 & Oxford & 1968-02-26 & 47.70979\\\\\n",
-       "\\end{tabular}\n"
-      ],
-      "text/markdown": [
-       "\n",
-       "A tibble: 3729 × 17\n",
-       "\n",
-       "| eid &lt;int&gt; | date &lt;date&gt; | code &lt;chr&gt; | value1 &lt;chr&gt; | value2 &lt;chr&gt; | value3 &lt;chr&gt; | meaning &lt;chr&gt; | name &lt;chr&gt; | value &lt;dbl&gt; | age_at_recruitment_f21022_0_0 &lt;dbl&gt; | sex_f31_0_0 &lt;fct&gt; | ethnic_background_f21000_0_0 &lt;fct&gt; | townsend_deprivation_index_at_recruitment_f189_0_0 &lt;dbl&gt; | date_of_attending_assessment_centre_f53_0_0 &lt;date&gt; | uk_biobank_assessment_centre_f54_0_0 &lt;chr&gt; | birth_date &lt;date&gt; | time &lt;dbl&gt; |\n",
-       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
-       "| 1005194 | 1993-06-30 | 246.. | 120.000 | 80.000 | NA | 163030003 | O/E - Systolic BP reading  | 120 | 65 | Male | White | 1.80551 | 2009-10-23 | Sheffield | 1944-10-23 | 48.68446 |\n",
-       "| 1005194 | 1993-06-30 | 246.. | 120.000 | 80.000 | NA | 163031004 | O/E - Diastolic BP reading |  80 | 65 | Male | White | 1.80551 | 2009-10-23 | Sheffield | 1944-10-23 | 48.68446 |\n",
-       "| 1005194 | 1997-07-14 | 246.. | 130.000 | 80.000 | NA | 163030003 | O/E - Systolic BP reading  | 130 | 65 | Male | White | 1.80551 | 2009-10-23 | Sheffield | 1944-10-23 | 52.72279 |\n",
-       "| 1005194 | 1997-07-14 | 246.. | 130.000 | 80.000 | NA | 163031004 | O/E - Diastolic BP reading |  80 | 65 | Male | White | 1.80551 | 2009-10-23 | Sheffield | 1944-10-23 | 52.72279 |\n",
-       "| 1005194 | 1999-01-21 | 246.. | 142.000 | 94.000 | NA | 163030003 | O/E - Systolic BP reading  | 142 | 65 | Male | White | 1.80551 | 2009-10-23 | Sheffield | 1944-10-23 | 54.24504 |\n",
-       "| 1005194 | 1999-01-21 | 246.. | 142.000 | 94.000 | NA | 163031004 | O/E - Diastolic BP reading |  94 | 65 | Male | White | 1.80551 | 2009-10-23 | Sheffield | 1944-10-23 | 54.24504 |\n",
-       "| 1005194 | 1999-01-27 | 246.. | 142.000 | 92.000 | NA | 163030003 | O/E - Systolic BP reading  | 142 | 65 | Male | White | 1.80551 | 2009-10-23 | Sheffield | 1944-10-23 | 54.26146 |\n",
-       "| 1005194 | 1999-01-27 | 246.. | 142.000 | 92.000 | NA | 163031004 | O/E - Diastolic BP reading |  92 | 65 | Male | White | 1.80551 | 2009-10-23 | Sheffield | 1944-10-23 | 54.26146 |\n",
-       "| 1005194 | 1999-10-06 | 246.. | 140.000 | 86.000 | NA | 163030003 | O/E - Systolic BP reading  | 140 | 65 | Male | White | 1.80551 | 2009-10-23 | Sheffield | 1944-10-23 | 54.95140 |\n",
-       "| 1005194 | 1999-10-06 | 246.. | 140.000 | 86.000 | NA | 163031004 | O/E - Diastolic BP reading |  86 | 65 | Male | White | 1.80551 | 2009-10-23 | Sheffield | 1944-10-23 | 54.95140 |\n",
-       "| 1005194 | 1999-12-08 | 246.. | 148.000 | 82.000 | NA | 163030003 | O/E - Systolic BP reading  | 148 | 65 | Male | White | 1.80551 | 2009-10-23 | Sheffield | 1944-10-23 | 55.12389 |\n",
-       "| 1005194 | 1999-12-08 | 246.. | 148.000 | 82.000 | NA | 163031004 | O/E - Diastolic BP reading |  82 | 65 | Male | White | 1.80551 | 2009-10-23 | Sheffield | 1944-10-23 | 55.12389 |\n",
-       "| 1005194 | 2000-06-19 | 246.. | 120.000 | 84.000 | NA | 163030003 | O/E - Systolic BP reading  | 120 | 65 | Male | White | 1.80551 | 2009-10-23 | Sheffield | 1944-10-23 | 55.65503 |\n",
-       "| 1005194 | 2000-06-19 | 246.. | 120.000 | 84.000 | NA | 163031004 | O/E - Diastolic BP reading |  84 | 65 | Male | White | 1.80551 | 2009-10-23 | Sheffield | 1944-10-23 | 55.65503 |\n",
-       "| 1005194 | 2001-01-05 | 246.. | 150.000 | 90.000 | NA | 163030003 | O/E - Systolic BP reading  | 150 | 65 | Male | White | 1.80551 | 2009-10-23 | Sheffield | 1944-10-23 | 56.20260 |\n",
-       "| 1005194 | 2001-01-05 | 246.. | 150.000 | 90.000 | NA | 163031004 | O/E - Diastolic BP reading |  90 | 65 | Male | White | 1.80551 | 2009-10-23 | Sheffield | 1944-10-23 | 56.20260 |\n",
-       "| 1005194 | 2001-02-02 | 246.. | 140.000 | 90.000 | NA | 163030003 | O/E - Systolic BP reading  | 140 | 65 | Male | White | 1.80551 | 2009-10-23 | Sheffield | 1944-10-23 | 56.27926 |\n",
-       "| 1005194 | 2001-02-02 | 246.. | 140.000 | 90.000 | NA | 163031004 | O/E - Diastolic BP reading |  90 | 65 | Male | White | 1.80551 | 2009-10-23 | Sheffield | 1944-10-23 | 56.27926 |\n",
-       "| 1005194 | 2001-07-05 | 246.. | 130.000 | 74.000 | NA | 163030003 | O/E - Systolic BP reading  | 130 | 65 | Male | White | 1.80551 | 2009-10-23 | Sheffield | 1944-10-23 | 56.69815 |\n",
-       "| 1005194 | 2001-07-05 | 246.. | 130.000 | 74.000 | NA | 163031004 | O/E - Diastolic BP reading |  74 | 65 | Male | White | 1.80551 | 2009-10-23 | Sheffield | 1944-10-23 | 56.69815 |\n",
-       "| 1005194 | 2002-05-01 | 246.. | 144.000 | 80.000 | NA | 163030003 | O/E - Systolic BP reading  | 144 | 65 | Male | White | 1.80551 | 2009-10-23 | Sheffield | 1944-10-23 | 57.51951 |\n",
-       "| 1005194 | 2002-05-01 | 246.. | 144.000 | 80.000 | NA | 163031004 | O/E - Diastolic BP reading |  80 | 65 | Male | White | 1.80551 | 2009-10-23 | Sheffield | 1944-10-23 | 57.51951 |\n",
-       "| 1005194 | 2003-03-03 | 246.. | 145.000 | 81.000 | NA | 163030003 | O/E - Systolic BP reading  | 145 | 65 | Male | White | 1.80551 | 2009-10-23 | Sheffield | 1944-10-23 | 58.35729 |\n",
-       "| 1005194 | 2003-03-03 | 246.. | 145.000 | 81.000 | NA | 163031004 | O/E - Diastolic BP reading |  81 | 65 | Male | White | 1.80551 | 2009-10-23 | Sheffield | 1944-10-23 | 58.35729 |\n",
-       "| 1005194 | 2003-03-18 | 246.. | 137.000 | 67.000 | NA | 163030003 | O/E - Systolic BP reading  | 137 | 65 | Male | White | 1.80551 | 2009-10-23 | Sheffield | 1944-10-23 | 58.39836 |\n",
-       "| 1005194 | 2003-03-18 | 246.. | 137.000 | 67.000 | NA | 163031004 | O/E - Diastolic BP reading |  67 | 65 | Male | White | 1.80551 | 2009-10-23 | Sheffield | 1944-10-23 | 58.39836 |\n",
-       "| 1005194 | 2003-03-18 | 246.. | 146.000 | 92.000 | NA | 163030003 | O/E - Systolic BP reading  | 146 | 65 | Male | White | 1.80551 | 2009-10-23 | Sheffield | 1944-10-23 | 58.39836 |\n",
-       "| 1005194 | 2003-03-18 | 246.. | 146.000 | 92.000 | NA | 163031004 | O/E - Diastolic BP reading |  92 | 65 | Male | White | 1.80551 | 2009-10-23 | Sheffield | 1944-10-23 | 58.39836 |\n",
-       "| 1005194 | 2003-03-19 | 246.. | 145.000 | 80.000 | NA | 163030003 | O/E - Systolic BP reading  | 145 | 65 | Male | White | 1.80551 | 2009-10-23 | Sheffield | 1944-10-23 | 58.40110 |\n",
-       "| 1005194 | 2003-03-19 | 246.. | 145.000 | 80.000 | NA | 163031004 | O/E - Diastolic BP reading |  80 | 65 | Male | White | 1.80551 | 2009-10-23 | Sheffield | 1944-10-23 | 58.40110 |\n",
-       "| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |\n",
-       "| 5996198 | 2005-09-28 | 2469. | 120.000 | NA | NA | 163030003 | O/E - Systolic BP reading  | 120 | 41 | Female | White | -0.297723 | 2009-02-26 | Oxford | 1968-02-26 | 37.58795 |\n",
-       "| 5996198 | 2005-09-28 | 246A. | 84.000  | NA | NA | 163031004 | O/E - Diastolic BP reading |  84 | 41 | Female | White | -0.297723 | 2009-02-26 | Oxford | 1968-02-26 | 37.58795 |\n",
-       "| 5996198 | 2006-03-10 | 2469. | 129.000 | NA | NA | 163030003 | O/E - Systolic BP reading  | 129 | 41 | Female | White | -0.297723 | 2009-02-26 | Oxford | 1968-02-26 | 38.03422 |\n",
-       "| 5996198 | 2006-03-10 | 246A. | 89.000  | NA | NA | 163031004 | O/E - Diastolic BP reading |  89 | 41 | Female | White | -0.297723 | 2009-02-26 | Oxford | 1968-02-26 | 38.03422 |\n",
-       "| 5996198 | 2007-09-19 | 2469. | 126.000 | NA | NA | 163030003 | O/E - Systolic BP reading  | 126 | 41 | Female | White | -0.297723 | 2009-02-26 | Oxford | 1968-02-26 | 39.56194 |\n",
-       "| 5996198 | 2007-09-19 | 246A. | 80.000  | NA | NA | 163031004 | O/E - Diastolic BP reading |  80 | 41 | Female | White | -0.297723 | 2009-02-26 | Oxford | 1968-02-26 | 39.56194 |\n",
-       "| 5996198 | 2012-08-09 | 2469. | 132.000 | NA | NA | 163030003 | O/E - Systolic BP reading  | 132 | 41 | Female | White | -0.297723 | 2009-02-26 | Oxford | 1968-02-26 | 44.45175 |\n",
-       "| 5996198 | 2012-08-09 | 246A. | 86.000  | NA | NA | 163031004 | O/E - Diastolic BP reading |  86 | 41 | Female | White | -0.297723 | 2009-02-26 | Oxford | 1968-02-26 | 44.45175 |\n",
-       "| 5996198 | 2012-11-07 | 2469. | 140.000 | NA | NA | 163030003 | O/E - Systolic BP reading  | 140 | 41 | Female | White | -0.297723 | 2009-02-26 | Oxford | 1968-02-26 | 44.69815 |\n",
-       "| 5996198 | 2012-11-07 | 246A. | 68.000  | NA | NA | 163031004 | O/E - Diastolic BP reading |  68 | 41 | Female | White | -0.297723 | 2009-02-26 | Oxford | 1968-02-26 | 44.69815 |\n",
-       "| 5996198 | 2013-03-06 | 2469. | 139.000 | NA | NA | 163030003 | O/E - Systolic BP reading  | 139 | 41 | Female | White | -0.297723 | 2009-02-26 | Oxford | 1968-02-26 | 45.02396 |\n",
-       "| 5996198 | 2013-03-06 | 246A. | 88.000  | NA | NA | 163031004 | O/E - Diastolic BP reading |  88 | 41 | Female | White | -0.297723 | 2009-02-26 | Oxford | 1968-02-26 | 45.02396 |\n",
-       "| 5996198 | 2014-01-17 | 2469. | 148.000 | NA | NA | 163030003 | O/E - Systolic BP reading  | 148 | 41 | Female | White | -0.297723 | 2009-02-26 | Oxford | 1968-02-26 | 45.89185 |\n",
-       "| 5996198 | 2014-01-17 | 246A. | 72.000  | NA | NA | 163031004 | O/E - Diastolic BP reading |  72 | 41 | Female | White | -0.297723 | 2009-02-26 | Oxford | 1968-02-26 | 45.89185 |\n",
-       "| 5996198 | 2014-02-03 | 2469. | 145.000 | NA | NA | 163030003 | O/E - Systolic BP reading  | 145 | 41 | Female | White | -0.297723 | 2009-02-26 | Oxford | 1968-02-26 | 45.93840 |\n",
-       "| 5996198 | 2014-02-03 | 246A. | 98.000  | NA | NA | 163031004 | O/E - Diastolic BP reading |  98 | 41 | Female | White | -0.297723 | 2009-02-26 | Oxford | 1968-02-26 | 45.93840 |\n",
-       "| 5996198 | 2014-02-07 | 2469. | 137.000 | NA | NA | 163030003 | O/E - Systolic BP reading  | 137 | 41 | Female | White | -0.297723 | 2009-02-26 | Oxford | 1968-02-26 | 45.94935 |\n",
-       "| 5996198 | 2014-02-07 | 246A. | 85.000  | NA | NA | 163031004 | O/E - Diastolic BP reading |  85 | 41 | Female | White | -0.297723 | 2009-02-26 | Oxford | 1968-02-26 | 45.94935 |\n",
-       "| 5996198 | 2014-03-18 | 2469. | 145.000 | NA | NA | 163030003 | O/E - Systolic BP reading  | 145 | 41 | Female | White | -0.297723 | 2009-02-26 | Oxford | 1968-02-26 | 46.05613 |\n",
-       "| 5996198 | 2014-03-18 | 246A. | 98.000  | NA | NA | 163031004 | O/E - Diastolic BP reading |  98 | 41 | Female | White | -0.297723 | 2009-02-26 | Oxford | 1968-02-26 | 46.05613 |\n",
-       "| 5996198 | 2014-04-11 | 2469. | 130.000 | NA | NA | 163030003 | O/E - Systolic BP reading  | 130 | 41 | Female | White | -0.297723 | 2009-02-26 | Oxford | 1968-02-26 | 46.12183 |\n",
-       "| 5996198 | 2014-04-11 | 246A. | 60.000  | NA | NA | 163031004 | O/E - Diastolic BP reading |  60 | 41 | Female | White | -0.297723 | 2009-02-26 | Oxford | 1968-02-26 | 46.12183 |\n",
-       "| 5996198 | 2014-05-14 | 2469. | 132.000 | NA | NA | 163030003 | O/E - Systolic BP reading  | 132 | 41 | Female | White | -0.297723 | 2009-02-26 | Oxford | 1968-02-26 | 46.21218 |\n",
-       "| 5996198 | 2014-05-14 | 246A. | 84.000  | NA | NA | 163031004 | O/E - Diastolic BP reading |  84 | 41 | Female | White | -0.297723 | 2009-02-26 | Oxford | 1968-02-26 | 46.21218 |\n",
-       "| 5996198 | 2015-09-22 | 2469. | 124.000 | NA | NA | 163030003 | O/E - Systolic BP reading  | 124 | 41 | Female | White | -0.297723 | 2009-02-26 | Oxford | 1968-02-26 | 47.57016 |\n",
-       "| 5996198 | 2015-09-22 | 246A. | 88.000  | NA | NA | 163031004 | O/E - Diastolic BP reading |  88 | 41 | Female | White | -0.297723 | 2009-02-26 | Oxford | 1968-02-26 | 47.57016 |\n",
-       "| 5996198 | 2015-10-01 | 2469. | 133.000 | NA | NA | 163030003 | O/E - Systolic BP reading  | 133 | 41 | Female | White | -0.297723 | 2009-02-26 | Oxford | 1968-02-26 | 47.59480 |\n",
-       "| 5996198 | 2015-10-01 | 246A. | 95.000  | NA | NA | 163031004 | O/E - Diastolic BP reading |  95 | 41 | Female | White | -0.297723 | 2009-02-26 | Oxford | 1968-02-26 | 47.59480 |\n",
-       "| 5996198 | 2015-11-12 | 2469. | 134.000 | NA | NA | 163030003 | O/E - Systolic BP reading  | 134 | 41 | Female | White | -0.297723 | 2009-02-26 | Oxford | 1968-02-26 | 47.70979 |\n",
-       "| 5996198 | 2015-11-12 | 246A. | 82.000  | NA | NA | 163031004 | O/E - Diastolic BP reading |  82 | 41 | Female | White | -0.297723 | 2009-02-26 | Oxford | 1968-02-26 | 47.70979 |\n",
-       "\n"
-      ],
-      "text/plain": [
-       "       eid     date       code   value1  value2 value3 meaning  \n",
-       "1      1005194 1993-06-30 246..  120.000 80.000 NA     163030003\n",
-       "2      1005194 1993-06-30 246..  120.000 80.000 NA     163031004\n",
-       "3      1005194 1997-07-14 246..  130.000 80.000 NA     163030003\n",
-       "4      1005194 1997-07-14 246..  130.000 80.000 NA     163031004\n",
-       "5      1005194 1999-01-21 246..  142.000 94.000 NA     163030003\n",
-       "6      1005194 1999-01-21 246..  142.000 94.000 NA     163031004\n",
-       "7      1005194 1999-01-27 246..  142.000 92.000 NA     163030003\n",
-       "8      1005194 1999-01-27 246..  142.000 92.000 NA     163031004\n",
-       "9      1005194 1999-10-06 246..  140.000 86.000 NA     163030003\n",
-       "10     1005194 1999-10-06 246..  140.000 86.000 NA     163031004\n",
-       "11     1005194 1999-12-08 246..  148.000 82.000 NA     163030003\n",
-       "12     1005194 1999-12-08 246..  148.000 82.000 NA     163031004\n",
-       "13     1005194 2000-06-19 246..  120.000 84.000 NA     163030003\n",
-       "14     1005194 2000-06-19 246..  120.000 84.000 NA     163031004\n",
-       "15     1005194 2001-01-05 246..  150.000 90.000 NA     163030003\n",
-       "16     1005194 2001-01-05 246..  150.000 90.000 NA     163031004\n",
-       "17     1005194 2001-02-02 246..  140.000 90.000 NA     163030003\n",
-       "18     1005194 2001-02-02 246..  140.000 90.000 NA     163031004\n",
-       "19     1005194 2001-07-05 246..  130.000 74.000 NA     163030003\n",
-       "20     1005194 2001-07-05 246..  130.000 74.000 NA     163031004\n",
-       "21     1005194 2002-05-01 246..  144.000 80.000 NA     163030003\n",
-       "22     1005194 2002-05-01 246..  144.000 80.000 NA     163031004\n",
-       "23     1005194 2003-03-03 246..  145.000 81.000 NA     163030003\n",
-       "24     1005194 2003-03-03 246..  145.000 81.000 NA     163031004\n",
-       "25     1005194 2003-03-18 246..  137.000 67.000 NA     163030003\n",
-       "26     1005194 2003-03-18 246..  137.000 67.000 NA     163031004\n",
-       "27     1005194 2003-03-18 246..  146.000 92.000 NA     163030003\n",
-       "28     1005194 2003-03-18 246..  146.000 92.000 NA     163031004\n",
-       "29     1005194 2003-03-19 246..  145.000 80.000 NA     163030003\n",
-       "30     1005194 2003-03-19 246..  145.000 80.000 NA     163031004\n",
-       "<U+22EE> <U+22EE> <U+22EE>   <U+22EE> <U+22EE> <U+22EE> <U+22EE> <U+22EE> \n",
-       "3700   5996198 2005-09-28 2469.  120.000 NA     NA     163030003\n",
-       "3701   5996198 2005-09-28 246A.  84.000  NA     NA     163031004\n",
-       "3702   5996198 2006-03-10 2469.  129.000 NA     NA     163030003\n",
-       "3703   5996198 2006-03-10 246A.  89.000  NA     NA     163031004\n",
-       "3704   5996198 2007-09-19 2469.  126.000 NA     NA     163030003\n",
-       "3705   5996198 2007-09-19 246A.  80.000  NA     NA     163031004\n",
-       "3706   5996198 2012-08-09 2469.  132.000 NA     NA     163030003\n",
-       "3707   5996198 2012-08-09 246A.  86.000  NA     NA     163031004\n",
-       "3708   5996198 2012-11-07 2469.  140.000 NA     NA     163030003\n",
-       "3709   5996198 2012-11-07 246A.  68.000  NA     NA     163031004\n",
-       "3710   5996198 2013-03-06 2469.  139.000 NA     NA     163030003\n",
-       "3711   5996198 2013-03-06 246A.  88.000  NA     NA     163031004\n",
-       "3712   5996198 2014-01-17 2469.  148.000 NA     NA     163030003\n",
-       "3713   5996198 2014-01-17 246A.  72.000  NA     NA     163031004\n",
-       "3714   5996198 2014-02-03 2469.  145.000 NA     NA     163030003\n",
-       "3715   5996198 2014-02-03 246A.  98.000  NA     NA     163031004\n",
-       "3716   5996198 2014-02-07 2469.  137.000 NA     NA     163030003\n",
-       "3717   5996198 2014-02-07 246A.  85.000  NA     NA     163031004\n",
-       "3718   5996198 2014-03-18 2469.  145.000 NA     NA     163030003\n",
-       "3719   5996198 2014-03-18 246A.  98.000  NA     NA     163031004\n",
-       "3720   5996198 2014-04-11 2469.  130.000 NA     NA     163030003\n",
-       "3721   5996198 2014-04-11 246A.  60.000  NA     NA     163031004\n",
-       "3722   5996198 2014-05-14 2469.  132.000 NA     NA     163030003\n",
-       "3723   5996198 2014-05-14 246A.  84.000  NA     NA     163031004\n",
-       "3724   5996198 2015-09-22 2469.  124.000 NA     NA     163030003\n",
-       "3725   5996198 2015-09-22 246A.  88.000  NA     NA     163031004\n",
-       "3726   5996198 2015-10-01 2469.  133.000 NA     NA     163030003\n",
-       "3727   5996198 2015-10-01 246A.  95.000  NA     NA     163031004\n",
-       "3728   5996198 2015-11-12 2469.  134.000 NA     NA     163030003\n",
-       "3729   5996198 2015-11-12 246A.  82.000  NA     NA     163031004\n",
-       "       name                       value  age_at_recruitment_f21022_0_0\n",
-       "1      O/E - Systolic BP reading  120    65                           \n",
-       "2      O/E - Diastolic BP reading  80    65                           \n",
-       "3      O/E - Systolic BP reading  130    65                           \n",
-       "4      O/E - Diastolic BP reading  80    65                           \n",
-       "5      O/E - Systolic BP reading  142    65                           \n",
-       "6      O/E - Diastolic BP reading  94    65                           \n",
-       "7      O/E - Systolic BP reading  142    65                           \n",
-       "8      O/E - Diastolic BP reading  92    65                           \n",
-       "9      O/E - Systolic BP reading  140    65                           \n",
-       "10     O/E - Diastolic BP reading  86    65                           \n",
-       "11     O/E - Systolic BP reading  148    65                           \n",
-       "12     O/E - Diastolic BP reading  82    65                           \n",
-       "13     O/E - Systolic BP reading  120    65                           \n",
-       "14     O/E - Diastolic BP reading  84    65                           \n",
-       "15     O/E - Systolic BP reading  150    65                           \n",
-       "16     O/E - Diastolic BP reading  90    65                           \n",
-       "17     O/E - Systolic BP reading  140    65                           \n",
-       "18     O/E - Diastolic BP reading  90    65                           \n",
-       "19     O/E - Systolic BP reading  130    65                           \n",
-       "20     O/E - Diastolic BP reading  74    65                           \n",
-       "21     O/E - Systolic BP reading  144    65                           \n",
-       "22     O/E - Diastolic BP reading  80    65                           \n",
-       "23     O/E - Systolic BP reading  145    65                           \n",
-       "24     O/E - Diastolic BP reading  81    65                           \n",
-       "25     O/E - Systolic BP reading  137    65                           \n",
-       "26     O/E - Diastolic BP reading  67    65                           \n",
-       "27     O/E - Systolic BP reading  146    65                           \n",
-       "28     O/E - Diastolic BP reading  92    65                           \n",
-       "29     O/E - Systolic BP reading  145    65                           \n",
-       "30     O/E - Diastolic BP reading  80    65                           \n",
-       "<U+22EE> <U+22EE>                   <U+22EE> <U+22EE>                     \n",
-       "3700   O/E - Systolic BP reading  120    41                           \n",
-       "3701   O/E - Diastolic BP reading  84    41                           \n",
-       "3702   O/E - Systolic BP reading  129    41                           \n",
-       "3703   O/E - Diastolic BP reading  89    41                           \n",
-       "3704   O/E - Systolic BP reading  126    41                           \n",
-       "3705   O/E - Diastolic BP reading  80    41                           \n",
-       "3706   O/E - Systolic BP reading  132    41                           \n",
-       "3707   O/E - Diastolic BP reading  86    41                           \n",
-       "3708   O/E - Systolic BP reading  140    41                           \n",
-       "3709   O/E - Diastolic BP reading  68    41                           \n",
-       "3710   O/E - Systolic BP reading  139    41                           \n",
-       "3711   O/E - Diastolic BP reading  88    41                           \n",
-       "3712   O/E - Systolic BP reading  148    41                           \n",
-       "3713   O/E - Diastolic BP reading  72    41                           \n",
-       "3714   O/E - Systolic BP reading  145    41                           \n",
-       "3715   O/E - Diastolic BP reading  98    41                           \n",
-       "3716   O/E - Systolic BP reading  137    41                           \n",
-       "3717   O/E - Diastolic BP reading  85    41                           \n",
-       "3718   O/E - Systolic BP reading  145    41                           \n",
-       "3719   O/E - Diastolic BP reading  98    41                           \n",
-       "3720   O/E - Systolic BP reading  130    41                           \n",
-       "3721   O/E - Diastolic BP reading  60    41                           \n",
-       "3722   O/E - Systolic BP reading  132    41                           \n",
-       "3723   O/E - Diastolic BP reading  84    41                           \n",
-       "3724   O/E - Systolic BP reading  124    41                           \n",
-       "3725   O/E - Diastolic BP reading  88    41                           \n",
-       "3726   O/E - Systolic BP reading  133    41                           \n",
-       "3727   O/E - Diastolic BP reading  95    41                           \n",
-       "3728   O/E - Systolic BP reading  134    41                           \n",
-       "3729   O/E - Diastolic BP reading  82    41                           \n",
-       "       sex_f31_0_0 ethnic_background_f21000_0_0\n",
-       "1      Male        White                       \n",
-       "2      Male        White                       \n",
-       "3      Male        White                       \n",
-       "4      Male        White                       \n",
-       "5      Male        White                       \n",
-       "6      Male        White                       \n",
-       "7      Male        White                       \n",
-       "8      Male        White                       \n",
-       "9      Male        White                       \n",
-       "10     Male        White                       \n",
-       "11     Male        White                       \n",
-       "12     Male        White                       \n",
-       "13     Male        White                       \n",
-       "14     Male        White                       \n",
-       "15     Male        White                       \n",
-       "16     Male        White                       \n",
-       "17     Male        White                       \n",
-       "18     Male        White                       \n",
-       "19     Male        White                       \n",
-       "20     Male        White                       \n",
-       "21     Male        White                       \n",
-       "22     Male        White                       \n",
-       "23     Male        White                       \n",
-       "24     Male        White                       \n",
-       "25     Male        White                       \n",
-       "26     Male        White                       \n",
-       "27     Male        White                       \n",
-       "28     Male        White                       \n",
-       "29     Male        White                       \n",
-       "30     Male        White                       \n",
-       "<U+22EE> <U+22EE>    <U+22EE>                    \n",
-       "3700   Female      White                       \n",
-       "3701   Female      White                       \n",
-       "3702   Female      White                       \n",
-       "3703   Female      White                       \n",
-       "3704   Female      White                       \n",
-       "3705   Female      White                       \n",
-       "3706   Female      White                       \n",
-       "3707   Female      White                       \n",
-       "3708   Female      White                       \n",
-       "3709   Female      White                       \n",
-       "3710   Female      White                       \n",
-       "3711   Female      White                       \n",
-       "3712   Female      White                       \n",
-       "3713   Female      White                       \n",
-       "3714   Female      White                       \n",
-       "3715   Female      White                       \n",
-       "3716   Female      White                       \n",
-       "3717   Female      White                       \n",
-       "3718   Female      White                       \n",
-       "3719   Female      White                       \n",
-       "3720   Female      White                       \n",
-       "3721   Female      White                       \n",
-       "3722   Female      White                       \n",
-       "3723   Female      White                       \n",
-       "3724   Female      White                       \n",
-       "3725   Female      White                       \n",
-       "3726   Female      White                       \n",
-       "3727   Female      White                       \n",
-       "3728   Female      White                       \n",
-       "3729   Female      White                       \n",
-       "       townsend_deprivation_index_at_recruitment_f189_0_0\n",
-       "1      1.80551                                           \n",
-       "2      1.80551                                           \n",
-       "3      1.80551                                           \n",
-       "4      1.80551                                           \n",
-       "5      1.80551                                           \n",
-       "6      1.80551                                           \n",
-       "7      1.80551                                           \n",
-       "8      1.80551                                           \n",
-       "9      1.80551                                           \n",
-       "10     1.80551                                           \n",
-       "11     1.80551                                           \n",
-       "12     1.80551                                           \n",
-       "13     1.80551                                           \n",
-       "14     1.80551                                           \n",
-       "15     1.80551                                           \n",
-       "16     1.80551                                           \n",
-       "17     1.80551                                           \n",
-       "18     1.80551                                           \n",
-       "19     1.80551                                           \n",
-       "20     1.80551                                           \n",
-       "21     1.80551                                           \n",
-       "22     1.80551                                           \n",
-       "23     1.80551                                           \n",
-       "24     1.80551                                           \n",
-       "25     1.80551                                           \n",
-       "26     1.80551                                           \n",
-       "27     1.80551                                           \n",
-       "28     1.80551                                           \n",
-       "29     1.80551                                           \n",
-       "30     1.80551                                           \n",
-       "<U+22EE> <U+22EE>                                          \n",
-       "3700   -0.297723                                         \n",
-       "3701   -0.297723                                         \n",
-       "3702   -0.297723                                         \n",
-       "3703   -0.297723                                         \n",
-       "3704   -0.297723                                         \n",
-       "3705   -0.297723                                         \n",
-       "3706   -0.297723                                         \n",
-       "3707   -0.297723                                         \n",
-       "3708   -0.297723                                         \n",
-       "3709   -0.297723                                         \n",
-       "3710   -0.297723                                         \n",
-       "3711   -0.297723                                         \n",
-       "3712   -0.297723                                         \n",
-       "3713   -0.297723                                         \n",
-       "3714   -0.297723                                         \n",
-       "3715   -0.297723                                         \n",
-       "3716   -0.297723                                         \n",
-       "3717   -0.297723                                         \n",
-       "3718   -0.297723                                         \n",
-       "3719   -0.297723                                         \n",
-       "3720   -0.297723                                         \n",
-       "3721   -0.297723                                         \n",
-       "3722   -0.297723                                         \n",
-       "3723   -0.297723                                         \n",
-       "3724   -0.297723                                         \n",
-       "3725   -0.297723                                         \n",
-       "3726   -0.297723                                         \n",
-       "3727   -0.297723                                         \n",
-       "3728   -0.297723                                         \n",
-       "3729   -0.297723                                         \n",
-       "       date_of_attending_assessment_centre_f53_0_0\n",
-       "1      2009-10-23                                 \n",
-       "2      2009-10-23                                 \n",
-       "3      2009-10-23                                 \n",
-       "4      2009-10-23                                 \n",
-       "5      2009-10-23                                 \n",
-       "6      2009-10-23                                 \n",
-       "7      2009-10-23                                 \n",
-       "8      2009-10-23                                 \n",
-       "9      2009-10-23                                 \n",
-       "10     2009-10-23                                 \n",
-       "11     2009-10-23                                 \n",
-       "12     2009-10-23                                 \n",
-       "13     2009-10-23                                 \n",
-       "14     2009-10-23                                 \n",
-       "15     2009-10-23                                 \n",
-       "16     2009-10-23                                 \n",
-       "17     2009-10-23                                 \n",
-       "18     2009-10-23                                 \n",
-       "19     2009-10-23                                 \n",
-       "20     2009-10-23                                 \n",
-       "21     2009-10-23                                 \n",
-       "22     2009-10-23                                 \n",
-       "23     2009-10-23                                 \n",
-       "24     2009-10-23                                 \n",
-       "25     2009-10-23                                 \n",
-       "26     2009-10-23                                 \n",
-       "27     2009-10-23                                 \n",
-       "28     2009-10-23                                 \n",
-       "29     2009-10-23                                 \n",
-       "30     2009-10-23                                 \n",
-       "<U+22EE> <U+22EE>                                   \n",
-       "3700   2009-02-26                                 \n",
-       "3701   2009-02-26                                 \n",
-       "3702   2009-02-26                                 \n",
-       "3703   2009-02-26                                 \n",
-       "3704   2009-02-26                                 \n",
-       "3705   2009-02-26                                 \n",
-       "3706   2009-02-26                                 \n",
-       "3707   2009-02-26                                 \n",
-       "3708   2009-02-26                                 \n",
-       "3709   2009-02-26                                 \n",
-       "3710   2009-02-26                                 \n",
-       "3711   2009-02-26                                 \n",
-       "3712   2009-02-26                                 \n",
-       "3713   2009-02-26                                 \n",
-       "3714   2009-02-26                                 \n",
-       "3715   2009-02-26                                 \n",
-       "3716   2009-02-26                                 \n",
-       "3717   2009-02-26                                 \n",
-       "3718   2009-02-26                                 \n",
-       "3719   2009-02-26                                 \n",
-       "3720   2009-02-26                                 \n",
-       "3721   2009-02-26                                 \n",
-       "3722   2009-02-26                                 \n",
-       "3723   2009-02-26                                 \n",
-       "3724   2009-02-26                                 \n",
-       "3725   2009-02-26                                 \n",
-       "3726   2009-02-26                                 \n",
-       "3727   2009-02-26                                 \n",
-       "3728   2009-02-26                                 \n",
-       "3729   2009-02-26                                 \n",
-       "       uk_biobank_assessment_centre_f54_0_0 birth_date time    \n",
-       "1      Sheffield                            1944-10-23 48.68446\n",
-       "2      Sheffield                            1944-10-23 48.68446\n",
-       "3      Sheffield                            1944-10-23 52.72279\n",
-       "4      Sheffield                            1944-10-23 52.72279\n",
-       "5      Sheffield                            1944-10-23 54.24504\n",
-       "6      Sheffield                            1944-10-23 54.24504\n",
-       "7      Sheffield                            1944-10-23 54.26146\n",
-       "8      Sheffield                            1944-10-23 54.26146\n",
-       "9      Sheffield                            1944-10-23 54.95140\n",
-       "10     Sheffield                            1944-10-23 54.95140\n",
-       "11     Sheffield                            1944-10-23 55.12389\n",
-       "12     Sheffield                            1944-10-23 55.12389\n",
-       "13     Sheffield                            1944-10-23 55.65503\n",
-       "14     Sheffield                            1944-10-23 55.65503\n",
-       "15     Sheffield                            1944-10-23 56.20260\n",
-       "16     Sheffield                            1944-10-23 56.20260\n",
-       "17     Sheffield                            1944-10-23 56.27926\n",
-       "18     Sheffield                            1944-10-23 56.27926\n",
-       "19     Sheffield                            1944-10-23 56.69815\n",
-       "20     Sheffield                            1944-10-23 56.69815\n",
-       "21     Sheffield                            1944-10-23 57.51951\n",
-       "22     Sheffield                            1944-10-23 57.51951\n",
-       "23     Sheffield                            1944-10-23 58.35729\n",
-       "24     Sheffield                            1944-10-23 58.35729\n",
-       "25     Sheffield                            1944-10-23 58.39836\n",
-       "26     Sheffield                            1944-10-23 58.39836\n",
-       "27     Sheffield                            1944-10-23 58.39836\n",
-       "28     Sheffield                            1944-10-23 58.39836\n",
-       "29     Sheffield                            1944-10-23 58.40110\n",
-       "30     Sheffield                            1944-10-23 58.40110\n",
-       "<U+22EE> <U+22EE>                             <U+22EE>   <U+22EE>\n",
-       "3700   Oxford                               1968-02-26 37.58795\n",
-       "3701   Oxford                               1968-02-26 37.58795\n",
-       "3702   Oxford                               1968-02-26 38.03422\n",
-       "3703   Oxford                               1968-02-26 38.03422\n",
-       "3704   Oxford                               1968-02-26 39.56194\n",
-       "3705   Oxford                               1968-02-26 39.56194\n",
-       "3706   Oxford                               1968-02-26 44.45175\n",
-       "3707   Oxford                               1968-02-26 44.45175\n",
-       "3708   Oxford                               1968-02-26 44.69815\n",
-       "3709   Oxford                               1968-02-26 44.69815\n",
-       "3710   Oxford                               1968-02-26 45.02396\n",
-       "3711   Oxford                               1968-02-26 45.02396\n",
-       "3712   Oxford                               1968-02-26 45.89185\n",
-       "3713   Oxford                               1968-02-26 45.89185\n",
-       "3714   Oxford                               1968-02-26 45.93840\n",
-       "3715   Oxford                               1968-02-26 45.93840\n",
-       "3716   Oxford                               1968-02-26 45.94935\n",
-       "3717   Oxford                               1968-02-26 45.94935\n",
-       "3718   Oxford                               1968-02-26 46.05613\n",
-       "3719   Oxford                               1968-02-26 46.05613\n",
-       "3720   Oxford                               1968-02-26 46.12183\n",
-       "3721   Oxford                               1968-02-26 46.12183\n",
-       "3722   Oxford                               1968-02-26 46.21218\n",
-       "3723   Oxford                               1968-02-26 46.21218\n",
-       "3724   Oxford                               1968-02-26 47.57016\n",
-       "3725   Oxford                               1968-02-26 47.57016\n",
-       "3726   Oxford                               1968-02-26 47.59480\n",
-       "3727   Oxford                               1968-02-26 47.59480\n",
-       "3728   Oxford                               1968-02-26 47.70979\n",
-       "3729   Oxford                               1968-02-26 47.70979"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "library(lubridate)\n",
-    "df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "163031004           4142442\n",
-       "163030003           3963088\n",
-       "1000731000000107    1670214\n",
-       "1022431000000105    1662102\n",
-       "1022541000000102    1630899\n",
-       "                     ...   \n",
-       "173842008                 1\n",
-       "77329001                  1\n",
-       "265404007                 1\n",
-       "81977006                  1\n",
-       "211427006                 1\n",
-       "Name: meaning, Length: 16366, dtype: int64"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "codes_gp_measurements.meaning.value_counts(normalize=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Basics"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "coding10 = pd.read_csv(f\"{path}/mapping/codings/coding10.tsv\", sep=\"\\t\").assign(coding = lambda x: x.coding.astype(\"int\")).rename(columns={\"coding\":\"uk_biobank_assessment_centre_f54_0_0\"})\n",
-    "coding10[\"uk_biobank_assessment_centre_f54_0_0\"] = coding10[\"uk_biobank_assessment_centre_f54_0_0\"].astype(\"int\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:34:06.176411Z",
-     "start_time": "2020-11-04T12:34:05.891730Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "502504\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>age_at_recruitment_f21022_0_0</th>\n",
-       "      <th>sex_f31_0_0</th>\n",
-       "      <th>ethnic_background_f21000_0_0</th>\n",
-       "      <th>townsend_deprivation_index_at_recruitment_f189_0_0</th>\n",
-       "      <th>date_of_attending_assessment_centre_f53_0_0</th>\n",
-       "      <th>uk_biobank_assessment_centre_f54_0_0</th>\n",
-       "      <th>birth_date</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>49.0</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>White</td>\n",
-       "      <td>-1.852930</td>\n",
-       "      <td>2009-11-12</td>\n",
-       "      <td>Sheffield</td>\n",
-       "      <td>1960-11-12</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1000020</td>\n",
-       "      <td>59.0</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>White</td>\n",
-       "      <td>0.204248</td>\n",
-       "      <td>2008-02-19</td>\n",
-       "      <td>Sheffield</td>\n",
-       "      <td>1949-02-19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1000037</td>\n",
-       "      <td>59.0</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>White</td>\n",
-       "      <td>-3.498860</td>\n",
-       "      <td>2008-11-11</td>\n",
-       "      <td>Sheffield</td>\n",
-       "      <td>1949-11-11</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000043</td>\n",
-       "      <td>63.0</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>White</td>\n",
-       "      <td>-5.351150</td>\n",
-       "      <td>2009-06-03</td>\n",
-       "      <td>Sheffield</td>\n",
-       "      <td>1946-06-03</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1000051</td>\n",
-       "      <td>51.0</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>White</td>\n",
-       "      <td>-1.799080</td>\n",
-       "      <td>2006-06-10</td>\n",
-       "      <td>Sheffield</td>\n",
-       "      <td>1955-06-10</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       eid  age_at_recruitment_f21022_0_0 sex_f31_0_0  \\\n",
-       "0  1000018                           49.0      Female   \n",
-       "1  1000020                           59.0        Male   \n",
-       "2  1000037                           59.0      Female   \n",
-       "3  1000043                           63.0        Male   \n",
-       "4  1000051                           51.0      Female   \n",
-       "\n",
-       "  ethnic_background_f21000_0_0  \\\n",
-       "0                        White   \n",
-       "1                        White   \n",
-       "2                        White   \n",
-       "3                        White   \n",
-       "4                        White   \n",
-       "\n",
-       "   townsend_deprivation_index_at_recruitment_f189_0_0  \\\n",
-       "0                                          -1.852930    \n",
-       "1                                           0.204248    \n",
-       "2                                          -3.498860    \n",
-       "3                                          -5.351150    \n",
-       "4                                          -1.799080    \n",
-       "\n",
-       "  date_of_attending_assessment_centre_f53_0_0  \\\n",
-       "0                                  2009-11-12   \n",
-       "1                                  2008-02-19   \n",
-       "2                                  2008-11-11   \n",
-       "3                                  2009-06-03   \n",
-       "4                                  2006-06-10   \n",
-       "\n",
-       "  uk_biobank_assessment_centre_f54_0_0  birth_date  \n",
-       "0                            Sheffield  1960-11-12  \n",
-       "1                            Sheffield  1949-02-19  \n",
-       "2                            Sheffield  1949-11-11  \n",
-       "3                            Sheffield  1946-06-03  \n",
-       "4                            Sheffield  1955-06-10  "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fields_basics = [\n",
-    "    \"21022\", # age at recruitment\n",
-    "    \"31\", # sex\n",
-    "    \"21000\", # ethnicity\n",
-    "    \"189\", # Townsend index\n",
-    "    \"53\", # date of baseline assessment\n",
-    "    \"54\", # assessment center\n",
-    "]\n",
-    "\n",
-    "temp = get_data_fields(fields_basics, data, data_field)\n",
-    "\n",
-    "temp[\"sex_f31_0_0\"] = temp[\"sex_f31_0_0\"].cat.set_categories([\"Female\", 'Male'], ordered=False)\n",
-    "\n",
-    "#temp[\"ethnic_background_f21000_0_0\"] = temp[\"ethnic_background_f21000_0_0\"].astype(\"string\")\n",
-    "\n",
-    "ethn_bg_def = {\"White\": [\"White\", \"British\", \"Irish\", \"Any other white background\"],\n",
-    "                \"Mixed\": [\"Mixed\", \"White and Black Caribbean\", \"White and Black African\", \"White and Asian\", \"Any other mixed background\"],  \n",
-    "                \"Asian\": [\"Asian or Asian British\", \"Indian\", \"Pakistani\", \"Bangladeshi\", \"Any other Asian background\"], \n",
-    "                \"Black\": [\"Black or Black British\", \"Caribbean\", \"African\", \"Any other Black background\"],\n",
-    "                \"Chinese\": [\"Chinese\"],  \n",
-    "                np.nan: [\"Other ethnic group\", \"Do not know\", \"Prefer not to answer\"]}\n",
-    "\n",
-    "ethn_bg_dict = {}\n",
-    "for key, values in ethn_bg_def.items(): \n",
-    "    for value in values:\n",
-    "        ethn_bg_dict[value]=key \n",
-    "        \n",
-    "temp[\"ethnic_background_f21000_0_0\"].replace(ethn_bg_dict, inplace=True)\n",
-    "temp[\"ethnic_background_f21000_0_0\"] = temp[\"ethnic_background_f21000_0_0\"].astype(\"category\")\n",
-    "\n",
-    "#\n",
-    "#temp[\"ethnic_background_f21000_0_0\"] = temp[\"ethnic_background_f21000_0_0\"].astype(\"category\").cat.set_categories(['White', 'Black', 'Asien', 'Mixed', 'Chinese'], ordered=False)\n",
-    "\n",
-    "basics = temp\n",
-    "print(len(temp))\n",
-    "\n",
-    "from dateutil.relativedelta import relativedelta\n",
-    "calc_birth_date = [date_of_attending_assessment_centre - relativedelta(years=age_at_recruitment) \n",
-    "                                                             for date_of_attending_assessment_centre, age_at_recruitment \n",
-    "                                                             in zip(basics[\"date_of_attending_assessment_centre_f53_0_0\"], basics[\"age_at_recruitment_f21022_0_0\"])]\n",
-    "\n",
-    "basics = basics.assign(birth_date = calc_birth_date)\n",
-    "basics[\"uk_biobank_assessment_centre_f54_0_0\"] = basics.assign(uk_biobank_assessment_centre_f54_0_0 = lambda x: x.uk_biobank_assessment_centre_f54_0_0.astype(\"int\")).merge(coding10, on=\"uk_biobank_assessment_centre_f54_0_0\")[\"meaning\"]\n",
-    "\n",
-    "\n",
-    "display(basics.head())\n",
-    "basics.to_feather(os.path.join(path, dataset_path, 'temp_basics.feather'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:34:06.186613Z",
-     "start_time": "2020-11-04T12:34:06.178111Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['White', 'Black', NaN, 'Asian', 'Mixed', 'Chinese']\n",
-      "Categories (5, object): ['White', 'Black', 'Asian', 'Mixed', 'Chinese']\n"
-     ]
-    }
-   ],
-   "source": [
-    " print(temp[\"ethnic_background_f21000_0_0\"].unique())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Questionnaire"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:34:06.389467Z",
-     "start_time": "2020-11-04T12:34:06.188206Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "502504\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>overall_health_rating_f2178_0_0</th>\n",
-       "      <th>smoking_status_f20116_0_0</th>\n",
-       "      <th>alcohol_intake_frequency_f1558_0_0</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>Fair</td>\n",
-       "      <td>Current</td>\n",
-       "      <td>Once or twice a week</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1000020</td>\n",
-       "      <td>Good</td>\n",
-       "      <td>Current</td>\n",
-       "      <td>Once or twice a week</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1000037</td>\n",
-       "      <td>Good</td>\n",
-       "      <td>Previous</td>\n",
-       "      <td>Once or twice a week</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000043</td>\n",
-       "      <td>Fair</td>\n",
-       "      <td>Previous</td>\n",
-       "      <td>Three or four times a week</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1000051</td>\n",
-       "      <td>Poor</td>\n",
-       "      <td>Never</td>\n",
-       "      <td>One to three times a month</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       eid overall_health_rating_f2178_0_0 smoking_status_f20116_0_0  \\\n",
-       "0  1000018                            Fair                   Current   \n",
-       "1  1000020                            Good                   Current   \n",
-       "2  1000037                            Good                  Previous   \n",
-       "3  1000043                            Fair                  Previous   \n",
-       "4  1000051                            Poor                     Never   \n",
-       "\n",
-       "  alcohol_intake_frequency_f1558_0_0  \n",
-       "0               Once or twice a week  \n",
-       "1               Once or twice a week  \n",
-       "2               Once or twice a week  \n",
-       "3         Three or four times a week  \n",
-       "4         One to three times a month  "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fields_questionnaire = [\n",
-    "    \"2178\", # Overall health\n",
-    "    \"20116\", # Smoking status\n",
-    "    \"1558\",\n",
-    "]\n",
-    "\n",
-    "temp = get_data_fields(fields_questionnaire, data, data_field)\n",
-    "\n",
-    "temp[\"overall_health_rating_f2178_0_0\"] = temp[\"overall_health_rating_f2178_0_0\"]\\\n",
-    "    .replace({\"Do not know\": np.nan, \"Prefer not to answer\": np.nan})\\\n",
-    "    .astype(\"category\").cat.set_categories(['Poor', 'Fair', 'Good', 'Excellent'], ordered=True)\n",
-    "\n",
-    "\n",
-    "temp[\"smoking_status_f20116_0_0\"] = temp[\"smoking_status_f20116_0_0\"]\\\n",
-    "    .replace({\"Prefer not to answer\": np.nan}, inplace=False)\\\n",
-    "    .astype(\"category\").cat.set_categories(['Current', 'Previous', 'Never'], ordered=True)\n",
-    "\n",
-    "temp[\"alcohol_intake_frequency_f1558_0_0\"] = temp[\"alcohol_intake_frequency_f1558_0_0\"]\\\n",
-    "    .replace({\"Prefer not to answer\": np.nan}, inplace=False)\\\n",
-    "    .astype(\"category\").cat.set_categories([\n",
-    "        'Daily or almost daily', \n",
-    "        'Three or four times a week', \n",
-    "        'Once or twice a week',\n",
-    "        'One to three times a month',\n",
-    "        'Special occasions only', \n",
-    "        'Never'], ordered=True)\n",
-    "\n",
-    "questionnaire = temp\n",
-    "print(len(temp))\n",
-    "display(temp.head())\n",
-    "\n",
-    "questionnaire.to_feather(os.path.join(path, dataset_path, 'temp_questionnaire.feather'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:34:06.398168Z",
-     "start_time": "2020-11-04T12:34:06.391082Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['Once or twice a week', 'Three or four times a week', 'One to three times a month', 'Daily or almost daily', 'Special occasions only', 'Never', NaN]\n",
-      "Categories (6, object): ['Daily or almost daily' < 'Three or four times a week' < 'Once or twice a week' < 'One to three times a month' < 'Special occasions only' < 'Never']\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(temp[\"alcohol_intake_frequency_f1558_0_0\"].unique())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Physical measurements"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:34:07.052989Z",
-     "start_time": "2020-11-04T12:34:06.400858Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "502504\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>body_mass_index_bmi_f21001_0_0</th>\n",
-       "      <th>weight_f21002_0_0</th>\n",
-       "      <th>pulse_wave_arterial_stiffness_index_f21021_0_0</th>\n",
-       "      <th>pulse_wave_reflection_index_f4195_0_0</th>\n",
-       "      <th>waist_circumference_f48_0_0</th>\n",
-       "      <th>hip_circumference_f49_0_0</th>\n",
-       "      <th>standing_height_f50_0_0</th>\n",
-       "      <th>trunk_fat_percentage_f23127_0_0</th>\n",
-       "      <th>body_fat_percentage_f23099_0_0</th>\n",
-       "      <th>basal_metabolic_rate_f23105_0_0</th>\n",
-       "      <th>forced_vital_capacity_fvc_best_measure_f20151_0_0</th>\n",
-       "      <th>forced_expiratory_volume_in_1second_fev1_best_measure_f20150_0_0</th>\n",
-       "      <th>fev1_fvc_ratio_zscore_f20258_0_0</th>\n",
-       "      <th>peak_expiratory_flow_pef_f3064_0_2</th>\n",
-       "      <th>peak_expiratory_flow_pef_f3064_0_1</th>\n",
-       "      <th>peak_expiratory_flow_pef_f3064_0_0</th>\n",
-       "      <th>systolic_blood_pressure_automated_reading_f4080</th>\n",
-       "      <th>diastolic_blood_pressure_automated_reading_f4079</th>\n",
-       "      <th>pulse_rate_automated_reading_f102</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>26.5557</td>\n",
-       "      <td>63.8</td>\n",
-       "      <td>7.2770</td>\n",
-       "      <td>80.0</td>\n",
-       "      <td>85.0</td>\n",
-       "      <td>107.0</td>\n",
-       "      <td>155.0</td>\n",
-       "      <td>37.5</td>\n",
-       "      <td>39.5</td>\n",
-       "      <td>5012.0</td>\n",
-       "      <td>3.21</td>\n",
-       "      <td>2.16</td>\n",
-       "      <td>1.978</td>\n",
-       "      <td>317.0</td>\n",
-       "      <td>312.0</td>\n",
-       "      <td>339.0</td>\n",
-       "      <td>159.5</td>\n",
-       "      <td>88.0</td>\n",
-       "      <td>50.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1000020</td>\n",
-       "      <td>22.7465</td>\n",
-       "      <td>70.7</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>87.8</td>\n",
-       "      <td>94.4</td>\n",
-       "      <td>176.3</td>\n",
-       "      <td>33.4</td>\n",
-       "      <td>28.7</td>\n",
-       "      <td>6171.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.375</td>\n",
-       "      <td>301.0</td>\n",
-       "      <td>496.0</td>\n",
-       "      <td>504.0</td>\n",
-       "      <td>133.0</td>\n",
-       "      <td>81.0</td>\n",
-       "      <td>74.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1000037</td>\n",
-       "      <td>32.4211</td>\n",
-       "      <td>78.9</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>101.0</td>\n",
-       "      <td>112.0</td>\n",
-       "      <td>156.0</td>\n",
-       "      <td>47.5</td>\n",
-       "      <td>48.4</td>\n",
-       "      <td>5397.0</td>\n",
-       "      <td>1.61</td>\n",
-       "      <td>1.27</td>\n",
-       "      <td>0.138</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>185.0</td>\n",
-       "      <td>208.0</td>\n",
-       "      <td>118.5</td>\n",
-       "      <td>78.0</td>\n",
-       "      <td>62.5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000043</td>\n",
-       "      <td>29.5679</td>\n",
-       "      <td>95.8</td>\n",
-       "      <td>11.1111</td>\n",
-       "      <td>78.0</td>\n",
-       "      <td>98.0</td>\n",
-       "      <td>104.0</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>27.6</td>\n",
-       "      <td>25.6</td>\n",
-       "      <td>8711.0</td>\n",
-       "      <td>4.14</td>\n",
-       "      <td>2.84</td>\n",
-       "      <td>1.096</td>\n",
-       "      <td>557.0</td>\n",
-       "      <td>513.0</td>\n",
-       "      <td>530.0</td>\n",
-       "      <td>141.5</td>\n",
-       "      <td>93.5</td>\n",
-       "      <td>64.5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1000051</td>\n",
-       "      <td>41.0222</td>\n",
-       "      <td>92.3</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>123.0</td>\n",
-       "      <td>129.0</td>\n",
-       "      <td>150.0</td>\n",
-       "      <td>48.9</td>\n",
-       "      <td>50.4</td>\n",
-       "      <td>6100.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.518</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>117.0</td>\n",
-       "      <td>81.0</td>\n",
-       "      <td>79.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       eid  body_mass_index_bmi_f21001_0_0  weight_f21002_0_0  \\\n",
-       "0  1000018                         26.5557               63.8   \n",
-       "1  1000020                         22.7465               70.7   \n",
-       "2  1000037                         32.4211               78.9   \n",
-       "3  1000043                         29.5679               95.8   \n",
-       "4  1000051                         41.0222               92.3   \n",
-       "\n",
-       "   pulse_wave_arterial_stiffness_index_f21021_0_0  \\\n",
-       "0                                          7.2770   \n",
-       "1                                             NaN   \n",
-       "2                                             NaN   \n",
-       "3                                         11.1111   \n",
-       "4                                             NaN   \n",
-       "\n",
-       "   pulse_wave_reflection_index_f4195_0_0  waist_circumference_f48_0_0  \\\n",
-       "0                                   80.0                         85.0   \n",
-       "1                                    NaN                         87.8   \n",
-       "2                                    NaN                        101.0   \n",
-       "3                                   78.0                         98.0   \n",
-       "4                                    NaN                        123.0   \n",
-       "\n",
-       "   hip_circumference_f49_0_0  standing_height_f50_0_0  \\\n",
-       "0                      107.0                    155.0   \n",
-       "1                       94.4                    176.3   \n",
-       "2                      112.0                    156.0   \n",
-       "3                      104.0                    180.0   \n",
-       "4                      129.0                    150.0   \n",
-       "\n",
-       "   trunk_fat_percentage_f23127_0_0  body_fat_percentage_f23099_0_0  \\\n",
-       "0                             37.5                            39.5   \n",
-       "1                             33.4                            28.7   \n",
-       "2                             47.5                            48.4   \n",
-       "3                             27.6                            25.6   \n",
-       "4                             48.9                            50.4   \n",
-       "\n",
-       "   basal_metabolic_rate_f23105_0_0  \\\n",
-       "0                           5012.0   \n",
-       "1                           6171.0   \n",
-       "2                           5397.0   \n",
-       "3                           8711.0   \n",
-       "4                           6100.0   \n",
-       "\n",
-       "   forced_vital_capacity_fvc_best_measure_f20151_0_0  \\\n",
-       "0                                               3.21   \n",
-       "1                                                NaN   \n",
-       "2                                               1.61   \n",
-       "3                                               4.14   \n",
-       "4                                                NaN   \n",
-       "\n",
-       "   forced_expiratory_volume_in_1second_fev1_best_measure_f20150_0_0  \\\n",
-       "0                                               2.16                  \n",
-       "1                                                NaN                  \n",
-       "2                                               1.27                  \n",
-       "3                                               2.84                  \n",
-       "4                                                NaN                  \n",
-       "\n",
-       "   fev1_fvc_ratio_zscore_f20258_0_0  peak_expiratory_flow_pef_f3064_0_2  \\\n",
-       "0                             1.978                               317.0   \n",
-       "1                             1.375                               301.0   \n",
-       "2                             0.138                                 NaN   \n",
-       "3                             1.096                               557.0   \n",
-       "4                             0.518                                 NaN   \n",
-       "\n",
-       "   peak_expiratory_flow_pef_f3064_0_1  peak_expiratory_flow_pef_f3064_0_0  \\\n",
-       "0                               312.0                               339.0   \n",
-       "1                               496.0                               504.0   \n",
-       "2                               185.0                               208.0   \n",
-       "3                               513.0                               530.0   \n",
-       "4                                 NaN                                 NaN   \n",
-       "\n",
-       "   systolic_blood_pressure_automated_reading_f4080  \\\n",
-       "0                                            159.5   \n",
-       "1                                            133.0   \n",
-       "2                                            118.5   \n",
-       "3                                            141.5   \n",
-       "4                                            117.0   \n",
-       "\n",
-       "   diastolic_blood_pressure_automated_reading_f4079  \\\n",
-       "0                                              88.0   \n",
-       "1                                              81.0   \n",
-       "2                                              78.0   \n",
-       "3                                              93.5   \n",
-       "4                                              81.0   \n",
-       "\n",
-       "   pulse_rate_automated_reading_f102  \n",
-       "0                               50.0  \n",
-       "1                               74.0  \n",
-       "2                               62.5  \n",
-       "3                               64.5  \n",
-       "4                               79.0  "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from statistics import mean\n",
-    "\n",
-    "fields_measurements = [\n",
-    "#    \"100313\", # Walking speed !!! MISSING !!!\n",
-    "    \"21001\", # BMI\n",
-    "    \"21002\", # weight\n",
-    "    \"4080\", # Syst. BP\n",
-    "    \"4079\", # Diast. BP\n",
-    "    \"102\",\n",
-    "    \"21021\",\n",
-    "    \"4195\",\n",
-    "    \"48\",\n",
-    "    \"49\",\n",
-    "    \"50\",\n",
-    "    \"23127\",\n",
-    "    \"23099\",\n",
-    "    \"23105\",\n",
-    "    \"20151\",\n",
-    "    \"20150\",\n",
-    "    \"20258\",\n",
-    "    \"3064\",\n",
-    "    \n",
-    "]\n",
-    "temp = get_data_fields(fields_measurements, data, data_field)\n",
-    "\n",
-    "sbp_cols = [\"systolic_blood_pressure_automated_reading_f4080_0_0\", \"systolic_blood_pressure_automated_reading_f4080_0_1\"]\n",
-    "dbp_cols = [\"diastolic_blood_pressure_automated_reading_f4079_0_0\", \"diastolic_blood_pressure_automated_reading_f4079_0_1\"]\n",
-    "pr_cols = [\"pulse_rate_automated_reading_f102_0_0\", \"pulse_rate_automated_reading_f102_0_1\"]\n",
-    "\n",
-    "temp = temp.assign(systolic_blood_pressure_automated_reading_f4080 = temp[sbp_cols].mean(axis=1),\n",
-    "                   diastolic_blood_pressure_automated_reading_f4079 = temp[dbp_cols].mean(axis=1),\n",
-    "                   pulse_rate_automated_reading_f102 = temp[pr_cols].mean(axis=1))\\\n",
-    "    .drop(sbp_cols + dbp_cols + pr_cols, axis=1)\n",
-    "\n",
-    "measurements = temp\n",
-    "print(len(temp))\n",
-    "display(temp.head())\n",
-    "\n",
-    "measurements.to_feather(os.path.join(path, dataset_path, 'temp_measurements.feather'))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Lab measurements"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:34:07.647955Z",
-     "start_time": "2020-11-04T12:34:07.055242Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "502504\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>basophill_count_f30160_0_0</th>\n",
-       "      <th>basophill_percentage_f30220_0_0</th>\n",
-       "      <th>eosinophill_count_f30150_0_0</th>\n",
-       "      <th>eosinophill_percentage_f30210_0_0</th>\n",
-       "      <th>haematocrit_percentage_f30030_0_0</th>\n",
-       "      <th>haemoglobin_concentration_f30020_0_0</th>\n",
-       "      <th>high_light_scatter_reticulocyte_count_f30300_0_0</th>\n",
-       "      <th>high_light_scatter_reticulocyte_percentage_f30290_0_0</th>\n",
-       "      <th>immature_reticulocyte_fraction_f30280_0_0</th>\n",
-       "      <th>...</th>\n",
-       "      <th>phosphate_f30810_0_0</th>\n",
-       "      <th>rheumatoid_factor_f30820_0_0</th>\n",
-       "      <th>shbg_f30830_0_0</th>\n",
-       "      <th>testosterone_f30850_0_0</th>\n",
-       "      <th>total_bilirubin_f30840_0_0</th>\n",
-       "      <th>total_protein_f30860_0_0</th>\n",
-       "      <th>triglycerides_f30870_0_0</th>\n",
-       "      <th>urate_f30880_0_0</th>\n",
-       "      <th>urea_f30670_0_0</th>\n",
-       "      <th>vitamin_d_f30890_0_0</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>0.04</td>\n",
-       "      <td>0.26</td>\n",
-       "      <td>0.25</td>\n",
-       "      <td>1.75</td>\n",
-       "      <td>39.79</td>\n",
-       "      <td>13.90</td>\n",
-       "      <td>0.022</td>\n",
-       "      <td>0.464</td>\n",
-       "      <td>0.378</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.422</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>70.11</td>\n",
-       "      <td>1.560</td>\n",
-       "      <td>7.41</td>\n",
-       "      <td>71.97</td>\n",
-       "      <td>1.247</td>\n",
-       "      <td>221.3</td>\n",
-       "      <td>5.48</td>\n",
-       "      <td>70.7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1000020</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0.30</td>\n",
-       "      <td>0.30</td>\n",
-       "      <td>2.50</td>\n",
-       "      <td>45.00</td>\n",
-       "      <td>15.60</td>\n",
-       "      <td>0.014</td>\n",
-       "      <td>0.290</td>\n",
-       "      <td>0.300</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.264</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>55.31</td>\n",
-       "      <td>12.237</td>\n",
-       "      <td>8.07</td>\n",
-       "      <td>78.45</td>\n",
-       "      <td>1.906</td>\n",
-       "      <td>374.7</td>\n",
-       "      <td>5.28</td>\n",
-       "      <td>35.9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1000037</td>\n",
-       "      <td>0.04</td>\n",
-       "      <td>0.57</td>\n",
-       "      <td>0.10</td>\n",
-       "      <td>1.43</td>\n",
-       "      <td>39.48</td>\n",
-       "      <td>13.58</td>\n",
-       "      <td>0.031</td>\n",
-       "      <td>0.686</td>\n",
-       "      <td>0.380</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000043</td>\n",
-       "      <td>0.02</td>\n",
-       "      <td>0.32</td>\n",
-       "      <td>0.11</td>\n",
-       "      <td>1.80</td>\n",
-       "      <td>44.31</td>\n",
-       "      <td>14.99</td>\n",
-       "      <td>0.025</td>\n",
-       "      <td>0.508</td>\n",
-       "      <td>0.250</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.928</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>31.63</td>\n",
-       "      <td>11.398</td>\n",
-       "      <td>8.65</td>\n",
-       "      <td>69.70</td>\n",
-       "      <td>5.184</td>\n",
-       "      <td>322.8</td>\n",
-       "      <td>6.67</td>\n",
-       "      <td>63.6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1000051</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 62 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       eid  basophill_count_f30160_0_0  basophill_percentage_f30220_0_0  \\\n",
-       "0  1000018                        0.04                             0.26   \n",
-       "1  1000020                        0.00                             0.30   \n",
-       "2  1000037                        0.04                             0.57   \n",
-       "3  1000043                        0.02                             0.32   \n",
-       "4  1000051                         NaN                              NaN   \n",
-       "\n",
-       "   eosinophill_count_f30150_0_0  eosinophill_percentage_f30210_0_0  \\\n",
-       "0                          0.25                               1.75   \n",
-       "1                          0.30                               2.50   \n",
-       "2                          0.10                               1.43   \n",
-       "3                          0.11                               1.80   \n",
-       "4                           NaN                                NaN   \n",
-       "\n",
-       "   haematocrit_percentage_f30030_0_0  haemoglobin_concentration_f30020_0_0  \\\n",
-       "0                              39.79                                 13.90   \n",
-       "1                              45.00                                 15.60   \n",
-       "2                              39.48                                 13.58   \n",
-       "3                              44.31                                 14.99   \n",
-       "4                                NaN                                   NaN   \n",
-       "\n",
-       "   high_light_scatter_reticulocyte_count_f30300_0_0  \\\n",
-       "0                                             0.022   \n",
-       "1                                             0.014   \n",
-       "2                                             0.031   \n",
-       "3                                             0.025   \n",
-       "4                                               NaN   \n",
-       "\n",
-       "   high_light_scatter_reticulocyte_percentage_f30290_0_0  \\\n",
-       "0                                              0.464       \n",
-       "1                                              0.290       \n",
-       "2                                              0.686       \n",
-       "3                                              0.508       \n",
-       "4                                                NaN       \n",
-       "\n",
-       "   immature_reticulocyte_fraction_f30280_0_0  ...  phosphate_f30810_0_0  \\\n",
-       "0                                      0.378  ...                 1.422   \n",
-       "1                                      0.300  ...                 1.264   \n",
-       "2                                      0.380  ...                   NaN   \n",
-       "3                                      0.250  ...                 0.928   \n",
-       "4                                        NaN  ...                   NaN   \n",
-       "\n",
-       "   rheumatoid_factor_f30820_0_0  shbg_f30830_0_0  testosterone_f30850_0_0  \\\n",
-       "0                           NaN            70.11                    1.560   \n",
-       "1                           NaN            55.31                   12.237   \n",
-       "2                           NaN              NaN                      NaN   \n",
-       "3                           NaN            31.63                   11.398   \n",
-       "4                           NaN              NaN                      NaN   \n",
-       "\n",
-       "   total_bilirubin_f30840_0_0  total_protein_f30860_0_0  \\\n",
-       "0                        7.41                     71.97   \n",
-       "1                        8.07                     78.45   \n",
-       "2                         NaN                       NaN   \n",
-       "3                        8.65                     69.70   \n",
-       "4                         NaN                       NaN   \n",
-       "\n",
-       "   triglycerides_f30870_0_0  urate_f30880_0_0  urea_f30670_0_0  \\\n",
-       "0                     1.247             221.3             5.48   \n",
-       "1                     1.906             374.7             5.28   \n",
-       "2                       NaN               NaN              NaN   \n",
-       "3                     5.184             322.8             6.67   \n",
-       "4                       NaN               NaN              NaN   \n",
-       "\n",
-       "   vitamin_d_f30890_0_0  \n",
-       "0                  70.7  \n",
-       "1                  35.9  \n",
-       "2                   NaN  \n",
-       "3                  63.6  \n",
-       "4                   NaN  \n",
-       "\n",
-       "[5 rows x 62 columns]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fields_blood_count = [\n",
-    "    \"30160\", #\tBasophill count\n",
-    "    \"30220\", #\tBasophill percentage\n",
-    "    \"30150\", #\tEosinophill count\n",
-    "    \"30210\", #\tEosinophill percentage\n",
-    "    \"30030\", #\tHaematocrit percentage\n",
-    "    \"30020\", #\tHaemoglobin concentration\n",
-    "    \"30300\", #\tHigh light scatter reticulocyte count\n",
-    "    \"30290\", #\tHigh light scatter reticulocyte percentage\n",
-    "    \"30280\", #\tImmature reticulocyte fraction\n",
-    "    \"30120\", #\tLymphocyte count\n",
-    "    \"30180\", #\tLymphocyte percentage\n",
-    "    \"30050\", #\tMean corpuscular haemoglobin\n",
-    "    \"30060\", #\tMean corpuscular haemoglobin concentration\n",
-    "    \"30040\", #\tMean corpuscular volume\n",
-    "    \"30100\", #\tMean platelet (thrombocyte) volume\n",
-    "    \"30260\", #\tMean reticulocyte volume\n",
-    "    \"30270\", #\tMean sphered cell volume\n",
-    "    \"30130\", #\tMonocyte count\n",
-    "    \"30190\", #\tMonocyte percentage\n",
-    "    \"30140\", #\tNeutrophill count\n",
-    "    \"30200\", #\tNeutrophill percentage\n",
-    "    \"30170\", #\tNucleated red blood cell count\n",
-    "    \"30230\", #\tNucleated red blood cell percentage\n",
-    "    \"30080\", #\tPlatelet count\n",
-    "    \"30090\", #\tPlatelet crit\n",
-    "    \"30110\", #\tPlatelet distribution width\n",
-    "    \"30010\", #\tRed blood cell (erythrocyte) count\n",
-    "    \"30070\", #\tRed blood cell (erythrocyte) distribution width\n",
-    "    \"30250\", #\tReticulocyte count\n",
-    "    \"30240\", #\tReticulocyte percentage\n",
-    "    \"30000\", #\tWhite blood cell (leukocyte) count\n",
-    "]\n",
-    "\n",
-    "fields_blood_biochemistry = [\n",
-    "    \"30620\",#\tAlanine aminotransferase\n",
-    "    \"30600\",#\tAlbumin\n",
-    "    \"30610\",#\tAlkaline phosphatase\n",
-    "    \"30630\",#\tApolipoprotein A\n",
-    "    \"30640\",#\tApolipoprotein B\n",
-    "    \"30650\",#\tAspartate aminotransferase\n",
-    "    \"30710\",#\tC-reactive protein\n",
-    "    \"30680\",#\tCalcium\n",
-    "    \"30690\",#\tCholesterol\n",
-    "    \"30700\",#\tCreatinine\n",
-    "    \"30720\",#\tCystatin C\n",
-    "    \"30660\",#\tDirect bilirubin\n",
-    "    \"30730\",#\tGamma glutamyltransferase\n",
-    "    \"30740\",#\tGlucose\n",
-    "    \"30750\",#\tGlycated haemoglobin (HbA1c)\n",
-    "    \"30760\",#\tHDL cholesterol\n",
-    "    \"30770\",#\tIGF-1\n",
-    "    \"30780\",#\tLDL direct\n",
-    "    \"30790\",#\tLipoprotein A\n",
-    "    \"30800\",#\tOestradiol\n",
-    "    \"30810\",#\tPhosphate\n",
-    "    \"30820\",#\tRheumatoid factor\n",
-    "    \"30830\",#\tSHBG\n",
-    "    \"30850\",#\tTestosterone\n",
-    "    \"30840\",#\tTotal bilirubin\n",
-    "    \"30860\",#\tTotal protein\n",
-    "    \"30870\",#\tTriglycerides\n",
-    "    \"30880\",#\tUrate\n",
-    "    \"30670\",#\tUrea\n",
-    "    \"30890\",#\tVitamin D\n",
-    "]\n",
-    "\n",
-    "fields_blood_infectious = [\n",
-    "    \"23000\", #\t1gG antigen for Herpes Simplex virus-1\n",
-    "    \"23001\", #\t2mgG unique antigen for Herpes Simplex virus-2\n",
-    "    \"23049\", #\tAntigen assay QC indicator\n",
-    "    \"23048\", #\tAntigen assay date\n",
-    "    \"23026\", #\tBK VP1 antigen for Human Polyomavirus BKV\n",
-    "    \"23039\", #\tCagA antigen for Helicobacter pylori\n",
-    "    \"23043\", #\tCatalase antigen for Helicobacter pylori\n",
-    "    \"23018\", #\tCore antigen for Hepatitis C Virus\n",
-    "    \"23030\", #\tE6 antigen for Human Papillomavirus type-16\n",
-    "    \"23031\", #\tE7 antigen for Human Papillomavirus type-16\n",
-    "    \"23006\", #\tEA-D antigen for Epstein-Barr Virus\n",
-    "    \"23004\", #\tEBNA-1 antigen for Epstein-Barr Virus\n",
-    "    \"23042\", #\tGroEL antigen for Helicobacter pylori\n",
-    "    \"23016\", #\tHBc antigen for Hepatitis B Virus\n",
-    "    \"23017\", #\tHBe antigen for Hepatitis B Virus\n",
-    "    \"23025\", #\tHIV-1 env antigen for Human Immunodeficiency Virus\n",
-    "    \"23024\", #\tHIV-1 gag antigen for Human Immunodeficiency Virus\n",
-    "    \"23023\", #\tHTLV-1 env antigen for Human T-Lymphotropic Virus 1\n",
-    "    \"23022\", #\tHTLV-1 gag antigen for Human T-Lymphotropic Virus 1\n",
-    "    \"23010\", #\tIE1A antigen for Human Herpesvirus-6\n",
-    "    \"23011\", #\tIE1B antigen for Human Herpesvirus-6\n",
-    "    \"23027\", #\tJC VP1 antigen for Human Polyomavirus JCV\n",
-    "    \"23015\", #\tK8.1 antigen for Kaposi's Sarcoma-Associated Herpesvirus\n",
-    "    \"23029\", #\tL1 antigen for Human Papillomavirus type-16\n",
-    "    \"23032\", #\tL1 antigen for Human Papillomavirus type-18\n",
-    "    \"23014\", #\tLANA antigen for Kaposi's Sarcoma-Associated Herpesvirus\n",
-    "    \"23028\", #\tMC VP1 antigen for Merkel Cell Polyomavirus\n",
-    "    \"23019\", #\tNS3 antigen for Hepatitis C Virus\n",
-    "    \"23041\", #\tOMP antigen for Helicobacter pylori\n",
-    "    \"23037\", #\tPorB antigen for Chlamydia trachomatis\n",
-    "    \"23013\", #\tU14 antigen for Human Herpesvirus-7\n",
-    "    \"23044\", #\tUreA antigen for Helicobacter pylori\n",
-    "    \"23003\", #\tVCA p18 antigen for Epstein-Barr Virus\n",
-    "    \"23040\", #\tVacA antigen for Helicobacter pylori\n",
-    "    \"23005\", #\tZEBRA antigen for Epstein-Barr Virus\n",
-    "    \"23002\", #\tgE / gI antigen for Varicella Zoster Virus\n",
-    "    \"23034\", #\tmomp A antigen for Chlamydia trachomatis\n",
-    "    \"23033\", #\tmomp D antigen for Chlamydia trachomatis\n",
-    "    \"23012\", #\tp101 k antigen for Human Herpesvirus-6\n",
-    "    \"23020\", #\tp22 antigen for Toxoplasma gondii\n",
-    "    \"23038\", #\tpGP3 antigen for Chlamydia trachomatis\n",
-    "    \"23009\", #\tpp 28 antigen for Human Cytomegalovirus\n",
-    "    \"23008\", #\tpp 52 antigen for Human Cytomegalovirus\n",
-    "    \"23007\", #\tpp150 Nter antigen for Human Cytomegalovirus\n",
-    "    \"23021\", #\tsag1 antigen for Toxoplasma gondii\n",
-    "    \"23035\", #\ttarp-D F1 antigen for Chlamydia trachomatis\n",
-    "    \"23036\", #\ttarp-D F2 antigen for Chlamydia trachomatis\n",
-    "]\n",
-    "\n",
-    "labs = temp = get_data_fields(fields_blood_count+fields_blood_biochemistry+fields_blood_infectious, data, data_field)\n",
-    "print(len(temp))\n",
-    "display(temp.head())\n",
-    "\n",
-    "labs.to_feather(os.path.join(path, dataset_path, 'temp_labs.feather'))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Family History"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:34:25.838958Z",
-     "start_time": "2020-11-04T12:34:07.649920Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "502504\n"
-     ]
-    }
-   ],
-   "source": [
-    "fh_list=[\"Heart disease\", \"Stroke\", \"High blood pressure\",  \"Diabetes\", \"Lung cancer\", \"Severe depression\", \"Parkinson's disease\", \"Alzheimer's disease/dementia\", \"Chronic bronchitis/emphysema\", \"Breast cancer\", \"Bowel cancer\"]\n",
-    "with open(os.path.join(path, dataset_path, 'fh_list.yaml'), 'w') as file: yaml.dump(fh_list, file, default_flow_style=False)\n",
-    "\n",
-    "fields_family_history = [\n",
-    "    \"20107\", # Family history \n",
-    "    \"20110\" # Family history\n",
-    "]\n",
-    "\n",
-    "raw = get_data_fields(fields_family_history, data, data_field)\n",
-    "temp = pd.melt(raw, id_vars=[\"eid\"], value_vars=raw.drop(\"eid\", axis=1).columns.to_list(), var_name = \"field\", value_name=\"family_history\").drop(\"field\", axis=1)\n",
-    "temp = temp[temp.family_history.isin(fh_list)].assign(family_history=temp[\"family_history\"].str.lower().replace(\" \", \"_\", regex=True))\n",
-    "\n",
-    "temp = temp.drop_duplicates().sort_values(\"eid\").reset_index().drop(\"index\", axis=1).assign(n=True)\n",
-    "temp = pd.pivot_table(temp, index=\"eid\", columns=\"family_history\", values=\"n\", observed=True).add_prefix('fh_')\n",
-    "family_history = temp = data[[\"eid\"]].copy().merge(temp, how=\"left\", on=\"eid\").fillna(False)\n",
-    "\n",
-    "print(len(temp))\n",
-    "temp.head()\n",
-    "\n",
-    "family_history.to_feather(os.path.join(path, dataset_path, 'temp_family_history.feather'))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Medications"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:34:25.842601Z",
-     "start_time": "2020-11-04T12:34:25.840607Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# https://list.essentialmeds.org/?showRemoved=0\n",
-    "# essential medicines WHO?!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:34:32.471831Z",
-     "start_time": "2020-11-04T12:34:25.844339Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "atc_mapping = pd.read_csv(f\"{path}/mapping/atc/atc_matched_list.csv\")\n",
-    "athena_concepts = pd.read_csv(f\"{data_path}/athena_vocabulary/CONCEPT.csv\", sep=\"\\t\").assign(vocabulary_id = lambda x: x.vocabulary_id.astype(\"string\"), concept_class_id = lambda x: x.concept_class_id.astype(\"string\"))\n",
-    "atc_concepts = athena_concepts[athena_concepts.vocabulary_id==\"ATC\"]\n",
-    "atc2_concepts = atc_concepts[atc_concepts.concept_class_id==\"ATC 2nd\"].sort_values(\"concept_code\")\n",
-    "medication_list = dict(zip([x.lower().replace(\" \", \"_\") for x in atc2_concepts.concept_name.to_list()], [[x] for x in atc2_concepts.concept_code.to_list()]))\n",
-    "medication_list_extra = {\n",
-    "    \"antihypertensives\": [\"C02\"],\n",
-    "    \"statins\": [\"C10A\", \"C10B\"],\n",
-    "    \"ass\": [\"B01\"],\n",
-    "    \"atypical_antipsychotics\" : [\"N05\"],\n",
-    "    \"glucocorticoids\" : [\"H02\"]                        \n",
-    "}\n",
-    "medication_list.update(medication_list_extra)\n",
-    "\n",
-    "with open(os.path.join(path, dataset_path, 'medication_list.yaml'), 'w') as file: yaml.dump(medication_list, file, default_flow_style=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:34:32.481137Z",
-     "start_time": "2020-11-04T12:34:32.473698Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "def had_medication_before(data, data_field, medications, atc_mapping):\n",
-    "    fields = [\"20003\"]\n",
-    "    raw = get_data_fields(fields, data, data_field)\n",
-    "    temp = pd.melt(raw, id_vars=[\"eid\"], value_vars=raw.drop(\"eid\", axis=1).columns.to_list(), var_name = \"field\", value_name=\"UKBB_code\").drop(\"field\", axis=1).drop_duplicates()\n",
-    "\n",
-    "    temp.UKBB_code = temp.UKBB_code.astype(str)\n",
-    "    temp = temp[temp.UKBB_code!=\"None\"].copy()\n",
-    "    temp = temp[temp.UKBB_code!=\"nan\"].copy()\n",
-    "    temp.UKBB_code = temp.UKBB_code.astype(int)\n",
-    "\n",
-    "    temp_atc = temp.merge(atc_mapping, how=\"left\", on=\"UKBB_code\").sort_values(\"eid\").reset_index(drop=True).dropna(subset=[\"ATC_code\"], axis=0)\n",
-    "    temp_atc.ATC_code = temp_atc.ATC_code.astype(\"string\")\n",
-    "    temp = data[[\"eid\"]].copy()\n",
-    "    for med, med_codes in tqdm(medication_list.items()):\n",
-    "        regex_str = \"^\"+\"|^\".join(med_codes)\n",
-    "        df = temp_atc[temp_atc.ATC_code.str.contains(regex_str, case=False)][[\"eid\"]]\\\n",
-    "            .drop_duplicates(subset=[\"eid\"])\\\n",
-    "            .assign(medication=True)\n",
-    "        temp[med] = temp.merge(df, how=\"left\", on=\"eid\").fillna(False).medication\n",
-    "        \n",
-    "    return temp.sort_values(\"eid\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:36:14.425612Z",
-     "start_time": "2020-11-04T12:34:32.482863Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4ea3a79139e04f94a00e3a35c527fa25",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(HTML(value=''), FloatProgress(value=0.0, max=98.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "502504\n"
-     ]
-    }
-   ],
-   "source": [
-    "medications = had_medication_before(data, data_field, medication_list, atc_mapping)\n",
-    "print(len(medications))\n",
-    "medications.head(10)\n",
-    "\n",
-    "medications.to_feather(os.path.join(path, dataset_path, 'temp_medications.feather'))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Diagnoses and events"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:37:14.667281Z",
-     "start_time": "2020-11-04T12:36:14.427693Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3146: DtypeWarning: Columns (5,6,9) have mixed types.Specify dtype option on import or set low_memory=False.\n",
-      "  interactivity=interactivity, compiler=compiler, result=result)\n",
-      "/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3146: DtypeWarning: Columns (11) have mixed types.Specify dtype option on import or set low_memory=False.\n",
-      "  interactivity=interactivity, compiler=compiler, result=result)\n"
-     ]
-    }
-   ],
-   "source": [
-    "vocab_dir = f\"{data_path}/athena_vocabulary_covid\"\n",
-    "vocab = {\n",
-    "    \"concept\": pd.read_csv(f\"{vocab_dir}/CONCEPT.csv\", sep='\\t'),\n",
-    "    \"domain\": pd.read_csv(f\"{vocab_dir}/DOMAIN.csv\", sep='\\t'),\n",
-    "    \"class\": pd.read_csv(f\"{vocab_dir}/CONCEPT_CLASS.csv\", sep='\\t'),\n",
-    "    \"relationship\": pd.read_csv(f\"{vocab_dir}/RELATIONSHIP.csv\", sep='\\t'),\n",
-    "    \"drug_strength\": pd.read_csv(f\"{vocab_dir}/DRUG_STRENGTH.csv\", sep='\\t'),\n",
-    "    \"vocabulary\": pd.read_csv(f\"{vocab_dir}/VOCABULARY.csv\", sep='\\t'),\n",
-    "    \"concept_synonym\": pd.read_csv(f\"{vocab_dir}/CONCEPT_SYNONYM.csv\", sep='\\t'),\n",
-    "    \"concept_ancestor\": pd.read_csv(f\"{vocab_dir}/CONCEPT_ANCESTOR.csv\", sep='\\t'),\n",
-    "    \"concept_relationship\": pd.read_csv(f\"{vocab_dir}/CONCEPT_RELATIONSHIP.csv\", sep='\\t')                       \n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Definitions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:37:14.772869Z",
-     "start_time": "2020-11-04T12:37:14.669541Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "coding1836 = pd.read_csv(f\"{path}/mapping/codings/coding1836.tsv\", sep=\"\\t\").rename(columns={\"coding\":\"code\"})\n",
-    "phecodes = pd.read_csv(f\"{path}/mapping/phecodes/phecode_icd10.csv\")\n",
-    "def phenotype_children(phecodes, phenotype_list):\n",
-    "    l={}\n",
-    "    phecodes = phecodes.dropna(subset=[\"Phenotype\"], axis=0)\n",
-    "    for ph, ph_names in phenotype_list.items():\n",
-    "        regex = \"|\".join(ph_names)\n",
-    "        l[ph] = list(phecodes[phecodes.Phenotype.str.contains(regex, case=False)].ICD10.str.replace(\"\\\\.\", \"\").str.slice(0, 3).unique())\n",
-    "    return l"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:37:14.851438Z",
-     "start_time": "2020-11-04T12:37:14.774599Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "snomed_core = pd.read_csv(f\"{path}/mapping/snomed_core_list.txt\", sep=\"|\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:37:14.909023Z",
-     "start_time": "2020-11-04T12:37:14.853286Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "snomed_core = snomed_core.query(\"SNOMED_CONCEPT_STATUS == 'Current'\").copy()\n",
-    "new = snomed_core.SNOMED_FSN.str.split(\"(\", n=1, expand=True)\n",
-    "snomed_core[\"snomed_name\"] = new[0].str.rstrip(' ')\n",
-    "snomed_core[\"snomed_type\"] = new[1].str.rstrip(')')\n",
-    "snomed_core_data = snomed_core#.query(\"(snomed_type=='disorder' | snomed_type=='finding') & USAGE>0.01\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:37:14.914973Z",
-     "start_time": "2020-11-04T12:37:14.910857Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "snomed_names = snomed_core_data.snomed_name.to_list()\n",
-    "snomed_names = [str(item).lower().strip().replace(\" \", \"_\").replace(\";\", \"\").replace(\",\", \"\") for item in snomed_names]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "phenotype_list_snomed = dict(zip(snomed_names, snomed_core_data.SNOMED_CID.to_list()))\n",
-    "snomed_df = pd.DataFrame.from_dict(phenotype_list_snomed, orient='index').reset_index()\n",
-    "snomed_df.columns = [\"diagnosis\", \"concept_code\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>ancestor_concept_id</th>\n",
-       "      <th>descendant_concept_id</th>\n",
-       "      <th>min_levels_of_separation</th>\n",
-       "      <th>max_levels_of_separation</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>375415</td>\n",
-       "      <td>4335743</td>\n",
-       "      <td>4</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>735979</td>\n",
-       "      <td>41070383</td>\n",
-       "      <td>3</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>529411</td>\n",
-       "      <td>43269406</td>\n",
-       "      <td>3</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>141960</td>\n",
-       "      <td>200164</td>\n",
-       "      <td>3</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>441840</td>\n",
-       "      <td>4096781</td>\n",
-       "      <td>4</td>\n",
-       "      <td>12</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>63586490</th>\n",
-       "      <td>45893522</td>\n",
-       "      <td>35412270</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>63586491</th>\n",
-       "      <td>45893522</td>\n",
-       "      <td>46275678</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>63586492</th>\n",
-       "      <td>45893522</td>\n",
-       "      <td>46275680</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>63586493</th>\n",
-       "      <td>45893522</td>\n",
-       "      <td>46275683</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>63586494</th>\n",
-       "      <td>45893522</td>\n",
-       "      <td>46275684</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>63586495 rows × 4 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          ancestor_concept_id  descendant_concept_id  \\\n",
-       "0                      375415                4335743   \n",
-       "1                      735979               41070383   \n",
-       "2                      529411               43269406   \n",
-       "3                      141960                 200164   \n",
-       "4                      441840                4096781   \n",
-       "...                       ...                    ...   \n",
-       "63586490             45893522               35412270   \n",
-       "63586491             45893522               46275678   \n",
-       "63586492             45893522               46275680   \n",
-       "63586493             45893522               46275683   \n",
-       "63586494             45893522               46275684   \n",
-       "\n",
-       "          min_levels_of_separation  max_levels_of_separation  \n",
-       "0                                4                         4  \n",
-       "1                                3                         5  \n",
-       "2                                3                         3  \n",
-       "3                                3                         6  \n",
-       "4                                4                        12  \n",
-       "...                            ...                       ...  \n",
-       "63586490                         2                         2  \n",
-       "63586491                         1                         1  \n",
-       "63586492                         2                         3  \n",
-       "63586493                         1                         2  \n",
-       "63586494                         2                         3  \n",
-       "\n",
-       "[63586495 rows x 4 columns]"
-      ]
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "vocab[\"concept_ancestor\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "concept_ids = vocab[\"concept\"].query(\"(vocabulary_id == 'SNOMED') | (vocabulary_id == 'ICD10CM')\")\n",
-    "concept_ids_icd10 = vocab[\"concept\"].query(\"vocabulary_id == 'ICD10CM'\").concept_id.to_list()\n",
-    "vocab_concept_ids = concept_ids.concept_id.to_list()\n",
-    "concept_ancestor = vocab[\"concept_ancestor\"][[\"ancestor_concept_id\", \"descendant_concept_id\"]].query(\"ancestor_concept_id == @vocab_concept_ids\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "concept_rel = vocab[\"concept_relationship\"][[\"concept_id_1\", \"concept_id_2\", \"relationship_id\"]].query(\"(concept_id_1 == @vocab_concept_ids) & (concept_id_2 == @concept_ids_icd10) & (relationship_id == 'Mapped from')\")\n",
-    "concept_mapping = concept_rel.rename(columns={\"concept_id_2\":\"concept_id\"}).merge(concept_ids, on=\"concept_id\").query(\"vocabulary_id == 'ICD10CM'\")[[\"concept_id_1\", \"concept_code\"]].rename(columns={\"concept_id_1\":\"concept_id_desc\",\"concept_code\":\"icd10\"})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_snomed_concept_id = snomed_df.merge(concept_ids[[\"concept_code\", \"concept_id\"]], on=\"concept_code\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#df_desc = df_snomed_concept_id.merge(concept_ancestor.rename(columns={\"ancestor_concept_id\":\"concept_id\"})[[\"concept_id\", \"descendant_concept_id\"]], on=\"concept_id\")\n",
-    "#df_desc = df_desc[[\"diagnosis\", \"concept_code\", \"concept_id\", \"descendant_concept_id\"]].rename(columns={\"descendant_concept_id\":\"concept_id_desc\"})\n",
-    "#df_desc_codes = df_desc.merge(concept_ids[[\"concept_id\", \"concept_code\"]].rename(columns={\"concept_id\":\"concept_id_desc\", \"concept_code\":\"concept_codes_desc\"}), on=\"concept_id_desc\").drop_duplicates().sort_values(\"concept_code\")\n",
-    "#df_desc_icd = df_desc_codes.merge(concept_mapping, on=\"concept_id_desc\")#.rename(columns={\"concept_id\":\"concept_id_desc\", \"concept_code\":\"concept_codes_desc\"}))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_icd = df_snomed_concept_id.merge(concept_mapping.rename(columns={\"concept_id_desc\":\"concept_id\"}), on=\"concept_id\")\n",
-    "df_mapped = df_icd[[\"diagnosis\", \"concept_code\", \"icd10\"]].drop_duplicates()\n",
-    "df_mapped[\"icd10\"] = df_mapped[\"icd10\"].str.replace(\".\", \"\")\n",
-    "df_mapped[\"meaning\"] = [e[:3] for e in df_mapped[\"icd10\"].to_list()]\n",
-    "icd10_codes = dict(df_mapped[[\"diagnosis\", \"meaning\"]].drop_duplicates().groupby(\"diagnosis\")[\"meaning\"].apply(list).to_dict())#set_index(\"diagnosis\", drop=True).to_dict()[\"meaning\"]#.sort_values(\"diagnosis\")"
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:39:55.342937Z",
-     "start_time": "2020-11-04T12:37:14.916906Z"
-    }
-   },
-   "source": [
-    "phenotype_list_snomed = dict(zip(snomed_names, snomed_core_data.SNOMED_CID.to_list()))\n",
-    "snomed_ids = vocab[\"concept\"].query(\"vocabulary_id == 'SNOMED'\").concept_id.to_list()\n",
-    "icd10_ids = vocab[\"concept\"].query(\"vocabulary_id == 'ICD10CM'\").concept_id.to_list()\n",
-    "\n",
-    "ph_to_icd10_mapping = {}\n",
-    "\n",
-    "def map_snomed_to_icd10(ph, snomed_code, concept, concept_ancestor, concept_relationship):\n",
-    "    concept_ids = concept.query(\"vocabulary_id == 'SNOMED' & concept_code == @snomed_code\").concept_id.to_list()\n",
-    "    snomed_desc_ids = concept_ancestor.query(\"ancestor_concept_id== @concept_ids\").descendant_concept_id.to_list()\n",
-    "    ph_desc = concept.query(\"concept_id == @snomed_desc_ids\").query(\"vocabulary_id == 'SNOMED'\")\n",
-    "    l_ph_desc_ids = ph_desc.concept_id.to_list()\n",
-    "    ph_icd10_ids = list(concept_relationship.query(\"concept_id_1==@l_ph_desc_ids\").query(\"concept_id_2 == @icd10_ids\").concept_id_2.unique())\n",
-    "    #ph_icd10_ids = list(concept_relationship.set_index(\"concept_id_1\").query(\"index==@l_ph_desc_ids\").query(\"concept_id_2 == @icd10_ids\").query(\"relationship_id == 'Mapped from'\").concept_id_2.unique()\n",
-    "    \n",
-    "    #ph_icd10_ids = list(temp.concept_id_2.unique())\n",
-    "    df = concept.query(\"concept_id == @ph_icd10_ids & vocabulary_id == 'ICD10CM'\")\n",
-    "    icd10_list = list(df[~df.concept_code.str.contains(\"OMOP\", na=False)].concept_code.unique())\n",
-    "    icd10_list = sorted(list(set([e[:3] for e in icd10_list])))\n",
-    "    #print(f\"{ph}: {icd10_list}\")\n",
-    "    return {ph: sorted(list(dict.fromkeys([str(e) for e in icd10_list])))}\n",
-    "\n",
-    "from joblib import Parallel, delayed\n",
-    "concept_ids = vocab[\"concept\"].query(\"(vocabulary_id == 'SNOMED') | (vocabulary_id == 'ICD10CM')\")\n",
-    "vocab_concept_ids = concept_ids.concept_id.to_list()\n",
-    "concept_ancestor = vocab[\"concept_ancestor\"][[\"ancestor_concept_id\", \"descendant_concept_id\"]].query(\"ancestor_concept_id == @vocab_concept_ids\")\n",
-    "concept_rel = vocab[\"concept_relationship\"][[\"concept_id_1\", \"concept_id_2\", \"relationship_id\"]].query(\"(concept_id_1 == @vocab_concept_ids) & (concept_id_2 == @vocab_concept_ids) & (relationship_id == 'Mapped from')\")\n",
-    "icd10_codes = Parallel(n_jobs=10, require=\"sharedmem\")(delayed(map_snomed_to_icd10)(ph, snomed_code, \n",
-    "                                                              concept_ids, concept_ancestor, concept_rel) for ph, snomed_code in tqdm(phenotype_list_snomed.items()))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:39:55.348009Z",
-     "start_time": "2020-11-04T12:39:55.344704Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "l10_snomed = {}\n",
-    "for ph in icd10_codes: l10_snomed.update({ph:icd10_codes[ph]})"
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:39:55.468545Z",
-     "start_time": "2020-11-04T12:39:55.349545Z"
-    }
-   },
-   "source": [
-    "phenotype_list_basic = {\n",
-    "    \"myocardial_infarction\": [\"Myocardial infarction\"],\n",
-    "    \"stroke\": [\"Cerebrovascular disease\"],\n",
-    "    \"diabetes1\" : [\"Type 1 diabetes\"],\n",
-    "    \"diabetes2\" : [\"Diabetes mellitus\", \"Type 2 diabetes\"],\n",
-    "    \"chronic_kidney_disease\": [\"Chronic kidney disease\", \"chronic renal failure\"],\n",
-    "    \"atrial_fibrillation\": [\"Atrial fibrillation\", \"Atrial flutter\", \"paroxysmal tachycardia\"],\n",
-    "    \"migraine\": [\"Migraine\"],\n",
-    "    \"rheumatoid_arthritis\": [\"Rheumatoid arthritis\"],\n",
-    "    \"systemic_lupus_erythematosus\": [\"Systemic lupus erythematosus\"],\n",
-    "    \"severe_mental_illness\": [\"Schizophrenia\", \"bipolar\", \"Major depressive disorder\"],\n",
-    "    \"erectile_dysfunction\" : [\"Erectile dysfunction\"],  \n",
-    "}\n",
-    "\n",
-    "l10_basic = phenotype_children(phecodes, phenotype_list_basic)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "l10_basic = {\n",
-    "    \"myocardial_infarction\": ['I21', 'I22', 'I23', 'I24', 'I25'],\n",
-    "    \"stroke\": ['G45', \"I63\", \"I64\"],\n",
-    "    \"diabetes1\" : ['E10'],\n",
-    "    \"diabetes2\" : ['E11', 'E12', 'E13', 'E14'],\n",
-    "    \"chronic_kidney_disease\": [\"I12\", \"N18\", \"N19\"],\n",
-    "    'atrial_fibrillation': ['I47', 'I48'],\n",
-    "    'migraine': ['G43', 'G44'],\n",
-    "    'rheumatoid_arthritis': ['J99', 'M05', 'M06', 'M08', 'M12', 'M13'],\n",
-    "    \"systemic_lupus_erythematosus\": ['M32'],\n",
-    "    'severe_mental_illness': ['F20', 'F25', 'F30', 'F31', 'F32', 'F33', 'F44'],\n",
-    "    \"erectile_dysfunction\" : ['F52', 'N48'],  \n",
-    "    \"liver_disease\":[\"K70\", \"K71\", \"K72\", \"K73\", \"K74\", \"K75\", \"K76\", \"K77\"],\n",
-    "    \"dementia\":['F00', 'F01', 'F02', 'F03'],\n",
-    "    \"copd\": ['J44']\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'myocardial_infarction': ['I21', 'I22', 'I23', 'I24', 'I25'],\n",
-       " 'stroke': ['G45', 'I63', 'I64'],\n",
-       " 'diabetes1': ['E10'],\n",
-       " 'diabetes2': ['E11', 'E12', 'E13', 'E14'],\n",
-       " 'chronic_kidney_disease': ['I12', 'N18', 'N19'],\n",
-       " 'atrial_fibrillation': ['I47', 'I48'],\n",
-       " 'migraine': ['G43', 'G44'],\n",
-       " 'rheumatoid_arthritis': ['J99', 'M05', 'M06', 'M08', 'M12', 'M13'],\n",
-       " 'systemic_lupus_erythematosus': ['M32'],\n",
-       " 'severe_mental_illness': ['F20', 'F25', 'F30', 'F31', 'F32', 'F33', 'F44'],\n",
-       " 'erectile_dysfunction': ['F52', 'N48'],\n",
-       " 'liver_disease': ['K70', 'K71', 'K72', 'K73', 'K74', 'K75', 'K76', 'K77'],\n",
-       " 'dementia': ['F00', 'F01', 'F02', 'F03'],\n",
-       " 'copd': ['J44']}"
-      ]
-     },
-     "execution_count": 49,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "l10_basic"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:39:55.473556Z",
-     "start_time": "2020-11-04T12:39:55.471051Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "l10_all = l10_basic\n",
-    "for key, value in l10_snomed.items(): \n",
-    "    if key not in l10_basic: l10_all[key] = value"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:39:55.620916Z",
-     "start_time": "2020-11-04T12:39:55.475137Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "l10 = {k: v for k, v in l10_all.items() if len(v)!=0}\n",
-    "\n",
-    "#phenotype_list = {k: v for k, v in phenotype_list.items() if k in list(l10.keys())}\n",
-    "\n",
-    "with open(os.path.join(path, dataset_path, 'phenotype_list.yaml'), 'w') as file: yaml.dump(l10, file, default_flow_style=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 1. Self Reported"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:43:13.288198Z",
-     "start_time": "2020-11-04T12:43:13.264683Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "coding609 = pd.read_csv(f\"{path}/mapping/codings/coding609.tsv\", sep=\"\\t\").rename(columns={\"coding\":\"code\"})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datetime import datetime, timedelta\n",
-    "\n",
-    "def datetime_from_dec_year(dec_year):\n",
-    "    start = dec_year\n",
-    "    year = int(start)\n",
-    "    rem = start - year\n",
-    "\n",
-    "    base = datetime(year, 1, 1)\n",
-    "    result = base + timedelta(seconds=(base.replace(year=base.year + 1) - base).total_seconds() * rem)\n",
-    "    #result.strftime(\"%Y-%m-%d\")\n",
-    "    return result.date()\n",
-    "\n",
-    "def extract_map_self_reported(data, data_field, code_map):\n",
-    "    pbar = tqdm(total=16)\n",
-    "    ### codes\n",
-    "    fields = [\"20002\"]; pbar.update(1)\n",
-    "    raw = get_data_fields_all(fields, data, data_field); pbar.update(1)\n",
-    "    col = \"noncancer_illness_code_selfreported_f20002\"; pbar.update(1)\n",
-    "    temp = pd.wide_to_long(raw, stubnames=[col], i=\"eid\", j=\"instance_index\", sep=\"_\", suffix=\"\\w+\").reset_index(); pbar.update(1)\n",
-    "    codes = temp.rename(columns={col:\"code\"})\\\n",
-    "        .assign(code=lambda x: x.code.astype(str))\\\n",
-    "        .replace(\"None\", np.nan) \\\n",
-    "        .replace(\"nan\", np.nan) \\\n",
-    "        .dropna(subset=[\"code\"], axis=0)\\\n",
-    "        .assign(code=lambda x: x.code.astype(int)) \\\n",
-    "        .merge(code_map, how=\"left\",on=\"code\") \\\n",
-    "        .dropna(subset=[\"meaning\"], axis=0)\\\n",
-    "        .sort_values([\"eid\", \"instance_index\"]) \\\n",
-    "        .reset_index(drop=True); pbar.update(1)\n",
-    "    \n",
-    "    ### dates\n",
-    "    fields = [\"20008\"]; pbar.update(1)\n",
-    "    raw = get_data_fields_all(fields, data, data_field); pbar.update(1)\n",
-    "    col=\"interpolated_year_when_noncancer_illness_first_diagnosed_f20008\"; pbar.update(1)\n",
-    "    temp = pd.wide_to_long(raw, stubnames=[col], i=\"eid\", j=\"instance_index\", sep=\"_\", suffix=\"\\w+\").reset_index(); pbar.update(1)\n",
-    "    dates = temp.rename(columns={col:\"date\"})\\\n",
-    "        .dropna(subset=[\"date\"], axis=0)\\\n",
-    "        .sort_values([\"eid\", \"instance_index\"]) \\\n",
-    "        .reset_index(drop=True); pbar.update(1)\n",
-    "\n",
-    "    dates = dates[dates.date!=-1]; pbar.update(1)\n",
-    "    dates = dates[dates.date!=-3]; pbar.update(1)\n",
-    "    dates.date = dates.date.apply(datetime_from_dec_year); pbar.update(1)\n",
-    "    \n",
-    "    test = codes.merge(dates, how=\"left\", on=[\"eid\", \"instance_index\"]).assign(origin=\"self_reported\").copy(); pbar.update(1)\n",
-    "    \n",
-    "    test[\"instance_index\"] = test[\"instance_index\"].astype(\"string\"); pbar.update(1)\n",
-    "    test[['instance','n']] = test.instance_index.str.split(\"_\",expand=True); pbar.update(1)\n",
-    "    pbar.close()\n",
-    "    \n",
-    "    return test[[\"eid\", \"origin\", 'instance','n', \"code\", \"meaning\", \"date\"]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tqdm.auto import tqdm"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:46:00.098893Z",
-     "start_time": "2020-11-04T12:43:13.432479Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4a6bf94594574b48b26a251987e4baf0",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(HTML(value=''), FloatProgress(value=0.0, max=16.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "codes_self_reported = extract_map_self_reported(data, data_field, coding609)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Load records"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "codes_gp_records = pd.read_feather(f\"{data_path}/1_decoded/codes_gp_diagnoses_210119.feather\").drop(\"level\", axis=1)\n",
-    "codes_hospital_records = pd.read_feather(f\"{data_path}/1_decoded/codes_hes_diagnoses_210120.feather\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Combine diagnoses and events"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:46:25.115010Z",
-     "start_time": "2020-11-04T12:46:08.961388Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>origin</th>\n",
-       "      <th>instance</th>\n",
-       "      <th>n</th>\n",
-       "      <th>code</th>\n",
-       "      <th>meaning</th>\n",
-       "      <th>date</th>\n",
-       "      <th>level</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>gp_read3</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>F171</td>\n",
-       "      <td>F17</td>\n",
-       "      <td>1976-01-01</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>gp_read3</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>O800</td>\n",
-       "      <td>O80</td>\n",
-       "      <td>1986-03-23</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>gp_read3</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>O800</td>\n",
-       "      <td>O80</td>\n",
-       "      <td>1989-05-25</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>gp_read3</td>\n",
-       "      <td>0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>Z824</td>\n",
-       "      <td>Z82</td>\n",
-       "      <td>1994-09-13</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>gp_read3</td>\n",
-       "      <td>0</td>\n",
-       "      <td>5</td>\n",
-       "      <td>Z867</td>\n",
-       "      <td>Z86</td>\n",
-       "      <td>1994-09-13</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       eid    origin instance  n  code meaning        date  level\n",
-       "0  1000018  gp_read3        0  1  F171     F17  1976-01-01    NaN\n",
-       "1  1000018  gp_read3        0  2  O800     O80  1986-03-23    NaN\n",
-       "2  1000018  gp_read3        0  3  O800     O80  1989-05-25    NaN\n",
-       "3  1000018  gp_read3        0  4  Z824     Z82  1994-09-13    NaN\n",
-       "4  1000018  gp_read3        0  5  Z867     Z86  1994-09-13    NaN"
-      ]
-     },
-     "execution_count": 61,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "diagnoses_codes = codes_self_reported.append(codes_hospital_records).append(codes_gp_records).sort_values([\"eid\", \"date\"]).dropna(subset=[\"date\"], axis=0).reset_index(drop=True)\n",
-    "diagnoses_codes.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "diagnoses_codes.reset_index(drop=True).assign(eid = lambda x: x.eid.astype(int),\n",
-    "                                              origin = lambda x: x.origin.astype(str),\n",
-    "                                              instance = lambda x: x.instance.astype(int),\n",
-    "                                              n = lambda x: x.n.astype(int),\n",
-    "                                              code = lambda x: x.code.astype(str), \n",
-    "                                              meaning = lambda x: x.meaning.astype(str))\\\n",
-    "    .to_feather(os.path.join(path, dataset_path, 'temp_diagnoses_codes.feather'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "diagnoses_codes = pd.read_feather(os.path.join(path, dataset_path, 'temp_diagnoses_codes.feather'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>origin</th>\n",
-       "      <th>instance</th>\n",
-       "      <th>n</th>\n",
-       "      <th>code</th>\n",
-       "      <th>meaning</th>\n",
-       "      <th>date</th>\n",
-       "      <th>level</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>gp_read3</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>F171</td>\n",
-       "      <td>F17</td>\n",
-       "      <td>1976-01-01</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>gp_read3</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>O800</td>\n",
-       "      <td>O80</td>\n",
-       "      <td>1986-03-23</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>gp_read3</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>O800</td>\n",
-       "      <td>O80</td>\n",
-       "      <td>1989-05-25</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>gp_read3</td>\n",
-       "      <td>0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>Z824</td>\n",
-       "      <td>Z82</td>\n",
-       "      <td>1994-09-13</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>gp_read3</td>\n",
-       "      <td>0</td>\n",
-       "      <td>5</td>\n",
-       "      <td>Z867</td>\n",
-       "      <td>Z86</td>\n",
-       "      <td>1994-09-13</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>32075621</th>\n",
-       "      <td>6025198</td>\n",
-       "      <td>hes_icd10</td>\n",
-       "      <td>6</td>\n",
-       "      <td>13</td>\n",
-       "      <td>['R945']</td>\n",
-       "      <td>R94</td>\n",
-       "      <td>2018-12-09</td>\n",
-       "      <td>2.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>32075622</th>\n",
-       "      <td>6025198</td>\n",
-       "      <td>hes_icd10</td>\n",
-       "      <td>6</td>\n",
-       "      <td>14</td>\n",
-       "      <td>['F171']</td>\n",
-       "      <td>F17</td>\n",
-       "      <td>2018-12-09</td>\n",
-       "      <td>2.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>32075623</th>\n",
-       "      <td>6025198</td>\n",
-       "      <td>hes_icd10</td>\n",
-       "      <td>6</td>\n",
-       "      <td>15</td>\n",
-       "      <td>['I10']</td>\n",
-       "      <td>I10</td>\n",
-       "      <td>2018-12-09</td>\n",
-       "      <td>2.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>32075624</th>\n",
-       "      <td>6025198</td>\n",
-       "      <td>hes_icd10</td>\n",
-       "      <td>6</td>\n",
-       "      <td>16</td>\n",
-       "      <td>['E780']</td>\n",
-       "      <td>E78</td>\n",
-       "      <td>2018-12-09</td>\n",
-       "      <td>2.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>32075625</th>\n",
-       "      <td>6025198</td>\n",
-       "      <td>hes_icd10</td>\n",
-       "      <td>6</td>\n",
-       "      <td>17</td>\n",
-       "      <td>['W802']</td>\n",
-       "      <td>W80</td>\n",
-       "      <td>2018-12-09</td>\n",
-       "      <td>3.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>32075626 rows × 8 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "              eid     origin  instance   n      code meaning        date  \\\n",
-       "0         1000018   gp_read3         0   1      F171     F17  1976-01-01   \n",
-       "1         1000018   gp_read3         0   2      O800     O80  1986-03-23   \n",
-       "2         1000018   gp_read3         0   3      O800     O80  1989-05-25   \n",
-       "3         1000018   gp_read3         0   4      Z824     Z82  1994-09-13   \n",
-       "4         1000018   gp_read3         0   5      Z867     Z86  1994-09-13   \n",
-       "...           ...        ...       ...  ..       ...     ...         ...   \n",
-       "32075621  6025198  hes_icd10         6  13  ['R945']     R94  2018-12-09   \n",
-       "32075622  6025198  hes_icd10         6  14  ['F171']     F17  2018-12-09   \n",
-       "32075623  6025198  hes_icd10         6  15   ['I10']     I10  2018-12-09   \n",
-       "32075624  6025198  hes_icd10         6  16  ['E780']     E78  2018-12-09   \n",
-       "32075625  6025198  hes_icd10         6  17  ['W802']     W80  2018-12-09   \n",
-       "\n",
-       "          level  \n",
-       "0           NaN  \n",
-       "1           NaN  \n",
-       "2           NaN  \n",
-       "3           NaN  \n",
-       "4           NaN  \n",
-       "...         ...  \n",
-       "32075621    2.0  \n",
-       "32075622    2.0  \n",
-       "32075623    2.0  \n",
-       "32075624    2.0  \n",
-       "32075625    3.0  \n",
-       "\n",
-       "[32075626 rows x 8 columns]"
-      ]
-     },
-     "execution_count": 64,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "diagnoses_codes"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:46:26.189927Z",
-     "start_time": "2020-11-04T12:46:25.117069Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from joblib import Parallel, delayed\n",
-    "from functools import reduce\n",
-    "from numba import jit\n",
-    "\n",
-    "def had_diagnosis_before_per_ph(df_before, ph, ph_codes, temp):\n",
-    "    df_ph = df_before[df_before.meaning.isin(ph_codes)][[\"eid\"]]\\\n",
-    "            .drop_duplicates(subset=[\"eid\"])\\\n",
-    "            .assign(phenotype=True) \n",
-    "    return temp.merge(df_ph, how=\"left\", on=\"eid\").fillna(False).phenotype\n",
-    "\n",
-    "def had_diagnosis_before(data, diagnoses_codes, phenotypes, time0=time0_col):\n",
-    "    diagnoses_codes_time = diagnoses_codes.merge(data[[\"eid\", time0]], how=\"left\", on=\"eid\")\n",
-    "    \n",
-    "    temp = data[[\"eid\"]].copy()\n",
-    "    df_before = diagnoses_codes_time[diagnoses_codes_time.date < diagnoses_codes_time[time0]]\n",
-    "                                                                                         \n",
-    "    df_phs = Parallel(n_jobs=20, require=\"sharedmem\")(delayed(had_diagnosis_before_per_ph)(df_before, ph, phenotypes[ph], temp) for ph in tqdm(list(phenotypes)))\n",
-    "    for ph, df_ph_series in zip(tqdm(list(phenotypes)), df_phs): temp[ph] = df_ph_series#temp.merge(df_ph, how=\"left\", on=\"eid\").fillna(False).phenotype\n",
-    "    \n",
-    "    return temp.sort_values(\"eid\")  #reduce(lambda left,right: pd.merge(left,right,on=['eid'], how='left'), df_phs)."
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {},
-   "source": [
-    "from joblib import Parallel, delayed\n",
-    "from functools import reduce\n",
-    "from numba import jit\n",
-    "\n",
-    "def had_diagnosis_before_per_ph(df_before, ph, ph_codes, temp):\n",
-    "    regex = \"|\".join(ph_codes)\n",
-    "    df_ph = df_before[df_before.meaning.str.contains(regex, case=False)][[\"eid\"]]\\\n",
-    "            .drop_duplicates(subset=[\"eid\"])\\\n",
-    "            .assign(phenotype=True)   \n",
-    "    return temp.merge(df_ph, how=\"left\", on=\"eid\").fillna(False).phenotype\n",
-    "\n",
-    "def had_diagnosis_before(data, diagnoses_codes, phenotypes, time0=time0_col):\n",
-    "    diagnoses_codes_time = diagnoses_codes.merge(data[[\"eid\", time0]], how=\"left\", on=\"eid\")\n",
-    "    \n",
-    "    temp = data[[\"eid\"]].copy()\n",
-    "    df_before = diagnoses_codes_time[diagnoses_codes_time.date < diagnoses_codes_time[time0]]\n",
-    "                                                                                         \n",
-    "    df_phs = Parallel(n_jobs=30, require=\"sharedmem\")(delayed(had_diagnosis_before_per_ph)(df_before, ph, phenotypes[ph], temp) for ph in tqdm(list(phenotypes)))\n",
-    "    for ph, ph_series in zip(tqdm(list(phenotypes)), df_phs): temp[ph] = ph_series\n",
-    "    \n",
-    "    return temp.sort_values(\"eid\")  #reduce(lambda left,right: pd.merge(left,right,on=['eid'], how='left'), df_phs)."
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {},
-   "source": [
-    "phenotypes = l10\n",
-    "time0 = time0_col\n",
-    "diagnoses_codes_time = diagnoses_codes.merge(data[[\"eid\", time0]], how=\"left\", on=\"eid\")\n",
-    "\n",
-    "temp = data[[\"eid\"]].copy()\n",
-    "df_before = diagnoses_codes_time[diagnoses_codes_time.date < diagnoses_codes_time[time0]]\n",
-    "\n",
-    "df_phs = Parallel(n_jobs=10, require=\"sharedmem\")(delayed(had_diagnosis_before_per_ph)(df_before, ph, phenotypes[ph], temp) for ph in tqdm(list(phenotypes)[0:100]))"
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {},
-   "source": [
-    "def had_diagnosis_before(data, diagnoses_codes, phenotypes, time0=time0_col):\n",
-    "    diagnoses_codes_time = diagnoses_codes.merge(data[[\"eid\", time0]], how=\"left\", on=\"eid\")\n",
-    "    \n",
-    "    temp = data[[\"eid\"]].copy()\n",
-    "    df_before = diagnoses_codes_time[diagnoses_codes_time.date < diagnoses_codes_time[time0]]\n",
-    "    for ph, ph_codes in tqdm(phenotypes.items()):\n",
-    "        regex = \"|\".join(ph_codes)\n",
-    "        df_ph = df_before[df_before.meaning.str.contains(regex, case=False)][[\"eid\"]]\\\n",
-    "            .drop_duplicates(subset=[\"eid\"])\\\n",
-    "            .assign(phenotype=True)\n",
-    "        temp[ph] = temp.merge(df_ph, how=\"left\", on=\"eid\").fillna(False).phenotype\n",
-    "       \n",
-    "    return temp.sort_values(\"eid\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 68,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T13:17:52.922023Z",
-     "start_time": "2020-11-04T12:46:26.191314Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
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-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(HTML(value=''), FloatProgress(value=0.0, max=2662.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "0bccfdff36944622b1a4b624acd2b955",
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-       "HBox(children=(HTML(value=''), FloatProgress(value=0.0, max=2662.0), HTML(value='')))"
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-     "metadata": {},
-     "output_type": "display_data"
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-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "502504\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>myocardial_infarction</th>\n",
-       "      <th>stroke</th>\n",
-       "      <th>diabetes1</th>\n",
-       "      <th>diabetes2</th>\n",
-       "      <th>chronic_kidney_disease</th>\n",
-       "      <th>atrial_fibrillation</th>\n",
-       "      <th>migraine</th>\n",
-       "      <th>rheumatoid_arthritis</th>\n",
-       "      <th>systemic_lupus_erythematosus</th>\n",
-       "      <th>...</th>\n",
-       "      <th>vulvitis</th>\n",
-       "      <th>vulvodynia</th>\n",
-       "      <th>vulvovaginitis</th>\n",
-       "      <th>waldenström_macroglobulinemia</th>\n",
-       "      <th>wheezing</th>\n",
-       "      <th>white_blood_cell_disorder</th>\n",
-       "      <th>worried_well</th>\n",
-       "      <th>wound_dehiscence</th>\n",
-       "      <th>wrist_joint_pain</th>\n",
-       "      <th>xerostomia</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1000020</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1000037</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000043</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1000051</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 2663 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       eid  myocardial_infarction  stroke  diabetes1  diabetes2  \\\n",
-       "0  1000018                  False   False      False      False   \n",
-       "1  1000020                  False   False      False      False   \n",
-       "2  1000037                  False   False      False      False   \n",
-       "3  1000043                  False   False      False      False   \n",
-       "4  1000051                  False   False      False       True   \n",
-       "\n",
-       "   chronic_kidney_disease  atrial_fibrillation  migraine  \\\n",
-       "0                    True                False     False   \n",
-       "1                   False                False     False   \n",
-       "2                   False                False      True   \n",
-       "3                   False                False     False   \n",
-       "4                   False                False     False   \n",
-       "\n",
-       "   rheumatoid_arthritis  systemic_lupus_erythematosus  ...  vulvitis  \\\n",
-       "0                 False                         False  ...     False   \n",
-       "1                 False                         False  ...     False   \n",
-       "2                  True                         False  ...     False   \n",
-       "3                 False                         False  ...     False   \n",
-       "4                 False                         False  ...     False   \n",
-       "\n",
-       "   vulvodynia  vulvovaginitis  waldenström_macroglobulinemia  wheezing  \\\n",
-       "0       False           False                          False     False   \n",
-       "1       False           False                          False     False   \n",
-       "2       False           False                          False     False   \n",
-       "3       False           False                          False      True   \n",
-       "4       False           False                          False     False   \n",
-       "\n",
-       "   white_blood_cell_disorder  worried_well  wound_dehiscence  \\\n",
-       "0                      False          True             False   \n",
-       "1                      False         False             False   \n",
-       "2                      False          True             False   \n",
-       "3                      False         False             False   \n",
-       "4                      False         False             False   \n",
-       "\n",
-       "   wrist_joint_pain  xerostomia  \n",
-       "0             False       False  \n",
-       "1             False       False  \n",
-       "2              True       False  \n",
-       "3             False       False  \n",
-       "4             False       False  \n",
-       "\n",
-       "[5 rows x 2663 columns]"
-      ]
-     },
-     "execution_count": 68,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "diagnoses = had_diagnosis_before(basics, diagnoses_codes, l10, time0=time0_col)\n",
-    "print(len(diagnoses))\n",
-    "\n",
-    "diagnoses.to_feather(os.path.join(path, dataset_path, 'temp_diagnoses.feather'))\n",
-    "\n",
-    "diagnoses.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 147,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "diagnoses = pd.read_feather(os.path.join(path, dataset_path, 'temp_diagnoses.feather'))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Add embeddings for Snomed Diagnoses"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Get SNOMED - node2vec dict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "snomed_embeddings = pd.read_csv(\"/data/analysis/ag-reils/steinfej/data/snomed_embeddings/snomed.emb.p1.q1.w20.l40.e200.graph_format.txt\", sep=\" \", header=None, skiprows=1)\n",
-    "snomed_embeddings.columns = [\"snomed_id\"]+list(snomed_embeddings.columns)[:-1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "metadata": {
-    "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>snomed_id</th>\n",
-       "      <th>0</th>\n",
-       "      <th>1</th>\n",
-       "      <th>2</th>\n",
-       "      <th>3</th>\n",
-       "      <th>4</th>\n",
-       "      <th>5</th>\n",
-       "      <th>6</th>\n",
-       "      <th>7</th>\n",
-       "      <th>8</th>\n",
-       "      <th>...</th>\n",
-       "      <th>190</th>\n",
-       "      <th>191</th>\n",
-       "      <th>192</th>\n",
-       "      <th>193</th>\n",
-       "      <th>194</th>\n",
-       "      <th>195</th>\n",
-       "      <th>196</th>\n",
-       "      <th>197</th>\n",
-       "      <th>198</th>\n",
-       "      <th>199</th>\n",
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-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>129265001</td>\n",
-       "      <td>0.027456</td>\n",
-       "      <td>0.171026</td>\n",
-       "      <td>-0.178822</td>\n",
-       "      <td>-0.038667</td>\n",
-       "      <td>0.299777</td>\n",
-       "      <td>0.082540</td>\n",
-       "      <td>-0.201284</td>\n",
-       "      <td>0.215164</td>\n",
-       "      <td>0.093241</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.211030</td>\n",
-       "      <td>0.488475</td>\n",
-       "      <td>0.082177</td>\n",
-       "      <td>-0.047102</td>\n",
-       "      <td>-0.086411</td>\n",
-       "      <td>-0.012141</td>\n",
-       "      <td>-0.258416</td>\n",
-       "      <td>-0.113295</td>\n",
-       "      <td>0.040249</td>\n",
-       "      <td>-0.116884</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>360224006</td>\n",
-       "      <td>-0.090187</td>\n",
-       "      <td>0.086986</td>\n",
-       "      <td>-0.184378</td>\n",
-       "      <td>-0.028722</td>\n",
-       "      <td>-0.010235</td>\n",
-       "      <td>0.242439</td>\n",
-       "      <td>-0.094009</td>\n",
-       "      <td>0.203015</td>\n",
-       "      <td>-0.097894</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.112750</td>\n",
-       "      <td>0.333906</td>\n",
-       "      <td>-0.018193</td>\n",
-       "      <td>0.020459</td>\n",
-       "      <td>-0.170226</td>\n",
-       "      <td>0.277866</td>\n",
-       "      <td>-0.007079</td>\n",
-       "      <td>-0.080419</td>\n",
-       "      <td>0.162456</td>\n",
-       "      <td>-0.101768</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>102272007</td>\n",
-       "      <td>0.553420</td>\n",
-       "      <td>0.460947</td>\n",
-       "      <td>0.143925</td>\n",
-       "      <td>-0.053785</td>\n",
-       "      <td>-0.849588</td>\n",
-       "      <td>0.467587</td>\n",
-       "      <td>-0.654922</td>\n",
-       "      <td>-0.010799</td>\n",
-       "      <td>0.510141</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.187371</td>\n",
-       "      <td>0.248607</td>\n",
-       "      <td>0.079687</td>\n",
-       "      <td>-0.354121</td>\n",
-       "      <td>0.412602</td>\n",
-       "      <td>0.582461</td>\n",
-       "      <td>-0.780365</td>\n",
-       "      <td>0.045450</td>\n",
-       "      <td>-0.196551</td>\n",
-       "      <td>0.045745</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>39937001</td>\n",
-       "      <td>0.123158</td>\n",
-       "      <td>0.019882</td>\n",
-       "      <td>0.050896</td>\n",
-       "      <td>0.037364</td>\n",
-       "      <td>0.200435</td>\n",
-       "      <td>0.312911</td>\n",
-       "      <td>-0.338977</td>\n",
-       "      <td>-0.092584</td>\n",
-       "      <td>-0.167741</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.057770</td>\n",
-       "      <td>-0.012834</td>\n",
-       "      <td>-0.139794</td>\n",
-       "      <td>0.180572</td>\n",
-       "      <td>0.190781</td>\n",
-       "      <td>0.104039</td>\n",
-       "      <td>-0.358235</td>\n",
-       "      <td>-0.137954</td>\n",
-       "      <td>-0.236551</td>\n",
-       "      <td>-0.206458</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>23583003</td>\n",
-       "      <td>0.423193</td>\n",
-       "      <td>0.384338</td>\n",
-       "      <td>-0.041503</td>\n",
-       "      <td>0.116848</td>\n",
-       "      <td>-0.055029</td>\n",
-       "      <td>0.149064</td>\n",
-       "      <td>-0.092810</td>\n",
-       "      <td>-0.050148</td>\n",
-       "      <td>0.113122</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.162375</td>\n",
-       "      <td>-0.076505</td>\n",
-       "      <td>-0.274352</td>\n",
-       "      <td>-0.099204</td>\n",
-       "      <td>-0.281887</td>\n",
-       "      <td>-0.266345</td>\n",
-       "      <td>-0.020257</td>\n",
-       "      <td>-0.003843</td>\n",
-       "      <td>-0.008804</td>\n",
-       "      <td>-0.286832</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 201 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   snomed_id         0         1         2         3         4         5  \\\n",
-       "0  129265001  0.027456  0.171026 -0.178822 -0.038667  0.299777  0.082540   \n",
-       "1  360224006 -0.090187  0.086986 -0.184378 -0.028722 -0.010235  0.242439   \n",
-       "2  102272007  0.553420  0.460947  0.143925 -0.053785 -0.849588  0.467587   \n",
-       "3   39937001  0.123158  0.019882  0.050896  0.037364  0.200435  0.312911   \n",
-       "4   23583003  0.423193  0.384338 -0.041503  0.116848 -0.055029  0.149064   \n",
-       "\n",
-       "          6         7         8  ...       190       191       192       193  \\\n",
-       "0 -0.201284  0.215164  0.093241  ...  0.211030  0.488475  0.082177 -0.047102   \n",
-       "1 -0.094009  0.203015 -0.097894  ...  0.112750  0.333906 -0.018193  0.020459   \n",
-       "2 -0.654922 -0.010799  0.510141  ...  0.187371  0.248607  0.079687 -0.354121   \n",
-       "3 -0.338977 -0.092584 -0.167741  ...  0.057770 -0.012834 -0.139794  0.180572   \n",
-       "4 -0.092810 -0.050148  0.113122  ...  0.162375 -0.076505 -0.274352 -0.099204   \n",
-       "\n",
-       "        194       195       196       197       198       199  \n",
-       "0 -0.086411 -0.012141 -0.258416 -0.113295  0.040249 -0.116884  \n",
-       "1 -0.170226  0.277866 -0.007079 -0.080419  0.162456 -0.101768  \n",
-       "2  0.412602  0.582461 -0.780365  0.045450 -0.196551  0.045745  \n",
-       "3  0.190781  0.104039 -0.358235 -0.137954 -0.236551 -0.206458  \n",
-       "4 -0.281887 -0.266345 -0.020257 -0.003843 -0.008804 -0.286832  \n",
-       "\n",
-       "[5 rows x 201 columns]"
-      ]
-     },
-     "execution_count": 64,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "snomed_embeddings.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "diagnoses_snomed = diagnoses.columns[13:].to_list()\n",
-    "diagnoses_snomed_dict = {}\n",
-    "for d in diagnoses_snomed: diagnoses_snomed_dict[d] = phenotype_list_snomed[d]\n",
-    "    \n",
-    "snomed_codes_used = list(phenotype_list_snomed.values())\n",
-    "snomed_codes_emb = snomed_embeddings.snomed_id.to_list()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "snomed_id_array = snomed_embeddings[[\"snomed_id\"]].values\n",
-    "node2vec_array = snomed_embeddings.iloc[:, 1:].values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "metadata": {
-    "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "456f56d7481648e48b74e9e76b675f01",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(FloatProgress(value=0.0, max=373286.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "snomed_arrays = {}\n",
-    "for sid, row in zip(tqdm(snomed_id_array), node2vec_array):\n",
-    "    snomed_arrays[sid[0]] = row"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Get Patient -> Snomed dict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 68,
-   "metadata": {
-    "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "05ac46541972426da8a5b5f7fe461c6e",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(FloatProgress(value=0.0, max=502504.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "diagnoses_array = diagnoses[diagnoses_snomed].values\n",
-    "eid_array = diagnoses[[\"eid\"]].values\n",
-    "\n",
-    "from numba import jit\n",
-    "import numpy as np\n",
-    "\n",
-    "patient_diagnoses = {}\n",
-    "for eid, row in zip(tqdm(eid_array), diagnoses_array):\n",
-    "    patient_diagnoses[eid[0]] = list(np.argwhere(row==True).flatten())\n",
-    "\n",
-    "#diagnoses.query(\"eid==1000092\")[diagnoses_snomed]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 69,
-   "metadata": {
-    "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f4c90d91d08e48119abcf8426cddd37e",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(FloatProgress(value=0.0, max=502504.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "patient_diagnoses_sid = {}\n",
-    "snomed_codes_emb_set = set(snomed_codes_emb)\n",
-    "for eid, p_d_col in tqdm(patient_diagnoses.items()):\n",
-    "    diagnoses_list = [diagnoses_snomed[i] for i in p_d_col]\n",
-    "    sid_list = [phenotype_list_snomed[i] for i in diagnoses_list]\n",
-    "    sid_list = [sid for sid in sid_list if sid in snomed_codes_emb_set]\n",
-    "    patient_diagnoses_sid[eid] = sid_list"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Get Patient -> node2vec average"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 70,
-   "metadata": {
-    "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "47e934dc64334c82893d23b458108fbf",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(FloatProgress(value=0.0, max=502504.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/steinfej/miniconda3/envs/python/lib/python3.7/site-packages/numpy/core/fromnumeric.py:3335: RuntimeWarning: Mean of empty slice.\n",
-      "  out=out, **kwargs)\n",
-      "/home/steinfej/miniconda3/envs/python/lib/python3.7/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars\n",
-      "  ret = ret.dtype.type(ret / rcount)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "patient_node2vec_dict = {}\n",
-    "for eid, sids in tqdm(patient_diagnoses_sid.items()):\n",
-    "    array_list =  [snomed_arrays[sid] for sid in sids]\n",
-    "    patient_node2vec_dict[eid] = np.mean(array_list, axis=0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "metadata": {
-    "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e1395c2d1bfc41f39697a8925b20d597",
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-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(FloatProgress(value=0.0, max=502504.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "array([ 0.37033895,  0.13321076, -0.10030267,  0.05625264, -0.08437331,\n",
-       "        0.11504787, -0.24833219, -0.11936529,  0.25239648, -0.32739932,\n",
-       "        0.06330914, -0.06493541, -0.28790415, -0.2885537 ,  0.02546298,\n",
-       "       -0.06123153, -0.17690672, -0.0867546 , -0.28337599, -0.08474496,\n",
-       "        0.31454532, -0.24158154,  0.15930864,  0.06124846,  0.07694602,\n",
-       "       -0.13752803,  0.13671128, -0.27988751, -0.03987635,  0.03632156,\n",
-       "       -0.13793361, -0.15857369, -0.16441636,  0.2412931 ,  0.20070578,\n",
-       "       -0.11412784,  0.02563965, -0.03560184,  0.17169744, -0.0153383 ,\n",
-       "        0.00117099,  0.1025362 , -0.06505568,  0.05646065, -0.02705149,\n",
-       "        0.04416442, -0.15798991, -0.10650637,  0.02082507, -0.21182802,\n",
-       "        0.13972325, -0.18089307, -0.12731068,  0.02907221, -0.19797107,\n",
-       "        0.19550177,  0.14941799,  0.21561857, -0.18085379,  0.10768238,\n",
-       "        0.12968045,  0.2082016 ,  0.03561408, -0.01122218, -0.27099816,\n",
-       "       -0.06029919, -0.18787618,  0.10084175, -0.07939234, -0.22951897,\n",
-       "       -0.36536359, -0.01050854, -0.21419807, -0.23562986,  0.02380316,\n",
-       "        0.1213157 , -0.1601396 ,  0.07218057, -0.04362593, -0.22303355,\n",
-       "       -0.23844839, -0.14799905,  0.22346404,  0.14218655,  0.39873181,\n",
-       "       -0.32965129,  0.19102432, -0.08894028, -0.20726567,  0.25343222,\n",
-       "        0.29939455,  0.20513029,  0.17430424, -0.05073184,  0.05827969,\n",
-       "        0.31626954,  0.0791522 , -0.21433214,  0.11027244,  0.0805151 ,\n",
-       "        0.08557279,  0.25260801, -0.00096969, -0.06177831,  0.0544524 ,\n",
-       "       -0.17340027,  0.09731447,  0.09982385, -0.074546  , -0.10350239,\n",
-       "       -0.25798929,  0.01222471,  0.1605315 ,  0.00191767,  0.11642527,\n",
-       "       -0.15387604, -0.01520803,  0.03220765, -0.13534233,  0.09154689,\n",
-       "        0.12562997, -0.01667786, -0.09286805,  0.14670483, -0.13097813,\n",
-       "       -0.38270284, -0.0129984 , -0.06352304,  0.12521014, -0.17354363,\n",
-       "       -0.16399127,  0.11488736,  0.39664734,  0.08557075, -0.24862126,\n",
-       "       -0.05882866,  0.23174289,  0.01765143, -0.10257617,  0.01208248,\n",
-       "       -0.06876405, -0.04180976,  0.07489962, -0.10724381,  0.29349823,\n",
-       "        0.17342743, -0.32044841, -0.01118798,  0.28508293,  0.16492201,\n",
-       "        0.03994952, -0.17309702,  0.0365077 ,  0.08619365, -0.21749571,\n",
-       "        0.01038752, -0.21414902, -0.18092813,  0.28433131, -0.12601774,\n",
-       "       -0.02147302,  0.51513453,  0.21506432, -0.12596341, -0.0413009 ,\n",
-       "        0.12595327,  0.08973215, -0.06285449, -0.03649041,  0.04217289,\n",
-       "       -0.02532634, -0.21734533, -0.08211848,  0.14014083,  0.13635479,\n",
-       "       -0.13163414, -0.08576438, -0.04016334,  0.23952563, -0.14895943,\n",
-       "        0.08564821, -0.04287149,  0.22135877, -0.06337237, -0.07189574,\n",
-       "       -0.1004494 ,  0.01225299, -0.13810014, -0.06814497, -0.16715941,\n",
-       "        0.07303671,  0.15891015, -0.04757821, -0.05777645, -0.0772216 ,\n",
-       "        0.16787738, -0.2112512 , -0.10142653, -0.19002809, -0.06400223])"
-      ]
-     },
-     "execution_count": 71,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# create imputation vector\n",
-    "arrays = [patient_node2vec_dict[key] for key in list(patient_node2vec_dict)]\n",
-    "arrays_ok = [array for array in tqdm(arrays) if ~np.isnan(array).any()]\n",
-    "imp_vector = np.mean(arrays_ok, axis=0)\n",
-    "imp_vector"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
-   "metadata": {
-    "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e2dd803c2e2a4cb68e71111979d235c2",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(FloatProgress(value=0.0, max=502504.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "for eid, array in tqdm(patient_node2vec_dict.items()):\n",
-    "    if np.isnan(array).any(): \n",
-    "        patient_node2vec_dict[eid] = imp_vector"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 73,
-   "metadata": {
-    "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a82c47a8f87a4df1bc81b78496edee0a",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(FloatProgress(value=0.0, max=502504.0), HTML(value='')))"
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-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "array_eids = [key for key in tqdm(list(patient_node2vec_dict))]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 74,
-   "metadata": {
-    "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "510554b8bed8456cbfd694b764286acb",
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-       "HBox(children=(FloatProgress(value=0.0, max=502504.0), HTML(value='')))"
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-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "ind_ok = [np.array([1]) if ~np.isnan(array).any() else np.array([0])  for array in tqdm(arrays)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 75,
-   "metadata": {
-    "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a85aec2f3e7644dc99c40781f999eb3c",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(FloatProgress(value=0.0, max=502504.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "arrays_emb = [patient_node2vec_dict[key] for key in tqdm(list(patient_node2vec_dict))]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 76,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "arrays_eids = np.reshape(np.stack(array_eids, axis=0),(-1,1)) \n",
-    "arrays_ind = np.stack(ind_ok, axis=0)\n",
-    "arrays_c = np.stack(arrays_emb, axis=0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 77,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "arrays_complete = np.concatenate([arrays_eids, arrays_ind, arrays_c], axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 78,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "diagnoses_emb = pd.DataFrame(data=arrays_complete, columns=[\"eid\"]+[\"node2vec_available\"]+[f\"node2vec_{e}\" for e in list(range(0, 200))])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 79,
-   "metadata": {
-    "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true
-    }
-   },
-   "outputs": [
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>node2vec_available</th>\n",
-       "      <th>node2vec_0</th>\n",
-       "      <th>node2vec_1</th>\n",
-       "      <th>node2vec_2</th>\n",
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-       "      <th>node2vec_4</th>\n",
-       "      <th>node2vec_5</th>\n",
-       "      <th>node2vec_6</th>\n",
-       "      <th>node2vec_7</th>\n",
-       "      <th>...</th>\n",
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-       "      <th>node2vec_191</th>\n",
-       "      <th>node2vec_192</th>\n",
-       "      <th>node2vec_193</th>\n",
-       "      <th>node2vec_194</th>\n",
-       "      <th>node2vec_195</th>\n",
-       "      <th>node2vec_196</th>\n",
-       "      <th>node2vec_197</th>\n",
-       "      <th>node2vec_198</th>\n",
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-       "      <td>-0.084373</td>\n",
-       "      <td>0.115048</td>\n",
-       "      <td>-0.248332</td>\n",
-       "      <td>-0.119365</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.073037</td>\n",
-       "      <td>0.15891</td>\n",
-       "      <td>-0.047578</td>\n",
-       "      <td>-0.057776</td>\n",
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-       "      <td>-0.101427</td>\n",
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-       "      <td>0.133211</td>\n",
-       "      <td>-0.100303</td>\n",
-       "      <td>0.056253</td>\n",
-       "      <td>-0.084373</td>\n",
-       "      <td>0.115048</td>\n",
-       "      <td>-0.248332</td>\n",
-       "      <td>-0.119365</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.073037</td>\n",
-       "      <td>0.15891</td>\n",
-       "      <td>-0.047578</td>\n",
-       "      <td>-0.057776</td>\n",
-       "      <td>-0.077222</td>\n",
-       "      <td>0.167877</td>\n",
-       "      <td>-0.211251</td>\n",
-       "      <td>-0.101427</td>\n",
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-       "      <td>-0.064002</td>\n",
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-       "      <th>2</th>\n",
-       "      <td>1000037.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.370339</td>\n",
-       "      <td>0.133211</td>\n",
-       "      <td>-0.100303</td>\n",
-       "      <td>0.056253</td>\n",
-       "      <td>-0.084373</td>\n",
-       "      <td>0.115048</td>\n",
-       "      <td>-0.248332</td>\n",
-       "      <td>-0.119365</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.073037</td>\n",
-       "      <td>0.15891</td>\n",
-       "      <td>-0.047578</td>\n",
-       "      <td>-0.057776</td>\n",
-       "      <td>-0.077222</td>\n",
-       "      <td>0.167877</td>\n",
-       "      <td>-0.211251</td>\n",
-       "      <td>-0.101427</td>\n",
-       "      <td>-0.190028</td>\n",
-       "      <td>-0.064002</td>\n",
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-       "      <td>0.133211</td>\n",
-       "      <td>-0.100303</td>\n",
-       "      <td>0.056253</td>\n",
-       "      <td>-0.084373</td>\n",
-       "      <td>0.115048</td>\n",
-       "      <td>-0.248332</td>\n",
-       "      <td>-0.119365</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.073037</td>\n",
-       "      <td>0.15891</td>\n",
-       "      <td>-0.047578</td>\n",
-       "      <td>-0.057776</td>\n",
-       "      <td>-0.077222</td>\n",
-       "      <td>0.167877</td>\n",
-       "      <td>-0.211251</td>\n",
-       "      <td>-0.101427</td>\n",
-       "      <td>-0.190028</td>\n",
-       "      <td>-0.064002</td>\n",
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-       "      <td>0.133211</td>\n",
-       "      <td>-0.100303</td>\n",
-       "      <td>0.056253</td>\n",
-       "      <td>-0.084373</td>\n",
-       "      <td>0.115048</td>\n",
-       "      <td>-0.248332</td>\n",
-       "      <td>-0.119365</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.073037</td>\n",
-       "      <td>0.15891</td>\n",
-       "      <td>-0.047578</td>\n",
-       "      <td>-0.057776</td>\n",
-       "      <td>-0.077222</td>\n",
-       "      <td>0.167877</td>\n",
-       "      <td>-0.211251</td>\n",
-       "      <td>-0.101427</td>\n",
-       "      <td>-0.190028</td>\n",
-       "      <td>-0.064002</td>\n",
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-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
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-       "    <tr>\n",
-       "      <th>502499</th>\n",
-       "      <td>6025150.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.370339</td>\n",
-       "      <td>0.133211</td>\n",
-       "      <td>-0.100303</td>\n",
-       "      <td>0.056253</td>\n",
-       "      <td>-0.084373</td>\n",
-       "      <td>0.115048</td>\n",
-       "      <td>-0.248332</td>\n",
-       "      <td>-0.119365</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.073037</td>\n",
-       "      <td>0.15891</td>\n",
-       "      <td>-0.047578</td>\n",
-       "      <td>-0.057776</td>\n",
-       "      <td>-0.077222</td>\n",
-       "      <td>0.167877</td>\n",
-       "      <td>-0.211251</td>\n",
-       "      <td>-0.101427</td>\n",
-       "      <td>-0.190028</td>\n",
-       "      <td>-0.064002</td>\n",
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-       "      <th>502500</th>\n",
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-       "      <td>0.115048</td>\n",
-       "      <td>-0.248332</td>\n",
-       "      <td>-0.119365</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.073037</td>\n",
-       "      <td>0.15891</td>\n",
-       "      <td>-0.047578</td>\n",
-       "      <td>-0.057776</td>\n",
-       "      <td>-0.077222</td>\n",
-       "      <td>0.167877</td>\n",
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-       "      <td>-0.101427</td>\n",
-       "      <td>-0.190028</td>\n",
-       "      <td>-0.064002</td>\n",
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-       "      <th>502501</th>\n",
-       "      <td>6025173.0</td>\n",
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-       "      <td>0.115048</td>\n",
-       "      <td>-0.248332</td>\n",
-       "      <td>-0.119365</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.073037</td>\n",
-       "      <td>0.15891</td>\n",
-       "      <td>-0.047578</td>\n",
-       "      <td>-0.057776</td>\n",
-       "      <td>-0.077222</td>\n",
-       "      <td>0.167877</td>\n",
-       "      <td>-0.211251</td>\n",
-       "      <td>-0.101427</td>\n",
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-       "    <tr>\n",
-       "      <th>502502</th>\n",
-       "      <td>6025182.0</td>\n",
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-       "      <td>0.115048</td>\n",
-       "      <td>-0.248332</td>\n",
-       "      <td>-0.119365</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.073037</td>\n",
-       "      <td>0.15891</td>\n",
-       "      <td>-0.047578</td>\n",
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-       "      <th>502503</th>\n",
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-       "      <td>-0.084373</td>\n",
-       "      <td>0.115048</td>\n",
-       "      <td>-0.248332</td>\n",
-       "      <td>-0.119365</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.073037</td>\n",
-       "      <td>0.15891</td>\n",
-       "      <td>-0.047578</td>\n",
-       "      <td>-0.057776</td>\n",
-       "      <td>-0.077222</td>\n",
-       "      <td>0.167877</td>\n",
-       "      <td>-0.211251</td>\n",
-       "      <td>-0.101427</td>\n",
-       "      <td>-0.190028</td>\n",
-       "      <td>-0.064002</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>502504 rows × 202 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "              eid  node2vec_available  node2vec_0  node2vec_1  node2vec_2  \\\n",
-       "0       1000018.0                 0.0    0.370339    0.133211   -0.100303   \n",
-       "1       1000020.0                 0.0    0.370339    0.133211   -0.100303   \n",
-       "2       1000037.0                 0.0    0.370339    0.133211   -0.100303   \n",
-       "3       1000043.0                 0.0    0.370339    0.133211   -0.100303   \n",
-       "4       1000051.0                 0.0    0.370339    0.133211   -0.100303   \n",
-       "...           ...                 ...         ...         ...         ...   \n",
-       "502499  6025150.0                 0.0    0.370339    0.133211   -0.100303   \n",
-       "502500  6025165.0                 0.0    0.370339    0.133211   -0.100303   \n",
-       "502501  6025173.0                 0.0    0.370339    0.133211   -0.100303   \n",
-       "502502  6025182.0                 0.0    0.370339    0.133211   -0.100303   \n",
-       "502503  6025198.0                 0.0    0.370339    0.133211   -0.100303   \n",
-       "\n",
-       "        node2vec_3  node2vec_4  node2vec_5  node2vec_6  node2vec_7  ...  \\\n",
-       "0         0.056253   -0.084373    0.115048   -0.248332   -0.119365  ...   \n",
-       "1         0.056253   -0.084373    0.115048   -0.248332   -0.119365  ...   \n",
-       "2         0.056253   -0.084373    0.115048   -0.248332   -0.119365  ...   \n",
-       "3         0.056253   -0.084373    0.115048   -0.248332   -0.119365  ...   \n",
-       "4         0.056253   -0.084373    0.115048   -0.248332   -0.119365  ...   \n",
-       "...            ...         ...         ...         ...         ...  ...   \n",
-       "502499    0.056253   -0.084373    0.115048   -0.248332   -0.119365  ...   \n",
-       "502500    0.056253   -0.084373    0.115048   -0.248332   -0.119365  ...   \n",
-       "502501    0.056253   -0.084373    0.115048   -0.248332   -0.119365  ...   \n",
-       "502502    0.056253   -0.084373    0.115048   -0.248332   -0.119365  ...   \n",
-       "502503    0.056253   -0.084373    0.115048   -0.248332   -0.119365  ...   \n",
-       "\n",
-       "        node2vec_190  node2vec_191  node2vec_192  node2vec_193  node2vec_194  \\\n",
-       "0           0.073037       0.15891     -0.047578     -0.057776     -0.077222   \n",
-       "1           0.073037       0.15891     -0.047578     -0.057776     -0.077222   \n",
-       "2           0.073037       0.15891     -0.047578     -0.057776     -0.077222   \n",
-       "3           0.073037       0.15891     -0.047578     -0.057776     -0.077222   \n",
-       "4           0.073037       0.15891     -0.047578     -0.057776     -0.077222   \n",
-       "...              ...           ...           ...           ...           ...   \n",
-       "502499      0.073037       0.15891     -0.047578     -0.057776     -0.077222   \n",
-       "502500      0.073037       0.15891     -0.047578     -0.057776     -0.077222   \n",
-       "502501      0.073037       0.15891     -0.047578     -0.057776     -0.077222   \n",
-       "502502      0.073037       0.15891     -0.047578     -0.057776     -0.077222   \n",
-       "502503      0.073037       0.15891     -0.047578     -0.057776     -0.077222   \n",
-       "\n",
-       "        node2vec_195  node2vec_196  node2vec_197  node2vec_198  node2vec_199  \n",
-       "0           0.167877     -0.211251     -0.101427     -0.190028     -0.064002  \n",
-       "1           0.167877     -0.211251     -0.101427     -0.190028     -0.064002  \n",
-       "2           0.167877     -0.211251     -0.101427     -0.190028     -0.064002  \n",
-       "3           0.167877     -0.211251     -0.101427     -0.190028     -0.064002  \n",
-       "4           0.167877     -0.211251     -0.101427     -0.190028     -0.064002  \n",
-       "...              ...           ...           ...           ...           ...  \n",
-       "502499      0.167877     -0.211251     -0.101427     -0.190028     -0.064002  \n",
-       "502500      0.167877     -0.211251     -0.101427     -0.190028     -0.064002  \n",
-       "502501      0.167877     -0.211251     -0.101427     -0.190028     -0.064002  \n",
-       "502502      0.167877     -0.211251     -0.101427     -0.190028     -0.064002  \n",
-       "502503      0.167877     -0.211251     -0.101427     -0.190028     -0.064002  \n",
-       "\n",
-       "[502504 rows x 202 columns]"
-      ]
-     },
-     "execution_count": 79,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "diagnoses_emb"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "diagnoses_emb.to_feather(os.path.join(path, dataset_path, 'temp_diagnoses_emb.feather'))\n",
-    "diagnoses_emb = pd.read_feather(os.path.join(path, dataset_path, 'temp_diagnoses_emb.feather'))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Merge Everything"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 69,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_dfs_dict = {\"basics\": pd.read_feather(os.path.join(path, dataset_path, 'temp_basics.feather')), \n",
-    "                 \"questionnaire\": pd.read_feather(os.path.join(path, dataset_path, 'temp_questionnaire.feather')), \n",
-    "                 \"measurements\": pd.read_feather(os.path.join(path, dataset_path, 'temp_measurements.feather')), \n",
-    "                 \"labs\": pd.read_feather(os.path.join(path, dataset_path, 'temp_labs.feather')), \n",
-    "                 \"family_history\": pd.read_feather(os.path.join(path, dataset_path, 'temp_family_history.feather')), \n",
-    "                 \"diagnoses\": pd.read_feather(os.path.join(path, dataset_path, 'temp_diagnoses.feather')),\n",
-    "                # \"diagnoses_emb\": pd.read_feather(os.path.join(path, dataset_path, 'temp_diagnoses_emb.feather')), \n",
-    "                 \"medications\": pd.read_feather(os.path.join(path, dataset_path, 'temp_medications.feather'))}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 70,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_cols_clean(df):\n",
-    "    df.columns = df.columns.str.replace(r'_0_0$', '').str.replace(r'_f[0-9]+$', '').str.replace(\"_automated_reading\", '')\n",
-    "    return df.columns\n",
-    "\n",
-    "def clean_df(df):\n",
-    "    df.columns = get_cols_clean(df)\n",
-    "    return df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "from functools import reduce\n",
-    "\n",
-    "data_baseline = reduce(lambda x, y: pd.merge(x, y, on = 'eid'), list(data_dfs_dict.values()))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_baseline = clean_df(data_baseline)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 73,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for col in [col for col in list(data_baseline.columns) if (\"_event\" in col) & (\"_time\" not in col)]:\n",
-    "    data_baseline[col] = data_baseline[col].astype(int)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 74,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "covariates = [col for col in list(data_baseline.columns) if not \"_event\" in col]\n",
-    "targets = [col for col in list(data_baseline.columns) if \"_event\" in col]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Exporting"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 75,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_cols = {}\n",
-    "for topic, df in data_dfs_dict.items(): \n",
-    "    data_cols[\"eid\"] = [\"admin\"]\n",
-    "    data_cols[topic]=list(get_cols_clean(df))[1:]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 76,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_cols_single = {}\n",
-    "for topic, columns in data_cols.items():\n",
-    "    for col in columns:\n",
-    "        data_cols_single[col] = topic"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 86,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>covariate</th>\n",
-       "      <th>dtype</th>\n",
-       "      <th>isTarget</th>\n",
-       "      <th>based_on</th>\n",
-       "      <th>aggr_fn</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>eid</td>\n",
-       "      <td>int</td>\n",
-       "      <td>False</td>\n",
-       "      <td>eid</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2</td>\n",
-       "      <td>age_at_recruitment</td>\n",
-       "      <td>float</td>\n",
-       "      <td>False</td>\n",
-       "      <td>basics</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>sex</td>\n",
-       "      <td>category</td>\n",
-       "      <td>False</td>\n",
-       "      <td>basics</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4</td>\n",
-       "      <td>ethnic_background</td>\n",
-       "      <td>category</td>\n",
-       "      <td>False</td>\n",
-       "      <td>basics</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5</td>\n",
-       "      <td>townsend_deprivation_index_at_recruitment</td>\n",
-       "      <td>float</td>\n",
-       "      <td>False</td>\n",
-       "      <td>basics</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2857</th>\n",
-       "      <td>2858</td>\n",
-       "      <td>surgical_dressings</td>\n",
-       "      <td>bool</td>\n",
-       "      <td>False</td>\n",
-       "      <td>medications</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2858</th>\n",
-       "      <td>2859</td>\n",
-       "      <td>statins</td>\n",
-       "      <td>bool</td>\n",
-       "      <td>False</td>\n",
-       "      <td>medications</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2859</th>\n",
-       "      <td>2860</td>\n",
-       "      <td>ass</td>\n",
-       "      <td>bool</td>\n",
-       "      <td>False</td>\n",
-       "      <td>medications</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2860</th>\n",
-       "      <td>2861</td>\n",
-       "      <td>atypical_antipsychotics</td>\n",
-       "      <td>bool</td>\n",
-       "      <td>False</td>\n",
-       "      <td>medications</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2861</th>\n",
-       "      <td>2862</td>\n",
-       "      <td>glucocorticoids</td>\n",
-       "      <td>bool</td>\n",
-       "      <td>False</td>\n",
-       "      <td>medications</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>2862 rows × 6 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        id                                  covariate     dtype  isTarget  \\\n",
-       "0        1                                        eid       int     False   \n",
-       "1        2                         age_at_recruitment     float     False   \n",
-       "2        3                                        sex  category     False   \n",
-       "3        4                          ethnic_background  category     False   \n",
-       "4        5  townsend_deprivation_index_at_recruitment     float     False   \n",
-       "...    ...                                        ...       ...       ...   \n",
-       "2857  2858                         surgical_dressings      bool     False   \n",
-       "2858  2859                                    statins      bool     False   \n",
-       "2859  2860                                        ass      bool     False   \n",
-       "2860  2861                    atypical_antipsychotics      bool     False   \n",
-       "2861  2862                            glucocorticoids      bool     False   \n",
-       "\n",
-       "         based_on  aggr_fn  \n",
-       "0             eid      NaN  \n",
-       "1          basics      NaN  \n",
-       "2          basics      NaN  \n",
-       "3          basics      NaN  \n",
-       "4          basics      NaN  \n",
-       "...           ...      ...  \n",
-       "2857  medications      NaN  \n",
-       "2858  medications      NaN  \n",
-       "2859  medications      NaN  \n",
-       "2860  medications      NaN  \n",
-       "2861  medications      NaN  \n",
-       "\n",
-       "[2862 rows x 6 columns]"
-      ]
-     },
-     "execution_count": 86,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dtypes = {\"int32\":\"int\", \"int64\":\"int\", \"float64\":\"float\", \"category\":\"category\", \"object\":\"category\", \"bool\":\"bool\"}\n",
-    "desc_dict = {\"id\": [*range(1, len(data_baseline.columns.to_list())+1)] , \n",
-    "             \"covariate\": data_baseline.columns.to_list(), \n",
-    "             \"dtype\":[dtypes[str(col)] for col in data_baseline.dtypes.to_list()], \n",
-    "             \"isTarget\":[True if col in targets else False for col in data_baseline.columns.to_list()],\n",
-    "            \"based_on\":[topic for col, topic in data_cols_single.items()],\n",
-    "            \"aggr_fn\": [np.nan for col in data_baseline.columns.to_list()]}\n",
-    "data_baseline_description = pd.DataFrame.from_dict(desc_dict)\n",
-    "data_baseline_description"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Exclusion Criteria"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 78,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#data_baseline_excl = data_baseline.copy().query(\"myocardial_infarction == False & coronary_heart_disease == False & statins == False\").reset_index(drop=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 79,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#data_baseline"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 87,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "feature_dict = {}\n",
-    "for group in data_baseline_description.based_on.unique(): feature_dict[group] = data_baseline_description.query(\"based_on==@group\").covariate.to_list()\n",
-    "with open(os.path.join(path, dataset_path, 'feature_list.yaml'), 'w') as file: yaml.dump(feature_dict, file, default_flow_style=False, allow_unicode=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 88,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#feature_dict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 89,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "### WRITE FEATURES IN YAML!!!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 90,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_baseline.to_feather(os.path.join(path, dataset_path, 'baseline_covariates.feather'))\n",
-    "#data_baseline_excl.to_feather(os.path.join(path, dataset_path, 'baseline_clinical_excl.feather'))\n",
-    "data_baseline_description.to_feather(os.path.join(path, dataset_path, 'baseline_covariates_description.feather'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#data_baseline.to_csv(os.path.join(path, dataset_path, 'baseline_clinical.csv'), index=False)\n",
-    "#data_baseline_description.to_csv(os.path.join(path, dataset_path, 'baseline_clinical_description.csv'), index=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# !!! REMEMBER IMPUTATION !!!"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "R [conda env:python]",
-   "language": "R",
-   "name": "conda-env-python-r"
-  },
-  "language_info": {
-   "codemirror_mode": "r",
-   "file_extension": ".r",
-   "mimetype": "text/x-r-source",
-   "name": "R",
-   "pygments_lexer": "r",
-   "version": "4.0.3"
-  },
-  "toc-autonumbering": true,
-  "toc-showcode": false,
-  "toc-showmarkdowntxt": false
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/neuralcvd/preprocessing/ukbb_tabular/1_preprocessing_dataportal.ipynb b/neuralcvd/preprocessing/ukbb_tabular/1_preprocessing_dataportal.ipynb
deleted file mode 100644
index a47d039..0000000
--- a/neuralcvd/preprocessing/ukbb_tabular/1_preprocessing_dataportal.ipynb
+++ /dev/null
@@ -1,5319 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 1. Data Portal Preprocessing"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-12-23T09:27:43.184073Z",
-     "start_time": "2020-12-23T09:27:38.838Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "-- \u001b[1mAttaching packages\u001b[22m --------------------------------------- tidyverse 1.3.0 --\n",
-      "\n",
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-      "\u001b[32mv\u001b[39m \u001b[34mtibble \u001b[39m 3.0.3     \u001b[32mv\u001b[39m \u001b[34mdplyr  \u001b[39m 1.0.2\n",
-      "\u001b[32mv\u001b[39m \u001b[34mtidyr  \u001b[39m 1.1.2     \u001b[32mv\u001b[39m \u001b[34mstringr\u001b[39m 1.4.0\n",
-      "\u001b[32mv\u001b[39m \u001b[34mreadr  \u001b[39m 1.3.1     \u001b[32mv\u001b[39m \u001b[34mforcats\u001b[39m 0.5.0\n",
-      "\n",
-      "-- \u001b[1mConflicts\u001b[22m ------------------------------------------ tidyverse_conflicts() --\n",
-      "\u001b[31mx\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mfilter()\u001b[39m masks \u001b[34mstats\u001b[39m::filter()\n",
-      "\u001b[31mx\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mlag()\u001b[39m    masks \u001b[34mstats\u001b[39m::lag()\n",
-      "\n",
-      "\n",
-      "Attaching package: 'lubridate'\n",
-      "\n",
-      "\n",
-      "The following objects are masked from 'package:base':\n",
-      "\n",
-      "    date, intersect, setdiff, union\n",
-      "\n",
-      "\n",
-      "\n",
-      "Attaching package: 'glue'\n",
-      "\n",
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-      "The following object is masked from 'package:dplyr':\n",
-      "\n",
-      "    collapse\n",
-      "\n",
-      "\n",
-      "\n",
-      "Attaching package: 'data.table'\n",
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-      "    hour, isoweek, mday, minute, month, quarter, second, wday, week,\n",
-      "    yday, year\n",
-      "\n",
-      "\n",
-      "The following objects are masked from 'package:dplyr':\n",
-      "\n",
-      "    between, first, last\n",
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-      "\n",
-      "    transpose\n",
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-      "Attaching package: 'magrittr'\n",
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-      "    set_names\n",
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-      "\n",
-      "    extract\n",
-      "\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "try(library(tidyverse), silent=TRUE)\n",
-    "library(lubridate)\n",
-    "library(glue)\n",
-    "library(data.table)\n",
-    "library(tidyfast)\n",
-    "library(\"magrittr\")\n",
-    "setwd(\"/\")\n",
-    "path = \"/data/analysis/ag-reils/ag-reils-shared/cardioRS/data/1_decoded\"\n",
-    "data_path = \"/data/analysis/ag-reils/ag-reils-shared/cardioRS/data\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style>\n",
-       ".list-inline {list-style: none; margin:0; padding: 0}\n",
-       ".list-inline>li {display: inline-block}\n",
-       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
-       "</style>\n",
-       "<ol class=list-inline><li>'death.txt'</li><li>'death_cause.txt'</li><li>'gp_clinical.txt'</li><li>'gp_registrations.txt'</li><li>'gp_scripts.txt'</li><li>'hesin.txt'</li><li>'hesin_critical.txt'</li><li>'hesin_delivery.txt'</li><li>'hesin_diag.txt'</li><li>'hesin_maternity.txt'</li><li>'hesin_oper.txt'</li><li>'hesin_psych.txt'</li></ol>\n"
-      ],
-      "text/latex": [
-       "\\begin{enumerate*}\n",
-       "\\item 'death.txt'\n",
-       "\\item 'death\\_cause.txt'\n",
-       "\\item 'gp\\_clinical.txt'\n",
-       "\\item 'gp\\_registrations.txt'\n",
-       "\\item 'gp\\_scripts.txt'\n",
-       "\\item 'hesin.txt'\n",
-       "\\item 'hesin\\_critical.txt'\n",
-       "\\item 'hesin\\_delivery.txt'\n",
-       "\\item 'hesin\\_diag.txt'\n",
-       "\\item 'hesin\\_maternity.txt'\n",
-       "\\item 'hesin\\_oper.txt'\n",
-       "\\item 'hesin\\_psych.txt'\n",
-       "\\end{enumerate*}\n"
-      ],
-      "text/markdown": [
-       "1. 'death.txt'\n",
-       "2. 'death_cause.txt'\n",
-       "3. 'gp_clinical.txt'\n",
-       "4. 'gp_registrations.txt'\n",
-       "5. 'gp_scripts.txt'\n",
-       "6. 'hesin.txt'\n",
-       "7. 'hesin_critical.txt'\n",
-       "8. 'hesin_delivery.txt'\n",
-       "9. 'hesin_diag.txt'\n",
-       "10. 'hesin_maternity.txt'\n",
-       "11. 'hesin_oper.txt'\n",
-       "12. 'hesin_psych.txt'\n",
-       "\n",
-       "\n"
-      ],
-      "text/plain": [
-       " [1] \"death.txt\"            \"death_cause.txt\"      \"gp_clinical.txt\"     \n",
-       " [4] \"gp_registrations.txt\" \"gp_scripts.txt\"       \"hesin.txt\"           \n",
-       " [7] \"hesin_critical.txt\"   \"hesin_delivery.txt\"   \"hesin_diag.txt\"      \n",
-       "[10] \"hesin_maternity.txt\"  \"hesin_oper.txt\"       \"hesin_psych.txt\"     "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "list.files(path = \"/data/project/uk_bb/cvd/data/ukb_downloads/updated_showcase_43098/ukb_data/records/\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load Athena Vocabulary"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Warning message in fread(glue(\"{vocab_dir}/CONCEPT.csv\"), sep = \"\\t\"):\n",
-      "\"Found and resolved improper quoting out-of-sample. First healed line 8811: <<2614984\t\"y set\" tubing for peritoneal dialysis\tDevice\tHCPCS\tHCPCS\tS\tA4719\t20020101\t20991231\t>>. If the fields are not quoted (e.g. field separator does not appear within any field), try quote=\"\" to avoid this warning.\"\n"
-     ]
-    }
-   ],
-   "source": [
-    "vocab_dir = glue(\"{data_path}/athena_vocabulary_covid\")\n",
-    "concept =fread(glue(\"{vocab_dir}/CONCEPT.csv\"), sep='\\t')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style>\n",
-       ".list-inline {list-style: none; margin:0; padding: 0}\n",
-       ".list-inline>li {display: inline-block}\n",
-       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
-       "</style>\n",
-       "<ol class=list-inline><li>'ABMS'</li><li>'ATC'</li><li>'CDM'</li><li>'CMS Place of Service'</li><li>'Concept Class'</li><li>'Condition Status'</li><li>'Condition Type'</li><li>'Cost'</li><li>'Cost Type'</li><li>'Currency'</li><li>'Death Type'</li><li>'Device Type'</li><li>'Domain'</li><li>'Drug Type'</li><li>'Episode'</li><li>'Ethnicity'</li><li>'Gender'</li><li>'HCPCS'</li><li>'HES Specialty'</li><li>'ICD10CM'</li><li>'ICD9CM'</li><li>'ICD9Proc'</li><li>'Korean Revenue Code'</li><li>'LOINC'</li><li>'Meas Type'</li><li>'Medicare Specialty'</li><li>'Metadata'</li><li>'NDC'</li><li>'NUCC'</li><li>'None'</li><li>'Note Type'</li><li>'OMOP Extension'</li><li>'OPCS4'</li><li>'OSM'</li><li>'Obs Period Type'</li><li>'Observation Type'</li><li>'PHDSC'</li><li>'Plan'</li><li>'Plan Stop Reason'</li><li>'Procedure Type'</li><li>'Race'</li><li>'Read'</li><li>'Relationship'</li><li>'Revenue Code'</li><li>'RxNorm'</li><li>'RxNorm Extension'</li><li>'SNOMED'</li><li>'SPL'</li><li>'Sponsor'</li><li>'Type Concept'</li><li>'UB04 Point of Origin'</li><li>'UB04 Pri Typ of Adm'</li><li>'UB04 Pt dis status'</li><li>'UB04 Typ bill'</li><li>'UCUM'</li><li>'US Census'</li><li>'Visit'</li><li>'Visit Type'</li><li>'Vocabulary'</li><li>'dm+d'</li></ol>\n"
-      ],
-      "text/latex": [
-       "\\begin{enumerate*}\n",
-       "\\item 'ABMS'\n",
-       "\\item 'ATC'\n",
-       "\\item 'CDM'\n",
-       "\\item 'CMS Place of Service'\n",
-       "\\item 'Concept Class'\n",
-       "\\item 'Condition Status'\n",
-       "\\item 'Condition Type'\n",
-       "\\item 'Cost'\n",
-       "\\item 'Cost Type'\n",
-       "\\item 'Currency'\n",
-       "\\item 'Death Type'\n",
-       "\\item 'Device Type'\n",
-       "\\item 'Domain'\n",
-       "\\item 'Drug Type'\n",
-       "\\item 'Episode'\n",
-       "\\item 'Ethnicity'\n",
-       "\\item 'Gender'\n",
-       "\\item 'HCPCS'\n",
-       "\\item 'HES Specialty'\n",
-       "\\item 'ICD10CM'\n",
-       "\\item 'ICD9CM'\n",
-       "\\item 'ICD9Proc'\n",
-       "\\item 'Korean Revenue Code'\n",
-       "\\item 'LOINC'\n",
-       "\\item 'Meas Type'\n",
-       "\\item 'Medicare Specialty'\n",
-       "\\item 'Metadata'\n",
-       "\\item 'NDC'\n",
-       "\\item 'NUCC'\n",
-       "\\item 'None'\n",
-       "\\item 'Note Type'\n",
-       "\\item 'OMOP Extension'\n",
-       "\\item 'OPCS4'\n",
-       "\\item 'OSM'\n",
-       "\\item 'Obs Period Type'\n",
-       "\\item 'Observation Type'\n",
-       "\\item 'PHDSC'\n",
-       "\\item 'Plan'\n",
-       "\\item 'Plan Stop Reason'\n",
-       "\\item 'Procedure Type'\n",
-       "\\item 'Race'\n",
-       "\\item 'Read'\n",
-       "\\item 'Relationship'\n",
-       "\\item 'Revenue Code'\n",
-       "\\item 'RxNorm'\n",
-       "\\item 'RxNorm Extension'\n",
-       "\\item 'SNOMED'\n",
-       "\\item 'SPL'\n",
-       "\\item 'Sponsor'\n",
-       "\\item 'Type Concept'\n",
-       "\\item 'UB04 Point of Origin'\n",
-       "\\item 'UB04 Pri Typ of Adm'\n",
-       "\\item 'UB04 Pt dis status'\n",
-       "\\item 'UB04 Typ bill'\n",
-       "\\item 'UCUM'\n",
-       "\\item 'US Census'\n",
-       "\\item 'Visit'\n",
-       "\\item 'Visit Type'\n",
-       "\\item 'Vocabulary'\n",
-       "\\item 'dm+d'\n",
-       "\\end{enumerate*}\n"
-      ],
-      "text/markdown": [
-       "1. 'ABMS'\n",
-       "2. 'ATC'\n",
-       "3. 'CDM'\n",
-       "4. 'CMS Place of Service'\n",
-       "5. 'Concept Class'\n",
-       "6. 'Condition Status'\n",
-       "7. 'Condition Type'\n",
-       "8. 'Cost'\n",
-       "9. 'Cost Type'\n",
-       "10. 'Currency'\n",
-       "11. 'Death Type'\n",
-       "12. 'Device Type'\n",
-       "13. 'Domain'\n",
-       "14. 'Drug Type'\n",
-       "15. 'Episode'\n",
-       "16. 'Ethnicity'\n",
-       "17. 'Gender'\n",
-       "18. 'HCPCS'\n",
-       "19. 'HES Specialty'\n",
-       "20. 'ICD10CM'\n",
-       "21. 'ICD9CM'\n",
-       "22. 'ICD9Proc'\n",
-       "23. 'Korean Revenue Code'\n",
-       "24. 'LOINC'\n",
-       "25. 'Meas Type'\n",
-       "26. 'Medicare Specialty'\n",
-       "27. 'Metadata'\n",
-       "28. 'NDC'\n",
-       "29. 'NUCC'\n",
-       "30. 'None'\n",
-       "31. 'Note Type'\n",
-       "32. 'OMOP Extension'\n",
-       "33. 'OPCS4'\n",
-       "34. 'OSM'\n",
-       "35. 'Obs Period Type'\n",
-       "36. 'Observation Type'\n",
-       "37. 'PHDSC'\n",
-       "38. 'Plan'\n",
-       "39. 'Plan Stop Reason'\n",
-       "40. 'Procedure Type'\n",
-       "41. 'Race'\n",
-       "42. 'Read'\n",
-       "43. 'Relationship'\n",
-       "44. 'Revenue Code'\n",
-       "45. 'RxNorm'\n",
-       "46. 'RxNorm Extension'\n",
-       "47. 'SNOMED'\n",
-       "48. 'SPL'\n",
-       "49. 'Sponsor'\n",
-       "50. 'Type Concept'\n",
-       "51. 'UB04 Point of Origin'\n",
-       "52. 'UB04 Pri Typ of Adm'\n",
-       "53. 'UB04 Pt dis status'\n",
-       "54. 'UB04 Typ bill'\n",
-       "55. 'UCUM'\n",
-       "56. 'US Census'\n",
-       "57. 'Visit'\n",
-       "58. 'Visit Type'\n",
-       "59. 'Vocabulary'\n",
-       "60. 'dm+d'\n",
-       "\n",
-       "\n"
-      ],
-      "text/plain": [
-       " [1] \"ABMS\"                 \"ATC\"                  \"CDM\"                 \n",
-       " [4] \"CMS Place of Service\" \"Concept Class\"        \"Condition Status\"    \n",
-       " [7] \"Condition Type\"       \"Cost\"                 \"Cost Type\"           \n",
-       "[10] \"Currency\"             \"Death Type\"           \"Device Type\"         \n",
-       "[13] \"Domain\"               \"Drug Type\"            \"Episode\"             \n",
-       "[16] \"Ethnicity\"            \"Gender\"               \"HCPCS\"               \n",
-       "[19] \"HES Specialty\"        \"ICD10CM\"              \"ICD9CM\"              \n",
-       "[22] \"ICD9Proc\"             \"Korean Revenue Code\"  \"LOINC\"               \n",
-       "[25] \"Meas Type\"            \"Medicare Specialty\"   \"Metadata\"            \n",
-       "[28] \"NDC\"                  \"NUCC\"                 \"None\"                \n",
-       "[31] \"Note Type\"            \"OMOP Extension\"       \"OPCS4\"               \n",
-       "[34] \"OSM\"                  \"Obs Period Type\"      \"Observation Type\"    \n",
-       "[37] \"PHDSC\"                \"Plan\"                 \"Plan Stop Reason\"    \n",
-       "[40] \"Procedure Type\"       \"Race\"                 \"Read\"                \n",
-       "[43] \"Relationship\"         \"Revenue Code\"         \"RxNorm\"              \n",
-       "[46] \"RxNorm Extension\"     \"SNOMED\"               \"SPL\"                 \n",
-       "[49] \"Sponsor\"              \"Type Concept\"         \"UB04 Point of Origin\"\n",
-       "[52] \"UB04 Pri Typ of Adm\"  \"UB04 Pt dis status\"   \"UB04 Typ bill\"       \n",
-       "[55] \"UCUM\"                 \"US Census\"            \"Visit\"               \n",
-       "[58] \"Visit Type\"           \"Vocabulary\"           \"dm+d\"                "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "unique(concept$vocabulary_id)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "relationship = fread(glue(\"{vocab_dir}/RELATIONSHIP.csv\"), sep='\\t')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "vocabulary =  fread(glue(\"{vocab_dir}/VOCABULARY.csv\"), sep='\\t')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "concept_relationship = fread(glue(\"{vocab_dir}/CONCEPT_RELATIONSHIP.csv\"), sep='\\t')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "## Diagnoses"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Hospital Episode Statistics"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hesin = fread(\"/data/project/uk_bb/cvd/data/ukb_downloads/updated_showcase_43098/ukb_data/records/hesin.txt\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hesin_diag = fread(\"/data/project/uk_bb/cvd/data/ukb_downloads/updated_showcase_43098/ukb_data/records/hesin_diag.txt\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hesin_critical = fread(\"/data/project/uk_bb/cvd/data/ukb_downloads/updated_showcase_43098/ukb_data/records/hesin_critical.txt\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hesin_psych = fread(\"/data/project/uk_bb/cvd/data/ukb_downloads/updated_showcase_43098/ukb_data/records/hesin_psych.txt\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hesin_delivery = fread(\"/data/project/uk_bb/cvd/data/ukb_downloads/updated_showcase_43098/ukb_data/records/hesin_delivery.txt\")\n",
-    "hesin_maternity = fread(\"/data/project/uk_bb/cvd/data/ukb_downloads/updated_showcase_43098/ukb_data/records/hesin_maternity.txt\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Diagnoses - ICD10"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "## icd9 to icd10 mapping\n",
-    "icd9to10_df = fread(\"/data/analysis/ag-reils/steinfej/code/umbrella/pre/ukbb/mapping/codings/coding1836.tsv\")\n",
-    "icd9to10_mapping = split(icd9to10_df$meaning, icd9to10_df$coding)\n",
-    "hesin_diag_icd9 = hesin_diag %>% filter(diag_icd9!=\"\") %>% rowwise() %>% mutate(diag_icd10 = list(icd9to10_mapping[[diag_icd9]])) %>% drop_na(diag_icd10)\n",
-    "hesin_diag = rbind(hesin_diag %>% filter(diag_icd9==\"\") %>% mutate(origin=\"hes_icd10\"), hesin_diag_icd9  %>% mutate(origin=\"hes_icd9\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hes_join = hesin[hesin_diag, on=c(\"eid\", \"ins_index\")]\n",
-    "hes_join = hes_join[, c(\"eid\", \"origin\",\"ins_index\", \"arr_index\", \"level\", \"epistart\", \"diag_icd10\")][order(eid, ins_index, arr_index),]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hes_join_date = hes_join %>% rename(date=\"epistart\") %>% mutate(date = ymd(as.Date(fast_strptime(date, \"%d/%m/%Y\"))))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hes_diagnoses = hes_join_date %>% drop_na(date) %>% rename(code = \"diag_icd10\") %>% mutate(instance=ins_index) %>% group_by(eid) %>% mutate(n = arr_index)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hes_diagnoses = hes_diagnoses %>% mutate(meaning=str_sub(code, 1, 3)) %>% select(c(eid, origin, instance, n, level, code, meaning, date))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "13502606"
-      ],
-      "text/latex": [
-       "13502606"
-      ],
-      "text/markdown": [
-       "13502606"
-      ],
-      "text/plain": [
-       "[1] 13502606"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<table>\n",
-       "<caption>A grouped_df: 6 × 8</caption>\n",
-       "<thead>\n",
-       "\t<tr><th scope=col>eid</th><th scope=col>origin</th><th scope=col>instance</th><th scope=col>n</th><th scope=col>level</th><th scope=col>code</th><th scope=col>meaning</th><th scope=col>date</th></tr>\n",
-       "\t<tr><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;date&gt;</th></tr>\n",
-       "</thead>\n",
-       "<tbody>\n",
-       "\t<tr><td>1003884</td><td>hes_icd10</td><td>52</td><td>0</td><td>1</td><td>E854</td><td>E85</td><td>2020-09-30</td></tr>\n",
-       "\t<tr><td>1003884</td><td>hes_icd10</td><td>52</td><td>1</td><td>2</td><td>N084</td><td>N08</td><td>2020-09-30</td></tr>\n",
-       "\t<tr><td>1003884</td><td>hes_icd10</td><td>52</td><td>2</td><td>2</td><td>E854</td><td>E85</td><td>2020-09-30</td></tr>\n",
-       "\t<tr><td>1003884</td><td>hes_icd10</td><td>52</td><td>3</td><td>2</td><td>K778</td><td>K77</td><td>2020-09-30</td></tr>\n",
-       "\t<tr><td>1003884</td><td>hes_icd10</td><td>52</td><td>4</td><td>2</td><td>E854</td><td>E85</td><td>2020-09-30</td></tr>\n",
-       "\t<tr><td>1003884</td><td>hes_icd10</td><td>52</td><td>5</td><td>2</td><td>G998</td><td>G99</td><td>2020-09-30</td></tr>\n",
-       "</tbody>\n",
-       "</table>\n"
-      ],
-      "text/latex": [
-       "A grouped\\_df: 6 × 8\n",
-       "\\begin{tabular}{llllllll}\n",
-       " eid & origin & instance & n & level & code & meaning & date\\\\\n",
-       " <int> & <chr> & <int> & <int> & <int> & <list> & <chr> & <date>\\\\\n",
-       "\\hline\n",
-       "\t 1003884 & hes\\_icd10 & 52 & 0 & 1 & E854 & E85 & 2020-09-30\\\\\n",
-       "\t 1003884 & hes\\_icd10 & 52 & 1 & 2 & N084 & N08 & 2020-09-30\\\\\n",
-       "\t 1003884 & hes\\_icd10 & 52 & 2 & 2 & E854 & E85 & 2020-09-30\\\\\n",
-       "\t 1003884 & hes\\_icd10 & 52 & 3 & 2 & K778 & K77 & 2020-09-30\\\\\n",
-       "\t 1003884 & hes\\_icd10 & 52 & 4 & 2 & E854 & E85 & 2020-09-30\\\\\n",
-       "\t 1003884 & hes\\_icd10 & 52 & 5 & 2 & G998 & G99 & 2020-09-30\\\\\n",
-       "\\end{tabular}\n"
-      ],
-      "text/markdown": [
-       "\n",
-       "A grouped_df: 6 × 8\n",
-       "\n",
-       "| eid &lt;int&gt; | origin &lt;chr&gt; | instance &lt;int&gt; | n &lt;int&gt; | level &lt;int&gt; | code &lt;list&gt; | meaning &lt;chr&gt; | date &lt;date&gt; |\n",
-       "|---|---|---|---|---|---|---|---|\n",
-       "| 1003884 | hes_icd10 | 52 | 0 | 1 | E854 | E85 | 2020-09-30 |\n",
-       "| 1003884 | hes_icd10 | 52 | 1 | 2 | N084 | N08 | 2020-09-30 |\n",
-       "| 1003884 | hes_icd10 | 52 | 2 | 2 | E854 | E85 | 2020-09-30 |\n",
-       "| 1003884 | hes_icd10 | 52 | 3 | 2 | K778 | K77 | 2020-09-30 |\n",
-       "| 1003884 | hes_icd10 | 52 | 4 | 2 | E854 | E85 | 2020-09-30 |\n",
-       "| 1003884 | hes_icd10 | 52 | 5 | 2 | G998 | G99 | 2020-09-30 |\n",
-       "\n"
-      ],
-      "text/plain": [
-       "  eid     origin    instance n level code meaning date      \n",
-       "1 1003884 hes_icd10 52       0 1     E854 E85     2020-09-30\n",
-       "2 1003884 hes_icd10 52       1 2     N084 N08     2020-09-30\n",
-       "3 1003884 hes_icd10 52       2 2     E854 E85     2020-09-30\n",
-       "4 1003884 hes_icd10 52       3 2     K778 K77     2020-09-30\n",
-       "5 1003884 hes_icd10 52       4 2     E854 E85     2020-09-30\n",
-       "6 1003884 hes_icd10 52       5 2     G998 G99     2020-09-30"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "nrow(hes_diagnoses)\n",
-    "head(hes_diagnoses %>% arrange(desc(date)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "arrow::write_feather(hes_diagnoses, glue(\"{path}/codes_hes_diagnoses_210120.feather\"))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Procedures - Snomed CT"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 230,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# just do opcs4 for now..., no good opcs3 mapping available => SnomedCT Mapping probably the most reasonable..."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 231,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hesin_oper = fread(\"/data/project/uk_bb/cvd/data/ukb_downloads/updated_showcase_43098/ukb_data/records/hesin_oper.txt\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 232,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hesin_oper[hesin_oper == \"\"] <- NA"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 233,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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xaD1EjaaxuIiyOLQQqNdseIiEqBYJBCo90xwpdSEPNAYJBCg+g+gIhKgWCQQqPd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\njwAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "plot without title"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 420,
-       "width": 420
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "library(visdat)\n",
-    "df = hesin_oper %>% sample_n(1000)\n",
-    "vis_miss(df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 234,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hesin_oper_pre = hesin_oper %>% rename(date=\"opdate\", code=\"oper4\") %>% \n",
-    "    mutate(date = ymd(as.Date(fast_strptime(date, \"%d/%m/%Y\"))))  %>%\n",
-    "    mutate(origin=\"hes_opcs4\", instance=ins_index) %>% group_by(eid) %>% mutate(n = arr_index) %>% select(eid, origin, instance, n, level, code, date)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 235,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "concept_ids_opcs4 = concept %>% filter(vocabulary_id == \"OPCS4\") %>% mutate(concept_code = str_replace(concept_code, \"\\\\.\", \"\"))\n",
-    "concept_ids_snomed = concept %>% filter(vocabulary_id == \"SNOMED\" & domain_id==\"Procedure\") \n",
-    "\n",
-    "# check necessary opcs4 concept ids\n",
-    "concept_ids = concept_ids_opcs4 %>% mutate(concept_id_1 = concept_id)\n",
-    "\n",
-    "cr_filtered = concept_relationship %>% filter(concept_id_1 %in% concept_ids_opcs4$concept_id) %>% filter(concept_id_2 %in% concept_ids_snomed$concept_id) %>% arrange(concept_id_1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 236,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mapping_opcs4_snomed = concept_ids_opcs4 %>% \n",
-    "    left_join(cr_filtered %>% select(concept_id_1, concept_id_2), by=c(\"concept_id\"=\"concept_id_1\")) %>% \n",
-    "    left_join(concept_ids_snomed %>% select(concept_id, concept_code, concept_name), by=c(\"concept_id_2\"=\"concept_id\")) %>% \n",
-    "    mutate(code = concept_code.x, meaning=concept_code.y, name=concept_name.y)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 237,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "7624501"
-      ],
-      "text/latex": [
-       "7624501"
-      ],
-      "text/markdown": [
-       "7624501"
-      ],
-      "text/plain": [
-       "[1] 7624501"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<table>\n",
-       "<caption>A grouped_df: 6 × 9</caption>\n",
-       "<thead>\n",
-       "\t<tr><th scope=col>eid</th><th scope=col>origin</th><th scope=col>instance</th><th scope=col>n</th><th scope=col>level</th><th scope=col>date</th><th scope=col>code</th><th scope=col>meaning</th><th scope=col>name</th></tr>\n",
-       "\t<tr><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;date&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
-       "</thead>\n",
-       "<tbody>\n",
-       "\t<tr><td>1000018</td><td>hes_opcs4</td><td>5</td><td>2</td><td>2</td><td>2018-08-29</td><td>Z942</td><td>387713003</td><td>Surgical procedure                     </td></tr>\n",
-       "\t<tr><td>1000018</td><td>hes_opcs4</td><td>5</td><td>1</td><td>2</td><td>2018-08-29</td><td>Z501</td><td>NA       </td><td>NA                                     </td></tr>\n",
-       "\t<tr><td>1000018</td><td>hes_opcs4</td><td>4</td><td>0</td><td>1</td><td>2016-11-08</td><td>Q108</td><td>11401008 </td><td>Dilation and curettage of uterus       </td></tr>\n",
-       "\t<tr><td>1000018</td><td>hes_opcs4</td><td>0</td><td>0</td><td>1</td><td>2005-06-02</td><td>V093</td><td>178397008</td><td>Reduction of fracture of zygomatic bone</td></tr>\n",
-       "\t<tr><td>1000018</td><td>hes_opcs4</td><td>2</td><td>0</td><td>1</td><td>NA</td><td>X998</td><td>NA       </td><td>NA                                     </td></tr>\n",
-       "\t<tr><td>1000018</td><td>hes_opcs4</td><td>0</td><td>1</td><td>2</td><td>2005-06-02</td><td>Z942</td><td>387713003</td><td>Surgical procedure                     </td></tr>\n",
-       "</tbody>\n",
-       "</table>\n"
-      ],
-      "text/latex": [
-       "A grouped\\_df: 6 × 9\n",
-       "\\begin{tabular}{lllllllll}\n",
-       " eid & origin & instance & n & level & date & code & meaning & name\\\\\n",
-       " <int> & <chr> & <int> & <int> & <int> & <date> & <chr> & <chr> & <chr>\\\\\n",
-       "\\hline\n",
-       "\t 1000018 & hes\\_opcs4 & 5 & 2 & 2 & 2018-08-29 & Z942 & 387713003 & Surgical procedure                     \\\\\n",
-       "\t 1000018 & hes\\_opcs4 & 5 & 1 & 2 & 2018-08-29 & Z501 & NA        & NA                                     \\\\\n",
-       "\t 1000018 & hes\\_opcs4 & 4 & 0 & 1 & 2016-11-08 & Q108 & 11401008  & Dilation and curettage of uterus       \\\\\n",
-       "\t 1000018 & hes\\_opcs4 & 0 & 0 & 1 & 2005-06-02 & V093 & 178397008 & Reduction of fracture of zygomatic bone\\\\\n",
-       "\t 1000018 & hes\\_opcs4 & 2 & 0 & 1 & NA & X998 & NA        & NA                                     \\\\\n",
-       "\t 1000018 & hes\\_opcs4 & 0 & 1 & 2 & 2005-06-02 & Z942 & 387713003 & Surgical procedure                     \\\\\n",
-       "\\end{tabular}\n"
-      ],
-      "text/markdown": [
-       "\n",
-       "A grouped_df: 6 × 9\n",
-       "\n",
-       "| eid &lt;int&gt; | origin &lt;chr&gt; | instance &lt;int&gt; | n &lt;int&gt; | level &lt;int&gt; | date &lt;date&gt; | code &lt;chr&gt; | meaning &lt;chr&gt; | name &lt;chr&gt; |\n",
-       "|---|---|---|---|---|---|---|---|---|\n",
-       "| 1000018 | hes_opcs4 | 5 | 2 | 2 | 2018-08-29 | Z942 | 387713003 | Surgical procedure                      |\n",
-       "| 1000018 | hes_opcs4 | 5 | 1 | 2 | 2018-08-29 | Z501 | NA        | NA                                      |\n",
-       "| 1000018 | hes_opcs4 | 4 | 0 | 1 | 2016-11-08 | Q108 | 11401008  | Dilation and curettage of uterus        |\n",
-       "| 1000018 | hes_opcs4 | 0 | 0 | 1 | 2005-06-02 | V093 | 178397008 | Reduction of fracture of zygomatic bone |\n",
-       "| 1000018 | hes_opcs4 | 2 | 0 | 1 | NA | X998 | NA        | NA                                      |\n",
-       "| 1000018 | hes_opcs4 | 0 | 1 | 2 | 2005-06-02 | Z942 | 387713003 | Surgical procedure                      |\n",
-       "\n"
-      ],
-      "text/plain": [
-       "  eid     origin    instance n level date       code meaning  \n",
-       "1 1000018 hes_opcs4 5        2 2     2018-08-29 Z942 387713003\n",
-       "2 1000018 hes_opcs4 5        1 2     2018-08-29 Z501 NA       \n",
-       "3 1000018 hes_opcs4 4        0 1     2016-11-08 Q108 11401008 \n",
-       "4 1000018 hes_opcs4 0        0 1     2005-06-02 V093 178397008\n",
-       "5 1000018 hes_opcs4 2        0 1     <NA>       X998 NA       \n",
-       "6 1000018 hes_opcs4 0        1 2     2005-06-02 Z942 387713003\n",
-       "  name                                   \n",
-       "1 Surgical procedure                     \n",
-       "2 NA                                     \n",
-       "3 Dilation and curettage of uterus       \n",
-       "4 Reduction of fracture of zygomatic bone\n",
-       "5 NA                                     \n",
-       "6 Surgical procedure                     "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "hes_procedures = hesin_oper_pre %>% left_join(mapping_opcs4_snomed %>% select(code, meaning, name), by=\"code\") %>% select(eid, origin, instance, n, level, date, code, meaning, name)\n",
-    "nrow(hesin_procedures)\n",
-    "head(hesin_procedures)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 238,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "arrow::write_feather(hes_procedures, glue(\"{path}/codes_hes_procedures_210119.feather\"))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Mortality Records - ICD10"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 239,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "death = fread(\"/data/project/uk_bb/cvd/data/ukb_downloads/updated_showcase_43098/ukb_data/records/death.txt\")\n",
-    "death_cause = fread(\"/data/project/uk_bb/cvd/data/ukb_downloads/updated_showcase_43098/ukb_data/records/death_cause.txt\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 240,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "death_join = death[death_cause, on=c(\"eid\", \"ins_index\")]\n",
-    "death_join = death_join[, c(\"eid\", \"ins_index\", \"arr_index\", \"level\", \"date_of_death\", \"cause_icd10\")][order(eid, ins_index, arr_index),]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 241,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "death_join_date = death_join %>% rename(date=\"date_of_death\") %>% rename(code = \"cause_icd10\") %>% mutate(date = ymd(as.Date(fast_strptime(date, \"%d/%m/%Y\"))))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 242,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "codes_death = death_join_date  %>% mutate(instance=0) %>% mutate(origin=\"death_records\") %>% group_by(eid) %>% mutate(n=row_number())\n",
-    "codes_death = codes_death %>% mutate(meaning=str_sub(code, 1, 3)) %>% select(c(eid, origin, instance, n, level, code, meaning, date))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 243,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<table>\n",
-       "<caption>A grouped_df: 6 × 8</caption>\n",
-       "<thead>\n",
-       "\t<tr><th scope=col>eid</th><th scope=col>origin</th><th scope=col>instance</th><th scope=col>n</th><th scope=col>level</th><th scope=col>code</th><th scope=col>meaning</th><th scope=col>date</th></tr>\n",
-       "\t<tr><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;date&gt;</th></tr>\n",
-       "</thead>\n",
-       "<tbody>\n",
-       "\t<tr><td>5285138</td><td>death_records</td><td>0</td><td>1</td><td>1</td><td>U071</td><td>U07</td><td>2020-11-01</td></tr>\n",
-       "\t<tr><td>5285138</td><td>death_records</td><td>0</td><td>2</td><td>2</td><td>J449</td><td>J44</td><td>2020-11-01</td></tr>\n",
-       "\t<tr><td>1206785</td><td>death_records</td><td>0</td><td>1</td><td>1</td><td>G309</td><td>G30</td><td>2020-10-29</td></tr>\n",
-       "\t<tr><td>1611467</td><td>death_records</td><td>0</td><td>1</td><td>1</td><td>C260</td><td>C26</td><td>2020-10-29</td></tr>\n",
-       "\t<tr><td>1611467</td><td>death_records</td><td>0</td><td>2</td><td>2</td><td>J189</td><td>J18</td><td>2020-10-29</td></tr>\n",
-       "\t<tr><td>2449704</td><td>death_records</td><td>0</td><td>1</td><td>1</td><td>U071</td><td>U07</td><td>2020-10-29</td></tr>\n",
-       "</tbody>\n",
-       "</table>\n"
-      ],
-      "text/latex": [
-       "A grouped\\_df: 6 × 8\n",
-       "\\begin{tabular}{llllllll}\n",
-       " eid & origin & instance & n & level & code & meaning & date\\\\\n",
-       " <int> & <chr> & <dbl> & <int> & <int> & <chr> & <chr> & <date>\\\\\n",
-       "\\hline\n",
-       "\t 5285138 & death\\_records & 0 & 1 & 1 & U071 & U07 & 2020-11-01\\\\\n",
-       "\t 5285138 & death\\_records & 0 & 2 & 2 & J449 & J44 & 2020-11-01\\\\\n",
-       "\t 1206785 & death\\_records & 0 & 1 & 1 & G309 & G30 & 2020-10-29\\\\\n",
-       "\t 1611467 & death\\_records & 0 & 1 & 1 & C260 & C26 & 2020-10-29\\\\\n",
-       "\t 1611467 & death\\_records & 0 & 2 & 2 & J189 & J18 & 2020-10-29\\\\\n",
-       "\t 2449704 & death\\_records & 0 & 1 & 1 & U071 & U07 & 2020-10-29\\\\\n",
-       "\\end{tabular}\n"
-      ],
-      "text/markdown": [
-       "\n",
-       "A grouped_df: 6 × 8\n",
-       "\n",
-       "| eid &lt;int&gt; | origin &lt;chr&gt; | instance &lt;dbl&gt; | n &lt;int&gt; | level &lt;int&gt; | code &lt;chr&gt; | meaning &lt;chr&gt; | date &lt;date&gt; |\n",
-       "|---|---|---|---|---|---|---|---|\n",
-       "| 5285138 | death_records | 0 | 1 | 1 | U071 | U07 | 2020-11-01 |\n",
-       "| 5285138 | death_records | 0 | 2 | 2 | J449 | J44 | 2020-11-01 |\n",
-       "| 1206785 | death_records | 0 | 1 | 1 | G309 | G30 | 2020-10-29 |\n",
-       "| 1611467 | death_records | 0 | 1 | 1 | C260 | C26 | 2020-10-29 |\n",
-       "| 1611467 | death_records | 0 | 2 | 2 | J189 | J18 | 2020-10-29 |\n",
-       "| 2449704 | death_records | 0 | 1 | 1 | U071 | U07 | 2020-10-29 |\n",
-       "\n"
-      ],
-      "text/plain": [
-       "  eid     origin        instance n level code meaning date      \n",
-       "1 5285138 death_records 0        1 1     U071 U07     2020-11-01\n",
-       "2 5285138 death_records 0        2 2     J449 J44     2020-11-01\n",
-       "3 1206785 death_records 0        1 1     G309 G30     2020-10-29\n",
-       "4 1611467 death_records 0        1 1     C260 C26     2020-10-29\n",
-       "5 1611467 death_records 0        2 2     J189 J18     2020-10-29\n",
-       "6 2449704 death_records 0        1 1     U071 U07     2020-10-29"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "head(codes_death %>% arrange(desc(date)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 244,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "arrow::write_feather(codes_death, glue(\"{path}/codes_death_records_210115.feather\"))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## GP Records"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-12-23T09:27:49.989427Z",
-     "start_time": "2020-12-23T09:27:48.579Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "gp_registrations = fread(\"/data/project/uk_bb/cvd/data/ukb_downloads/updated_showcase_43098/ukb_data/records/gp_registrations.txt\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-12-23T11:13:46.064068Z",
-     "start_time": "2020-12-23T11:11:59.825Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "gp_clinical = fread(\"/data/project/uk_bb/cvd/data/ukb_downloads/updated_showcase_43098/ukb_data/records/gp_clinical.txt\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "gp_clinical[gp_clinical == \"\"] <- NA"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "gp_clinical = gp_clinical %>% rename(date=\"event_dt\") %>% mutate(date = ymd(as.Date(fast_strptime(date, \"%d/%m/%Y\"))))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# clean_dates\n",
-    "# These data are provided in a form which is as close as possible to how they were issued from their source supplier, in order to avoid potential systematic error or bias by attempting to ‘clean’ them by\n",
-    "# removing or altering invalid or erroneous information. However, to protect individuals, alterations have been made to dates in relation to participant date of birth as follows:\n",
-    "\n",
-    "# - where clinical event or prescription date precedes participant date of birth it has been altered to 01/01/1901.\n",
-    "# - Where the date matches participant date of birth it has been altered to 02/02/1902.\n",
-    "# - Where the date follows participant date of birth but is in the year of their birth it has been altered to 03/03/1903.\n",
-    "# - Where the date was in the future this has been changed to 07/07/2037 as these are likely to have been entered as a place-holder or other system default."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "gp_clinical = gp_clinical %>% filter(date!=\"2037-07-07\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 190,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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9+Qls5WuvRE2m+vl+P9x9fX/fGKsx93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\nQJEIccD/Aij7gl1wHb1gAAAAAElFTkSuQmCC",
-      "text/plain": [
-       "plot without title"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 420,
-       "width": 420
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "library(visdat)\n",
-    "df = gp_clinical %>% sample_n(1000)\n",
-    "vis_miss(df)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Diagnoses - ICD10"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "readv2_icd10 = head(fread(\"/data/analysis/ag-reils/steinfej/code/umbrella/pre/ukbb/mapping/gp_codings/read_v2_icd10.csv\"), -3) %>% rename(read_2=\"read_code\", code =\"icd10_code\") %>% select(read_2, code)\n",
-    "readv3_icd10 = head(fread(\"/data/analysis/ag-reils/steinfej/code/umbrella/pre/ukbb/mapping/gp_codings/read_ctv3_icd10.csv\"), -3)%>% rename(read_3=\"read_code\", code=\"icd10_code\") %>% select(read_3, code)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 210,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Joining, by = \"read_2\"\n",
-      "\n",
-      "Joining, by = \"read_3\"\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "gp_diagnoses_pre = gp_clinical %>% filter(read_2 %in% readv2_icd10$read_2 | read_3 %in% readv3_icd10$read_3)\n",
-    "gp_diagnoses_readv2 = gp_diagnoses_pre %>% filter(!is.na(read_2)) %>% left_join(readv2_icd10, on=\"read_2\") %>% drop_na(code) %>% mutate(origin=\"gp_read2\") %>% select(eid, origin, code, date)\n",
-    "gp_diagnoses_readv3 = gp_diagnoses_pre %>% filter(!is.na(read_3)) %>% left_join(readv3_icd10, on=\"read_3\") %>% drop_na(code) %>% mutate(origin=\"gp_read3\") %>% select(eid, origin, code, date)\n",
-    "gp_diagnoses_raw = rbind(gp_diagnoses_readv2, gp_diagnoses_readv3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 273,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "17781180"
-      ],
-      "text/latex": [
-       "17781180"
-      ],
-      "text/markdown": [
-       "17781180"
-      ],
-      "text/plain": [
-       "[1] 17781180"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<table>\n",
-       "<caption>A grouped_df: 6 × 8</caption>\n",
-       "<thead>\n",
-       "\t<tr><th scope=col>eid</th><th scope=col>origin</th><th scope=col>instance</th><th scope=col>n</th><th scope=col>level</th><th scope=col>code</th><th scope=col>meaning</th><th scope=col>date</th></tr>\n",
-       "\t<tr><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;lgl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;date&gt;</th></tr>\n",
-       "</thead>\n",
-       "<tbody>\n",
-       "\t<tr><td>3375683</td><td>gp_read3</td><td>0</td><td> 5</td><td>NA</td><td>Z016</td><td>Z01</td><td>2019-01-01</td></tr>\n",
-       "\t<tr><td>5397213</td><td>gp_read3</td><td>0</td><td>42</td><td>NA</td><td>Z016</td><td>Z01</td><td>2019-01-01</td></tr>\n",
-       "\t<tr><td>4141959</td><td>gp_read2</td><td>0</td><td>54</td><td>NA</td><td>L309</td><td>L30</td><td>2017-09-29</td></tr>\n",
-       "\t<tr><td>5812168</td><td>gp_read2</td><td>0</td><td>24</td><td>NA</td><td>H609</td><td>H60</td><td>2017-09-28</td></tr>\n",
-       "\t<tr><td>1246037</td><td>gp_read2</td><td>0</td><td>26</td><td>NA</td><td>B379</td><td>B37</td><td>2017-09-22</td></tr>\n",
-       "\t<tr><td>2850667</td><td>gp_read2</td><td>0</td><td>12</td><td>NA</td><td>L988</td><td>L98</td><td>2017-09-22</td></tr>\n",
-       "</tbody>\n",
-       "</table>\n"
-      ],
-      "text/latex": [
-       "A grouped\\_df: 6 × 8\n",
-       "\\begin{tabular}{llllllll}\n",
-       " eid & origin & instance & n & level & code & meaning & date\\\\\n",
-       " <int> & <chr> & <dbl> & <int> & <lgl> & <chr> & <chr> & <date>\\\\\n",
-       "\\hline\n",
-       "\t 3375683 & gp\\_read3 & 0 &  5 & NA & Z016 & Z01 & 2019-01-01\\\\\n",
-       "\t 5397213 & gp\\_read3 & 0 & 42 & NA & Z016 & Z01 & 2019-01-01\\\\\n",
-       "\t 4141959 & gp\\_read2 & 0 & 54 & NA & L309 & L30 & 2017-09-29\\\\\n",
-       "\t 5812168 & gp\\_read2 & 0 & 24 & NA & H609 & H60 & 2017-09-28\\\\\n",
-       "\t 1246037 & gp\\_read2 & 0 & 26 & NA & B379 & B37 & 2017-09-22\\\\\n",
-       "\t 2850667 & gp\\_read2 & 0 & 12 & NA & L988 & L98 & 2017-09-22\\\\\n",
-       "\\end{tabular}\n"
-      ],
-      "text/markdown": [
-       "\n",
-       "A grouped_df: 6 × 8\n",
-       "\n",
-       "| eid &lt;int&gt; | origin &lt;chr&gt; | instance &lt;dbl&gt; | n &lt;int&gt; | level &lt;lgl&gt; | code &lt;chr&gt; | meaning &lt;chr&gt; | date &lt;date&gt; |\n",
-       "|---|---|---|---|---|---|---|---|\n",
-       "| 3375683 | gp_read3 | 0 |  5 | NA | Z016 | Z01 | 2019-01-01 |\n",
-       "| 5397213 | gp_read3 | 0 | 42 | NA | Z016 | Z01 | 2019-01-01 |\n",
-       "| 4141959 | gp_read2 | 0 | 54 | NA | L309 | L30 | 2017-09-29 |\n",
-       "| 5812168 | gp_read2 | 0 | 24 | NA | H609 | H60 | 2017-09-28 |\n",
-       "| 1246037 | gp_read2 | 0 | 26 | NA | B379 | B37 | 2017-09-22 |\n",
-       "| 2850667 | gp_read2 | 0 | 12 | NA | L988 | L98 | 2017-09-22 |\n",
-       "\n"
-      ],
-      "text/plain": [
-       "  eid     origin   instance n  level code meaning date      \n",
-       "1 3375683 gp_read3 0         5 NA    Z016 Z01     2019-01-01\n",
-       "2 5397213 gp_read3 0        42 NA    Z016 Z01     2019-01-01\n",
-       "3 4141959 gp_read2 0        54 NA    L309 L30     2017-09-29\n",
-       "4 5812168 gp_read2 0        24 NA    H609 H60     2017-09-28\n",
-       "5 1246037 gp_read2 0        26 NA    B379 B37     2017-09-22\n",
-       "6 2850667 gp_read2 0        12 NA    L988 L98     2017-09-22"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "gp_diagnoses = gp_diagnoses_raw %>% mutate(instance=0, level=NA) %>% distinct() %>% group_by(eid) %>% mutate(n = row_number()) %>% mutate(meaning=str_sub(code, 1, 3)) %>% select(c(eid, origin, instance, n, level, code, meaning, date))\n",
-    "nrow(gp_diagnoses)\n",
-    "head(gp_diagnoses %>% arrange(desc(date)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 212,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "arrow::write_feather(gp_diagnoses, glue(\"{path}/codes_gp_diagnoses_210119.feather\"))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Procedures - Snomed CT"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "readv2_opcs4 = head(fread(\"/data/analysis/ag-reils/steinfej/code/umbrella/pre/ukbb/mapping/gp_codings/read_v2_opcs4.csv\"), -3) %>% rename(read_2=\"read_code\", code =\"opcs_4.2_code\") %>% select(read_2, code)\n",
-    "readv3_opcs4 = head(fread(\"/data/analysis/ag-reils/steinfej/code/umbrella/pre/ukbb/mapping/gp_codings/read_ctv3_opcs4.csv\"), -3)%>% rename(read_3=\"read_code\", code=\"opcs4_code\") %>% select(read_3, code)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 214,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "gp_procedures_pre = gp_clinical %>% filter(read_2 %in% readv2_opcs4$read_2 | read_3 %in% readv3_opcs4$read_3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 215,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Joining, by = \"read_2\"\n",
-      "\n",
-      "Joining, by = \"read_3\"\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "gp_procedures_readv2 = gp_procedures_pre %>% filter(!is.na(read_2)) %>% left_join(readv2_opcs4, on=\"read_2\") %>% drop_na(code) %>% mutate(origin=\"gp_read2\") %>% select(eid, origin, code, date)\n",
-    "gp_procedures_readv3 = gp_procedures_pre %>% filter(!is.na(read_3)) %>% left_join(readv3_opcs4, on=\"read_3\") %>% drop_na(code) %>% mutate(origin=\"gp_read3\") %>% select(eid, origin, code, date)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 216,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "gp_procedures_raw = rbind(gp_procedures_readv2, gp_procedures_readv3) %>% mutate(instance=0, level=NA) %>% distinct() %>% group_by(eid) %>% mutate(n = row_number()) "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 217,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# opcs4 to snomed mapping\n",
-    "\n",
-    "concept_ids_opcs4 = concept %>% filter(vocabulary_id == \"OPCS4\") %>% mutate(concept_code = str_replace(concept_code, \"\\\\.\", \"\"))\n",
-    "concept_ids_snomed = concept %>% filter(vocabulary_id == \"SNOMED\" & domain_id==\"Procedure\") \n",
-    "\n",
-    "# check necessary opcs4 concept ids\n",
-    "concept_ids = concept_ids_opcs4 %>% mutate(concept_id_1 = concept_id)\n",
-    "cr_filtered = concept_relationship %>% filter(concept_id_1 %in% concept_ids_opcs4$concept_id) %>% filter(concept_id_2 %in% concept_ids_snomed$concept_id) %>% arrange(concept_id_1)\n",
-    "\n",
-    "mapping_opcs4_snomed = concept_ids_opcs4 %>% \n",
-    "    left_join(cr_filtered %>% select(concept_id_1, concept_id_2), by=c(\"concept_id\"=\"concept_id_1\")) %>% \n",
-    "    left_join(concept_ids_snomed %>% select(concept_id, concept_code, concept_name), by=c(\"concept_id_2\"=\"concept_id\")) %>% \n",
-    "    mutate(code = concept_code.x, meaning=concept_code.y, name=concept_name.y)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 272,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "5689793"
-      ],
-      "text/latex": [
-       "5689793"
-      ],
-      "text/markdown": [
-       "5689793"
-      ],
-      "text/plain": [
-       "[1] 5689793"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<table>\n",
-       "<caption>A grouped_df: 6 × 9</caption>\n",
-       "<thead>\n",
-       "\t<tr><th scope=col>eid</th><th scope=col>origin</th><th scope=col>instance</th><th scope=col>n</th><th scope=col>level</th><th scope=col>date</th><th scope=col>code</th><th scope=col>meaning</th><th scope=col>name</th></tr>\n",
-       "\t<tr><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;lgl&gt;</th><th scope=col>&lt;date&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
-       "</thead>\n",
-       "<tbody>\n",
-       "\t<tr><td>1124200</td><td>gp_read2</td><td>0</td><td>35</td><td>NA</td><td>2017-09-20</td><td>D071</td><td>172630005</td><td>Syringing ear to remove wax                 </td></tr>\n",
-       "\t<tr><td>1369937</td><td>gp_read2</td><td>0</td><td>65</td><td>NA</td><td>2017-09-20</td><td>X36 </td><td>82078001 </td><td>Collection of blood specimen for laboratory </td></tr>\n",
-       "\t<tr><td>1444398</td><td>gp_read2</td><td>0</td><td> 6</td><td>NA</td><td>2017-09-20</td><td>U333</td><td>448489007</td><td>Removal of ambulatory blood pressure monitor</td></tr>\n",
-       "\t<tr><td>1494963</td><td>gp_read2</td><td>0</td><td>12</td><td>NA</td><td>2017-09-20</td><td>X378</td><td>76601001 </td><td>Intramuscular injection                     </td></tr>\n",
-       "\t<tr><td>1612512</td><td>gp_read2</td><td>0</td><td>11</td><td>NA</td><td>2017-09-20</td><td>X378</td><td>76601001 </td><td>Intramuscular injection                     </td></tr>\n",
-       "\t<tr><td>1630054</td><td>gp_read2</td><td>0</td><td>52</td><td>NA</td><td>2017-09-20</td><td>X37 </td><td>76601001 </td><td>Intramuscular injection                     </td></tr>\n",
-       "</tbody>\n",
-       "</table>\n"
-      ],
-      "text/latex": [
-       "A grouped\\_df: 6 × 9\n",
-       "\\begin{tabular}{lllllllll}\n",
-       " eid & origin & instance & n & level & date & code & meaning & name\\\\\n",
-       " <int> & <chr> & <dbl> & <int> & <lgl> & <date> & <chr> & <chr> & <chr>\\\\\n",
-       "\\hline\n",
-       "\t 1124200 & gp\\_read2 & 0 & 35 & NA & 2017-09-20 & D071 & 172630005 & Syringing ear to remove wax                 \\\\\n",
-       "\t 1369937 & gp\\_read2 & 0 & 65 & NA & 2017-09-20 & X36  & 82078001  & Collection of blood specimen for laboratory \\\\\n",
-       "\t 1444398 & gp\\_read2 & 0 &  6 & NA & 2017-09-20 & U333 & 448489007 & Removal of ambulatory blood pressure monitor\\\\\n",
-       "\t 1494963 & gp\\_read2 & 0 & 12 & NA & 2017-09-20 & X378 & 76601001  & Intramuscular injection                     \\\\\n",
-       "\t 1612512 & gp\\_read2 & 0 & 11 & NA & 2017-09-20 & X378 & 76601001  & Intramuscular injection                     \\\\\n",
-       "\t 1630054 & gp\\_read2 & 0 & 52 & NA & 2017-09-20 & X37  & 76601001  & Intramuscular injection                     \\\\\n",
-       "\\end{tabular}\n"
-      ],
-      "text/markdown": [
-       "\n",
-       "A grouped_df: 6 × 9\n",
-       "\n",
-       "| eid &lt;int&gt; | origin &lt;chr&gt; | instance &lt;dbl&gt; | n &lt;int&gt; | level &lt;lgl&gt; | date &lt;date&gt; | code &lt;chr&gt; | meaning &lt;chr&gt; | name &lt;chr&gt; |\n",
-       "|---|---|---|---|---|---|---|---|---|\n",
-       "| 1124200 | gp_read2 | 0 | 35 | NA | 2017-09-20 | D071 | 172630005 | Syringing ear to remove wax                  |\n",
-       "| 1369937 | gp_read2 | 0 | 65 | NA | 2017-09-20 | X36  | 82078001  | Collection of blood specimen for laboratory  |\n",
-       "| 1444398 | gp_read2 | 0 |  6 | NA | 2017-09-20 | U333 | 448489007 | Removal of ambulatory blood pressure monitor |\n",
-       "| 1494963 | gp_read2 | 0 | 12 | NA | 2017-09-20 | X378 | 76601001  | Intramuscular injection                      |\n",
-       "| 1612512 | gp_read2 | 0 | 11 | NA | 2017-09-20 | X378 | 76601001  | Intramuscular injection                      |\n",
-       "| 1630054 | gp_read2 | 0 | 52 | NA | 2017-09-20 | X37  | 76601001  | Intramuscular injection                      |\n",
-       "\n"
-      ],
-      "text/plain": [
-       "  eid     origin   instance n  level date       code meaning  \n",
-       "1 1124200 gp_read2 0        35 NA    2017-09-20 D071 172630005\n",
-       "2 1369937 gp_read2 0        65 NA    2017-09-20 X36  82078001 \n",
-       "3 1444398 gp_read2 0         6 NA    2017-09-20 U333 448489007\n",
-       "4 1494963 gp_read2 0        12 NA    2017-09-20 X378 76601001 \n",
-       "5 1612512 gp_read2 0        11 NA    2017-09-20 X378 76601001 \n",
-       "6 1630054 gp_read2 0        52 NA    2017-09-20 X37  76601001 \n",
-       "  name                                        \n",
-       "1 Syringing ear to remove wax                 \n",
-       "2 Collection of blood specimen for laboratory \n",
-       "3 Removal of ambulatory blood pressure monitor\n",
-       "4 Intramuscular injection                     \n",
-       "5 Intramuscular injection                     \n",
-       "6 Intramuscular injection                     "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "gp_procedures = gp_procedures_raw %>% left_join(mapping_opcs4_snomed %>% select(code, meaning, name), by=\"code\") %>% select(eid, origin, instance, n, level, date, code, meaning, name) %>% arrange(eid, date)\n",
-    "nrow(gp_procedures)\n",
-    "head(gp_procedures %>% arrange(desc(date)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 222,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "arrow::write_feather(gp_procedures, glue(\"{path}/codes_gp_procedures_210119.feather\"))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Measurements - Snomed CT"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 257,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "readv2_readv3 = head(fread(\"/data/analysis/ag-reils/steinfej/code/umbrella/pre/ukbb/mapping/gp_codings/read_v2_read_ctv3.csv\"), -3) %>% rename(read_2=\"READV2_CODE\", code =\"READV3_CODE\", name =\"TERMV3_DESC\") %>% select(read_2, code)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 250,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "gp_meas = gp_clinical %>% filter(!is.na(value1)) %>% distinct()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "gp_meas_readv2 = gp_meas %>% filter(!is.na(read_2)) %>% left_join(readv2_readv3, by=\"read_2\")\n",
-    "gp_meas_readv2 "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "gp_meas_readv3 = gp_meas %>% filter(!is.na(read_3)) %>% mutate(code=read_3)\n",
-    "gp_meas_readv3 "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "gp_meas_all = rbind(gp_meas_readv2, gp_meas_readv3) %>% distinct() %>% group_by(eid) "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 296,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "readv3_lkp = head(fread(\"/data/analysis/ag-reils/steinfej/code/umbrella/pre/ukbb/mapping/gp_codings/read_ctv3_lkp.csv\"), -3)%>% rename(code=\"read_code\", name =\"term_description\") %>% select(code, name)\n",
-    "readv3_sct = fread(\"/data/analysis/ag-reils/steinfej/code/umbrella/pre/ukbb/mapping/gp_codings/CTV3SCTMAP.csv\")%>% rename(SCUI=\"V1\", STUI=\"V2\", TCUI=\"V3\", TTUI=\"V4\")%>% rename(code=\"SCUI\", meaning=\"TCUI\") %>% select(code, meaning)\n",
-    "#readct_sct = fread(\"/data/analysis/ag-reils/steinfej/code/umbrella/pre/ukbb/mapping/gp_codings/RCTSCTMAP.csv\")%>% rename(SCUI=\"V1\", STUI=\"V2\", TCUI=\"V3\", TTUI=\"V4\")#%>% rename(code=\"read_code\", name =\"term_description\") %>% select(code, name)#"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 266,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "gp_meas = gp_meas_all %>% left_join(readv3_lkp, by=\"code\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 302,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "concept_ids_snomed = concept %>% filter(vocabulary_id == \"SNOMED\") %>% rename(name=\"concept_name\", meaning=\"concept_code\") %>% select(meaning, name)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 309,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "gp_meas_uncleaned = gp_meas_all %>% left_join(readv3_sct, by=\"code\") %>% left_join(concept_ids_snomed, by=\"meaning\") %>% distinct()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "gp_meas_cleaned_1 = gp_meas_uncleaned %>% select(eid, date, code, value1, value2, value3, meaning, name) %>% distinct() %>% filter(value1!=0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "gp_meas_cleaned_2 = gp_meas_cleaned_1 %>% ungroup() %>% filter(!is.na(meaning))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 376,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Warning message in mask$eval_all_filter(dots, env_filter):\n",
-      "\"NAs introduced by coercion\"\n",
-      "Warning message in mask$eval_all_filter(dots, env_filter):\n",
-      "\"NAs introduced by coercion\"\n"
-     ]
-    }
-   ],
-   "source": [
-    "double_df = gp_meas_cleaned_2 %>% filter(!is.na(as.numeric(value1)) & !is.na(as.numeric(value2))) "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 410,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# clean blood pressure and map to systolic and diastolic\n",
-    "bp_double_mapped = double_df %>% filter(name %in% c('O/E - blood pressure reading', 'O/E - BP reading normal', 'O/E - BP reading raised',\n",
-    "                'O/E - BP borderline raised', 'O/E - Systolic BP reading', 'O/E - Diastolic BP reading', 'Sitting blood pressure', \"Average home systolic blood pressure\",\n",
-    "                'Standing blood pressure','24 hr blood pressure monitoring')) %>% \n",
-    "     #filter(name %in% c('O/E - Systolic BP reading', 'O/E - Diastolic BP reading', \"Average home systolic blood pressure\")) %>%\n",
-    "    filter(as.numeric(value1)>0) %>% \n",
-    "    mutate(value_high = pmax(as.numeric(value1), as.numeric(value2)), value_low = pmin(as.numeric(value1), as.numeric(value2))) %>% \n",
-    "    filter(value_high>40 & value_low>20 & value_high<400 & value_low<300)  %>% rename(\"163030003\" = \"value_high\", \"163031004\" = \"value_low\") %>% \n",
-    "    select(-c(meaning, name)) %>% pivot_longer(c(\"163030003\", \"163031004\"), names_to=\"meaning\", values_to=\"value\") %>% left_join(concept_ids_snomed, by=\"meaning\") %>% distinct() %>% arrange(eid) %>%\n",
-    "    select(eid, date, code, value1, value2, value3, meaning, name, value)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 422,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Warning message in mask$eval_all_filter(dots, env_filter):\n",
-      "\"NAs introduced by coercion\"\n",
-      "Warning message in mask$eval_all_filter(dots, env_filter):\n",
-      "\"NAs introduced by coercion\"\n",
-      "Warning message:\n",
-      "\"Problem with `mutate()` input `value`.\n",
-      "\u001b[34mi\u001b[39m NAs introduced by coercion\n",
-      "\u001b[34mi\u001b[39m Input `value` is `case_when(...)`.\"\n",
-      "Warning message in eval_tidy(pair$lhs, env = default_env):\n",
-      "\"NAs introduced by coercion\"\n",
-      "Warning message:\n",
-      "\"Problem with `mutate()` input `value`.\n",
-      "\u001b[34mi\u001b[39m NAs introduced by coercion\n",
-      "\u001b[34mi\u001b[39m Input `value` is `case_when(...)`.\"\n",
-      "Warning message in eval_tidy(pair$rhs, env = default_env):\n",
-      "\"NAs introduced by coercion\"\n",
-      "Warning message:\n",
-      "\"Problem with `mutate()` input `value`.\n",
-      "\u001b[34mi\u001b[39m NAs introduced by coercion\n",
-      "\u001b[34mi\u001b[39m Input `value` is `case_when(...)`.\"\n",
-      "Warning message in eval_tidy(pair$lhs, env = default_env):\n",
-      "\"NAs introduced by coercion\"\n",
-      "Warning message:\n",
-      "\"Problem with `mutate()` input `value`.\n",
-      "\u001b[34mi\u001b[39m NAs introduced by coercion\n",
-      "\u001b[34mi\u001b[39m Input `value` is `case_when(...)`.\"\n",
-      "Warning message in eval_tidy(pair$rhs, env = default_env):\n",
-      "\"NAs introduced by coercion\"\n"
-     ]
-    }
-   ],
-   "source": [
-    "gp_meas_single = gp_meas_cleaned_2 %>% filter(is.na(as.numeric(value1)) | is.na(as.numeric(value2))) %>%\n",
-    "    mutate(value=case_when(!is.na(as.numeric(value1)) ~ as.numeric(value1), is.na(as.numeric(value1)) ~ as.numeric(value2))) %>% filter(!is.na(value))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "gp_meas_cleaned_3 = rbind(gp_meas_single, bp_double_mapped) %>% distinct() %>% arrange(eid, date)\n",
-    "gp_meas_cleaned_3"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 428,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "arrow::write_feather(gp_meas_cleaned_3, glue(\"{path}/codes_gp_measurements_210120.feather\"))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Prescriptions - RXNorm"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-12-23T09:28:34.328613Z",
-     "start_time": "2020-12-23T09:27:50.059Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "gp_scripts = fread(\"/data/project/uk_bb/cvd/data/ukb_downloads/updated_showcase_43098/ukb_data/records/gp_scripts.txt\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "gp_scripts[gp_scripts == \"\"] <- NA"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "gp_scripts = gp_scripts %>% mutate(date = ymd(as.Date(fast_strptime(issue_date, \"%d/%m/%Y\"))))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "gp_scripts_names_available = gp_scripts %>% filter(!is.na(drug_name))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 75,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "gp_scripts_read_available = gp_scripts %>% filter(is.na(drug_name))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "drug_names = (gp_scripts_names_available %>% count(drug_name, sort=TRUE))$drug_name"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "library(jsonlite)\n",
-    "write_json(drug_names, glue(\"{path}/drug_names.json\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "name_umls_link = arrow::read_feather(glue(\"{path}/drug_names_umls_linked.feather\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "drugs_rxnorm = arrow::read_feather(glue(\"{path}/drug_names_umls_linked_rxnorm.feather\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "rx_mapping = concept %>% filter(vocabulary_id %in% c('RxNorm','RxNorm Extension')) %>% select(concept_code, concept_name) %>% rename(rx_code =\"concept_code\", name=\"concept_name\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 69,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Joining, by = \"rx_code\"\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "rx_norm_mapping_table = drugs_rxnorm %>% select(drug_name, rx_code) %>% filter(rx_code != \"\") %>% distinct() %>% left_join(rx_mapping, on=\"rx_code\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 80,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Joining, by = \"drug_name\"\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<table>\n",
-       "<caption>A data.table: 56629816 × 5</caption>\n",
-       "<thead>\n",
-       "\t<tr><th scope=col>eid</th><th scope=col>date</th><th scope=col>drug_name</th><th scope=col>rx_code</th><th scope=col>name</th></tr>\n",
-       "\t<tr><th scope=col>&lt;int&gt;</th><th scope=col>&lt;date&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
-       "</thead>\n",
-       "<tbody>\n",
-       "\t<tr><td>1000018</td><td>1996-11-30</td><td>Doxycycline 100mg capsules                 </td><td>3640 </td><td>doxycycline         </td></tr>\n",
-       "\t<tr><td>1000018</td><td>1997-09-12</td><td>Propranolol 40mg tablets                   </td><td>8787 </td><td>propranolol         </td></tr>\n",
-       "\t<tr><td>1000018</td><td>1997-10-10</td><td>Propranolol 40mg tablets                   </td><td>8787 </td><td>propranolol         </td></tr>\n",
-       "\t<tr><td>1000018</td><td>1997-10-10</td><td>Noriday 350microgram tablets (Pfizer Ltd)  </td><td>NA   </td><td>NA                  </td></tr>\n",
-       "\t<tr><td>1000018</td><td>1998-01-19</td><td>Propranolol 40mg tablets                   </td><td>8787 </td><td>propranolol         </td></tr>\n",
-       "\t<tr><td>1000018</td><td>1998-01-19</td><td>Propranolol 80mg tablets                   </td><td>8787 </td><td>propranolol         </td></tr>\n",
-       "\t<tr><td>1000018</td><td>1998-01-19</td><td>Gamolenic acid 40mg capsules               </td><td>25605</td><td>gamma-linolenic acid</td></tr>\n",
-       "\t<tr><td>1000018</td><td>1998-03-13</td><td>Propranolol 80mg tablets                   </td><td>8787 </td><td>propranolol         </td></tr>\n",
-       "\t<tr><td>1000018</td><td>1998-03-25</td><td>Amoxicillin 250mg capsules                 </td><td>723  </td><td>amoxicillin         </td></tr>\n",
-       "\t<tr><td>1000018</td><td>1998-04-16</td><td>Amoxicillin 250mg capsules                 </td><td>723  </td><td>amoxicillin         </td></tr>\n",
-       "\t<tr><td>1000018</td><td>1998-04-24</td><td>Metronidazole 200mg tablets                </td><td>6922 </td><td>metronidazole       </td></tr>\n",
-       "\t<tr><td>1000018</td><td>1998-06-19</td><td>Mebeverine 135mg tablets                   </td><td>29410</td><td>mebeverine          </td></tr>\n",
-       "\t<tr><td>1000018</td><td>1998-06-19</td><td>Propranolol 80mg tablets                   </td><td>8787 </td><td>propranolol         </td></tr>\n",
-       "\t<tr><td>1000018</td><td>1998-08-14</td><td>Gentisone HC ear drops (AMCo)              </td><td>5492 </td><td>hydrocortisone      </td></tr>\n",
-       "\t<tr><td>1000018</td><td>1999-01-20</td><td>Gamolenic acid 40mg capsules               </td><td>25605</td><td>gamma-linolenic acid</td></tr>\n",
-       "\t<tr><td>1000018</td><td>1999-01-20</td><td>Gentisone HC ear drops (AMCo)              </td><td>5492 </td><td>hydrocortisone      </td></tr>\n",
-       "\t<tr><td>1000018</td><td>2001-01-12</td><td>Aciclovir 200mg dispersible tablets        </td><td>281  </td><td>acyclovir           </td></tr>\n",
-       "\t<tr><td>1000018</td><td>2001-01-12</td><td>Doxycycline 100mg capsules                 </td><td>3640 </td><td>doxycycline         </td></tr>\n",
-       "\t<tr><td>1000018</td><td>2003-11-17</td><td>Aciclovir 200mg dispersible tablets        </td><td>281  </td><td>acyclovir           </td></tr>\n",
-       "\t<tr><td>1000018</td><td>2003-11-17</td><td>Doxycycline 100mg capsules                 </td><td>3640 </td><td>doxycycline         </td></tr>\n",
-       "\t<tr><td>1000018</td><td>2003-11-18</td><td>Doxycycline 100mg capsules                 </td><td>3640 </td><td>doxycycline         </td></tr>\n",
-       "\t<tr><td>1000018</td><td>2003-11-24</td><td>Beclometasone 50micrograms/dose nasal spray</td><td>1347 </td><td>beclomethasone      </td></tr>\n",
-       "\t<tr><td>1000018</td><td>2005-06-08</td><td>Atenolol 50mg tablets                      </td><td>1202 </td><td>atenolol            </td></tr>\n",
-       "\t<tr><td>1000018</td><td>2005-06-09</td><td>Atenolol 50mg tablets                      </td><td>1202 </td><td>atenolol            </td></tr>\n",
-       "\t<tr><td>1000018</td><td>2005-06-23</td><td>Atenolol 50mg tablets                      </td><td>1202 </td><td>atenolol            </td></tr>\n",
-       "\t<tr><td>1000018</td><td>2005-06-24</td><td>Atenolol 50mg tablets                      </td><td>1202 </td><td>atenolol            </td></tr>\n",
-       "\t<tr><td>1000018</td><td>2005-09-01</td><td>Atenolol 50mg tablets                      </td><td>1202 </td><td>atenolol            </td></tr>\n",
-       "\t<tr><td>1000018</td><td>2005-10-07</td><td>Atenolol 50mg tablets                      </td><td>1202 </td><td>atenolol            </td></tr>\n",
-       "\t<tr><td>1000018</td><td>2005-11-08</td><td>Atenolol 50mg tablets                      </td><td>1202 </td><td>atenolol            </td></tr>\n",
-       "\t<tr><td>1000018</td><td>2005-12-05</td><td>Atenolol 50mg tablets                      </td><td>1202 </td><td>atenolol            </td></tr>\n",
-       "\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
-       "\t<tr><td>6025198</td><td>2015-01-23</td><td>Ramipril 2.5mg capsules </td><td>35296</td><td>ramipril   </td></tr>\n",
-       "\t<tr><td>6025198</td><td>2015-01-23</td><td>Simvastatin 40mg tablets</td><td>36567</td><td>simvastatin</td></tr>\n",
-       "\t<tr><td>6025198</td><td>2015-01-23</td><td>Fenbid 5% gel (AMCo)    </td><td>NA   </td><td>NA         </td></tr>\n",
-       "\t<tr><td>6025198</td><td>2015-02-27</td><td>Ramipril 2.5mg capsules </td><td>35296</td><td>ramipril   </td></tr>\n",
-       "\t<tr><td>6025198</td><td>2015-02-27</td><td>Simvastatin 40mg tablets</td><td>36567</td><td>simvastatin</td></tr>\n",
-       "\t<tr><td>6025198</td><td>2015-02-27</td><td>Fenbid 5% gel (AMCo)    </td><td>NA   </td><td>NA         </td></tr>\n",
-       "\t<tr><td>6025198</td><td>2015-05-05</td><td>Ramipril 2.5mg capsules </td><td>35296</td><td>ramipril   </td></tr>\n",
-       "\t<tr><td>6025198</td><td>2015-05-05</td><td>Simvastatin 40mg tablets</td><td>36567</td><td>simvastatin</td></tr>\n",
-       "\t<tr><td>6025198</td><td>2015-05-05</td><td>Fenbid 5% gel (AMCo)    </td><td>NA   </td><td>NA         </td></tr>\n",
-       "\t<tr><td>6025198</td><td>2015-06-24</td><td>Ramipril 2.5mg capsules </td><td>35296</td><td>ramipril   </td></tr>\n",
-       "\t<tr><td>6025198</td><td>2015-06-24</td><td>Simvastatin 40mg tablets</td><td>36567</td><td>simvastatin</td></tr>\n",
-       "\t<tr><td>6025198</td><td>2015-06-24</td><td>Fenbid 5% gel (AMCo)    </td><td>NA   </td><td>NA         </td></tr>\n",
-       "\t<tr><td>6025198</td><td>2015-08-19</td><td>Ramipril 2.5mg capsules </td><td>35296</td><td>ramipril   </td></tr>\n",
-       "\t<tr><td>6025198</td><td>2015-08-19</td><td>Simvastatin 40mg tablets</td><td>36567</td><td>simvastatin</td></tr>\n",
-       "\t<tr><td>6025198</td><td>2015-08-19</td><td>Fenbid 5% gel (AMCo)    </td><td>NA   </td><td>NA         </td></tr>\n",
-       "\t<tr><td>6025198</td><td>2015-10-14</td><td>Ramipril 2.5mg capsules </td><td>35296</td><td>ramipril   </td></tr>\n",
-       "\t<tr><td>6025198</td><td>2015-10-14</td><td>Simvastatin 40mg tablets</td><td>36567</td><td>simvastatin</td></tr>\n",
-       "\t<tr><td>6025198</td><td>2015-10-14</td><td>Fenbid 5% gel (AMCo)    </td><td>NA   </td><td>NA         </td></tr>\n",
-       "\t<tr><td>6025198</td><td>2015-12-10</td><td>Ramipril 2.5mg capsules </td><td>35296</td><td>ramipril   </td></tr>\n",
-       "\t<tr><td>6025198</td><td>2015-12-10</td><td>Simvastatin 40mg tablets</td><td>36567</td><td>simvastatin</td></tr>\n",
-       "\t<tr><td>6025198</td><td>2015-12-10</td><td>Fenbid 5% gel (AMCo)    </td><td>NA   </td><td>NA         </td></tr>\n",
-       "\t<tr><td>6025198</td><td>2016-02-03</td><td>Ramipril 2.5mg capsules </td><td>35296</td><td>ramipril   </td></tr>\n",
-       "\t<tr><td>6025198</td><td>2016-02-03</td><td>Simvastatin 40mg tablets</td><td>36567</td><td>simvastatin</td></tr>\n",
-       "\t<tr><td>6025198</td><td>2016-02-03</td><td>Fenbid 5% gel (AMCo)    </td><td>NA   </td><td>NA         </td></tr>\n",
-       "\t<tr><td>6025198</td><td>2016-04-06</td><td>Ramipril 2.5mg capsules </td><td>35296</td><td>ramipril   </td></tr>\n",
-       "\t<tr><td>6025198</td><td>2016-04-06</td><td>Simvastatin 40mg tablets</td><td>36567</td><td>simvastatin</td></tr>\n",
-       "\t<tr><td>6025198</td><td>2016-04-06</td><td>Fenbid 5% gel (AMCo)    </td><td>NA   </td><td>NA         </td></tr>\n",
-       "\t<tr><td>6025198</td><td>2016-05-25</td><td>Ramipril 2.5mg capsules </td><td>35296</td><td>ramipril   </td></tr>\n",
-       "\t<tr><td>6025198</td><td>2016-05-25</td><td>Simvastatin 40mg tablets</td><td>36567</td><td>simvastatin</td></tr>\n",
-       "\t<tr><td>6025198</td><td>2016-05-25</td><td>Fenbid 5% gel (AMCo)    </td><td>NA   </td><td>NA         </td></tr>\n",
-       "</tbody>\n",
-       "</table>\n"
-      ],
-      "text/latex": [
-       "A data.table: 56629816 × 5\n",
-       "\\begin{tabular}{lllll}\n",
-       " eid & date & drug\\_name & rx\\_code & name\\\\\n",
-       " <int> & <date> & <chr> & <chr> & <chr>\\\\\n",
-       "\\hline\n",
-       "\t 1000018 & 1996-11-30 & Doxycycline 100mg capsules                  & 3640  & doxycycline         \\\\\n",
-       "\t 1000018 & 1997-09-12 & Propranolol 40mg tablets                    & 8787  & propranolol         \\\\\n",
-       "\t 1000018 & 1997-10-10 & Propranolol 40mg tablets                    & 8787  & propranolol         \\\\\n",
-       "\t 1000018 & 1997-10-10 & Noriday 350microgram tablets (Pfizer Ltd)   & NA    & NA                  \\\\\n",
-       "\t 1000018 & 1998-01-19 & Propranolol 40mg tablets                    & 8787  & propranolol         \\\\\n",
-       "\t 1000018 & 1998-01-19 & Propranolol 80mg tablets                    & 8787  & propranolol         \\\\\n",
-       "\t 1000018 & 1998-01-19 & Gamolenic acid 40mg capsules                & 25605 & gamma-linolenic acid\\\\\n",
-       "\t 1000018 & 1998-03-13 & Propranolol 80mg tablets                    & 8787  & propranolol         \\\\\n",
-       "\t 1000018 & 1998-03-25 & Amoxicillin 250mg capsules                  & 723   & amoxicillin         \\\\\n",
-       "\t 1000018 & 1998-04-16 & Amoxicillin 250mg capsules                  & 723   & amoxicillin         \\\\\n",
-       "\t 1000018 & 1998-04-24 & Metronidazole 200mg tablets                 & 6922  & metronidazole       \\\\\n",
-       "\t 1000018 & 1998-06-19 & Mebeverine 135mg tablets                    & 29410 & mebeverine          \\\\\n",
-       "\t 1000018 & 1998-06-19 & Propranolol 80mg tablets                    & 8787  & propranolol         \\\\\n",
-       "\t 1000018 & 1998-08-14 & Gentisone HC ear drops (AMCo)               & 5492  & hydrocortisone      \\\\\n",
-       "\t 1000018 & 1999-01-20 & Gamolenic acid 40mg capsules                & 25605 & gamma-linolenic acid\\\\\n",
-       "\t 1000018 & 1999-01-20 & Gentisone HC ear drops (AMCo)               & 5492  & hydrocortisone      \\\\\n",
-       "\t 1000018 & 2001-01-12 & Aciclovir 200mg dispersible tablets         & 281   & acyclovir           \\\\\n",
-       "\t 1000018 & 2001-01-12 & Doxycycline 100mg capsules                  & 3640  & doxycycline         \\\\\n",
-       "\t 1000018 & 2003-11-17 & Aciclovir 200mg dispersible tablets         & 281   & acyclovir           \\\\\n",
-       "\t 1000018 & 2003-11-17 & Doxycycline 100mg capsules                  & 3640  & doxycycline         \\\\\n",
-       "\t 1000018 & 2003-11-18 & Doxycycline 100mg capsules                  & 3640  & doxycycline         \\\\\n",
-       "\t 1000018 & 2003-11-24 & Beclometasone 50micrograms/dose nasal spray & 1347  & beclomethasone      \\\\\n",
-       "\t 1000018 & 2005-06-08 & Atenolol 50mg tablets                       & 1202  & atenolol            \\\\\n",
-       "\t 1000018 & 2005-06-09 & Atenolol 50mg tablets                       & 1202  & atenolol            \\\\\n",
-       "\t 1000018 & 2005-06-23 & Atenolol 50mg tablets                       & 1202  & atenolol            \\\\\n",
-       "\t 1000018 & 2005-06-24 & Atenolol 50mg tablets                       & 1202  & atenolol            \\\\\n",
-       "\t 1000018 & 2005-09-01 & Atenolol 50mg tablets                       & 1202  & atenolol            \\\\\n",
-       "\t 1000018 & 2005-10-07 & Atenolol 50mg tablets                       & 1202  & atenolol            \\\\\n",
-       "\t 1000018 & 2005-11-08 & Atenolol 50mg tablets                       & 1202  & atenolol            \\\\\n",
-       "\t 1000018 & 2005-12-05 & Atenolol 50mg tablets                       & 1202  & atenolol            \\\\\n",
-       "\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
-       "\t 6025198 & 2015-01-23 & Ramipril 2.5mg capsules  & 35296 & ramipril   \\\\\n",
-       "\t 6025198 & 2015-01-23 & Simvastatin 40mg tablets & 36567 & simvastatin\\\\\n",
-       "\t 6025198 & 2015-01-23 & Fenbid 5\\% gel (AMCo)     & NA    & NA         \\\\\n",
-       "\t 6025198 & 2015-02-27 & Ramipril 2.5mg capsules  & 35296 & ramipril   \\\\\n",
-       "\t 6025198 & 2015-02-27 & Simvastatin 40mg tablets & 36567 & simvastatin\\\\\n",
-       "\t 6025198 & 2015-02-27 & Fenbid 5\\% gel (AMCo)     & NA    & NA         \\\\\n",
-       "\t 6025198 & 2015-05-05 & Ramipril 2.5mg capsules  & 35296 & ramipril   \\\\\n",
-       "\t 6025198 & 2015-05-05 & Simvastatin 40mg tablets & 36567 & simvastatin\\\\\n",
-       "\t 6025198 & 2015-05-05 & Fenbid 5\\% gel (AMCo)     & NA    & NA         \\\\\n",
-       "\t 6025198 & 2015-06-24 & Ramipril 2.5mg capsules  & 35296 & ramipril   \\\\\n",
-       "\t 6025198 & 2015-06-24 & Simvastatin 40mg tablets & 36567 & simvastatin\\\\\n",
-       "\t 6025198 & 2015-06-24 & Fenbid 5\\% gel (AMCo)     & NA    & NA         \\\\\n",
-       "\t 6025198 & 2015-08-19 & Ramipril 2.5mg capsules  & 35296 & ramipril   \\\\\n",
-       "\t 6025198 & 2015-08-19 & Simvastatin 40mg tablets & 36567 & simvastatin\\\\\n",
-       "\t 6025198 & 2015-08-19 & Fenbid 5\\% gel (AMCo)     & NA    & NA         \\\\\n",
-       "\t 6025198 & 2015-10-14 & Ramipril 2.5mg capsules  & 35296 & ramipril   \\\\\n",
-       "\t 6025198 & 2015-10-14 & Simvastatin 40mg tablets & 36567 & simvastatin\\\\\n",
-       "\t 6025198 & 2015-10-14 & Fenbid 5\\% gel (AMCo)     & NA    & NA         \\\\\n",
-       "\t 6025198 & 2015-12-10 & Ramipril 2.5mg capsules  & 35296 & ramipril   \\\\\n",
-       "\t 6025198 & 2015-12-10 & Simvastatin 40mg tablets & 36567 & simvastatin\\\\\n",
-       "\t 6025198 & 2015-12-10 & Fenbid 5\\% gel (AMCo)     & NA    & NA         \\\\\n",
-       "\t 6025198 & 2016-02-03 & Ramipril 2.5mg capsules  & 35296 & ramipril   \\\\\n",
-       "\t 6025198 & 2016-02-03 & Simvastatin 40mg tablets & 36567 & simvastatin\\\\\n",
-       "\t 6025198 & 2016-02-03 & Fenbid 5\\% gel (AMCo)     & NA    & NA         \\\\\n",
-       "\t 6025198 & 2016-04-06 & Ramipril 2.5mg capsules  & 35296 & ramipril   \\\\\n",
-       "\t 6025198 & 2016-04-06 & Simvastatin 40mg tablets & 36567 & simvastatin\\\\\n",
-       "\t 6025198 & 2016-04-06 & Fenbid 5\\% gel (AMCo)     & NA    & NA         \\\\\n",
-       "\t 6025198 & 2016-05-25 & Ramipril 2.5mg capsules  & 35296 & ramipril   \\\\\n",
-       "\t 6025198 & 2016-05-25 & Simvastatin 40mg tablets & 36567 & simvastatin\\\\\n",
-       "\t 6025198 & 2016-05-25 & Fenbid 5\\% gel (AMCo)     & NA    & NA         \\\\\n",
-       "\\end{tabular}\n"
-      ],
-      "text/markdown": [
-       "\n",
-       "A data.table: 56629816 × 5\n",
-       "\n",
-       "| eid &lt;int&gt; | date &lt;date&gt; | drug_name &lt;chr&gt; | rx_code &lt;chr&gt; | name &lt;chr&gt; |\n",
-       "|---|---|---|---|---|\n",
-       "| 1000018 | 1996-11-30 | Doxycycline 100mg capsules                  | 3640  | doxycycline          |\n",
-       "| 1000018 | 1997-09-12 | Propranolol 40mg tablets                    | 8787  | propranolol          |\n",
-       "| 1000018 | 1997-10-10 | Propranolol 40mg tablets                    | 8787  | propranolol          |\n",
-       "| 1000018 | 1997-10-10 | Noriday 350microgram tablets (Pfizer Ltd)   | NA    | NA                   |\n",
-       "| 1000018 | 1998-01-19 | Propranolol 40mg tablets                    | 8787  | propranolol          |\n",
-       "| 1000018 | 1998-01-19 | Propranolol 80mg tablets                    | 8787  | propranolol          |\n",
-       "| 1000018 | 1998-01-19 | Gamolenic acid 40mg capsules                | 25605 | gamma-linolenic acid |\n",
-       "| 1000018 | 1998-03-13 | Propranolol 80mg tablets                    | 8787  | propranolol          |\n",
-       "| 1000018 | 1998-03-25 | Amoxicillin 250mg capsules                  | 723   | amoxicillin          |\n",
-       "| 1000018 | 1998-04-16 | Amoxicillin 250mg capsules                  | 723   | amoxicillin          |\n",
-       "| 1000018 | 1998-04-24 | Metronidazole 200mg tablets                 | 6922  | metronidazole        |\n",
-       "| 1000018 | 1998-06-19 | Mebeverine 135mg tablets                    | 29410 | mebeverine           |\n",
-       "| 1000018 | 1998-06-19 | Propranolol 80mg tablets                    | 8787  | propranolol          |\n",
-       "| 1000018 | 1998-08-14 | Gentisone HC ear drops (AMCo)               | 5492  | hydrocortisone       |\n",
-       "| 1000018 | 1999-01-20 | Gamolenic acid 40mg capsules                | 25605 | gamma-linolenic acid |\n",
-       "| 1000018 | 1999-01-20 | Gentisone HC ear drops (AMCo)               | 5492  | hydrocortisone       |\n",
-       "| 1000018 | 2001-01-12 | Aciclovir 200mg dispersible tablets         | 281   | acyclovir            |\n",
-       "| 1000018 | 2001-01-12 | Doxycycline 100mg capsules                  | 3640  | doxycycline          |\n",
-       "| 1000018 | 2003-11-17 | Aciclovir 200mg dispersible tablets         | 281   | acyclovir            |\n",
-       "| 1000018 | 2003-11-17 | Doxycycline 100mg capsules                  | 3640  | doxycycline          |\n",
-       "| 1000018 | 2003-11-18 | Doxycycline 100mg capsules                  | 3640  | doxycycline          |\n",
-       "| 1000018 | 2003-11-24 | Beclometasone 50micrograms/dose nasal spray | 1347  | beclomethasone       |\n",
-       "| 1000018 | 2005-06-08 | Atenolol 50mg tablets                       | 1202  | atenolol             |\n",
-       "| 1000018 | 2005-06-09 | Atenolol 50mg tablets                       | 1202  | atenolol             |\n",
-       "| 1000018 | 2005-06-23 | Atenolol 50mg tablets                       | 1202  | atenolol             |\n",
-       "| 1000018 | 2005-06-24 | Atenolol 50mg tablets                       | 1202  | atenolol             |\n",
-       "| 1000018 | 2005-09-01 | Atenolol 50mg tablets                       | 1202  | atenolol             |\n",
-       "| 1000018 | 2005-10-07 | Atenolol 50mg tablets                       | 1202  | atenolol             |\n",
-       "| 1000018 | 2005-11-08 | Atenolol 50mg tablets                       | 1202  | atenolol             |\n",
-       "| 1000018 | 2005-12-05 | Atenolol 50mg tablets                       | 1202  | atenolol             |\n",
-       "| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |\n",
-       "| 6025198 | 2015-01-23 | Ramipril 2.5mg capsules  | 35296 | ramipril    |\n",
-       "| 6025198 | 2015-01-23 | Simvastatin 40mg tablets | 36567 | simvastatin |\n",
-       "| 6025198 | 2015-01-23 | Fenbid 5% gel (AMCo)     | NA    | NA          |\n",
-       "| 6025198 | 2015-02-27 | Ramipril 2.5mg capsules  | 35296 | ramipril    |\n",
-       "| 6025198 | 2015-02-27 | Simvastatin 40mg tablets | 36567 | simvastatin |\n",
-       "| 6025198 | 2015-02-27 | Fenbid 5% gel (AMCo)     | NA    | NA          |\n",
-       "| 6025198 | 2015-05-05 | Ramipril 2.5mg capsules  | 35296 | ramipril    |\n",
-       "| 6025198 | 2015-05-05 | Simvastatin 40mg tablets | 36567 | simvastatin |\n",
-       "| 6025198 | 2015-05-05 | Fenbid 5% gel (AMCo)     | NA    | NA          |\n",
-       "| 6025198 | 2015-06-24 | Ramipril 2.5mg capsules  | 35296 | ramipril    |\n",
-       "| 6025198 | 2015-06-24 | Simvastatin 40mg tablets | 36567 | simvastatin |\n",
-       "| 6025198 | 2015-06-24 | Fenbid 5% gel (AMCo)     | NA    | NA          |\n",
-       "| 6025198 | 2015-08-19 | Ramipril 2.5mg capsules  | 35296 | ramipril    |\n",
-       "| 6025198 | 2015-08-19 | Simvastatin 40mg tablets | 36567 | simvastatin |\n",
-       "| 6025198 | 2015-08-19 | Fenbid 5% gel (AMCo)     | NA    | NA          |\n",
-       "| 6025198 | 2015-10-14 | Ramipril 2.5mg capsules  | 35296 | ramipril    |\n",
-       "| 6025198 | 2015-10-14 | Simvastatin 40mg tablets | 36567 | simvastatin |\n",
-       "| 6025198 | 2015-10-14 | Fenbid 5% gel (AMCo)     | NA    | NA          |\n",
-       "| 6025198 | 2015-12-10 | Ramipril 2.5mg capsules  | 35296 | ramipril    |\n",
-       "| 6025198 | 2015-12-10 | Simvastatin 40mg tablets | 36567 | simvastatin |\n",
-       "| 6025198 | 2015-12-10 | Fenbid 5% gel (AMCo)     | NA    | NA          |\n",
-       "| 6025198 | 2016-02-03 | Ramipril 2.5mg capsules  | 35296 | ramipril    |\n",
-       "| 6025198 | 2016-02-03 | Simvastatin 40mg tablets | 36567 | simvastatin |\n",
-       "| 6025198 | 2016-02-03 | Fenbid 5% gel (AMCo)     | NA    | NA          |\n",
-       "| 6025198 | 2016-04-06 | Ramipril 2.5mg capsules  | 35296 | ramipril    |\n",
-       "| 6025198 | 2016-04-06 | Simvastatin 40mg tablets | 36567 | simvastatin |\n",
-       "| 6025198 | 2016-04-06 | Fenbid 5% gel (AMCo)     | NA    | NA          |\n",
-       "| 6025198 | 2016-05-25 | Ramipril 2.5mg capsules  | 35296 | ramipril    |\n",
-       "| 6025198 | 2016-05-25 | Simvastatin 40mg tablets | 36567 | simvastatin |\n",
-       "| 6025198 | 2016-05-25 | Fenbid 5% gel (AMCo)     | NA    | NA          |\n",
-       "\n"
-      ],
-      "text/plain": [
-       "         eid     date       drug_name                                   rx_code\n",
-       "1        1000018 1996-11-30 Doxycycline 100mg capsules                  3640   \n",
-       "2        1000018 1997-09-12 Propranolol 40mg tablets                    8787   \n",
-       "3        1000018 1997-10-10 Propranolol 40mg tablets                    8787   \n",
-       "4        1000018 1997-10-10 Noriday 350microgram tablets (Pfizer Ltd)   NA     \n",
-       "5        1000018 1998-01-19 Propranolol 40mg tablets                    8787   \n",
-       "6        1000018 1998-01-19 Propranolol 80mg tablets                    8787   \n",
-       "7        1000018 1998-01-19 Gamolenic acid 40mg capsules                25605  \n",
-       "8        1000018 1998-03-13 Propranolol 80mg tablets                    8787   \n",
-       "9        1000018 1998-03-25 Amoxicillin 250mg capsules                  723    \n",
-       "10       1000018 1998-04-16 Amoxicillin 250mg capsules                  723    \n",
-       "11       1000018 1998-04-24 Metronidazole 200mg tablets                 6922   \n",
-       "12       1000018 1998-06-19 Mebeverine 135mg tablets                    29410  \n",
-       "13       1000018 1998-06-19 Propranolol 80mg tablets                    8787   \n",
-       "14       1000018 1998-08-14 Gentisone HC ear drops (AMCo)               5492   \n",
-       "15       1000018 1999-01-20 Gamolenic acid 40mg capsules                25605  \n",
-       "16       1000018 1999-01-20 Gentisone HC ear drops (AMCo)               5492   \n",
-       "17       1000018 2001-01-12 Aciclovir 200mg dispersible tablets         281    \n",
-       "18       1000018 2001-01-12 Doxycycline 100mg capsules                  3640   \n",
-       "19       1000018 2003-11-17 Aciclovir 200mg dispersible tablets         281    \n",
-       "20       1000018 2003-11-17 Doxycycline 100mg capsules                  3640   \n",
-       "21       1000018 2003-11-18 Doxycycline 100mg capsules                  3640   \n",
-       "22       1000018 2003-11-24 Beclometasone 50micrograms/dose nasal spray 1347   \n",
-       "23       1000018 2005-06-08 Atenolol 50mg tablets                       1202   \n",
-       "24       1000018 2005-06-09 Atenolol 50mg tablets                       1202   \n",
-       "25       1000018 2005-06-23 Atenolol 50mg tablets                       1202   \n",
-       "26       1000018 2005-06-24 Atenolol 50mg tablets                       1202   \n",
-       "27       1000018 2005-09-01 Atenolol 50mg tablets                       1202   \n",
-       "28       1000018 2005-10-07 Atenolol 50mg tablets                       1202   \n",
-       "29       1000018 2005-11-08 Atenolol 50mg tablets                       1202   \n",
-       "30       1000018 2005-12-05 Atenolol 50mg tablets                       1202   \n",
-       "<U+22EE>   <U+22EE> <U+22EE>   <U+22EE>                                    <U+22EE>\n",
-       "56629787 6025198 2015-01-23 Ramipril 2.5mg capsules                     35296  \n",
-       "56629788 6025198 2015-01-23 Simvastatin 40mg tablets                    36567  \n",
-       "56629789 6025198 2015-01-23 Fenbid 5% gel (AMCo)                        NA     \n",
-       "56629790 6025198 2015-02-27 Ramipril 2.5mg capsules                     35296  \n",
-       "56629791 6025198 2015-02-27 Simvastatin 40mg tablets                    36567  \n",
-       "56629792 6025198 2015-02-27 Fenbid 5% gel (AMCo)                        NA     \n",
-       "56629793 6025198 2015-05-05 Ramipril 2.5mg capsules                     35296  \n",
-       "56629794 6025198 2015-05-05 Simvastatin 40mg tablets                    36567  \n",
-       "56629795 6025198 2015-05-05 Fenbid 5% gel (AMCo)                        NA     \n",
-       "56629796 6025198 2015-06-24 Ramipril 2.5mg capsules                     35296  \n",
-       "56629797 6025198 2015-06-24 Simvastatin 40mg tablets                    36567  \n",
-       "56629798 6025198 2015-06-24 Fenbid 5% gel (AMCo)                        NA     \n",
-       "56629799 6025198 2015-08-19 Ramipril 2.5mg capsules                     35296  \n",
-       "56629800 6025198 2015-08-19 Simvastatin 40mg tablets                    36567  \n",
-       "56629801 6025198 2015-08-19 Fenbid 5% gel (AMCo)                        NA     \n",
-       "56629802 6025198 2015-10-14 Ramipril 2.5mg capsules                     35296  \n",
-       "56629803 6025198 2015-10-14 Simvastatin 40mg tablets                    36567  \n",
-       "56629804 6025198 2015-10-14 Fenbid 5% gel (AMCo)                        NA     \n",
-       "56629805 6025198 2015-12-10 Ramipril 2.5mg capsules                     35296  \n",
-       "56629806 6025198 2015-12-10 Simvastatin 40mg tablets                    36567  \n",
-       "56629807 6025198 2015-12-10 Fenbid 5% gel (AMCo)                        NA     \n",
-       "56629808 6025198 2016-02-03 Ramipril 2.5mg capsules                     35296  \n",
-       "56629809 6025198 2016-02-03 Simvastatin 40mg tablets                    36567  \n",
-       "56629810 6025198 2016-02-03 Fenbid 5% gel (AMCo)                        NA     \n",
-       "56629811 6025198 2016-04-06 Ramipril 2.5mg capsules                     35296  \n",
-       "56629812 6025198 2016-04-06 Simvastatin 40mg tablets                    36567  \n",
-       "56629813 6025198 2016-04-06 Fenbid 5% gel (AMCo)                        NA     \n",
-       "56629814 6025198 2016-05-25 Ramipril 2.5mg capsules                     35296  \n",
-       "56629815 6025198 2016-05-25 Simvastatin 40mg tablets                    36567  \n",
-       "56629816 6025198 2016-05-25 Fenbid 5% gel (AMCo)                        NA     \n",
-       "         name                \n",
-       "1        doxycycline         \n",
-       "2        propranolol         \n",
-       "3        propranolol         \n",
-       "4        NA                  \n",
-       "5        propranolol         \n",
-       "6        propranolol         \n",
-       "7        gamma-linolenic acid\n",
-       "8        propranolol         \n",
-       "9        amoxicillin         \n",
-       "10       amoxicillin         \n",
-       "11       metronidazole       \n",
-       "12       mebeverine          \n",
-       "13       propranolol         \n",
-       "14       hydrocortisone      \n",
-       "15       gamma-linolenic acid\n",
-       "16       hydrocortisone      \n",
-       "17       acyclovir           \n",
-       "18       doxycycline         \n",
-       "19       acyclovir           \n",
-       "20       doxycycline         \n",
-       "21       doxycycline         \n",
-       "22       beclomethasone      \n",
-       "23       atenolol            \n",
-       "24       atenolol            \n",
-       "25       atenolol            \n",
-       "26       atenolol            \n",
-       "27       atenolol            \n",
-       "28       atenolol            \n",
-       "29       atenolol            \n",
-       "30       atenolol            \n",
-       "<U+22EE>   <U+22EE>            \n",
-       "56629787 ramipril            \n",
-       "56629788 simvastatin         \n",
-       "56629789 NA                  \n",
-       "56629790 ramipril            \n",
-       "56629791 simvastatin         \n",
-       "56629792 NA                  \n",
-       "56629793 ramipril            \n",
-       "56629794 simvastatin         \n",
-       "56629795 NA                  \n",
-       "56629796 ramipril            \n",
-       "56629797 simvastatin         \n",
-       "56629798 NA                  \n",
-       "56629799 ramipril            \n",
-       "56629800 simvastatin         \n",
-       "56629801 NA                  \n",
-       "56629802 ramipril            \n",
-       "56629803 simvastatin         \n",
-       "56629804 NA                  \n",
-       "56629805 ramipril            \n",
-       "56629806 simvastatin         \n",
-       "56629807 NA                  \n",
-       "56629808 ramipril            \n",
-       "56629809 simvastatin         \n",
-       "56629810 NA                  \n",
-       "56629811 ramipril            \n",
-       "56629812 simvastatin         \n",
-       "56629813 NA                  \n",
-       "56629814 ramipril            \n",
-       "56629815 simvastatin         \n",
-       "56629816 NA                  "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "gp_scripts_rxnorm = gp_scripts_names_available %>% left_join(rx_norm_mapping_table, on=\"drug_name\") %>% select(eid, date, drug_name, rx_code, name) %>% distinct()\n",
-    "gp_scripts_rxnorm "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<dl>\n",
-       "\t<dt>$platform</dt>\n",
-       "\t\t<dd>'x86_64-conda-linux-gnu'</dd>\n",
-       "\t<dt>$arch</dt>\n",
-       "\t\t<dd>'x86_64'</dd>\n",
-       "\t<dt>$os</dt>\n",
-       "\t\t<dd>'linux-gnu'</dd>\n",
-       "\t<dt>$system</dt>\n",
-       "\t\t<dd>'x86_64, linux-gnu'</dd>\n",
-       "\t<dt>$status</dt>\n",
-       "\t\t<dd>''</dd>\n",
-       "\t<dt>$major</dt>\n",
-       "\t\t<dd>'4'</dd>\n",
-       "\t<dt>$minor</dt>\n",
-       "\t\t<dd>'0.3'</dd>\n",
-       "\t<dt>$year</dt>\n",
-       "\t\t<dd>'2020'</dd>\n",
-       "\t<dt>$month</dt>\n",
-       "\t\t<dd>'10'</dd>\n",
-       "\t<dt>$day</dt>\n",
-       "\t\t<dd>'10'</dd>\n",
-       "\t<dt>$`svn rev`</dt>\n",
-       "\t\t<dd>'79318'</dd>\n",
-       "\t<dt>$language</dt>\n",
-       "\t\t<dd>'R'</dd>\n",
-       "\t<dt>$version.string</dt>\n",
-       "\t\t<dd>'R version 4.0.3 (2020-10-10)'</dd>\n",
-       "\t<dt>$nickname</dt>\n",
-       "\t\t<dd>'Bunny-Wunnies Freak Out'</dd>\n",
-       "</dl>\n"
-      ],
-      "text/latex": [
-       "\\begin{description}\n",
-       "\\item[\\$platform] 'x86\\_64-conda-linux-gnu'\n",
-       "\\item[\\$arch] 'x86\\_64'\n",
-       "\\item[\\$os] 'linux-gnu'\n",
-       "\\item[\\$system] 'x86\\_64, linux-gnu'\n",
-       "\\item[\\$status] ''\n",
-       "\\item[\\$major] '4'\n",
-       "\\item[\\$minor] '0.3'\n",
-       "\\item[\\$year] '2020'\n",
-       "\\item[\\$month] '10'\n",
-       "\\item[\\$day] '10'\n",
-       "\\item[\\$`svn rev`] '79318'\n",
-       "\\item[\\$language] 'R'\n",
-       "\\item[\\$version.string] 'R version 4.0.3 (2020-10-10)'\n",
-       "\\item[\\$nickname] 'Bunny-Wunnies Freak Out'\n",
-       "\\end{description}\n"
-      ],
-      "text/markdown": [
-       "$platform\n",
-       ":   'x86_64-conda-linux-gnu'\n",
-       "$arch\n",
-       ":   'x86_64'\n",
-       "$os\n",
-       ":   'linux-gnu'\n",
-       "$system\n",
-       ":   'x86_64, linux-gnu'\n",
-       "$status\n",
-       ":   ''\n",
-       "$major\n",
-       ":   '4'\n",
-       "$minor\n",
-       ":   '0.3'\n",
-       "$year\n",
-       ":   '2020'\n",
-       "$month\n",
-       ":   '10'\n",
-       "$day\n",
-       ":   '10'\n",
-       "$`svn rev`\n",
-       ":   '79318'\n",
-       "$language\n",
-       ":   'R'\n",
-       "$version.string\n",
-       ":   'R version 4.0.3 (2020-10-10)'\n",
-       "$nickname\n",
-       ":   'Bunny-Wunnies Freak Out'\n",
-       "\n",
-       "\n"
-      ],
-      "text/plain": [
-       "$platform\n",
-       "[1] \"x86_64-conda-linux-gnu\"\n",
-       "\n",
-       "$arch\n",
-       "[1] \"x86_64\"\n",
-       "\n",
-       "$os\n",
-       "[1] \"linux-gnu\"\n",
-       "\n",
-       "$system\n",
-       "[1] \"x86_64, linux-gnu\"\n",
-       "\n",
-       "$status\n",
-       "[1] \"\"\n",
-       "\n",
-       "$major\n",
-       "[1] \"4\"\n",
-       "\n",
-       "$minor\n",
-       "[1] \"0.3\"\n",
-       "\n",
-       "$year\n",
-       "[1] \"2020\"\n",
-       "\n",
-       "$month\n",
-       "[1] \"10\"\n",
-       "\n",
-       "$day\n",
-       "[1] \"10\"\n",
-       "\n",
-       "$`svn rev`\n",
-       "[1] \"79318\"\n",
-       "\n",
-       "$language\n",
-       "[1] \"R\"\n",
-       "\n",
-       "$version.string\n",
-       "[1] \"R version 4.0.3 (2020-10-10)\"\n",
-       "\n",
-       "$nickname\n",
-       "[1] \"Bunny-Wunnies Freak Out\"\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "R.Version()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 87,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "arrow::write_feather(gp_scripts_rxnorm, glue(\"{path}/codes_gp_prescription_scispacy_210121.feather\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "### OLD NOT WORKING TRASH"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 83,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "readv2_readv3 = head(fread(\"/data/analysis/ag-reils/steinfej/code/umbrella/pre/ukbb/mapping/gp_codings/read_v2_read_ctv3.csv\"), -3) %>% rename(read_2=\"READV2_CODE\", code =\"READV3_CODE\", name =\"TERMV3_DESC\") %>% select(read_2, code)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 84,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "gp_scripts_read3 = gp_scripts_read_available %>% left_join(readv2_readv3, by=\"read_2\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 86,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<table>\n",
-       "<caption>A data.table: 0 × 10</caption>\n",
-       "<thead>\n",
-       "\t<tr><th scope=col>eid</th><th scope=col>data_provider</th><th scope=col>issue_date</th><th scope=col>read_2</th><th scope=col>bnf_code</th><th scope=col>dmd_code</th><th scope=col>drug_name</th><th scope=col>quantity</th><th scope=col>date</th><th scope=col>code</th></tr>\n",
-       "\t<tr><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int64&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;date&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
-       "</thead>\n",
-       "<tbody>\n",
-       "</tbody>\n",
-       "</table>\n"
-      ],
-      "text/latex": [
-       "A data.table: 0 × 10\n",
-       "\\begin{tabular}{llllllllll}\n",
-       " eid & data\\_provider & issue\\_date & read\\_2 & bnf\\_code & dmd\\_code & drug\\_name & quantity & date & code\\\\\n",
-       " <int> & <int> & <chr> & <chr> & <chr> & <int64> & <chr> & <chr> & <date> & <chr>\\\\\n",
-       "\\hline\n",
-       "\\end{tabular}\n"
-      ],
-      "text/markdown": [
-       "\n",
-       "A data.table: 0 × 10\n",
-       "\n",
-       "| eid &lt;int&gt; | data_provider &lt;int&gt; | issue_date &lt;chr&gt; | read_2 &lt;chr&gt; | bnf_code &lt;chr&gt; | dmd_code &lt;int64&gt; | drug_name &lt;chr&gt; | quantity &lt;chr&gt; | date &lt;date&gt; | code &lt;chr&gt; |\n",
-       "|---|---|---|---|---|---|---|---|---|---|\n",
-       "\n"
-      ],
-      "text/plain": [
-       "     eid data_provider issue_date read_2 bnf_code dmd_code drug_name quantity\n",
-       "     date code"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "gp_scripts_read3 %>% drop_na(code)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bnf_snomed_mapping = fread(\"/data/analysis/ag-reils/steinfej/code/umbrella/pre/ukbb/mapping/gp_codings/bnf_snomed_mapping.csv\") %>%\n",
-    "    rename(bnf_code=\"BNF Code\", sct_code =\"SNOMED Code\") %>% \n",
-    "    filter(`VMP / VMPP/ AMP / AMPP`==\"VMP\") %>% \n",
-    "    filter(bnf_code != \"\") %>% mutate(bnf_map = str_sub(bnf_code, 1, 7))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "gp_test = gp_scripts %>% sample_n(1000) %>% filter(bnf_code != \"\") %>% mutate(bnf_code =str_replace_all(bnf_code, \"\\\\.\", \"\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 100,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<table>\n",
-       "<caption>A data.table: 58742 × 11</caption>\n",
-       "<thead>\n",
-       "\t<tr><th scope=col>eid</th><th scope=col>data_provider</th><th scope=col>issue_date</th><th scope=col>read_2</th><th scope=col>bnf_code</th><th scope=col>dmd_code</th><th scope=col>drug_name</th><th scope=col>quantity</th><th scope=col>date</th><th scope=col>bnf_map</th><th scope=col>sct_code</th></tr>\n",
-       "\t<tr><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int64&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;date&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int64&gt;</th></tr>\n",
-       "</thead>\n",
-       "<tbody>\n",
-       "\t<tr><td>5157903</td><td>3</td><td>25/05/2010</td><td>NA</td><td>0602010000</td><td>NA</td><td>Levothyroxine sodium 100microgram tablets</td><td>56 tablet(s) - 100 micrograms</td><td>2010-05-25</td><td>0602010</td><td>       374294006</td></tr>\n",
-       "\t<tr><td>5157903</td><td>3</td><td>25/05/2010</td><td>NA</td><td>0602010000</td><td>NA</td><td>Levothyroxine sodium 100microgram tablets</td><td>56 tablet(s) - 100 micrograms</td><td>2010-05-25</td><td>0602010</td><td>       374295007</td></tr>\n",
-       "\t<tr><td>5157903</td><td>3</td><td>25/05/2010</td><td>NA</td><td>0602010000</td><td>NA</td><td>Levothyroxine sodium 100microgram tablets</td><td>56 tablet(s) - 100 micrograms</td><td>2010-05-25</td><td>0602010</td><td>       374296008</td></tr>\n",
-       "\t<tr><td>5157903</td><td>3</td><td>25/05/2010</td><td>NA</td><td>0602010000</td><td>NA</td><td>Levothyroxine sodium 100microgram tablets</td><td>56 tablet(s) - 100 micrograms</td><td>2010-05-25</td><td>0602010</td><td>       325315001</td></tr>\n",
-       "\t<tr><td>5157903</td><td>3</td><td>25/05/2010</td><td>NA</td><td>0602010000</td><td>NA</td><td>Levothyroxine sodium 100microgram tablets</td><td>56 tablet(s) - 100 micrograms</td><td>2010-05-25</td><td>0602010</td><td>       325316000</td></tr>\n",
-       "\t<tr><td>5157903</td><td>3</td><td>25/05/2010</td><td>NA</td><td>0602010000</td><td>NA</td><td>Levothyroxine sodium 100microgram tablets</td><td>56 tablet(s) - 100 micrograms</td><td>2010-05-25</td><td>0602010</td><td>8584011000001100</td></tr>\n",
-       "\t<tr><td>5157903</td><td>3</td><td>25/05/2010</td><td>NA</td><td>0602010000</td><td>NA</td><td>Levothyroxine sodium 100microgram tablets</td><td>56 tablet(s) - 100 micrograms</td><td>2010-05-25</td><td>0602010</td><td>8584111000001104</td></tr>\n",
-       "\t<tr><td>5157903</td><td>3</td><td>25/05/2010</td><td>NA</td><td>0602010000</td><td>NA</td><td>Levothyroxine sodium 100microgram tablets</td><td>56 tablet(s) - 100 micrograms</td><td>2010-05-25</td><td>0602010</td><td>8584211000001105</td></tr>\n",
-       "\t<tr><td>5157903</td><td>3</td><td>25/05/2010</td><td>NA</td><td>0602010000</td><td>NA</td><td>Levothyroxine sodium 100microgram tablets</td><td>56 tablet(s) - 100 micrograms</td><td>2010-05-25</td><td>0602010</td><td>8584411000001109</td></tr>\n",
-       "\t<tr><td>5157903</td><td>3</td><td>25/05/2010</td><td>NA</td><td>0602010000</td><td>NA</td><td>Levothyroxine sodium 100microgram tablets</td><td>56 tablet(s) - 100 micrograms</td><td>2010-05-25</td><td>0602010</td><td>8584511000001108</td></tr>\n",
-       "\t<tr><td>5157903</td><td>3</td><td>25/05/2010</td><td>NA</td><td>0602010000</td><td>NA</td><td>Levothyroxine sodium 100microgram tablets</td><td>56 tablet(s) - 100 micrograms</td><td>2010-05-25</td><td>0602010</td><td>8584611000001107</td></tr>\n",
-       "\t<tr><td>5157903</td><td>3</td><td>25/05/2010</td><td>NA</td><td>0602010000</td><td>NA</td><td>Levothyroxine sodium 100microgram tablets</td><td>56 tablet(s) - 100 micrograms</td><td>2010-05-25</td><td>0602010</td><td>8584711000001103</td></tr>\n",
-       "\t<tr><td>5157903</td><td>3</td><td>25/05/2010</td><td>NA</td><td>0602010000</td><td>NA</td><td>Levothyroxine sodium 100microgram tablets</td><td>56 tablet(s) - 100 micrograms</td><td>2010-05-25</td><td>0602010</td><td>8584311000001102</td></tr>\n",
-       "\t<tr><td>5157903</td><td>3</td><td>25/05/2010</td><td>NA</td><td>0602010000</td><td>NA</td><td>Levothyroxine sodium 100microgram tablets</td><td>56 tablet(s) - 100 micrograms</td><td>2010-05-25</td><td>0602010</td><td>8584811000001106</td></tr>\n",
-       "\t<tr><td>5157903</td><td>3</td><td>25/05/2010</td><td>NA</td><td>0602010000</td><td>NA</td><td>Levothyroxine sodium 100microgram tablets</td><td>56 tablet(s) - 100 micrograms</td><td>2010-05-25</td><td>0602010</td><td>8584911000001101</td></tr>\n",
-       "\t<tr><td>5157903</td><td>3</td><td>25/05/2010</td><td>NA</td><td>0602010000</td><td>NA</td><td>Levothyroxine sodium 100microgram tablets</td><td>56 tablet(s) - 100 micrograms</td><td>2010-05-25</td><td>0602010</td><td>8585011000001101</td></tr>\n",
-       "\t<tr><td>5157903</td><td>3</td><td>25/05/2010</td><td>NA</td><td>0602010000</td><td>NA</td><td>Levothyroxine sodium 100microgram tablets</td><td>56 tablet(s) - 100 micrograms</td><td>2010-05-25</td><td>0602010</td><td>8585111000001100</td></tr>\n",
-       "\t<tr><td>5157903</td><td>3</td><td>25/05/2010</td><td>NA</td><td>0602010000</td><td>NA</td><td>Levothyroxine sodium 100microgram tablets</td><td>56 tablet(s) - 100 micrograms</td><td>2010-05-25</td><td>0602010</td><td>8586611000001103</td></tr>\n",
-       "\t<tr><td>5157903</td><td>3</td><td>25/05/2010</td><td>NA</td><td>0602010000</td><td>NA</td><td>Levothyroxine sodium 100microgram tablets</td><td>56 tablet(s) - 100 micrograms</td><td>2010-05-25</td><td>0602010</td><td>8586711000001107</td></tr>\n",
-       "\t<tr><td>5157903</td><td>3</td><td>25/05/2010</td><td>NA</td><td>0602010000</td><td>NA</td><td>Levothyroxine sodium 100microgram tablets</td><td>56 tablet(s) - 100 micrograms</td><td>2010-05-25</td><td>0602010</td><td>8585211000001106</td></tr>\n",
-       "\t<tr><td>5157903</td><td>3</td><td>25/05/2010</td><td>NA</td><td>0602010000</td><td>NA</td><td>Levothyroxine sodium 100microgram tablets</td><td>56 tablet(s) - 100 micrograms</td><td>2010-05-25</td><td>0602010</td><td>8585411000001105</td></tr>\n",
-       "\t<tr><td>5157903</td><td>3</td><td>25/05/2010</td><td>NA</td><td>0602010000</td><td>NA</td><td>Levothyroxine sodium 100microgram tablets</td><td>56 tablet(s) - 100 micrograms</td><td>2010-05-25</td><td>0602010</td><td>8585311000001103</td></tr>\n",
-       "\t<tr><td>5157903</td><td>3</td><td>25/05/2010</td><td>NA</td><td>0602010000</td><td>NA</td><td>Levothyroxine sodium 100microgram tablets</td><td>56 tablet(s) - 100 micrograms</td><td>2010-05-25</td><td>0602010</td><td>8585511000001109</td></tr>\n",
-       "\t<tr><td>5157903</td><td>3</td><td>25/05/2010</td><td>NA</td><td>0602010000</td><td>NA</td><td>Levothyroxine sodium 100microgram tablets</td><td>56 tablet(s) - 100 micrograms</td><td>2010-05-25</td><td>0602010</td><td>8585811000001107</td></tr>\n",
-       "\t<tr><td>5157903</td><td>3</td><td>25/05/2010</td><td>NA</td><td>0602010000</td><td>NA</td><td>Levothyroxine sodium 100microgram tablets</td><td>56 tablet(s) - 100 micrograms</td><td>2010-05-25</td><td>0602010</td><td>8585611000001108</td></tr>\n",
-       "\t<tr><td>5157903</td><td>3</td><td>25/05/2010</td><td>NA</td><td>0602010000</td><td>NA</td><td>Levothyroxine sodium 100microgram tablets</td><td>56 tablet(s) - 100 micrograms</td><td>2010-05-25</td><td>0602010</td><td>8585911000001102</td></tr>\n",
-       "\t<tr><td>5157903</td><td>3</td><td>25/05/2010</td><td>NA</td><td>0602010000</td><td>NA</td><td>Levothyroxine sodium 100microgram tablets</td><td>56 tablet(s) - 100 micrograms</td><td>2010-05-25</td><td>0602010</td><td>8585711000001104</td></tr>\n",
-       "\t<tr><td>5157903</td><td>3</td><td>25/05/2010</td><td>NA</td><td>0602010000</td><td>NA</td><td>Levothyroxine sodium 100microgram tablets</td><td>56 tablet(s) - 100 micrograms</td><td>2010-05-25</td><td>0602010</td><td>8586811000001104</td></tr>\n",
-       "\t<tr><td>5157903</td><td>3</td><td>25/05/2010</td><td>NA</td><td>0602010000</td><td>NA</td><td>Levothyroxine sodium 100microgram tablets</td><td>56 tablet(s) - 100 micrograms</td><td>2010-05-25</td><td>0602010</td><td>8586011000001105</td></tr>\n",
-       "\t<tr><td>5157903</td><td>3</td><td>25/05/2010</td><td>NA</td><td>0602010000</td><td>NA</td><td>Levothyroxine sodium 100microgram tablets</td><td>56 tablet(s) - 100 micrograms</td><td>2010-05-25</td><td>0602010</td><td>8586111000001106</td></tr>\n",
-       "\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
-       "\t<tr><td>1918594</td><td>3</td><td>27/10/2004</td><td>NA</td><td>0407010200</td><td>NA</td><td>Co-codamol 8mg/500mg tablets</td><td>200 tablet(s)</td><td>2004-10-27</td><td>0407010</td><td>12301811000001104</td></tr>\n",
-       "\t<tr><td>1918594</td><td>3</td><td>27/10/2004</td><td>NA</td><td>0407010200</td><td>NA</td><td>Co-codamol 8mg/500mg tablets</td><td>200 tablet(s)</td><td>2004-10-27</td><td>0407010</td><td>13004211000001106</td></tr>\n",
-       "\t<tr><td>1918594</td><td>3</td><td>27/10/2004</td><td>NA</td><td>0407010200</td><td>NA</td><td>Co-codamol 8mg/500mg tablets</td><td>200 tablet(s)</td><td>2004-10-27</td><td>0407010</td><td>13004111000001100</td></tr>\n",
-       "\t<tr><td>1918594</td><td>3</td><td>27/10/2004</td><td>NA</td><td>0407010200</td><td>NA</td><td>Co-codamol 8mg/500mg tablets</td><td>200 tablet(s)</td><td>2004-10-27</td><td>0407010</td><td>13004311000001103</td></tr>\n",
-       "\t<tr><td>1918594</td><td>3</td><td>27/10/2004</td><td>NA</td><td>0407010200</td><td>NA</td><td>Co-codamol 8mg/500mg tablets</td><td>200 tablet(s)</td><td>2004-10-27</td><td>0407010</td><td>13892911000001107</td></tr>\n",
-       "\t<tr><td>1918594</td><td>3</td><td>27/10/2004</td><td>NA</td><td>0407010200</td><td>NA</td><td>Co-codamol 8mg/500mg tablets</td><td>200 tablet(s)</td><td>2004-10-27</td><td>0407010</td><td>13892811000001102</td></tr>\n",
-       "\t<tr><td>1918594</td><td>3</td><td>27/10/2004</td><td>NA</td><td>0407010200</td><td>NA</td><td>Co-codamol 8mg/500mg tablets</td><td>200 tablet(s)</td><td>2004-10-27</td><td>0407010</td><td>13893011000001104</td></tr>\n",
-       "\t<tr><td>1918594</td><td>3</td><td>27/10/2004</td><td>NA</td><td>0407010200</td><td>NA</td><td>Co-codamol 8mg/500mg tablets</td><td>200 tablet(s)</td><td>2004-10-27</td><td>0407010</td><td>13893811000001105</td></tr>\n",
-       "\t<tr><td>1918594</td><td>3</td><td>27/10/2004</td><td>NA</td><td>0407010200</td><td>NA</td><td>Co-codamol 8mg/500mg tablets</td><td>200 tablet(s)</td><td>2004-10-27</td><td>0407010</td><td>16073011000001100</td></tr>\n",
-       "\t<tr><td>1918594</td><td>3</td><td>27/10/2004</td><td>NA</td><td>0407010200</td><td>NA</td><td>Co-codamol 8mg/500mg tablets</td><td>200 tablet(s)</td><td>2004-10-27</td><td>0407010</td><td>16665611000001108</td></tr>\n",
-       "\t<tr><td>1918594</td><td>3</td><td>27/10/2004</td><td>NA</td><td>0407010200</td><td>NA</td><td>Co-codamol 8mg/500mg tablets</td><td>200 tablet(s)</td><td>2004-10-27</td><td>0407010</td><td>16757111000001108</td></tr>\n",
-       "\t<tr><td>1918594</td><td>3</td><td>27/10/2004</td><td>NA</td><td>0407010200</td><td>NA</td><td>Co-codamol 8mg/500mg tablets</td><td>200 tablet(s)</td><td>2004-10-27</td><td>0407010</td><td>17853711000001103</td></tr>\n",
-       "\t<tr><td>1918594</td><td>3</td><td>27/10/2004</td><td>NA</td><td>0407010200</td><td>NA</td><td>Co-codamol 8mg/500mg tablets</td><td>200 tablet(s)</td><td>2004-10-27</td><td>0407010</td><td>18084511000001109</td></tr>\n",
-       "\t<tr><td>1918594</td><td>3</td><td>27/10/2004</td><td>NA</td><td>0407010200</td><td>NA</td><td>Co-codamol 8mg/500mg tablets</td><td>200 tablet(s)</td><td>2004-10-27</td><td>0407010</td><td>18595211000001106</td></tr>\n",
-       "\t<tr><td>1918594</td><td>3</td><td>27/10/2004</td><td>NA</td><td>0407010200</td><td>NA</td><td>Co-codamol 8mg/500mg tablets</td><td>200 tablet(s)</td><td>2004-10-27</td><td>0407010</td><td>18615611000001103</td></tr>\n",
-       "\t<tr><td>1918594</td><td>3</td><td>27/10/2004</td><td>NA</td><td>0407010200</td><td>NA</td><td>Co-codamol 8mg/500mg tablets</td><td>200 tablet(s)</td><td>2004-10-27</td><td>0407010</td><td>19200211000001107</td></tr>\n",
-       "\t<tr><td>1918594</td><td>3</td><td>27/10/2004</td><td>NA</td><td>0407010200</td><td>NA</td><td>Co-codamol 8mg/500mg tablets</td><td>200 tablet(s)</td><td>2004-10-27</td><td>0407010</td><td>19230711000001108</td></tr>\n",
-       "\t<tr><td>1918594</td><td>3</td><td>27/10/2004</td><td>NA</td><td>0407010200</td><td>NA</td><td>Co-codamol 8mg/500mg tablets</td><td>200 tablet(s)</td><td>2004-10-27</td><td>0407010</td><td>19805611000001105</td></tr>\n",
-       "\t<tr><td>1918594</td><td>3</td><td>27/10/2004</td><td>NA</td><td>0407010200</td><td>NA</td><td>Co-codamol 8mg/500mg tablets</td><td>200 tablet(s)</td><td>2004-10-27</td><td>0407010</td><td>21636411000001107</td></tr>\n",
-       "\t<tr><td>1918594</td><td>3</td><td>27/10/2004</td><td>NA</td><td>0407010200</td><td>NA</td><td>Co-codamol 8mg/500mg tablets</td><td>200 tablet(s)</td><td>2004-10-27</td><td>0407010</td><td>24014911000001102</td></tr>\n",
-       "\t<tr><td>1918594</td><td>3</td><td>27/10/2004</td><td>NA</td><td>0407010200</td><td>NA</td><td>Co-codamol 8mg/500mg tablets</td><td>200 tablet(s)</td><td>2004-10-27</td><td>0407010</td><td>24580811000001103</td></tr>\n",
-       "\t<tr><td>1918594</td><td>3</td><td>27/10/2004</td><td>NA</td><td>0407010200</td><td>NA</td><td>Co-codamol 8mg/500mg tablets</td><td>200 tablet(s)</td><td>2004-10-27</td><td>0407010</td><td>32637711000001103</td></tr>\n",
-       "\t<tr><td>1918594</td><td>3</td><td>27/10/2004</td><td>NA</td><td>0407010200</td><td>NA</td><td>Co-codamol 8mg/500mg tablets</td><td>200 tablet(s)</td><td>2004-10-27</td><td>0407010</td><td>33568211000001100</td></tr>\n",
-       "\t<tr><td>1918594</td><td>3</td><td>27/10/2004</td><td>NA</td><td>0407010200</td><td>NA</td><td>Co-codamol 8mg/500mg tablets</td><td>200 tablet(s)</td><td>2004-10-27</td><td>0407010</td><td>34625311000001101</td></tr>\n",
-       "\t<tr><td>1918594</td><td>3</td><td>27/10/2004</td><td>NA</td><td>0407010200</td><td>NA</td><td>Co-codamol 8mg/500mg tablets</td><td>200 tablet(s)</td><td>2004-10-27</td><td>0407010</td><td>34878611000001106</td></tr>\n",
-       "\t<tr><td>1918594</td><td>3</td><td>27/10/2004</td><td>NA</td><td>0407010200</td><td>NA</td><td>Co-codamol 8mg/500mg tablets</td><td>200 tablet(s)</td><td>2004-10-27</td><td>0407010</td><td>36441411000001100</td></tr>\n",
-       "\t<tr><td>1918594</td><td>3</td><td>27/10/2004</td><td>NA</td><td>0407010200</td><td>NA</td><td>Co-codamol 8mg/500mg tablets</td><td>200 tablet(s)</td><td>2004-10-27</td><td>0407010</td><td>36752911000001107</td></tr>\n",
-       "\t<tr><td>1918594</td><td>3</td><td>27/10/2004</td><td>NA</td><td>0407010200</td><td>NA</td><td>Co-codamol 8mg/500mg tablets</td><td>200 tablet(s)</td><td>2004-10-27</td><td>0407010</td><td>38063911000001100</td></tr>\n",
-       "\t<tr><td>1918594</td><td>3</td><td>27/10/2004</td><td>NA</td><td>0407010200</td><td>NA</td><td>Co-codamol 8mg/500mg tablets</td><td>200 tablet(s)</td><td>2004-10-27</td><td>0407010</td><td>38555311000001107</td></tr>\n",
-       "\t<tr><td>1918594</td><td>3</td><td>27/10/2004</td><td>NA</td><td>0407010200</td><td>NA</td><td>Co-codamol 8mg/500mg tablets</td><td>200 tablet(s)</td><td>2004-10-27</td><td>0407010</td><td>38555211000001104</td></tr>\n",
-       "</tbody>\n",
-       "</table>\n"
-      ],
-      "text/latex": [
-       "A data.table: 58742 × 11\n",
-       "\\begin{tabular}{lllllllllll}\n",
-       " eid & data\\_provider & issue\\_date & read\\_2 & bnf\\_code & dmd\\_code & drug\\_name & quantity & date & bnf\\_map & sct\\_code\\\\\n",
-       " <int> & <int> & <chr> & <chr> & <chr> & <int64> & <chr> & <chr> & <date> & <chr> & <int64>\\\\\n",
-       "\\hline\n",
-       "\t 5157903 & 3 & 25/05/2010 & NA & 0602010000 & NA & Levothyroxine sodium 100microgram tablets & 56 tablet(s) - 100 micrograms & 2010-05-25 & 0602010 &        374294006\\\\\n",
-       "\t 5157903 & 3 & 25/05/2010 & NA & 0602010000 & NA & Levothyroxine sodium 100microgram tablets & 56 tablet(s) - 100 micrograms & 2010-05-25 & 0602010 &        374295007\\\\\n",
-       "\t 5157903 & 3 & 25/05/2010 & NA & 0602010000 & NA & Levothyroxine sodium 100microgram tablets & 56 tablet(s) - 100 micrograms & 2010-05-25 & 0602010 &        374296008\\\\\n",
-       "\t 5157903 & 3 & 25/05/2010 & NA & 0602010000 & NA & Levothyroxine sodium 100microgram tablets & 56 tablet(s) - 100 micrograms & 2010-05-25 & 0602010 &        325315001\\\\\n",
-       "\t 5157903 & 3 & 25/05/2010 & NA & 0602010000 & NA & Levothyroxine sodium 100microgram tablets & 56 tablet(s) - 100 micrograms & 2010-05-25 & 0602010 &        325316000\\\\\n",
-       "\t 5157903 & 3 & 25/05/2010 & NA & 0602010000 & NA & Levothyroxine sodium 100microgram tablets & 56 tablet(s) - 100 micrograms & 2010-05-25 & 0602010 & 8584011000001100\\\\\n",
-       "\t 5157903 & 3 & 25/05/2010 & NA & 0602010000 & NA & Levothyroxine sodium 100microgram tablets & 56 tablet(s) - 100 micrograms & 2010-05-25 & 0602010 & 8584111000001104\\\\\n",
-       "\t 5157903 & 3 & 25/05/2010 & NA & 0602010000 & NA & Levothyroxine sodium 100microgram tablets & 56 tablet(s) - 100 micrograms & 2010-05-25 & 0602010 & 8584211000001105\\\\\n",
-       "\t 5157903 & 3 & 25/05/2010 & NA & 0602010000 & NA & Levothyroxine sodium 100microgram tablets & 56 tablet(s) - 100 micrograms & 2010-05-25 & 0602010 & 8584411000001109\\\\\n",
-       "\t 5157903 & 3 & 25/05/2010 & NA & 0602010000 & NA & Levothyroxine sodium 100microgram tablets & 56 tablet(s) - 100 micrograms & 2010-05-25 & 0602010 & 8584511000001108\\\\\n",
-       "\t 5157903 & 3 & 25/05/2010 & NA & 0602010000 & NA & Levothyroxine sodium 100microgram tablets & 56 tablet(s) - 100 micrograms & 2010-05-25 & 0602010 & 8584611000001107\\\\\n",
-       "\t 5157903 & 3 & 25/05/2010 & NA & 0602010000 & NA & Levothyroxine sodium 100microgram tablets & 56 tablet(s) - 100 micrograms & 2010-05-25 & 0602010 & 8584711000001103\\\\\n",
-       "\t 5157903 & 3 & 25/05/2010 & NA & 0602010000 & NA & Levothyroxine sodium 100microgram tablets & 56 tablet(s) - 100 micrograms & 2010-05-25 & 0602010 & 8584311000001102\\\\\n",
-       "\t 5157903 & 3 & 25/05/2010 & NA & 0602010000 & NA & Levothyroxine sodium 100microgram tablets & 56 tablet(s) - 100 micrograms & 2010-05-25 & 0602010 & 8584811000001106\\\\\n",
-       "\t 5157903 & 3 & 25/05/2010 & NA & 0602010000 & NA & Levothyroxine sodium 100microgram tablets & 56 tablet(s) - 100 micrograms & 2010-05-25 & 0602010 & 8584911000001101\\\\\n",
-       "\t 5157903 & 3 & 25/05/2010 & NA & 0602010000 & NA & Levothyroxine sodium 100microgram tablets & 56 tablet(s) - 100 micrograms & 2010-05-25 & 0602010 & 8585011000001101\\\\\n",
-       "\t 5157903 & 3 & 25/05/2010 & NA & 0602010000 & NA & Levothyroxine sodium 100microgram tablets & 56 tablet(s) - 100 micrograms & 2010-05-25 & 0602010 & 8585111000001100\\\\\n",
-       "\t 5157903 & 3 & 25/05/2010 & NA & 0602010000 & NA & Levothyroxine sodium 100microgram tablets & 56 tablet(s) - 100 micrograms & 2010-05-25 & 0602010 & 8586611000001103\\\\\n",
-       "\t 5157903 & 3 & 25/05/2010 & NA & 0602010000 & NA & Levothyroxine sodium 100microgram tablets & 56 tablet(s) - 100 micrograms & 2010-05-25 & 0602010 & 8586711000001107\\\\\n",
-       "\t 5157903 & 3 & 25/05/2010 & NA & 0602010000 & NA & Levothyroxine sodium 100microgram tablets & 56 tablet(s) - 100 micrograms & 2010-05-25 & 0602010 & 8585211000001106\\\\\n",
-       "\t 5157903 & 3 & 25/05/2010 & NA & 0602010000 & NA & Levothyroxine sodium 100microgram tablets & 56 tablet(s) - 100 micrograms & 2010-05-25 & 0602010 & 8585411000001105\\\\\n",
-       "\t 5157903 & 3 & 25/05/2010 & NA & 0602010000 & NA & Levothyroxine sodium 100microgram tablets & 56 tablet(s) - 100 micrograms & 2010-05-25 & 0602010 & 8585311000001103\\\\\n",
-       "\t 5157903 & 3 & 25/05/2010 & NA & 0602010000 & NA & Levothyroxine sodium 100microgram tablets & 56 tablet(s) - 100 micrograms & 2010-05-25 & 0602010 & 8585511000001109\\\\\n",
-       "\t 5157903 & 3 & 25/05/2010 & NA & 0602010000 & NA & Levothyroxine sodium 100microgram tablets & 56 tablet(s) - 100 micrograms & 2010-05-25 & 0602010 & 8585811000001107\\\\\n",
-       "\t 5157903 & 3 & 25/05/2010 & NA & 0602010000 & NA & Levothyroxine sodium 100microgram tablets & 56 tablet(s) - 100 micrograms & 2010-05-25 & 0602010 & 8585611000001108\\\\\n",
-       "\t 5157903 & 3 & 25/05/2010 & NA & 0602010000 & NA & Levothyroxine sodium 100microgram tablets & 56 tablet(s) - 100 micrograms & 2010-05-25 & 0602010 & 8585911000001102\\\\\n",
-       "\t 5157903 & 3 & 25/05/2010 & NA & 0602010000 & NA & Levothyroxine sodium 100microgram tablets & 56 tablet(s) - 100 micrograms & 2010-05-25 & 0602010 & 8585711000001104\\\\\n",
-       "\t 5157903 & 3 & 25/05/2010 & NA & 0602010000 & NA & Levothyroxine sodium 100microgram tablets & 56 tablet(s) - 100 micrograms & 2010-05-25 & 0602010 & 8586811000001104\\\\\n",
-       "\t 5157903 & 3 & 25/05/2010 & NA & 0602010000 & NA & Levothyroxine sodium 100microgram tablets & 56 tablet(s) - 100 micrograms & 2010-05-25 & 0602010 & 8586011000001105\\\\\n",
-       "\t 5157903 & 3 & 25/05/2010 & NA & 0602010000 & NA & Levothyroxine sodium 100microgram tablets & 56 tablet(s) - 100 micrograms & 2010-05-25 & 0602010 & 8586111000001106\\\\\n",
-       "\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
-       "\t 1918594 & 3 & 27/10/2004 & NA & 0407010200 & NA & Co-codamol 8mg/500mg tablets & 200 tablet(s) & 2004-10-27 & 0407010 & 12301811000001104\\\\\n",
-       "\t 1918594 & 3 & 27/10/2004 & NA & 0407010200 & NA & Co-codamol 8mg/500mg tablets & 200 tablet(s) & 2004-10-27 & 0407010 & 13004211000001106\\\\\n",
-       "\t 1918594 & 3 & 27/10/2004 & NA & 0407010200 & NA & Co-codamol 8mg/500mg tablets & 200 tablet(s) & 2004-10-27 & 0407010 & 13004111000001100\\\\\n",
-       "\t 1918594 & 3 & 27/10/2004 & NA & 0407010200 & NA & Co-codamol 8mg/500mg tablets & 200 tablet(s) & 2004-10-27 & 0407010 & 13004311000001103\\\\\n",
-       "\t 1918594 & 3 & 27/10/2004 & NA & 0407010200 & NA & Co-codamol 8mg/500mg tablets & 200 tablet(s) & 2004-10-27 & 0407010 & 13892911000001107\\\\\n",
-       "\t 1918594 & 3 & 27/10/2004 & NA & 0407010200 & NA & Co-codamol 8mg/500mg tablets & 200 tablet(s) & 2004-10-27 & 0407010 & 13892811000001102\\\\\n",
-       "\t 1918594 & 3 & 27/10/2004 & NA & 0407010200 & NA & Co-codamol 8mg/500mg tablets & 200 tablet(s) & 2004-10-27 & 0407010 & 13893011000001104\\\\\n",
-       "\t 1918594 & 3 & 27/10/2004 & NA & 0407010200 & NA & Co-codamol 8mg/500mg tablets & 200 tablet(s) & 2004-10-27 & 0407010 & 13893811000001105\\\\\n",
-       "\t 1918594 & 3 & 27/10/2004 & NA & 0407010200 & NA & Co-codamol 8mg/500mg tablets & 200 tablet(s) & 2004-10-27 & 0407010 & 16073011000001100\\\\\n",
-       "\t 1918594 & 3 & 27/10/2004 & NA & 0407010200 & NA & Co-codamol 8mg/500mg tablets & 200 tablet(s) & 2004-10-27 & 0407010 & 16665611000001108\\\\\n",
-       "\t 1918594 & 3 & 27/10/2004 & NA & 0407010200 & NA & Co-codamol 8mg/500mg tablets & 200 tablet(s) & 2004-10-27 & 0407010 & 16757111000001108\\\\\n",
-       "\t 1918594 & 3 & 27/10/2004 & NA & 0407010200 & NA & Co-codamol 8mg/500mg tablets & 200 tablet(s) & 2004-10-27 & 0407010 & 17853711000001103\\\\\n",
-       "\t 1918594 & 3 & 27/10/2004 & NA & 0407010200 & NA & Co-codamol 8mg/500mg tablets & 200 tablet(s) & 2004-10-27 & 0407010 & 18084511000001109\\\\\n",
-       "\t 1918594 & 3 & 27/10/2004 & NA & 0407010200 & NA & Co-codamol 8mg/500mg tablets & 200 tablet(s) & 2004-10-27 & 0407010 & 18595211000001106\\\\\n",
-       "\t 1918594 & 3 & 27/10/2004 & NA & 0407010200 & NA & Co-codamol 8mg/500mg tablets & 200 tablet(s) & 2004-10-27 & 0407010 & 18615611000001103\\\\\n",
-       "\t 1918594 & 3 & 27/10/2004 & NA & 0407010200 & NA & Co-codamol 8mg/500mg tablets & 200 tablet(s) & 2004-10-27 & 0407010 & 19200211000001107\\\\\n",
-       "\t 1918594 & 3 & 27/10/2004 & NA & 0407010200 & NA & Co-codamol 8mg/500mg tablets & 200 tablet(s) & 2004-10-27 & 0407010 & 19230711000001108\\\\\n",
-       "\t 1918594 & 3 & 27/10/2004 & NA & 0407010200 & NA & Co-codamol 8mg/500mg tablets & 200 tablet(s) & 2004-10-27 & 0407010 & 19805611000001105\\\\\n",
-       "\t 1918594 & 3 & 27/10/2004 & NA & 0407010200 & NA & Co-codamol 8mg/500mg tablets & 200 tablet(s) & 2004-10-27 & 0407010 & 21636411000001107\\\\\n",
-       "\t 1918594 & 3 & 27/10/2004 & NA & 0407010200 & NA & Co-codamol 8mg/500mg tablets & 200 tablet(s) & 2004-10-27 & 0407010 & 24014911000001102\\\\\n",
-       "\t 1918594 & 3 & 27/10/2004 & NA & 0407010200 & NA & Co-codamol 8mg/500mg tablets & 200 tablet(s) & 2004-10-27 & 0407010 & 24580811000001103\\\\\n",
-       "\t 1918594 & 3 & 27/10/2004 & NA & 0407010200 & NA & Co-codamol 8mg/500mg tablets & 200 tablet(s) & 2004-10-27 & 0407010 & 32637711000001103\\\\\n",
-       "\t 1918594 & 3 & 27/10/2004 & NA & 0407010200 & NA & Co-codamol 8mg/500mg tablets & 200 tablet(s) & 2004-10-27 & 0407010 & 33568211000001100\\\\\n",
-       "\t 1918594 & 3 & 27/10/2004 & NA & 0407010200 & NA & Co-codamol 8mg/500mg tablets & 200 tablet(s) & 2004-10-27 & 0407010 & 34625311000001101\\\\\n",
-       "\t 1918594 & 3 & 27/10/2004 & NA & 0407010200 & NA & Co-codamol 8mg/500mg tablets & 200 tablet(s) & 2004-10-27 & 0407010 & 34878611000001106\\\\\n",
-       "\t 1918594 & 3 & 27/10/2004 & NA & 0407010200 & NA & Co-codamol 8mg/500mg tablets & 200 tablet(s) & 2004-10-27 & 0407010 & 36441411000001100\\\\\n",
-       "\t 1918594 & 3 & 27/10/2004 & NA & 0407010200 & NA & Co-codamol 8mg/500mg tablets & 200 tablet(s) & 2004-10-27 & 0407010 & 36752911000001107\\\\\n",
-       "\t 1918594 & 3 & 27/10/2004 & NA & 0407010200 & NA & Co-codamol 8mg/500mg tablets & 200 tablet(s) & 2004-10-27 & 0407010 & 38063911000001100\\\\\n",
-       "\t 1918594 & 3 & 27/10/2004 & NA & 0407010200 & NA & Co-codamol 8mg/500mg tablets & 200 tablet(s) & 2004-10-27 & 0407010 & 38555311000001107\\\\\n",
-       "\t 1918594 & 3 & 27/10/2004 & NA & 0407010200 & NA & Co-codamol 8mg/500mg tablets & 200 tablet(s) & 2004-10-27 & 0407010 & 38555211000001104\\\\\n",
-       "\\end{tabular}\n"
-      ],
-      "text/markdown": [
-       "\n",
-       "A data.table: 58742 × 11\n",
-       "\n",
-       "| eid &lt;int&gt; | data_provider &lt;int&gt; | issue_date &lt;chr&gt; | read_2 &lt;chr&gt; | bnf_code &lt;chr&gt; | dmd_code &lt;int64&gt; | drug_name &lt;chr&gt; | quantity &lt;chr&gt; | date &lt;date&gt; | bnf_map &lt;chr&gt; | sct_code &lt;int64&gt; |\n",
-       "|---|---|---|---|---|---|---|---|---|---|---|\n",
-       "| 5157903 | 3 | 25/05/2010 | NA | 0602010000 | NA | Levothyroxine sodium 100microgram tablets | 56 tablet(s) - 100 micrograms | 2010-05-25 | 0602010 |        374294006 |\n",
-       "| 5157903 | 3 | 25/05/2010 | NA | 0602010000 | NA | Levothyroxine sodium 100microgram tablets | 56 tablet(s) - 100 micrograms | 2010-05-25 | 0602010 |        374295007 |\n",
-       "| 5157903 | 3 | 25/05/2010 | NA | 0602010000 | NA | Levothyroxine sodium 100microgram tablets | 56 tablet(s) - 100 micrograms | 2010-05-25 | 0602010 |        374296008 |\n",
-       "| 5157903 | 3 | 25/05/2010 | NA | 0602010000 | NA | Levothyroxine sodium 100microgram tablets | 56 tablet(s) - 100 micrograms | 2010-05-25 | 0602010 |        325315001 |\n",
-       "| 5157903 | 3 | 25/05/2010 | NA | 0602010000 | NA | Levothyroxine sodium 100microgram tablets | 56 tablet(s) - 100 micrograms | 2010-05-25 | 0602010 |        325316000 |\n",
-       "| 5157903 | 3 | 25/05/2010 | NA | 0602010000 | NA | Levothyroxine sodium 100microgram tablets | 56 tablet(s) - 100 micrograms | 2010-05-25 | 0602010 | 8584011000001100 |\n",
-       "| 5157903 | 3 | 25/05/2010 | NA | 0602010000 | NA | Levothyroxine sodium 100microgram tablets | 56 tablet(s) - 100 micrograms | 2010-05-25 | 0602010 | 8584111000001104 |\n",
-       "| 5157903 | 3 | 25/05/2010 | NA | 0602010000 | NA | Levothyroxine sodium 100microgram tablets | 56 tablet(s) - 100 micrograms | 2010-05-25 | 0602010 | 8584211000001105 |\n",
-       "| 5157903 | 3 | 25/05/2010 | NA | 0602010000 | NA | Levothyroxine sodium 100microgram tablets | 56 tablet(s) - 100 micrograms | 2010-05-25 | 0602010 | 8584411000001109 |\n",
-       "| 5157903 | 3 | 25/05/2010 | NA | 0602010000 | NA | Levothyroxine sodium 100microgram tablets | 56 tablet(s) - 100 micrograms | 2010-05-25 | 0602010 | 8584511000001108 |\n",
-       "| 5157903 | 3 | 25/05/2010 | NA | 0602010000 | NA | Levothyroxine sodium 100microgram tablets | 56 tablet(s) - 100 micrograms | 2010-05-25 | 0602010 | 8584611000001107 |\n",
-       "| 5157903 | 3 | 25/05/2010 | NA | 0602010000 | NA | Levothyroxine sodium 100microgram tablets | 56 tablet(s) - 100 micrograms | 2010-05-25 | 0602010 | 8584711000001103 |\n",
-       "| 5157903 | 3 | 25/05/2010 | NA | 0602010000 | NA | Levothyroxine sodium 100microgram tablets | 56 tablet(s) - 100 micrograms | 2010-05-25 | 0602010 | 8584311000001102 |\n",
-       "| 5157903 | 3 | 25/05/2010 | NA | 0602010000 | NA | Levothyroxine sodium 100microgram tablets | 56 tablet(s) - 100 micrograms | 2010-05-25 | 0602010 | 8584811000001106 |\n",
-       "| 5157903 | 3 | 25/05/2010 | NA | 0602010000 | NA | Levothyroxine sodium 100microgram tablets | 56 tablet(s) - 100 micrograms | 2010-05-25 | 0602010 | 8584911000001101 |\n",
-       "| 5157903 | 3 | 25/05/2010 | NA | 0602010000 | NA | Levothyroxine sodium 100microgram tablets | 56 tablet(s) - 100 micrograms | 2010-05-25 | 0602010 | 8585011000001101 |\n",
-       "| 5157903 | 3 | 25/05/2010 | NA | 0602010000 | NA | Levothyroxine sodium 100microgram tablets | 56 tablet(s) - 100 micrograms | 2010-05-25 | 0602010 | 8585111000001100 |\n",
-       "| 5157903 | 3 | 25/05/2010 | NA | 0602010000 | NA | Levothyroxine sodium 100microgram tablets | 56 tablet(s) - 100 micrograms | 2010-05-25 | 0602010 | 8586611000001103 |\n",
-       "| 5157903 | 3 | 25/05/2010 | NA | 0602010000 | NA | Levothyroxine sodium 100microgram tablets | 56 tablet(s) - 100 micrograms | 2010-05-25 | 0602010 | 8586711000001107 |\n",
-       "| 5157903 | 3 | 25/05/2010 | NA | 0602010000 | NA | Levothyroxine sodium 100microgram tablets | 56 tablet(s) - 100 micrograms | 2010-05-25 | 0602010 | 8585211000001106 |\n",
-       "| 5157903 | 3 | 25/05/2010 | NA | 0602010000 | NA | Levothyroxine sodium 100microgram tablets | 56 tablet(s) - 100 micrograms | 2010-05-25 | 0602010 | 8585411000001105 |\n",
-       "| 5157903 | 3 | 25/05/2010 | NA | 0602010000 | NA | Levothyroxine sodium 100microgram tablets | 56 tablet(s) - 100 micrograms | 2010-05-25 | 0602010 | 8585311000001103 |\n",
-       "| 5157903 | 3 | 25/05/2010 | NA | 0602010000 | NA | Levothyroxine sodium 100microgram tablets | 56 tablet(s) - 100 micrograms | 2010-05-25 | 0602010 | 8585511000001109 |\n",
-       "| 5157903 | 3 | 25/05/2010 | NA | 0602010000 | NA | Levothyroxine sodium 100microgram tablets | 56 tablet(s) - 100 micrograms | 2010-05-25 | 0602010 | 8585811000001107 |\n",
-       "| 5157903 | 3 | 25/05/2010 | NA | 0602010000 | NA | Levothyroxine sodium 100microgram tablets | 56 tablet(s) - 100 micrograms | 2010-05-25 | 0602010 | 8585611000001108 |\n",
-       "| 5157903 | 3 | 25/05/2010 | NA | 0602010000 | NA | Levothyroxine sodium 100microgram tablets | 56 tablet(s) - 100 micrograms | 2010-05-25 | 0602010 | 8585911000001102 |\n",
-       "| 5157903 | 3 | 25/05/2010 | NA | 0602010000 | NA | Levothyroxine sodium 100microgram tablets | 56 tablet(s) - 100 micrograms | 2010-05-25 | 0602010 | 8585711000001104 |\n",
-       "| 5157903 | 3 | 25/05/2010 | NA | 0602010000 | NA | Levothyroxine sodium 100microgram tablets | 56 tablet(s) - 100 micrograms | 2010-05-25 | 0602010 | 8586811000001104 |\n",
-       "| 5157903 | 3 | 25/05/2010 | NA | 0602010000 | NA | Levothyroxine sodium 100microgram tablets | 56 tablet(s) - 100 micrograms | 2010-05-25 | 0602010 | 8586011000001105 |\n",
-       "| 5157903 | 3 | 25/05/2010 | NA | 0602010000 | NA | Levothyroxine sodium 100microgram tablets | 56 tablet(s) - 100 micrograms | 2010-05-25 | 0602010 | 8586111000001106 |\n",
-       "| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |\n",
-       "| 1918594 | 3 | 27/10/2004 | NA | 0407010200 | NA | Co-codamol 8mg/500mg tablets | 200 tablet(s) | 2004-10-27 | 0407010 | 12301811000001104 |\n",
-       "| 1918594 | 3 | 27/10/2004 | NA | 0407010200 | NA | Co-codamol 8mg/500mg tablets | 200 tablet(s) | 2004-10-27 | 0407010 | 13004211000001106 |\n",
-       "| 1918594 | 3 | 27/10/2004 | NA | 0407010200 | NA | Co-codamol 8mg/500mg tablets | 200 tablet(s) | 2004-10-27 | 0407010 | 13004111000001100 |\n",
-       "| 1918594 | 3 | 27/10/2004 | NA | 0407010200 | NA | Co-codamol 8mg/500mg tablets | 200 tablet(s) | 2004-10-27 | 0407010 | 13004311000001103 |\n",
-       "| 1918594 | 3 | 27/10/2004 | NA | 0407010200 | NA | Co-codamol 8mg/500mg tablets | 200 tablet(s) | 2004-10-27 | 0407010 | 13892911000001107 |\n",
-       "| 1918594 | 3 | 27/10/2004 | NA | 0407010200 | NA | Co-codamol 8mg/500mg tablets | 200 tablet(s) | 2004-10-27 | 0407010 | 13892811000001102 |\n",
-       "| 1918594 | 3 | 27/10/2004 | NA | 0407010200 | NA | Co-codamol 8mg/500mg tablets | 200 tablet(s) | 2004-10-27 | 0407010 | 13893011000001104 |\n",
-       "| 1918594 | 3 | 27/10/2004 | NA | 0407010200 | NA | Co-codamol 8mg/500mg tablets | 200 tablet(s) | 2004-10-27 | 0407010 | 13893811000001105 |\n",
-       "| 1918594 | 3 | 27/10/2004 | NA | 0407010200 | NA | Co-codamol 8mg/500mg tablets | 200 tablet(s) | 2004-10-27 | 0407010 | 16073011000001100 |\n",
-       "| 1918594 | 3 | 27/10/2004 | NA | 0407010200 | NA | Co-codamol 8mg/500mg tablets | 200 tablet(s) | 2004-10-27 | 0407010 | 16665611000001108 |\n",
-       "| 1918594 | 3 | 27/10/2004 | NA | 0407010200 | NA | Co-codamol 8mg/500mg tablets | 200 tablet(s) | 2004-10-27 | 0407010 | 16757111000001108 |\n",
-       "| 1918594 | 3 | 27/10/2004 | NA | 0407010200 | NA | Co-codamol 8mg/500mg tablets | 200 tablet(s) | 2004-10-27 | 0407010 | 17853711000001103 |\n",
-       "| 1918594 | 3 | 27/10/2004 | NA | 0407010200 | NA | Co-codamol 8mg/500mg tablets | 200 tablet(s) | 2004-10-27 | 0407010 | 18084511000001109 |\n",
-       "| 1918594 | 3 | 27/10/2004 | NA | 0407010200 | NA | Co-codamol 8mg/500mg tablets | 200 tablet(s) | 2004-10-27 | 0407010 | 18595211000001106 |\n",
-       "| 1918594 | 3 | 27/10/2004 | NA | 0407010200 | NA | Co-codamol 8mg/500mg tablets | 200 tablet(s) | 2004-10-27 | 0407010 | 18615611000001103 |\n",
-       "| 1918594 | 3 | 27/10/2004 | NA | 0407010200 | NA | Co-codamol 8mg/500mg tablets | 200 tablet(s) | 2004-10-27 | 0407010 | 19200211000001107 |\n",
-       "| 1918594 | 3 | 27/10/2004 | NA | 0407010200 | NA | Co-codamol 8mg/500mg tablets | 200 tablet(s) | 2004-10-27 | 0407010 | 19230711000001108 |\n",
-       "| 1918594 | 3 | 27/10/2004 | NA | 0407010200 | NA | Co-codamol 8mg/500mg tablets | 200 tablet(s) | 2004-10-27 | 0407010 | 19805611000001105 |\n",
-       "| 1918594 | 3 | 27/10/2004 | NA | 0407010200 | NA | Co-codamol 8mg/500mg tablets | 200 tablet(s) | 2004-10-27 | 0407010 | 21636411000001107 |\n",
-       "| 1918594 | 3 | 27/10/2004 | NA | 0407010200 | NA | Co-codamol 8mg/500mg tablets | 200 tablet(s) | 2004-10-27 | 0407010 | 24014911000001102 |\n",
-       "| 1918594 | 3 | 27/10/2004 | NA | 0407010200 | NA | Co-codamol 8mg/500mg tablets | 200 tablet(s) | 2004-10-27 | 0407010 | 24580811000001103 |\n",
-       "| 1918594 | 3 | 27/10/2004 | NA | 0407010200 | NA | Co-codamol 8mg/500mg tablets | 200 tablet(s) | 2004-10-27 | 0407010 | 32637711000001103 |\n",
-       "| 1918594 | 3 | 27/10/2004 | NA | 0407010200 | NA | Co-codamol 8mg/500mg tablets | 200 tablet(s) | 2004-10-27 | 0407010 | 33568211000001100 |\n",
-       "| 1918594 | 3 | 27/10/2004 | NA | 0407010200 | NA | Co-codamol 8mg/500mg tablets | 200 tablet(s) | 2004-10-27 | 0407010 | 34625311000001101 |\n",
-       "| 1918594 | 3 | 27/10/2004 | NA | 0407010200 | NA | Co-codamol 8mg/500mg tablets | 200 tablet(s) | 2004-10-27 | 0407010 | 34878611000001106 |\n",
-       "| 1918594 | 3 | 27/10/2004 | NA | 0407010200 | NA | Co-codamol 8mg/500mg tablets | 200 tablet(s) | 2004-10-27 | 0407010 | 36441411000001100 |\n",
-       "| 1918594 | 3 | 27/10/2004 | NA | 0407010200 | NA | Co-codamol 8mg/500mg tablets | 200 tablet(s) | 2004-10-27 | 0407010 | 36752911000001107 |\n",
-       "| 1918594 | 3 | 27/10/2004 | NA | 0407010200 | NA | Co-codamol 8mg/500mg tablets | 200 tablet(s) | 2004-10-27 | 0407010 | 38063911000001100 |\n",
-       "| 1918594 | 3 | 27/10/2004 | NA | 0407010200 | NA | Co-codamol 8mg/500mg tablets | 200 tablet(s) | 2004-10-27 | 0407010 | 38555311000001107 |\n",
-       "| 1918594 | 3 | 27/10/2004 | NA | 0407010200 | NA | Co-codamol 8mg/500mg tablets | 200 tablet(s) | 2004-10-27 | 0407010 | 38555211000001104 |\n",
-       "\n"
-      ],
-      "text/plain": [
-       "       eid     data_provider issue_date read_2 bnf_code   dmd_code\n",
-       "1      5157903 3             25/05/2010 NA     0602010000 NA      \n",
-       "2      5157903 3             25/05/2010 NA     0602010000 NA      \n",
-       "3      5157903 3             25/05/2010 NA     0602010000 NA      \n",
-       "4      5157903 3             25/05/2010 NA     0602010000 NA      \n",
-       "5      5157903 3             25/05/2010 NA     0602010000 NA      \n",
-       "6      5157903 3             25/05/2010 NA     0602010000 NA      \n",
-       "7      5157903 3             25/05/2010 NA     0602010000 NA      \n",
-       "8      5157903 3             25/05/2010 NA     0602010000 NA      \n",
-       "9      5157903 3             25/05/2010 NA     0602010000 NA      \n",
-       "10     5157903 3             25/05/2010 NA     0602010000 NA      \n",
-       "11     5157903 3             25/05/2010 NA     0602010000 NA      \n",
-       "12     5157903 3             25/05/2010 NA     0602010000 NA      \n",
-       "13     5157903 3             25/05/2010 NA     0602010000 NA      \n",
-       "14     5157903 3             25/05/2010 NA     0602010000 NA      \n",
-       "15     5157903 3             25/05/2010 NA     0602010000 NA      \n",
-       "16     5157903 3             25/05/2010 NA     0602010000 NA      \n",
-       "17     5157903 3             25/05/2010 NA     0602010000 NA      \n",
-       "18     5157903 3             25/05/2010 NA     0602010000 NA      \n",
-       "19     5157903 3             25/05/2010 NA     0602010000 NA      \n",
-       "20     5157903 3             25/05/2010 NA     0602010000 NA      \n",
-       "21     5157903 3             25/05/2010 NA     0602010000 NA      \n",
-       "22     5157903 3             25/05/2010 NA     0602010000 NA      \n",
-       "23     5157903 3             25/05/2010 NA     0602010000 NA      \n",
-       "24     5157903 3             25/05/2010 NA     0602010000 NA      \n",
-       "25     5157903 3             25/05/2010 NA     0602010000 NA      \n",
-       "26     5157903 3             25/05/2010 NA     0602010000 NA      \n",
-       "27     5157903 3             25/05/2010 NA     0602010000 NA      \n",
-       "28     5157903 3             25/05/2010 NA     0602010000 NA      \n",
-       "29     5157903 3             25/05/2010 NA     0602010000 NA      \n",
-       "30     5157903 3             25/05/2010 NA     0602010000 NA      \n",
-       "<U+22EE> <U+22EE> <U+22EE>      <U+22EE>   <U+22EE> <U+22EE>   <U+22EE>\n",
-       "58713  1918594 3             27/10/2004 NA     0407010200 NA      \n",
-       "58714  1918594 3             27/10/2004 NA     0407010200 NA      \n",
-       "58715  1918594 3             27/10/2004 NA     0407010200 NA      \n",
-       "58716  1918594 3             27/10/2004 NA     0407010200 NA      \n",
-       "58717  1918594 3             27/10/2004 NA     0407010200 NA      \n",
-       "58718  1918594 3             27/10/2004 NA     0407010200 NA      \n",
-       "58719  1918594 3             27/10/2004 NA     0407010200 NA      \n",
-       "58720  1918594 3             27/10/2004 NA     0407010200 NA      \n",
-       "58721  1918594 3             27/10/2004 NA     0407010200 NA      \n",
-       "58722  1918594 3             27/10/2004 NA     0407010200 NA      \n",
-       "58723  1918594 3             27/10/2004 NA     0407010200 NA      \n",
-       "58724  1918594 3             27/10/2004 NA     0407010200 NA      \n",
-       "58725  1918594 3             27/10/2004 NA     0407010200 NA      \n",
-       "58726  1918594 3             27/10/2004 NA     0407010200 NA      \n",
-       "58727  1918594 3             27/10/2004 NA     0407010200 NA      \n",
-       "58728  1918594 3             27/10/2004 NA     0407010200 NA      \n",
-       "58729  1918594 3             27/10/2004 NA     0407010200 NA      \n",
-       "58730  1918594 3             27/10/2004 NA     0407010200 NA      \n",
-       "58731  1918594 3             27/10/2004 NA     0407010200 NA      \n",
-       "58732  1918594 3             27/10/2004 NA     0407010200 NA      \n",
-       "58733  1918594 3             27/10/2004 NA     0407010200 NA      \n",
-       "58734  1918594 3             27/10/2004 NA     0407010200 NA      \n",
-       "58735  1918594 3             27/10/2004 NA     0407010200 NA      \n",
-       "58736  1918594 3             27/10/2004 NA     0407010200 NA      \n",
-       "58737  1918594 3             27/10/2004 NA     0407010200 NA      \n",
-       "58738  1918594 3             27/10/2004 NA     0407010200 NA      \n",
-       "58739  1918594 3             27/10/2004 NA     0407010200 NA      \n",
-       "58740  1918594 3             27/10/2004 NA     0407010200 NA      \n",
-       "58741  1918594 3             27/10/2004 NA     0407010200 NA      \n",
-       "58742  1918594 3             27/10/2004 NA     0407010200 NA      \n",
-       "       drug_name                                 quantity                     \n",
-       "1      Levothyroxine sodium 100microgram tablets 56 tablet(s) - 100 micrograms\n",
-       "2      Levothyroxine sodium 100microgram tablets 56 tablet(s) - 100 micrograms\n",
-       "3      Levothyroxine sodium 100microgram tablets 56 tablet(s) - 100 micrograms\n",
-       "4      Levothyroxine sodium 100microgram tablets 56 tablet(s) - 100 micrograms\n",
-       "5      Levothyroxine sodium 100microgram tablets 56 tablet(s) - 100 micrograms\n",
-       "6      Levothyroxine sodium 100microgram tablets 56 tablet(s) - 100 micrograms\n",
-       "7      Levothyroxine sodium 100microgram tablets 56 tablet(s) - 100 micrograms\n",
-       "8      Levothyroxine sodium 100microgram tablets 56 tablet(s) - 100 micrograms\n",
-       "9      Levothyroxine sodium 100microgram tablets 56 tablet(s) - 100 micrograms\n",
-       "10     Levothyroxine sodium 100microgram tablets 56 tablet(s) - 100 micrograms\n",
-       "11     Levothyroxine sodium 100microgram tablets 56 tablet(s) - 100 micrograms\n",
-       "12     Levothyroxine sodium 100microgram tablets 56 tablet(s) - 100 micrograms\n",
-       "13     Levothyroxine sodium 100microgram tablets 56 tablet(s) - 100 micrograms\n",
-       "14     Levothyroxine sodium 100microgram tablets 56 tablet(s) - 100 micrograms\n",
-       "15     Levothyroxine sodium 100microgram tablets 56 tablet(s) - 100 micrograms\n",
-       "16     Levothyroxine sodium 100microgram tablets 56 tablet(s) - 100 micrograms\n",
-       "17     Levothyroxine sodium 100microgram tablets 56 tablet(s) - 100 micrograms\n",
-       "18     Levothyroxine sodium 100microgram tablets 56 tablet(s) - 100 micrograms\n",
-       "19     Levothyroxine sodium 100microgram tablets 56 tablet(s) - 100 micrograms\n",
-       "20     Levothyroxine sodium 100microgram tablets 56 tablet(s) - 100 micrograms\n",
-       "21     Levothyroxine sodium 100microgram tablets 56 tablet(s) - 100 micrograms\n",
-       "22     Levothyroxine sodium 100microgram tablets 56 tablet(s) - 100 micrograms\n",
-       "23     Levothyroxine sodium 100microgram tablets 56 tablet(s) - 100 micrograms\n",
-       "24     Levothyroxine sodium 100microgram tablets 56 tablet(s) - 100 micrograms\n",
-       "25     Levothyroxine sodium 100microgram tablets 56 tablet(s) - 100 micrograms\n",
-       "26     Levothyroxine sodium 100microgram tablets 56 tablet(s) - 100 micrograms\n",
-       "27     Levothyroxine sodium 100microgram tablets 56 tablet(s) - 100 micrograms\n",
-       "28     Levothyroxine sodium 100microgram tablets 56 tablet(s) - 100 micrograms\n",
-       "29     Levothyroxine sodium 100microgram tablets 56 tablet(s) - 100 micrograms\n",
-       "30     Levothyroxine sodium 100microgram tablets 56 tablet(s) - 100 micrograms\n",
-       "<U+22EE> <U+22EE>                                  <U+22EE>                     \n",
-       "58713  Co-codamol 8mg/500mg tablets              200 tablet(s)                \n",
-       "58714  Co-codamol 8mg/500mg tablets              200 tablet(s)                \n",
-       "58715  Co-codamol 8mg/500mg tablets              200 tablet(s)                \n",
-       "58716  Co-codamol 8mg/500mg tablets              200 tablet(s)                \n",
-       "58717  Co-codamol 8mg/500mg tablets              200 tablet(s)                \n",
-       "58718  Co-codamol 8mg/500mg tablets              200 tablet(s)                \n",
-       "58719  Co-codamol 8mg/500mg tablets              200 tablet(s)                \n",
-       "58720  Co-codamol 8mg/500mg tablets              200 tablet(s)                \n",
-       "58721  Co-codamol 8mg/500mg tablets              200 tablet(s)                \n",
-       "58722  Co-codamol 8mg/500mg tablets              200 tablet(s)                \n",
-       "58723  Co-codamol 8mg/500mg tablets              200 tablet(s)                \n",
-       "58724  Co-codamol 8mg/500mg tablets              200 tablet(s)                \n",
-       "58725  Co-codamol 8mg/500mg tablets              200 tablet(s)                \n",
-       "58726  Co-codamol 8mg/500mg tablets              200 tablet(s)                \n",
-       "58727  Co-codamol 8mg/500mg tablets              200 tablet(s)                \n",
-       "58728  Co-codamol 8mg/500mg tablets              200 tablet(s)                \n",
-       "58729  Co-codamol 8mg/500mg tablets              200 tablet(s)                \n",
-       "58730  Co-codamol 8mg/500mg tablets              200 tablet(s)                \n",
-       "58731  Co-codamol 8mg/500mg tablets              200 tablet(s)                \n",
-       "58732  Co-codamol 8mg/500mg tablets              200 tablet(s)                \n",
-       "58733  Co-codamol 8mg/500mg tablets              200 tablet(s)                \n",
-       "58734  Co-codamol 8mg/500mg tablets              200 tablet(s)                \n",
-       "58735  Co-codamol 8mg/500mg tablets              200 tablet(s)                \n",
-       "58736  Co-codamol 8mg/500mg tablets              200 tablet(s)                \n",
-       "58737  Co-codamol 8mg/500mg tablets              200 tablet(s)                \n",
-       "58738  Co-codamol 8mg/500mg tablets              200 tablet(s)                \n",
-       "58739  Co-codamol 8mg/500mg tablets              200 tablet(s)                \n",
-       "58740  Co-codamol 8mg/500mg tablets              200 tablet(s)                \n",
-       "58741  Co-codamol 8mg/500mg tablets              200 tablet(s)                \n",
-       "58742  Co-codamol 8mg/500mg tablets              200 tablet(s)                \n",
-       "       date       bnf_map sct_code         \n",
-       "1      2010-05-25 0602010        374294006 \n",
-       "2      2010-05-25 0602010        374295007 \n",
-       "3      2010-05-25 0602010        374296008 \n",
-       "4      2010-05-25 0602010        325315001 \n",
-       "5      2010-05-25 0602010        325316000 \n",
-       "6      2010-05-25 0602010 8584011000001100 \n",
-       "7      2010-05-25 0602010 8584111000001104 \n",
-       "8      2010-05-25 0602010 8584211000001105 \n",
-       "9      2010-05-25 0602010 8584411000001109 \n",
-       "10     2010-05-25 0602010 8584511000001108 \n",
-       "11     2010-05-25 0602010 8584611000001107 \n",
-       "12     2010-05-25 0602010 8584711000001103 \n",
-       "13     2010-05-25 0602010 8584311000001102 \n",
-       "14     2010-05-25 0602010 8584811000001106 \n",
-       "15     2010-05-25 0602010 8584911000001101 \n",
-       "16     2010-05-25 0602010 8585011000001101 \n",
-       "17     2010-05-25 0602010 8585111000001100 \n",
-       "18     2010-05-25 0602010 8586611000001103 \n",
-       "19     2010-05-25 0602010 8586711000001107 \n",
-       "20     2010-05-25 0602010 8585211000001106 \n",
-       "21     2010-05-25 0602010 8585411000001105 \n",
-       "22     2010-05-25 0602010 8585311000001103 \n",
-       "23     2010-05-25 0602010 8585511000001109 \n",
-       "24     2010-05-25 0602010 8585811000001107 \n",
-       "25     2010-05-25 0602010 8585611000001108 \n",
-       "26     2010-05-25 0602010 8585911000001102 \n",
-       "27     2010-05-25 0602010 8585711000001104 \n",
-       "28     2010-05-25 0602010 8586811000001104 \n",
-       "29     2010-05-25 0602010 8586011000001105 \n",
-       "30     2010-05-25 0602010 8586111000001106 \n",
-       "<U+22EE> <U+22EE>   <U+22EE> <U+22EE>         \n",
-       "58713  2004-10-27 0407010 12301811000001104\n",
-       "58714  2004-10-27 0407010 13004211000001106\n",
-       "58715  2004-10-27 0407010 13004111000001100\n",
-       "58716  2004-10-27 0407010 13004311000001103\n",
-       "58717  2004-10-27 0407010 13892911000001107\n",
-       "58718  2004-10-27 0407010 13892811000001102\n",
-       "58719  2004-10-27 0407010 13893011000001104\n",
-       "58720  2004-10-27 0407010 13893811000001105\n",
-       "58721  2004-10-27 0407010 16073011000001100\n",
-       "58722  2004-10-27 0407010 16665611000001108\n",
-       "58723  2004-10-27 0407010 16757111000001108\n",
-       "58724  2004-10-27 0407010 17853711000001103\n",
-       "58725  2004-10-27 0407010 18084511000001109\n",
-       "58726  2004-10-27 0407010 18595211000001106\n",
-       "58727  2004-10-27 0407010 18615611000001103\n",
-       "58728  2004-10-27 0407010 19200211000001107\n",
-       "58729  2004-10-27 0407010 19230711000001108\n",
-       "58730  2004-10-27 0407010 19805611000001105\n",
-       "58731  2004-10-27 0407010 21636411000001107\n",
-       "58732  2004-10-27 0407010 24014911000001102\n",
-       "58733  2004-10-27 0407010 24580811000001103\n",
-       "58734  2004-10-27 0407010 32637711000001103\n",
-       "58735  2004-10-27 0407010 33568211000001100\n",
-       "58736  2004-10-27 0407010 34625311000001101\n",
-       "58737  2004-10-27 0407010 34878611000001106\n",
-       "58738  2004-10-27 0407010 36441411000001100\n",
-       "58739  2004-10-27 0407010 36752911000001107\n",
-       "58740  2004-10-27 0407010 38063911000001100\n",
-       "58741  2004-10-27 0407010 38555311000001107\n",
-       "58742  2004-10-27 0407010 38555211000001104"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "gp_scripts_pre = gp_test %>% mutate(bnf_map = str_sub(bnf_code, 1, 7)) %>% left_join(bnf_snomed_mapping %>% select(bnf_map, sct_code), by=\"bnf_map\") %>% distinct() %>% drop_na(sct_code)\n",
-    "gp_scripts_pre "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 180,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<table>\n",
-       "<caption>A data.table: 5842014 × 10</caption>\n",
-       "<thead>\n",
-       "\t<tr><th scope=col>concept_id</th><th scope=col>concept_name</th><th scope=col>domain_id</th><th scope=col>vocabulary_id</th><th scope=col>concept_class_id</th><th scope=col>standard_concept</th><th scope=col>concept_code</th><th scope=col>valid_start_date</th><th scope=col>valid_end_date</th><th scope=col>invalid_reason</th></tr>\n",
-       "\t<tr><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
-       "</thead>\n",
-       "<tbody>\n",
-       "\t<tr><td>45756805</td><td>Pediatric Cardiology                                   </td><td>Provider</td><td>ABMS</td><td>Physician Specialty</td><td>S</td><td>OMOP4821938</td><td>19700101</td><td>20991231</td><td></td></tr>\n",
-       "\t<tr><td>45756804</td><td>Pediatric Anesthesiology                               </td><td>Provider</td><td>ABMS</td><td>Physician Specialty</td><td>S</td><td>OMOP4821939</td><td>19700101</td><td>20991231</td><td></td></tr>\n",
-       "\t<tr><td>45756803</td><td>Pathology-Anatomic/Pathology-Clinical                  </td><td>Provider</td><td>ABMS</td><td>Physician Specialty</td><td>S</td><td>OMOP4821940</td><td>19700101</td><td>20991231</td><td></td></tr>\n",
-       "\t<tr><td>45756802</td><td>Pathology - Pediatric                                  </td><td>Provider</td><td>ABMS</td><td>Physician Specialty</td><td>S</td><td>OMOP4821941</td><td>19700101</td><td>20991231</td><td></td></tr>\n",
-       "\t<tr><td>45756801</td><td>Pathology - Molecular Genetic                          </td><td>Provider</td><td>ABMS</td><td>Physician Specialty</td><td>S</td><td>OMOP4821942</td><td>19700101</td><td>20991231</td><td></td></tr>\n",
-       "\t<tr><td>45756800</td><td>Microbiology                                           </td><td>Provider</td><td>ABMS</td><td>Physician Specialty</td><td>S</td><td>OMOP4821943</td><td>19700101</td><td>20991231</td><td></td></tr>\n",
-       "\t<tr><td>45756799</td><td>Pathology - Hematology                                 </td><td>Provider</td><td>ABMS</td><td>Physician Specialty</td><td>S</td><td>OMOP4821944</td><td>19700101</td><td>20991231</td><td></td></tr>\n",
-       "\t<tr><td>45756798</td><td>Pathology - Forensic                                   </td><td>Provider</td><td>ABMS</td><td>Physician Specialty</td><td>S</td><td>OMOP4821945</td><td>19700101</td><td>20991231</td><td></td></tr>\n",
-       "\t<tr><td>45756797</td><td>Pathology - Clinical                                   </td><td>Provider</td><td>ABMS</td><td>Physician Specialty</td><td>S</td><td>OMOP4821946</td><td>19700101</td><td>20991231</td><td></td></tr>\n",
-       "\t<tr><td>45756796</td><td>Pathology - Chemical                                   </td><td>Provider</td><td>ABMS</td><td>Physician Specialty</td><td>S</td><td>OMOP4821947</td><td>19700101</td><td>20991231</td><td></td></tr>\n",
-       "\t<tr><td>45756795</td><td>Pathology - Anatomic                                   </td><td>Provider</td><td>ABMS</td><td>Physician Specialty</td><td>S</td><td>OMOP4821948</td><td>19700101</td><td>20991231</td><td></td></tr>\n",
-       "\t<tr><td>45756794</td><td>Orthopaedic Sports Medicine                            </td><td>Provider</td><td>ABMS</td><td>Physician Specialty</td><td>S</td><td>OMOP4821949</td><td>19700101</td><td>20991231</td><td></td></tr>\n",
-       "\t<tr><td>45756793</td><td>Nuclear Radiology                                      </td><td>Provider</td><td>ABMS</td><td>Physician Specialty</td><td>S</td><td>OMOP4821950</td><td>19700101</td><td>20991231</td><td></td></tr>\n",
-       "\t<tr><td>45756792</td><td>Neurotology                                            </td><td>Provider</td><td>ABMS</td><td>Physician Specialty</td><td>S</td><td>OMOP4821951</td><td>19700101</td><td>20991231</td><td></td></tr>\n",
-       "\t<tr><td>45756791</td><td>Neuroradiology                                         </td><td>Provider</td><td>ABMS</td><td>Physician Specialty</td><td>S</td><td>OMOP4821952</td><td>19700101</td><td>20991231</td><td></td></tr>\n",
-       "\t<tr><td>45756790</td><td>Neuropathology                                         </td><td>Provider</td><td>ABMS</td><td>Physician Specialty</td><td>S</td><td>OMOP4821953</td><td>19700101</td><td>20991231</td><td></td></tr>\n",
-       "\t<tr><td>45756789</td><td>Neuromuscular Medicine                                 </td><td>Provider</td><td>ABMS</td><td>Physician Specialty</td><td>S</td><td>OMOP4821954</td><td>19700101</td><td>20991231</td><td></td></tr>\n",
-       "\t<tr><td>45756788</td><td>Neurology with Special Qualification in Child Neurology</td><td>Provider</td><td>ABMS</td><td>Physician Specialty</td><td>S</td><td>OMOP4821955</td><td>19700101</td><td>20991231</td><td></td></tr>\n",
-       "\t<tr><td>45756787</td><td>Neurodevelopmental Disabilities                        </td><td>Provider</td><td>ABMS</td><td>Physician Specialty</td><td>S</td><td>OMOP4821956</td><td>19700101</td><td>20991231</td><td></td></tr>\n",
-       "\t<tr><td>45756786</td><td>Neonatal-Perinatal Medicine                            </td><td>Provider</td><td>ABMS</td><td>Physician Specialty</td><td>S</td><td>OMOP4821957</td><td>19700101</td><td>20991231</td><td></td></tr>\n",
-       "\t<tr><td>45756785</td><td>Molecular Genetic Pathology                            </td><td>Provider</td><td>ABMS</td><td>Physician Specialty</td><td> </td><td>OMOP4821958</td><td>19700101</td><td>20991231</td><td></td></tr>\n",
-       "\t<tr><td>45756784</td><td>Medical Toxicology                                     </td><td>Provider</td><td>ABMS</td><td>Physician Specialty</td><td> </td><td>OMOP4821959</td><td>19700101</td><td>20991231</td><td></td></tr>\n",
-       "\t<tr><td>45756783</td><td>Medical Physics                                        </td><td>Provider</td><td>ABMS</td><td>Physician Specialty</td><td>S</td><td>OMOP4821960</td><td>19700101</td><td>20991231</td><td></td></tr>\n",
-       "\t<tr><td>45756782</td><td>Medical Genetics and Genomics                          </td><td>Provider</td><td>ABMS</td><td>Physician Specialty</td><td> </td><td>OMOP4821961</td><td>19700101</td><td>20991231</td><td></td></tr>\n",
-       "\t<tr><td>45756781</td><td>Medical Biochemical Genetics                           </td><td>Provider</td><td>ABMS</td><td>Physician Specialty</td><td>S</td><td>OMOP4821962</td><td>19700101</td><td>20991231</td><td></td></tr>\n",
-       "\t<tr><td>45756780</td><td>Maternal and Fetal Medicine                            </td><td>Provider</td><td>ABMS</td><td>Physician Specialty</td><td>S</td><td>OMOP4821963</td><td>19700101</td><td>20991231</td><td></td></tr>\n",
-       "\t<tr><td>45756779</td><td>Interventional Cardiology                              </td><td>Provider</td><td>ABMS</td><td>Physician Specialty</td><td> </td><td>OMOP4821964</td><td>19700101</td><td>20991231</td><td></td></tr>\n",
-       "\t<tr><td>45756778</td><td>Internal Medicine - Critical Care Medicine             </td><td>Provider</td><td>ABMS</td><td>Physician Specialty</td><td>S</td><td>OMOP4821965</td><td>19700101</td><td>20991231</td><td></td></tr>\n",
-       "\t<tr><td>45756777</td><td>Hospice and Palliative Medicine                        </td><td>Provider</td><td>ABMS</td><td>Physician Specialty</td><td>S</td><td>OMOP4821966</td><td>19700101</td><td>20991231</td><td></td></tr>\n",
-       "\t<tr><td>45756776</td><td>Geriatric Psychiatry                                   </td><td>Provider</td><td>ABMS</td><td>Physician Specialty</td><td>S</td><td>OMOP4821967</td><td>19700101</td><td>20991231</td><td></td></tr>\n",
-       "\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
-       "\t<tr><td>36080587</td><td>Ruxience             </td><td>Drug</td><td>dm+d</td><td>Brand Name</td><td></td><td>OMOP4977061</td><td>19700101</td><td>20991231</td><td></td></tr>\n",
-       "\t<tr><td>36081833</td><td>Uropyrine            </td><td>Drug</td><td>dm+d</td><td>Brand Name</td><td></td><td>OMOP4977062</td><td>19700101</td><td>20991231</td><td></td></tr>\n",
-       "\t<tr><td>36081455</td><td>Thyrotardin-Inject   </td><td>Drug</td><td>dm+d</td><td>Brand Name</td><td></td><td>OMOP4977063</td><td>19700101</td><td>20991231</td><td></td></tr>\n",
-       "\t<tr><td>36070943</td><td>Adynovi              </td><td>Drug</td><td>dm+d</td><td>Brand Name</td><td></td><td>OMOP4977064</td><td>19700101</td><td>20991231</td><td></td></tr>\n",
-       "\t<tr><td>36073151</td><td>Combiprasal          </td><td>Drug</td><td>dm+d</td><td>Brand Name</td><td></td><td>OMOP4977065</td><td>19700101</td><td>20991231</td><td></td></tr>\n",
-       "\t<tr><td>36046146</td><td>PulmoClear           </td><td>Drug</td><td>dm+d</td><td>Brand Name</td><td></td><td>OMOP4977066</td><td>19700101</td><td>20991231</td><td></td></tr>\n",
-       "\t<tr><td>36071153</td><td>Ambelina             </td><td>Drug</td><td>dm+d</td><td>Brand Name</td><td></td><td>OMOP4977067</td><td>19700101</td><td>20991231</td><td></td></tr>\n",
-       "\t<tr><td>36046221</td><td>AlfacalEss           </td><td>Drug</td><td>dm+d</td><td>Brand Name</td><td></td><td>OMOP4977068</td><td>19700101</td><td>20991231</td><td></td></tr>\n",
-       "\t<tr><td>36081267</td><td>TauriDose            </td><td>Drug</td><td>dm+d</td><td>Brand Name</td><td></td><td>OMOP4977069</td><td>19700101</td><td>20991231</td><td></td></tr>\n",
-       "\t<tr><td>36046222</td><td>UroFlush             </td><td>Drug</td><td>dm+d</td><td>Brand Name</td><td></td><td>OMOP4977070</td><td>19700101</td><td>20991231</td><td></td></tr>\n",
-       "\t<tr><td>36076935</td><td>IroFate              </td><td>Drug</td><td>dm+d</td><td>Brand Name</td><td></td><td>OMOP4977071</td><td>19700101</td><td>20991231</td><td></td></tr>\n",
-       "\t<tr><td>36080331</td><td>RestorD3             </td><td>Drug</td><td>dm+d</td><td>Brand Name</td><td></td><td>OMOP4977072</td><td>19700101</td><td>20991231</td><td></td></tr>\n",
-       "\t<tr><td>36081270</td><td>Tavlesse             </td><td>Drug</td><td>dm+d</td><td>Brand Name</td><td></td><td>OMOP4977073</td><td>19700101</td><td>20991231</td><td></td></tr>\n",
-       "\t<tr><td>36077754</td><td>MagnaLac             </td><td>Drug</td><td>dm+d</td><td>Brand Name</td><td></td><td>OMOP4977074</td><td>19700101</td><td>20991231</td><td></td></tr>\n",
-       "\t<tr><td>36068497</td><td>Dutrozen             </td><td>Drug</td><td>dm+d</td><td>Brand Name</td><td></td><td>OMOP4977075</td><td>19700101</td><td>20991231</td><td></td></tr>\n",
-       "\t<tr><td>36080621</td><td>Scenesse             </td><td>Drug</td><td>dm+d</td><td>Brand Name</td><td></td><td>OMOP4977076</td><td>19700101</td><td>20991231</td><td></td></tr>\n",
-       "\t<tr><td>36077757</td><td>MagnaTrate           </td><td>Drug</td><td>dm+d</td><td>Brand Name</td><td></td><td>OMOP4977077</td><td>19700101</td><td>20991231</td><td></td></tr>\n",
-       "\t<tr><td>36076553</td><td>Gonasi               </td><td>Drug</td><td>dm+d</td><td>Brand Name</td><td></td><td>OMOP4977078</td><td>19700101</td><td>20991231</td><td></td></tr>\n",
-       "\t<tr><td>36077330</td><td>Lenzetto             </td><td>Drug</td><td>dm+d</td><td>Brand Name</td><td></td><td>OMOP4977079</td><td>19700101</td><td>20991231</td><td></td></tr>\n",
-       "\t<tr><td>36071438</td><td>Aspire Allergy Relief</td><td>Drug</td><td>dm+d</td><td>Brand Name</td><td></td><td>OMOP4977080</td><td>19700101</td><td>20991231</td><td></td></tr>\n",
-       "\t<tr><td>36078445</td><td>Myloxifin            </td><td>Drug</td><td>dm+d</td><td>Brand Name</td><td></td><td>OMOP4977081</td><td>19700101</td><td>20991231</td><td></td></tr>\n",
-       "\t<tr><td>36070312</td><td>Polivy               </td><td>Drug</td><td>dm+d</td><td>Brand Name</td><td></td><td>OMOP4977082</td><td>19700101</td><td>20991231</td><td></td></tr>\n",
-       "\t<tr><td>36078737</td><td>Norditropin Flexpro  </td><td>Drug</td><td>dm+d</td><td>Brand Name</td><td></td><td>OMOP4977083</td><td>19700101</td><td>20991231</td><td></td></tr>\n",
-       "\t<tr><td>36079462</td><td>PF                   </td><td>Drug</td><td>dm+d</td><td>Brand Name</td><td></td><td>OMOP4977084</td><td>19700101</td><td>20991231</td><td></td></tr>\n",
-       "\t<tr><td>36082335</td><td>Zirabev              </td><td>Drug</td><td>dm+d</td><td>Brand Name</td><td></td><td>OMOP4977085</td><td>19700101</td><td>20991231</td><td></td></tr>\n",
-       "\t<tr><td>36080079</td><td>Quest L-Lysine       </td><td>Drug</td><td>dm+d</td><td>Brand Name</td><td></td><td>OMOP4977086</td><td>19700101</td><td>20991231</td><td></td></tr>\n",
-       "\t<tr><td>36076118</td><td>FoliSol              </td><td>Drug</td><td>dm+d</td><td>Brand Name</td><td></td><td>OMOP4977087</td><td>19700101</td><td>20991231</td><td></td></tr>\n",
-       "\t<tr><td>36049404</td><td>Munuza               </td><td>Drug</td><td>dm+d</td><td>Brand Name</td><td></td><td>OMOP4977088</td><td>19700101</td><td>20991231</td><td></td></tr>\n",
-       "\t<tr><td>36076134</td><td>FORTUM MONO          </td><td>Drug</td><td>dm+d</td><td>Brand Name</td><td></td><td>OMOP4977089</td><td>19700101</td><td>20991231</td><td></td></tr>\n",
-       "\t<tr><td>36076588</td><td>Hashmats Health A-Z  </td><td>Drug</td><td>dm+d</td><td>Brand Name</td><td></td><td>OMOP4977090</td><td>19700101</td><td>20991231</td><td></td></tr>\n",
-       "</tbody>\n",
-       "</table>\n"
-      ],
-      "text/latex": [
-       "A data.table: 5842014 × 10\n",
-       "\\begin{tabular}{llllllllll}\n",
-       " concept\\_id & concept\\_name & domain\\_id & vocabulary\\_id & concept\\_class\\_id & standard\\_concept & concept\\_code & valid\\_start\\_date & valid\\_end\\_date & invalid\\_reason\\\\\n",
-       " <int> & <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <int> & <int> & <chr>\\\\\n",
-       "\\hline\n",
-       "\t 45756805 & Pediatric Cardiology                                    & Provider & ABMS & Physician Specialty & S & OMOP4821938 & 19700101 & 20991231 & \\\\\n",
-       "\t 45756804 & Pediatric Anesthesiology                                & Provider & ABMS & Physician Specialty & S & OMOP4821939 & 19700101 & 20991231 & \\\\\n",
-       "\t 45756803 & Pathology-Anatomic/Pathology-Clinical                   & Provider & ABMS & Physician Specialty & S & OMOP4821940 & 19700101 & 20991231 & \\\\\n",
-       "\t 45756802 & Pathology - Pediatric                                   & Provider & ABMS & Physician Specialty & S & OMOP4821941 & 19700101 & 20991231 & \\\\\n",
-       "\t 45756801 & Pathology - Molecular Genetic                           & Provider & ABMS & Physician Specialty & S & OMOP4821942 & 19700101 & 20991231 & \\\\\n",
-       "\t 45756800 & Microbiology                                            & Provider & ABMS & Physician Specialty & S & OMOP4821943 & 19700101 & 20991231 & \\\\\n",
-       "\t 45756799 & Pathology - Hematology                                  & Provider & ABMS & Physician Specialty & S & OMOP4821944 & 19700101 & 20991231 & \\\\\n",
-       "\t 45756798 & Pathology - Forensic                                    & Provider & ABMS & Physician Specialty & S & OMOP4821945 & 19700101 & 20991231 & \\\\\n",
-       "\t 45756797 & Pathology - Clinical                                    & Provider & ABMS & Physician Specialty & S & OMOP4821946 & 19700101 & 20991231 & \\\\\n",
-       "\t 45756796 & Pathology - Chemical                                    & Provider & ABMS & Physician Specialty & S & OMOP4821947 & 19700101 & 20991231 & \\\\\n",
-       "\t 45756795 & Pathology - Anatomic                                    & Provider & ABMS & Physician Specialty & S & OMOP4821948 & 19700101 & 20991231 & \\\\\n",
-       "\t 45756794 & Orthopaedic Sports Medicine                             & Provider & ABMS & Physician Specialty & S & OMOP4821949 & 19700101 & 20991231 & \\\\\n",
-       "\t 45756793 & Nuclear Radiology                                       & Provider & ABMS & Physician Specialty & S & OMOP4821950 & 19700101 & 20991231 & \\\\\n",
-       "\t 45756792 & Neurotology                                             & Provider & ABMS & Physician Specialty & S & OMOP4821951 & 19700101 & 20991231 & \\\\\n",
-       "\t 45756791 & Neuroradiology                                          & Provider & ABMS & Physician Specialty & S & OMOP4821952 & 19700101 & 20991231 & \\\\\n",
-       "\t 45756790 & Neuropathology                                          & Provider & ABMS & Physician Specialty & S & OMOP4821953 & 19700101 & 20991231 & \\\\\n",
-       "\t 45756789 & Neuromuscular Medicine                                  & Provider & ABMS & Physician Specialty & S & OMOP4821954 & 19700101 & 20991231 & \\\\\n",
-       "\t 45756788 & Neurology with Special Qualification in Child Neurology & Provider & ABMS & Physician Specialty & S & OMOP4821955 & 19700101 & 20991231 & \\\\\n",
-       "\t 45756787 & Neurodevelopmental Disabilities                         & Provider & ABMS & Physician Specialty & S & OMOP4821956 & 19700101 & 20991231 & \\\\\n",
-       "\t 45756786 & Neonatal-Perinatal Medicine                             & Provider & ABMS & Physician Specialty & S & OMOP4821957 & 19700101 & 20991231 & \\\\\n",
-       "\t 45756785 & Molecular Genetic Pathology                             & Provider & ABMS & Physician Specialty &   & OMOP4821958 & 19700101 & 20991231 & \\\\\n",
-       "\t 45756784 & Medical Toxicology                                      & Provider & ABMS & Physician Specialty &   & OMOP4821959 & 19700101 & 20991231 & \\\\\n",
-       "\t 45756783 & Medical Physics                                         & Provider & ABMS & Physician Specialty & S & OMOP4821960 & 19700101 & 20991231 & \\\\\n",
-       "\t 45756782 & Medical Genetics and Genomics                           & Provider & ABMS & Physician Specialty &   & OMOP4821961 & 19700101 & 20991231 & \\\\\n",
-       "\t 45756781 & Medical Biochemical Genetics                            & Provider & ABMS & Physician Specialty & S & OMOP4821962 & 19700101 & 20991231 & \\\\\n",
-       "\t 45756780 & Maternal and Fetal Medicine                             & Provider & ABMS & Physician Specialty & S & OMOP4821963 & 19700101 & 20991231 & \\\\\n",
-       "\t 45756779 & Interventional Cardiology                               & Provider & ABMS & Physician Specialty &   & OMOP4821964 & 19700101 & 20991231 & \\\\\n",
-       "\t 45756778 & Internal Medicine - Critical Care Medicine              & Provider & ABMS & Physician Specialty & S & OMOP4821965 & 19700101 & 20991231 & \\\\\n",
-       "\t 45756777 & Hospice and Palliative Medicine                         & Provider & ABMS & Physician Specialty & S & OMOP4821966 & 19700101 & 20991231 & \\\\\n",
-       "\t 45756776 & Geriatric Psychiatry                                    & Provider & ABMS & Physician Specialty & S & OMOP4821967 & 19700101 & 20991231 & \\\\\n",
-       "\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
-       "\t 36080587 & Ruxience              & Drug & dm+d & Brand Name &  & OMOP4977061 & 19700101 & 20991231 & \\\\\n",
-       "\t 36081833 & Uropyrine             & Drug & dm+d & Brand Name &  & OMOP4977062 & 19700101 & 20991231 & \\\\\n",
-       "\t 36081455 & Thyrotardin-Inject    & Drug & dm+d & Brand Name &  & OMOP4977063 & 19700101 & 20991231 & \\\\\n",
-       "\t 36070943 & Adynovi               & Drug & dm+d & Brand Name &  & OMOP4977064 & 19700101 & 20991231 & \\\\\n",
-       "\t 36073151 & Combiprasal           & Drug & dm+d & Brand Name &  & OMOP4977065 & 19700101 & 20991231 & \\\\\n",
-       "\t 36046146 & PulmoClear            & Drug & dm+d & Brand Name &  & OMOP4977066 & 19700101 & 20991231 & \\\\\n",
-       "\t 36071153 & Ambelina              & Drug & dm+d & Brand Name &  & OMOP4977067 & 19700101 & 20991231 & \\\\\n",
-       "\t 36046221 & AlfacalEss            & Drug & dm+d & Brand Name &  & OMOP4977068 & 19700101 & 20991231 & \\\\\n",
-       "\t 36081267 & TauriDose             & Drug & dm+d & Brand Name &  & OMOP4977069 & 19700101 & 20991231 & \\\\\n",
-       "\t 36046222 & UroFlush              & Drug & dm+d & Brand Name &  & OMOP4977070 & 19700101 & 20991231 & \\\\\n",
-       "\t 36076935 & IroFate               & Drug & dm+d & Brand Name &  & OMOP4977071 & 19700101 & 20991231 & \\\\\n",
-       "\t 36080331 & RestorD3              & Drug & dm+d & Brand Name &  & OMOP4977072 & 19700101 & 20991231 & \\\\\n",
-       "\t 36081270 & Tavlesse              & Drug & dm+d & Brand Name &  & OMOP4977073 & 19700101 & 20991231 & \\\\\n",
-       "\t 36077754 & MagnaLac              & Drug & dm+d & Brand Name &  & OMOP4977074 & 19700101 & 20991231 & \\\\\n",
-       "\t 36068497 & Dutrozen              & Drug & dm+d & Brand Name &  & OMOP4977075 & 19700101 & 20991231 & \\\\\n",
-       "\t 36080621 & Scenesse              & Drug & dm+d & Brand Name &  & OMOP4977076 & 19700101 & 20991231 & \\\\\n",
-       "\t 36077757 & MagnaTrate            & Drug & dm+d & Brand Name &  & OMOP4977077 & 19700101 & 20991231 & \\\\\n",
-       "\t 36076553 & Gonasi                & Drug & dm+d & Brand Name &  & OMOP4977078 & 19700101 & 20991231 & \\\\\n",
-       "\t 36077330 & Lenzetto              & Drug & dm+d & Brand Name &  & OMOP4977079 & 19700101 & 20991231 & \\\\\n",
-       "\t 36071438 & Aspire Allergy Relief & Drug & dm+d & Brand Name &  & OMOP4977080 & 19700101 & 20991231 & \\\\\n",
-       "\t 36078445 & Myloxifin             & Drug & dm+d & Brand Name &  & OMOP4977081 & 19700101 & 20991231 & \\\\\n",
-       "\t 36070312 & Polivy                & Drug & dm+d & Brand Name &  & OMOP4977082 & 19700101 & 20991231 & \\\\\n",
-       "\t 36078737 & Norditropin Flexpro   & Drug & dm+d & Brand Name &  & OMOP4977083 & 19700101 & 20991231 & \\\\\n",
-       "\t 36079462 & PF                    & Drug & dm+d & Brand Name &  & OMOP4977084 & 19700101 & 20991231 & \\\\\n",
-       "\t 36082335 & Zirabev               & Drug & dm+d & Brand Name &  & OMOP4977085 & 19700101 & 20991231 & \\\\\n",
-       "\t 36080079 & Quest L-Lysine        & Drug & dm+d & Brand Name &  & OMOP4977086 & 19700101 & 20991231 & \\\\\n",
-       "\t 36076118 & FoliSol               & Drug & dm+d & Brand Name &  & OMOP4977087 & 19700101 & 20991231 & \\\\\n",
-       "\t 36049404 & Munuza                & Drug & dm+d & Brand Name &  & OMOP4977088 & 19700101 & 20991231 & \\\\\n",
-       "\t 36076134 & FORTUM MONO           & Drug & dm+d & Brand Name &  & OMOP4977089 & 19700101 & 20991231 & \\\\\n",
-       "\t 36076588 & Hashmats Health A-Z   & Drug & dm+d & Brand Name &  & OMOP4977090 & 19700101 & 20991231 & \\\\\n",
-       "\\end{tabular}\n"
-      ],
-      "text/markdown": [
-       "\n",
-       "A data.table: 5842014 × 10\n",
-       "\n",
-       "| concept_id &lt;int&gt; | concept_name &lt;chr&gt; | domain_id &lt;chr&gt; | vocabulary_id &lt;chr&gt; | concept_class_id &lt;chr&gt; | standard_concept &lt;chr&gt; | concept_code &lt;chr&gt; | valid_start_date &lt;int&gt; | valid_end_date &lt;int&gt; | invalid_reason &lt;chr&gt; |\n",
-       "|---|---|---|---|---|---|---|---|---|---|\n",
-       "| 45756805 | Pediatric Cardiology                                    | Provider | ABMS | Physician Specialty | S | OMOP4821938 | 19700101 | 20991231 | <!----> |\n",
-       "| 45756804 | Pediatric Anesthesiology                                | Provider | ABMS | Physician Specialty | S | OMOP4821939 | 19700101 | 20991231 | <!----> |\n",
-       "| 45756803 | Pathology-Anatomic/Pathology-Clinical                   | Provider | ABMS | Physician Specialty | S | OMOP4821940 | 19700101 | 20991231 | <!----> |\n",
-       "| 45756802 | Pathology - Pediatric                                   | Provider | ABMS | Physician Specialty | S | OMOP4821941 | 19700101 | 20991231 | <!----> |\n",
-       "| 45756801 | Pathology - Molecular Genetic                           | Provider | ABMS | Physician Specialty | S | OMOP4821942 | 19700101 | 20991231 | <!----> |\n",
-       "| 45756800 | Microbiology                                            | Provider | ABMS | Physician Specialty | S | OMOP4821943 | 19700101 | 20991231 | <!----> |\n",
-       "| 45756799 | Pathology - Hematology                                  | Provider | ABMS | Physician Specialty | S | OMOP4821944 | 19700101 | 20991231 | <!----> |\n",
-       "| 45756798 | Pathology - Forensic                                    | Provider | ABMS | Physician Specialty | S | OMOP4821945 | 19700101 | 20991231 | <!----> |\n",
-       "| 45756797 | Pathology - Clinical                                    | Provider | ABMS | Physician Specialty | S | OMOP4821946 | 19700101 | 20991231 | <!----> |\n",
-       "| 45756796 | Pathology - Chemical                                    | Provider | ABMS | Physician Specialty | S | OMOP4821947 | 19700101 | 20991231 | <!----> |\n",
-       "| 45756795 | Pathology - Anatomic                                    | Provider | ABMS | Physician Specialty | S | OMOP4821948 | 19700101 | 20991231 | <!----> |\n",
-       "| 45756794 | Orthopaedic Sports Medicine                             | Provider | ABMS | Physician Specialty | S | OMOP4821949 | 19700101 | 20991231 | <!----> |\n",
-       "| 45756793 | Nuclear Radiology                                       | Provider | ABMS | Physician Specialty | S | OMOP4821950 | 19700101 | 20991231 | <!----> |\n",
-       "| 45756792 | Neurotology                                             | Provider | ABMS | Physician Specialty | S | OMOP4821951 | 19700101 | 20991231 | <!----> |\n",
-       "| 45756791 | Neuroradiology                                          | Provider | ABMS | Physician Specialty | S | OMOP4821952 | 19700101 | 20991231 | <!----> |\n",
-       "| 45756790 | Neuropathology                                          | Provider | ABMS | Physician Specialty | S | OMOP4821953 | 19700101 | 20991231 | <!----> |\n",
-       "| 45756789 | Neuromuscular Medicine                                  | Provider | ABMS | Physician Specialty | S | OMOP4821954 | 19700101 | 20991231 | <!----> |\n",
-       "| 45756788 | Neurology with Special Qualification in Child Neurology | Provider | ABMS | Physician Specialty | S | OMOP4821955 | 19700101 | 20991231 | <!----> |\n",
-       "| 45756787 | Neurodevelopmental Disabilities                         | Provider | ABMS | Physician Specialty | S | OMOP4821956 | 19700101 | 20991231 | <!----> |\n",
-       "| 45756786 | Neonatal-Perinatal Medicine                             | Provider | ABMS | Physician Specialty | S | OMOP4821957 | 19700101 | 20991231 | <!----> |\n",
-       "| 45756785 | Molecular Genetic Pathology                             | Provider | ABMS | Physician Specialty | <!----> | OMOP4821958 | 19700101 | 20991231 | <!----> |\n",
-       "| 45756784 | Medical Toxicology                                      | Provider | ABMS | Physician Specialty | <!----> | OMOP4821959 | 19700101 | 20991231 | <!----> |\n",
-       "| 45756783 | Medical Physics                                         | Provider | ABMS | Physician Specialty | S | OMOP4821960 | 19700101 | 20991231 | <!----> |\n",
-       "| 45756782 | Medical Genetics and Genomics                           | Provider | ABMS | Physician Specialty | <!----> | OMOP4821961 | 19700101 | 20991231 | <!----> |\n",
-       "| 45756781 | Medical Biochemical Genetics                            | Provider | ABMS | Physician Specialty | S | OMOP4821962 | 19700101 | 20991231 | <!----> |\n",
-       "| 45756780 | Maternal and Fetal Medicine                             | Provider | ABMS | Physician Specialty | S | OMOP4821963 | 19700101 | 20991231 | <!----> |\n",
-       "| 45756779 | Interventional Cardiology                               | Provider | ABMS | Physician Specialty | <!----> | OMOP4821964 | 19700101 | 20991231 | <!----> |\n",
-       "| 45756778 | Internal Medicine - Critical Care Medicine              | Provider | ABMS | Physician Specialty | S | OMOP4821965 | 19700101 | 20991231 | <!----> |\n",
-       "| 45756777 | Hospice and Palliative Medicine                         | Provider | ABMS | Physician Specialty | S | OMOP4821966 | 19700101 | 20991231 | <!----> |\n",
-       "| 45756776 | Geriatric Psychiatry                                    | Provider | ABMS | Physician Specialty | S | OMOP4821967 | 19700101 | 20991231 | <!----> |\n",
-       "| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |\n",
-       "| 36080587 | Ruxience              | Drug | dm+d | Brand Name | <!----> | OMOP4977061 | 19700101 | 20991231 | <!----> |\n",
-       "| 36081833 | Uropyrine             | Drug | dm+d | Brand Name | <!----> | OMOP4977062 | 19700101 | 20991231 | <!----> |\n",
-       "| 36081455 | Thyrotardin-Inject    | Drug | dm+d | Brand Name | <!----> | OMOP4977063 | 19700101 | 20991231 | <!----> |\n",
-       "| 36070943 | Adynovi               | Drug | dm+d | Brand Name | <!----> | OMOP4977064 | 19700101 | 20991231 | <!----> |\n",
-       "| 36073151 | Combiprasal           | Drug | dm+d | Brand Name | <!----> | OMOP4977065 | 19700101 | 20991231 | <!----> |\n",
-       "| 36046146 | PulmoClear            | Drug | dm+d | Brand Name | <!----> | OMOP4977066 | 19700101 | 20991231 | <!----> |\n",
-       "| 36071153 | Ambelina              | Drug | dm+d | Brand Name | <!----> | OMOP4977067 | 19700101 | 20991231 | <!----> |\n",
-       "| 36046221 | AlfacalEss            | Drug | dm+d | Brand Name | <!----> | OMOP4977068 | 19700101 | 20991231 | <!----> |\n",
-       "| 36081267 | TauriDose             | Drug | dm+d | Brand Name | <!----> | OMOP4977069 | 19700101 | 20991231 | <!----> |\n",
-       "| 36046222 | UroFlush              | Drug | dm+d | Brand Name | <!----> | OMOP4977070 | 19700101 | 20991231 | <!----> |\n",
-       "| 36076935 | IroFate               | Drug | dm+d | Brand Name | <!----> | OMOP4977071 | 19700101 | 20991231 | <!----> |\n",
-       "| 36080331 | RestorD3              | Drug | dm+d | Brand Name | <!----> | OMOP4977072 | 19700101 | 20991231 | <!----> |\n",
-       "| 36081270 | Tavlesse              | Drug | dm+d | Brand Name | <!----> | OMOP4977073 | 19700101 | 20991231 | <!----> |\n",
-       "| 36077754 | MagnaLac              | Drug | dm+d | Brand Name | <!----> | OMOP4977074 | 19700101 | 20991231 | <!----> |\n",
-       "| 36068497 | Dutrozen              | Drug | dm+d | Brand Name | <!----> | OMOP4977075 | 19700101 | 20991231 | <!----> |\n",
-       "| 36080621 | Scenesse              | Drug | dm+d | Brand Name | <!----> | OMOP4977076 | 19700101 | 20991231 | <!----> |\n",
-       "| 36077757 | MagnaTrate            | Drug | dm+d | Brand Name | <!----> | OMOP4977077 | 19700101 | 20991231 | <!----> |\n",
-       "| 36076553 | Gonasi                | Drug | dm+d | Brand Name | <!----> | OMOP4977078 | 19700101 | 20991231 | <!----> |\n",
-       "| 36077330 | Lenzetto              | Drug | dm+d | Brand Name | <!----> | OMOP4977079 | 19700101 | 20991231 | <!----> |\n",
-       "| 36071438 | Aspire Allergy Relief | Drug | dm+d | Brand Name | <!----> | OMOP4977080 | 19700101 | 20991231 | <!----> |\n",
-       "| 36078445 | Myloxifin             | Drug | dm+d | Brand Name | <!----> | OMOP4977081 | 19700101 | 20991231 | <!----> |\n",
-       "| 36070312 | Polivy                | Drug | dm+d | Brand Name | <!----> | OMOP4977082 | 19700101 | 20991231 | <!----> |\n",
-       "| 36078737 | Norditropin Flexpro   | Drug | dm+d | Brand Name | <!----> | OMOP4977083 | 19700101 | 20991231 | <!----> |\n",
-       "| 36079462 | PF                    | Drug | dm+d | Brand Name | <!----> | OMOP4977084 | 19700101 | 20991231 | <!----> |\n",
-       "| 36082335 | Zirabev               | Drug | dm+d | Brand Name | <!----> | OMOP4977085 | 19700101 | 20991231 | <!----> |\n",
-       "| 36080079 | Quest L-Lysine        | Drug | dm+d | Brand Name | <!----> | OMOP4977086 | 19700101 | 20991231 | <!----> |\n",
-       "| 36076118 | FoliSol               | Drug | dm+d | Brand Name | <!----> | OMOP4977087 | 19700101 | 20991231 | <!----> |\n",
-       "| 36049404 | Munuza                | Drug | dm+d | Brand Name | <!----> | OMOP4977088 | 19700101 | 20991231 | <!----> |\n",
-       "| 36076134 | FORTUM MONO           | Drug | dm+d | Brand Name | <!----> | OMOP4977089 | 19700101 | 20991231 | <!----> |\n",
-       "| 36076588 | Hashmats Health A-Z   | Drug | dm+d | Brand Name | <!----> | OMOP4977090 | 19700101 | 20991231 | <!----> |\n",
-       "\n"
-      ],
-      "text/plain": [
-       "        concept_id concept_name                                           \n",
-       "1       45756805   Pediatric Cardiology                                   \n",
-       "2       45756804   Pediatric Anesthesiology                               \n",
-       "3       45756803   Pathology-Anatomic/Pathology-Clinical                  \n",
-       "4       45756802   Pathology - Pediatric                                  \n",
-       "5       45756801   Pathology - Molecular Genetic                          \n",
-       "6       45756800   Microbiology                                           \n",
-       "7       45756799   Pathology - Hematology                                 \n",
-       "8       45756798   Pathology - Forensic                                   \n",
-       "9       45756797   Pathology - Clinical                                   \n",
-       "10      45756796   Pathology - Chemical                                   \n",
-       "11      45756795   Pathology - Anatomic                                   \n",
-       "12      45756794   Orthopaedic Sports Medicine                            \n",
-       "13      45756793   Nuclear Radiology                                      \n",
-       "14      45756792   Neurotology                                            \n",
-       "15      45756791   Neuroradiology                                         \n",
-       "16      45756790   Neuropathology                                         \n",
-       "17      45756789   Neuromuscular Medicine                                 \n",
-       "18      45756788   Neurology with Special Qualification in Child Neurology\n",
-       "19      45756787   Neurodevelopmental Disabilities                        \n",
-       "20      45756786   Neonatal-Perinatal Medicine                            \n",
-       "21      45756785   Molecular Genetic Pathology                            \n",
-       "22      45756784   Medical Toxicology                                     \n",
-       "23      45756783   Medical Physics                                        \n",
-       "24      45756782   Medical Genetics and Genomics                          \n",
-       "25      45756781   Medical Biochemical Genetics                           \n",
-       "26      45756780   Maternal and Fetal Medicine                            \n",
-       "27      45756779   Interventional Cardiology                              \n",
-       "28      45756778   Internal Medicine - Critical Care Medicine             \n",
-       "29      45756777   Hospice and Palliative Medicine                        \n",
-       "30      45756776   Geriatric Psychiatry                                   \n",
-       "<U+22EE>  <U+22EE>   <U+22EE>                                               \n",
-       "5841985 36080587   Ruxience                                               \n",
-       "5841986 36081833   Uropyrine                                              \n",
-       "5841987 36081455   Thyrotardin-Inject                                     \n",
-       "5841988 36070943   Adynovi                                                \n",
-       "5841989 36073151   Combiprasal                                            \n",
-       "5841990 36046146   PulmoClear                                             \n",
-       "5841991 36071153   Ambelina                                               \n",
-       "5841992 36046221   AlfacalEss                                             \n",
-       "5841993 36081267   TauriDose                                              \n",
-       "5841994 36046222   UroFlush                                               \n",
-       "5841995 36076935   IroFate                                                \n",
-       "5841996 36080331   RestorD3                                               \n",
-       "5841997 36081270   Tavlesse                                               \n",
-       "5841998 36077754   MagnaLac                                               \n",
-       "5841999 36068497   Dutrozen                                               \n",
-       "5842000 36080621   Scenesse                                               \n",
-       "5842001 36077757   MagnaTrate                                             \n",
-       "5842002 36076553   Gonasi                                                 \n",
-       "5842003 36077330   Lenzetto                                               \n",
-       "5842004 36071438   Aspire Allergy Relief                                  \n",
-       "5842005 36078445   Myloxifin                                              \n",
-       "5842006 36070312   Polivy                                                 \n",
-       "5842007 36078737   Norditropin Flexpro                                    \n",
-       "5842008 36079462   PF                                                     \n",
-       "5842009 36082335   Zirabev                                                \n",
-       "5842010 36080079   Quest L-Lysine                                         \n",
-       "5842011 36076118   FoliSol                                                \n",
-       "5842012 36049404   Munuza                                                 \n",
-       "5842013 36076134   FORTUM MONO                                            \n",
-       "5842014 36076588   Hashmats Health A-Z                                    \n",
-       "        domain_id vocabulary_id concept_class_id    standard_concept\n",
-       "1       Provider  ABMS          Physician Specialty S               \n",
-       "2       Provider  ABMS          Physician Specialty S               \n",
-       "3       Provider  ABMS          Physician Specialty S               \n",
-       "4       Provider  ABMS          Physician Specialty S               \n",
-       "5       Provider  ABMS          Physician Specialty S               \n",
-       "6       Provider  ABMS          Physician Specialty S               \n",
-       "7       Provider  ABMS          Physician Specialty S               \n",
-       "8       Provider  ABMS          Physician Specialty S               \n",
-       "9       Provider  ABMS          Physician Specialty S               \n",
-       "10      Provider  ABMS          Physician Specialty S               \n",
-       "11      Provider  ABMS          Physician Specialty S               \n",
-       "12      Provider  ABMS          Physician Specialty S               \n",
-       "13      Provider  ABMS          Physician Specialty S               \n",
-       "14      Provider  ABMS          Physician Specialty S               \n",
-       "15      Provider  ABMS          Physician Specialty S               \n",
-       "16      Provider  ABMS          Physician Specialty S               \n",
-       "17      Provider  ABMS          Physician Specialty S               \n",
-       "18      Provider  ABMS          Physician Specialty S               \n",
-       "19      Provider  ABMS          Physician Specialty S               \n",
-       "20      Provider  ABMS          Physician Specialty S               \n",
-       "21      Provider  ABMS          Physician Specialty                 \n",
-       "22      Provider  ABMS          Physician Specialty                 \n",
-       "23      Provider  ABMS          Physician Specialty S               \n",
-       "24      Provider  ABMS          Physician Specialty                 \n",
-       "25      Provider  ABMS          Physician Specialty S               \n",
-       "26      Provider  ABMS          Physician Specialty S               \n",
-       "27      Provider  ABMS          Physician Specialty                 \n",
-       "28      Provider  ABMS          Physician Specialty S               \n",
-       "29      Provider  ABMS          Physician Specialty S               \n",
-       "30      Provider  ABMS          Physician Specialty S               \n",
-       "<U+22EE>  <U+22EE>  <U+22EE>      <U+22EE>            <U+22EE>        \n",
-       "5841985 Drug      dm+d          Brand Name                          \n",
-       "5841986 Drug      dm+d          Brand Name                          \n",
-       "5841987 Drug      dm+d          Brand Name                          \n",
-       "5841988 Drug      dm+d          Brand Name                          \n",
-       "5841989 Drug      dm+d          Brand Name                          \n",
-       "5841990 Drug      dm+d          Brand Name                          \n",
-       "5841991 Drug      dm+d          Brand Name                          \n",
-       "5841992 Drug      dm+d          Brand Name                          \n",
-       "5841993 Drug      dm+d          Brand Name                          \n",
-       "5841994 Drug      dm+d          Brand Name                          \n",
-       "5841995 Drug      dm+d          Brand Name                          \n",
-       "5841996 Drug      dm+d          Brand Name                          \n",
-       "5841997 Drug      dm+d          Brand Name                          \n",
-       "5841998 Drug      dm+d          Brand Name                          \n",
-       "5841999 Drug      dm+d          Brand Name                          \n",
-       "5842000 Drug      dm+d          Brand Name                          \n",
-       "5842001 Drug      dm+d          Brand Name                          \n",
-       "5842002 Drug      dm+d          Brand Name                          \n",
-       "5842003 Drug      dm+d          Brand Name                          \n",
-       "5842004 Drug      dm+d          Brand Name                          \n",
-       "5842005 Drug      dm+d          Brand Name                          \n",
-       "5842006 Drug      dm+d          Brand Name                          \n",
-       "5842007 Drug      dm+d          Brand Name                          \n",
-       "5842008 Drug      dm+d          Brand Name                          \n",
-       "5842009 Drug      dm+d          Brand Name                          \n",
-       "5842010 Drug      dm+d          Brand Name                          \n",
-       "5842011 Drug      dm+d          Brand Name                          \n",
-       "5842012 Drug      dm+d          Brand Name                          \n",
-       "5842013 Drug      dm+d          Brand Name                          \n",
-       "5842014 Drug      dm+d          Brand Name                          \n",
-       "        concept_code valid_start_date valid_end_date invalid_reason\n",
-       "1       OMOP4821938  19700101         20991231                     \n",
-       "2       OMOP4821939  19700101         20991231                     \n",
-       "3       OMOP4821940  19700101         20991231                     \n",
-       "4       OMOP4821941  19700101         20991231                     \n",
-       "5       OMOP4821942  19700101         20991231                     \n",
-       "6       OMOP4821943  19700101         20991231                     \n",
-       "7       OMOP4821944  19700101         20991231                     \n",
-       "8       OMOP4821945  19700101         20991231                     \n",
-       "9       OMOP4821946  19700101         20991231                     \n",
-       "10      OMOP4821947  19700101         20991231                     \n",
-       "11      OMOP4821948  19700101         20991231                     \n",
-       "12      OMOP4821949  19700101         20991231                     \n",
-       "13      OMOP4821950  19700101         20991231                     \n",
-       "14      OMOP4821951  19700101         20991231                     \n",
-       "15      OMOP4821952  19700101         20991231                     \n",
-       "16      OMOP4821953  19700101         20991231                     \n",
-       "17      OMOP4821954  19700101         20991231                     \n",
-       "18      OMOP4821955  19700101         20991231                     \n",
-       "19      OMOP4821956  19700101         20991231                     \n",
-       "20      OMOP4821957  19700101         20991231                     \n",
-       "21      OMOP4821958  19700101         20991231                     \n",
-       "22      OMOP4821959  19700101         20991231                     \n",
-       "23      OMOP4821960  19700101         20991231                     \n",
-       "24      OMOP4821961  19700101         20991231                     \n",
-       "25      OMOP4821962  19700101         20991231                     \n",
-       "26      OMOP4821963  19700101         20991231                     \n",
-       "27      OMOP4821964  19700101         20991231                     \n",
-       "28      OMOP4821965  19700101         20991231                     \n",
-       "29      OMOP4821966  19700101         20991231                     \n",
-       "30      OMOP4821967  19700101         20991231                     \n",
-       "<U+22EE>  <U+22EE>     <U+22EE>         <U+22EE>       <U+22EE>      \n",
-       "5841985 OMOP4977061  19700101         20991231                     \n",
-       "5841986 OMOP4977062  19700101         20991231                     \n",
-       "5841987 OMOP4977063  19700101         20991231                     \n",
-       "5841988 OMOP4977064  19700101         20991231                     \n",
-       "5841989 OMOP4977065  19700101         20991231                     \n",
-       "5841990 OMOP4977066  19700101         20991231                     \n",
-       "5841991 OMOP4977067  19700101         20991231                     \n",
-       "5841992 OMOP4977068  19700101         20991231                     \n",
-       "5841993 OMOP4977069  19700101         20991231                     \n",
-       "5841994 OMOP4977070  19700101         20991231                     \n",
-       "5841995 OMOP4977071  19700101         20991231                     \n",
-       "5841996 OMOP4977072  19700101         20991231                     \n",
-       "5841997 OMOP4977073  19700101         20991231                     \n",
-       "5841998 OMOP4977074  19700101         20991231                     \n",
-       "5841999 OMOP4977075  19700101         20991231                     \n",
-       "5842000 OMOP4977076  19700101         20991231                     \n",
-       "5842001 OMOP4977077  19700101         20991231                     \n",
-       "5842002 OMOP4977078  19700101         20991231                     \n",
-       "5842003 OMOP4977079  19700101         20991231                     \n",
-       "5842004 OMOP4977080  19700101         20991231                     \n",
-       "5842005 OMOP4977081  19700101         20991231                     \n",
-       "5842006 OMOP4977082  19700101         20991231                     \n",
-       "5842007 OMOP4977083  19700101         20991231                     \n",
-       "5842008 OMOP4977084  19700101         20991231                     \n",
-       "5842009 OMOP4977085  19700101         20991231                     \n",
-       "5842010 OMOP4977086  19700101         20991231                     \n",
-       "5842011 OMOP4977087  19700101         20991231                     \n",
-       "5842012 OMOP4977088  19700101         20991231                     \n",
-       "5842013 OMOP4977089  19700101         20991231                     \n",
-       "5842014 OMOP4977090  19700101         20991231                     "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "concept"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 185,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# opcs4 to snomed mapping\n",
-    "bnf_map = bnf_snomed_mapping %>% select(bnf_map, sct_code) %>% mutate(sct_code = as.character(sct_code)) %>% distinct()\n",
-    "concept_ids_snomed_input = concept %>% filter(vocabulary_id == \"SNOMED\" & concept_code %in% unique(bnf_map$sct_code)) %>% mutate(concept_code = str_replace(concept_code, \"\\\\.\", \"\"))\n",
-    "concept_ids_snomed_substance = concept %>% filter(vocabulary_id == \"SNOMED\" & domain_id ==\"Drug\", concept_class_id==\"Substance\") %>% mutate(concept_code = str_replace(concept_code, \"\\\\.\", \"\"))\n",
-    "#concept_ids_snomed_ingred = concept %>% filter(vocabulary_id == \"SNOMED\" & domain_id==\"Procedure\") "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 191,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# check necessary opcs4 concept ids\n",
-    "#concept_ids = unique(concept_ids_snomed_input %>% mutate(concept_id_1 = concept_id))$concept_id_1\n",
-    "cr_filtered = concept_relationship %>% \n",
-    "    filter(concept_id_1 %in% concept_ids_snomed_input$concept_id) %>% \n",
-    "    filter(concept_id_2 %in% concept_ids_snomed_substance$concept_id) %>% \n",
-    "    filter(relationship_id %in% c(\"Has active ing\")) %>%\n",
-    "    arrange(concept_id_1) "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 196,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mapping_snomed_substance = concept_ids_snomed_input %>% \n",
-    "    left_join(cr_filtered %>% select(concept_id_1, concept_id_2), by=c(\"concept_id\"=\"concept_id_1\")) %>% \n",
-    "    left_join(concept_ids_snomed_substance %>% select(concept_id, concept_code, concept_name), by=c(\"concept_id_2\"=\"concept_id\")) %>% \n",
-    "    mutate(code = concept_code.x, meaning=concept_code.y, name=concept_name.y) %>% select(code, meaning, name)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 201,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<table>\n",
-       "<caption>A data.table: 5859 × 6</caption>\n",
-       "<thead>\n",
-       "\t<tr><th scope=col>eid</th><th scope=col>bnf_code</th><th scope=col>drug_name</th><th scope=col>date</th><th scope=col>meaning</th><th scope=col>name</th></tr>\n",
-       "\t<tr><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;date&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
-       "</thead>\n",
-       "<tbody>\n",
-       "\t<tr><td>5157903</td><td>0602010000</td><td>Levothyroxine sodium 100microgram tablets </td><td>2010-05-25</td><td>710809001</td><td>Levothyroxine      </td></tr>\n",
-       "\t<tr><td>5157903</td><td>0602010000</td><td>Levothyroxine sodium 100microgram tablets </td><td>2010-05-25</td><td>61275002 </td><td>Liothyronine       </td></tr>\n",
-       "\t<tr><td>5157903</td><td>0602010000</td><td>Levothyroxine sodium 100microgram tablets </td><td>2010-05-25</td><td>NA       </td><td>NA                 </td></tr>\n",
-       "\t<tr><td>3039302</td><td>0310000000</td><td>Pseudoephedrine hydrochloride 60mg tablets</td><td>2004-12-03</td><td>372900003</td><td>Pseudoephedrine    </td></tr>\n",
-       "\t<tr><td>3039302</td><td>0310000000</td><td>Pseudoephedrine hydrochloride 60mg tablets</td><td>2004-12-03</td><td>NA       </td><td>NA                 </td></tr>\n",
-       "\t<tr><td>3039302</td><td>0310000000</td><td>Pseudoephedrine hydrochloride 60mg tablets</td><td>2004-12-03</td><td>387517004</td><td>Paracetamol        </td></tr>\n",
-       "\t<tr><td>3039302</td><td>0310000000</td><td>Pseudoephedrine hydrochloride 60mg tablets</td><td>2004-12-03</td><td>372771005</td><td>Phenylephrine      </td></tr>\n",
-       "\t<tr><td>3039302</td><td>0310000000</td><td>Pseudoephedrine hydrochloride 60mg tablets</td><td>2004-12-03</td><td>387464001</td><td>Phenylpropanolamine</td></tr>\n",
-       "\t<tr><td>3039302</td><td>0310000000</td><td>Pseudoephedrine hydrochloride 60mg tablets</td><td>2004-12-03</td><td>387207008</td><td>Ibuprofen          </td></tr>\n",
-       "\t<tr><td>4865631</td><td>0206020000</td><td>Felodipine 10mg modified-release tablets  </td><td>2003-07-21</td><td>372754009</td><td>Verapamil          </td></tr>\n",
-       "\t<tr><td>4865631</td><td>0206020000</td><td>Felodipine 10mg modified-release tablets  </td><td>2003-07-21</td><td>386864001</td><td>Amlodipine         </td></tr>\n",
-       "\t<tr><td>4865631</td><td>0206020000</td><td>Felodipine 10mg modified-release tablets  </td><td>2003-07-21</td><td>NA       </td><td>NA                 </td></tr>\n",
-       "\t<tr><td>4865631</td><td>0206020000</td><td>Felodipine 10mg modified-release tablets  </td><td>2003-07-21</td><td>395764001</td><td>Lacidipine         </td></tr>\n",
-       "\t<tr><td>4865631</td><td>0206020000</td><td>Felodipine 10mg modified-release tablets  </td><td>2003-07-21</td><td>395986007</td><td>Lercanidipine      </td></tr>\n",
-       "\t<tr><td>4865631</td><td>0206020000</td><td>Felodipine 10mg modified-release tablets  </td><td>2003-07-21</td><td>372502001</td><td>Nicardipine        </td></tr>\n",
-       "\t<tr><td>4865631</td><td>0206020000</td><td>Felodipine 10mg modified-release tablets  </td><td>2003-07-21</td><td>387490003</td><td>Nifedipine         </td></tr>\n",
-       "\t<tr><td>4865631</td><td>0206020000</td><td>Felodipine 10mg modified-release tablets  </td><td>2003-07-21</td><td>386862002</td><td>Isradipine         </td></tr>\n",
-       "\t<tr><td>4865631</td><td>0206020000</td><td>Felodipine 10mg modified-release tablets  </td><td>2003-07-21</td><td>386861009</td><td>Nisoldipine        </td></tr>\n",
-       "\t<tr><td>4865631</td><td>0206020000</td><td>Felodipine 10mg modified-release tablets  </td><td>2003-07-21</td><td>387502003</td><td>Nimodipine         </td></tr>\n",
-       "\t<tr><td>4865631</td><td>0206020000</td><td>Felodipine 10mg modified-release tablets  </td><td>2003-07-21</td><td>372793000</td><td>Diltiazem          </td></tr>\n",
-       "\t<tr><td>4865631</td><td>0206020000</td><td>Felodipine 10mg modified-release tablets  </td><td>2003-07-21</td><td>386876001</td><td>Valsartan          </td></tr>\n",
-       "\t<tr><td>4865631</td><td>0206020000</td><td>Felodipine 10mg modified-release tablets  </td><td>2003-07-21</td><td>386863007</td><td>Felodipine         </td></tr>\n",
-       "\t<tr><td>3164812</td><td>0407020000</td><td>Tramadol 50mg capsules                    </td><td>2014-04-24</td><td>387322000</td><td>Dihydrocodeine     </td></tr>\n",
-       "\t<tr><td>3164812</td><td>0407020000</td><td>Tramadol 50mg capsules                    </td><td>2014-04-24</td><td>386858008</td><td>Tramadol           </td></tr>\n",
-       "\t<tr><td>3164812</td><td>0407020000</td><td>Tramadol 50mg capsules                    </td><td>2014-04-24</td><td>387298007</td><td>Meperidine         </td></tr>\n",
-       "\t<tr><td>3164812</td><td>0407020000</td><td>Tramadol 50mg capsules                    </td><td>2014-04-24</td><td>387494007</td><td>Codeine            </td></tr>\n",
-       "\t<tr><td>3164812</td><td>0407020000</td><td>Tramadol 50mg capsules                    </td><td>2014-04-24</td><td>387173000</td><td>Buprenorphine      </td></tr>\n",
-       "\t<tr><td>3164812</td><td>0407020000</td><td>Tramadol 50mg capsules                    </td><td>2014-04-24</td><td>373529000</td><td>Morphine           </td></tr>\n",
-       "\t<tr><td>3164812</td><td>0407020000</td><td>Tramadol 50mg capsules                    </td><td>2014-04-24</td><td>NA       </td><td>NA                 </td></tr>\n",
-       "\t<tr><td>3164812</td><td>0407020000</td><td>Tramadol 50mg capsules                    </td><td>2014-04-24</td><td>387341002</td><td>Diamorphine        </td></tr>\n",
-       "\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
-       "\t<tr><td>2086145</td><td>1001040100</td><td>diclofenac sodium EC tablets 50mg          </td><td>2006-05-12</td><td>387135004</td><td>Allopurinol     </td></tr>\n",
-       "\t<tr><td>2086145</td><td>1001040100</td><td>diclofenac sodium EC tablets 50mg          </td><td>2006-05-12</td><td>NA       </td><td>NA              </td></tr>\n",
-       "\t<tr><td>2086145</td><td>1001040100</td><td>diclofenac sodium EC tablets 50mg          </td><td>2006-05-12</td><td>387108008</td><td>Sulfinpyrazone  </td></tr>\n",
-       "\t<tr><td>2086145</td><td>1001040100</td><td>diclofenac sodium EC tablets 50mg          </td><td>2006-05-12</td><td>387365004</td><td>Probenecid      </td></tr>\n",
-       "\t<tr><td>2086145</td><td>1001040100</td><td>diclofenac sodium EC tablets 50mg          </td><td>2006-05-12</td><td>395858003</td><td>Rasburicase     </td></tr>\n",
-       "\t<tr><td>2086145</td><td>1001040100</td><td>diclofenac sodium EC tablets 50mg          </td><td>2006-05-12</td><td>441743008</td><td>Febuxostat      </td></tr>\n",
-       "\t<tr><td>2086145</td><td>1001040100</td><td>diclofenac sodium EC tablets 50mg          </td><td>2006-05-12</td><td>698025001</td><td>Benzbromarone   </td></tr>\n",
-       "\t<tr><td>3448543</td><td>1306020100</td><td>Erythromycin 250mg gastro-resistant tablets</td><td>2007-09-21</td><td>126097006</td><td>Ethinylestradiol</td></tr>\n",
-       "\t<tr><td>3448543</td><td>1306020100</td><td>Erythromycin 250mg gastro-resistant tablets</td><td>2007-09-21</td><td>126119006</td><td>Cyproterone     </td></tr>\n",
-       "\t<tr><td>3448543</td><td>1306020100</td><td>Erythromycin 250mg gastro-resistant tablets</td><td>2007-09-21</td><td>NA       </td><td>NA              </td></tr>\n",
-       "\t<tr><td>4991635</td><td>0202030000</td><td>Spironolactone 25mg tablets                </td><td>2014-12-12</td><td>387503008</td><td>Amiloride       </td></tr>\n",
-       "\t<tr><td>4991635</td><td>0202030000</td><td>Spironolactone 25mg tablets                </td><td>2014-12-12</td><td>387078006</td><td>Spironolactone  </td></tr>\n",
-       "\t<tr><td>4991635</td><td>0202030000</td><td>Spironolactone 25mg tablets                </td><td>2014-12-12</td><td>387053007</td><td>Triamterene     </td></tr>\n",
-       "\t<tr><td>4991635</td><td>0202030000</td><td>Spironolactone 25mg tablets                </td><td>2014-12-12</td><td>407010008</td><td>Eplerenone      </td></tr>\n",
-       "\t<tr><td>1918594</td><td>0407010200</td><td>Co-codamol 8mg/500mg tablets               </td><td>2004-10-27</td><td>387517004</td><td>Paracetamol     </td></tr>\n",
-       "\t<tr><td>1918594</td><td>0407010200</td><td>Co-codamol 8mg/500mg tablets               </td><td>2004-10-27</td><td>387322000</td><td>Dihydrocodeine  </td></tr>\n",
-       "\t<tr><td>1918594</td><td>0407010200</td><td>Co-codamol 8mg/500mg tablets               </td><td>2004-10-27</td><td>387494007</td><td>Codeine         </td></tr>\n",
-       "\t<tr><td>1918594</td><td>0407010200</td><td>Co-codamol 8mg/500mg tablets               </td><td>2004-10-27</td><td>387458008</td><td>Aspirin         </td></tr>\n",
-       "\t<tr><td>1918594</td><td>0407010200</td><td>Co-codamol 8mg/500mg tablets               </td><td>2004-10-27</td><td>NA       </td><td>NA              </td></tr>\n",
-       "\t<tr><td>1918594</td><td>0407010200</td><td>Co-codamol 8mg/500mg tablets               </td><td>2004-10-27</td><td>419768004</td><td>Nefopam         </td></tr>\n",
-       "\t<tr><td>1918594</td><td>0407010200</td><td>Co-codamol 8mg/500mg tablets               </td><td>2004-10-27</td><td>387302004</td><td>Isometheptene   </td></tr>\n",
-       "\t<tr><td>1918594</td><td>0407010200</td><td>Co-codamol 8mg/500mg tablets               </td><td>2004-10-27</td><td>372771005</td><td>Phenylephrine   </td></tr>\n",
-       "\t<tr><td>1918594</td><td>0407010200</td><td>Co-codamol 8mg/500mg tablets               </td><td>2004-10-27</td><td>372682005</td><td>Diphenhydramine </td></tr>\n",
-       "\t<tr><td>1918594</td><td>0407010200</td><td>Co-codamol 8mg/500mg tablets               </td><td>2004-10-27</td><td>372900003</td><td>Pseudoephedrine </td></tr>\n",
-       "\t<tr><td>1918594</td><td>0407010200</td><td>Co-codamol 8mg/500mg tablets               </td><td>2004-10-27</td><td>87174009 </td><td>Guaifenesin     </td></tr>\n",
-       "\t<tr><td>1918594</td><td>0407010200</td><td>Co-codamol 8mg/500mg tablets               </td><td>2004-10-27</td><td>255641001</td><td>Caffeine        </td></tr>\n",
-       "\t<tr><td>1918594</td><td>0407010200</td><td>Co-codamol 8mg/500mg tablets               </td><td>2004-10-27</td><td>387358007</td><td>Ephedrine       </td></tr>\n",
-       "\t<tr><td>1918594</td><td>0407010200</td><td>Co-codamol 8mg/500mg tablets               </td><td>2004-10-27</td><td>43706004 </td><td>Ascorbic acid   </td></tr>\n",
-       "\t<tr><td>1918594</td><td>0407010200</td><td>Co-codamol 8mg/500mg tablets               </td><td>2004-10-27</td><td>396486005</td><td>Pholcodine      </td></tr>\n",
-       "\t<tr><td>1918594</td><td>0407010200</td><td>Co-codamol 8mg/500mg tablets               </td><td>2004-10-27</td><td>387207008</td><td>Ibuprofen       </td></tr>\n",
-       "</tbody>\n",
-       "</table>\n"
-      ],
-      "text/latex": [
-       "A data.table: 5859 × 6\n",
-       "\\begin{tabular}{llllll}\n",
-       " eid & bnf\\_code & drug\\_name & date & meaning & name\\\\\n",
-       " <int> & <chr> & <chr> & <date> & <chr> & <chr>\\\\\n",
-       "\\hline\n",
-       "\t 5157903 & 0602010000 & Levothyroxine sodium 100microgram tablets  & 2010-05-25 & 710809001 & Levothyroxine      \\\\\n",
-       "\t 5157903 & 0602010000 & Levothyroxine sodium 100microgram tablets  & 2010-05-25 & 61275002  & Liothyronine       \\\\\n",
-       "\t 5157903 & 0602010000 & Levothyroxine sodium 100microgram tablets  & 2010-05-25 & NA        & NA                 \\\\\n",
-       "\t 3039302 & 0310000000 & Pseudoephedrine hydrochloride 60mg tablets & 2004-12-03 & 372900003 & Pseudoephedrine    \\\\\n",
-       "\t 3039302 & 0310000000 & Pseudoephedrine hydrochloride 60mg tablets & 2004-12-03 & NA        & NA                 \\\\\n",
-       "\t 3039302 & 0310000000 & Pseudoephedrine hydrochloride 60mg tablets & 2004-12-03 & 387517004 & Paracetamol        \\\\\n",
-       "\t 3039302 & 0310000000 & Pseudoephedrine hydrochloride 60mg tablets & 2004-12-03 & 372771005 & Phenylephrine      \\\\\n",
-       "\t 3039302 & 0310000000 & Pseudoephedrine hydrochloride 60mg tablets & 2004-12-03 & 387464001 & Phenylpropanolamine\\\\\n",
-       "\t 3039302 & 0310000000 & Pseudoephedrine hydrochloride 60mg tablets & 2004-12-03 & 387207008 & Ibuprofen          \\\\\n",
-       "\t 4865631 & 0206020000 & Felodipine 10mg modified-release tablets   & 2003-07-21 & 372754009 & Verapamil          \\\\\n",
-       "\t 4865631 & 0206020000 & Felodipine 10mg modified-release tablets   & 2003-07-21 & 386864001 & Amlodipine         \\\\\n",
-       "\t 4865631 & 0206020000 & Felodipine 10mg modified-release tablets   & 2003-07-21 & NA        & NA                 \\\\\n",
-       "\t 4865631 & 0206020000 & Felodipine 10mg modified-release tablets   & 2003-07-21 & 395764001 & Lacidipine         \\\\\n",
-       "\t 4865631 & 0206020000 & Felodipine 10mg modified-release tablets   & 2003-07-21 & 395986007 & Lercanidipine      \\\\\n",
-       "\t 4865631 & 0206020000 & Felodipine 10mg modified-release tablets   & 2003-07-21 & 372502001 & Nicardipine        \\\\\n",
-       "\t 4865631 & 0206020000 & Felodipine 10mg modified-release tablets   & 2003-07-21 & 387490003 & Nifedipine         \\\\\n",
-       "\t 4865631 & 0206020000 & Felodipine 10mg modified-release tablets   & 2003-07-21 & 386862002 & Isradipine         \\\\\n",
-       "\t 4865631 & 0206020000 & Felodipine 10mg modified-release tablets   & 2003-07-21 & 386861009 & Nisoldipine        \\\\\n",
-       "\t 4865631 & 0206020000 & Felodipine 10mg modified-release tablets   & 2003-07-21 & 387502003 & Nimodipine         \\\\\n",
-       "\t 4865631 & 0206020000 & Felodipine 10mg modified-release tablets   & 2003-07-21 & 372793000 & Diltiazem          \\\\\n",
-       "\t 4865631 & 0206020000 & Felodipine 10mg modified-release tablets   & 2003-07-21 & 386876001 & Valsartan          \\\\\n",
-       "\t 4865631 & 0206020000 & Felodipine 10mg modified-release tablets   & 2003-07-21 & 386863007 & Felodipine         \\\\\n",
-       "\t 3164812 & 0407020000 & Tramadol 50mg capsules                     & 2014-04-24 & 387322000 & Dihydrocodeine     \\\\\n",
-       "\t 3164812 & 0407020000 & Tramadol 50mg capsules                     & 2014-04-24 & 386858008 & Tramadol           \\\\\n",
-       "\t 3164812 & 0407020000 & Tramadol 50mg capsules                     & 2014-04-24 & 387298007 & Meperidine         \\\\\n",
-       "\t 3164812 & 0407020000 & Tramadol 50mg capsules                     & 2014-04-24 & 387494007 & Codeine            \\\\\n",
-       "\t 3164812 & 0407020000 & Tramadol 50mg capsules                     & 2014-04-24 & 387173000 & Buprenorphine      \\\\\n",
-       "\t 3164812 & 0407020000 & Tramadol 50mg capsules                     & 2014-04-24 & 373529000 & Morphine           \\\\\n",
-       "\t 3164812 & 0407020000 & Tramadol 50mg capsules                     & 2014-04-24 & NA        & NA                 \\\\\n",
-       "\t 3164812 & 0407020000 & Tramadol 50mg capsules                     & 2014-04-24 & 387341002 & Diamorphine        \\\\\n",
-       "\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
-       "\t 2086145 & 1001040100 & diclofenac sodium EC tablets 50mg           & 2006-05-12 & 387135004 & Allopurinol     \\\\\n",
-       "\t 2086145 & 1001040100 & diclofenac sodium EC tablets 50mg           & 2006-05-12 & NA        & NA              \\\\\n",
-       "\t 2086145 & 1001040100 & diclofenac sodium EC tablets 50mg           & 2006-05-12 & 387108008 & Sulfinpyrazone  \\\\\n",
-       "\t 2086145 & 1001040100 & diclofenac sodium EC tablets 50mg           & 2006-05-12 & 387365004 & Probenecid      \\\\\n",
-       "\t 2086145 & 1001040100 & diclofenac sodium EC tablets 50mg           & 2006-05-12 & 395858003 & Rasburicase     \\\\\n",
-       "\t 2086145 & 1001040100 & diclofenac sodium EC tablets 50mg           & 2006-05-12 & 441743008 & Febuxostat      \\\\\n",
-       "\t 2086145 & 1001040100 & diclofenac sodium EC tablets 50mg           & 2006-05-12 & 698025001 & Benzbromarone   \\\\\n",
-       "\t 3448543 & 1306020100 & Erythromycin 250mg gastro-resistant tablets & 2007-09-21 & 126097006 & Ethinylestradiol\\\\\n",
-       "\t 3448543 & 1306020100 & Erythromycin 250mg gastro-resistant tablets & 2007-09-21 & 126119006 & Cyproterone     \\\\\n",
-       "\t 3448543 & 1306020100 & Erythromycin 250mg gastro-resistant tablets & 2007-09-21 & NA        & NA              \\\\\n",
-       "\t 4991635 & 0202030000 & Spironolactone 25mg tablets                 & 2014-12-12 & 387503008 & Amiloride       \\\\\n",
-       "\t 4991635 & 0202030000 & Spironolactone 25mg tablets                 & 2014-12-12 & 387078006 & Spironolactone  \\\\\n",
-       "\t 4991635 & 0202030000 & Spironolactone 25mg tablets                 & 2014-12-12 & 387053007 & Triamterene     \\\\\n",
-       "\t 4991635 & 0202030000 & Spironolactone 25mg tablets                 & 2014-12-12 & 407010008 & Eplerenone      \\\\\n",
-       "\t 1918594 & 0407010200 & Co-codamol 8mg/500mg tablets                & 2004-10-27 & 387517004 & Paracetamol     \\\\\n",
-       "\t 1918594 & 0407010200 & Co-codamol 8mg/500mg tablets                & 2004-10-27 & 387322000 & Dihydrocodeine  \\\\\n",
-       "\t 1918594 & 0407010200 & Co-codamol 8mg/500mg tablets                & 2004-10-27 & 387494007 & Codeine         \\\\\n",
-       "\t 1918594 & 0407010200 & Co-codamol 8mg/500mg tablets                & 2004-10-27 & 387458008 & Aspirin         \\\\\n",
-       "\t 1918594 & 0407010200 & Co-codamol 8mg/500mg tablets                & 2004-10-27 & NA        & NA              \\\\\n",
-       "\t 1918594 & 0407010200 & Co-codamol 8mg/500mg tablets                & 2004-10-27 & 419768004 & Nefopam         \\\\\n",
-       "\t 1918594 & 0407010200 & Co-codamol 8mg/500mg tablets                & 2004-10-27 & 387302004 & Isometheptene   \\\\\n",
-       "\t 1918594 & 0407010200 & Co-codamol 8mg/500mg tablets                & 2004-10-27 & 372771005 & Phenylephrine   \\\\\n",
-       "\t 1918594 & 0407010200 & Co-codamol 8mg/500mg tablets                & 2004-10-27 & 372682005 & Diphenhydramine \\\\\n",
-       "\t 1918594 & 0407010200 & Co-codamol 8mg/500mg tablets                & 2004-10-27 & 372900003 & Pseudoephedrine \\\\\n",
-       "\t 1918594 & 0407010200 & Co-codamol 8mg/500mg tablets                & 2004-10-27 & 87174009  & Guaifenesin     \\\\\n",
-       "\t 1918594 & 0407010200 & Co-codamol 8mg/500mg tablets                & 2004-10-27 & 255641001 & Caffeine        \\\\\n",
-       "\t 1918594 & 0407010200 & Co-codamol 8mg/500mg tablets                & 2004-10-27 & 387358007 & Ephedrine       \\\\\n",
-       "\t 1918594 & 0407010200 & Co-codamol 8mg/500mg tablets                & 2004-10-27 & 43706004  & Ascorbic acid   \\\\\n",
-       "\t 1918594 & 0407010200 & Co-codamol 8mg/500mg tablets                & 2004-10-27 & 396486005 & Pholcodine      \\\\\n",
-       "\t 1918594 & 0407010200 & Co-codamol 8mg/500mg tablets                & 2004-10-27 & 387207008 & Ibuprofen       \\\\\n",
-       "\\end{tabular}\n"
-      ],
-      "text/markdown": [
-       "\n",
-       "A data.table: 5859 × 6\n",
-       "\n",
-       "| eid &lt;int&gt; | bnf_code &lt;chr&gt; | drug_name &lt;chr&gt; | date &lt;date&gt; | meaning &lt;chr&gt; | name &lt;chr&gt; |\n",
-       "|---|---|---|---|---|---|\n",
-       "| 5157903 | 0602010000 | Levothyroxine sodium 100microgram tablets  | 2010-05-25 | 710809001 | Levothyroxine       |\n",
-       "| 5157903 | 0602010000 | Levothyroxine sodium 100microgram tablets  | 2010-05-25 | 61275002  | Liothyronine        |\n",
-       "| 5157903 | 0602010000 | Levothyroxine sodium 100microgram tablets  | 2010-05-25 | NA        | NA                  |\n",
-       "| 3039302 | 0310000000 | Pseudoephedrine hydrochloride 60mg tablets | 2004-12-03 | 372900003 | Pseudoephedrine     |\n",
-       "| 3039302 | 0310000000 | Pseudoephedrine hydrochloride 60mg tablets | 2004-12-03 | NA        | NA                  |\n",
-       "| 3039302 | 0310000000 | Pseudoephedrine hydrochloride 60mg tablets | 2004-12-03 | 387517004 | Paracetamol         |\n",
-       "| 3039302 | 0310000000 | Pseudoephedrine hydrochloride 60mg tablets | 2004-12-03 | 372771005 | Phenylephrine       |\n",
-       "| 3039302 | 0310000000 | Pseudoephedrine hydrochloride 60mg tablets | 2004-12-03 | 387464001 | Phenylpropanolamine |\n",
-       "| 3039302 | 0310000000 | Pseudoephedrine hydrochloride 60mg tablets | 2004-12-03 | 387207008 | Ibuprofen           |\n",
-       "| 4865631 | 0206020000 | Felodipine 10mg modified-release tablets   | 2003-07-21 | 372754009 | Verapamil           |\n",
-       "| 4865631 | 0206020000 | Felodipine 10mg modified-release tablets   | 2003-07-21 | 386864001 | Amlodipine          |\n",
-       "| 4865631 | 0206020000 | Felodipine 10mg modified-release tablets   | 2003-07-21 | NA        | NA                  |\n",
-       "| 4865631 | 0206020000 | Felodipine 10mg modified-release tablets   | 2003-07-21 | 395764001 | Lacidipine          |\n",
-       "| 4865631 | 0206020000 | Felodipine 10mg modified-release tablets   | 2003-07-21 | 395986007 | Lercanidipine       |\n",
-       "| 4865631 | 0206020000 | Felodipine 10mg modified-release tablets   | 2003-07-21 | 372502001 | Nicardipine         |\n",
-       "| 4865631 | 0206020000 | Felodipine 10mg modified-release tablets   | 2003-07-21 | 387490003 | Nifedipine          |\n",
-       "| 4865631 | 0206020000 | Felodipine 10mg modified-release tablets   | 2003-07-21 | 386862002 | Isradipine          |\n",
-       "| 4865631 | 0206020000 | Felodipine 10mg modified-release tablets   | 2003-07-21 | 386861009 | Nisoldipine         |\n",
-       "| 4865631 | 0206020000 | Felodipine 10mg modified-release tablets   | 2003-07-21 | 387502003 | Nimodipine          |\n",
-       "| 4865631 | 0206020000 | Felodipine 10mg modified-release tablets   | 2003-07-21 | 372793000 | Diltiazem           |\n",
-       "| 4865631 | 0206020000 | Felodipine 10mg modified-release tablets   | 2003-07-21 | 386876001 | Valsartan           |\n",
-       "| 4865631 | 0206020000 | Felodipine 10mg modified-release tablets   | 2003-07-21 | 386863007 | Felodipine          |\n",
-       "| 3164812 | 0407020000 | Tramadol 50mg capsules                     | 2014-04-24 | 387322000 | Dihydrocodeine      |\n",
-       "| 3164812 | 0407020000 | Tramadol 50mg capsules                     | 2014-04-24 | 386858008 | Tramadol            |\n",
-       "| 3164812 | 0407020000 | Tramadol 50mg capsules                     | 2014-04-24 | 387298007 | Meperidine          |\n",
-       "| 3164812 | 0407020000 | Tramadol 50mg capsules                     | 2014-04-24 | 387494007 | Codeine             |\n",
-       "| 3164812 | 0407020000 | Tramadol 50mg capsules                     | 2014-04-24 | 387173000 | Buprenorphine       |\n",
-       "| 3164812 | 0407020000 | Tramadol 50mg capsules                     | 2014-04-24 | 373529000 | Morphine            |\n",
-       "| 3164812 | 0407020000 | Tramadol 50mg capsules                     | 2014-04-24 | NA        | NA                  |\n",
-       "| 3164812 | 0407020000 | Tramadol 50mg capsules                     | 2014-04-24 | 387341002 | Diamorphine         |\n",
-       "| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |\n",
-       "| 2086145 | 1001040100 | diclofenac sodium EC tablets 50mg           | 2006-05-12 | 387135004 | Allopurinol      |\n",
-       "| 2086145 | 1001040100 | diclofenac sodium EC tablets 50mg           | 2006-05-12 | NA        | NA               |\n",
-       "| 2086145 | 1001040100 | diclofenac sodium EC tablets 50mg           | 2006-05-12 | 387108008 | Sulfinpyrazone   |\n",
-       "| 2086145 | 1001040100 | diclofenac sodium EC tablets 50mg           | 2006-05-12 | 387365004 | Probenecid       |\n",
-       "| 2086145 | 1001040100 | diclofenac sodium EC tablets 50mg           | 2006-05-12 | 395858003 | Rasburicase      |\n",
-       "| 2086145 | 1001040100 | diclofenac sodium EC tablets 50mg           | 2006-05-12 | 441743008 | Febuxostat       |\n",
-       "| 2086145 | 1001040100 | diclofenac sodium EC tablets 50mg           | 2006-05-12 | 698025001 | Benzbromarone    |\n",
-       "| 3448543 | 1306020100 | Erythromycin 250mg gastro-resistant tablets | 2007-09-21 | 126097006 | Ethinylestradiol |\n",
-       "| 3448543 | 1306020100 | Erythromycin 250mg gastro-resistant tablets | 2007-09-21 | 126119006 | Cyproterone      |\n",
-       "| 3448543 | 1306020100 | Erythromycin 250mg gastro-resistant tablets | 2007-09-21 | NA        | NA               |\n",
-       "| 4991635 | 0202030000 | Spironolactone 25mg tablets                 | 2014-12-12 | 387503008 | Amiloride        |\n",
-       "| 4991635 | 0202030000 | Spironolactone 25mg tablets                 | 2014-12-12 | 387078006 | Spironolactone   |\n",
-       "| 4991635 | 0202030000 | Spironolactone 25mg tablets                 | 2014-12-12 | 387053007 | Triamterene      |\n",
-       "| 4991635 | 0202030000 | Spironolactone 25mg tablets                 | 2014-12-12 | 407010008 | Eplerenone       |\n",
-       "| 1918594 | 0407010200 | Co-codamol 8mg/500mg tablets                | 2004-10-27 | 387517004 | Paracetamol      |\n",
-       "| 1918594 | 0407010200 | Co-codamol 8mg/500mg tablets                | 2004-10-27 | 387322000 | Dihydrocodeine   |\n",
-       "| 1918594 | 0407010200 | Co-codamol 8mg/500mg tablets                | 2004-10-27 | 387494007 | Codeine          |\n",
-       "| 1918594 | 0407010200 | Co-codamol 8mg/500mg tablets                | 2004-10-27 | 387458008 | Aspirin          |\n",
-       "| 1918594 | 0407010200 | Co-codamol 8mg/500mg tablets                | 2004-10-27 | NA        | NA               |\n",
-       "| 1918594 | 0407010200 | Co-codamol 8mg/500mg tablets                | 2004-10-27 | 419768004 | Nefopam          |\n",
-       "| 1918594 | 0407010200 | Co-codamol 8mg/500mg tablets                | 2004-10-27 | 387302004 | Isometheptene    |\n",
-       "| 1918594 | 0407010200 | Co-codamol 8mg/500mg tablets                | 2004-10-27 | 372771005 | Phenylephrine    |\n",
-       "| 1918594 | 0407010200 | Co-codamol 8mg/500mg tablets                | 2004-10-27 | 372682005 | Diphenhydramine  |\n",
-       "| 1918594 | 0407010200 | Co-codamol 8mg/500mg tablets                | 2004-10-27 | 372900003 | Pseudoephedrine  |\n",
-       "| 1918594 | 0407010200 | Co-codamol 8mg/500mg tablets                | 2004-10-27 | 87174009  | Guaifenesin      |\n",
-       "| 1918594 | 0407010200 | Co-codamol 8mg/500mg tablets                | 2004-10-27 | 255641001 | Caffeine         |\n",
-       "| 1918594 | 0407010200 | Co-codamol 8mg/500mg tablets                | 2004-10-27 | 387358007 | Ephedrine        |\n",
-       "| 1918594 | 0407010200 | Co-codamol 8mg/500mg tablets                | 2004-10-27 | 43706004  | Ascorbic acid    |\n",
-       "| 1918594 | 0407010200 | Co-codamol 8mg/500mg tablets                | 2004-10-27 | 396486005 | Pholcodine       |\n",
-       "| 1918594 | 0407010200 | Co-codamol 8mg/500mg tablets                | 2004-10-27 | 387207008 | Ibuprofen        |\n",
-       "\n"
-      ],
-      "text/plain": [
-       "       eid     bnf_code   drug_name                                  \n",
-       "1      5157903 0602010000 Levothyroxine sodium 100microgram tablets  \n",
-       "2      5157903 0602010000 Levothyroxine sodium 100microgram tablets  \n",
-       "3      5157903 0602010000 Levothyroxine sodium 100microgram tablets  \n",
-       "4      3039302 0310000000 Pseudoephedrine hydrochloride 60mg tablets \n",
-       "5      3039302 0310000000 Pseudoephedrine hydrochloride 60mg tablets \n",
-       "6      3039302 0310000000 Pseudoephedrine hydrochloride 60mg tablets \n",
-       "7      3039302 0310000000 Pseudoephedrine hydrochloride 60mg tablets \n",
-       "8      3039302 0310000000 Pseudoephedrine hydrochloride 60mg tablets \n",
-       "9      3039302 0310000000 Pseudoephedrine hydrochloride 60mg tablets \n",
-       "10     4865631 0206020000 Felodipine 10mg modified-release tablets   \n",
-       "11     4865631 0206020000 Felodipine 10mg modified-release tablets   \n",
-       "12     4865631 0206020000 Felodipine 10mg modified-release tablets   \n",
-       "13     4865631 0206020000 Felodipine 10mg modified-release tablets   \n",
-       "14     4865631 0206020000 Felodipine 10mg modified-release tablets   \n",
-       "15     4865631 0206020000 Felodipine 10mg modified-release tablets   \n",
-       "16     4865631 0206020000 Felodipine 10mg modified-release tablets   \n",
-       "17     4865631 0206020000 Felodipine 10mg modified-release tablets   \n",
-       "18     4865631 0206020000 Felodipine 10mg modified-release tablets   \n",
-       "19     4865631 0206020000 Felodipine 10mg modified-release tablets   \n",
-       "20     4865631 0206020000 Felodipine 10mg modified-release tablets   \n",
-       "21     4865631 0206020000 Felodipine 10mg modified-release tablets   \n",
-       "22     4865631 0206020000 Felodipine 10mg modified-release tablets   \n",
-       "23     3164812 0407020000 Tramadol 50mg capsules                     \n",
-       "24     3164812 0407020000 Tramadol 50mg capsules                     \n",
-       "25     3164812 0407020000 Tramadol 50mg capsules                     \n",
-       "26     3164812 0407020000 Tramadol 50mg capsules                     \n",
-       "27     3164812 0407020000 Tramadol 50mg capsules                     \n",
-       "28     3164812 0407020000 Tramadol 50mg capsules                     \n",
-       "29     3164812 0407020000 Tramadol 50mg capsules                     \n",
-       "30     3164812 0407020000 Tramadol 50mg capsules                     \n",
-       "<U+22EE> <U+22EE> <U+22EE>   <U+22EE>                                   \n",
-       "5830   2086145 1001040100 diclofenac sodium EC tablets 50mg          \n",
-       "5831   2086145 1001040100 diclofenac sodium EC tablets 50mg          \n",
-       "5832   2086145 1001040100 diclofenac sodium EC tablets 50mg          \n",
-       "5833   2086145 1001040100 diclofenac sodium EC tablets 50mg          \n",
-       "5834   2086145 1001040100 diclofenac sodium EC tablets 50mg          \n",
-       "5835   2086145 1001040100 diclofenac sodium EC tablets 50mg          \n",
-       "5836   2086145 1001040100 diclofenac sodium EC tablets 50mg          \n",
-       "5837   3448543 1306020100 Erythromycin 250mg gastro-resistant tablets\n",
-       "5838   3448543 1306020100 Erythromycin 250mg gastro-resistant tablets\n",
-       "5839   3448543 1306020100 Erythromycin 250mg gastro-resistant tablets\n",
-       "5840   4991635 0202030000 Spironolactone 25mg tablets                \n",
-       "5841   4991635 0202030000 Spironolactone 25mg tablets                \n",
-       "5842   4991635 0202030000 Spironolactone 25mg tablets                \n",
-       "5843   4991635 0202030000 Spironolactone 25mg tablets                \n",
-       "5844   1918594 0407010200 Co-codamol 8mg/500mg tablets               \n",
-       "5845   1918594 0407010200 Co-codamol 8mg/500mg tablets               \n",
-       "5846   1918594 0407010200 Co-codamol 8mg/500mg tablets               \n",
-       "5847   1918594 0407010200 Co-codamol 8mg/500mg tablets               \n",
-       "5848   1918594 0407010200 Co-codamol 8mg/500mg tablets               \n",
-       "5849   1918594 0407010200 Co-codamol 8mg/500mg tablets               \n",
-       "5850   1918594 0407010200 Co-codamol 8mg/500mg tablets               \n",
-       "5851   1918594 0407010200 Co-codamol 8mg/500mg tablets               \n",
-       "5852   1918594 0407010200 Co-codamol 8mg/500mg tablets               \n",
-       "5853   1918594 0407010200 Co-codamol 8mg/500mg tablets               \n",
-       "5854   1918594 0407010200 Co-codamol 8mg/500mg tablets               \n",
-       "5855   1918594 0407010200 Co-codamol 8mg/500mg tablets               \n",
-       "5856   1918594 0407010200 Co-codamol 8mg/500mg tablets               \n",
-       "5857   1918594 0407010200 Co-codamol 8mg/500mg tablets               \n",
-       "5858   1918594 0407010200 Co-codamol 8mg/500mg tablets               \n",
-       "5859   1918594 0407010200 Co-codamol 8mg/500mg tablets               \n",
-       "       date       meaning   name               \n",
-       "1      2010-05-25 710809001 Levothyroxine      \n",
-       "2      2010-05-25 61275002  Liothyronine       \n",
-       "3      2010-05-25 NA        NA                 \n",
-       "4      2004-12-03 372900003 Pseudoephedrine    \n",
-       "5      2004-12-03 NA        NA                 \n",
-       "6      2004-12-03 387517004 Paracetamol        \n",
-       "7      2004-12-03 372771005 Phenylephrine      \n",
-       "8      2004-12-03 387464001 Phenylpropanolamine\n",
-       "9      2004-12-03 387207008 Ibuprofen          \n",
-       "10     2003-07-21 372754009 Verapamil          \n",
-       "11     2003-07-21 386864001 Amlodipine         \n",
-       "12     2003-07-21 NA        NA                 \n",
-       "13     2003-07-21 395764001 Lacidipine         \n",
-       "14     2003-07-21 395986007 Lercanidipine      \n",
-       "15     2003-07-21 372502001 Nicardipine        \n",
-       "16     2003-07-21 387490003 Nifedipine         \n",
-       "17     2003-07-21 386862002 Isradipine         \n",
-       "18     2003-07-21 386861009 Nisoldipine        \n",
-       "19     2003-07-21 387502003 Nimodipine         \n",
-       "20     2003-07-21 372793000 Diltiazem          \n",
-       "21     2003-07-21 386876001 Valsartan          \n",
-       "22     2003-07-21 386863007 Felodipine         \n",
-       "23     2014-04-24 387322000 Dihydrocodeine     \n",
-       "24     2014-04-24 386858008 Tramadol           \n",
-       "25     2014-04-24 387298007 Meperidine         \n",
-       "26     2014-04-24 387494007 Codeine            \n",
-       "27     2014-04-24 387173000 Buprenorphine      \n",
-       "28     2014-04-24 373529000 Morphine           \n",
-       "29     2014-04-24 NA        NA                 \n",
-       "30     2014-04-24 387341002 Diamorphine        \n",
-       "<U+22EE> <U+22EE>   <U+22EE>  <U+22EE>           \n",
-       "5830   2006-05-12 387135004 Allopurinol        \n",
-       "5831   2006-05-12 NA        NA                 \n",
-       "5832   2006-05-12 387108008 Sulfinpyrazone     \n",
-       "5833   2006-05-12 387365004 Probenecid         \n",
-       "5834   2006-05-12 395858003 Rasburicase        \n",
-       "5835   2006-05-12 441743008 Febuxostat         \n",
-       "5836   2006-05-12 698025001 Benzbromarone      \n",
-       "5837   2007-09-21 126097006 Ethinylestradiol   \n",
-       "5838   2007-09-21 126119006 Cyproterone        \n",
-       "5839   2007-09-21 NA        NA                 \n",
-       "5840   2014-12-12 387503008 Amiloride          \n",
-       "5841   2014-12-12 387078006 Spironolactone     \n",
-       "5842   2014-12-12 387053007 Triamterene        \n",
-       "5843   2014-12-12 407010008 Eplerenone         \n",
-       "5844   2004-10-27 387517004 Paracetamol        \n",
-       "5845   2004-10-27 387322000 Dihydrocodeine     \n",
-       "5846   2004-10-27 387494007 Codeine            \n",
-       "5847   2004-10-27 387458008 Aspirin            \n",
-       "5848   2004-10-27 NA        NA                 \n",
-       "5849   2004-10-27 419768004 Nefopam            \n",
-       "5850   2004-10-27 387302004 Isometheptene      \n",
-       "5851   2004-10-27 372771005 Phenylephrine      \n",
-       "5852   2004-10-27 372682005 Diphenhydramine    \n",
-       "5853   2004-10-27 372900003 Pseudoephedrine    \n",
-       "5854   2004-10-27 87174009  Guaifenesin        \n",
-       "5855   2004-10-27 255641001 Caffeine           \n",
-       "5856   2004-10-27 387358007 Ephedrine          \n",
-       "5857   2004-10-27 43706004  Ascorbic acid      \n",
-       "5858   2004-10-27 396486005 Pholcodine         \n",
-       "5859   2004-10-27 387207008 Ibuprofen          "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "gp_scripts_pre %>% mutate(sct_code=as.character(sct_code)) %>% left_join(mapping_snomed_substance, by=c(\"sct_code\"=\"code\")) %>% select(eid, bnf_code, drug_name, date, meaning, name) %>% distinct()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 197,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<table>\n",
-       "<caption>A data.table: 17239 × 3</caption>\n",
-       "<thead>\n",
-       "\t<tr><th scope=col>code</th><th scope=col>meaning</th><th scope=col>name</th></tr>\n",
-       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
-       "</thead>\n",
-       "<tbody>\n",
-       "\t<tr><td>10050811000001105</td><td>NA       </td><td>NA                </td></tr>\n",
-       "\t<tr><td>10051011000001108</td><td>NA       </td><td>NA                </td></tr>\n",
-       "\t<tr><td>10051911000001107</td><td>NA       </td><td>NA                </td></tr>\n",
-       "\t<tr><td>10052111000001104</td><td>385544005</td><td>Pegfilgrastim     </td></tr>\n",
-       "\t<tr><td>10063711000001101</td><td>391852006</td><td>Bismuth subnitrate</td></tr>\n",
-       "\t<tr><td>10063811000001109</td><td>126189002</td><td>Desmopressin      </td></tr>\n",
-       "\t<tr><td>10063911000001104</td><td>126189002</td><td>Desmopressin      </td></tr>\n",
-       "\t<tr><td>10064111000001100</td><td>NA       </td><td>NA                </td></tr>\n",
-       "\t<tr><td>10064211000001106</td><td>NA       </td><td>NA                </td></tr>\n",
-       "\t<tr><td>10064311000001103</td><td>NA       </td><td>NA                </td></tr>\n",
-       "\t<tr><td>10064511000001109</td><td>NA       </td><td>NA                </td></tr>\n",
-       "\t<tr><td>10064611000001108</td><td>372672009</td><td>Cromolyn          </td></tr>\n",
-       "\t<tr><td>10064711000001104</td><td>387458008</td><td>Aspirin           </td></tr>\n",
-       "\t<tr><td>10075311000001106</td><td>NA       </td><td>NA                </td></tr>\n",
-       "\t<tr><td>10075411000001104</td><td>NA       </td><td>NA                </td></tr>\n",
-       "\t<tr><td>10075511000001100</td><td>373529000</td><td>Morphine          </td></tr>\n",
-       "\t<tr><td>10075611000001101</td><td>372515005</td><td>Salmeterol        </td></tr>\n",
-       "\t<tr><td>10097111000001108</td><td>NA       </td><td>NA                </td></tr>\n",
-       "\t<tr><td>10097211000001102</td><td>411530000</td><td>Insulin glulisine </td></tr>\n",
-       "\t<tr><td>10140711000001108</td><td>43706004 </td><td>Ascorbic acid     </td></tr>\n",
-       "\t<tr><td>10140811000001100</td><td>396065004</td><td>Folinic acid      </td></tr>\n",
-       "\t<tr><td>10140911000001105</td><td>406439009</td><td>Daptomycin        </td></tr>\n",
-       "\t<tr><td>10141011000001102</td><td>NA       </td><td>NA                </td></tr>\n",
-       "\t<tr><td>10141211000001107</td><td>NA       </td><td>NA                </td></tr>\n",
-       "\t<tr><td>10141311000001104</td><td>NA       </td><td>NA                </td></tr>\n",
-       "\t<tr><td>10141411000001106</td><td>NA       </td><td>NA                </td></tr>\n",
-       "\t<tr><td>10141511000001105</td><td>NA       </td><td>NA                </td></tr>\n",
-       "\t<tr><td>10141611000001109</td><td>NA       </td><td>NA                </td></tr>\n",
-       "\t<tr><td>10142111000001106</td><td>421924006</td><td>Rotigotine        </td></tr>\n",
-       "\t<tr><td>10143511000001109</td><td>421924006</td><td>Rotigotine        </td></tr>\n",
-       "\t<tr><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
-       "\t<tr><td>9864911000001103</td><td>NA       </td><td>NA                                   </td></tr>\n",
-       "\t<tr><td>9865011000001103</td><td>NA       </td><td>NA                                   </td></tr>\n",
-       "\t<tr><td>9865111000001102</td><td>NA       </td><td>NA                                   </td></tr>\n",
-       "\t<tr><td>9865211000001108</td><td>424967004</td><td>Lumiracoxib                          </td></tr>\n",
-       "\t<tr><td>9865311000001100</td><td>424967004</td><td>Lumiracoxib                          </td></tr>\n",
-       "\t<tr><td>9865411000001107</td><td>NA       </td><td>NA                                   </td></tr>\n",
-       "\t<tr><td>9865511000001106</td><td>NA       </td><td>NA                                   </td></tr>\n",
-       "\t<tr><td>9865611000001105</td><td>NA       </td><td>NA                                   </td></tr>\n",
-       "\t<tr><td>9865711000001101</td><td>NA       </td><td>NA                                   </td></tr>\n",
-       "\t<tr><td>9865811000001109</td><td>NA       </td><td>NA                                   </td></tr>\n",
-       "\t<tr><td>9865911000001104</td><td>NA       </td><td>NA                                   </td></tr>\n",
-       "\t<tr><td>9866111000001108</td><td>37237003 </td><td>Vitamin E and vitamin E derivative   </td></tr>\n",
-       "\t<tr><td>9866211000001102</td><td>NA       </td><td>NA                                   </td></tr>\n",
-       "\t<tr><td>9866311000001105</td><td>NA       </td><td>NA                                   </td></tr>\n",
-       "\t<tr><td>9870711000001101</td><td>8631001  </td><td>Potassium chloride                   </td></tr>\n",
-       "\t<tr><td>9884211000001109</td><td>NA       </td><td>NA                                   </td></tr>\n",
-       "\t<tr><td>9884311000001101</td><td>NA       </td><td>NA                                   </td></tr>\n",
-       "\t<tr><td>9884411000001108</td><td>NA       </td><td>NA                                   </td></tr>\n",
-       "\t<tr><td>9884511000001107</td><td>NA       </td><td>NA                                   </td></tr>\n",
-       "\t<tr><td>9884611000001106</td><td>NA       </td><td>NA                                   </td></tr>\n",
-       "\t<tr><td>9884711000001102</td><td>NA       </td><td>NA                                   </td></tr>\n",
-       "\t<tr><td>9884811000001105</td><td>387162007</td><td>Senna                                </td></tr>\n",
-       "\t<tr><td>9884811000001105</td><td>387128007</td><td>Cascara                              </td></tr>\n",
-       "\t<tr><td>9889211000001108</td><td>NA       </td><td>NA                                   </td></tr>\n",
-       "\t<tr><td>9904311000001107</td><td>NA       </td><td>NA                                   </td></tr>\n",
-       "\t<tr><td>9904411000001100</td><td>412374001</td><td>Haemophilus influenzae type b vaccine</td></tr>\n",
-       "\t<tr><td>9904411000001100</td><td>424891007</td><td>Meningococcus vaccine                </td></tr>\n",
-       "\t<tr><td>9904511000001101</td><td>387107003</td><td>Prilocaine                           </td></tr>\n",
-       "\t<tr><td>9904511000001101</td><td>387480006</td><td>Lidocaine                            </td></tr>\n",
-       "\t<tr><td>9992611000001108</td><td>NA       </td><td>NA                                   </td></tr>\n",
-       "</tbody>\n",
-       "</table>\n"
-      ],
-      "text/latex": [
-       "A data.table: 17239 × 3\n",
-       "\\begin{tabular}{lll}\n",
-       " code & meaning & name\\\\\n",
-       " <chr> & <chr> & <chr>\\\\\n",
-       "\\hline\n",
-       "\t 10050811000001105 & NA        & NA                \\\\\n",
-       "\t 10051011000001108 & NA        & NA                \\\\\n",
-       "\t 10051911000001107 & NA        & NA                \\\\\n",
-       "\t 10052111000001104 & 385544005 & Pegfilgrastim     \\\\\n",
-       "\t 10063711000001101 & 391852006 & Bismuth subnitrate\\\\\n",
-       "\t 10063811000001109 & 126189002 & Desmopressin      \\\\\n",
-       "\t 10063911000001104 & 126189002 & Desmopressin      \\\\\n",
-       "\t 10064111000001100 & NA        & NA                \\\\\n",
-       "\t 10064211000001106 & NA        & NA                \\\\\n",
-       "\t 10064311000001103 & NA        & NA                \\\\\n",
-       "\t 10064511000001109 & NA        & NA                \\\\\n",
-       "\t 10064611000001108 & 372672009 & Cromolyn          \\\\\n",
-       "\t 10064711000001104 & 387458008 & Aspirin           \\\\\n",
-       "\t 10075311000001106 & NA        & NA                \\\\\n",
-       "\t 10075411000001104 & NA        & NA                \\\\\n",
-       "\t 10075511000001100 & 373529000 & Morphine          \\\\\n",
-       "\t 10075611000001101 & 372515005 & Salmeterol        \\\\\n",
-       "\t 10097111000001108 & NA        & NA                \\\\\n",
-       "\t 10097211000001102 & 411530000 & Insulin glulisine \\\\\n",
-       "\t 10140711000001108 & 43706004  & Ascorbic acid     \\\\\n",
-       "\t 10140811000001100 & 396065004 & Folinic acid      \\\\\n",
-       "\t 10140911000001105 & 406439009 & Daptomycin        \\\\\n",
-       "\t 10141011000001102 & NA        & NA                \\\\\n",
-       "\t 10141211000001107 & NA        & NA                \\\\\n",
-       "\t 10141311000001104 & NA        & NA                \\\\\n",
-       "\t 10141411000001106 & NA        & NA                \\\\\n",
-       "\t 10141511000001105 & NA        & NA                \\\\\n",
-       "\t 10141611000001109 & NA        & NA                \\\\\n",
-       "\t 10142111000001106 & 421924006 & Rotigotine        \\\\\n",
-       "\t 10143511000001109 & 421924006 & Rotigotine        \\\\\n",
-       "\t ⋮ & ⋮ & ⋮\\\\\n",
-       "\t 9864911000001103 & NA        & NA                                   \\\\\n",
-       "\t 9865011000001103 & NA        & NA                                   \\\\\n",
-       "\t 9865111000001102 & NA        & NA                                   \\\\\n",
-       "\t 9865211000001108 & 424967004 & Lumiracoxib                          \\\\\n",
-       "\t 9865311000001100 & 424967004 & Lumiracoxib                          \\\\\n",
-       "\t 9865411000001107 & NA        & NA                                   \\\\\n",
-       "\t 9865511000001106 & NA        & NA                                   \\\\\n",
-       "\t 9865611000001105 & NA        & NA                                   \\\\\n",
-       "\t 9865711000001101 & NA        & NA                                   \\\\\n",
-       "\t 9865811000001109 & NA        & NA                                   \\\\\n",
-       "\t 9865911000001104 & NA        & NA                                   \\\\\n",
-       "\t 9866111000001108 & 37237003  & Vitamin E and vitamin E derivative   \\\\\n",
-       "\t 9866211000001102 & NA        & NA                                   \\\\\n",
-       "\t 9866311000001105 & NA        & NA                                   \\\\\n",
-       "\t 9870711000001101 & 8631001   & Potassium chloride                   \\\\\n",
-       "\t 9884211000001109 & NA        & NA                                   \\\\\n",
-       "\t 9884311000001101 & NA        & NA                                   \\\\\n",
-       "\t 9884411000001108 & NA        & NA                                   \\\\\n",
-       "\t 9884511000001107 & NA        & NA                                   \\\\\n",
-       "\t 9884611000001106 & NA        & NA                                   \\\\\n",
-       "\t 9884711000001102 & NA        & NA                                   \\\\\n",
-       "\t 9884811000001105 & 387162007 & Senna                                \\\\\n",
-       "\t 9884811000001105 & 387128007 & Cascara                              \\\\\n",
-       "\t 9889211000001108 & NA        & NA                                   \\\\\n",
-       "\t 9904311000001107 & NA        & NA                                   \\\\\n",
-       "\t 9904411000001100 & 412374001 & Haemophilus influenzae type b vaccine\\\\\n",
-       "\t 9904411000001100 & 424891007 & Meningococcus vaccine                \\\\\n",
-       "\t 9904511000001101 & 387107003 & Prilocaine                           \\\\\n",
-       "\t 9904511000001101 & 387480006 & Lidocaine                            \\\\\n",
-       "\t 9992611000001108 & NA        & NA                                   \\\\\n",
-       "\\end{tabular}\n"
-      ],
-      "text/markdown": [
-       "\n",
-       "A data.table: 17239 × 3\n",
-       "\n",
-       "| code &lt;chr&gt; | meaning &lt;chr&gt; | name &lt;chr&gt; |\n",
-       "|---|---|---|\n",
-       "| 10050811000001105 | NA        | NA                 |\n",
-       "| 10051011000001108 | NA        | NA                 |\n",
-       "| 10051911000001107 | NA        | NA                 |\n",
-       "| 10052111000001104 | 385544005 | Pegfilgrastim      |\n",
-       "| 10063711000001101 | 391852006 | Bismuth subnitrate |\n",
-       "| 10063811000001109 | 126189002 | Desmopressin       |\n",
-       "| 10063911000001104 | 126189002 | Desmopressin       |\n",
-       "| 10064111000001100 | NA        | NA                 |\n",
-       "| 10064211000001106 | NA        | NA                 |\n",
-       "| 10064311000001103 | NA        | NA                 |\n",
-       "| 10064511000001109 | NA        | NA                 |\n",
-       "| 10064611000001108 | 372672009 | Cromolyn           |\n",
-       "| 10064711000001104 | 387458008 | Aspirin            |\n",
-       "| 10075311000001106 | NA        | NA                 |\n",
-       "| 10075411000001104 | NA        | NA                 |\n",
-       "| 10075511000001100 | 373529000 | Morphine           |\n",
-       "| 10075611000001101 | 372515005 | Salmeterol         |\n",
-       "| 10097111000001108 | NA        | NA                 |\n",
-       "| 10097211000001102 | 411530000 | Insulin glulisine  |\n",
-       "| 10140711000001108 | 43706004  | Ascorbic acid      |\n",
-       "| 10140811000001100 | 396065004 | Folinic acid       |\n",
-       "| 10140911000001105 | 406439009 | Daptomycin         |\n",
-       "| 10141011000001102 | NA        | NA                 |\n",
-       "| 10141211000001107 | NA        | NA                 |\n",
-       "| 10141311000001104 | NA        | NA                 |\n",
-       "| 10141411000001106 | NA        | NA                 |\n",
-       "| 10141511000001105 | NA        | NA                 |\n",
-       "| 10141611000001109 | NA        | NA                 |\n",
-       "| 10142111000001106 | 421924006 | Rotigotine         |\n",
-       "| 10143511000001109 | 421924006 | Rotigotine         |\n",
-       "| ⋮ | ⋮ | ⋮ |\n",
-       "| 9864911000001103 | NA        | NA                                    |\n",
-       "| 9865011000001103 | NA        | NA                                    |\n",
-       "| 9865111000001102 | NA        | NA                                    |\n",
-       "| 9865211000001108 | 424967004 | Lumiracoxib                           |\n",
-       "| 9865311000001100 | 424967004 | Lumiracoxib                           |\n",
-       "| 9865411000001107 | NA        | NA                                    |\n",
-       "| 9865511000001106 | NA        | NA                                    |\n",
-       "| 9865611000001105 | NA        | NA                                    |\n",
-       "| 9865711000001101 | NA        | NA                                    |\n",
-       "| 9865811000001109 | NA        | NA                                    |\n",
-       "| 9865911000001104 | NA        | NA                                    |\n",
-       "| 9866111000001108 | 37237003  | Vitamin E and vitamin E derivative    |\n",
-       "| 9866211000001102 | NA        | NA                                    |\n",
-       "| 9866311000001105 | NA        | NA                                    |\n",
-       "| 9870711000001101 | 8631001   | Potassium chloride                    |\n",
-       "| 9884211000001109 | NA        | NA                                    |\n",
-       "| 9884311000001101 | NA        | NA                                    |\n",
-       "| 9884411000001108 | NA        | NA                                    |\n",
-       "| 9884511000001107 | NA        | NA                                    |\n",
-       "| 9884611000001106 | NA        | NA                                    |\n",
-       "| 9884711000001102 | NA        | NA                                    |\n",
-       "| 9884811000001105 | 387162007 | Senna                                 |\n",
-       "| 9884811000001105 | 387128007 | Cascara                               |\n",
-       "| 9889211000001108 | NA        | NA                                    |\n",
-       "| 9904311000001107 | NA        | NA                                    |\n",
-       "| 9904411000001100 | 412374001 | Haemophilus influenzae type b vaccine |\n",
-       "| 9904411000001100 | 424891007 | Meningococcus vaccine                 |\n",
-       "| 9904511000001101 | 387107003 | Prilocaine                            |\n",
-       "| 9904511000001101 | 387480006 | Lidocaine                             |\n",
-       "| 9992611000001108 | NA        | NA                                    |\n",
-       "\n"
-      ],
-      "text/plain": [
-       "       code              meaning   name                                 \n",
-       "1      10050811000001105 NA        NA                                   \n",
-       "2      10051011000001108 NA        NA                                   \n",
-       "3      10051911000001107 NA        NA                                   \n",
-       "4      10052111000001104 385544005 Pegfilgrastim                        \n",
-       "5      10063711000001101 391852006 Bismuth subnitrate                   \n",
-       "6      10063811000001109 126189002 Desmopressin                         \n",
-       "7      10063911000001104 126189002 Desmopressin                         \n",
-       "8      10064111000001100 NA        NA                                   \n",
-       "9      10064211000001106 NA        NA                                   \n",
-       "10     10064311000001103 NA        NA                                   \n",
-       "11     10064511000001109 NA        NA                                   \n",
-       "12     10064611000001108 372672009 Cromolyn                             \n",
-       "13     10064711000001104 387458008 Aspirin                              \n",
-       "14     10075311000001106 NA        NA                                   \n",
-       "15     10075411000001104 NA        NA                                   \n",
-       "16     10075511000001100 373529000 Morphine                             \n",
-       "17     10075611000001101 372515005 Salmeterol                           \n",
-       "18     10097111000001108 NA        NA                                   \n",
-       "19     10097211000001102 411530000 Insulin glulisine                    \n",
-       "20     10140711000001108 43706004  Ascorbic acid                        \n",
-       "21     10140811000001100 396065004 Folinic acid                         \n",
-       "22     10140911000001105 406439009 Daptomycin                           \n",
-       "23     10141011000001102 NA        NA                                   \n",
-       "24     10141211000001107 NA        NA                                   \n",
-       "25     10141311000001104 NA        NA                                   \n",
-       "26     10141411000001106 NA        NA                                   \n",
-       "27     10141511000001105 NA        NA                                   \n",
-       "28     10141611000001109 NA        NA                                   \n",
-       "29     10142111000001106 421924006 Rotigotine                           \n",
-       "30     10143511000001109 421924006 Rotigotine                           \n",
-       "<U+22EE> <U+22EE>          <U+22EE>  <U+22EE>                             \n",
-       "17210  9864911000001103  NA        NA                                   \n",
-       "17211  9865011000001103  NA        NA                                   \n",
-       "17212  9865111000001102  NA        NA                                   \n",
-       "17213  9865211000001108  424967004 Lumiracoxib                          \n",
-       "17214  9865311000001100  424967004 Lumiracoxib                          \n",
-       "17215  9865411000001107  NA        NA                                   \n",
-       "17216  9865511000001106  NA        NA                                   \n",
-       "17217  9865611000001105  NA        NA                                   \n",
-       "17218  9865711000001101  NA        NA                                   \n",
-       "17219  9865811000001109  NA        NA                                   \n",
-       "17220  9865911000001104  NA        NA                                   \n",
-       "17221  9866111000001108  37237003  Vitamin E and vitamin E derivative   \n",
-       "17222  9866211000001102  NA        NA                                   \n",
-       "17223  9866311000001105  NA        NA                                   \n",
-       "17224  9870711000001101  8631001   Potassium chloride                   \n",
-       "17225  9884211000001109  NA        NA                                   \n",
-       "17226  9884311000001101  NA        NA                                   \n",
-       "17227  9884411000001108  NA        NA                                   \n",
-       "17228  9884511000001107  NA        NA                                   \n",
-       "17229  9884611000001106  NA        NA                                   \n",
-       "17230  9884711000001102  NA        NA                                   \n",
-       "17231  9884811000001105  387162007 Senna                                \n",
-       "17232  9884811000001105  387128007 Cascara                              \n",
-       "17233  9889211000001108  NA        NA                                   \n",
-       "17234  9904311000001107  NA        NA                                   \n",
-       "17235  9904411000001100  412374001 Haemophilus influenzae type b vaccine\n",
-       "17236  9904411000001100  424891007 Meningococcus vaccine                \n",
-       "17237  9904511000001101  387107003 Prilocaine                           \n",
-       "17238  9904511000001101  387480006 Lidocaine                            \n",
-       "17239  9992611000001108  NA        NA                                   "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "mapping_snomed_substance "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<table>\n",
-       "<caption>A data.table: 14898465 × 10</caption>\n",
-       "<thead>\n",
-       "\t<tr><th scope=col>eid</th><th scope=col>data_provider</th><th scope=col>issue_date</th><th scope=col>read_2</th><th scope=col>bnf_code</th><th scope=col>dmd_code</th><th scope=col>drug_name</th><th scope=col>quantity</th><th scope=col>date</th><th scope=col>code</th></tr>\n",
-       "\t<tr><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int64&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;date&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
-       "</thead>\n",
-       "<tbody>\n",
-       "\t<tr><td>1000020</td><td>2</td><td>06/10/2016</td><td>dher.</td><td>04065400</td><td>NA</td><td>Prochlorperazine 5mg tablets</td><td>28.000</td><td>2016-10-06</td><td>NA</td></tr>\n",
-       "\t<tr><td>1000360</td><td>4</td><td>17/09/1993</td><td>a23y.</td><td>NA      </td><td>NA</td><td>NA                          </td><td>NA    </td><td>1993-09-17</td><td>NA</td></tr>\n",
-       "\t<tr><td>1000360</td><td>4</td><td>15/07/1996</td><td>j28B.</td><td>NA      </td><td>NA</td><td>NA                          </td><td>NA    </td><td>1996-07-15</td><td>NA</td></tr>\n",
-       "\t<tr><td>1000360</td><td>4</td><td>12/06/1997</td><td>j28B.</td><td>NA      </td><td>NA</td><td>NA                          </td><td>NA    </td><td>1997-06-12</td><td>NA</td></tr>\n",
-       "\t<tr><td>1000360</td><td>4</td><td>26/06/1998</td><td>c8o1.</td><td>NA      </td><td>NA</td><td>NA                          </td><td>NA    </td><td>1998-06-26</td><td>NA</td></tr>\n",
-       "\t<tr><td>1000360</td><td>4</td><td>26/06/1998</td><td>l81z.</td><td>NA      </td><td>NA</td><td>NA                          </td><td>NA    </td><td>1998-06-26</td><td>NA</td></tr>\n",
-       "\t<tr><td>1000360</td><td>4</td><td>01/09/1998</td><td>l81z.</td><td>NA      </td><td>NA</td><td>NA                          </td><td>NA    </td><td>1998-09-01</td><td>NA</td></tr>\n",
-       "\t<tr><td>1000360</td><td>4</td><td>19/11/1998</td><td>l81z.</td><td>NA      </td><td>NA</td><td>NA                          </td><td>NA    </td><td>1998-11-19</td><td>NA</td></tr>\n",
-       "\t<tr><td>1000360</td><td>4</td><td>19/05/1999</td><td>aj1t.</td><td>NA      </td><td>NA</td><td>NA                          </td><td>NA    </td><td>1999-05-19</td><td>NA</td></tr>\n",
-       "\t<tr><td>1000360</td><td>4</td><td>15/12/1999</td><td>aj1t.</td><td>NA      </td><td>NA</td><td>NA                          </td><td>NA    </td><td>1999-12-15</td><td>NA</td></tr>\n",
-       "\t<tr><td>1000360</td><td>4</td><td>15/12/1999</td><td>l881.</td><td>NA      </td><td>NA</td><td>NA                          </td><td>NA    </td><td>1999-12-15</td><td>NA</td></tr>\n",
-       "\t<tr><td>1000360</td><td>4</td><td>07/06/2000</td><td>l881.</td><td>NA      </td><td>NA</td><td>NA                          </td><td>NA    </td><td>2000-06-07</td><td>NA</td></tr>\n",
-       "\t<tr><td>1000360</td><td>4</td><td>04/07/2000</td><td>l881.</td><td>NA      </td><td>NA</td><td>NA                          </td><td>NA    </td><td>2000-07-04</td><td>NA</td></tr>\n",
-       "\t<tr><td>1000360</td><td>4</td><td>04/10/2000</td><td>l881.</td><td>NA      </td><td>NA</td><td>NA                          </td><td>NA    </td><td>2000-10-04</td><td>NA</td></tr>\n",
-       "\t<tr><td>1000360</td><td>4</td><td>08/11/2000</td><td>l881.</td><td>NA      </td><td>NA</td><td>NA                          </td><td>NA    </td><td>2000-11-08</td><td>NA</td></tr>\n",
-       "\t<tr><td>1000360</td><td>4</td><td>03/01/2001</td><td>l881.</td><td>NA      </td><td>NA</td><td>NA                          </td><td>NA    </td><td>2001-01-03</td><td>NA</td></tr>\n",
-       "\t<tr><td>1000360</td><td>4</td><td>14/02/2001</td><td>l881.</td><td>NA      </td><td>NA</td><td>NA                          </td><td>NA    </td><td>2001-02-14</td><td>NA</td></tr>\n",
-       "\t<tr><td>1000360</td><td>4</td><td>19/04/2001</td><td>l881.</td><td>NA      </td><td>NA</td><td>NA                          </td><td>NA    </td><td>2001-04-19</td><td>NA</td></tr>\n",
-       "\t<tr><td>1000360</td><td>4</td><td>16/07/2001</td><td>dher.</td><td>NA      </td><td>NA</td><td>NA                          </td><td>NA    </td><td>2001-07-16</td><td>NA</td></tr>\n",
-       "\t<tr><td>1000360</td><td>4</td><td>19/08/2002</td><td>da91.</td><td>NA      </td><td>NA</td><td>NA                          </td><td>NA    </td><td>2002-08-19</td><td>NA</td></tr>\n",
-       "\t<tr><td>1000360</td><td>4</td><td>10/09/2002</td><td>da91.</td><td>NA      </td><td>NA</td><td>NA                          </td><td>NA    </td><td>2002-09-10</td><td>NA</td></tr>\n",
-       "\t<tr><td>1000360</td><td>4</td><td>07/10/2002</td><td>da91.</td><td>NA      </td><td>NA</td><td>NA                          </td><td>NA    </td><td>2002-10-07</td><td>NA</td></tr>\n",
-       "\t<tr><td>1000360</td><td>4</td><td>05/11/2002</td><td>da91.</td><td>NA      </td><td>NA</td><td>NA                          </td><td>NA    </td><td>2002-11-05</td><td>NA</td></tr>\n",
-       "\t<tr><td>1000360</td><td>4</td><td>05/12/2002</td><td>da91.</td><td>NA      </td><td>NA</td><td>NA                          </td><td>NA    </td><td>2002-12-05</td><td>NA</td></tr>\n",
-       "\t<tr><td>1000360</td><td>4</td><td>30/12/2002</td><td>da91.</td><td>NA      </td><td>NA</td><td>NA                          </td><td>NA    </td><td>2002-12-30</td><td>NA</td></tr>\n",
-       "\t<tr><td>1000360</td><td>4</td><td>30/01/2003</td><td>da91.</td><td>NA      </td><td>NA</td><td>NA                          </td><td>NA    </td><td>2003-01-30</td><td>NA</td></tr>\n",
-       "\t<tr><td>1000360</td><td>4</td><td>18/03/2003</td><td>da91.</td><td>NA      </td><td>NA</td><td>NA                          </td><td>NA    </td><td>2003-03-18</td><td>NA</td></tr>\n",
-       "\t<tr><td>1000360</td><td>4</td><td>17/04/2003</td><td>da91.</td><td>NA      </td><td>NA</td><td>NA                          </td><td>NA    </td><td>2003-04-17</td><td>NA</td></tr>\n",
-       "\t<tr><td>1000360</td><td>4</td><td>13/05/2003</td><td>da91.</td><td>NA      </td><td>NA</td><td>NA                          </td><td>NA    </td><td>2003-05-13</td><td>NA</td></tr>\n",
-       "\t<tr><td>1000360</td><td>4</td><td>09/06/2003</td><td>da91.</td><td>NA      </td><td>NA</td><td>NA                          </td><td>NA    </td><td>2003-06-09</td><td>NA</td></tr>\n",
-       "\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
-       "\t<tr><td>6025042</td><td>4</td><td>12/01/2016</td><td>bxd5.</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>2016-01-12</td><td>NA</td></tr>\n",
-       "\t<tr><td>6025042</td><td>4</td><td>09/02/2016</td><td>bxd5.</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>2016-02-09</td><td>NA</td></tr>\n",
-       "\t<tr><td>6025042</td><td>4</td><td>08/03/2016</td><td>bxd5.</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>2016-03-08</td><td>NA</td></tr>\n",
-       "\t<tr><td>6025042</td><td>4</td><td>05/04/2016</td><td>bxd5.</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>2016-04-05</td><td>NA</td></tr>\n",
-       "\t<tr><td>6025042</td><td>4</td><td>02/06/2016</td><td>d1d3.</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>2016-06-02</td><td>NA</td></tr>\n",
-       "\t<tr><td>6025042</td><td>4</td><td>02/06/2016</td><td>dnj2.</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>2016-06-02</td><td>NA</td></tr>\n",
-       "\t<tr><td>6025042</td><td>4</td><td>02/06/2016</td><td>dnj2.</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>2016-06-02</td><td>NA</td></tr>\n",
-       "\t<tr><td>6025042</td><td>4</td><td>23/06/2016</td><td>d1d3.</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>2016-06-23</td><td>NA</td></tr>\n",
-       "\t<tr><td>6025042</td><td>4</td><td>07/07/2016</td><td>dnj2.</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>2016-07-07</td><td>NA</td></tr>\n",
-       "\t<tr><td>6025042</td><td>4</td><td>05/08/2016</td><td>dnj2.</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>2016-08-05</td><td>NA</td></tr>\n",
-       "\t<tr><td>6025042</td><td>4</td><td>31/08/2016</td><td>dnj2.</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>2016-08-31</td><td>NA</td></tr>\n",
-       "\t<tr><td>6025042</td><td>4</td><td>30/09/2016</td><td>dnj2.</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>2016-09-30</td><td>NA</td></tr>\n",
-       "\t<tr><td>6025042</td><td>4</td><td>26/10/2016</td><td>dnj2.</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>2016-10-26</td><td>NA</td></tr>\n",
-       "\t<tr><td>6025042</td><td>4</td><td>22/11/2016</td><td>dnj2.</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>2016-11-22</td><td>NA</td></tr>\n",
-       "\t<tr><td>6025042</td><td>4</td><td>20/12/2016</td><td>dnj2.</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>2016-12-20</td><td>NA</td></tr>\n",
-       "\t<tr><td>6025042</td><td>4</td><td>25/01/2017</td><td>dnj2.</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>2017-01-25</td><td>NA</td></tr>\n",
-       "\t<tr><td>6025042</td><td>4</td><td>17/02/2017</td><td>dnj2.</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>2017-02-17</td><td>NA</td></tr>\n",
-       "\t<tr><td>6025042</td><td>4</td><td>08/03/2017</td><td>a4ez.</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>2017-03-08</td><td>NA</td></tr>\n",
-       "\t<tr><td>6025042</td><td>4</td><td>20/03/2017</td><td>dnj2.</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>2017-03-20</td><td>NA</td></tr>\n",
-       "\t<tr><td>6025042</td><td>4</td><td>31/03/2017</td><td>dia2.</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>2017-03-31</td><td>NA</td></tr>\n",
-       "\t<tr><td>6025042</td><td>4</td><td>31/03/2017</td><td>dia2.</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>2017-03-31</td><td>NA</td></tr>\n",
-       "\t<tr><td>6025042</td><td>4</td><td>31/03/2017</td><td>dnj2.</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>2017-03-31</td><td>NA</td></tr>\n",
-       "\t<tr><td>6025042</td><td>4</td><td>06/04/2017</td><td>dia2.</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>2017-04-06</td><td>NA</td></tr>\n",
-       "\t<tr><td>6025042</td><td>4</td><td>11/05/2017</td><td>dnj2.</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>2017-05-11</td><td>NA</td></tr>\n",
-       "\t<tr><td>6025042</td><td>4</td><td>05/06/2017</td><td>dnj2.</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>2017-06-05</td><td>NA</td></tr>\n",
-       "\t<tr><td>6025042</td><td>4</td><td>05/06/2017</td><td>m412.</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>2017-06-05</td><td>NA</td></tr>\n",
-       "\t<tr><td>6025042</td><td>4</td><td>12/07/2017</td><td>dnj2.</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>2017-07-12</td><td>NA</td></tr>\n",
-       "\t<tr><td>6025042</td><td>4</td><td>18/07/2017</td><td>m272.</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>2017-07-18</td><td>NA</td></tr>\n",
-       "\t<tr><td>6025042</td><td>4</td><td>09/08/2017</td><td>dnj2.</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>2017-08-09</td><td>NA</td></tr>\n",
-       "\t<tr><td>6025042</td><td>4</td><td>13/09/2017</td><td>dnj2.</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>2017-09-13</td><td>NA</td></tr>\n",
-       "</tbody>\n",
-       "</table>\n"
-      ],
-      "text/latex": [
-       "A data.table: 14898465 × 10\n",
-       "\\begin{tabular}{llllllllll}\n",
-       " eid & data\\_provider & issue\\_date & read\\_2 & bnf\\_code & dmd\\_code & drug\\_name & quantity & date & code\\\\\n",
-       " <int> & <int> & <chr> & <chr> & <chr> & <int64> & <chr> & <chr> & <date> & <chr>\\\\\n",
-       "\\hline\n",
-       "\t 1000020 & 2 & 06/10/2016 & dher. & 04065400 & NA & Prochlorperazine 5mg tablets & 28.000 & 2016-10-06 & NA\\\\\n",
-       "\t 1000360 & 4 & 17/09/1993 & a23y. & NA       & NA & NA                           & NA     & 1993-09-17 & NA\\\\\n",
-       "\t 1000360 & 4 & 15/07/1996 & j28B. & NA       & NA & NA                           & NA     & 1996-07-15 & NA\\\\\n",
-       "\t 1000360 & 4 & 12/06/1997 & j28B. & NA       & NA & NA                           & NA     & 1997-06-12 & NA\\\\\n",
-       "\t 1000360 & 4 & 26/06/1998 & c8o1. & NA       & NA & NA                           & NA     & 1998-06-26 & NA\\\\\n",
-       "\t 1000360 & 4 & 26/06/1998 & l81z. & NA       & NA & NA                           & NA     & 1998-06-26 & NA\\\\\n",
-       "\t 1000360 & 4 & 01/09/1998 & l81z. & NA       & NA & NA                           & NA     & 1998-09-01 & NA\\\\\n",
-       "\t 1000360 & 4 & 19/11/1998 & l81z. & NA       & NA & NA                           & NA     & 1998-11-19 & NA\\\\\n",
-       "\t 1000360 & 4 & 19/05/1999 & aj1t. & NA       & NA & NA                           & NA     & 1999-05-19 & NA\\\\\n",
-       "\t 1000360 & 4 & 15/12/1999 & aj1t. & NA       & NA & NA                           & NA     & 1999-12-15 & NA\\\\\n",
-       "\t 1000360 & 4 & 15/12/1999 & l881. & NA       & NA & NA                           & NA     & 1999-12-15 & NA\\\\\n",
-       "\t 1000360 & 4 & 07/06/2000 & l881. & NA       & NA & NA                           & NA     & 2000-06-07 & NA\\\\\n",
-       "\t 1000360 & 4 & 04/07/2000 & l881. & NA       & NA & NA                           & NA     & 2000-07-04 & NA\\\\\n",
-       "\t 1000360 & 4 & 04/10/2000 & l881. & NA       & NA & NA                           & NA     & 2000-10-04 & NA\\\\\n",
-       "\t 1000360 & 4 & 08/11/2000 & l881. & NA       & NA & NA                           & NA     & 2000-11-08 & NA\\\\\n",
-       "\t 1000360 & 4 & 03/01/2001 & l881. & NA       & NA & NA                           & NA     & 2001-01-03 & NA\\\\\n",
-       "\t 1000360 & 4 & 14/02/2001 & l881. & NA       & NA & NA                           & NA     & 2001-02-14 & NA\\\\\n",
-       "\t 1000360 & 4 & 19/04/2001 & l881. & NA       & NA & NA                           & NA     & 2001-04-19 & NA\\\\\n",
-       "\t 1000360 & 4 & 16/07/2001 & dher. & NA       & NA & NA                           & NA     & 2001-07-16 & NA\\\\\n",
-       "\t 1000360 & 4 & 19/08/2002 & da91. & NA       & NA & NA                           & NA     & 2002-08-19 & NA\\\\\n",
-       "\t 1000360 & 4 & 10/09/2002 & da91. & NA       & NA & NA                           & NA     & 2002-09-10 & NA\\\\\n",
-       "\t 1000360 & 4 & 07/10/2002 & da91. & NA       & NA & NA                           & NA     & 2002-10-07 & NA\\\\\n",
-       "\t 1000360 & 4 & 05/11/2002 & da91. & NA       & NA & NA                           & NA     & 2002-11-05 & NA\\\\\n",
-       "\t 1000360 & 4 & 05/12/2002 & da91. & NA       & NA & NA                           & NA     & 2002-12-05 & NA\\\\\n",
-       "\t 1000360 & 4 & 30/12/2002 & da91. & NA       & NA & NA                           & NA     & 2002-12-30 & NA\\\\\n",
-       "\t 1000360 & 4 & 30/01/2003 & da91. & NA       & NA & NA                           & NA     & 2003-01-30 & NA\\\\\n",
-       "\t 1000360 & 4 & 18/03/2003 & da91. & NA       & NA & NA                           & NA     & 2003-03-18 & NA\\\\\n",
-       "\t 1000360 & 4 & 17/04/2003 & da91. & NA       & NA & NA                           & NA     & 2003-04-17 & NA\\\\\n",
-       "\t 1000360 & 4 & 13/05/2003 & da91. & NA       & NA & NA                           & NA     & 2003-05-13 & NA\\\\\n",
-       "\t 1000360 & 4 & 09/06/2003 & da91. & NA       & NA & NA                           & NA     & 2003-06-09 & NA\\\\\n",
-       "\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
-       "\t 6025042 & 4 & 12/01/2016 & bxd5. & NA & NA & NA & NA & 2016-01-12 & NA\\\\\n",
-       "\t 6025042 & 4 & 09/02/2016 & bxd5. & NA & NA & NA & NA & 2016-02-09 & NA\\\\\n",
-       "\t 6025042 & 4 & 08/03/2016 & bxd5. & NA & NA & NA & NA & 2016-03-08 & NA\\\\\n",
-       "\t 6025042 & 4 & 05/04/2016 & bxd5. & NA & NA & NA & NA & 2016-04-05 & NA\\\\\n",
-       "\t 6025042 & 4 & 02/06/2016 & d1d3. & NA & NA & NA & NA & 2016-06-02 & NA\\\\\n",
-       "\t 6025042 & 4 & 02/06/2016 & dnj2. & NA & NA & NA & NA & 2016-06-02 & NA\\\\\n",
-       "\t 6025042 & 4 & 02/06/2016 & dnj2. & NA & NA & NA & NA & 2016-06-02 & NA\\\\\n",
-       "\t 6025042 & 4 & 23/06/2016 & d1d3. & NA & NA & NA & NA & 2016-06-23 & NA\\\\\n",
-       "\t 6025042 & 4 & 07/07/2016 & dnj2. & NA & NA & NA & NA & 2016-07-07 & NA\\\\\n",
-       "\t 6025042 & 4 & 05/08/2016 & dnj2. & NA & NA & NA & NA & 2016-08-05 & NA\\\\\n",
-       "\t 6025042 & 4 & 31/08/2016 & dnj2. & NA & NA & NA & NA & 2016-08-31 & NA\\\\\n",
-       "\t 6025042 & 4 & 30/09/2016 & dnj2. & NA & NA & NA & NA & 2016-09-30 & NA\\\\\n",
-       "\t 6025042 & 4 & 26/10/2016 & dnj2. & NA & NA & NA & NA & 2016-10-26 & NA\\\\\n",
-       "\t 6025042 & 4 & 22/11/2016 & dnj2. & NA & NA & NA & NA & 2016-11-22 & NA\\\\\n",
-       "\t 6025042 & 4 & 20/12/2016 & dnj2. & NA & NA & NA & NA & 2016-12-20 & NA\\\\\n",
-       "\t 6025042 & 4 & 25/01/2017 & dnj2. & NA & NA & NA & NA & 2017-01-25 & NA\\\\\n",
-       "\t 6025042 & 4 & 17/02/2017 & dnj2. & NA & NA & NA & NA & 2017-02-17 & NA\\\\\n",
-       "\t 6025042 & 4 & 08/03/2017 & a4ez. & NA & NA & NA & NA & 2017-03-08 & NA\\\\\n",
-       "\t 6025042 & 4 & 20/03/2017 & dnj2. & NA & NA & NA & NA & 2017-03-20 & NA\\\\\n",
-       "\t 6025042 & 4 & 31/03/2017 & dia2. & NA & NA & NA & NA & 2017-03-31 & NA\\\\\n",
-       "\t 6025042 & 4 & 31/03/2017 & dia2. & NA & NA & NA & NA & 2017-03-31 & NA\\\\\n",
-       "\t 6025042 & 4 & 31/03/2017 & dnj2. & NA & NA & NA & NA & 2017-03-31 & NA\\\\\n",
-       "\t 6025042 & 4 & 06/04/2017 & dia2. & NA & NA & NA & NA & 2017-04-06 & NA\\\\\n",
-       "\t 6025042 & 4 & 11/05/2017 & dnj2. & NA & NA & NA & NA & 2017-05-11 & NA\\\\\n",
-       "\t 6025042 & 4 & 05/06/2017 & dnj2. & NA & NA & NA & NA & 2017-06-05 & NA\\\\\n",
-       "\t 6025042 & 4 & 05/06/2017 & m412. & NA & NA & NA & NA & 2017-06-05 & NA\\\\\n",
-       "\t 6025042 & 4 & 12/07/2017 & dnj2. & NA & NA & NA & NA & 2017-07-12 & NA\\\\\n",
-       "\t 6025042 & 4 & 18/07/2017 & m272. & NA & NA & NA & NA & 2017-07-18 & NA\\\\\n",
-       "\t 6025042 & 4 & 09/08/2017 & dnj2. & NA & NA & NA & NA & 2017-08-09 & NA\\\\\n",
-       "\t 6025042 & 4 & 13/09/2017 & dnj2. & NA & NA & NA & NA & 2017-09-13 & NA\\\\\n",
-       "\\end{tabular}\n"
-      ],
-      "text/markdown": [
-       "\n",
-       "A data.table: 14898465 × 10\n",
-       "\n",
-       "| eid &lt;int&gt; | data_provider &lt;int&gt; | issue_date &lt;chr&gt; | read_2 &lt;chr&gt; | bnf_code &lt;chr&gt; | dmd_code &lt;int64&gt; | drug_name &lt;chr&gt; | quantity &lt;chr&gt; | date &lt;date&gt; | code &lt;chr&gt; |\n",
-       "|---|---|---|---|---|---|---|---|---|---|\n",
-       "| 1000020 | 2 | 06/10/2016 | dher. | 04065400 | NA | Prochlorperazine 5mg tablets | 28.000 | 2016-10-06 | NA |\n",
-       "| 1000360 | 4 | 17/09/1993 | a23y. | NA       | NA | NA                           | NA     | 1993-09-17 | NA |\n",
-       "| 1000360 | 4 | 15/07/1996 | j28B. | NA       | NA | NA                           | NA     | 1996-07-15 | NA |\n",
-       "| 1000360 | 4 | 12/06/1997 | j28B. | NA       | NA | NA                           | NA     | 1997-06-12 | NA |\n",
-       "| 1000360 | 4 | 26/06/1998 | c8o1. | NA       | NA | NA                           | NA     | 1998-06-26 | NA |\n",
-       "| 1000360 | 4 | 26/06/1998 | l81z. | NA       | NA | NA                           | NA     | 1998-06-26 | NA |\n",
-       "| 1000360 | 4 | 01/09/1998 | l81z. | NA       | NA | NA                           | NA     | 1998-09-01 | NA |\n",
-       "| 1000360 | 4 | 19/11/1998 | l81z. | NA       | NA | NA                           | NA     | 1998-11-19 | NA |\n",
-       "| 1000360 | 4 | 19/05/1999 | aj1t. | NA       | NA | NA                           | NA     | 1999-05-19 | NA |\n",
-       "| 1000360 | 4 | 15/12/1999 | aj1t. | NA       | NA | NA                           | NA     | 1999-12-15 | NA |\n",
-       "| 1000360 | 4 | 15/12/1999 | l881. | NA       | NA | NA                           | NA     | 1999-12-15 | NA |\n",
-       "| 1000360 | 4 | 07/06/2000 | l881. | NA       | NA | NA                           | NA     | 2000-06-07 | NA |\n",
-       "| 1000360 | 4 | 04/07/2000 | l881. | NA       | NA | NA                           | NA     | 2000-07-04 | NA |\n",
-       "| 1000360 | 4 | 04/10/2000 | l881. | NA       | NA | NA                           | NA     | 2000-10-04 | NA |\n",
-       "| 1000360 | 4 | 08/11/2000 | l881. | NA       | NA | NA                           | NA     | 2000-11-08 | NA |\n",
-       "| 1000360 | 4 | 03/01/2001 | l881. | NA       | NA | NA                           | NA     | 2001-01-03 | NA |\n",
-       "| 1000360 | 4 | 14/02/2001 | l881. | NA       | NA | NA                           | NA     | 2001-02-14 | NA |\n",
-       "| 1000360 | 4 | 19/04/2001 | l881. | NA       | NA | NA                           | NA     | 2001-04-19 | NA |\n",
-       "| 1000360 | 4 | 16/07/2001 | dher. | NA       | NA | NA                           | NA     | 2001-07-16 | NA |\n",
-       "| 1000360 | 4 | 19/08/2002 | da91. | NA       | NA | NA                           | NA     | 2002-08-19 | NA |\n",
-       "| 1000360 | 4 | 10/09/2002 | da91. | NA       | NA | NA                           | NA     | 2002-09-10 | NA |\n",
-       "| 1000360 | 4 | 07/10/2002 | da91. | NA       | NA | NA                           | NA     | 2002-10-07 | NA |\n",
-       "| 1000360 | 4 | 05/11/2002 | da91. | NA       | NA | NA                           | NA     | 2002-11-05 | NA |\n",
-       "| 1000360 | 4 | 05/12/2002 | da91. | NA       | NA | NA                           | NA     | 2002-12-05 | NA |\n",
-       "| 1000360 | 4 | 30/12/2002 | da91. | NA       | NA | NA                           | NA     | 2002-12-30 | NA |\n",
-       "| 1000360 | 4 | 30/01/2003 | da91. | NA       | NA | NA                           | NA     | 2003-01-30 | NA |\n",
-       "| 1000360 | 4 | 18/03/2003 | da91. | NA       | NA | NA                           | NA     | 2003-03-18 | NA |\n",
-       "| 1000360 | 4 | 17/04/2003 | da91. | NA       | NA | NA                           | NA     | 2003-04-17 | NA |\n",
-       "| 1000360 | 4 | 13/05/2003 | da91. | NA       | NA | NA                           | NA     | 2003-05-13 | NA |\n",
-       "| 1000360 | 4 | 09/06/2003 | da91. | NA       | NA | NA                           | NA     | 2003-06-09 | NA |\n",
-       "| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |\n",
-       "| 6025042 | 4 | 12/01/2016 | bxd5. | NA | NA | NA | NA | 2016-01-12 | NA |\n",
-       "| 6025042 | 4 | 09/02/2016 | bxd5. | NA | NA | NA | NA | 2016-02-09 | NA |\n",
-       "| 6025042 | 4 | 08/03/2016 | bxd5. | NA | NA | NA | NA | 2016-03-08 | NA |\n",
-       "| 6025042 | 4 | 05/04/2016 | bxd5. | NA | NA | NA | NA | 2016-04-05 | NA |\n",
-       "| 6025042 | 4 | 02/06/2016 | d1d3. | NA | NA | NA | NA | 2016-06-02 | NA |\n",
-       "| 6025042 | 4 | 02/06/2016 | dnj2. | NA | NA | NA | NA | 2016-06-02 | NA |\n",
-       "| 6025042 | 4 | 02/06/2016 | dnj2. | NA | NA | NA | NA | 2016-06-02 | NA |\n",
-       "| 6025042 | 4 | 23/06/2016 | d1d3. | NA | NA | NA | NA | 2016-06-23 | NA |\n",
-       "| 6025042 | 4 | 07/07/2016 | dnj2. | NA | NA | NA | NA | 2016-07-07 | NA |\n",
-       "| 6025042 | 4 | 05/08/2016 | dnj2. | NA | NA | NA | NA | 2016-08-05 | NA |\n",
-       "| 6025042 | 4 | 31/08/2016 | dnj2. | NA | NA | NA | NA | 2016-08-31 | NA |\n",
-       "| 6025042 | 4 | 30/09/2016 | dnj2. | NA | NA | NA | NA | 2016-09-30 | NA |\n",
-       "| 6025042 | 4 | 26/10/2016 | dnj2. | NA | NA | NA | NA | 2016-10-26 | NA |\n",
-       "| 6025042 | 4 | 22/11/2016 | dnj2. | NA | NA | NA | NA | 2016-11-22 | NA |\n",
-       "| 6025042 | 4 | 20/12/2016 | dnj2. | NA | NA | NA | NA | 2016-12-20 | NA |\n",
-       "| 6025042 | 4 | 25/01/2017 | dnj2. | NA | NA | NA | NA | 2017-01-25 | NA |\n",
-       "| 6025042 | 4 | 17/02/2017 | dnj2. | NA | NA | NA | NA | 2017-02-17 | NA |\n",
-       "| 6025042 | 4 | 08/03/2017 | a4ez. | NA | NA | NA | NA | 2017-03-08 | NA |\n",
-       "| 6025042 | 4 | 20/03/2017 | dnj2. | NA | NA | NA | NA | 2017-03-20 | NA |\n",
-       "| 6025042 | 4 | 31/03/2017 | dia2. | NA | NA | NA | NA | 2017-03-31 | NA |\n",
-       "| 6025042 | 4 | 31/03/2017 | dia2. | NA | NA | NA | NA | 2017-03-31 | NA |\n",
-       "| 6025042 | 4 | 31/03/2017 | dnj2. | NA | NA | NA | NA | 2017-03-31 | NA |\n",
-       "| 6025042 | 4 | 06/04/2017 | dia2. | NA | NA | NA | NA | 2017-04-06 | NA |\n",
-       "| 6025042 | 4 | 11/05/2017 | dnj2. | NA | NA | NA | NA | 2017-05-11 | NA |\n",
-       "| 6025042 | 4 | 05/06/2017 | dnj2. | NA | NA | NA | NA | 2017-06-05 | NA |\n",
-       "| 6025042 | 4 | 05/06/2017 | m412. | NA | NA | NA | NA | 2017-06-05 | NA |\n",
-       "| 6025042 | 4 | 12/07/2017 | dnj2. | NA | NA | NA | NA | 2017-07-12 | NA |\n",
-       "| 6025042 | 4 | 18/07/2017 | m272. | NA | NA | NA | NA | 2017-07-18 | NA |\n",
-       "| 6025042 | 4 | 09/08/2017 | dnj2. | NA | NA | NA | NA | 2017-08-09 | NA |\n",
-       "| 6025042 | 4 | 13/09/2017 | dnj2. | NA | NA | NA | NA | 2017-09-13 | NA |\n",
-       "\n"
-      ],
-      "text/plain": [
-       "         eid     data_provider issue_date read_2 bnf_code dmd_code\n",
-       "1        1000020 2             06/10/2016 dher.  04065400 NA      \n",
-       "2        1000360 4             17/09/1993 a23y.  NA       NA      \n",
-       "3        1000360 4             15/07/1996 j28B.  NA       NA      \n",
-       "4        1000360 4             12/06/1997 j28B.  NA       NA      \n",
-       "5        1000360 4             26/06/1998 c8o1.  NA       NA      \n",
-       "6        1000360 4             26/06/1998 l81z.  NA       NA      \n",
-       "7        1000360 4             01/09/1998 l81z.  NA       NA      \n",
-       "8        1000360 4             19/11/1998 l81z.  NA       NA      \n",
-       "9        1000360 4             19/05/1999 aj1t.  NA       NA      \n",
-       "10       1000360 4             15/12/1999 aj1t.  NA       NA      \n",
-       "11       1000360 4             15/12/1999 l881.  NA       NA      \n",
-       "12       1000360 4             07/06/2000 l881.  NA       NA      \n",
-       "13       1000360 4             04/07/2000 l881.  NA       NA      \n",
-       "14       1000360 4             04/10/2000 l881.  NA       NA      \n",
-       "15       1000360 4             08/11/2000 l881.  NA       NA      \n",
-       "16       1000360 4             03/01/2001 l881.  NA       NA      \n",
-       "17       1000360 4             14/02/2001 l881.  NA       NA      \n",
-       "18       1000360 4             19/04/2001 l881.  NA       NA      \n",
-       "19       1000360 4             16/07/2001 dher.  NA       NA      \n",
-       "20       1000360 4             19/08/2002 da91.  NA       NA      \n",
-       "21       1000360 4             10/09/2002 da91.  NA       NA      \n",
-       "22       1000360 4             07/10/2002 da91.  NA       NA      \n",
-       "23       1000360 4             05/11/2002 da91.  NA       NA      \n",
-       "24       1000360 4             05/12/2002 da91.  NA       NA      \n",
-       "25       1000360 4             30/12/2002 da91.  NA       NA      \n",
-       "26       1000360 4             30/01/2003 da91.  NA       NA      \n",
-       "27       1000360 4             18/03/2003 da91.  NA       NA      \n",
-       "28       1000360 4             17/04/2003 da91.  NA       NA      \n",
-       "29       1000360 4             13/05/2003 da91.  NA       NA      \n",
-       "30       1000360 4             09/06/2003 da91.  NA       NA      \n",
-       "<U+22EE>   <U+22EE> <U+22EE>      <U+22EE>   <U+22EE> <U+22EE> <U+22EE>\n",
-       "14898436 6025042 4             12/01/2016 bxd5.  NA       NA      \n",
-       "14898437 6025042 4             09/02/2016 bxd5.  NA       NA      \n",
-       "14898438 6025042 4             08/03/2016 bxd5.  NA       NA      \n",
-       "14898439 6025042 4             05/04/2016 bxd5.  NA       NA      \n",
-       "14898440 6025042 4             02/06/2016 d1d3.  NA       NA      \n",
-       "14898441 6025042 4             02/06/2016 dnj2.  NA       NA      \n",
-       "14898442 6025042 4             02/06/2016 dnj2.  NA       NA      \n",
-       "14898443 6025042 4             23/06/2016 d1d3.  NA       NA      \n",
-       "14898444 6025042 4             07/07/2016 dnj2.  NA       NA      \n",
-       "14898445 6025042 4             05/08/2016 dnj2.  NA       NA      \n",
-       "14898446 6025042 4             31/08/2016 dnj2.  NA       NA      \n",
-       "14898447 6025042 4             30/09/2016 dnj2.  NA       NA      \n",
-       "14898448 6025042 4             26/10/2016 dnj2.  NA       NA      \n",
-       "14898449 6025042 4             22/11/2016 dnj2.  NA       NA      \n",
-       "14898450 6025042 4             20/12/2016 dnj2.  NA       NA      \n",
-       "14898451 6025042 4             25/01/2017 dnj2.  NA       NA      \n",
-       "14898452 6025042 4             17/02/2017 dnj2.  NA       NA      \n",
-       "14898453 6025042 4             08/03/2017 a4ez.  NA       NA      \n",
-       "14898454 6025042 4             20/03/2017 dnj2.  NA       NA      \n",
-       "14898455 6025042 4             31/03/2017 dia2.  NA       NA      \n",
-       "14898456 6025042 4             31/03/2017 dia2.  NA       NA      \n",
-       "14898457 6025042 4             31/03/2017 dnj2.  NA       NA      \n",
-       "14898458 6025042 4             06/04/2017 dia2.  NA       NA      \n",
-       "14898459 6025042 4             11/05/2017 dnj2.  NA       NA      \n",
-       "14898460 6025042 4             05/06/2017 dnj2.  NA       NA      \n",
-       "14898461 6025042 4             05/06/2017 m412.  NA       NA      \n",
-       "14898462 6025042 4             12/07/2017 dnj2.  NA       NA      \n",
-       "14898463 6025042 4             18/07/2017 m272.  NA       NA      \n",
-       "14898464 6025042 4             09/08/2017 dnj2.  NA       NA      \n",
-       "14898465 6025042 4             13/09/2017 dnj2.  NA       NA      \n",
-       "         drug_name                    quantity date       code  \n",
-       "1        Prochlorperazine 5mg tablets 28.000   2016-10-06 NA    \n",
-       "2        NA                           NA       1993-09-17 NA    \n",
-       "3        NA                           NA       1996-07-15 NA    \n",
-       "4        NA                           NA       1997-06-12 NA    \n",
-       "5        NA                           NA       1998-06-26 NA    \n",
-       "6        NA                           NA       1998-06-26 NA    \n",
-       "7        NA                           NA       1998-09-01 NA    \n",
-       "8        NA                           NA       1998-11-19 NA    \n",
-       "9        NA                           NA       1999-05-19 NA    \n",
-       "10       NA                           NA       1999-12-15 NA    \n",
-       "11       NA                           NA       1999-12-15 NA    \n",
-       "12       NA                           NA       2000-06-07 NA    \n",
-       "13       NA                           NA       2000-07-04 NA    \n",
-       "14       NA                           NA       2000-10-04 NA    \n",
-       "15       NA                           NA       2000-11-08 NA    \n",
-       "16       NA                           NA       2001-01-03 NA    \n",
-       "17       NA                           NA       2001-02-14 NA    \n",
-       "18       NA                           NA       2001-04-19 NA    \n",
-       "19       NA                           NA       2001-07-16 NA    \n",
-       "20       NA                           NA       2002-08-19 NA    \n",
-       "21       NA                           NA       2002-09-10 NA    \n",
-       "22       NA                           NA       2002-10-07 NA    \n",
-       "23       NA                           NA       2002-11-05 NA    \n",
-       "24       NA                           NA       2002-12-05 NA    \n",
-       "25       NA                           NA       2002-12-30 NA    \n",
-       "26       NA                           NA       2003-01-30 NA    \n",
-       "27       NA                           NA       2003-03-18 NA    \n",
-       "28       NA                           NA       2003-04-17 NA    \n",
-       "29       NA                           NA       2003-05-13 NA    \n",
-       "30       NA                           NA       2003-06-09 NA    \n",
-       "<U+22EE>   <U+22EE>                     <U+22EE> <U+22EE>   <U+22EE>\n",
-       "14898436 NA                           NA       2016-01-12 NA    \n",
-       "14898437 NA                           NA       2016-02-09 NA    \n",
-       "14898438 NA                           NA       2016-03-08 NA    \n",
-       "14898439 NA                           NA       2016-04-05 NA    \n",
-       "14898440 NA                           NA       2016-06-02 NA    \n",
-       "14898441 NA                           NA       2016-06-02 NA    \n",
-       "14898442 NA                           NA       2016-06-02 NA    \n",
-       "14898443 NA                           NA       2016-06-23 NA    \n",
-       "14898444 NA                           NA       2016-07-07 NA    \n",
-       "14898445 NA                           NA       2016-08-05 NA    \n",
-       "14898446 NA                           NA       2016-08-31 NA    \n",
-       "14898447 NA                           NA       2016-09-30 NA    \n",
-       "14898448 NA                           NA       2016-10-26 NA    \n",
-       "14898449 NA                           NA       2016-11-22 NA    \n",
-       "14898450 NA                           NA       2016-12-20 NA    \n",
-       "14898451 NA                           NA       2017-01-25 NA    \n",
-       "14898452 NA                           NA       2017-02-17 NA    \n",
-       "14898453 NA                           NA       2017-03-08 NA    \n",
-       "14898454 NA                           NA       2017-03-20 NA    \n",
-       "14898455 NA                           NA       2017-03-31 NA    \n",
-       "14898456 NA                           NA       2017-03-31 NA    \n",
-       "14898457 NA                           NA       2017-03-31 NA    \n",
-       "14898458 NA                           NA       2017-04-06 NA    \n",
-       "14898459 NA                           NA       2017-05-11 NA    \n",
-       "14898460 NA                           NA       2017-06-05 NA    \n",
-       "14898461 NA                           NA       2017-06-05 NA    \n",
-       "14898462 NA                           NA       2017-07-12 NA    \n",
-       "14898463 NA                           NA       2017-07-18 NA    \n",
-       "14898464 NA                           NA       2017-08-09 NA    \n",
-       "14898465 NA                           NA       2017-09-13 NA    "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "gp_scripts_read2 = gp_scripts %>% filter(!is.na(read_2)) %>% arrange(eid)\n",
-    "gp_scripts_read3 = gp_scripts_read2 %>% left_join(readv2_readv3, by=\"read_2\")\n",
-    "gp_scripts_read3"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<table>\n",
-       "<caption>A data.table: 57704542 × 9</caption>\n",
-       "<thead>\n",
-       "\t<tr><th scope=col>eid</th><th scope=col>data_provider</th><th scope=col>issue_date</th><th scope=col>read_2</th><th scope=col>bnf_code</th><th scope=col>dmd_code</th><th scope=col>drug_name</th><th scope=col>quantity</th><th scope=col>date</th></tr>\n",
-       "\t<tr><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int64&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;date&gt;</th></tr>\n",
-       "</thead>\n",
-       "<tbody>\n",
-       "\t<tr><td>1000018</td><td>3</td><td>30/11/1996</td><td>NA</td><td>13.06.02.01.00</td><td>NA</td><td>Doxycycline 100mg capsules                 </td><td>8 capsule(s) - 100 mg          </td><td>1996-11-30</td></tr>\n",
-       "\t<tr><td>1000018</td><td>3</td><td>12/09/1997</td><td>NA</td><td>02.03.02.02.00</td><td>NA</td><td>Propranolol 40mg tablets                   </td><td>56 tablet(s) - 40 mg           </td><td>1997-09-12</td></tr>\n",
-       "\t<tr><td>1000018</td><td>3</td><td>10/10/1997</td><td>NA</td><td>02.03.02.02.00</td><td>NA</td><td>Propranolol 40mg tablets                   </td><td>168 tablet(s) - 40 mg          </td><td>1997-10-10</td></tr>\n",
-       "\t<tr><td>1000018</td><td>3</td><td>10/10/1997</td><td>NA</td><td>07.03.02.01.00</td><td>NA</td><td>Noriday 350microgram tablets (Pfizer Ltd)  </td><td>168 tablet(s) - 350 micrograms </td><td>1997-10-10</td></tr>\n",
-       "\t<tr><td>1000018</td><td>3</td><td>19/01/1998</td><td>NA</td><td>02.03.02.02.00</td><td>NA</td><td>Propranolol 40mg tablets                   </td><td>168 tablet(s) - 40 mg          </td><td>1998-01-19</td></tr>\n",
-       "\t<tr><td>1000018</td><td>3</td><td>19/01/1998</td><td>NA</td><td>02.03.02.02.00</td><td>NA</td><td>Propranolol 80mg tablets                   </td><td>112 tablet(s) - 80 mg          </td><td>1998-01-19</td></tr>\n",
-       "\t<tr><td>1000018</td><td>3</td><td>19/01/1998</td><td>NA</td><td>06.07.02.00.00</td><td>NA</td><td>Gamolenic acid 40mg capsules               </td><td>336 capsule(s) - 40 mg         </td><td>1998-01-19</td></tr>\n",
-       "\t<tr><td>1000018</td><td>3</td><td>13/03/1998</td><td>NA</td><td>02.03.02.02.00</td><td>NA</td><td>Propranolol 80mg tablets                   </td><td>112 tablet(s) - 80 mg          </td><td>1998-03-13</td></tr>\n",
-       "\t<tr><td>1000018</td><td>3</td><td>25/03/1998</td><td>NA</td><td>05.01.01.03.00</td><td>NA</td><td>Amoxicillin 250mg capsules                 </td><td>15 capsule(s) - 250 mg         </td><td>1998-03-25</td></tr>\n",
-       "\t<tr><td>1000018</td><td>3</td><td>16/04/1998</td><td>NA</td><td>05.01.01.03.00</td><td>NA</td><td>Amoxicillin 250mg capsules                 </td><td>15 capsule(s) - 250 mg         </td><td>1998-04-16</td></tr>\n",
-       "\t<tr><td>1000018</td><td>3</td><td>24/04/1998</td><td>NA</td><td>05.04.03.00.00</td><td>NA</td><td>Metronidazole 200mg tablets                </td><td>15 tablet(s) - 200 mg          </td><td>1998-04-24</td></tr>\n",
-       "\t<tr><td>1000018</td><td>3</td><td>19/06/1998</td><td>NA</td><td>01.02.02.00.00</td><td>NA</td><td>Mebeverine 135mg tablets                   </td><td>84 tablet(s) - 135 mg          </td><td>1998-06-19</td></tr>\n",
-       "\t<tr><td>1000018</td><td>3</td><td>19/06/1998</td><td>NA</td><td>02.03.02.02.00</td><td>NA</td><td>Propranolol 80mg tablets                   </td><td>112 tablet(s) - 80 mg          </td><td>1998-06-19</td></tr>\n",
-       "\t<tr><td>1000018</td><td>3</td><td>14/08/1998</td><td>NA</td><td>12.01.01.02.00</td><td>NA</td><td>Gentisone HC ear drops (AMCo)              </td><td>10 millilitre                  </td><td>1998-08-14</td></tr>\n",
-       "\t<tr><td>1000018</td><td>3</td><td>20/01/1999</td><td>NA</td><td>06.07.02.00.00</td><td>NA</td><td>Gamolenic acid 40mg capsules               </td><td>336 capsule(s) - 40 mg         </td><td>1999-01-20</td></tr>\n",
-       "\t<tr><td>1000018</td><td>3</td><td>20/01/1999</td><td>NA</td><td>12.01.01.02.00</td><td>NA</td><td>Gentisone HC ear drops (AMCo)              </td><td>10 millilitre                  </td><td>1999-01-20</td></tr>\n",
-       "\t<tr><td>1000018</td><td>3</td><td>12/01/2001</td><td>NA</td><td>05.03.02.01.00</td><td>NA</td><td>Aciclovir 200mg dispersible tablets        </td><td>35 tablet(s) - 200 mg          </td><td>2001-01-12</td></tr>\n",
-       "\t<tr><td>1000018</td><td>3</td><td>12/01/2001</td><td>NA</td><td>13.06.02.01.00</td><td>NA</td><td>Doxycycline 100mg capsules                 </td><td>10 capsule(s) - 100 mg         </td><td>2001-01-12</td></tr>\n",
-       "\t<tr><td>1000018</td><td>3</td><td>17/11/2003</td><td>NA</td><td>05.03.02.01.00</td><td>NA</td><td>Aciclovir 200mg dispersible tablets        </td><td>35 tablet(s) - 200 mg          </td><td>2003-11-17</td></tr>\n",
-       "\t<tr><td>1000018</td><td>3</td><td>17/11/2003</td><td>NA</td><td>13.06.02.01.00</td><td>NA</td><td>Doxycycline 100mg capsules                 </td><td>8 capsule(s) - 100 mg          </td><td>2003-11-17</td></tr>\n",
-       "\t<tr><td>1000018</td><td>3</td><td>18/11/2003</td><td>NA</td><td>13.06.02.01.00</td><td>NA</td><td>Doxycycline 100mg capsules                 </td><td>8 capsule(s) - 100 mg          </td><td>2003-11-18</td></tr>\n",
-       "\t<tr><td>1000018</td><td>3</td><td>24/11/2003</td><td>NA</td><td>12.02.01.00.00</td><td>NA</td><td>Beclometasone 50micrograms/dose nasal spray</td><td>1 spray(s) - 50 micrograms/dose</td><td>2003-11-24</td></tr>\n",
-       "\t<tr><td>1000018</td><td>3</td><td>08/06/2005</td><td>NA</td><td>02.04.00.00.00</td><td>NA</td><td>Atenolol 50mg tablets                      </td><td>28 tablet(s) - 50 mg           </td><td>2005-06-08</td></tr>\n",
-       "\t<tr><td>1000018</td><td>3</td><td>09/06/2005</td><td>NA</td><td>02.04.00.00.00</td><td>NA</td><td>Atenolol 50mg tablets                      </td><td>28 tablet(s) - 50 mg           </td><td>2005-06-09</td></tr>\n",
-       "\t<tr><td>1000018</td><td>3</td><td>23/06/2005</td><td>NA</td><td>02.04.00.00.00</td><td>NA</td><td>Atenolol 50mg tablets                      </td><td>28 tablet(s) - 50 mg           </td><td>2005-06-23</td></tr>\n",
-       "\t<tr><td>1000018</td><td>3</td><td>24/06/2005</td><td>NA</td><td>02.04.00.00.00</td><td>NA</td><td>Atenolol 50mg tablets                      </td><td>28 tablet(s) - 50 mg           </td><td>2005-06-24</td></tr>\n",
-       "\t<tr><td>1000018</td><td>3</td><td>01/09/2005</td><td>NA</td><td>02.04.00.00.00</td><td>NA</td><td>Atenolol 50mg tablets                      </td><td>28 tablet(s) - 50 mg           </td><td>2005-09-01</td></tr>\n",
-       "\t<tr><td>1000018</td><td>3</td><td>07/10/2005</td><td>NA</td><td>02.04.00.00.00</td><td>NA</td><td>Atenolol 50mg tablets                      </td><td>28 tablet(s) - 50 mg           </td><td>2005-10-07</td></tr>\n",
-       "\t<tr><td>1000018</td><td>3</td><td>08/11/2005</td><td>NA</td><td>02.04.00.00.00</td><td>NA</td><td>Atenolol 50mg tablets                      </td><td>28 tablet(s) - 50 mg           </td><td>2005-11-08</td></tr>\n",
-       "\t<tr><td>1000018</td><td>3</td><td>05/12/2005</td><td>NA</td><td>02.04.00.00.00</td><td>NA</td><td>Atenolol 50mg tablets                      </td><td>28 tablet(s) - 50 mg           </td><td>2005-12-05</td></tr>\n",
-       "\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
-       "\t<tr><td>6025198</td><td>3</td><td>23/01/2015</td><td>NA</td><td>02.05.05.01.00</td><td>NA</td><td>Ramipril 2.5mg capsules </td><td>2*28 capsule</td><td>2015-01-23</td></tr>\n",
-       "\t<tr><td>6025198</td><td>3</td><td>23/01/2015</td><td>NA</td><td>02.12.04.00.00</td><td>NA</td><td>Simvastatin 40mg tablets</td><td>2*28 tablet </td><td>2015-01-23</td></tr>\n",
-       "\t<tr><td>6025198</td><td>3</td><td>23/01/2015</td><td>NA</td><td>10.03.02.01.00</td><td>NA</td><td>Fenbid 5% gel (AMCo)    </td><td>100 gram    </td><td>2015-01-23</td></tr>\n",
-       "\t<tr><td>6025198</td><td>3</td><td>27/02/2015</td><td>NA</td><td>02.05.05.01.00</td><td>NA</td><td>Ramipril 2.5mg capsules </td><td>56 capsule  </td><td>2015-02-27</td></tr>\n",
-       "\t<tr><td>6025198</td><td>3</td><td>27/02/2015</td><td>NA</td><td>02.12.04.00.00</td><td>NA</td><td>Simvastatin 40mg tablets</td><td>56 tablet   </td><td>2015-02-27</td></tr>\n",
-       "\t<tr><td>6025198</td><td>3</td><td>27/02/2015</td><td>NA</td><td>10.03.02.01.00</td><td>NA</td><td>Fenbid 5% gel (AMCo)    </td><td>100 gram    </td><td>2015-02-27</td></tr>\n",
-       "\t<tr><td>6025198</td><td>3</td><td>05/05/2015</td><td>NA</td><td>02.05.05.01.00</td><td>NA</td><td>Ramipril 2.5mg capsules </td><td>56 capsule  </td><td>2015-05-05</td></tr>\n",
-       "\t<tr><td>6025198</td><td>3</td><td>05/05/2015</td><td>NA</td><td>02.12.04.00.00</td><td>NA</td><td>Simvastatin 40mg tablets</td><td>56 tablet   </td><td>2015-05-05</td></tr>\n",
-       "\t<tr><td>6025198</td><td>3</td><td>05/05/2015</td><td>NA</td><td>10.03.02.01.00</td><td>NA</td><td>Fenbid 5% gel (AMCo)    </td><td>100 gram    </td><td>2015-05-05</td></tr>\n",
-       "\t<tr><td>6025198</td><td>3</td><td>24/06/2015</td><td>NA</td><td>02.05.05.01.00</td><td>NA</td><td>Ramipril 2.5mg capsules </td><td>56 capsule  </td><td>2015-06-24</td></tr>\n",
-       "\t<tr><td>6025198</td><td>3</td><td>24/06/2015</td><td>NA</td><td>02.12.04.00.00</td><td>NA</td><td>Simvastatin 40mg tablets</td><td>56 tablet   </td><td>2015-06-24</td></tr>\n",
-       "\t<tr><td>6025198</td><td>3</td><td>24/06/2015</td><td>NA</td><td>10.03.02.01.00</td><td>NA</td><td>Fenbid 5% gel (AMCo)    </td><td>100 gram    </td><td>2015-06-24</td></tr>\n",
-       "\t<tr><td>6025198</td><td>3</td><td>19/08/2015</td><td>NA</td><td>02.05.05.01.00</td><td>NA</td><td>Ramipril 2.5mg capsules </td><td>56 capsule  </td><td>2015-08-19</td></tr>\n",
-       "\t<tr><td>6025198</td><td>3</td><td>19/08/2015</td><td>NA</td><td>02.12.04.00.00</td><td>NA</td><td>Simvastatin 40mg tablets</td><td>56 tablet   </td><td>2015-08-19</td></tr>\n",
-       "\t<tr><td>6025198</td><td>3</td><td>19/08/2015</td><td>NA</td><td>10.03.02.01.00</td><td>NA</td><td>Fenbid 5% gel (AMCo)    </td><td>100 gram    </td><td>2015-08-19</td></tr>\n",
-       "\t<tr><td>6025198</td><td>3</td><td>14/10/2015</td><td>NA</td><td>02.05.05.01.00</td><td>NA</td><td>Ramipril 2.5mg capsules </td><td>56 capsule  </td><td>2015-10-14</td></tr>\n",
-       "\t<tr><td>6025198</td><td>3</td><td>14/10/2015</td><td>NA</td><td>02.12.04.00.00</td><td>NA</td><td>Simvastatin 40mg tablets</td><td>56 tablet   </td><td>2015-10-14</td></tr>\n",
-       "\t<tr><td>6025198</td><td>3</td><td>14/10/2015</td><td>NA</td><td>10.03.02.01.00</td><td>NA</td><td>Fenbid 5% gel (AMCo)    </td><td>100 gram    </td><td>2015-10-14</td></tr>\n",
-       "\t<tr><td>6025198</td><td>3</td><td>10/12/2015</td><td>NA</td><td>02.05.05.01.00</td><td>NA</td><td>Ramipril 2.5mg capsules </td><td>56 capsule  </td><td>2015-12-10</td></tr>\n",
-       "\t<tr><td>6025198</td><td>3</td><td>10/12/2015</td><td>NA</td><td>02.12.04.00.00</td><td>NA</td><td>Simvastatin 40mg tablets</td><td>56 tablet   </td><td>2015-12-10</td></tr>\n",
-       "\t<tr><td>6025198</td><td>3</td><td>10/12/2015</td><td>NA</td><td>10.03.02.01.00</td><td>NA</td><td>Fenbid 5% gel (AMCo)    </td><td>100 gram    </td><td>2015-12-10</td></tr>\n",
-       "\t<tr><td>6025198</td><td>3</td><td>03/02/2016</td><td>NA</td><td>02.05.05.01.00</td><td>NA</td><td>Ramipril 2.5mg capsules </td><td>56 capsule  </td><td>2016-02-03</td></tr>\n",
-       "\t<tr><td>6025198</td><td>3</td><td>03/02/2016</td><td>NA</td><td>02.12.04.00.00</td><td>NA</td><td>Simvastatin 40mg tablets</td><td>56 tablet   </td><td>2016-02-03</td></tr>\n",
-       "\t<tr><td>6025198</td><td>3</td><td>03/02/2016</td><td>NA</td><td>10.03.02.01.00</td><td>NA</td><td>Fenbid 5% gel (AMCo)    </td><td>100 gram    </td><td>2016-02-03</td></tr>\n",
-       "\t<tr><td>6025198</td><td>3</td><td>06/04/2016</td><td>NA</td><td>02.05.05.01.00</td><td>NA</td><td>Ramipril 2.5mg capsules </td><td>56 capsule  </td><td>2016-04-06</td></tr>\n",
-       "\t<tr><td>6025198</td><td>3</td><td>06/04/2016</td><td>NA</td><td>02.12.04.00.00</td><td>NA</td><td>Simvastatin 40mg tablets</td><td>56 tablet   </td><td>2016-04-06</td></tr>\n",
-       "\t<tr><td>6025198</td><td>3</td><td>06/04/2016</td><td>NA</td><td>10.03.02.01.00</td><td>NA</td><td>Fenbid 5% gel (AMCo)    </td><td>100 gram    </td><td>2016-04-06</td></tr>\n",
-       "\t<tr><td>6025198</td><td>3</td><td>25/05/2016</td><td>NA</td><td>02.05.05.01.00</td><td>NA</td><td>Ramipril 2.5mg capsules </td><td>56 capsule  </td><td>2016-05-25</td></tr>\n",
-       "\t<tr><td>6025198</td><td>3</td><td>25/05/2016</td><td>NA</td><td>02.12.04.00.00</td><td>NA</td><td>Simvastatin 40mg tablets</td><td>56 tablet   </td><td>2016-05-25</td></tr>\n",
-       "\t<tr><td>6025198</td><td>3</td><td>25/05/2016</td><td>NA</td><td>10.03.02.01.00</td><td>NA</td><td>Fenbid 5% gel (AMCo)    </td><td>100 gram    </td><td>2016-05-25</td></tr>\n",
-       "</tbody>\n",
-       "</table>\n"
-      ],
-      "text/latex": [
-       "A data.table: 57704542 × 9\n",
-       "\\begin{tabular}{lllllllll}\n",
-       " eid & data\\_provider & issue\\_date & read\\_2 & bnf\\_code & dmd\\_code & drug\\_name & quantity & date\\\\\n",
-       " <int> & <int> & <chr> & <chr> & <chr> & <int64> & <chr> & <chr> & <date>\\\\\n",
-       "\\hline\n",
-       "\t 1000018 & 3 & 30/11/1996 & NA & 13.06.02.01.00 & NA & Doxycycline 100mg capsules                  & 8 capsule(s) - 100 mg           & 1996-11-30\\\\\n",
-       "\t 1000018 & 3 & 12/09/1997 & NA & 02.03.02.02.00 & NA & Propranolol 40mg tablets                    & 56 tablet(s) - 40 mg            & 1997-09-12\\\\\n",
-       "\t 1000018 & 3 & 10/10/1997 & NA & 02.03.02.02.00 & NA & Propranolol 40mg tablets                    & 168 tablet(s) - 40 mg           & 1997-10-10\\\\\n",
-       "\t 1000018 & 3 & 10/10/1997 & NA & 07.03.02.01.00 & NA & Noriday 350microgram tablets (Pfizer Ltd)   & 168 tablet(s) - 350 micrograms  & 1997-10-10\\\\\n",
-       "\t 1000018 & 3 & 19/01/1998 & NA & 02.03.02.02.00 & NA & Propranolol 40mg tablets                    & 168 tablet(s) - 40 mg           & 1998-01-19\\\\\n",
-       "\t 1000018 & 3 & 19/01/1998 & NA & 02.03.02.02.00 & NA & Propranolol 80mg tablets                    & 112 tablet(s) - 80 mg           & 1998-01-19\\\\\n",
-       "\t 1000018 & 3 & 19/01/1998 & NA & 06.07.02.00.00 & NA & Gamolenic acid 40mg capsules                & 336 capsule(s) - 40 mg          & 1998-01-19\\\\\n",
-       "\t 1000018 & 3 & 13/03/1998 & NA & 02.03.02.02.00 & NA & Propranolol 80mg tablets                    & 112 tablet(s) - 80 mg           & 1998-03-13\\\\\n",
-       "\t 1000018 & 3 & 25/03/1998 & NA & 05.01.01.03.00 & NA & Amoxicillin 250mg capsules                  & 15 capsule(s) - 250 mg          & 1998-03-25\\\\\n",
-       "\t 1000018 & 3 & 16/04/1998 & NA & 05.01.01.03.00 & NA & Amoxicillin 250mg capsules                  & 15 capsule(s) - 250 mg          & 1998-04-16\\\\\n",
-       "\t 1000018 & 3 & 24/04/1998 & NA & 05.04.03.00.00 & NA & Metronidazole 200mg tablets                 & 15 tablet(s) - 200 mg           & 1998-04-24\\\\\n",
-       "\t 1000018 & 3 & 19/06/1998 & NA & 01.02.02.00.00 & NA & Mebeverine 135mg tablets                    & 84 tablet(s) - 135 mg           & 1998-06-19\\\\\n",
-       "\t 1000018 & 3 & 19/06/1998 & NA & 02.03.02.02.00 & NA & Propranolol 80mg tablets                    & 112 tablet(s) - 80 mg           & 1998-06-19\\\\\n",
-       "\t 1000018 & 3 & 14/08/1998 & NA & 12.01.01.02.00 & NA & Gentisone HC ear drops (AMCo)               & 10 millilitre                   & 1998-08-14\\\\\n",
-       "\t 1000018 & 3 & 20/01/1999 & NA & 06.07.02.00.00 & NA & Gamolenic acid 40mg capsules                & 336 capsule(s) - 40 mg          & 1999-01-20\\\\\n",
-       "\t 1000018 & 3 & 20/01/1999 & NA & 12.01.01.02.00 & NA & Gentisone HC ear drops (AMCo)               & 10 millilitre                   & 1999-01-20\\\\\n",
-       "\t 1000018 & 3 & 12/01/2001 & NA & 05.03.02.01.00 & NA & Aciclovir 200mg dispersible tablets         & 35 tablet(s) - 200 mg           & 2001-01-12\\\\\n",
-       "\t 1000018 & 3 & 12/01/2001 & NA & 13.06.02.01.00 & NA & Doxycycline 100mg capsules                  & 10 capsule(s) - 100 mg          & 2001-01-12\\\\\n",
-       "\t 1000018 & 3 & 17/11/2003 & NA & 05.03.02.01.00 & NA & Aciclovir 200mg dispersible tablets         & 35 tablet(s) - 200 mg           & 2003-11-17\\\\\n",
-       "\t 1000018 & 3 & 17/11/2003 & NA & 13.06.02.01.00 & NA & Doxycycline 100mg capsules                  & 8 capsule(s) - 100 mg           & 2003-11-17\\\\\n",
-       "\t 1000018 & 3 & 18/11/2003 & NA & 13.06.02.01.00 & NA & Doxycycline 100mg capsules                  & 8 capsule(s) - 100 mg           & 2003-11-18\\\\\n",
-       "\t 1000018 & 3 & 24/11/2003 & NA & 12.02.01.00.00 & NA & Beclometasone 50micrograms/dose nasal spray & 1 spray(s) - 50 micrograms/dose & 2003-11-24\\\\\n",
-       "\t 1000018 & 3 & 08/06/2005 & NA & 02.04.00.00.00 & NA & Atenolol 50mg tablets                       & 28 tablet(s) - 50 mg            & 2005-06-08\\\\\n",
-       "\t 1000018 & 3 & 09/06/2005 & NA & 02.04.00.00.00 & NA & Atenolol 50mg tablets                       & 28 tablet(s) - 50 mg            & 2005-06-09\\\\\n",
-       "\t 1000018 & 3 & 23/06/2005 & NA & 02.04.00.00.00 & NA & Atenolol 50mg tablets                       & 28 tablet(s) - 50 mg            & 2005-06-23\\\\\n",
-       "\t 1000018 & 3 & 24/06/2005 & NA & 02.04.00.00.00 & NA & Atenolol 50mg tablets                       & 28 tablet(s) - 50 mg            & 2005-06-24\\\\\n",
-       "\t 1000018 & 3 & 01/09/2005 & NA & 02.04.00.00.00 & NA & Atenolol 50mg tablets                       & 28 tablet(s) - 50 mg            & 2005-09-01\\\\\n",
-       "\t 1000018 & 3 & 07/10/2005 & NA & 02.04.00.00.00 & NA & Atenolol 50mg tablets                       & 28 tablet(s) - 50 mg            & 2005-10-07\\\\\n",
-       "\t 1000018 & 3 & 08/11/2005 & NA & 02.04.00.00.00 & NA & Atenolol 50mg tablets                       & 28 tablet(s) - 50 mg            & 2005-11-08\\\\\n",
-       "\t 1000018 & 3 & 05/12/2005 & NA & 02.04.00.00.00 & NA & Atenolol 50mg tablets                       & 28 tablet(s) - 50 mg            & 2005-12-05\\\\\n",
-       "\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
-       "\t 6025198 & 3 & 23/01/2015 & NA & 02.05.05.01.00 & NA & Ramipril 2.5mg capsules  & 2*28 capsule & 2015-01-23\\\\\n",
-       "\t 6025198 & 3 & 23/01/2015 & NA & 02.12.04.00.00 & NA & Simvastatin 40mg tablets & 2*28 tablet  & 2015-01-23\\\\\n",
-       "\t 6025198 & 3 & 23/01/2015 & NA & 10.03.02.01.00 & NA & Fenbid 5\\% gel (AMCo)     & 100 gram     & 2015-01-23\\\\\n",
-       "\t 6025198 & 3 & 27/02/2015 & NA & 02.05.05.01.00 & NA & Ramipril 2.5mg capsules  & 56 capsule   & 2015-02-27\\\\\n",
-       "\t 6025198 & 3 & 27/02/2015 & NA & 02.12.04.00.00 & NA & Simvastatin 40mg tablets & 56 tablet    & 2015-02-27\\\\\n",
-       "\t 6025198 & 3 & 27/02/2015 & NA & 10.03.02.01.00 & NA & Fenbid 5\\% gel (AMCo)     & 100 gram     & 2015-02-27\\\\\n",
-       "\t 6025198 & 3 & 05/05/2015 & NA & 02.05.05.01.00 & NA & Ramipril 2.5mg capsules  & 56 capsule   & 2015-05-05\\\\\n",
-       "\t 6025198 & 3 & 05/05/2015 & NA & 02.12.04.00.00 & NA & Simvastatin 40mg tablets & 56 tablet    & 2015-05-05\\\\\n",
-       "\t 6025198 & 3 & 05/05/2015 & NA & 10.03.02.01.00 & NA & Fenbid 5\\% gel (AMCo)     & 100 gram     & 2015-05-05\\\\\n",
-       "\t 6025198 & 3 & 24/06/2015 & NA & 02.05.05.01.00 & NA & Ramipril 2.5mg capsules  & 56 capsule   & 2015-06-24\\\\\n",
-       "\t 6025198 & 3 & 24/06/2015 & NA & 02.12.04.00.00 & NA & Simvastatin 40mg tablets & 56 tablet    & 2015-06-24\\\\\n",
-       "\t 6025198 & 3 & 24/06/2015 & NA & 10.03.02.01.00 & NA & Fenbid 5\\% gel (AMCo)     & 100 gram     & 2015-06-24\\\\\n",
-       "\t 6025198 & 3 & 19/08/2015 & NA & 02.05.05.01.00 & NA & Ramipril 2.5mg capsules  & 56 capsule   & 2015-08-19\\\\\n",
-       "\t 6025198 & 3 & 19/08/2015 & NA & 02.12.04.00.00 & NA & Simvastatin 40mg tablets & 56 tablet    & 2015-08-19\\\\\n",
-       "\t 6025198 & 3 & 19/08/2015 & NA & 10.03.02.01.00 & NA & Fenbid 5\\% gel (AMCo)     & 100 gram     & 2015-08-19\\\\\n",
-       "\t 6025198 & 3 & 14/10/2015 & NA & 02.05.05.01.00 & NA & Ramipril 2.5mg capsules  & 56 capsule   & 2015-10-14\\\\\n",
-       "\t 6025198 & 3 & 14/10/2015 & NA & 02.12.04.00.00 & NA & Simvastatin 40mg tablets & 56 tablet    & 2015-10-14\\\\\n",
-       "\t 6025198 & 3 & 14/10/2015 & NA & 10.03.02.01.00 & NA & Fenbid 5\\% gel (AMCo)     & 100 gram     & 2015-10-14\\\\\n",
-       "\t 6025198 & 3 & 10/12/2015 & NA & 02.05.05.01.00 & NA & Ramipril 2.5mg capsules  & 56 capsule   & 2015-12-10\\\\\n",
-       "\t 6025198 & 3 & 10/12/2015 & NA & 02.12.04.00.00 & NA & Simvastatin 40mg tablets & 56 tablet    & 2015-12-10\\\\\n",
-       "\t 6025198 & 3 & 10/12/2015 & NA & 10.03.02.01.00 & NA & Fenbid 5\\% gel (AMCo)     & 100 gram     & 2015-12-10\\\\\n",
-       "\t 6025198 & 3 & 03/02/2016 & NA & 02.05.05.01.00 & NA & Ramipril 2.5mg capsules  & 56 capsule   & 2016-02-03\\\\\n",
-       "\t 6025198 & 3 & 03/02/2016 & NA & 02.12.04.00.00 & NA & Simvastatin 40mg tablets & 56 tablet    & 2016-02-03\\\\\n",
-       "\t 6025198 & 3 & 03/02/2016 & NA & 10.03.02.01.00 & NA & Fenbid 5\\% gel (AMCo)     & 100 gram     & 2016-02-03\\\\\n",
-       "\t 6025198 & 3 & 06/04/2016 & NA & 02.05.05.01.00 & NA & Ramipril 2.5mg capsules  & 56 capsule   & 2016-04-06\\\\\n",
-       "\t 6025198 & 3 & 06/04/2016 & NA & 02.12.04.00.00 & NA & Simvastatin 40mg tablets & 56 tablet    & 2016-04-06\\\\\n",
-       "\t 6025198 & 3 & 06/04/2016 & NA & 10.03.02.01.00 & NA & Fenbid 5\\% gel (AMCo)     & 100 gram     & 2016-04-06\\\\\n",
-       "\t 6025198 & 3 & 25/05/2016 & NA & 02.05.05.01.00 & NA & Ramipril 2.5mg capsules  & 56 capsule   & 2016-05-25\\\\\n",
-       "\t 6025198 & 3 & 25/05/2016 & NA & 02.12.04.00.00 & NA & Simvastatin 40mg tablets & 56 tablet    & 2016-05-25\\\\\n",
-       "\t 6025198 & 3 & 25/05/2016 & NA & 10.03.02.01.00 & NA & Fenbid 5\\% gel (AMCo)     & 100 gram     & 2016-05-25\\\\\n",
-       "\\end{tabular}\n"
-      ],
-      "text/markdown": [
-       "\n",
-       "A data.table: 57704542 × 9\n",
-       "\n",
-       "| eid &lt;int&gt; | data_provider &lt;int&gt; | issue_date &lt;chr&gt; | read_2 &lt;chr&gt; | bnf_code &lt;chr&gt; | dmd_code &lt;int64&gt; | drug_name &lt;chr&gt; | quantity &lt;chr&gt; | date &lt;date&gt; |\n",
-       "|---|---|---|---|---|---|---|---|---|\n",
-       "| 1000018 | 3 | 30/11/1996 | NA | 13.06.02.01.00 | NA | Doxycycline 100mg capsules                  | 8 capsule(s) - 100 mg           | 1996-11-30 |\n",
-       "| 1000018 | 3 | 12/09/1997 | NA | 02.03.02.02.00 | NA | Propranolol 40mg tablets                    | 56 tablet(s) - 40 mg            | 1997-09-12 |\n",
-       "| 1000018 | 3 | 10/10/1997 | NA | 02.03.02.02.00 | NA | Propranolol 40mg tablets                    | 168 tablet(s) - 40 mg           | 1997-10-10 |\n",
-       "| 1000018 | 3 | 10/10/1997 | NA | 07.03.02.01.00 | NA | Noriday 350microgram tablets (Pfizer Ltd)   | 168 tablet(s) - 350 micrograms  | 1997-10-10 |\n",
-       "| 1000018 | 3 | 19/01/1998 | NA | 02.03.02.02.00 | NA | Propranolol 40mg tablets                    | 168 tablet(s) - 40 mg           | 1998-01-19 |\n",
-       "| 1000018 | 3 | 19/01/1998 | NA | 02.03.02.02.00 | NA | Propranolol 80mg tablets                    | 112 tablet(s) - 80 mg           | 1998-01-19 |\n",
-       "| 1000018 | 3 | 19/01/1998 | NA | 06.07.02.00.00 | NA | Gamolenic acid 40mg capsules                | 336 capsule(s) - 40 mg          | 1998-01-19 |\n",
-       "| 1000018 | 3 | 13/03/1998 | NA | 02.03.02.02.00 | NA | Propranolol 80mg tablets                    | 112 tablet(s) - 80 mg           | 1998-03-13 |\n",
-       "| 1000018 | 3 | 25/03/1998 | NA | 05.01.01.03.00 | NA | Amoxicillin 250mg capsules                  | 15 capsule(s) - 250 mg          | 1998-03-25 |\n",
-       "| 1000018 | 3 | 16/04/1998 | NA | 05.01.01.03.00 | NA | Amoxicillin 250mg capsules                  | 15 capsule(s) - 250 mg          | 1998-04-16 |\n",
-       "| 1000018 | 3 | 24/04/1998 | NA | 05.04.03.00.00 | NA | Metronidazole 200mg tablets                 | 15 tablet(s) - 200 mg           | 1998-04-24 |\n",
-       "| 1000018 | 3 | 19/06/1998 | NA | 01.02.02.00.00 | NA | Mebeverine 135mg tablets                    | 84 tablet(s) - 135 mg           | 1998-06-19 |\n",
-       "| 1000018 | 3 | 19/06/1998 | NA | 02.03.02.02.00 | NA | Propranolol 80mg tablets                    | 112 tablet(s) - 80 mg           | 1998-06-19 |\n",
-       "| 1000018 | 3 | 14/08/1998 | NA | 12.01.01.02.00 | NA | Gentisone HC ear drops (AMCo)               | 10 millilitre                   | 1998-08-14 |\n",
-       "| 1000018 | 3 | 20/01/1999 | NA | 06.07.02.00.00 | NA | Gamolenic acid 40mg capsules                | 336 capsule(s) - 40 mg          | 1999-01-20 |\n",
-       "| 1000018 | 3 | 20/01/1999 | NA | 12.01.01.02.00 | NA | Gentisone HC ear drops (AMCo)               | 10 millilitre                   | 1999-01-20 |\n",
-       "| 1000018 | 3 | 12/01/2001 | NA | 05.03.02.01.00 | NA | Aciclovir 200mg dispersible tablets         | 35 tablet(s) - 200 mg           | 2001-01-12 |\n",
-       "| 1000018 | 3 | 12/01/2001 | NA | 13.06.02.01.00 | NA | Doxycycline 100mg capsules                  | 10 capsule(s) - 100 mg          | 2001-01-12 |\n",
-       "| 1000018 | 3 | 17/11/2003 | NA | 05.03.02.01.00 | NA | Aciclovir 200mg dispersible tablets         | 35 tablet(s) - 200 mg           | 2003-11-17 |\n",
-       "| 1000018 | 3 | 17/11/2003 | NA | 13.06.02.01.00 | NA | Doxycycline 100mg capsules                  | 8 capsule(s) - 100 mg           | 2003-11-17 |\n",
-       "| 1000018 | 3 | 18/11/2003 | NA | 13.06.02.01.00 | NA | Doxycycline 100mg capsules                  | 8 capsule(s) - 100 mg           | 2003-11-18 |\n",
-       "| 1000018 | 3 | 24/11/2003 | NA | 12.02.01.00.00 | NA | Beclometasone 50micrograms/dose nasal spray | 1 spray(s) - 50 micrograms/dose | 2003-11-24 |\n",
-       "| 1000018 | 3 | 08/06/2005 | NA | 02.04.00.00.00 | NA | Atenolol 50mg tablets                       | 28 tablet(s) - 50 mg            | 2005-06-08 |\n",
-       "| 1000018 | 3 | 09/06/2005 | NA | 02.04.00.00.00 | NA | Atenolol 50mg tablets                       | 28 tablet(s) - 50 mg            | 2005-06-09 |\n",
-       "| 1000018 | 3 | 23/06/2005 | NA | 02.04.00.00.00 | NA | Atenolol 50mg tablets                       | 28 tablet(s) - 50 mg            | 2005-06-23 |\n",
-       "| 1000018 | 3 | 24/06/2005 | NA | 02.04.00.00.00 | NA | Atenolol 50mg tablets                       | 28 tablet(s) - 50 mg            | 2005-06-24 |\n",
-       "| 1000018 | 3 | 01/09/2005 | NA | 02.04.00.00.00 | NA | Atenolol 50mg tablets                       | 28 tablet(s) - 50 mg            | 2005-09-01 |\n",
-       "| 1000018 | 3 | 07/10/2005 | NA | 02.04.00.00.00 | NA | Atenolol 50mg tablets                       | 28 tablet(s) - 50 mg            | 2005-10-07 |\n",
-       "| 1000018 | 3 | 08/11/2005 | NA | 02.04.00.00.00 | NA | Atenolol 50mg tablets                       | 28 tablet(s) - 50 mg            | 2005-11-08 |\n",
-       "| 1000018 | 3 | 05/12/2005 | NA | 02.04.00.00.00 | NA | Atenolol 50mg tablets                       | 28 tablet(s) - 50 mg            | 2005-12-05 |\n",
-       "| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |\n",
-       "| 6025198 | 3 | 23/01/2015 | NA | 02.05.05.01.00 | NA | Ramipril 2.5mg capsules  | 2*28 capsule | 2015-01-23 |\n",
-       "| 6025198 | 3 | 23/01/2015 | NA | 02.12.04.00.00 | NA | Simvastatin 40mg tablets | 2*28 tablet  | 2015-01-23 |\n",
-       "| 6025198 | 3 | 23/01/2015 | NA | 10.03.02.01.00 | NA | Fenbid 5% gel (AMCo)     | 100 gram     | 2015-01-23 |\n",
-       "| 6025198 | 3 | 27/02/2015 | NA | 02.05.05.01.00 | NA | Ramipril 2.5mg capsules  | 56 capsule   | 2015-02-27 |\n",
-       "| 6025198 | 3 | 27/02/2015 | NA | 02.12.04.00.00 | NA | Simvastatin 40mg tablets | 56 tablet    | 2015-02-27 |\n",
-       "| 6025198 | 3 | 27/02/2015 | NA | 10.03.02.01.00 | NA | Fenbid 5% gel (AMCo)     | 100 gram     | 2015-02-27 |\n",
-       "| 6025198 | 3 | 05/05/2015 | NA | 02.05.05.01.00 | NA | Ramipril 2.5mg capsules  | 56 capsule   | 2015-05-05 |\n",
-       "| 6025198 | 3 | 05/05/2015 | NA | 02.12.04.00.00 | NA | Simvastatin 40mg tablets | 56 tablet    | 2015-05-05 |\n",
-       "| 6025198 | 3 | 05/05/2015 | NA | 10.03.02.01.00 | NA | Fenbid 5% gel (AMCo)     | 100 gram     | 2015-05-05 |\n",
-       "| 6025198 | 3 | 24/06/2015 | NA | 02.05.05.01.00 | NA | Ramipril 2.5mg capsules  | 56 capsule   | 2015-06-24 |\n",
-       "| 6025198 | 3 | 24/06/2015 | NA | 02.12.04.00.00 | NA | Simvastatin 40mg tablets | 56 tablet    | 2015-06-24 |\n",
-       "| 6025198 | 3 | 24/06/2015 | NA | 10.03.02.01.00 | NA | Fenbid 5% gel (AMCo)     | 100 gram     | 2015-06-24 |\n",
-       "| 6025198 | 3 | 19/08/2015 | NA | 02.05.05.01.00 | NA | Ramipril 2.5mg capsules  | 56 capsule   | 2015-08-19 |\n",
-       "| 6025198 | 3 | 19/08/2015 | NA | 02.12.04.00.00 | NA | Simvastatin 40mg tablets | 56 tablet    | 2015-08-19 |\n",
-       "| 6025198 | 3 | 19/08/2015 | NA | 10.03.02.01.00 | NA | Fenbid 5% gel (AMCo)     | 100 gram     | 2015-08-19 |\n",
-       "| 6025198 | 3 | 14/10/2015 | NA | 02.05.05.01.00 | NA | Ramipril 2.5mg capsules  | 56 capsule   | 2015-10-14 |\n",
-       "| 6025198 | 3 | 14/10/2015 | NA | 02.12.04.00.00 | NA | Simvastatin 40mg tablets | 56 tablet    | 2015-10-14 |\n",
-       "| 6025198 | 3 | 14/10/2015 | NA | 10.03.02.01.00 | NA | Fenbid 5% gel (AMCo)     | 100 gram     | 2015-10-14 |\n",
-       "| 6025198 | 3 | 10/12/2015 | NA | 02.05.05.01.00 | NA | Ramipril 2.5mg capsules  | 56 capsule   | 2015-12-10 |\n",
-       "| 6025198 | 3 | 10/12/2015 | NA | 02.12.04.00.00 | NA | Simvastatin 40mg tablets | 56 tablet    | 2015-12-10 |\n",
-       "| 6025198 | 3 | 10/12/2015 | NA | 10.03.02.01.00 | NA | Fenbid 5% gel (AMCo)     | 100 gram     | 2015-12-10 |\n",
-       "| 6025198 | 3 | 03/02/2016 | NA | 02.05.05.01.00 | NA | Ramipril 2.5mg capsules  | 56 capsule   | 2016-02-03 |\n",
-       "| 6025198 | 3 | 03/02/2016 | NA | 02.12.04.00.00 | NA | Simvastatin 40mg tablets | 56 tablet    | 2016-02-03 |\n",
-       "| 6025198 | 3 | 03/02/2016 | NA | 10.03.02.01.00 | NA | Fenbid 5% gel (AMCo)     | 100 gram     | 2016-02-03 |\n",
-       "| 6025198 | 3 | 06/04/2016 | NA | 02.05.05.01.00 | NA | Ramipril 2.5mg capsules  | 56 capsule   | 2016-04-06 |\n",
-       "| 6025198 | 3 | 06/04/2016 | NA | 02.12.04.00.00 | NA | Simvastatin 40mg tablets | 56 tablet    | 2016-04-06 |\n",
-       "| 6025198 | 3 | 06/04/2016 | NA | 10.03.02.01.00 | NA | Fenbid 5% gel (AMCo)     | 100 gram     | 2016-04-06 |\n",
-       "| 6025198 | 3 | 25/05/2016 | NA | 02.05.05.01.00 | NA | Ramipril 2.5mg capsules  | 56 capsule   | 2016-05-25 |\n",
-       "| 6025198 | 3 | 25/05/2016 | NA | 02.12.04.00.00 | NA | Simvastatin 40mg tablets | 56 tablet    | 2016-05-25 |\n",
-       "| 6025198 | 3 | 25/05/2016 | NA | 10.03.02.01.00 | NA | Fenbid 5% gel (AMCo)     | 100 gram     | 2016-05-25 |\n",
-       "\n"
-      ],
-      "text/plain": [
-       "         eid     data_provider issue_date read_2 bnf_code       dmd_code\n",
-       "1        1000018 3             30/11/1996 NA     13.06.02.01.00 NA      \n",
-       "2        1000018 3             12/09/1997 NA     02.03.02.02.00 NA      \n",
-       "3        1000018 3             10/10/1997 NA     02.03.02.02.00 NA      \n",
-       "4        1000018 3             10/10/1997 NA     07.03.02.01.00 NA      \n",
-       "5        1000018 3             19/01/1998 NA     02.03.02.02.00 NA      \n",
-       "6        1000018 3             19/01/1998 NA     02.03.02.02.00 NA      \n",
-       "7        1000018 3             19/01/1998 NA     06.07.02.00.00 NA      \n",
-       "8        1000018 3             13/03/1998 NA     02.03.02.02.00 NA      \n",
-       "9        1000018 3             25/03/1998 NA     05.01.01.03.00 NA      \n",
-       "10       1000018 3             16/04/1998 NA     05.01.01.03.00 NA      \n",
-       "11       1000018 3             24/04/1998 NA     05.04.03.00.00 NA      \n",
-       "12       1000018 3             19/06/1998 NA     01.02.02.00.00 NA      \n",
-       "13       1000018 3             19/06/1998 NA     02.03.02.02.00 NA      \n",
-       "14       1000018 3             14/08/1998 NA     12.01.01.02.00 NA      \n",
-       "15       1000018 3             20/01/1999 NA     06.07.02.00.00 NA      \n",
-       "16       1000018 3             20/01/1999 NA     12.01.01.02.00 NA      \n",
-       "17       1000018 3             12/01/2001 NA     05.03.02.01.00 NA      \n",
-       "18       1000018 3             12/01/2001 NA     13.06.02.01.00 NA      \n",
-       "19       1000018 3             17/11/2003 NA     05.03.02.01.00 NA      \n",
-       "20       1000018 3             17/11/2003 NA     13.06.02.01.00 NA      \n",
-       "21       1000018 3             18/11/2003 NA     13.06.02.01.00 NA      \n",
-       "22       1000018 3             24/11/2003 NA     12.02.01.00.00 NA      \n",
-       "23       1000018 3             08/06/2005 NA     02.04.00.00.00 NA      \n",
-       "24       1000018 3             09/06/2005 NA     02.04.00.00.00 NA      \n",
-       "25       1000018 3             23/06/2005 NA     02.04.00.00.00 NA      \n",
-       "26       1000018 3             24/06/2005 NA     02.04.00.00.00 NA      \n",
-       "27       1000018 3             01/09/2005 NA     02.04.00.00.00 NA      \n",
-       "28       1000018 3             07/10/2005 NA     02.04.00.00.00 NA      \n",
-       "29       1000018 3             08/11/2005 NA     02.04.00.00.00 NA      \n",
-       "30       1000018 3             05/12/2005 NA     02.04.00.00.00 NA      \n",
-       "<U+22EE>   <U+22EE> <U+22EE>      <U+22EE>   <U+22EE> <U+22EE>       <U+22EE>\n",
-       "57704513 6025198 3             23/01/2015 NA     02.05.05.01.00 NA      \n",
-       "57704514 6025198 3             23/01/2015 NA     02.12.04.00.00 NA      \n",
-       "57704515 6025198 3             23/01/2015 NA     10.03.02.01.00 NA      \n",
-       "57704516 6025198 3             27/02/2015 NA     02.05.05.01.00 NA      \n",
-       "57704517 6025198 3             27/02/2015 NA     02.12.04.00.00 NA      \n",
-       "57704518 6025198 3             27/02/2015 NA     10.03.02.01.00 NA      \n",
-       "57704519 6025198 3             05/05/2015 NA     02.05.05.01.00 NA      \n",
-       "57704520 6025198 3             05/05/2015 NA     02.12.04.00.00 NA      \n",
-       "57704521 6025198 3             05/05/2015 NA     10.03.02.01.00 NA      \n",
-       "57704522 6025198 3             24/06/2015 NA     02.05.05.01.00 NA      \n",
-       "57704523 6025198 3             24/06/2015 NA     02.12.04.00.00 NA      \n",
-       "57704524 6025198 3             24/06/2015 NA     10.03.02.01.00 NA      \n",
-       "57704525 6025198 3             19/08/2015 NA     02.05.05.01.00 NA      \n",
-       "57704526 6025198 3             19/08/2015 NA     02.12.04.00.00 NA      \n",
-       "57704527 6025198 3             19/08/2015 NA     10.03.02.01.00 NA      \n",
-       "57704528 6025198 3             14/10/2015 NA     02.05.05.01.00 NA      \n",
-       "57704529 6025198 3             14/10/2015 NA     02.12.04.00.00 NA      \n",
-       "57704530 6025198 3             14/10/2015 NA     10.03.02.01.00 NA      \n",
-       "57704531 6025198 3             10/12/2015 NA     02.05.05.01.00 NA      \n",
-       "57704532 6025198 3             10/12/2015 NA     02.12.04.00.00 NA      \n",
-       "57704533 6025198 3             10/12/2015 NA     10.03.02.01.00 NA      \n",
-       "57704534 6025198 3             03/02/2016 NA     02.05.05.01.00 NA      \n",
-       "57704535 6025198 3             03/02/2016 NA     02.12.04.00.00 NA      \n",
-       "57704536 6025198 3             03/02/2016 NA     10.03.02.01.00 NA      \n",
-       "57704537 6025198 3             06/04/2016 NA     02.05.05.01.00 NA      \n",
-       "57704538 6025198 3             06/04/2016 NA     02.12.04.00.00 NA      \n",
-       "57704539 6025198 3             06/04/2016 NA     10.03.02.01.00 NA      \n",
-       "57704540 6025198 3             25/05/2016 NA     02.05.05.01.00 NA      \n",
-       "57704541 6025198 3             25/05/2016 NA     02.12.04.00.00 NA      \n",
-       "57704542 6025198 3             25/05/2016 NA     10.03.02.01.00 NA      \n",
-       "         drug_name                                  \n",
-       "1        Doxycycline 100mg capsules                 \n",
-       "2        Propranolol 40mg tablets                   \n",
-       "3        Propranolol 40mg tablets                   \n",
-       "4        Noriday 350microgram tablets (Pfizer Ltd)  \n",
-       "5        Propranolol 40mg tablets                   \n",
-       "6        Propranolol 80mg tablets                   \n",
-       "7        Gamolenic acid 40mg capsules               \n",
-       "8        Propranolol 80mg tablets                   \n",
-       "9        Amoxicillin 250mg capsules                 \n",
-       "10       Amoxicillin 250mg capsules                 \n",
-       "11       Metronidazole 200mg tablets                \n",
-       "12       Mebeverine 135mg tablets                   \n",
-       "13       Propranolol 80mg tablets                   \n",
-       "14       Gentisone HC ear drops (AMCo)              \n",
-       "15       Gamolenic acid 40mg capsules               \n",
-       "16       Gentisone HC ear drops (AMCo)              \n",
-       "17       Aciclovir 200mg dispersible tablets        \n",
-       "18       Doxycycline 100mg capsules                 \n",
-       "19       Aciclovir 200mg dispersible tablets        \n",
-       "20       Doxycycline 100mg capsules                 \n",
-       "21       Doxycycline 100mg capsules                 \n",
-       "22       Beclometasone 50micrograms/dose nasal spray\n",
-       "23       Atenolol 50mg tablets                      \n",
-       "24       Atenolol 50mg tablets                      \n",
-       "25       Atenolol 50mg tablets                      \n",
-       "26       Atenolol 50mg tablets                      \n",
-       "27       Atenolol 50mg tablets                      \n",
-       "28       Atenolol 50mg tablets                      \n",
-       "29       Atenolol 50mg tablets                      \n",
-       "30       Atenolol 50mg tablets                      \n",
-       "<U+22EE>   <U+22EE>                                   \n",
-       "57704513 Ramipril 2.5mg capsules                    \n",
-       "57704514 Simvastatin 40mg tablets                   \n",
-       "57704515 Fenbid 5% gel (AMCo)                       \n",
-       "57704516 Ramipril 2.5mg capsules                    \n",
-       "57704517 Simvastatin 40mg tablets                   \n",
-       "57704518 Fenbid 5% gel (AMCo)                       \n",
-       "57704519 Ramipril 2.5mg capsules                    \n",
-       "57704520 Simvastatin 40mg tablets                   \n",
-       "57704521 Fenbid 5% gel (AMCo)                       \n",
-       "57704522 Ramipril 2.5mg capsules                    \n",
-       "57704523 Simvastatin 40mg tablets                   \n",
-       "57704524 Fenbid 5% gel (AMCo)                       \n",
-       "57704525 Ramipril 2.5mg capsules                    \n",
-       "57704526 Simvastatin 40mg tablets                   \n",
-       "57704527 Fenbid 5% gel (AMCo)                       \n",
-       "57704528 Ramipril 2.5mg capsules                    \n",
-       "57704529 Simvastatin 40mg tablets                   \n",
-       "57704530 Fenbid 5% gel (AMCo)                       \n",
-       "57704531 Ramipril 2.5mg capsules                    \n",
-       "57704532 Simvastatin 40mg tablets                   \n",
-       "57704533 Fenbid 5% gel (AMCo)                       \n",
-       "57704534 Ramipril 2.5mg capsules                    \n",
-       "57704535 Simvastatin 40mg tablets                   \n",
-       "57704536 Fenbid 5% gel (AMCo)                       \n",
-       "57704537 Ramipril 2.5mg capsules                    \n",
-       "57704538 Simvastatin 40mg tablets                   \n",
-       "57704539 Fenbid 5% gel (AMCo)                       \n",
-       "57704540 Ramipril 2.5mg capsules                    \n",
-       "57704541 Simvastatin 40mg tablets                   \n",
-       "57704542 Fenbid 5% gel (AMCo)                       \n",
-       "         quantity                        date      \n",
-       "1        8 capsule(s) - 100 mg           1996-11-30\n",
-       "2        56 tablet(s) - 40 mg            1997-09-12\n",
-       "3        168 tablet(s) - 40 mg           1997-10-10\n",
-       "4        168 tablet(s) - 350 micrograms  1997-10-10\n",
-       "5        168 tablet(s) - 40 mg           1998-01-19\n",
-       "6        112 tablet(s) - 80 mg           1998-01-19\n",
-       "7        336 capsule(s) - 40 mg          1998-01-19\n",
-       "8        112 tablet(s) - 80 mg           1998-03-13\n",
-       "9        15 capsule(s) - 250 mg          1998-03-25\n",
-       "10       15 capsule(s) - 250 mg          1998-04-16\n",
-       "11       15 tablet(s) - 200 mg           1998-04-24\n",
-       "12       84 tablet(s) - 135 mg           1998-06-19\n",
-       "13       112 tablet(s) - 80 mg           1998-06-19\n",
-       "14       10 millilitre                   1998-08-14\n",
-       "15       336 capsule(s) - 40 mg          1999-01-20\n",
-       "16       10 millilitre                   1999-01-20\n",
-       "17       35 tablet(s) - 200 mg           2001-01-12\n",
-       "18       10 capsule(s) - 100 mg          2001-01-12\n",
-       "19       35 tablet(s) - 200 mg           2003-11-17\n",
-       "20       8 capsule(s) - 100 mg           2003-11-17\n",
-       "21       8 capsule(s) - 100 mg           2003-11-18\n",
-       "22       1 spray(s) - 50 micrograms/dose 2003-11-24\n",
-       "23       28 tablet(s) - 50 mg            2005-06-08\n",
-       "24       28 tablet(s) - 50 mg            2005-06-09\n",
-       "25       28 tablet(s) - 50 mg            2005-06-23\n",
-       "26       28 tablet(s) - 50 mg            2005-06-24\n",
-       "27       28 tablet(s) - 50 mg            2005-09-01\n",
-       "28       28 tablet(s) - 50 mg            2005-10-07\n",
-       "29       28 tablet(s) - 50 mg            2005-11-08\n",
-       "30       28 tablet(s) - 50 mg            2005-12-05\n",
-       "<U+22EE>   <U+22EE>                        <U+22EE>  \n",
-       "57704513 2*28 capsule                    2015-01-23\n",
-       "57704514 2*28 tablet                     2015-01-23\n",
-       "57704515 100 gram                        2015-01-23\n",
-       "57704516 56 capsule                      2015-02-27\n",
-       "57704517 56 tablet                       2015-02-27\n",
-       "57704518 100 gram                        2015-02-27\n",
-       "57704519 56 capsule                      2015-05-05\n",
-       "57704520 56 tablet                       2015-05-05\n",
-       "57704521 100 gram                        2015-05-05\n",
-       "57704522 56 capsule                      2015-06-24\n",
-       "57704523 56 tablet                       2015-06-24\n",
-       "57704524 100 gram                        2015-06-24\n",
-       "57704525 56 capsule                      2015-08-19\n",
-       "57704526 56 tablet                       2015-08-19\n",
-       "57704527 100 gram                        2015-08-19\n",
-       "57704528 56 capsule                      2015-10-14\n",
-       "57704529 56 tablet                       2015-10-14\n",
-       "57704530 100 gram                        2015-10-14\n",
-       "57704531 56 capsule                      2015-12-10\n",
-       "57704532 56 tablet                       2015-12-10\n",
-       "57704533 100 gram                        2015-12-10\n",
-       "57704534 56 capsule                      2016-02-03\n",
-       "57704535 56 tablet                       2016-02-03\n",
-       "57704536 100 gram                        2016-02-03\n",
-       "57704537 56 capsule                      2016-04-06\n",
-       "57704538 56 tablet                       2016-04-06\n",
-       "57704539 100 gram                        2016-04-06\n",
-       "57704540 56 capsule                      2016-05-25\n",
-       "57704541 56 tablet                       2016-05-25\n",
-       "57704542 100 gram                        2016-05-25"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "gp_scripts %>% filter(!is.na(read_2) | !is.na(dmd_code)) %>% arrange(eid)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 137,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<table>\n",
-       "<caption>A data.table: 6350891 × 8</caption>\n",
-       "<thead>\n",
-       "\t<tr><th scope=col>eid</th><th scope=col>data_provider</th><th scope=col>issue_date</th><th scope=col>read_2</th><th scope=col>bnf_code</th><th scope=col>dmd_code</th><th scope=col>drug_name</th><th scope=col>quantity</th></tr>\n",
-       "\t<tr><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int64&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
-       "</thead>\n",
-       "<tbody>\n",
-       "\t<tr><td>1000614</td><td>1</td><td>22/04/2004</td><td>djiG.00</td><td></td><td>         322637009</td><td>Tramadol modified release capsule 100mg                                                     </td><td>30.000 </td></tr>\n",
-       "\t<tr><td>1000614</td><td>1</td><td>23/09/2004</td><td>e311.  </td><td></td><td>         323509004</td><td>Amoxicillin capsules 250mg                                                                  </td><td>21.000 </td></tr>\n",
-       "\t<tr><td>1000614</td><td>1</td><td>28/10/2005</td><td>c13J.00</td><td></td><td>         320139002</td><td>Salbutamol cfc free inhaler 100micrograms/inhalation                                        </td><td>2.000  </td></tr>\n",
-       "\t<tr><td>1000614</td><td>1</td><td>28/10/2005</td><td>e311.  </td><td></td><td>         323509004</td><td>Amoxicillin capsules 250mg                                                                  </td><td>15.000 </td></tr>\n",
-       "\t<tr><td>1000614</td><td>1</td><td>28/10/2005</td><td>k32w.00</td><td></td><td>         330295009</td><td>Chloramphenicol ophthalmic ointment 1%                                                      </td><td>4.000  </td></tr>\n",
-       "\t<tr><td>1000614</td><td>1</td><td>11/11/2005</td><td>c61t.00</td><td></td><td>         320531008</td><td>Beclometasone 250micrograms/dose inhaler                                                    </td><td>2.000  </td></tr>\n",
-       "\t<tr><td>1000614</td><td>1</td><td>28/11/2005</td><td>fe6j.00</td><td></td><td>         325444003</td><td>Prednisolone sodium phosphate soluble tablet 5mg                                            </td><td>30.000 </td></tr>\n",
-       "\t<tr><td>1000614</td><td>1</td><td>08/12/2005</td><td>ecc2.00</td><td></td><td>         324431001</td><td>Trimethoprim tablets 200mg                                                                  </td><td>14.000 </td></tr>\n",
-       "\t<tr><td>1000614</td><td>1</td><td>27/10/2006</td><td>e311.  </td><td></td><td>         323509004</td><td>Amoxicillin capsules 250mg                                                                  </td><td>21.000 </td></tr>\n",
-       "\t<tr><td>1000614</td><td>1</td><td>29/12/2006</td><td>e311.  </td><td></td><td>         323509004</td><td>Amoxicillin capsules 250mg                                                                  </td><td>21.000 </td></tr>\n",
-       "\t<tr><td>1000614</td><td>1</td><td>03/01/2007</td><td>n45n.00</td><td></td><td>  7374311000001101</td><td>Revaxis adsorbed vaccine -                                                                  </td><td>1.000  </td></tr>\n",
-       "\t<tr><td>1000614</td><td>1</td><td>03/01/2007</td><td>n45n.00</td><td></td><td>  7374311000001101</td><td>Revaxis adsorbed vaccine -                                                                  </td><td>1.000  </td></tr>\n",
-       "\t<tr><td>1000614</td><td>1</td><td>21/05/2007</td><td>e311.  </td><td></td><td>         323509004</td><td>Amoxicillin capsules 250mg                                                                  </td><td>21.000 </td></tr>\n",
-       "\t<tr><td>1000614</td><td>1</td><td>18/07/2007</td><td>c13T.00</td><td></td><td> 34015001000027103</td><td>Ventolin evohaler 100 100microgram/inhalation Pressurised inhalation (Glaxo Wellcome UK Ltd)</td><td>2.000  </td></tr>\n",
-       "\t<tr><td>1000614</td><td>1</td><td>30/07/2007</td><td>c8p3.00</td><td></td><td>         320828002</td><td>Fexofenadine tablets 180mg                                                                  </td><td>30.000 </td></tr>\n",
-       "\t<tr><td>1000614</td><td>1</td><td>30/07/2007</td><td>m313.00</td><td></td><td> 14608811000001105</td><td>Calamine lotion -                                                                           </td><td>200.000</td></tr>\n",
-       "\t<tr><td>1000614</td><td>1</td><td>03/09/2008</td><td>dher.  </td><td></td><td>   784511000001103</td><td>Prochlorperazine tablets 5mg                                                                </td><td>84.000 </td></tr>\n",
-       "\t<tr><td>1000614</td><td>1</td><td>12/11/2008</td><td>e311.  </td><td></td><td>         323509004</td><td>Amoxicillin capsules 250mg                                                                  </td><td>21.000 </td></tr>\n",
-       "\t<tr><td>1000614</td><td>1</td><td>15/12/2008</td><td>da91.00</td><td></td><td>         321987003</td><td>Citalopram tablets 20mg                                                                     </td><td>28.000 </td></tr>\n",
-       "\t<tr><td>1000614</td><td>1</td><td>12/01/2009</td><td>da91.00</td><td></td><td>         321987003</td><td>Citalopram 20mg tablets                                                                     </td><td>56.000 </td></tr>\n",
-       "\t<tr><td>1000614</td><td>1</td><td>12/01/2009</td><td>da91.00</td><td></td><td>         321987003</td><td>Citalopram tablets 20mg                                                                     </td><td>56.000 </td></tr>\n",
-       "\t<tr><td>1000614</td><td>1</td><td>12/01/2009</td><td>diaU.00</td><td></td><td>136665001000027109</td><td>Paracetamol with codeine phosphate tablets 500mg + 15mg                                     </td><td>100.000</td></tr>\n",
-       "\t<tr><td>1000614</td><td>1</td><td>03/02/2009</td><td>e312.  </td><td></td><td>         323510009</td><td>Amoxicillin capsules 500mg                                                                  </td><td>21.000 </td></tr>\n",
-       "\t<tr><td>1000614</td><td>1</td><td>02/10/2009</td><td>c61t.00</td><td></td><td>         320531008</td><td>BECLOMETASONE inh 250micrograms/actuation                                                   </td><td>2.000  </td></tr>\n",
-       "\t<tr><td>1000614</td><td>1</td><td>02/10/2009</td><td>c61t.00</td><td></td><td>         320531008</td><td>Beclometasone 250micrograms/dose inhaler                                                    </td><td>2.000  </td></tr>\n",
-       "\t<tr><td>1000614</td><td>1</td><td>02/10/2009</td><td>da91.00</td><td></td><td>         321987003</td><td>CITALOPRAM tabs 20mg                                                                        </td><td>56.000 </td></tr>\n",
-       "\t<tr><td>1000614</td><td>1</td><td>26/11/2009</td><td>c61t.00</td><td></td><td>         320531008</td><td>BECLOMETASONE inh 250micrograms/actuation                                                   </td><td>2.000  </td></tr>\n",
-       "\t<tr><td>1000614</td><td>1</td><td>26/11/2009</td><td>da91.00</td><td></td><td>         321987003</td><td>CITALOPRAM tabs 20mg                                                                        </td><td>56.000 </td></tr>\n",
-       "\t<tr><td>1000614</td><td>1</td><td>02/12/2009</td><td>c13T.00</td><td></td><td> 34015001000027103</td><td>Ventolin evohaler 100 100microgram/inhalation Pressurised inhalation (Glaxo Wellcome UK Ltd)</td><td>2.000  </td></tr>\n",
-       "\t<tr><td>1000614</td><td>1</td><td>10/02/2010</td><td>da91.00</td><td></td><td>         321987003</td><td>CITALOPRAM tabs 20mg                                                                        </td><td>56.000 </td></tr>\n",
-       "\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
-       "\t<tr><td>6024123</td><td>1</td><td>16/11/2016</td><td>d73u.00</td><td></td><td>      321785001</td><td>Clomipramine 10mg capsules               </td><td>56.000 </td></tr>\n",
-       "\t<tr><td>6024123</td><td>1</td><td>16/11/2016</td><td>d73u.00</td><td></td><td>      321785001</td><td>Clomipramine 10mg capsules               </td><td>56.000 </td></tr>\n",
-       "\t<tr><td>6024123</td><td>1</td><td>16/11/2016</td><td>diaq.00</td><td></td><td>      322344006</td><td>Co-codamol 8mg/500mg capsules            </td><td>132.000</td></tr>\n",
-       "\t<tr><td>6024123</td><td>1</td><td>16/11/2016</td><td>diaq.00</td><td></td><td>      322344006</td><td>Co-codamol 8mg/500mg capsules            </td><td>132.000</td></tr>\n",
-       "\t<tr><td>6024123</td><td>1</td><td>16/11/2016</td><td>ja18.00</td><td></td><td>439911000001107</td><td><span style=white-space:pre-wrap>Algesal cream (Thornton &amp; Ross Ltd)      </span></td><td>50.000 </td></tr>\n",
-       "\t<tr><td>6024123</td><td>1</td><td>16/11/2016</td><td>ja18.00</td><td></td><td>439911000001107</td><td><span style=white-space:pre-wrap>Algesal cream (Thornton &amp; Ross Ltd)      </span></td><td>50.000 </td></tr>\n",
-       "\t<tr><td>6024123</td><td>1</td><td>14/12/2016</td><td>diaq.00</td><td></td><td>      322344006</td><td>Co-codamol 8mg/500mg capsules            </td><td>132.000</td></tr>\n",
-       "\t<tr><td>6024123</td><td>1</td><td>10/01/2017</td><td>a6b1.00</td><td></td><td>      317291008</td><td>Omeprazole 20mg gastro-resistant capsules</td><td>56.000 </td></tr>\n",
-       "\t<tr><td>6024123</td><td>1</td><td>10/01/2017</td><td>d73u.00</td><td></td><td>      321785001</td><td>Clomipramine 10mg capsules               </td><td>56.000 </td></tr>\n",
-       "\t<tr><td>6024123</td><td>1</td><td>10/01/2017</td><td>diaq.00</td><td></td><td>      322344006</td><td>Co-codamol 8mg/500mg capsules            </td><td>132.000</td></tr>\n",
-       "\t<tr><td>6024123</td><td>1</td><td>10/01/2017</td><td>ja18.00</td><td></td><td>439911000001107</td><td><span style=white-space:pre-wrap>Algesal cream (Thornton &amp; Ross Ltd)      </span></td><td>50.000 </td></tr>\n",
-       "\t<tr><td>6024123</td><td>1</td><td>07/02/2017</td><td>diaq.00</td><td></td><td>      322344006</td><td>Co-codamol 8mg/500mg capsules            </td><td>132.000</td></tr>\n",
-       "\t<tr><td>6024123</td><td>1</td><td>07/02/2017</td><td>diaq.00</td><td></td><td>      322344006</td><td>Co-codamol 8mg/500mg capsules            </td><td>132.000</td></tr>\n",
-       "\t<tr><td>6024123</td><td>1</td><td>03/03/2017</td><td>diaq.00</td><td></td><td>      322344006</td><td>Co-codamol 8mg/500mg capsules            </td><td>132.000</td></tr>\n",
-       "\t<tr><td>6024123</td><td>1</td><td>03/03/2017</td><td>ja18.00</td><td></td><td>439911000001107</td><td><span style=white-space:pre-wrap>Algesal cream (Thornton &amp; Ross Ltd)      </span></td><td>50.000 </td></tr>\n",
-       "\t<tr><td>6024123</td><td>1</td><td>28/03/2017</td><td>a6b1.00</td><td></td><td>      317291008</td><td>Omeprazole 20mg gastro-resistant capsules</td><td>56.000 </td></tr>\n",
-       "\t<tr><td>6024123</td><td>1</td><td>28/03/2017</td><td>d73u.00</td><td></td><td>      321785001</td><td>Clomipramine 10mg capsules               </td><td>56.000 </td></tr>\n",
-       "\t<tr><td>6024123</td><td>1</td><td>28/03/2017</td><td>diaq.00</td><td></td><td>      322344006</td><td>Co-codamol 8mg/500mg capsules            </td><td>132.000</td></tr>\n",
-       "\t<tr><td>6024123</td><td>1</td><td>28/03/2017</td><td>ja18.00</td><td></td><td>439911000001107</td><td><span style=white-space:pre-wrap>Algesal cream (Thornton &amp; Ross Ltd)      </span></td><td>50.000 </td></tr>\n",
-       "\t<tr><td>6024123</td><td>1</td><td>28/03/2017</td><td>ja18.00</td><td></td><td>439911000001107</td><td><span style=white-space:pre-wrap>Algesal cream (Thornton &amp; Ross Ltd)      </span></td><td>50.000 </td></tr>\n",
-       "\t<tr><td>6024123</td><td>1</td><td>02/05/2017</td><td>diaq.00</td><td></td><td>      322344006</td><td>Co-codamol 8mg/500mg capsules            </td><td>132.000</td></tr>\n",
-       "\t<tr><td>6024123</td><td>1</td><td>02/05/2017</td><td>diaq.00</td><td></td><td>      322344006</td><td>Co-codamol 8mg/500mg capsules            </td><td>132.000</td></tr>\n",
-       "\t<tr><td>6024123</td><td>1</td><td>16/05/2017</td><td>a6b1.00</td><td></td><td>      317291008</td><td>Omeprazole 20mg gastro-resistant capsules</td><td>56.000 </td></tr>\n",
-       "\t<tr><td>6024123</td><td>1</td><td>16/05/2017</td><td>a6b1.00</td><td></td><td>      317291008</td><td>Omeprazole 20mg gastro-resistant capsules</td><td>56.000 </td></tr>\n",
-       "\t<tr><td>6024123</td><td>1</td><td>16/05/2017</td><td>d73u.00</td><td></td><td>      321785001</td><td>Clomipramine 10mg capsules               </td><td>56.000 </td></tr>\n",
-       "\t<tr><td>6024123</td><td>1</td><td>16/05/2017</td><td>d73u.00</td><td></td><td>      321785001</td><td>Clomipramine 10mg capsules               </td><td>56.000 </td></tr>\n",
-       "\t<tr><td>6024123</td><td>1</td><td>16/05/2017</td><td>diaq.00</td><td></td><td>      322344006</td><td>Co-codamol 8mg/500mg capsules            </td><td>132.000</td></tr>\n",
-       "\t<tr><td>6024123</td><td>1</td><td>16/05/2017</td><td>diaq.00</td><td></td><td>      322344006</td><td>Co-codamol 8mg/500mg capsules            </td><td>132.000</td></tr>\n",
-       "\t<tr><td>6024123</td><td>1</td><td>16/05/2017</td><td>ja18.00</td><td></td><td>439911000001107</td><td><span style=white-space:pre-wrap>Algesal cream (Thornton &amp; Ross Ltd)      </span></td><td>50.000 </td></tr>\n",
-       "\t<tr><td>6024123</td><td>1</td><td>16/05/2017</td><td>ja18.00</td><td></td><td>439911000001107</td><td><span style=white-space:pre-wrap>Algesal cream (Thornton &amp; Ross Ltd)      </span></td><td>50.000 </td></tr>\n",
-       "</tbody>\n",
-       "</table>\n"
-      ],
-      "text/latex": [
-       "A data.table: 6350891 × 8\n",
-       "\\begin{tabular}{llllllll}\n",
-       " eid & data\\_provider & issue\\_date & read\\_2 & bnf\\_code & dmd\\_code & drug\\_name & quantity\\\\\n",
-       " <int> & <int> & <chr> & <chr> & <chr> & <int64> & <chr> & <chr>\\\\\n",
-       "\\hline\n",
-       "\t 1000614 & 1 & 22/04/2004 & djiG.00 &  &          322637009 & Tramadol modified release capsule 100mg                                                      & 30.000 \\\\\n",
-       "\t 1000614 & 1 & 23/09/2004 & e311.   &  &          323509004 & Amoxicillin capsules 250mg                                                                   & 21.000 \\\\\n",
-       "\t 1000614 & 1 & 28/10/2005 & c13J.00 &  &          320139002 & Salbutamol cfc free inhaler 100micrograms/inhalation                                         & 2.000  \\\\\n",
-       "\t 1000614 & 1 & 28/10/2005 & e311.   &  &          323509004 & Amoxicillin capsules 250mg                                                                   & 15.000 \\\\\n",
-       "\t 1000614 & 1 & 28/10/2005 & k32w.00 &  &          330295009 & Chloramphenicol ophthalmic ointment 1\\%                                                       & 4.000  \\\\\n",
-       "\t 1000614 & 1 & 11/11/2005 & c61t.00 &  &          320531008 & Beclometasone 250micrograms/dose inhaler                                                     & 2.000  \\\\\n",
-       "\t 1000614 & 1 & 28/11/2005 & fe6j.00 &  &          325444003 & Prednisolone sodium phosphate soluble tablet 5mg                                             & 30.000 \\\\\n",
-       "\t 1000614 & 1 & 08/12/2005 & ecc2.00 &  &          324431001 & Trimethoprim tablets 200mg                                                                   & 14.000 \\\\\n",
-       "\t 1000614 & 1 & 27/10/2006 & e311.   &  &          323509004 & Amoxicillin capsules 250mg                                                                   & 21.000 \\\\\n",
-       "\t 1000614 & 1 & 29/12/2006 & e311.   &  &          323509004 & Amoxicillin capsules 250mg                                                                   & 21.000 \\\\\n",
-       "\t 1000614 & 1 & 03/01/2007 & n45n.00 &  &   7374311000001101 & Revaxis adsorbed vaccine -                                                                   & 1.000  \\\\\n",
-       "\t 1000614 & 1 & 03/01/2007 & n45n.00 &  &   7374311000001101 & Revaxis adsorbed vaccine -                                                                   & 1.000  \\\\\n",
-       "\t 1000614 & 1 & 21/05/2007 & e311.   &  &          323509004 & Amoxicillin capsules 250mg                                                                   & 21.000 \\\\\n",
-       "\t 1000614 & 1 & 18/07/2007 & c13T.00 &  &  34015001000027103 & Ventolin evohaler 100 100microgram/inhalation Pressurised inhalation (Glaxo Wellcome UK Ltd) & 2.000  \\\\\n",
-       "\t 1000614 & 1 & 30/07/2007 & c8p3.00 &  &          320828002 & Fexofenadine tablets 180mg                                                                   & 30.000 \\\\\n",
-       "\t 1000614 & 1 & 30/07/2007 & m313.00 &  &  14608811000001105 & Calamine lotion -                                                                            & 200.000\\\\\n",
-       "\t 1000614 & 1 & 03/09/2008 & dher.   &  &    784511000001103 & Prochlorperazine tablets 5mg                                                                 & 84.000 \\\\\n",
-       "\t 1000614 & 1 & 12/11/2008 & e311.   &  &          323509004 & Amoxicillin capsules 250mg                                                                   & 21.000 \\\\\n",
-       "\t 1000614 & 1 & 15/12/2008 & da91.00 &  &          321987003 & Citalopram tablets 20mg                                                                      & 28.000 \\\\\n",
-       "\t 1000614 & 1 & 12/01/2009 & da91.00 &  &          321987003 & Citalopram 20mg tablets                                                                      & 56.000 \\\\\n",
-       "\t 1000614 & 1 & 12/01/2009 & da91.00 &  &          321987003 & Citalopram tablets 20mg                                                                      & 56.000 \\\\\n",
-       "\t 1000614 & 1 & 12/01/2009 & diaU.00 &  & 136665001000027109 & Paracetamol with codeine phosphate tablets 500mg + 15mg                                      & 100.000\\\\\n",
-       "\t 1000614 & 1 & 03/02/2009 & e312.   &  &          323510009 & Amoxicillin capsules 500mg                                                                   & 21.000 \\\\\n",
-       "\t 1000614 & 1 & 02/10/2009 & c61t.00 &  &          320531008 & BECLOMETASONE inh 250micrograms/actuation                                                    & 2.000  \\\\\n",
-       "\t 1000614 & 1 & 02/10/2009 & c61t.00 &  &          320531008 & Beclometasone 250micrograms/dose inhaler                                                     & 2.000  \\\\\n",
-       "\t 1000614 & 1 & 02/10/2009 & da91.00 &  &          321987003 & CITALOPRAM tabs 20mg                                                                         & 56.000 \\\\\n",
-       "\t 1000614 & 1 & 26/11/2009 & c61t.00 &  &          320531008 & BECLOMETASONE inh 250micrograms/actuation                                                    & 2.000  \\\\\n",
-       "\t 1000614 & 1 & 26/11/2009 & da91.00 &  &          321987003 & CITALOPRAM tabs 20mg                                                                         & 56.000 \\\\\n",
-       "\t 1000614 & 1 & 02/12/2009 & c13T.00 &  &  34015001000027103 & Ventolin evohaler 100 100microgram/inhalation Pressurised inhalation (Glaxo Wellcome UK Ltd) & 2.000  \\\\\n",
-       "\t 1000614 & 1 & 10/02/2010 & da91.00 &  &          321987003 & CITALOPRAM tabs 20mg                                                                         & 56.000 \\\\\n",
-       "\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
-       "\t 6024123 & 1 & 16/11/2016 & d73u.00 &  &       321785001 & Clomipramine 10mg capsules                & 56.000 \\\\\n",
-       "\t 6024123 & 1 & 16/11/2016 & d73u.00 &  &       321785001 & Clomipramine 10mg capsules                & 56.000 \\\\\n",
-       "\t 6024123 & 1 & 16/11/2016 & diaq.00 &  &       322344006 & Co-codamol 8mg/500mg capsules             & 132.000\\\\\n",
-       "\t 6024123 & 1 & 16/11/2016 & diaq.00 &  &       322344006 & Co-codamol 8mg/500mg capsules             & 132.000\\\\\n",
-       "\t 6024123 & 1 & 16/11/2016 & ja18.00 &  & 439911000001107 & Algesal cream (Thornton \\& Ross Ltd)       & 50.000 \\\\\n",
-       "\t 6024123 & 1 & 16/11/2016 & ja18.00 &  & 439911000001107 & Algesal cream (Thornton \\& Ross Ltd)       & 50.000 \\\\\n",
-       "\t 6024123 & 1 & 14/12/2016 & diaq.00 &  &       322344006 & Co-codamol 8mg/500mg capsules             & 132.000\\\\\n",
-       "\t 6024123 & 1 & 10/01/2017 & a6b1.00 &  &       317291008 & Omeprazole 20mg gastro-resistant capsules & 56.000 \\\\\n",
-       "\t 6024123 & 1 & 10/01/2017 & d73u.00 &  &       321785001 & Clomipramine 10mg capsules                & 56.000 \\\\\n",
-       "\t 6024123 & 1 & 10/01/2017 & diaq.00 &  &       322344006 & Co-codamol 8mg/500mg capsules             & 132.000\\\\\n",
-       "\t 6024123 & 1 & 10/01/2017 & ja18.00 &  & 439911000001107 & Algesal cream (Thornton \\& Ross Ltd)       & 50.000 \\\\\n",
-       "\t 6024123 & 1 & 07/02/2017 & diaq.00 &  &       322344006 & Co-codamol 8mg/500mg capsules             & 132.000\\\\\n",
-       "\t 6024123 & 1 & 07/02/2017 & diaq.00 &  &       322344006 & Co-codamol 8mg/500mg capsules             & 132.000\\\\\n",
-       "\t 6024123 & 1 & 03/03/2017 & diaq.00 &  &       322344006 & Co-codamol 8mg/500mg capsules             & 132.000\\\\\n",
-       "\t 6024123 & 1 & 03/03/2017 & ja18.00 &  & 439911000001107 & Algesal cream (Thornton \\& Ross Ltd)       & 50.000 \\\\\n",
-       "\t 6024123 & 1 & 28/03/2017 & a6b1.00 &  &       317291008 & Omeprazole 20mg gastro-resistant capsules & 56.000 \\\\\n",
-       "\t 6024123 & 1 & 28/03/2017 & d73u.00 &  &       321785001 & Clomipramine 10mg capsules                & 56.000 \\\\\n",
-       "\t 6024123 & 1 & 28/03/2017 & diaq.00 &  &       322344006 & Co-codamol 8mg/500mg capsules             & 132.000\\\\\n",
-       "\t 6024123 & 1 & 28/03/2017 & ja18.00 &  & 439911000001107 & Algesal cream (Thornton \\& Ross Ltd)       & 50.000 \\\\\n",
-       "\t 6024123 & 1 & 28/03/2017 & ja18.00 &  & 439911000001107 & Algesal cream (Thornton \\& Ross Ltd)       & 50.000 \\\\\n",
-       "\t 6024123 & 1 & 02/05/2017 & diaq.00 &  &       322344006 & Co-codamol 8mg/500mg capsules             & 132.000\\\\\n",
-       "\t 6024123 & 1 & 02/05/2017 & diaq.00 &  &       322344006 & Co-codamol 8mg/500mg capsules             & 132.000\\\\\n",
-       "\t 6024123 & 1 & 16/05/2017 & a6b1.00 &  &       317291008 & Omeprazole 20mg gastro-resistant capsules & 56.000 \\\\\n",
-       "\t 6024123 & 1 & 16/05/2017 & a6b1.00 &  &       317291008 & Omeprazole 20mg gastro-resistant capsules & 56.000 \\\\\n",
-       "\t 6024123 & 1 & 16/05/2017 & d73u.00 &  &       321785001 & Clomipramine 10mg capsules                & 56.000 \\\\\n",
-       "\t 6024123 & 1 & 16/05/2017 & d73u.00 &  &       321785001 & Clomipramine 10mg capsules                & 56.000 \\\\\n",
-       "\t 6024123 & 1 & 16/05/2017 & diaq.00 &  &       322344006 & Co-codamol 8mg/500mg capsules             & 132.000\\\\\n",
-       "\t 6024123 & 1 & 16/05/2017 & diaq.00 &  &       322344006 & Co-codamol 8mg/500mg capsules             & 132.000\\\\\n",
-       "\t 6024123 & 1 & 16/05/2017 & ja18.00 &  & 439911000001107 & Algesal cream (Thornton \\& Ross Ltd)       & 50.000 \\\\\n",
-       "\t 6024123 & 1 & 16/05/2017 & ja18.00 &  & 439911000001107 & Algesal cream (Thornton \\& Ross Ltd)       & 50.000 \\\\\n",
-       "\\end{tabular}\n"
-      ],
-      "text/markdown": [
-       "\n",
-       "A data.table: 6350891 × 8\n",
-       "\n",
-       "| eid &lt;int&gt; | data_provider &lt;int&gt; | issue_date &lt;chr&gt; | read_2 &lt;chr&gt; | bnf_code &lt;chr&gt; | dmd_code &lt;int64&gt; | drug_name &lt;chr&gt; | quantity &lt;chr&gt; |\n",
-       "|---|---|---|---|---|---|---|---|\n",
-       "| 1000614 | 1 | 22/04/2004 | djiG.00 | <!----> |          322637009 | Tramadol modified release capsule 100mg                                                      | 30.000  |\n",
-       "| 1000614 | 1 | 23/09/2004 | e311.   | <!----> |          323509004 | Amoxicillin capsules 250mg                                                                   | 21.000  |\n",
-       "| 1000614 | 1 | 28/10/2005 | c13J.00 | <!----> |          320139002 | Salbutamol cfc free inhaler 100micrograms/inhalation                                         | 2.000   |\n",
-       "| 1000614 | 1 | 28/10/2005 | e311.   | <!----> |          323509004 | Amoxicillin capsules 250mg                                                                   | 15.000  |\n",
-       "| 1000614 | 1 | 28/10/2005 | k32w.00 | <!----> |          330295009 | Chloramphenicol ophthalmic ointment 1%                                                       | 4.000   |\n",
-       "| 1000614 | 1 | 11/11/2005 | c61t.00 | <!----> |          320531008 | Beclometasone 250micrograms/dose inhaler                                                     | 2.000   |\n",
-       "| 1000614 | 1 | 28/11/2005 | fe6j.00 | <!----> |          325444003 | Prednisolone sodium phosphate soluble tablet 5mg                                             | 30.000  |\n",
-       "| 1000614 | 1 | 08/12/2005 | ecc2.00 | <!----> |          324431001 | Trimethoprim tablets 200mg                                                                   | 14.000  |\n",
-       "| 1000614 | 1 | 27/10/2006 | e311.   | <!----> |          323509004 | Amoxicillin capsules 250mg                                                                   | 21.000  |\n",
-       "| 1000614 | 1 | 29/12/2006 | e311.   | <!----> |          323509004 | Amoxicillin capsules 250mg                                                                   | 21.000  |\n",
-       "| 1000614 | 1 | 03/01/2007 | n45n.00 | <!----> |   7374311000001101 | Revaxis adsorbed vaccine -                                                                   | 1.000   |\n",
-       "| 1000614 | 1 | 03/01/2007 | n45n.00 | <!----> |   7374311000001101 | Revaxis adsorbed vaccine -                                                                   | 1.000   |\n",
-       "| 1000614 | 1 | 21/05/2007 | e311.   | <!----> |          323509004 | Amoxicillin capsules 250mg                                                                   | 21.000  |\n",
-       "| 1000614 | 1 | 18/07/2007 | c13T.00 | <!----> |  34015001000027103 | Ventolin evohaler 100 100microgram/inhalation Pressurised inhalation (Glaxo Wellcome UK Ltd) | 2.000   |\n",
-       "| 1000614 | 1 | 30/07/2007 | c8p3.00 | <!----> |          320828002 | Fexofenadine tablets 180mg                                                                   | 30.000  |\n",
-       "| 1000614 | 1 | 30/07/2007 | m313.00 | <!----> |  14608811000001105 | Calamine lotion -                                                                            | 200.000 |\n",
-       "| 1000614 | 1 | 03/09/2008 | dher.   | <!----> |    784511000001103 | Prochlorperazine tablets 5mg                                                                 | 84.000  |\n",
-       "| 1000614 | 1 | 12/11/2008 | e311.   | <!----> |          323509004 | Amoxicillin capsules 250mg                                                                   | 21.000  |\n",
-       "| 1000614 | 1 | 15/12/2008 | da91.00 | <!----> |          321987003 | Citalopram tablets 20mg                                                                      | 28.000  |\n",
-       "| 1000614 | 1 | 12/01/2009 | da91.00 | <!----> |          321987003 | Citalopram 20mg tablets                                                                      | 56.000  |\n",
-       "| 1000614 | 1 | 12/01/2009 | da91.00 | <!----> |          321987003 | Citalopram tablets 20mg                                                                      | 56.000  |\n",
-       "| 1000614 | 1 | 12/01/2009 | diaU.00 | <!----> | 136665001000027109 | Paracetamol with codeine phosphate tablets 500mg + 15mg                                      | 100.000 |\n",
-       "| 1000614 | 1 | 03/02/2009 | e312.   | <!----> |          323510009 | Amoxicillin capsules 500mg                                                                   | 21.000  |\n",
-       "| 1000614 | 1 | 02/10/2009 | c61t.00 | <!----> |          320531008 | BECLOMETASONE inh 250micrograms/actuation                                                    | 2.000   |\n",
-       "| 1000614 | 1 | 02/10/2009 | c61t.00 | <!----> |          320531008 | Beclometasone 250micrograms/dose inhaler                                                     | 2.000   |\n",
-       "| 1000614 | 1 | 02/10/2009 | da91.00 | <!----> |          321987003 | CITALOPRAM tabs 20mg                                                                         | 56.000  |\n",
-       "| 1000614 | 1 | 26/11/2009 | c61t.00 | <!----> |          320531008 | BECLOMETASONE inh 250micrograms/actuation                                                    | 2.000   |\n",
-       "| 1000614 | 1 | 26/11/2009 | da91.00 | <!----> |          321987003 | CITALOPRAM tabs 20mg                                                                         | 56.000  |\n",
-       "| 1000614 | 1 | 02/12/2009 | c13T.00 | <!----> |  34015001000027103 | Ventolin evohaler 100 100microgram/inhalation Pressurised inhalation (Glaxo Wellcome UK Ltd) | 2.000   |\n",
-       "| 1000614 | 1 | 10/02/2010 | da91.00 | <!----> |          321987003 | CITALOPRAM tabs 20mg                                                                         | 56.000  |\n",
-       "| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |\n",
-       "| 6024123 | 1 | 16/11/2016 | d73u.00 | <!----> |       321785001 | Clomipramine 10mg capsules                | 56.000  |\n",
-       "| 6024123 | 1 | 16/11/2016 | d73u.00 | <!----> |       321785001 | Clomipramine 10mg capsules                | 56.000  |\n",
-       "| 6024123 | 1 | 16/11/2016 | diaq.00 | <!----> |       322344006 | Co-codamol 8mg/500mg capsules             | 132.000 |\n",
-       "| 6024123 | 1 | 16/11/2016 | diaq.00 | <!----> |       322344006 | Co-codamol 8mg/500mg capsules             | 132.000 |\n",
-       "| 6024123 | 1 | 16/11/2016 | ja18.00 | <!----> | 439911000001107 | Algesal cream (Thornton &amp; Ross Ltd)       | 50.000  |\n",
-       "| 6024123 | 1 | 16/11/2016 | ja18.00 | <!----> | 439911000001107 | Algesal cream (Thornton &amp; Ross Ltd)       | 50.000  |\n",
-       "| 6024123 | 1 | 14/12/2016 | diaq.00 | <!----> |       322344006 | Co-codamol 8mg/500mg capsules             | 132.000 |\n",
-       "| 6024123 | 1 | 10/01/2017 | a6b1.00 | <!----> |       317291008 | Omeprazole 20mg gastro-resistant capsules | 56.000  |\n",
-       "| 6024123 | 1 | 10/01/2017 | d73u.00 | <!----> |       321785001 | Clomipramine 10mg capsules                | 56.000  |\n",
-       "| 6024123 | 1 | 10/01/2017 | diaq.00 | <!----> |       322344006 | Co-codamol 8mg/500mg capsules             | 132.000 |\n",
-       "| 6024123 | 1 | 10/01/2017 | ja18.00 | <!----> | 439911000001107 | Algesal cream (Thornton &amp; Ross Ltd)       | 50.000  |\n",
-       "| 6024123 | 1 | 07/02/2017 | diaq.00 | <!----> |       322344006 | Co-codamol 8mg/500mg capsules             | 132.000 |\n",
-       "| 6024123 | 1 | 07/02/2017 | diaq.00 | <!----> |       322344006 | Co-codamol 8mg/500mg capsules             | 132.000 |\n",
-       "| 6024123 | 1 | 03/03/2017 | diaq.00 | <!----> |       322344006 | Co-codamol 8mg/500mg capsules             | 132.000 |\n",
-       "| 6024123 | 1 | 03/03/2017 | ja18.00 | <!----> | 439911000001107 | Algesal cream (Thornton &amp; Ross Ltd)       | 50.000  |\n",
-       "| 6024123 | 1 | 28/03/2017 | a6b1.00 | <!----> |       317291008 | Omeprazole 20mg gastro-resistant capsules | 56.000  |\n",
-       "| 6024123 | 1 | 28/03/2017 | d73u.00 | <!----> |       321785001 | Clomipramine 10mg capsules                | 56.000  |\n",
-       "| 6024123 | 1 | 28/03/2017 | diaq.00 | <!----> |       322344006 | Co-codamol 8mg/500mg capsules             | 132.000 |\n",
-       "| 6024123 | 1 | 28/03/2017 | ja18.00 | <!----> | 439911000001107 | Algesal cream (Thornton &amp; Ross Ltd)       | 50.000  |\n",
-       "| 6024123 | 1 | 28/03/2017 | ja18.00 | <!----> | 439911000001107 | Algesal cream (Thornton &amp; Ross Ltd)       | 50.000  |\n",
-       "| 6024123 | 1 | 02/05/2017 | diaq.00 | <!----> |       322344006 | Co-codamol 8mg/500mg capsules             | 132.000 |\n",
-       "| 6024123 | 1 | 02/05/2017 | diaq.00 | <!----> |       322344006 | Co-codamol 8mg/500mg capsules             | 132.000 |\n",
-       "| 6024123 | 1 | 16/05/2017 | a6b1.00 | <!----> |       317291008 | Omeprazole 20mg gastro-resistant capsules | 56.000  |\n",
-       "| 6024123 | 1 | 16/05/2017 | a6b1.00 | <!----> |       317291008 | Omeprazole 20mg gastro-resistant capsules | 56.000  |\n",
-       "| 6024123 | 1 | 16/05/2017 | d73u.00 | <!----> |       321785001 | Clomipramine 10mg capsules                | 56.000  |\n",
-       "| 6024123 | 1 | 16/05/2017 | d73u.00 | <!----> |       321785001 | Clomipramine 10mg capsules                | 56.000  |\n",
-       "| 6024123 | 1 | 16/05/2017 | diaq.00 | <!----> |       322344006 | Co-codamol 8mg/500mg capsules             | 132.000 |\n",
-       "| 6024123 | 1 | 16/05/2017 | diaq.00 | <!----> |       322344006 | Co-codamol 8mg/500mg capsules             | 132.000 |\n",
-       "| 6024123 | 1 | 16/05/2017 | ja18.00 | <!----> | 439911000001107 | Algesal cream (Thornton &amp; Ross Ltd)       | 50.000  |\n",
-       "| 6024123 | 1 | 16/05/2017 | ja18.00 | <!----> | 439911000001107 | Algesal cream (Thornton &amp; Ross Ltd)       | 50.000  |\n",
-       "\n"
-      ],
-      "text/plain": [
-       "        eid     data_provider issue_date read_2  bnf_code dmd_code          \n",
-       "1       1000614 1             22/04/2004 djiG.00                   322637009\n",
-       "2       1000614 1             23/09/2004 e311.                     323509004\n",
-       "3       1000614 1             28/10/2005 c13J.00                   320139002\n",
-       "4       1000614 1             28/10/2005 e311.                     323509004\n",
-       "5       1000614 1             28/10/2005 k32w.00                   330295009\n",
-       "6       1000614 1             11/11/2005 c61t.00                   320531008\n",
-       "7       1000614 1             28/11/2005 fe6j.00                   325444003\n",
-       "8       1000614 1             08/12/2005 ecc2.00                   324431001\n",
-       "9       1000614 1             27/10/2006 e311.                     323509004\n",
-       "10      1000614 1             29/12/2006 e311.                     323509004\n",
-       "11      1000614 1             03/01/2007 n45n.00            7374311000001101\n",
-       "12      1000614 1             03/01/2007 n45n.00            7374311000001101\n",
-       "13      1000614 1             21/05/2007 e311.                     323509004\n",
-       "14      1000614 1             18/07/2007 c13T.00           34015001000027103\n",
-       "15      1000614 1             30/07/2007 c8p3.00                   320828002\n",
-       "16      1000614 1             30/07/2007 m313.00           14608811000001105\n",
-       "17      1000614 1             03/09/2008 dher.               784511000001103\n",
-       "18      1000614 1             12/11/2008 e311.                     323509004\n",
-       "19      1000614 1             15/12/2008 da91.00                   321987003\n",
-       "20      1000614 1             12/01/2009 da91.00                   321987003\n",
-       "21      1000614 1             12/01/2009 da91.00                   321987003\n",
-       "22      1000614 1             12/01/2009 diaU.00          136665001000027109\n",
-       "23      1000614 1             03/02/2009 e312.                     323510009\n",
-       "24      1000614 1             02/10/2009 c61t.00                   320531008\n",
-       "25      1000614 1             02/10/2009 c61t.00                   320531008\n",
-       "26      1000614 1             02/10/2009 da91.00                   321987003\n",
-       "27      1000614 1             26/11/2009 c61t.00                   320531008\n",
-       "28      1000614 1             26/11/2009 da91.00                   321987003\n",
-       "29      1000614 1             02/12/2009 c13T.00           34015001000027103\n",
-       "30      1000614 1             10/02/2010 da91.00                   321987003\n",
-       "<U+22EE>  <U+22EE> <U+22EE>      <U+22EE>   <U+22EE> <U+22EE> <U+22EE>          \n",
-       "6350862 6024123 1             16/11/2016 d73u.00                321785001   \n",
-       "6350863 6024123 1             16/11/2016 d73u.00                321785001   \n",
-       "6350864 6024123 1             16/11/2016 diaq.00                322344006   \n",
-       "6350865 6024123 1             16/11/2016 diaq.00                322344006   \n",
-       "6350866 6024123 1             16/11/2016 ja18.00          439911000001107   \n",
-       "6350867 6024123 1             16/11/2016 ja18.00          439911000001107   \n",
-       "6350868 6024123 1             14/12/2016 diaq.00                322344006   \n",
-       "6350869 6024123 1             10/01/2017 a6b1.00                317291008   \n",
-       "6350870 6024123 1             10/01/2017 d73u.00                321785001   \n",
-       "6350871 6024123 1             10/01/2017 diaq.00                322344006   \n",
-       "6350872 6024123 1             10/01/2017 ja18.00          439911000001107   \n",
-       "6350873 6024123 1             07/02/2017 diaq.00                322344006   \n",
-       "6350874 6024123 1             07/02/2017 diaq.00                322344006   \n",
-       "6350875 6024123 1             03/03/2017 diaq.00                322344006   \n",
-       "6350876 6024123 1             03/03/2017 ja18.00          439911000001107   \n",
-       "6350877 6024123 1             28/03/2017 a6b1.00                317291008   \n",
-       "6350878 6024123 1             28/03/2017 d73u.00                321785001   \n",
-       "6350879 6024123 1             28/03/2017 diaq.00                322344006   \n",
-       "6350880 6024123 1             28/03/2017 ja18.00          439911000001107   \n",
-       "6350881 6024123 1             28/03/2017 ja18.00          439911000001107   \n",
-       "6350882 6024123 1             02/05/2017 diaq.00                322344006   \n",
-       "6350883 6024123 1             02/05/2017 diaq.00                322344006   \n",
-       "6350884 6024123 1             16/05/2017 a6b1.00                317291008   \n",
-       "6350885 6024123 1             16/05/2017 a6b1.00                317291008   \n",
-       "6350886 6024123 1             16/05/2017 d73u.00                321785001   \n",
-       "6350887 6024123 1             16/05/2017 d73u.00                321785001   \n",
-       "6350888 6024123 1             16/05/2017 diaq.00                322344006   \n",
-       "6350889 6024123 1             16/05/2017 diaq.00                322344006   \n",
-       "6350890 6024123 1             16/05/2017 ja18.00          439911000001107   \n",
-       "6350891 6024123 1             16/05/2017 ja18.00          439911000001107   \n",
-       "        drug_name                                                                                   \n",
-       "1       Tramadol modified release capsule 100mg                                                     \n",
-       "2       Amoxicillin capsules 250mg                                                                  \n",
-       "3       Salbutamol cfc free inhaler 100micrograms/inhalation                                        \n",
-       "4       Amoxicillin capsules 250mg                                                                  \n",
-       "5       Chloramphenicol ophthalmic ointment 1%                                                      \n",
-       "6       Beclometasone 250micrograms/dose inhaler                                                    \n",
-       "7       Prednisolone sodium phosphate soluble tablet 5mg                                            \n",
-       "8       Trimethoprim tablets 200mg                                                                  \n",
-       "9       Amoxicillin capsules 250mg                                                                  \n",
-       "10      Amoxicillin capsules 250mg                                                                  \n",
-       "11      Revaxis adsorbed vaccine -                                                                  \n",
-       "12      Revaxis adsorbed vaccine -                                                                  \n",
-       "13      Amoxicillin capsules 250mg                                                                  \n",
-       "14      Ventolin evohaler 100 100microgram/inhalation Pressurised inhalation (Glaxo Wellcome UK Ltd)\n",
-       "15      Fexofenadine tablets 180mg                                                                  \n",
-       "16      Calamine lotion -                                                                           \n",
-       "17      Prochlorperazine tablets 5mg                                                                \n",
-       "18      Amoxicillin capsules 250mg                                                                  \n",
-       "19      Citalopram tablets 20mg                                                                     \n",
-       "20      Citalopram 20mg tablets                                                                     \n",
-       "21      Citalopram tablets 20mg                                                                     \n",
-       "22      Paracetamol with codeine phosphate tablets 500mg + 15mg                                     \n",
-       "23      Amoxicillin capsules 500mg                                                                  \n",
-       "24      BECLOMETASONE inh 250micrograms/actuation                                                   \n",
-       "25      Beclometasone 250micrograms/dose inhaler                                                    \n",
-       "26      CITALOPRAM tabs 20mg                                                                        \n",
-       "27      BECLOMETASONE inh 250micrograms/actuation                                                   \n",
-       "28      CITALOPRAM tabs 20mg                                                                        \n",
-       "29      Ventolin evohaler 100 100microgram/inhalation Pressurised inhalation (Glaxo Wellcome UK Ltd)\n",
-       "30      CITALOPRAM tabs 20mg                                                                        \n",
-       "<U+22EE>  <U+22EE>                                                                                    \n",
-       "6350862 Clomipramine 10mg capsules                                                                  \n",
-       "6350863 Clomipramine 10mg capsules                                                                  \n",
-       "6350864 Co-codamol 8mg/500mg capsules                                                               \n",
-       "6350865 Co-codamol 8mg/500mg capsules                                                               \n",
-       "6350866 Algesal cream (Thornton & Ross Ltd)                                                         \n",
-       "6350867 Algesal cream (Thornton & Ross Ltd)                                                         \n",
-       "6350868 Co-codamol 8mg/500mg capsules                                                               \n",
-       "6350869 Omeprazole 20mg gastro-resistant capsules                                                   \n",
-       "6350870 Clomipramine 10mg capsules                                                                  \n",
-       "6350871 Co-codamol 8mg/500mg capsules                                                               \n",
-       "6350872 Algesal cream (Thornton & Ross Ltd)                                                         \n",
-       "6350873 Co-codamol 8mg/500mg capsules                                                               \n",
-       "6350874 Co-codamol 8mg/500mg capsules                                                               \n",
-       "6350875 Co-codamol 8mg/500mg capsules                                                               \n",
-       "6350876 Algesal cream (Thornton & Ross Ltd)                                                         \n",
-       "6350877 Omeprazole 20mg gastro-resistant capsules                                                   \n",
-       "6350878 Clomipramine 10mg capsules                                                                  \n",
-       "6350879 Co-codamol 8mg/500mg capsules                                                               \n",
-       "6350880 Algesal cream (Thornton & Ross Ltd)                                                         \n",
-       "6350881 Algesal cream (Thornton & Ross Ltd)                                                         \n",
-       "6350882 Co-codamol 8mg/500mg capsules                                                               \n",
-       "6350883 Co-codamol 8mg/500mg capsules                                                               \n",
-       "6350884 Omeprazole 20mg gastro-resistant capsules                                                   \n",
-       "6350885 Omeprazole 20mg gastro-resistant capsules                                                   \n",
-       "6350886 Clomipramine 10mg capsules                                                                  \n",
-       "6350887 Clomipramine 10mg capsules                                                                  \n",
-       "6350888 Co-codamol 8mg/500mg capsules                                                               \n",
-       "6350889 Co-codamol 8mg/500mg capsules                                                               \n",
-       "6350890 Algesal cream (Thornton & Ross Ltd)                                                         \n",
-       "6350891 Algesal cream (Thornton & Ross Ltd)                                                         \n",
-       "        quantity\n",
-       "1       30.000  \n",
-       "2       21.000  \n",
-       "3       2.000   \n",
-       "4       15.000  \n",
-       "5       4.000   \n",
-       "6       2.000   \n",
-       "7       30.000  \n",
-       "8       14.000  \n",
-       "9       21.000  \n",
-       "10      21.000  \n",
-       "11      1.000   \n",
-       "12      1.000   \n",
-       "13      21.000  \n",
-       "14      2.000   \n",
-       "15      30.000  \n",
-       "16      200.000 \n",
-       "17      84.000  \n",
-       "18      21.000  \n",
-       "19      28.000  \n",
-       "20      56.000  \n",
-       "21      56.000  \n",
-       "22      100.000 \n",
-       "23      21.000  \n",
-       "24      2.000   \n",
-       "25      2.000   \n",
-       "26      56.000  \n",
-       "27      2.000   \n",
-       "28      56.000  \n",
-       "29      2.000   \n",
-       "30      56.000  \n",
-       "<U+22EE>  <U+22EE>\n",
-       "6350862 56.000  \n",
-       "6350863 56.000  \n",
-       "6350864 132.000 \n",
-       "6350865 132.000 \n",
-       "6350866 50.000  \n",
-       "6350867 50.000  \n",
-       "6350868 132.000 \n",
-       "6350869 56.000  \n",
-       "6350870 56.000  \n",
-       "6350871 132.000 \n",
-       "6350872 50.000  \n",
-       "6350873 132.000 \n",
-       "6350874 132.000 \n",
-       "6350875 132.000 \n",
-       "6350876 50.000  \n",
-       "6350877 56.000  \n",
-       "6350878 56.000  \n",
-       "6350879 132.000 \n",
-       "6350880 50.000  \n",
-       "6350881 50.000  \n",
-       "6350882 132.000 \n",
-       "6350883 132.000 \n",
-       "6350884 56.000  \n",
-       "6350885 56.000  \n",
-       "6350886 56.000  \n",
-       "6350887 56.000  \n",
-       "6350888 132.000 \n",
-       "6350889 132.000 \n",
-       "6350890 50.000  \n",
-       "6350891 50.000  "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "gp_scripts %>% filter(!is.na(dmd_code))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-12-23T10:02:40.116702Z",
-     "start_time": "2020-12-23T10:02:38.610Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "bnf_map = fread(\"data/analysis/ag-reils/steinfej/code/umbrella/pre/ukbb/mapping/bnf_codings.csv\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-12-23T10:31:07.600869Z",
-     "start_time": "2020-12-23T10:31:06.168Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "bnf_map_clean = bnf_map %>% mutate(code=str_sub(BNF_Presentation_Code, 1, 6)) %>% \n",
-    "    distinct() %>% \n",
-    "    mutate(label=case_when(str_detect(BNF_Paragraph, \"DUMMY\")~BNF_Section, TRUE ~ BNF_Paragraph)) %>%\n",
-    "    select(code, label, BNF_Subparagraph, BNF_Paragraph, BNF_Section, BNF_Chapter) %>% distinct()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-12-23T11:07:06.205751Z",
-     "start_time": "2020-12-23T11:06:09.173Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "test = gp_scripts %>% mutate(code=str_replace_all(bnf_code, \".\", \"\"))#%>% mutate(code=str_sub(str_remove_all(bnf_code, \".\"), 1, 6))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-12-23T11:11:36.597661Z",
-     "start_time": "2020-12-23T11:11:36.527Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Classes 'data.table' and 'data.frame':\t47242673 obs. of  9 variables:\n",
-      " $ eid          : int  1000018 1000018 1000018 1000018 1000018 1000018 1000018 1000018 1000018 1000018 ...\n",
-      " $ data_provider: int  3 3 3 3 3 3 3 3 3 3 ...\n",
-      " $ issue_date   : chr  \"30/11/1996\" \"12/09/1997\" \"10/10/1997\" \"10/10/1997\" ...\n",
-      " $ read_2       : chr  \"\" \"\" \"\" \"\" ...\n",
-      " $ bnf_code     : chr  \"13.06.02.01.00\" \"02.03.02.02.00\" \"02.03.02.02.00\" \"07.03.02.01.00\" ...\n",
-      " $ dmd_code     :integer64 NA NA NA NA NA NA NA NA ... \n",
-      " $ drug_name    : chr  \"Doxycycline 100mg capsules\" \"Propranolol 40mg tablets\" \"Propranolol 40mg tablets\" \"Noriday 350microgram tablets (Pfizer Ltd)\" ...\n",
-      " $ quantity     : chr  \"8 capsule(s) - 100 mg\" \"56 tablet(s) - 40 mg\" \"168 tablet(s) - 40 mg\" \"168 tablet(s) - 350 micrograms\" ...\n",
-      " $ code         : chr  \"\" \"\" \"\" \"\" ...\n",
-      " - attr(*, \".internal.selfref\")=<externalptr> \n"
-     ]
-    }
-   ],
-   "source": [
-    "str(test)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-12-23T09:33:59.219590Z",
-     "start_time": "2020-12-23T09:33:57.816Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "library(visdat)\n",
-    "library(naniar)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-12-23T09:32:17.589534Z",
-     "start_time": "2020-12-23T09:32:16.261Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Classes 'data.table' and 'data.frame':\t47242673 obs. of  8 variables:\n",
-      " $ eid          : int  1000018 1000018 1000018 1000018 1000018 1000018 1000018 1000018 1000018 1000018 ...\n",
-      " $ data_provider: int  3 3 3 3 3 3 3 3 3 3 ...\n",
-      " $ issue_date   : chr  \"30/11/1996\" \"12/09/1997\" \"10/10/1997\" \"10/10/1997\" ...\n",
-      " $ read_2       : chr  \"\" \"\" \"\" \"\" ...\n",
-      " $ bnf_code     : chr  \"13.06.02.01.00\" \"02.03.02.02.00\" \"02.03.02.02.00\" \"07.03.02.01.00\" ...\n",
-      " $ dmd_code     :integer64 NA NA NA NA NA NA NA NA ... \n",
-      " $ drug_name    : chr  \"Doxycycline 100mg capsules\" \"Propranolol 40mg tablets\" \"Propranolol 40mg tablets\" \"Noriday 350microgram tablets (Pfizer Ltd)\" ...\n",
-      " $ quantity     : chr  \"8 capsule(s) - 100 mg\" \"56 tablet(s) - 40 mg\" \"168 tablet(s) - 40 mg\" \"168 tablet(s) - 350 micrograms\" ...\n",
-      " - attr(*, \".internal.selfref\")=<externalptr> \n"
-     ]
-    }
-   ],
-   "source": [
-    "library(Hmisc)\n",
-    "str(gp_scripts)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-12-23T09:34:08.262982Z",
-     "start_time": "2020-12-23T09:34:01.976Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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1m41oPJRWYsX/Cg+Bc2rF8wChFS/jzD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",
-      "text/plain": [
-       "plot without title"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 420,
-       "width": 420
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "library(visdat)\n",
-    "df = gp_scripts %>% sample_n(1000)\n",
-    "vis_dat(df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-12-23T09:34:22.373844Z",
-     "start_time": "2020-12-23T09:34:20.650Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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oISIUkICbCAkKDEFklCSFAiJAkhQYmQJIQEJUKSEBKUCElCSIAFhARYQEhQYtdO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\n",
-      "text/plain": [
-       "plot without title"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 420,
-       "width": 420
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "library(naniar)\n",
-    "df = gp_scripts %>% sample_n(1000)\n",
-    "vis_miss(df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data/analysis/ag-reils/steinfej/code/umbrella/pre/ukbb/mapping/bnf_codings.csv"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "nrow(gp_registrations)\n",
-    "head(gp_registrations)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "nrow(gp_clinical)\n",
-    "head(gp_clinical)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "str(gp_clinical)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "hide_input": false,
-  "kernelspec": {
-   "display_name": "R [conda env:python]",
-   "language": "R",
-   "name": "conda-env-python-r"
-  },
-  "language_info": {
-   "codemirror_mode": "r",
-   "file_extension": ".r",
-   "mimetype": "text/x-r-source",
-   "name": "R",
-   "pygments_lexer": "r",
-   "version": "4.0.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/neuralcvd/preprocessing/ukbb_tabular/1_preprocessing_dataportal_spacy.ipynb b/neuralcvd/preprocessing/ukbb_tabular/1_preprocessing_dataportal_spacy.ipynb
deleted file mode 100644
index 1063a03..0000000
--- a/neuralcvd/preprocessing/ukbb_tabular/1_preprocessing_dataportal_spacy.ipynb
+++ /dev/null
@@ -1,997 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 1. Data Portal Preprocessing"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from scispacy.linking import EntityLinker\n",
-    "import spacy\n",
-    "import scispacy"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tqdm.auto import tqdm"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "path = \"/data/analysis/ag-reils/ag-reils-shared/cardioRS/data/1_decoded\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "nlp = spacy.load(\"en_core_sci_lg\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "linker_umls = EntityLinker(name=\"umls\", threshold=0.85)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#linker_rxnorm = EntityLinker(resolve_abbreviations=True, name=\"rxnorm\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "nlp.add_pipe(linker_umls)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import json\n",
-    "with open(f\"{path}/drug_names.json\") as f:\n",
-    "    drug_names = json.load(f)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tqdm.auto import tqdm"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 178,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "71eef9cdf3894f378b58a7ecd6518dbc",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(HTML(value=''), FloatProgress(value=0.0, max=80681.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/scispacy/candidate_generation.py:284: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
-      "  extended_neighbors[empty_vectors_boolean_flags] = numpy.array(neighbors)[:-1]\n",
-      "/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/scispacy/candidate_generation.py:285: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
-      "  extended_distances[empty_vectors_boolean_flags] = numpy.array(distances)[:-1]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>drug_index</th>\n",
-       "      <th>drug_name</th>\n",
-       "      <th>CUI</th>\n",
-       "      <th>TUI</th>\n",
-       "      <th>name</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>999.0</td>\n",
-       "      <td>Simvastatin 40mg tablets</td>\n",
-       "      <td>C0074554</td>\n",
-       "      <td>[T109, T121]</td>\n",
-       "      <td>simvastatin</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>999.0</td>\n",
-       "      <td>Simvastatin 40mg tablets</td>\n",
-       "      <td>C0039225</td>\n",
-       "      <td>[T122]</td>\n",
-       "      <td>Tablet Dosage Form</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>999.0</td>\n",
-       "      <td>Simvastatin 40mg tablets</td>\n",
-       "      <td>C0993159</td>\n",
-       "      <td>[T122]</td>\n",
-       "      <td>Oral Tablet</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>999.0</td>\n",
-       "      <td>Bendroflumethiazide 2.5mg tablets</td>\n",
-       "      <td>C0992038</td>\n",
-       "      <td>[T200]</td>\n",
-       "      <td>Bendroflumethiazide 2.5 MG</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>999.0</td>\n",
-       "      <td>Bendroflumethiazide 2.5mg tablets</td>\n",
-       "      <td>C0004975</td>\n",
-       "      <td>[T109, T121]</td>\n",
-       "      <td>bendroflumethiazide</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>261724</th>\n",
-       "      <td>999.0</td>\n",
-       "      <td>zinc oxide with icthammol, salicylic acid and ...</td>\n",
-       "      <td>C0036079</td>\n",
-       "      <td>[T109, T121]</td>\n",
-       "      <td>salicylic acid</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>261725</th>\n",
-       "      <td>999.0</td>\n",
-       "      <td>zinc oxide with icthammol, salicylic acid and ...</td>\n",
-       "      <td>C0073983</td>\n",
-       "      <td>[T109, T121]</td>\n",
-       "      <td>salsalate</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>261726</th>\n",
-       "      <td>999.0</td>\n",
-       "      <td>zinc oxide with icthammol, salicylic acid and ...</td>\n",
-       "      <td>C0004057</td>\n",
-       "      <td>[T109, T121]</td>\n",
-       "      <td>aspirin</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>261727</th>\n",
-       "      <td>999.0</td>\n",
-       "      <td>zinc oxide with icthammol, salicylic acid and ...</td>\n",
-       "      <td>C3547570</td>\n",
-       "      <td>[T044]</td>\n",
-       "      <td>salicylic acid binding</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>261728</th>\n",
-       "      <td>999.0</td>\n",
-       "      <td>zyban 150mg tab</td>\n",
-       "      <td>C0751626</td>\n",
-       "      <td>[T109, T121]</td>\n",
-       "      <td>Zyban</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>261729 rows × 5 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        drug_index                                          drug_name  \\\n",
-       "0            999.0                           Simvastatin 40mg tablets   \n",
-       "1            999.0                           Simvastatin 40mg tablets   \n",
-       "2            999.0                           Simvastatin 40mg tablets   \n",
-       "3            999.0                  Bendroflumethiazide 2.5mg tablets   \n",
-       "4            999.0                  Bendroflumethiazide 2.5mg tablets   \n",
-       "...            ...                                                ...   \n",
-       "261724       999.0  zinc oxide with icthammol, salicylic acid and ...   \n",
-       "261725       999.0  zinc oxide with icthammol, salicylic acid and ...   \n",
-       "261726       999.0  zinc oxide with icthammol, salicylic acid and ...   \n",
-       "261727       999.0  zinc oxide with icthammol, salicylic acid and ...   \n",
-       "261728       999.0                                    zyban 150mg tab   \n",
-       "\n",
-       "             CUI           TUI                        name  \n",
-       "0       C0074554  [T109, T121]                 simvastatin  \n",
-       "1       C0039225        [T122]          Tablet Dosage Form  \n",
-       "2       C0993159        [T122]                 Oral Tablet  \n",
-       "3       C0992038        [T200]  Bendroflumethiazide 2.5 MG  \n",
-       "4       C0004975  [T109, T121]         bendroflumethiazide  \n",
-       "...          ...           ...                         ...  \n",
-       "261724  C0036079  [T109, T121]              salicylic acid  \n",
-       "261725  C0073983  [T109, T121]                   salsalate  \n",
-       "261726  C0004057  [T109, T121]                     aspirin  \n",
-       "261727  C3547570        [T044]      salicylic acid binding  \n",
-       "261728  C0751626  [T109, T121]                       Zyban  \n",
-       "\n",
-       "[261729 rows x 5 columns]"
-      ]
-     },
-     "execution_count": 178,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "def extract_umls_cuis(drug_name, nlp, linker):\n",
-    "    rows=[]\n",
-    "    doc=nlp(drug_name)\n",
-    "    entities = doc.ents\n",
-    "    if len(entities)>0: \n",
-    "        #entity = entities[0]._.kb_ents ### loop through all and do the filter for types later -> get all substances linked to name!\n",
-    "        for entity in entities:\n",
-    "            if len(entity)>0:\n",
-    "                for ent in entity._.kb_ents:\n",
-    "                    link = linker_umls.kb.cui_to_entity[ent[0]]\n",
-    "                    rows.append({\"drug_index\": index, \"drug_name\": drug_name, \"CUI\":link.concept_id, \"TUI\":link.types, \"name\": link.canonical_name})\n",
-    "    return rows\n",
-    "\n",
-    "from joblib import Parallel, delayed\n",
-    "rows =  Parallel(n_jobs=1)(delayed(extract_umls_cuis)(drug_name, nlp, linker_umls) for drug_name in tqdm(drug_names))\n",
-    "                    \n",
-    "drugs_umls = pd.DataFrame({\"drug_index\": [],\"drug_name\": [], \"CUI\":[], \"TUI\": [],\"name\": []}).append([item for sublist in rows for item in sublist], ignore_index=True)\n",
-    "drugs_umls"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 184,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "drugs_umls_cleaned = drugs_umls[drugs_umls.TUI.str.join(\" \").str.contains(\"T109\") | \n",
-    "           drugs_umls.TUI.str.join(\" \").str.contains(\"T121\") | \n",
-    "           drugs_umls.TUI.str.join(\" \").str.contains(\"T116\") |\n",
-    "           drugs_umls.TUI.str.join(\" \").str.contains(\"127\") ][[\"drug_name\", \"CUI\", \"TUI\", \"name\"]].reset_index(drop=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "drugs_umls_cleaned = pd.read_feather(f\"{path}/drug_names_umls_linked.feather\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\"\"\" UTS (UMLS Terminology Services) API client \"\"\"\n",
-    "import json\n",
-    "from pathlib import Path\n",
-    "import requests\n",
-    "from lxml.html import fromstring\n",
-    "\n",
-    "\n",
-    "class UtsClient:\n",
-    "    \"\"\"All the UTS REST API requests are handled through this client\"\"\"\n",
-    "    def __init__(self, apikey=None):\n",
-    "        if apikey is None:\n",
-    "            file_key = Path(__file__).resolve().parents[1] / 'uts-api-key.txt'\n",
-    "            self.apikey = open(file_key).read().rstrip()\n",
-    "            self.apikey = '47669881-3e07-4acb-b087-d9b767b02ce8'\n",
-    "        else:\n",
-    "            self.apikey = apikey\n",
-    "        self.service = \"http://umlsks.nlm.nih.gov\"\n",
-    "        self.headers = {\n",
-    "            \"Content-type\": \"application/x-www-form-urlencoded\",\n",
-    "            \"Accept\": \"text/plain\",\n",
-    "            \"User-Agent\": \"python\"\n",
-    "        }\n",
-    "        self.tgt = None\n",
-    "        self.base_uri = \"https://uts-ws.nlm.nih.gov\"\n",
-    "        self.version = \"current\"\n",
-    "\n",
-    "    def gettgt(self):\n",
-    "        \"\"\"Retrieve a ticket granting ticket\"\"\"\n",
-    "        auth_uri = \"https://utslogin.nlm.nih.gov\"\n",
-    "        params = {\"apikey\": self.apikey}\n",
-    "        auth_endpoint = \"/cas/v1/api-key\"\n",
-    "        r = requests.post(auth_uri + auth_endpoint, data=params,\n",
-    "                          headers=self.headers)\n",
-    "        response = fromstring(r.text)\n",
-    "        # extract the entire URL needed from the HTML form (action attribute)\n",
-    "        # returned - looks similar to\n",
-    "        # https://utslogin.nlm.nih.gov/cas/v1/tickets/TGT-36471-aYqNLN2rFIJPXKzxwdTNC5ZT7z3B3cTAKfSc5ndHQcUxeaDOLN-cas\n",
-    "        # we make a POST call to this URL in the getst method\n",
-    "        self.tgt = response.xpath('//form/@action')[0]\n",
-    "\n",
-    "    def getst(self):\n",
-    "        \"\"\"Request a single-use service ticket\"\"\"\n",
-    "        if self.tgt is None:\n",
-    "            self.gettgt()\n",
-    "        params = {\"service\": self.service}\n",
-    "        r = requests.post(self.tgt, data=params, headers=self.headers)\n",
-    "        return r.text\n",
-    "\n",
-    "    def query_get(self, endpoint, query):\n",
-    "        r = requests.get(self.base_uri+endpoint, params=query)\n",
-    "        if r.status_code == 404:\n",
-    "            return\n",
-    "        return json.loads(r.text)\n",
-    "                   \n",
-    "    def get_concept_rxnorm_atoms(self, cui, tok):\n",
-    "        endpoint = f\"/rest/content/{self.version}/CUI/{cui}/atoms\"\n",
-    "        query = {\"ticket\": self.getst(), \"sabs\": \"RXNORM\"}\n",
-    "        rst = self.query_get(endpoint, query)\n",
-    "        # If MSH term can't be found by CUI, use the defining word to search\n",
-    "        if rst is None:\n",
-    "            endpoint = f\"/rest/search/{self.version}\"\n",
-    "            query = {\"ticket\": self.getst(),\n",
-    "                     \"string\": tok, \"searchType\": \"exact\"}\n",
-    "            rst = self.query_get(endpoint, query)\n",
-    "            if rst is not None:\n",
-    "                search_max = 5\n",
-    "                for rec in rst['result']['results']:\n",
-    "                    # search MSH until found\n",
-    "                    endpoint = (f\"/rest/content/{self.version}\"\n",
-    "                                f\"/CUI/{rec['ui']}/atoms\")\n",
-    "                    query = {\"ticket\": self.getst(), \"sabs\": \"RXNORM\"}\n",
-    "                    rst = self.query_get(endpoint, query)\n",
-    "                    if rst is not None:\n",
-    "                        return rst\n",
-    "                    else:\n",
-    "                        search_max -= 1\n",
-    "                    if search_max == 0:\n",
-    "                        return None\n",
-    "        return rst"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "umls_client = UtsClient(apikey=\"47669881-3e07-4acb-b087-d9b767b02ce8\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "5826a0aad9b041d993e70ab753c1cf90",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(HTML(value=''), FloatProgress(value=0.0, max=67624.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>drug_name</th>\n",
-       "      <th>CUI</th>\n",
-       "      <th>TUI</th>\n",
-       "      <th>name</th>\n",
-       "      <th>rx_code</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Simvastatin 40mg tablets</td>\n",
-       "      <td>C0074554</td>\n",
-       "      <td>[T109, T121]</td>\n",
-       "      <td>simvastatin</td>\n",
-       "      <td>[36567]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Bendroflumethiazide 2.5mg tablets</td>\n",
-       "      <td>C0004975</td>\n",
-       "      <td>[T109, T121]</td>\n",
-       "      <td>bendroflumethiazide</td>\n",
-       "      <td>[1369]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Aspirin 75mg dispersible tablets</td>\n",
-       "      <td>C0004057</td>\n",
-       "      <td>[T109, T121]</td>\n",
-       "      <td>aspirin</td>\n",
-       "      <td>[1191]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Omeprazole 20mg gastro-resistant capsules</td>\n",
-       "      <td>C0028978</td>\n",
-       "      <td>[T109, T121]</td>\n",
-       "      <td>omeprazole</td>\n",
-       "      <td>[7646]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Omeprazole 20mg gastro-resistant capsules</td>\n",
-       "      <td>C0771315</td>\n",
-       "      <td>[T109, T121]</td>\n",
-       "      <td>omeprazole sodium</td>\n",
-       "      <td>[1792108]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>67619</th>\n",
-       "      <td>zinc oxide with icthammol, salicylic acid and ...</td>\n",
-       "      <td>C0043491</td>\n",
-       "      <td>[T121, T197]</td>\n",
-       "      <td>zinc oxide</td>\n",
-       "      <td>[]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>67620</th>\n",
-       "      <td>zinc oxide with icthammol, salicylic acid and ...</td>\n",
-       "      <td>C0036079</td>\n",
-       "      <td>[T109, T121]</td>\n",
-       "      <td>salicylic acid</td>\n",
-       "      <td>[]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>67621</th>\n",
-       "      <td>zinc oxide with icthammol, salicylic acid and ...</td>\n",
-       "      <td>C0073983</td>\n",
-       "      <td>[T109, T121]</td>\n",
-       "      <td>salsalate</td>\n",
-       "      <td>[]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>67622</th>\n",
-       "      <td>zinc oxide with icthammol, salicylic acid and ...</td>\n",
-       "      <td>C0004057</td>\n",
-       "      <td>[T109, T121]</td>\n",
-       "      <td>aspirin</td>\n",
-       "      <td>[]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>67623</th>\n",
-       "      <td>zyban 150mg tab</td>\n",
-       "      <td>C0751626</td>\n",
-       "      <td>[T109, T121]</td>\n",
-       "      <td>Zyban</td>\n",
-       "      <td>[]</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>67624 rows × 5 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                               drug_name       CUI  \\\n",
-       "0                               Simvastatin 40mg tablets  C0074554   \n",
-       "1                      Bendroflumethiazide 2.5mg tablets  C0004975   \n",
-       "2                       Aspirin 75mg dispersible tablets  C0004057   \n",
-       "3              Omeprazole 20mg gastro-resistant capsules  C0028978   \n",
-       "4              Omeprazole 20mg gastro-resistant capsules  C0771315   \n",
-       "...                                                  ...       ...   \n",
-       "67619  zinc oxide with icthammol, salicylic acid and ...  C0043491   \n",
-       "67620  zinc oxide with icthammol, salicylic acid and ...  C0036079   \n",
-       "67621  zinc oxide with icthammol, salicylic acid and ...  C0073983   \n",
-       "67622  zinc oxide with icthammol, salicylic acid and ...  C0004057   \n",
-       "67623                                    zyban 150mg tab  C0751626   \n",
-       "\n",
-       "                TUI                 name    rx_code  \n",
-       "0      [T109, T121]          simvastatin    [36567]  \n",
-       "1      [T109, T121]  bendroflumethiazide     [1369]  \n",
-       "2      [T109, T121]              aspirin     [1191]  \n",
-       "3      [T109, T121]           omeprazole     [7646]  \n",
-       "4      [T109, T121]    omeprazole sodium  [1792108]  \n",
-       "...             ...                  ...        ...  \n",
-       "67619  [T121, T197]           zinc oxide         []  \n",
-       "67620  [T109, T121]       salicylic acid         []  \n",
-       "67621  [T109, T121]            salsalate         []  \n",
-       "67622  [T109, T121]              aspirin         []  \n",
-       "67623  [T109, T121]                Zyban         []  \n",
-       "\n",
-       "[67624 rows x 5 columns]"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_cui_to_rxnorm = drugs_umls_cleaned.copy()\n",
-    "\n",
-    "def map_cui_to_rxnorm(i, cui, toks, umls_client):\n",
-    "    i_codes = []\n",
-    "    for tok in toks:\n",
-    "        req = umls_client.get_concept_rxnorm_atoms(cui=cui, tok=tok)\n",
-    "        try: code = req[\"result\"][0][\"code\"].rsplit(\"/\",1)[1]\n",
-    "        except: code = \"\"\n",
-    "        i_codes.append(code)\n",
-    "    return list(set(i_codes))\n",
-    "    \n",
-    "from joblib import Parallel, delayed\n",
-    "drug_rx_codes =  Parallel(n_jobs=40)(delayed(map_cui_to_rxnorm)(i, df_cui_to_rxnorm.iloc[i, 1], df_cui_to_rxnorm.iloc[i, 2], umls_client) for i in tqdm(range(len(df_cui_to_rxnorm))))\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "drug_rx_codes_cleaned = [\"\".join([x for x in drug if x]) for drug in drug_rx_codes]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>drug_name</th>\n",
-       "      <th>CUI</th>\n",
-       "      <th>TUI</th>\n",
-       "      <th>name</th>\n",
-       "      <th>rx_code</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Simvastatin 40mg tablets</td>\n",
-       "      <td>C0074554</td>\n",
-       "      <td>[T109, T121]</td>\n",
-       "      <td>simvastatin</td>\n",
-       "      <td>36567</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Bendroflumethiazide 2.5mg tablets</td>\n",
-       "      <td>C0004975</td>\n",
-       "      <td>[T109, T121]</td>\n",
-       "      <td>bendroflumethiazide</td>\n",
-       "      <td>1369</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Aspirin 75mg dispersible tablets</td>\n",
-       "      <td>C0004057</td>\n",
-       "      <td>[T109, T121]</td>\n",
-       "      <td>aspirin</td>\n",
-       "      <td>1191</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Omeprazole 20mg gastro-resistant capsules</td>\n",
-       "      <td>C0028978</td>\n",
-       "      <td>[T109, T121]</td>\n",
-       "      <td>omeprazole</td>\n",
-       "      <td>7646</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Omeprazole 20mg gastro-resistant capsules</td>\n",
-       "      <td>C0771315</td>\n",
-       "      <td>[T109, T121]</td>\n",
-       "      <td>omeprazole sodium</td>\n",
-       "      <td>1792108</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>67619</th>\n",
-       "      <td>zinc oxide with icthammol, salicylic acid and ...</td>\n",
-       "      <td>C0043491</td>\n",
-       "      <td>[T121, T197]</td>\n",
-       "      <td>zinc oxide</td>\n",
-       "      <td></td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>67620</th>\n",
-       "      <td>zinc oxide with icthammol, salicylic acid and ...</td>\n",
-       "      <td>C0036079</td>\n",
-       "      <td>[T109, T121]</td>\n",
-       "      <td>salicylic acid</td>\n",
-       "      <td></td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>67621</th>\n",
-       "      <td>zinc oxide with icthammol, salicylic acid and ...</td>\n",
-       "      <td>C0073983</td>\n",
-       "      <td>[T109, T121]</td>\n",
-       "      <td>salsalate</td>\n",
-       "      <td></td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>67622</th>\n",
-       "      <td>zinc oxide with icthammol, salicylic acid and ...</td>\n",
-       "      <td>C0004057</td>\n",
-       "      <td>[T109, T121]</td>\n",
-       "      <td>aspirin</td>\n",
-       "      <td></td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>67623</th>\n",
-       "      <td>zyban 150mg tab</td>\n",
-       "      <td>C0751626</td>\n",
-       "      <td>[T109, T121]</td>\n",
-       "      <td>Zyban</td>\n",
-       "      <td></td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>67624 rows × 5 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                               drug_name       CUI  \\\n",
-       "0                               Simvastatin 40mg tablets  C0074554   \n",
-       "1                      Bendroflumethiazide 2.5mg tablets  C0004975   \n",
-       "2                       Aspirin 75mg dispersible tablets  C0004057   \n",
-       "3              Omeprazole 20mg gastro-resistant capsules  C0028978   \n",
-       "4              Omeprazole 20mg gastro-resistant capsules  C0771315   \n",
-       "...                                                  ...       ...   \n",
-       "67619  zinc oxide with icthammol, salicylic acid and ...  C0043491   \n",
-       "67620  zinc oxide with icthammol, salicylic acid and ...  C0036079   \n",
-       "67621  zinc oxide with icthammol, salicylic acid and ...  C0073983   \n",
-       "67622  zinc oxide with icthammol, salicylic acid and ...  C0004057   \n",
-       "67623                                    zyban 150mg tab  C0751626   \n",
-       "\n",
-       "                TUI                 name  rx_code  \n",
-       "0      [T109, T121]          simvastatin    36567  \n",
-       "1      [T109, T121]  bendroflumethiazide     1369  \n",
-       "2      [T109, T121]              aspirin     1191  \n",
-       "3      [T109, T121]           omeprazole     7646  \n",
-       "4      [T109, T121]    omeprazole sodium  1792108  \n",
-       "...             ...                  ...      ...  \n",
-       "67619  [T121, T197]           zinc oxide           \n",
-       "67620  [T109, T121]       salicylic acid           \n",
-       "67621  [T109, T121]            salsalate           \n",
-       "67622  [T109, T121]              aspirin           \n",
-       "67623  [T109, T121]                Zyban           \n",
-       "\n",
-       "[67624 rows x 5 columns]"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_cui_to_rxnorm[\"rx_code\"] = drug_rx_codes_cleaned\n",
-    "df_cui_to_rxnorm"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_cui_to_rxnorm.to_feather(f\"{path}/drug_names_umls_linked_rxnorm.feather\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>drug_name</th>\n",
-       "      <th>CUI</th>\n",
-       "      <th>TUI</th>\n",
-       "      <th>name</th>\n",
-       "      <th>rx_code</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Simvastatin 40mg tablets</td>\n",
-       "      <td>C0074554</td>\n",
-       "      <td>[T109, T121]</td>\n",
-       "      <td>simvastatin</td>\n",
-       "      <td>[36567]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Bendroflumethiazide 2.5mg tablets</td>\n",
-       "      <td>C0004975</td>\n",
-       "      <td>[T109, T121]</td>\n",
-       "      <td>bendroflumethiazide</td>\n",
-       "      <td>[1369]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Aspirin 75mg dispersible tablets</td>\n",
-       "      <td>C0004057</td>\n",
-       "      <td>[T109, T121]</td>\n",
-       "      <td>aspirin</td>\n",
-       "      <td>[1191]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Omeprazole 20mg gastro-resistant capsules</td>\n",
-       "      <td>C0028978</td>\n",
-       "      <td>[T109, T121]</td>\n",
-       "      <td>omeprazole</td>\n",
-       "      <td>[7646]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Omeprazole 20mg gastro-resistant capsules</td>\n",
-       "      <td>C0771315</td>\n",
-       "      <td>[T109, T121]</td>\n",
-       "      <td>omeprazole sodium</td>\n",
-       "      <td>[1792108]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>Levothyroxine sodium 100microgram tablets</td>\n",
-       "      <td>C0079691</td>\n",
-       "      <td>[T116, T121, T125]</td>\n",
-       "      <td>levothyroxine sodium</td>\n",
-       "      <td>[40144]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>Levothyroxine sodium 100microgram tablets</td>\n",
-       "      <td>C0040165</td>\n",
-       "      <td>[T116, T121, T125]</td>\n",
-       "      <td>levothyroxine</td>\n",
-       "      <td>[10582]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>Levothyroxine sodium 100microgram tablets</td>\n",
-       "      <td>C1881373</td>\n",
-       "      <td>[T109, T121]</td>\n",
-       "      <td>Synthetic Levothyroxine</td>\n",
-       "      <td>[]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>Amlodipine 5mg tablets</td>\n",
-       "      <td>C0051696</td>\n",
-       "      <td>[T109, T121]</td>\n",
-       "      <td>amlodipine</td>\n",
-       "      <td>[17767]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>Paracetamol 500mg tablets</td>\n",
-       "      <td>C0000970</td>\n",
-       "      <td>[T109, T121]</td>\n",
-       "      <td>acetaminophen</td>\n",
-       "      <td>[161]</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                   drug_name       CUI                 TUI  \\\n",
-       "0                   Simvastatin 40mg tablets  C0074554        [T109, T121]   \n",
-       "1          Bendroflumethiazide 2.5mg tablets  C0004975        [T109, T121]   \n",
-       "2           Aspirin 75mg dispersible tablets  C0004057        [T109, T121]   \n",
-       "3  Omeprazole 20mg gastro-resistant capsules  C0028978        [T109, T121]   \n",
-       "4  Omeprazole 20mg gastro-resistant capsules  C0771315        [T109, T121]   \n",
-       "5  Levothyroxine sodium 100microgram tablets  C0079691  [T116, T121, T125]   \n",
-       "6  Levothyroxine sodium 100microgram tablets  C0040165  [T116, T121, T125]   \n",
-       "7  Levothyroxine sodium 100microgram tablets  C1881373        [T109, T121]   \n",
-       "8                     Amlodipine 5mg tablets  C0051696        [T109, T121]   \n",
-       "9                  Paracetamol 500mg tablets  C0000970        [T109, T121]   \n",
-       "\n",
-       "                      name    rx_code  \n",
-       "0              simvastatin    [36567]  \n",
-       "1      bendroflumethiazide     [1369]  \n",
-       "2                  aspirin     [1191]  \n",
-       "3               omeprazole     [7646]  \n",
-       "4        omeprazole sodium  [1792108]  \n",
-       "5     levothyroxine sodium    [40144]  \n",
-       "6            levothyroxine    [10582]  \n",
-       "7  Synthetic Levothyroxine         []  \n",
-       "8               amlodipine    [17767]  \n",
-       "9            acetaminophen      [161]  "
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_cui_to_rxnorm"
-   ]
-  }
- ],
- "metadata": {
-  "hide_input": false,
-  "kernelspec": {
-   "display_name": "Python [conda env:miniconda3-pl1.x]",
-   "language": "python",
-   "name": "conda-env-miniconda3-pl1.x-py"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/neuralcvd/preprocessing/ukbb_tabular/2_preprocessing_clinical-interactions.ipynb b/neuralcvd/preprocessing/ukbb_tabular/2_preprocessing_clinical-interactions.ipynb
deleted file mode 100644
index 9338687..0000000
--- a/neuralcvd/preprocessing/ukbb_tabular/2_preprocessing_clinical-interactions.ipynb
+++ /dev/null
@@ -1,5692 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Preprocessing"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:31:49.436340Z",
-     "start_time": "2020-11-04T12:31:48.732042Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import os\n",
-    "import yaml\n",
-    "from tqdm.notebook import trange, tqdm\n",
-    "dataset_name = \"cvd_interactions\"\n",
-    "path = \"/data/analysis/ag-reils/steinfej/code/umbrella/pre/ukbb\"\n",
-    "data_path = \"/data/analysis/ag-reils/ag-reils-shared/cardioRS/data\"\n",
-    "dataset_path = f\"{data_path}/2_datasets_pre/{dataset_name}\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:31:49.895222Z",
-     "start_time": "2020-11-04T12:31:49.891332Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from pathlib import Path\n",
-    "Path(dataset_path).mkdir(parents=True, exist_ok=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:33:14.171198Z",
-     "start_time": "2020-11-04T12:31:50.204540Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "data = pd.read_feather(f\"{data_path}/1_decoded/ukb_data.feather\")\n",
-    "data_field = pd.read_feather(f\"{data_path}/1_decoded/ukb_data_field.feather\")\n",
-    "data_columns = data.columns.to_list()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Mappings + Vocabulary"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:34:05.867152Z",
-     "start_time": "2020-11-04T12:33:16.878773Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "502505\n",
-      "502504\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Drop obviouse missing data\n",
-    "print(len(data))\n",
-    "data = data.dropna(subset=[\"sex_f31_0_0\"], axis=0)\n",
-    "print(len(data))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Starting information"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:34:05.872216Z",
-     "start_time": "2020-11-04T12:34:05.869505Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "time0_col=\"birth_date\"\n",
-    "# time0_col=\"date_of_attending_assessment_centre_f53_0_0\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Baseline covariates"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:34:05.889725Z",
-     "start_time": "2020-11-04T12:34:05.874587Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "def get_fields(fields, data, data_field):\n",
-    "    f = data_field[data_field[\"field.showcase\"].isin(fields) & data_field[\"field.tab\"].str.contains(\"f\\\\.\\\\d+\\\\.0\\\\.\\\\d\")].copy()\n",
-    "    f[\"field\"] = pd.Categorical(f[\"field.showcase\"], categories=fields, ordered=True)\n",
-    "    f = f.sort_values(\"field\").reset_index().drop(\"field\", axis=1)\n",
-    "    return f\n",
-    "\n",
-    "def get_fields_all(fields, data, data_field):\n",
-    "    f = data_field[data_field[\"field.showcase\"].isin(fields)].copy()\n",
-    "    f[\"field\"] = pd.Categorical(f[\"field.showcase\"], categories=fields, ordered=True)\n",
-    "    f = f.sort_values(\"field\").reset_index().drop(\"field\", axis=1)\n",
-    "    return f\n",
-    "\n",
-    "def get_data_fields(fields, data, data_field):\n",
-    "    f = get_fields(fields, data, data_field)\n",
-    "    return data[[\"eid\"]+f[\"col.name\"].to_list()].copy()\n",
-    "\n",
-    "def get_data_fields_all(fields, data, data_field):\n",
-    "    f = get_fields_all(fields, data, data_field)\n",
-    "    return data[[\"eid\"]+f[\"col.name\"].to_list()].copy()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Basics"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:34:06.176411Z",
-     "start_time": "2020-11-04T12:34:05.891730Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "502504\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>age_at_recruitment_f21022_0_0</th>\n",
-       "      <th>sex_f31_0_0</th>\n",
-       "      <th>ethnic_background_f21000_0_0</th>\n",
-       "      <th>townsend_deprivation_index_at_recruitment_f189_0_0</th>\n",
-       "      <th>date_of_attending_assessment_centre_f53_0_0</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>49.0</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>White</td>\n",
-       "      <td>-1.852930</td>\n",
-       "      <td>2009-11-12</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1000020</td>\n",
-       "      <td>59.0</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>White</td>\n",
-       "      <td>0.204248</td>\n",
-       "      <td>2008-02-19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1000037</td>\n",
-       "      <td>59.0</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>White</td>\n",
-       "      <td>-3.498860</td>\n",
-       "      <td>2008-11-11</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000043</td>\n",
-       "      <td>63.0</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>White</td>\n",
-       "      <td>-5.351150</td>\n",
-       "      <td>2009-06-03</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1000051</td>\n",
-       "      <td>51.0</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>White</td>\n",
-       "      <td>-1.799080</td>\n",
-       "      <td>2006-06-10</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       eid  age_at_recruitment_f21022_0_0 sex_f31_0_0  \\\n",
-       "0  1000018                           49.0      Female   \n",
-       "1  1000020                           59.0        Male   \n",
-       "2  1000037                           59.0      Female   \n",
-       "3  1000043                           63.0        Male   \n",
-       "4  1000051                           51.0      Female   \n",
-       "\n",
-       "  ethnic_background_f21000_0_0  \\\n",
-       "0                        White   \n",
-       "1                        White   \n",
-       "2                        White   \n",
-       "3                        White   \n",
-       "4                        White   \n",
-       "\n",
-       "   townsend_deprivation_index_at_recruitment_f189_0_0  \\\n",
-       "0                                          -1.852930    \n",
-       "1                                           0.204248    \n",
-       "2                                          -3.498860    \n",
-       "3                                          -5.351150    \n",
-       "4                                          -1.799080    \n",
-       "\n",
-       "  date_of_attending_assessment_centre_f53_0_0  \n",
-       "0                                  2009-11-12  \n",
-       "1                                  2008-02-19  \n",
-       "2                                  2008-11-11  \n",
-       "3                                  2009-06-03  \n",
-       "4                                  2006-06-10  "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fields_basics = [\n",
-    "    \"21022\", # age at recruitment\n",
-    "    \"31\", # sex\n",
-    "    \"21000\", # ethnicity\n",
-    "    \"189\", # Townsend index\n",
-    "    \"53\", # date of baseline assessment\n",
-    "]\n",
-    "\n",
-    "temp = get_data_fields(fields_basics, data, data_field)\n",
-    "\n",
-    "temp[\"sex_f31_0_0\"] = temp[\"sex_f31_0_0\"].cat.set_categories([\"Female\", 'Male'], ordered=False)\n",
-    "\n",
-    "#temp[\"ethnic_background_f21000_0_0\"] = temp[\"ethnic_background_f21000_0_0\"].astype(\"string\")\n",
-    "\n",
-    "ethn_bg_def = {\"White\": [\"White\", \"British\", \"Irish\", \"Any other white background\"],\n",
-    "                \"Mixed\": [\"Mixed\", \"White and Black Caribbean\", \"White and Black African\", \"White and Asian\", \"Any other mixed background\"],  \n",
-    "                \"Asian\": [\"Asian or Asian British\", \"Indian\", \"Pakistani\", \"Bangladeshi\", \"Any other Asian background\"], \n",
-    "                \"Black\": [\"Black or Black British\", \"Caribbean\", \"African\", \"Any other Black background\"],\n",
-    "                \"Chinese\": [\"Chinese\"],  \n",
-    "                np.nan: [\"Other ethnic group\", \"Do not know\", \"Prefer not to answer\"]}\n",
-    "\n",
-    "ethn_bg_dict = {}\n",
-    "for key, values in ethn_bg_def.items(): \n",
-    "    for value in values:\n",
-    "        ethn_bg_dict[value]=key \n",
-    "        \n",
-    "temp[\"ethnic_background_f21000_0_0\"].replace(ethn_bg_dict, inplace=True)\n",
-    "temp[\"ethnic_background_f21000_0_0\"] = temp[\"ethnic_background_f21000_0_0\"].astype(\"category\")\n",
-    "\n",
-    "#\n",
-    "#temp[\"ethnic_background_f21000_0_0\"] = temp[\"ethnic_background_f21000_0_0\"].astype(\"category\").cat.set_categories(['White', 'Black', 'Asien', 'Mixed', 'Chinese'], ordered=False)\n",
-    "\n",
-    "basics = temp\n",
-    "print(len(temp))\n",
-    "display(temp.head())\n",
-    "\n",
-    "from dateutil.relativedelta import relativedelta\n",
-    "calc_birth_date = [date_of_attending_assessment_centre - relativedelta(years=age_at_recruitment) \n",
-    "                                                             for date_of_attending_assessment_centre, age_at_recruitment \n",
-    "                                                             in zip(basics[\"date_of_attending_assessment_centre_f53_0_0\"], basics[\"age_at_recruitment_f21022_0_0\"])]\n",
-    "basics = basics.assign(birth_date = calc_birth_date)\n",
-    "\n",
-    "\n",
-    "basics.to_feather(os.path.join(path, dataset_path, 'temp_basics.feather'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:34:06.186613Z",
-     "start_time": "2020-11-04T12:34:06.178111Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[White, Black, NaN, Asian, Mixed, Chinese]\n",
-      "Categories (5, object): [White, Black, Asian, Mixed, Chinese]\n"
-     ]
-    }
-   ],
-   "source": [
-    " print(temp[\"ethnic_background_f21000_0_0\"].unique())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Questionnaire"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:34:06.389467Z",
-     "start_time": "2020-11-04T12:34:06.188206Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "502504\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>overall_health_rating_f2178_0_0</th>\n",
-       "      <th>smoking_status_f20116_0_0</th>\n",
-       "      <th>alcohol_intake_frequency_f1558_0_0</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>Fair</td>\n",
-       "      <td>Current</td>\n",
-       "      <td>Once or twice a week</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1000020</td>\n",
-       "      <td>Good</td>\n",
-       "      <td>Current</td>\n",
-       "      <td>Once or twice a week</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1000037</td>\n",
-       "      <td>Good</td>\n",
-       "      <td>Previous</td>\n",
-       "      <td>Once or twice a week</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000043</td>\n",
-       "      <td>Fair</td>\n",
-       "      <td>Previous</td>\n",
-       "      <td>Three or four times a week</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1000051</td>\n",
-       "      <td>Poor</td>\n",
-       "      <td>Never</td>\n",
-       "      <td>One to three times a month</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       eid overall_health_rating_f2178_0_0 smoking_status_f20116_0_0  \\\n",
-       "0  1000018                            Fair                   Current   \n",
-       "1  1000020                            Good                   Current   \n",
-       "2  1000037                            Good                  Previous   \n",
-       "3  1000043                            Fair                  Previous   \n",
-       "4  1000051                            Poor                     Never   \n",
-       "\n",
-       "  alcohol_intake_frequency_f1558_0_0  \n",
-       "0               Once or twice a week  \n",
-       "1               Once or twice a week  \n",
-       "2               Once or twice a week  \n",
-       "3         Three or four times a week  \n",
-       "4         One to three times a month  "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fields_questionnaire = [\n",
-    "    \"2178\", # Overall health\n",
-    "    \"20116\", # Smoking status\n",
-    "    \"1558\",\n",
-    "]\n",
-    "\n",
-    "temp = get_data_fields(fields_questionnaire, data, data_field)\n",
-    "\n",
-    "temp[\"overall_health_rating_f2178_0_0\"] = temp[\"overall_health_rating_f2178_0_0\"]\\\n",
-    "    .replace({\"Do not know\": np.nan, \"Prefer not to answer\": np.nan})\\\n",
-    "    .astype(\"category\").cat.set_categories(['Poor', 'Fair', 'Good', 'Excellent'], ordered=True)\n",
-    "\n",
-    "\n",
-    "temp[\"smoking_status_f20116_0_0\"] = temp[\"smoking_status_f20116_0_0\"]\\\n",
-    "    .replace({\"Prefer not to answer\": np.nan}, inplace=False)\\\n",
-    "    .astype(\"category\").cat.set_categories(['Current', 'Previous', 'Never'], ordered=True)\n",
-    "\n",
-    "temp[\"alcohol_intake_frequency_f1558_0_0\"] = temp[\"alcohol_intake_frequency_f1558_0_0\"]\\\n",
-    "    .replace({\"Prefer not to answer\": np.nan}, inplace=False)\\\n",
-    "    .astype(\"category\").cat.set_categories([\n",
-    "        'Daily or almost daily', \n",
-    "        'Three or four times a week', \n",
-    "        'Once or twice a week',\n",
-    "        'One to three times a month',\n",
-    "        'Special occasions only', \n",
-    "        'Never'], ordered=True)\n",
-    "\n",
-    "questionnaire = temp\n",
-    "print(len(temp))\n",
-    "display(temp.head())\n",
-    "\n",
-    "questionnaire.to_feather(os.path.join(path, dataset_path, 'temp_questionnaire.feather'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:34:06.398168Z",
-     "start_time": "2020-11-04T12:34:06.391082Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[Once or twice a week, Three or four times a week, One to three times a month, Daily or almost daily, Special occasions only, Never, NaN]\n",
-      "Categories (6, object): [Daily or almost daily < Three or four times a week < Once or twice a week < One to three times a month < Special occasions only < Never]\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(temp[\"alcohol_intake_frequency_f1558_0_0\"].unique())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Physical measurements"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:34:07.052989Z",
-     "start_time": "2020-11-04T12:34:06.400858Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "502504\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>body_mass_index_bmi_f21001_0_0</th>\n",
-       "      <th>weight_f21002_0_0</th>\n",
-       "      <th>pulse_wave_arterial_stiffness_index_f21021_0_0</th>\n",
-       "      <th>pulse_wave_reflection_index_f4195_0_0</th>\n",
-       "      <th>waist_circumference_f48_0_0</th>\n",
-       "      <th>hip_circumference_f49_0_0</th>\n",
-       "      <th>standing_height_f50_0_0</th>\n",
-       "      <th>trunk_fat_percentage_f23127_0_0</th>\n",
-       "      <th>body_fat_percentage_f23099_0_0</th>\n",
-       "      <th>basal_metabolic_rate_f23105_0_0</th>\n",
-       "      <th>forced_vital_capacity_fvc_best_measure_f20151_0_0</th>\n",
-       "      <th>forced_expiratory_volume_in_1second_fev1_best_measure_f20150_0_0</th>\n",
-       "      <th>fev1_fvc_ratio_zscore_f20258_0_0</th>\n",
-       "      <th>peak_expiratory_flow_pef_f3064_0_2</th>\n",
-       "      <th>peak_expiratory_flow_pef_f3064_0_1</th>\n",
-       "      <th>peak_expiratory_flow_pef_f3064_0_0</th>\n",
-       "      <th>systolic_blood_pressure_automated_reading_f4080</th>\n",
-       "      <th>diastolic_blood_pressure_automated_reading_f4079</th>\n",
-       "      <th>pulse_rate_automated_reading_f102</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>26.5557</td>\n",
-       "      <td>63.8</td>\n",
-       "      <td>7.2770</td>\n",
-       "      <td>80.0</td>\n",
-       "      <td>85.0</td>\n",
-       "      <td>107.0</td>\n",
-       "      <td>155.0</td>\n",
-       "      <td>37.5</td>\n",
-       "      <td>39.5</td>\n",
-       "      <td>5012.0</td>\n",
-       "      <td>3.21</td>\n",
-       "      <td>2.16</td>\n",
-       "      <td>1.978</td>\n",
-       "      <td>317.0</td>\n",
-       "      <td>312.0</td>\n",
-       "      <td>339.0</td>\n",
-       "      <td>159.5</td>\n",
-       "      <td>88.0</td>\n",
-       "      <td>50.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1000020</td>\n",
-       "      <td>22.7465</td>\n",
-       "      <td>70.7</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>87.8</td>\n",
-       "      <td>94.4</td>\n",
-       "      <td>176.3</td>\n",
-       "      <td>33.4</td>\n",
-       "      <td>28.7</td>\n",
-       "      <td>6171.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.375</td>\n",
-       "      <td>301.0</td>\n",
-       "      <td>496.0</td>\n",
-       "      <td>504.0</td>\n",
-       "      <td>133.0</td>\n",
-       "      <td>81.0</td>\n",
-       "      <td>74.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1000037</td>\n",
-       "      <td>32.4211</td>\n",
-       "      <td>78.9</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>101.0</td>\n",
-       "      <td>112.0</td>\n",
-       "      <td>156.0</td>\n",
-       "      <td>47.5</td>\n",
-       "      <td>48.4</td>\n",
-       "      <td>5397.0</td>\n",
-       "      <td>1.61</td>\n",
-       "      <td>1.27</td>\n",
-       "      <td>0.138</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>185.0</td>\n",
-       "      <td>208.0</td>\n",
-       "      <td>118.5</td>\n",
-       "      <td>78.0</td>\n",
-       "      <td>62.5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000043</td>\n",
-       "      <td>29.5679</td>\n",
-       "      <td>95.8</td>\n",
-       "      <td>11.1111</td>\n",
-       "      <td>78.0</td>\n",
-       "      <td>98.0</td>\n",
-       "      <td>104.0</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>27.6</td>\n",
-       "      <td>25.6</td>\n",
-       "      <td>8711.0</td>\n",
-       "      <td>4.14</td>\n",
-       "      <td>2.84</td>\n",
-       "      <td>1.096</td>\n",
-       "      <td>557.0</td>\n",
-       "      <td>513.0</td>\n",
-       "      <td>530.0</td>\n",
-       "      <td>141.5</td>\n",
-       "      <td>93.5</td>\n",
-       "      <td>64.5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1000051</td>\n",
-       "      <td>41.0222</td>\n",
-       "      <td>92.3</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>123.0</td>\n",
-       "      <td>129.0</td>\n",
-       "      <td>150.0</td>\n",
-       "      <td>48.9</td>\n",
-       "      <td>50.4</td>\n",
-       "      <td>6100.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.518</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>117.0</td>\n",
-       "      <td>81.0</td>\n",
-       "      <td>79.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       eid  body_mass_index_bmi_f21001_0_0  weight_f21002_0_0  \\\n",
-       "0  1000018                         26.5557               63.8   \n",
-       "1  1000020                         22.7465               70.7   \n",
-       "2  1000037                         32.4211               78.9   \n",
-       "3  1000043                         29.5679               95.8   \n",
-       "4  1000051                         41.0222               92.3   \n",
-       "\n",
-       "   pulse_wave_arterial_stiffness_index_f21021_0_0  \\\n",
-       "0                                          7.2770   \n",
-       "1                                             NaN   \n",
-       "2                                             NaN   \n",
-       "3                                         11.1111   \n",
-       "4                                             NaN   \n",
-       "\n",
-       "   pulse_wave_reflection_index_f4195_0_0  waist_circumference_f48_0_0  \\\n",
-       "0                                   80.0                         85.0   \n",
-       "1                                    NaN                         87.8   \n",
-       "2                                    NaN                        101.0   \n",
-       "3                                   78.0                         98.0   \n",
-       "4                                    NaN                        123.0   \n",
-       "\n",
-       "   hip_circumference_f49_0_0  standing_height_f50_0_0  \\\n",
-       "0                      107.0                    155.0   \n",
-       "1                       94.4                    176.3   \n",
-       "2                      112.0                    156.0   \n",
-       "3                      104.0                    180.0   \n",
-       "4                      129.0                    150.0   \n",
-       "\n",
-       "   trunk_fat_percentage_f23127_0_0  body_fat_percentage_f23099_0_0  \\\n",
-       "0                             37.5                            39.5   \n",
-       "1                             33.4                            28.7   \n",
-       "2                             47.5                            48.4   \n",
-       "3                             27.6                            25.6   \n",
-       "4                             48.9                            50.4   \n",
-       "\n",
-       "   basal_metabolic_rate_f23105_0_0  \\\n",
-       "0                           5012.0   \n",
-       "1                           6171.0   \n",
-       "2                           5397.0   \n",
-       "3                           8711.0   \n",
-       "4                           6100.0   \n",
-       "\n",
-       "   forced_vital_capacity_fvc_best_measure_f20151_0_0  \\\n",
-       "0                                               3.21   \n",
-       "1                                                NaN   \n",
-       "2                                               1.61   \n",
-       "3                                               4.14   \n",
-       "4                                                NaN   \n",
-       "\n",
-       "   forced_expiratory_volume_in_1second_fev1_best_measure_f20150_0_0  \\\n",
-       "0                                               2.16                  \n",
-       "1                                                NaN                  \n",
-       "2                                               1.27                  \n",
-       "3                                               2.84                  \n",
-       "4                                                NaN                  \n",
-       "\n",
-       "   fev1_fvc_ratio_zscore_f20258_0_0  peak_expiratory_flow_pef_f3064_0_2  \\\n",
-       "0                             1.978                               317.0   \n",
-       "1                             1.375                               301.0   \n",
-       "2                             0.138                                 NaN   \n",
-       "3                             1.096                               557.0   \n",
-       "4                             0.518                                 NaN   \n",
-       "\n",
-       "   peak_expiratory_flow_pef_f3064_0_1  peak_expiratory_flow_pef_f3064_0_0  \\\n",
-       "0                               312.0                               339.0   \n",
-       "1                               496.0                               504.0   \n",
-       "2                               185.0                               208.0   \n",
-       "3                               513.0                               530.0   \n",
-       "4                                 NaN                                 NaN   \n",
-       "\n",
-       "   systolic_blood_pressure_automated_reading_f4080  \\\n",
-       "0                                            159.5   \n",
-       "1                                            133.0   \n",
-       "2                                            118.5   \n",
-       "3                                            141.5   \n",
-       "4                                            117.0   \n",
-       "\n",
-       "   diastolic_blood_pressure_automated_reading_f4079  \\\n",
-       "0                                              88.0   \n",
-       "1                                              81.0   \n",
-       "2                                              78.0   \n",
-       "3                                              93.5   \n",
-       "4                                              81.0   \n",
-       "\n",
-       "   pulse_rate_automated_reading_f102  \n",
-       "0                               50.0  \n",
-       "1                               74.0  \n",
-       "2                               62.5  \n",
-       "3                               64.5  \n",
-       "4                               79.0  "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from statistics import mean\n",
-    "\n",
-    "fields_measurements = [\n",
-    "#    \"100313\", # Walking speed !!! MISSING !!!\n",
-    "    \"21001\", # BMI\n",
-    "    \"21002\", # weight\n",
-    "    \"4080\", # Syst. BP\n",
-    "    \"4079\", # Diast. BP\n",
-    "    \"102\",\n",
-    "    \"21021\",\n",
-    "    \"4195\",\n",
-    "    \"48\",\n",
-    "    \"49\",\n",
-    "    \"50\",\n",
-    "    \"23127\",\n",
-    "    \"23099\",\n",
-    "    \"23105\",\n",
-    "    \"20151\",\n",
-    "    \"20150\",\n",
-    "    \"20258\",\n",
-    "    \"3064\",\n",
-    "    \n",
-    "]\n",
-    "temp = get_data_fields(fields_measurements, data, data_field)\n",
-    "\n",
-    "sbp_cols = [\"systolic_blood_pressure_automated_reading_f4080_0_0\", \"systolic_blood_pressure_automated_reading_f4080_0_1\"]\n",
-    "dbp_cols = [\"diastolic_blood_pressure_automated_reading_f4079_0_0\", \"diastolic_blood_pressure_automated_reading_f4079_0_1\"]\n",
-    "pr_cols = [\"pulse_rate_automated_reading_f102_0_0\", \"pulse_rate_automated_reading_f102_0_1\"]\n",
-    "\n",
-    "temp = temp.assign(systolic_blood_pressure_automated_reading_f4080 = temp[sbp_cols].mean(axis=1),\n",
-    "                   diastolic_blood_pressure_automated_reading_f4079 = temp[dbp_cols].mean(axis=1),\n",
-    "                   pulse_rate_automated_reading_f102 = temp[pr_cols].mean(axis=1))\\\n",
-    "    .drop(sbp_cols + dbp_cols + pr_cols, axis=1)\n",
-    "\n",
-    "measurements = temp\n",
-    "print(len(temp))\n",
-    "display(temp.head())\n",
-    "\n",
-    "measurements.to_feather(os.path.join(path, dataset_path, 'temp_measurements.feather'))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Lab measurements"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:34:07.647955Z",
-     "start_time": "2020-11-04T12:34:07.055242Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "502504\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>basophill_count_f30160_0_0</th>\n",
-       "      <th>basophill_percentage_f30220_0_0</th>\n",
-       "      <th>eosinophill_count_f30150_0_0</th>\n",
-       "      <th>eosinophill_percentage_f30210_0_0</th>\n",
-       "      <th>haematocrit_percentage_f30030_0_0</th>\n",
-       "      <th>haemoglobin_concentration_f30020_0_0</th>\n",
-       "      <th>high_light_scatter_reticulocyte_count_f30300_0_0</th>\n",
-       "      <th>high_light_scatter_reticulocyte_percentage_f30290_0_0</th>\n",
-       "      <th>immature_reticulocyte_fraction_f30280_0_0</th>\n",
-       "      <th>...</th>\n",
-       "      <th>phosphate_f30810_0_0</th>\n",
-       "      <th>rheumatoid_factor_f30820_0_0</th>\n",
-       "      <th>shbg_f30830_0_0</th>\n",
-       "      <th>testosterone_f30850_0_0</th>\n",
-       "      <th>total_bilirubin_f30840_0_0</th>\n",
-       "      <th>total_protein_f30860_0_0</th>\n",
-       "      <th>triglycerides_f30870_0_0</th>\n",
-       "      <th>urate_f30880_0_0</th>\n",
-       "      <th>urea_f30670_0_0</th>\n",
-       "      <th>vitamin_d_f30890_0_0</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>0.04</td>\n",
-       "      <td>0.26</td>\n",
-       "      <td>0.25</td>\n",
-       "      <td>1.75</td>\n",
-       "      <td>39.79</td>\n",
-       "      <td>13.90</td>\n",
-       "      <td>0.022</td>\n",
-       "      <td>0.464</td>\n",
-       "      <td>0.378</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.422</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>70.11</td>\n",
-       "      <td>1.560</td>\n",
-       "      <td>7.41</td>\n",
-       "      <td>71.97</td>\n",
-       "      <td>1.247</td>\n",
-       "      <td>221.3</td>\n",
-       "      <td>5.48</td>\n",
-       "      <td>70.7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1000020</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0.30</td>\n",
-       "      <td>0.30</td>\n",
-       "      <td>2.50</td>\n",
-       "      <td>45.00</td>\n",
-       "      <td>15.60</td>\n",
-       "      <td>0.014</td>\n",
-       "      <td>0.290</td>\n",
-       "      <td>0.300</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.264</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>55.31</td>\n",
-       "      <td>12.237</td>\n",
-       "      <td>8.07</td>\n",
-       "      <td>78.45</td>\n",
-       "      <td>1.906</td>\n",
-       "      <td>374.7</td>\n",
-       "      <td>5.28</td>\n",
-       "      <td>35.9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1000037</td>\n",
-       "      <td>0.04</td>\n",
-       "      <td>0.57</td>\n",
-       "      <td>0.10</td>\n",
-       "      <td>1.43</td>\n",
-       "      <td>39.48</td>\n",
-       "      <td>13.58</td>\n",
-       "      <td>0.031</td>\n",
-       "      <td>0.686</td>\n",
-       "      <td>0.380</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000043</td>\n",
-       "      <td>0.02</td>\n",
-       "      <td>0.32</td>\n",
-       "      <td>0.11</td>\n",
-       "      <td>1.80</td>\n",
-       "      <td>44.31</td>\n",
-       "      <td>14.99</td>\n",
-       "      <td>0.025</td>\n",
-       "      <td>0.508</td>\n",
-       "      <td>0.250</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.928</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>31.63</td>\n",
-       "      <td>11.398</td>\n",
-       "      <td>8.65</td>\n",
-       "      <td>69.70</td>\n",
-       "      <td>5.184</td>\n",
-       "      <td>322.8</td>\n",
-       "      <td>6.67</td>\n",
-       "      <td>63.6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1000051</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 62 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       eid  basophill_count_f30160_0_0  basophill_percentage_f30220_0_0  \\\n",
-       "0  1000018                        0.04                             0.26   \n",
-       "1  1000020                        0.00                             0.30   \n",
-       "2  1000037                        0.04                             0.57   \n",
-       "3  1000043                        0.02                             0.32   \n",
-       "4  1000051                         NaN                              NaN   \n",
-       "\n",
-       "   eosinophill_count_f30150_0_0  eosinophill_percentage_f30210_0_0  \\\n",
-       "0                          0.25                               1.75   \n",
-       "1                          0.30                               2.50   \n",
-       "2                          0.10                               1.43   \n",
-       "3                          0.11                               1.80   \n",
-       "4                           NaN                                NaN   \n",
-       "\n",
-       "   haematocrit_percentage_f30030_0_0  haemoglobin_concentration_f30020_0_0  \\\n",
-       "0                              39.79                                 13.90   \n",
-       "1                              45.00                                 15.60   \n",
-       "2                              39.48                                 13.58   \n",
-       "3                              44.31                                 14.99   \n",
-       "4                                NaN                                   NaN   \n",
-       "\n",
-       "   high_light_scatter_reticulocyte_count_f30300_0_0  \\\n",
-       "0                                             0.022   \n",
-       "1                                             0.014   \n",
-       "2                                             0.031   \n",
-       "3                                             0.025   \n",
-       "4                                               NaN   \n",
-       "\n",
-       "   high_light_scatter_reticulocyte_percentage_f30290_0_0  \\\n",
-       "0                                              0.464       \n",
-       "1                                              0.290       \n",
-       "2                                              0.686       \n",
-       "3                                              0.508       \n",
-       "4                                                NaN       \n",
-       "\n",
-       "   immature_reticulocyte_fraction_f30280_0_0  ...  phosphate_f30810_0_0  \\\n",
-       "0                                      0.378  ...                 1.422   \n",
-       "1                                      0.300  ...                 1.264   \n",
-       "2                                      0.380  ...                   NaN   \n",
-       "3                                      0.250  ...                 0.928   \n",
-       "4                                        NaN  ...                   NaN   \n",
-       "\n",
-       "   rheumatoid_factor_f30820_0_0  shbg_f30830_0_0  testosterone_f30850_0_0  \\\n",
-       "0                           NaN            70.11                    1.560   \n",
-       "1                           NaN            55.31                   12.237   \n",
-       "2                           NaN              NaN                      NaN   \n",
-       "3                           NaN            31.63                   11.398   \n",
-       "4                           NaN              NaN                      NaN   \n",
-       "\n",
-       "   total_bilirubin_f30840_0_0  total_protein_f30860_0_0  \\\n",
-       "0                        7.41                     71.97   \n",
-       "1                        8.07                     78.45   \n",
-       "2                         NaN                       NaN   \n",
-       "3                        8.65                     69.70   \n",
-       "4                         NaN                       NaN   \n",
-       "\n",
-       "   triglycerides_f30870_0_0  urate_f30880_0_0  urea_f30670_0_0  \\\n",
-       "0                     1.247             221.3             5.48   \n",
-       "1                     1.906             374.7             5.28   \n",
-       "2                       NaN               NaN              NaN   \n",
-       "3                     5.184             322.8             6.67   \n",
-       "4                       NaN               NaN              NaN   \n",
-       "\n",
-       "   vitamin_d_f30890_0_0  \n",
-       "0                  70.7  \n",
-       "1                  35.9  \n",
-       "2                   NaN  \n",
-       "3                  63.6  \n",
-       "4                   NaN  \n",
-       "\n",
-       "[5 rows x 62 columns]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fields_blood_count = [\n",
-    "    \"30160\", #\tBasophill count\n",
-    "    \"30220\", #\tBasophill percentage\n",
-    "    \"30150\", #\tEosinophill count\n",
-    "    \"30210\", #\tEosinophill percentage\n",
-    "    \"30030\", #\tHaematocrit percentage\n",
-    "    \"30020\", #\tHaemoglobin concentration\n",
-    "    \"30300\", #\tHigh light scatter reticulocyte count\n",
-    "    \"30290\", #\tHigh light scatter reticulocyte percentage\n",
-    "    \"30280\", #\tImmature reticulocyte fraction\n",
-    "    \"30120\", #\tLymphocyte count\n",
-    "    \"30180\", #\tLymphocyte percentage\n",
-    "    \"30050\", #\tMean corpuscular haemoglobin\n",
-    "    \"30060\", #\tMean corpuscular haemoglobin concentration\n",
-    "    \"30040\", #\tMean corpuscular volume\n",
-    "    \"30100\", #\tMean platelet (thrombocyte) volume\n",
-    "    \"30260\", #\tMean reticulocyte volume\n",
-    "    \"30270\", #\tMean sphered cell volume\n",
-    "    \"30130\", #\tMonocyte count\n",
-    "    \"30190\", #\tMonocyte percentage\n",
-    "    \"30140\", #\tNeutrophill count\n",
-    "    \"30200\", #\tNeutrophill percentage\n",
-    "    \"30170\", #\tNucleated red blood cell count\n",
-    "    \"30230\", #\tNucleated red blood cell percentage\n",
-    "    \"30080\", #\tPlatelet count\n",
-    "    \"30090\", #\tPlatelet crit\n",
-    "    \"30110\", #\tPlatelet distribution width\n",
-    "    \"30010\", #\tRed blood cell (erythrocyte) count\n",
-    "    \"30070\", #\tRed blood cell (erythrocyte) distribution width\n",
-    "    \"30250\", #\tReticulocyte count\n",
-    "    \"30240\", #\tReticulocyte percentage\n",
-    "    \"30000\", #\tWhite blood cell (leukocyte) count\n",
-    "]\n",
-    "\n",
-    "fields_blood_biochemistry = [\n",
-    "    \"30620\",#\tAlanine aminotransferase\n",
-    "    \"30600\",#\tAlbumin\n",
-    "    \"30610\",#\tAlkaline phosphatase\n",
-    "    \"30630\",#\tApolipoprotein A\n",
-    "    \"30640\",#\tApolipoprotein B\n",
-    "    \"30650\",#\tAspartate aminotransferase\n",
-    "    \"30710\",#\tC-reactive protein\n",
-    "    \"30680\",#\tCalcium\n",
-    "    \"30690\",#\tCholesterol\n",
-    "    \"30700\",#\tCreatinine\n",
-    "    \"30720\",#\tCystatin C\n",
-    "    \"30660\",#\tDirect bilirubin\n",
-    "    \"30730\",#\tGamma glutamyltransferase\n",
-    "    \"30740\",#\tGlucose\n",
-    "    \"30750\",#\tGlycated haemoglobin (HbA1c)\n",
-    "    \"30760\",#\tHDL cholesterol\n",
-    "    \"30770\",#\tIGF-1\n",
-    "    \"30780\",#\tLDL direct\n",
-    "    \"30790\",#\tLipoprotein A\n",
-    "    \"30800\",#\tOestradiol\n",
-    "    \"30810\",#\tPhosphate\n",
-    "    \"30820\",#\tRheumatoid factor\n",
-    "    \"30830\",#\tSHBG\n",
-    "    \"30850\",#\tTestosterone\n",
-    "    \"30840\",#\tTotal bilirubin\n",
-    "    \"30860\",#\tTotal protein\n",
-    "    \"30870\",#\tTriglycerides\n",
-    "    \"30880\",#\tUrate\n",
-    "    \"30670\",#\tUrea\n",
-    "    \"30890\",#\tVitamin D\n",
-    "]\n",
-    "\n",
-    "fields_blood_infectious = [\n",
-    "    \"23000\", #\t1gG antigen for Herpes Simplex virus-1\n",
-    "    \"23001\", #\t2mgG unique antigen for Herpes Simplex virus-2\n",
-    "    \"23049\", #\tAntigen assay QC indicator\n",
-    "    \"23048\", #\tAntigen assay date\n",
-    "    \"23026\", #\tBK VP1 antigen for Human Polyomavirus BKV\n",
-    "    \"23039\", #\tCagA antigen for Helicobacter pylori\n",
-    "    \"23043\", #\tCatalase antigen for Helicobacter pylori\n",
-    "    \"23018\", #\tCore antigen for Hepatitis C Virus\n",
-    "    \"23030\", #\tE6 antigen for Human Papillomavirus type-16\n",
-    "    \"23031\", #\tE7 antigen for Human Papillomavirus type-16\n",
-    "    \"23006\", #\tEA-D antigen for Epstein-Barr Virus\n",
-    "    \"23004\", #\tEBNA-1 antigen for Epstein-Barr Virus\n",
-    "    \"23042\", #\tGroEL antigen for Helicobacter pylori\n",
-    "    \"23016\", #\tHBc antigen for Hepatitis B Virus\n",
-    "    \"23017\", #\tHBe antigen for Hepatitis B Virus\n",
-    "    \"23025\", #\tHIV-1 env antigen for Human Immunodeficiency Virus\n",
-    "    \"23024\", #\tHIV-1 gag antigen for Human Immunodeficiency Virus\n",
-    "    \"23023\", #\tHTLV-1 env antigen for Human T-Lymphotropic Virus 1\n",
-    "    \"23022\", #\tHTLV-1 gag antigen for Human T-Lymphotropic Virus 1\n",
-    "    \"23010\", #\tIE1A antigen for Human Herpesvirus-6\n",
-    "    \"23011\", #\tIE1B antigen for Human Herpesvirus-6\n",
-    "    \"23027\", #\tJC VP1 antigen for Human Polyomavirus JCV\n",
-    "    \"23015\", #\tK8.1 antigen for Kaposi's Sarcoma-Associated Herpesvirus\n",
-    "    \"23029\", #\tL1 antigen for Human Papillomavirus type-16\n",
-    "    \"23032\", #\tL1 antigen for Human Papillomavirus type-18\n",
-    "    \"23014\", #\tLANA antigen for Kaposi's Sarcoma-Associated Herpesvirus\n",
-    "    \"23028\", #\tMC VP1 antigen for Merkel Cell Polyomavirus\n",
-    "    \"23019\", #\tNS3 antigen for Hepatitis C Virus\n",
-    "    \"23041\", #\tOMP antigen for Helicobacter pylori\n",
-    "    \"23037\", #\tPorB antigen for Chlamydia trachomatis\n",
-    "    \"23013\", #\tU14 antigen for Human Herpesvirus-7\n",
-    "    \"23044\", #\tUreA antigen for Helicobacter pylori\n",
-    "    \"23003\", #\tVCA p18 antigen for Epstein-Barr Virus\n",
-    "    \"23040\", #\tVacA antigen for Helicobacter pylori\n",
-    "    \"23005\", #\tZEBRA antigen for Epstein-Barr Virus\n",
-    "    \"23002\", #\tgE / gI antigen for Varicella Zoster Virus\n",
-    "    \"23034\", #\tmomp A antigen for Chlamydia trachomatis\n",
-    "    \"23033\", #\tmomp D antigen for Chlamydia trachomatis\n",
-    "    \"23012\", #\tp101 k antigen for Human Herpesvirus-6\n",
-    "    \"23020\", #\tp22 antigen for Toxoplasma gondii\n",
-    "    \"23038\", #\tpGP3 antigen for Chlamydia trachomatis\n",
-    "    \"23009\", #\tpp 28 antigen for Human Cytomegalovirus\n",
-    "    \"23008\", #\tpp 52 antigen for Human Cytomegalovirus\n",
-    "    \"23007\", #\tpp150 Nter antigen for Human Cytomegalovirus\n",
-    "    \"23021\", #\tsag1 antigen for Toxoplasma gondii\n",
-    "    \"23035\", #\ttarp-D F1 antigen for Chlamydia trachomatis\n",
-    "    \"23036\", #\ttarp-D F2 antigen for Chlamydia trachomatis\n",
-    "]\n",
-    "\n",
-    "labs = temp = get_data_fields(fields_blood_count+fields_blood_biochemistry+fields_blood_infectious, data, data_field)\n",
-    "print(len(temp))\n",
-    "display(temp.head())\n",
-    "\n",
-    "labs.to_feather(os.path.join(path, dataset_path, 'temp_labs.feather'))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Family History"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:34:25.838958Z",
-     "start_time": "2020-11-04T12:34:07.649920Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "502504\n"
-     ]
-    }
-   ],
-   "source": [
-    "fh_list=[\"Heart disease\", \"Stroke\", \"High blood pressure\",  \"Diabetes\", \"Lung cancer\", \"Severe depression\", \"Parkinson's disease\", \"Alzheimer's disease/dementia\", \"Chronic bronchitis/emphysema\", \"Breast cancer\", \"Bowel cancer\"]\n",
-    "with open(os.path.join(path, dataset_path, 'fh_list.yaml'), 'w') as file: yaml.dump(fh_list, file, default_flow_style=False)\n",
-    "\n",
-    "fields_family_history = [\n",
-    "    \"20107\", # Family history \n",
-    "    \"20110\" # Family history\n",
-    "]\n",
-    "\n",
-    "raw = get_data_fields(fields_family_history, data, data_field)\n",
-    "temp = pd.melt(raw, id_vars=[\"eid\"], value_vars=raw.drop(\"eid\", axis=1).columns.to_list(), var_name = \"field\", value_name=\"family_history\").drop(\"field\", axis=1)\n",
-    "temp = temp[temp.family_history.isin(fh_list)].assign(family_history=temp[\"family_history\"].str.lower().replace(\" \", \"_\", regex=True))\n",
-    "\n",
-    "temp = temp.drop_duplicates().sort_values(\"eid\").reset_index().drop(\"index\", axis=1).assign(n=True)\n",
-    "temp = pd.pivot_table(temp, index=\"eid\", columns=\"family_history\", values=\"n\", observed=True).add_prefix('fh_')\n",
-    "family_history = temp = data[[\"eid\"]].copy().merge(temp, how=\"left\", on=\"eid\").fillna(False)\n",
-    "\n",
-    "print(len(temp))\n",
-    "temp.head()\n",
-    "\n",
-    "family_history.to_feather(os.path.join(path, dataset_path, 'temp_family_history.feather'))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Medications"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:34:25.842601Z",
-     "start_time": "2020-11-04T12:34:25.840607Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# https://list.essentialmeds.org/?showRemoved=0\n",
-    "# essential medicines WHO?!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:34:32.471831Z",
-     "start_time": "2020-11-04T12:34:25.844339Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "atc_mapping = pd.read_csv(f\"{path}/mapping/atc/atc_matched_list.csv\")\n",
-    "athena_concepts = pd.read_csv(f\"{data_path}/athena_vocabulary/CONCEPT.csv\", sep=\"\\t\").assign(vocabulary_id = lambda x: x.vocabulary_id.astype(\"string\"), concept_class_id = lambda x: x.concept_class_id.astype(\"string\"))\n",
-    "atc_concepts = athena_concepts[athena_concepts.vocabulary_id==\"ATC\"]\n",
-    "atc2_concepts = atc_concepts[atc_concepts.concept_class_id==\"ATC 2nd\"].sort_values(\"concept_code\")\n",
-    "medication_list = dict(zip([x.lower().replace(\" \", \"_\") for x in atc2_concepts.concept_name.to_list()], [[x] for x in atc2_concepts.concept_code.to_list()]))\n",
-    "medication_list_extra = {\n",
-    "    \"antihypertensives\": [\"C02\"],\n",
-    "    \"statins\": [\"C10A\", \"C10B\"],\n",
-    "    \"ass\": [\"B01\"],\n",
-    "    \"atypical_antipsychotics\" : [\"N05\"],\n",
-    "    \"glucocorticoids\" : [\"H02\"]                        \n",
-    "}\n",
-    "medication_list.update(medication_list_extra)\n",
-    "\n",
-    "with open(os.path.join(path, dataset_path, 'medication_list.yaml'), 'w') as file: yaml.dump(medication_list, file, default_flow_style=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def had_medication_before(data, data_field, medications, atc_mapping):\n",
-    "    fields = [\"20003\"]\n",
-    "    raw = get_data_fields(fields, data, data_field)\n",
-    "    temp = pd.melt(raw, id_vars=[\"eid\"], value_vars=raw.drop(\"eid\", axis=1).columns.to_list(), var_name = \"field\", value_name=\"UKBB_code\").drop(\"field\", axis=1).drop_duplicates()\n",
-    "\n",
-    "    temp.UKBB_code = temp.UKBB_code.astype(str)\n",
-    "    temp = temp[temp.UKBB_code!=\"None\"].copy()\n",
-    "    temp.UKBB_code = temp.UKBB_code.astype(int)\n",
-    "\n",
-    "    temp_atc = temp.merge(atc_mapping, how=\"left\", on=\"UKBB_code\").sort_values(\"eid\").reset_index(drop=True).dropna(subset=[\"ATC_code\"], axis=0)\n",
-    "    temp_atc.ATC_code = temp_atc.ATC_code.astype(\"string\")\n",
-    "    temp = data[[\"eid\"]].copy()\n",
-    "    for med, med_codes in tqdm(medication_list.items()):\n",
-    "        regex_str = \"^\"+\"|^\".join(med_codes)\n",
-    "        df = temp_atc[temp_atc.ATC_code.str.contains(regex_str, case=False)][[\"eid\"]]\\\n",
-    "            .drop_duplicates(subset=[\"eid\"])\\\n",
-    "            .assign(medication=True)\n",
-    "        temp[med] = temp.merge(df, how=\"left\", on=\"eid\").fillna(False).medication\n",
-    "        \n",
-    "    return temp.sort_values(\"eid\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:34:32.481137Z",
-     "start_time": "2020-11-04T12:34:32.473698Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "def had_diagnosis_before_per_ph(df_before, ph, ph_codes, temp):\n",
-    "   # regex = \"|\".join(ph_codes)\n",
-    "    #df_ph = df_before.set_index(\"meaning\").loc[ph_codes][[\"eid\"]]\\\n",
-    "    #        .drop_duplicates(subset=[\"eid\"])\\\n",
-    "    #        .assign(phenotype=True) \n",
-    "    df_ph = df_before[df_before.meaning.isin(ph_codes)][[\"eid\"]]\\\n",
-    "            .drop_duplicates(subset=[\"eid\"])\\\n",
-    "            .assign(phenotype=True) \n",
-    "    #df_ph = df_before[df_before.meaning.str.contains(regex, case=False)][[\"eid\"]]\\\n",
-    "    #        .drop_duplicates(subset=[\"eid\"])\\\n",
-    "    #        .assign(phenotype=True)   \n",
-    "    return temp.merge(df_ph, how=\"left\", on=\"eid\").fillna(False).phenotype\n",
-    "\n",
-    "def had_diagnosis_before(data, diagnoses_codes, phenotypes, time0=time0_col):\n",
-    "    diagnoses_codes_time = diagnoses_codes.merge(data[[\"eid\", time0]], how=\"left\", on=\"eid\")\n",
-    "    \n",
-    "    temp = data[[\"eid\"]].copy()\n",
-    "    df_before = diagnoses_codes_time[diagnoses_codes_time.date < diagnoses_codes_time[time0]]\n",
-    "                                                                                         \n",
-    "    df_phs = Parallel(n_jobs=20, require=\"sharedmem\")(delayed(had_diagnosis_before_per_ph)(df_before, ph, phenotypes[ph], temp) for ph in tqdm(list(phenotypes)))\n",
-    "    for ph, df_ph_series in zip(tqdm(list(phenotypes)), df_phs): temp[ph] = df_ph_series#temp.merge(df_ph, how=\"left\", on=\"eid\").fillna(False).phenotype\n",
-    "    \n",
-    "    return temp.sort_values(\"eid\")  #reduce(lambda left,right: pd.merge(left,right,on=['eid'], how='left'), df_phs)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:36:14.425612Z",
-     "start_time": "2020-11-04T12:34:32.482863Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "13ef8609a6b64b219928981b7752c608",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(FloatProgress(value=0.0, max=98.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "502504\n"
-     ]
-    }
-   ],
-   "source": [
-    "medications = had_medication_before(data, data_field, medication_list, atc_mapping)\n",
-    "print(len(medications))\n",
-    "medications.head(100)\n",
-    "\n",
-    "medications.to_feather(os.path.join(path, dataset_path, 'temp_medications.feather'))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Diagnoses and events"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:37:14.667281Z",
-     "start_time": "2020-11-04T12:36:14.427693Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/steinfej/miniconda3/envs/python/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3072: DtypeWarning: Columns (5,6,9) have mixed types.Specify dtype option on import or set low_memory=False.\n",
-      "  interactivity=interactivity, compiler=compiler, result=result)\n",
-      "/home/steinfej/miniconda3/envs/python/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3072: DtypeWarning: Columns (11) have mixed types.Specify dtype option on import or set low_memory=False.\n",
-      "  interactivity=interactivity, compiler=compiler, result=result)\n"
-     ]
-    }
-   ],
-   "source": [
-    "vocab_dir = f\"{data_path}/athena_vocabulary_covid\"\n",
-    "vocab = {\n",
-    "    \"concept\": pd.read_csv(f\"{vocab_dir}/CONCEPT.csv\", sep='\\t'),\n",
-    "    \"domain\": pd.read_csv(f\"{vocab_dir}/DOMAIN.csv\", sep='\\t'),\n",
-    "    \"class\": pd.read_csv(f\"{vocab_dir}/CONCEPT_CLASS.csv\", sep='\\t'),\n",
-    "    \"relationship\": pd.read_csv(f\"{vocab_dir}/RELATIONSHIP.csv\", sep='\\t'),\n",
-    "    \"drug_strength\": pd.read_csv(f\"{vocab_dir}/DRUG_STRENGTH.csv\", sep='\\t'),\n",
-    "    \"vocabulary\": pd.read_csv(f\"{vocab_dir}/VOCABULARY.csv\", sep='\\t'),\n",
-    "    \"concept_synonym\": pd.read_csv(f\"{vocab_dir}/CONCEPT_SYNONYM.csv\", sep='\\t'),\n",
-    "    \"concept_ancestor\": pd.read_csv(f\"{vocab_dir}/CONCEPT_ANCESTOR.csv\", sep='\\t'),\n",
-    "    \"concept_relationship\": pd.read_csv(f\"{vocab_dir}/CONCEPT_RELATIONSHIP.csv\", sep='\\t')                       \n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Definitions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:37:14.772869Z",
-     "start_time": "2020-11-04T12:37:14.669541Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "coding1836 = pd.read_csv(f\"{path}/mapping/codings/coding1836.tsv\", sep=\"\\t\").rename(columns={\"coding\":\"code\"})\n",
-    "phecodes = pd.read_csv(f\"{path}/mapping/phecodes/phecode_icd10.csv\")\n",
-    "def phenotype_children(phecodes, phenotype_list):\n",
-    "    l={}\n",
-    "    phecodes = phecodes.dropna(subset=[\"Phenotype\"], axis=0)\n",
-    "    for ph, ph_names in phenotype_list.items():\n",
-    "        regex = \"|\".join(ph_names)\n",
-    "        l[ph] = list(phecodes[phecodes.Phenotype.str.contains(regex, case=False)].ICD10.str.replace(\"\\\\.\", \"\").str.slice(0, 3).unique())\n",
-    "    return l"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:37:14.851438Z",
-     "start_time": "2020-11-04T12:37:14.774599Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "snomed_core = pd.read_csv(f\"{path}/mapping/snomed_core_list.txt\", sep=\"|\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:37:14.909023Z",
-     "start_time": "2020-11-04T12:37:14.853286Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "snomed_core = snomed_core.query(\"SNOMED_CONCEPT_STATUS == 'Current'\").copy()\n",
-    "new = snomed_core.SNOMED_FSN.str.split(\"(\", n=1, expand=True)\n",
-    "snomed_core[\"snomed_name\"] = new[0].str.rstrip(' ')\n",
-    "snomed_core[\"snomed_type\"] = new[1].str.rstrip(')')\n",
-    "snomed_core_data = snomed_core#.query(\"(snomed_type=='disorder' | snomed_type=='finding') & USAGE>0.01\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:37:14.914973Z",
-     "start_time": "2020-11-04T12:37:14.910857Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "snomed_names = snomed_core_data.snomed_name.to_list()\n",
-    "snomed_names = [str(item).lower().strip().replace(\" \", \"_\").replace(\";\", \"\").replace(\",\", \"\") for item in snomed_names]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:39:55.342937Z",
-     "start_time": "2020-11-04T12:37:14.916906Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "989d1908f8624958a92da3602ad125f1",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(FloatProgress(value=0.0, max=6181.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "phenotype_list_snomed = dict(zip(snomed_names, snomed_core_data.SNOMED_CID.to_list()))\n",
-    "snomed_ids = vocab[\"concept\"].query(\"vocabulary_id == 'SNOMED'\").concept_id.to_list()\n",
-    "icd10_ids = vocab[\"concept\"].query(\"vocabulary_id == 'ICD10CM'\").concept_id.to_list()\n",
-    "\n",
-    "ph_to_icd10_mapping = {}\n",
-    "\n",
-    "def map_snomed_to_icd10(ph, snomed_code, concept, concept_ancestor, concept_relationship):\n",
-    "    concept_ids = concept.query(\"vocabulary_id == 'SNOMED' & concept_code == @snomed_code\").concept_id.to_list()\n",
-    "    snomed_desc_ids = concept_ancestor.query(\"ancestor_concept_id== @concept_ids\").descendant_concept_id.to_list()\n",
-    "    ph_desc = concept.query(\"concept_id == @snomed_desc_ids\").query(\"vocabulary_id == 'SNOMED'\")\n",
-    "    l_ph_desc_ids = ph_desc.concept_id.to_list()\n",
-    "    ph_icd10_ids = list(concept_relationship.query(\"concept_id_1==@l_ph_desc_ids\").query(\"concept_id_2 == @icd10_ids\").concept_id_2.unique())\n",
-    "    #ph_icd10_ids = list(concept_relationship.set_index(\"concept_id_1\").query(\"index==@l_ph_desc_ids\").query(\"concept_id_2 == @icd10_ids\").query(\"relationship_id == 'Mapped from'\").concept_id_2.unique()\n",
-    "    \n",
-    "    #ph_icd10_ids = list(temp.concept_id_2.unique())\n",
-    "    df = concept.query(\"concept_id == @ph_icd10_ids & vocabulary_id == 'ICD10CM'\")\n",
-    "    icd10_list = list(df[~df.concept_code.str.contains(\"OMOP\", na=False)].concept_code.unique())\n",
-    "    icd10_list = sorted(list(set([e[:3] for e in icd10_list])))\n",
-    "    #print(f\"{ph}: {icd10_list}\")\n",
-    "    return {ph: sorted(list(dict.fromkeys([str(e) for e in icd10_list])))}\n",
-    "\n",
-    "from joblib import Parallel, delayed\n",
-    "concept_ids = vocab[\"concept\"].query(\"(vocabulary_id == 'SNOMED') | (vocabulary_id == 'ICD10CM')\")\n",
-    "vocab_concept_ids = concept_ids.concept_id.to_list()\n",
-    "concept_ancestor = vocab[\"concept_ancestor\"][[\"ancestor_concept_id\", \"descendant_concept_id\"]].query(\"ancestor_concept_id == @vocab_concept_ids\")\n",
-    "concept_rel = vocab[\"concept_relationship\"][[\"concept_id_1\", \"concept_id_2\", \"relationship_id\"]].query(\"(concept_id_1 == @vocab_concept_ids) & (concept_id_2 == @vocab_concept_ids) & (relationship_id == 'Mapped from')\")\n",
-    "icd10_codes = Parallel(n_jobs=10, require=\"sharedmem\")(delayed(map_snomed_to_icd10)(ph, snomed_code, \n",
-    "                                                              concept_ids, concept_ancestor, concept_rel) for ph, snomed_code in tqdm(phenotype_list_snomed.items()))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:39:55.348009Z",
-     "start_time": "2020-11-04T12:39:55.344704Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "l10_snomed = {}\n",
-    "for ph in icd10_codes: l10_snomed.update(ph)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:39:55.468545Z",
-     "start_time": "2020-11-04T12:39:55.349545Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "phenotype_list_basic = {\n",
-    "    \"coronary_heart_disease\": [\"Ischemic heart disease\"],\n",
-    "    \"myocardial_infarction\": [\"Myocardial infarction\"],\n",
-    "    \"stroke\": [\"Cerebrovascular disease\"],\n",
-    "    \"diabetes1\" : [\"Type 1 diabetes\"],\n",
-    "    \"diabetes2\" : [\"Diabetes mellitus\", \"Type 2 diabetes\"],\n",
-    "    \"chronic_kidney_disease\": [\"Chronic kidney disease\", \"chronic renal failure\"],\n",
-    "    \"atrial_fibrillation\": [\"Atrial fibrillation\", \"Atrial flutter\", \"paroxysmal tachycardia\"],\n",
-    "    \"migraine\": [\"Migraine\"],\n",
-    "    \"rheumatoid_arthritis\": [\"Rheumatoid arthritis\"],\n",
-    "    \"systemic_lupus_erythematosus\": [\"Systemic lupus erythematosus\"],\n",
-    "    \"severe_mental_illness\": [\"Schizophrenia\", \"bipolar\", \"Major depressive disorder\"],\n",
-    "    \"erectile_dysfunction\" : [\"Erectile dysfunction\"],  \n",
-    "}\n",
-    "\n",
-    "l10_basic = phenotype_children(phecodes, phenotype_list_basic)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:39:55.473556Z",
-     "start_time": "2020-11-04T12:39:55.471051Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "l10_all = l10_basic\n",
-    "for key, value in l10_snomed.items(): \n",
-    "    if key not in l10_basic: l10_all[key] = value"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:39:55.620916Z",
-     "start_time": "2020-11-04T12:39:55.475137Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "l10 = {k: v for k, v in l10_all.items() if len(v)!=0}\n",
-    "\n",
-    "#phenotype_list = {k: v for k, v in phenotype_list.items() if k in list(l10.keys())}\n",
-    "\n",
-    "with open(os.path.join(path, dataset_path, 'phenotype_list.yaml'), 'w') as file: yaml.dump(l10, file, default_flow_style=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 1. Self Reported"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:43:13.288198Z",
-     "start_time": "2020-11-04T12:43:13.264683Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "coding609 = pd.read_csv(f\"{path}/mapping/codings/coding609.tsv\", sep=\"\\t\").rename(columns={\"coding\":\"code\"})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:43:13.304423Z",
-     "start_time": "2020-11-04T12:43:13.289982Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from datetime import datetime, timedelta\n",
-    "\n",
-    "def datetime_from_dec_year(dec_year):\n",
-    "    start = dec_year\n",
-    "    year = int(start)\n",
-    "    rem = start - year\n",
-    "\n",
-    "    base = datetime(year, 1, 1)\n",
-    "    result = base + timedelta(seconds=(base.replace(year=base.year + 1) - base).total_seconds() * rem)\n",
-    "    #result.strftime(\"%Y-%m-%d\")\n",
-    "    return result.date()\n",
-    "\n",
-    "def extract_map_self_reported(data, data_field, code_map):\n",
-    "    pbar = tqdm(total=16)\n",
-    "    ### codes\n",
-    "    fields = [\"20002\"]; pbar.update(1)\n",
-    "    raw = get_data_fields_all(fields, data, data_field); pbar.update(1)\n",
-    "    col = \"noncancer_illness_code_selfreported_f20002\"; pbar.update(1)\n",
-    "    temp = pd.wide_to_long(raw, stubnames=[col], i=\"eid\", j=\"instance_index\", sep=\"_\", suffix=\"\\w+\").reset_index(); pbar.update(1)\n",
-    "    codes = temp.rename(columns={col:\"code\"})\\\n",
-    "        .assign(code=lambda x: x.code.astype(str))\\\n",
-    "        .replace(\"None\", np.nan) \\\n",
-    "        .dropna(subset=[\"code\"], axis=0)\\\n",
-    "        .assign(code=lambda x: x.code.astype(int)) \\\n",
-    "        .merge(code_map, how=\"left\",on=\"code\") \\\n",
-    "        .dropna(subset=[\"meaning\"], axis=0)\\\n",
-    "        .sort_values([\"eid\", \"instance_index\"]) \\\n",
-    "        .reset_index(drop=True); pbar.update(1)\n",
-    "    \n",
-    "    ### dates\n",
-    "    fields = [\"20008\"]; pbar.update(1)\n",
-    "    raw = get_data_fields_all(fields, data, data_field); pbar.update(1)\n",
-    "    col=\"interpolated_year_when_noncancer_illness_first_diagnosed_f20008\"; pbar.update(1)\n",
-    "    temp = pd.wide_to_long(raw, stubnames=[col], i=\"eid\", j=\"instance_index\", sep=\"_\", suffix=\"\\w+\").reset_index(); pbar.update(1)\n",
-    "    dates = temp.rename(columns={col:\"date\"})\\\n",
-    "        .dropna(subset=[\"date\"], axis=0)\\\n",
-    "        .sort_values([\"eid\", \"instance_index\"]) \\\n",
-    "        .reset_index(drop=True); pbar.update(1)\n",
-    "\n",
-    "    dates = dates[dates.date!=-1]; pbar.update(1)\n",
-    "    dates = dates[dates.date!=-3]; pbar.update(1)\n",
-    "    dates.date = dates.date.apply(datetime_from_dec_year); pbar.update(1)\n",
-    "    \n",
-    "    test = codes.merge(dates, how=\"left\", on=[\"eid\", \"instance_index\"]).assign(origin=\"self_reported\").copy(); pbar.update(1)\n",
-    "    \n",
-    "    test[\"instance_index\"] = test[\"instance_index\"].astype(\"string\"); pbar.update(1)\n",
-    "    test[['instance','n']] = test.instance_index.str.split(\"_\",expand=True); pbar.update(1)\n",
-    "    pbar.close()\n",
-    "    \n",
-    "    return test[[\"eid\", \"origin\", 'instance','n', \"code\", \"meaning\", \"date\"]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:46:00.098893Z",
-     "start_time": "2020-11-04T12:43:13.432479Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "7af3e55ecb424d5fbbd303f1f5b67a66",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(FloatProgress(value=0.0, max=16.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "codes_self_reported = extract_map_self_reported(data, data_field, coding609)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 2. Primary Care"
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {},
-   "source": [
-    "# 2. Primary Care\n",
-    "fields = c(\n",
-    "    42040 # GP clinical event records\n",
-    ")\n",
-    "# extract covariates at baseline (Instance 0)\n",
-    "def = data_field %>% filter((field.showcase %in% fields),ignore.case = TRUE)\n",
-    "diagnoses_primary_care = data[, append(\"eid\", def$col.name)]\n",
-    "head(diagnoses_primary_care)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 3. Hospital episode statistics"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:46:08.959617Z",
-     "start_time": "2020-11-04T12:46:00.100618Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "codes_hospital_records = pd.read_feather(f\"{data_path}/1_decoded/codes_hospital_records.feather\").drop(\"level\", axis=1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Combine diagnoses and events"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:46:25.115010Z",
-     "start_time": "2020-11-04T12:46:08.961388Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>origin</th>\n",
-       "      <th>instance</th>\n",
-       "      <th>n</th>\n",
-       "      <th>code</th>\n",
-       "      <th>meaning</th>\n",
-       "      <th>date</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>hes_icd10</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>S0240</td>\n",
-       "      <td>S02</td>\n",
-       "      <td>2005-06-02</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>hes_icd10</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>W188</td>\n",
-       "      <td>W18</td>\n",
-       "      <td>2005-06-02</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>hes_icd10</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>K37</td>\n",
-       "      <td>K37</td>\n",
-       "      <td>1998-05-11</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>hes_icd10</td>\n",
-       "      <td>0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>K37</td>\n",
-       "      <td>K37</td>\n",
-       "      <td>1998-05-16</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>hes_icd10</td>\n",
-       "      <td>0</td>\n",
-       "      <td>5</td>\n",
-       "      <td>K37</td>\n",
-       "      <td>K37</td>\n",
-       "      <td>1998-06-01</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       eid     origin instance  n   code meaning        date\n",
-       "0  1000018  hes_icd10        0  1  S0240     S02  2005-06-02\n",
-       "1  1000018  hes_icd10        0  2   W188     W18  2005-06-02\n",
-       "2  1000018  hes_icd10        0  3    K37     K37  1998-05-11\n",
-       "3  1000018  hes_icd10        0  4    K37     K37  1998-05-16\n",
-       "4  1000018  hes_icd10        0  5    K37     K37  1998-06-01"
-      ]
-     },
-     "execution_count": 51,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "diagnoses_codes = codes_self_reported.append(codes_hospital_records).sort_values([\"eid\", \"instance\", \"n\"]).dropna(subset=[\"date\"], axis=0)\n",
-    "diagnoses_codes.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "RangeIndex: 13339466 entries, 0 to 13339465\n",
-      "Data columns (total 7 columns):\n",
-      " #   Column    Dtype \n",
-      "---  ------    ----- \n",
-      " 0   eid       int64 \n",
-      " 1   origin    object\n",
-      " 2   instance  object\n",
-      " 3   n         object\n",
-      " 4   code      object\n",
-      " 5   meaning   object\n",
-      " 6   date      object\n",
-      "dtypes: int64(1), object(6)\n",
-      "memory usage: 712.4+ MB\n"
-     ]
-    }
-   ],
-   "source": [
-    "diagnoses_codes.reset_index(drop=True).info()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "RangeIndex: 12547626 entries, 0 to 12547625\n",
-      "Data columns (total 7 columns):\n",
-      " #   Column    Dtype  \n",
-      "---  ------    -----  \n",
-      " 0   eid       int32  \n",
-      " 1   origin    object \n",
-      " 2   instance  float64\n",
-      " 3   n         int32  \n",
-      " 4   code      object \n",
-      " 5   meaning   object \n",
-      " 6   date      object \n",
-      "dtypes: float64(1), int32(2), object(4)\n",
-      "memory usage: 574.4+ MB\n"
-     ]
-    }
-   ],
-   "source": [
-    "codes_hospital_records.info()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "diagnoses_codes.reset_index(drop=True).assign(eid = lambda x: x.eid.astype(int),\n",
-    "                                              origin = lambda x: x.origin.astype(str),\n",
-    "                                              instance = lambda x: x.instance.astype(int),\n",
-    "                                              n = lambda x: x.n.astype(int),\n",
-    "                                              code = lambda x: x.code.astype(str), \n",
-    "                                              meaning = lambda x: x.meaning.astype(str))\\\n",
-    "    .to_feather(os.path.join(path, dataset_path, 'temp_diagnoses_codes.feather'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "diagnoses_codes = pd.read_feather(os.path.join(path, dataset_path, 'temp_diagnoses_codes.feather'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:46:26.189927Z",
-     "start_time": "2020-11-04T12:46:25.117069Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from joblib import Parallel, delayed\n",
-    "from functools import reduce\n",
-    "from numba import jit\n",
-    "\n",
-    "def had_diagnosis_before_per_ph(df_before, ph, ph_codes, temp):\n",
-    "   # regex = \"|\".join(ph_codes)\n",
-    "    #df_ph = df_before.set_index(\"meaning\").loc[ph_codes][[\"eid\"]]\\\n",
-    "    #        .drop_duplicates(subset=[\"eid\"])\\\n",
-    "    #        .assign(phenotype=True) \n",
-    "    df_ph = df_before[df_before.meaning.isin(ph_codes)][[\"eid\"]]\\\n",
-    "            .drop_duplicates(subset=[\"eid\"])\\\n",
-    "            .assign(phenotype=True) \n",
-    "    #df_ph = df_before[df_before.meaning.str.contains(regex, case=False)][[\"eid\"]]\\\n",
-    "    #        .drop_duplicates(subset=[\"eid\"])\\\n",
-    "    #        .assign(phenotype=True)   \n",
-    "    return temp.merge(df_ph, how=\"left\", on=\"eid\").fillna(False).phenotype\n",
-    "\n",
-    "def had_diagnosis_before(data, diagnoses_codes, phenotypes, time0=time0_col):\n",
-    "    diagnoses_codes_time = diagnoses_codes.merge(data[[\"eid\", time0]], how=\"left\", on=\"eid\")\n",
-    "    \n",
-    "    temp = data[[\"eid\"]].copy()\n",
-    "    df_before = diagnoses_codes_time[diagnoses_codes_time.date < diagnoses_codes_time[time0]]\n",
-    "                                                                                         \n",
-    "    df_phs = Parallel(n_jobs=20, require=\"sharedmem\")(delayed(had_diagnosis_before_per_ph)(df_before, ph, phenotypes[ph], temp) for ph in tqdm(list(phenotypes)))\n",
-    "    for ph, df_ph_series in zip(tqdm(list(phenotypes)), df_phs): temp[ph] = df_ph_series#temp.merge(df_ph, how=\"left\", on=\"eid\").fillna(False).phenotype\n",
-    "    \n",
-    "    return temp.sort_values(\"eid\")  #reduce(lambda left,right: pd.merge(left,right,on=['eid'], how='left'), df_phs)."
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "source": [
-    "from joblib import Parallel, delayed\n",
-    "from functools import reduce\n",
-    "from numba import jit\n",
-    "\n",
-    "def had_diagnosis_before_per_ph(df_before, ph, ph_codes, temp):\n",
-    "    regex = \"|\".join(ph_codes)\n",
-    "    df_ph = df_before[df_before.meaning.str.contains(regex, case=False)][[\"eid\"]]\\\n",
-    "            .drop_duplicates(subset=[\"eid\"])\\\n",
-    "            .assign(phenotype=True)   \n",
-    "    return temp.merge(df_ph, how=\"left\", on=\"eid\").fillna(False).phenotype\n",
-    "\n",
-    "def had_diagnosis_before(data, diagnoses_codes, phenotypes, time0=time0_col):\n",
-    "    diagnoses_codes_time = diagnoses_codes.merge(data[[\"eid\", time0]], how=\"left\", on=\"eid\")\n",
-    "    \n",
-    "    temp = data[[\"eid\"]].copy()\n",
-    "    df_before = diagnoses_codes_time[diagnoses_codes_time.date < diagnoses_codes_time[time0]]\n",
-    "                                                                                         \n",
-    "    df_phs = Parallel(n_jobs=30, require=\"sharedmem\")(delayed(had_diagnosis_before_per_ph)(df_before, ph, phenotypes[ph], temp) for ph in tqdm(list(phenotypes)))\n",
-    "    for ph, ph_series in zip(tqdm(list(phenotypes)), df_phs): temp[ph] = ph_series\n",
-    "    \n",
-    "    return temp.sort_values(\"eid\")  #reduce(lambda left,right: pd.merge(left,right,on=['eid'], how='left'), df_phs)."
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "source": [
-    "phenotypes = l10\n",
-    "time0 = time0_col\n",
-    "diagnoses_codes_time = diagnoses_codes.merge(data[[\"eid\", time0]], how=\"left\", on=\"eid\")\n",
-    "\n",
-    "temp = data[[\"eid\"]].copy()\n",
-    "df_before = diagnoses_codes_time[diagnoses_codes_time.date < diagnoses_codes_time[time0]]\n",
-    "\n",
-    "df_phs = Parallel(n_jobs=10, require=\"sharedmem\")(delayed(had_diagnosis_before_per_ph)(df_before, ph, phenotypes[ph], temp) for ph in tqdm(list(phenotypes)[0:100]))"
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "source": [
-    "def had_diagnosis_before(data, diagnoses_codes, phenotypes, time0=time0_col):\n",
-    "    diagnoses_codes_time = diagnoses_codes.merge(data[[\"eid\", time0]], how=\"left\", on=\"eid\")\n",
-    "    \n",
-    "    temp = data[[\"eid\"]].copy()\n",
-    "    df_before = diagnoses_codes_time[diagnoses_codes_time.date < diagnoses_codes_time[time0]]\n",
-    "    for ph, ph_codes in tqdm(phenotypes.items()):\n",
-    "        regex = \"|\".join(ph_codes)\n",
-    "        df_ph = df_before[df_before.meaning.str.contains(regex, case=False)][[\"eid\"]]\\\n",
-    "            .drop_duplicates(subset=[\"eid\"])\\\n",
-    "            .assign(phenotype=True)\n",
-    "        temp[ph] = temp.merge(df_ph, how=\"left\", on=\"eid\").fillna(False).phenotype\n",
-    "       \n",
-    "    return temp.sort_values(\"eid\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T13:17:52.922023Z",
-     "start_time": "2020-11-04T12:46:26.191314Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6bea6215cd7a462ea5e12e755d4a70ed",
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-      },
-      "text/plain": [
-       "HBox(children=(FloatProgress(value=0.0, max=3521.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
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-       "HBox(children=(FloatProgress(value=0.0, max=3521.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "502504\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>coronary_heart_disease</th>\n",
-       "      <th>myocardial_infarction</th>\n",
-       "      <th>stroke</th>\n",
-       "      <th>diabetes1</th>\n",
-       "      <th>diabetes2</th>\n",
-       "      <th>chronic_kidney_disease</th>\n",
-       "      <th>atrial_fibrillation</th>\n",
-       "      <th>migraine</th>\n",
-       "      <th>rheumatoid_arthritis</th>\n",
-       "      <th>...</th>\n",
-       "      <th>sleep_terror_disorder</th>\n",
-       "      <th>acute_frontal_sinusitis</th>\n",
-       "      <th>benign_neoplasm_of_pancreas</th>\n",
-       "      <th>primary_malignant_neoplasm_of_soft_tissues_of_lower_limb</th>\n",
-       "      <th>neoplasm_of_uncertain_behavior_of_neck</th>\n",
-       "      <th>injury_of_peroneal_nerve</th>\n",
-       "      <th>dupuytren's_disease</th>\n",
-       "      <th>stem_cell_donor</th>\n",
-       "      <th>endemic_goiter</th>\n",
-       "      <th>diplegic_cerebral_palsy</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1000020</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1000037</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000043</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1000051</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 3522 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       eid  coronary_heart_disease  myocardial_infarction  stroke  diabetes1  \\\n",
-       "0  1000018                   False                  False   False      False   \n",
-       "1  1000020                   False                  False   False      False   \n",
-       "2  1000037                   False                  False   False      False   \n",
-       "3  1000043                   False                  False   False      False   \n",
-       "4  1000051                   False                  False   False      False   \n",
-       "\n",
-       "   diabetes2  chronic_kidney_disease  atrial_fibrillation  migraine  \\\n",
-       "0      False                   False                False     False   \n",
-       "1      False                   False                False     False   \n",
-       "2      False                   False                False     False   \n",
-       "3      False                   False                False     False   \n",
-       "4      False                   False                False     False   \n",
-       "\n",
-       "   rheumatoid_arthritis  ...  sleep_terror_disorder  acute_frontal_sinusitis  \\\n",
-       "0                 False  ...                  False                    False   \n",
-       "1                 False  ...                  False                    False   \n",
-       "2                 False  ...                  False                    False   \n",
-       "3                 False  ...                  False                    False   \n",
-       "4                 False  ...                  False                    False   \n",
-       "\n",
-       "   benign_neoplasm_of_pancreas  \\\n",
-       "0                        False   \n",
-       "1                        False   \n",
-       "2                        False   \n",
-       "3                        False   \n",
-       "4                        False   \n",
-       "\n",
-       "   primary_malignant_neoplasm_of_soft_tissues_of_lower_limb  \\\n",
-       "0                                              False          \n",
-       "1                                              False          \n",
-       "2                                              False          \n",
-       "3                                              False          \n",
-       "4                                              False          \n",
-       "\n",
-       "   neoplasm_of_uncertain_behavior_of_neck  injury_of_peroneal_nerve  \\\n",
-       "0                                   False                     False   \n",
-       "1                                   False                     False   \n",
-       "2                                   False                     False   \n",
-       "3                                   False                     False   \n",
-       "4                                   False                     False   \n",
-       "\n",
-       "   dupuytren's_disease  stem_cell_donor  endemic_goiter  \\\n",
-       "0                False            False           False   \n",
-       "1                False            False           False   \n",
-       "2                False            False           False   \n",
-       "3                False            False           False   \n",
-       "4                False            False           False   \n",
-       "\n",
-       "   diplegic_cerebral_palsy  \n",
-       "0                    False  \n",
-       "1                    False  \n",
-       "2                    False  \n",
-       "3                    False  \n",
-       "4                    False  \n",
-       "\n",
-       "[5 rows x 3522 columns]"
-      ]
-     },
-     "execution_count": 61,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "diagnoses = had_diagnosis_before(basics, diagnoses_codes, l10, time0=time0_col)\n",
-    "print(len(diagnoses))\n",
-    "\n",
-    "diagnoses.to_feather(os.path.join(path, dataset_path, 'temp_diagnoses.feather'))\n",
-    "\n",
-    "diagnoses.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "diagnoses = pd.read_feather(os.path.join(path, dataset_path, 'temp_diagnoses.feather'))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "source": [
-    "# Add embeddings for Snomed Diagnoses"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "source": [
-    "#### Get SNOMED - node2vec dict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "outputs": [],
-   "source": [
-    "snomed_embeddings = pd.read_csv(\"/data/analysis/ag-reils/steinfej/data/snomed_embeddings/snomed.emb.p1.q1.w20.l40.e200.graph_format.txt\", sep=\" \", header=None, skiprows=1)\n",
-    "snomed_embeddings.columns = [\"snomed_id\"]+list(snomed_embeddings.columns)[:-1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "metadata": {
-    "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true,
-     "source_hidden": true
-    }
-   },
-   "outputs": [
-    {
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-       "\n",
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-       "\n",
-       "    .dataframe thead th {\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>snomed_id</th>\n",
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-       "      <th>0</th>\n",
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-       "      <td>0.027456</td>\n",
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-       "      <td>-0.178822</td>\n",
-       "      <td>-0.038667</td>\n",
-       "      <td>0.299777</td>\n",
-       "      <td>0.082540</td>\n",
-       "      <td>-0.201284</td>\n",
-       "      <td>0.215164</td>\n",
-       "      <td>0.093241</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.211030</td>\n",
-       "      <td>0.488475</td>\n",
-       "      <td>0.082177</td>\n",
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-       "      <td>-0.086411</td>\n",
-       "      <td>-0.012141</td>\n",
-       "      <td>-0.258416</td>\n",
-       "      <td>-0.113295</td>\n",
-       "      <td>0.040249</td>\n",
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-       "      <td>-0.184378</td>\n",
-       "      <td>-0.028722</td>\n",
-       "      <td>-0.010235</td>\n",
-       "      <td>0.242439</td>\n",
-       "      <td>-0.094009</td>\n",
-       "      <td>0.203015</td>\n",
-       "      <td>-0.097894</td>\n",
-       "      <td>...</td>\n",
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-       "      <td>0.333906</td>\n",
-       "      <td>-0.018193</td>\n",
-       "      <td>0.020459</td>\n",
-       "      <td>-0.170226</td>\n",
-       "      <td>0.277866</td>\n",
-       "      <td>-0.007079</td>\n",
-       "      <td>-0.080419</td>\n",
-       "      <td>0.162456</td>\n",
-       "      <td>-0.101768</td>\n",
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-       "      <th>2</th>\n",
-       "      <td>102272007</td>\n",
-       "      <td>0.553420</td>\n",
-       "      <td>0.460947</td>\n",
-       "      <td>0.143925</td>\n",
-       "      <td>-0.053785</td>\n",
-       "      <td>-0.849588</td>\n",
-       "      <td>0.467587</td>\n",
-       "      <td>-0.654922</td>\n",
-       "      <td>-0.010799</td>\n",
-       "      <td>0.510141</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.187371</td>\n",
-       "      <td>0.248607</td>\n",
-       "      <td>0.079687</td>\n",
-       "      <td>-0.354121</td>\n",
-       "      <td>0.412602</td>\n",
-       "      <td>0.582461</td>\n",
-       "      <td>-0.780365</td>\n",
-       "      <td>0.045450</td>\n",
-       "      <td>-0.196551</td>\n",
-       "      <td>0.045745</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>39937001</td>\n",
-       "      <td>0.123158</td>\n",
-       "      <td>0.019882</td>\n",
-       "      <td>0.050896</td>\n",
-       "      <td>0.037364</td>\n",
-       "      <td>0.200435</td>\n",
-       "      <td>0.312911</td>\n",
-       "      <td>-0.338977</td>\n",
-       "      <td>-0.092584</td>\n",
-       "      <td>-0.167741</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.057770</td>\n",
-       "      <td>-0.012834</td>\n",
-       "      <td>-0.139794</td>\n",
-       "      <td>0.180572</td>\n",
-       "      <td>0.190781</td>\n",
-       "      <td>0.104039</td>\n",
-       "      <td>-0.358235</td>\n",
-       "      <td>-0.137954</td>\n",
-       "      <td>-0.236551</td>\n",
-       "      <td>-0.206458</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>23583003</td>\n",
-       "      <td>0.423193</td>\n",
-       "      <td>0.384338</td>\n",
-       "      <td>-0.041503</td>\n",
-       "      <td>0.116848</td>\n",
-       "      <td>-0.055029</td>\n",
-       "      <td>0.149064</td>\n",
-       "      <td>-0.092810</td>\n",
-       "      <td>-0.050148</td>\n",
-       "      <td>0.113122</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.162375</td>\n",
-       "      <td>-0.076505</td>\n",
-       "      <td>-0.274352</td>\n",
-       "      <td>-0.099204</td>\n",
-       "      <td>-0.281887</td>\n",
-       "      <td>-0.266345</td>\n",
-       "      <td>-0.020257</td>\n",
-       "      <td>-0.003843</td>\n",
-       "      <td>-0.008804</td>\n",
-       "      <td>-0.286832</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 201 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   snomed_id         0         1         2         3         4         5  \\\n",
-       "0  129265001  0.027456  0.171026 -0.178822 -0.038667  0.299777  0.082540   \n",
-       "1  360224006 -0.090187  0.086986 -0.184378 -0.028722 -0.010235  0.242439   \n",
-       "2  102272007  0.553420  0.460947  0.143925 -0.053785 -0.849588  0.467587   \n",
-       "3   39937001  0.123158  0.019882  0.050896  0.037364  0.200435  0.312911   \n",
-       "4   23583003  0.423193  0.384338 -0.041503  0.116848 -0.055029  0.149064   \n",
-       "\n",
-       "          6         7         8  ...       190       191       192       193  \\\n",
-       "0 -0.201284  0.215164  0.093241  ...  0.211030  0.488475  0.082177 -0.047102   \n",
-       "1 -0.094009  0.203015 -0.097894  ...  0.112750  0.333906 -0.018193  0.020459   \n",
-       "2 -0.654922 -0.010799  0.510141  ...  0.187371  0.248607  0.079687 -0.354121   \n",
-       "3 -0.338977 -0.092584 -0.167741  ...  0.057770 -0.012834 -0.139794  0.180572   \n",
-       "4 -0.092810 -0.050148  0.113122  ...  0.162375 -0.076505 -0.274352 -0.099204   \n",
-       "\n",
-       "        194       195       196       197       198       199  \n",
-       "0 -0.086411 -0.012141 -0.258416 -0.113295  0.040249 -0.116884  \n",
-       "1 -0.170226  0.277866 -0.007079 -0.080419  0.162456 -0.101768  \n",
-       "2  0.412602  0.582461 -0.780365  0.045450 -0.196551  0.045745  \n",
-       "3  0.190781  0.104039 -0.358235 -0.137954 -0.236551 -0.206458  \n",
-       "4 -0.281887 -0.266345 -0.020257 -0.003843 -0.008804 -0.286832  \n",
-       "\n",
-       "[5 rows x 201 columns]"
-      ]
-     },
-     "execution_count": 64,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "snomed_embeddings.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "outputs": [],
-   "source": [
-    "diagnoses_snomed = diagnoses.columns[13:].to_list()\n",
-    "diagnoses_snomed_dict = {}\n",
-    "for d in diagnoses_snomed: diagnoses_snomed_dict[d] = phenotype_list_snomed[d]\n",
-    "    \n",
-    "snomed_codes_used = list(phenotype_list_snomed.values())\n",
-    "snomed_codes_emb = snomed_embeddings.snomed_id.to_list()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "outputs": [],
-   "source": [
-    "snomed_id_array = snomed_embeddings[[\"snomed_id\"]].values\n",
-    "node2vec_array = snomed_embeddings.iloc[:, 1:].values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "metadata": {
-    "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true,
-     "source_hidden": true
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "456f56d7481648e48b74e9e76b675f01",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(FloatProgress(value=0.0, max=373286.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "snomed_arrays = {}\n",
-    "for sid, row in zip(tqdm(snomed_id_array), node2vec_array):\n",
-    "    snomed_arrays[sid[0]] = row"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "source": [
-    "#### Get Patient -> Snomed dict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 68,
-   "metadata": {
-    "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true,
-     "source_hidden": true
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "05ac46541972426da8a5b5f7fe461c6e",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(FloatProgress(value=0.0, max=502504.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "diagnoses_array = diagnoses[diagnoses_snomed].values\n",
-    "eid_array = diagnoses[[\"eid\"]].values\n",
-    "\n",
-    "from numba import jit\n",
-    "import numpy as np\n",
-    "\n",
-    "patient_diagnoses = {}\n",
-    "for eid, row in zip(tqdm(eid_array), diagnoses_array):\n",
-    "    patient_diagnoses[eid[0]] = list(np.argwhere(row==True).flatten())\n",
-    "\n",
-    "#diagnoses.query(\"eid==1000092\")[diagnoses_snomed]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 69,
-   "metadata": {
-    "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true,
-     "source_hidden": true
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f4c90d91d08e48119abcf8426cddd37e",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(FloatProgress(value=0.0, max=502504.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "patient_diagnoses_sid = {}\n",
-    "snomed_codes_emb_set = set(snomed_codes_emb)\n",
-    "for eid, p_d_col in tqdm(patient_diagnoses.items()):\n",
-    "    diagnoses_list = [diagnoses_snomed[i] for i in p_d_col]\n",
-    "    sid_list = [phenotype_list_snomed[i] for i in diagnoses_list]\n",
-    "    sid_list = [sid for sid in sid_list if sid in snomed_codes_emb_set]\n",
-    "    patient_diagnoses_sid[eid] = sid_list"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "source": [
-    "#### Get Patient -> node2vec average"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 70,
-   "metadata": {
-    "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true,
-     "source_hidden": true
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "47e934dc64334c82893d23b458108fbf",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(FloatProgress(value=0.0, max=502504.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/steinfej/miniconda3/envs/python/lib/python3.7/site-packages/numpy/core/fromnumeric.py:3335: RuntimeWarning: Mean of empty slice.\n",
-      "  out=out, **kwargs)\n",
-      "/home/steinfej/miniconda3/envs/python/lib/python3.7/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars\n",
-      "  ret = ret.dtype.type(ret / rcount)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "patient_node2vec_dict = {}\n",
-    "for eid, sids in tqdm(patient_diagnoses_sid.items()):\n",
-    "    array_list =  [snomed_arrays[sid] for sid in sids]\n",
-    "    patient_node2vec_dict[eid] = np.mean(array_list, axis=0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "metadata": {
-    "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true,
-     "source_hidden": true
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e1395c2d1bfc41f39697a8925b20d597",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(FloatProgress(value=0.0, max=502504.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "array([ 0.37033895,  0.13321076, -0.10030267,  0.05625264, -0.08437331,\n",
-       "        0.11504787, -0.24833219, -0.11936529,  0.25239648, -0.32739932,\n",
-       "        0.06330914, -0.06493541, -0.28790415, -0.2885537 ,  0.02546298,\n",
-       "       -0.06123153, -0.17690672, -0.0867546 , -0.28337599, -0.08474496,\n",
-       "        0.31454532, -0.24158154,  0.15930864,  0.06124846,  0.07694602,\n",
-       "       -0.13752803,  0.13671128, -0.27988751, -0.03987635,  0.03632156,\n",
-       "       -0.13793361, -0.15857369, -0.16441636,  0.2412931 ,  0.20070578,\n",
-       "       -0.11412784,  0.02563965, -0.03560184,  0.17169744, -0.0153383 ,\n",
-       "        0.00117099,  0.1025362 , -0.06505568,  0.05646065, -0.02705149,\n",
-       "        0.04416442, -0.15798991, -0.10650637,  0.02082507, -0.21182802,\n",
-       "        0.13972325, -0.18089307, -0.12731068,  0.02907221, -0.19797107,\n",
-       "        0.19550177,  0.14941799,  0.21561857, -0.18085379,  0.10768238,\n",
-       "        0.12968045,  0.2082016 ,  0.03561408, -0.01122218, -0.27099816,\n",
-       "       -0.06029919, -0.18787618,  0.10084175, -0.07939234, -0.22951897,\n",
-       "       -0.36536359, -0.01050854, -0.21419807, -0.23562986,  0.02380316,\n",
-       "        0.1213157 , -0.1601396 ,  0.07218057, -0.04362593, -0.22303355,\n",
-       "       -0.23844839, -0.14799905,  0.22346404,  0.14218655,  0.39873181,\n",
-       "       -0.32965129,  0.19102432, -0.08894028, -0.20726567,  0.25343222,\n",
-       "        0.29939455,  0.20513029,  0.17430424, -0.05073184,  0.05827969,\n",
-       "        0.31626954,  0.0791522 , -0.21433214,  0.11027244,  0.0805151 ,\n",
-       "        0.08557279,  0.25260801, -0.00096969, -0.06177831,  0.0544524 ,\n",
-       "       -0.17340027,  0.09731447,  0.09982385, -0.074546  , -0.10350239,\n",
-       "       -0.25798929,  0.01222471,  0.1605315 ,  0.00191767,  0.11642527,\n",
-       "       -0.15387604, -0.01520803,  0.03220765, -0.13534233,  0.09154689,\n",
-       "        0.12562997, -0.01667786, -0.09286805,  0.14670483, -0.13097813,\n",
-       "       -0.38270284, -0.0129984 , -0.06352304,  0.12521014, -0.17354363,\n",
-       "       -0.16399127,  0.11488736,  0.39664734,  0.08557075, -0.24862126,\n",
-       "       -0.05882866,  0.23174289,  0.01765143, -0.10257617,  0.01208248,\n",
-       "       -0.06876405, -0.04180976,  0.07489962, -0.10724381,  0.29349823,\n",
-       "        0.17342743, -0.32044841, -0.01118798,  0.28508293,  0.16492201,\n",
-       "        0.03994952, -0.17309702,  0.0365077 ,  0.08619365, -0.21749571,\n",
-       "        0.01038752, -0.21414902, -0.18092813,  0.28433131, -0.12601774,\n",
-       "       -0.02147302,  0.51513453,  0.21506432, -0.12596341, -0.0413009 ,\n",
-       "        0.12595327,  0.08973215, -0.06285449, -0.03649041,  0.04217289,\n",
-       "       -0.02532634, -0.21734533, -0.08211848,  0.14014083,  0.13635479,\n",
-       "       -0.13163414, -0.08576438, -0.04016334,  0.23952563, -0.14895943,\n",
-       "        0.08564821, -0.04287149,  0.22135877, -0.06337237, -0.07189574,\n",
-       "       -0.1004494 ,  0.01225299, -0.13810014, -0.06814497, -0.16715941,\n",
-       "        0.07303671,  0.15891015, -0.04757821, -0.05777645, -0.0772216 ,\n",
-       "        0.16787738, -0.2112512 , -0.10142653, -0.19002809, -0.06400223])"
-      ]
-     },
-     "execution_count": 71,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# create imputation vector\n",
-    "arrays = [patient_node2vec_dict[key] for key in list(patient_node2vec_dict)]\n",
-    "arrays_ok = [array for array in tqdm(arrays) if ~np.isnan(array).any()]\n",
-    "imp_vector = np.mean(arrays_ok, axis=0)\n",
-    "imp_vector"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
-   "metadata": {
-    "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true,
-     "source_hidden": true
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e2dd803c2e2a4cb68e71111979d235c2",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(FloatProgress(value=0.0, max=502504.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "for eid, array in tqdm(patient_node2vec_dict.items()):\n",
-    "    if np.isnan(array).any(): \n",
-    "        patient_node2vec_dict[eid] = imp_vector"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 73,
-   "metadata": {
-    "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true,
-     "source_hidden": true
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a82c47a8f87a4df1bc81b78496edee0a",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(FloatProgress(value=0.0, max=502504.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "array_eids = [key for key in tqdm(list(patient_node2vec_dict))]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 74,
-   "metadata": {
-    "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true,
-     "source_hidden": true
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "510554b8bed8456cbfd694b764286acb",
-       "version_major": 2,
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-      },
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-       "HBox(children=(FloatProgress(value=0.0, max=502504.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "ind_ok = [np.array([1]) if ~np.isnan(array).any() else np.array([0])  for array in tqdm(arrays)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 75,
-   "metadata": {
-    "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true,
-     "source_hidden": true
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a85aec2f3e7644dc99c40781f999eb3c",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(FloatProgress(value=0.0, max=502504.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "arrays_emb = [patient_node2vec_dict[key] for key in tqdm(list(patient_node2vec_dict))]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 76,
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "outputs": [],
-   "source": [
-    "arrays_eids = np.reshape(np.stack(array_eids, axis=0),(-1,1)) \n",
-    "arrays_ind = np.stack(ind_ok, axis=0)\n",
-    "arrays_c = np.stack(arrays_emb, axis=0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 77,
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "outputs": [],
-   "source": [
-    "arrays_complete = np.concatenate([arrays_eids, arrays_ind, arrays_c], axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 78,
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "outputs": [],
-   "source": [
-    "diagnoses_emb = pd.DataFrame(data=arrays_complete, columns=[\"eid\"]+[\"node2vec_available\"]+[f\"node2vec_{e}\" for e in list(range(0, 200))])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 79,
-   "metadata": {
-    "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true,
-     "source_hidden": true
-    }
-   },
-   "outputs": [
-    {
-     "data": {
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-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>node2vec_available</th>\n",
-       "      <th>node2vec_0</th>\n",
-       "      <th>node2vec_1</th>\n",
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-       "      <th>node2vec_6</th>\n",
-       "      <th>node2vec_7</th>\n",
-       "      <th>...</th>\n",
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-       "      <th>node2vec_192</th>\n",
-       "      <th>node2vec_193</th>\n",
-       "      <th>node2vec_194</th>\n",
-       "      <th>node2vec_195</th>\n",
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-       "      <th>node2vec_197</th>\n",
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-       "      <td>-0.248332</td>\n",
-       "      <td>-0.119365</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.073037</td>\n",
-       "      <td>0.15891</td>\n",
-       "      <td>-0.047578</td>\n",
-       "      <td>-0.057776</td>\n",
-       "      <td>-0.077222</td>\n",
-       "      <td>0.167877</td>\n",
-       "      <td>-0.211251</td>\n",
-       "      <td>-0.101427</td>\n",
-       "      <td>-0.190028</td>\n",
-       "      <td>-0.064002</td>\n",
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-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1000020.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.370339</td>\n",
-       "      <td>0.133211</td>\n",
-       "      <td>-0.100303</td>\n",
-       "      <td>0.056253</td>\n",
-       "      <td>-0.084373</td>\n",
-       "      <td>0.115048</td>\n",
-       "      <td>-0.248332</td>\n",
-       "      <td>-0.119365</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.073037</td>\n",
-       "      <td>0.15891</td>\n",
-       "      <td>-0.047578</td>\n",
-       "      <td>-0.057776</td>\n",
-       "      <td>-0.077222</td>\n",
-       "      <td>0.167877</td>\n",
-       "      <td>-0.211251</td>\n",
-       "      <td>-0.101427</td>\n",
-       "      <td>-0.190028</td>\n",
-       "      <td>-0.064002</td>\n",
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-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1000037.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.370339</td>\n",
-       "      <td>0.133211</td>\n",
-       "      <td>-0.100303</td>\n",
-       "      <td>0.056253</td>\n",
-       "      <td>-0.084373</td>\n",
-       "      <td>0.115048</td>\n",
-       "      <td>-0.248332</td>\n",
-       "      <td>-0.119365</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.073037</td>\n",
-       "      <td>0.15891</td>\n",
-       "      <td>-0.047578</td>\n",
-       "      <td>-0.057776</td>\n",
-       "      <td>-0.077222</td>\n",
-       "      <td>0.167877</td>\n",
-       "      <td>-0.211251</td>\n",
-       "      <td>-0.101427</td>\n",
-       "      <td>-0.190028</td>\n",
-       "      <td>-0.064002</td>\n",
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-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000043.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.370339</td>\n",
-       "      <td>0.133211</td>\n",
-       "      <td>-0.100303</td>\n",
-       "      <td>0.056253</td>\n",
-       "      <td>-0.084373</td>\n",
-       "      <td>0.115048</td>\n",
-       "      <td>-0.248332</td>\n",
-       "      <td>-0.119365</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.073037</td>\n",
-       "      <td>0.15891</td>\n",
-       "      <td>-0.047578</td>\n",
-       "      <td>-0.057776</td>\n",
-       "      <td>-0.077222</td>\n",
-       "      <td>0.167877</td>\n",
-       "      <td>-0.211251</td>\n",
-       "      <td>-0.101427</td>\n",
-       "      <td>-0.190028</td>\n",
-       "      <td>-0.064002</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1000051.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.370339</td>\n",
-       "      <td>0.133211</td>\n",
-       "      <td>-0.100303</td>\n",
-       "      <td>0.056253</td>\n",
-       "      <td>-0.084373</td>\n",
-       "      <td>0.115048</td>\n",
-       "      <td>-0.248332</td>\n",
-       "      <td>-0.119365</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.073037</td>\n",
-       "      <td>0.15891</td>\n",
-       "      <td>-0.047578</td>\n",
-       "      <td>-0.057776</td>\n",
-       "      <td>-0.077222</td>\n",
-       "      <td>0.167877</td>\n",
-       "      <td>-0.211251</td>\n",
-       "      <td>-0.101427</td>\n",
-       "      <td>-0.190028</td>\n",
-       "      <td>-0.064002</td>\n",
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-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
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-       "      <td>...</td>\n",
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-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
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-       "    <tr>\n",
-       "      <th>502499</th>\n",
-       "      <td>6025150.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.370339</td>\n",
-       "      <td>0.133211</td>\n",
-       "      <td>-0.100303</td>\n",
-       "      <td>0.056253</td>\n",
-       "      <td>-0.084373</td>\n",
-       "      <td>0.115048</td>\n",
-       "      <td>-0.248332</td>\n",
-       "      <td>-0.119365</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.073037</td>\n",
-       "      <td>0.15891</td>\n",
-       "      <td>-0.047578</td>\n",
-       "      <td>-0.057776</td>\n",
-       "      <td>-0.077222</td>\n",
-       "      <td>0.167877</td>\n",
-       "      <td>-0.211251</td>\n",
-       "      <td>-0.101427</td>\n",
-       "      <td>-0.190028</td>\n",
-       "      <td>-0.064002</td>\n",
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-       "    <tr>\n",
-       "      <th>502500</th>\n",
-       "      <td>6025165.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.370339</td>\n",
-       "      <td>0.133211</td>\n",
-       "      <td>-0.100303</td>\n",
-       "      <td>0.056253</td>\n",
-       "      <td>-0.084373</td>\n",
-       "      <td>0.115048</td>\n",
-       "      <td>-0.248332</td>\n",
-       "      <td>-0.119365</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.073037</td>\n",
-       "      <td>0.15891</td>\n",
-       "      <td>-0.047578</td>\n",
-       "      <td>-0.057776</td>\n",
-       "      <td>-0.077222</td>\n",
-       "      <td>0.167877</td>\n",
-       "      <td>-0.211251</td>\n",
-       "      <td>-0.101427</td>\n",
-       "      <td>-0.190028</td>\n",
-       "      <td>-0.064002</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>502501</th>\n",
-       "      <td>6025173.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.370339</td>\n",
-       "      <td>0.133211</td>\n",
-       "      <td>-0.100303</td>\n",
-       "      <td>0.056253</td>\n",
-       "      <td>-0.084373</td>\n",
-       "      <td>0.115048</td>\n",
-       "      <td>-0.248332</td>\n",
-       "      <td>-0.119365</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.073037</td>\n",
-       "      <td>0.15891</td>\n",
-       "      <td>-0.047578</td>\n",
-       "      <td>-0.057776</td>\n",
-       "      <td>-0.077222</td>\n",
-       "      <td>0.167877</td>\n",
-       "      <td>-0.211251</td>\n",
-       "      <td>-0.101427</td>\n",
-       "      <td>-0.190028</td>\n",
-       "      <td>-0.064002</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>502502</th>\n",
-       "      <td>6025182.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.370339</td>\n",
-       "      <td>0.133211</td>\n",
-       "      <td>-0.100303</td>\n",
-       "      <td>0.056253</td>\n",
-       "      <td>-0.084373</td>\n",
-       "      <td>0.115048</td>\n",
-       "      <td>-0.248332</td>\n",
-       "      <td>-0.119365</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.073037</td>\n",
-       "      <td>0.15891</td>\n",
-       "      <td>-0.047578</td>\n",
-       "      <td>-0.057776</td>\n",
-       "      <td>-0.077222</td>\n",
-       "      <td>0.167877</td>\n",
-       "      <td>-0.211251</td>\n",
-       "      <td>-0.101427</td>\n",
-       "      <td>-0.190028</td>\n",
-       "      <td>-0.064002</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>502503</th>\n",
-       "      <td>6025198.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.370339</td>\n",
-       "      <td>0.133211</td>\n",
-       "      <td>-0.100303</td>\n",
-       "      <td>0.056253</td>\n",
-       "      <td>-0.084373</td>\n",
-       "      <td>0.115048</td>\n",
-       "      <td>-0.248332</td>\n",
-       "      <td>-0.119365</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.073037</td>\n",
-       "      <td>0.15891</td>\n",
-       "      <td>-0.047578</td>\n",
-       "      <td>-0.057776</td>\n",
-       "      <td>-0.077222</td>\n",
-       "      <td>0.167877</td>\n",
-       "      <td>-0.211251</td>\n",
-       "      <td>-0.101427</td>\n",
-       "      <td>-0.190028</td>\n",
-       "      <td>-0.064002</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>502504 rows × 202 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "              eid  node2vec_available  node2vec_0  node2vec_1  node2vec_2  \\\n",
-       "0       1000018.0                 0.0    0.370339    0.133211   -0.100303   \n",
-       "1       1000020.0                 0.0    0.370339    0.133211   -0.100303   \n",
-       "2       1000037.0                 0.0    0.370339    0.133211   -0.100303   \n",
-       "3       1000043.0                 0.0    0.370339    0.133211   -0.100303   \n",
-       "4       1000051.0                 0.0    0.370339    0.133211   -0.100303   \n",
-       "...           ...                 ...         ...         ...         ...   \n",
-       "502499  6025150.0                 0.0    0.370339    0.133211   -0.100303   \n",
-       "502500  6025165.0                 0.0    0.370339    0.133211   -0.100303   \n",
-       "502501  6025173.0                 0.0    0.370339    0.133211   -0.100303   \n",
-       "502502  6025182.0                 0.0    0.370339    0.133211   -0.100303   \n",
-       "502503  6025198.0                 0.0    0.370339    0.133211   -0.100303   \n",
-       "\n",
-       "        node2vec_3  node2vec_4  node2vec_5  node2vec_6  node2vec_7  ...  \\\n",
-       "0         0.056253   -0.084373    0.115048   -0.248332   -0.119365  ...   \n",
-       "1         0.056253   -0.084373    0.115048   -0.248332   -0.119365  ...   \n",
-       "2         0.056253   -0.084373    0.115048   -0.248332   -0.119365  ...   \n",
-       "3         0.056253   -0.084373    0.115048   -0.248332   -0.119365  ...   \n",
-       "4         0.056253   -0.084373    0.115048   -0.248332   -0.119365  ...   \n",
-       "...            ...         ...         ...         ...         ...  ...   \n",
-       "502499    0.056253   -0.084373    0.115048   -0.248332   -0.119365  ...   \n",
-       "502500    0.056253   -0.084373    0.115048   -0.248332   -0.119365  ...   \n",
-       "502501    0.056253   -0.084373    0.115048   -0.248332   -0.119365  ...   \n",
-       "502502    0.056253   -0.084373    0.115048   -0.248332   -0.119365  ...   \n",
-       "502503    0.056253   -0.084373    0.115048   -0.248332   -0.119365  ...   \n",
-       "\n",
-       "        node2vec_190  node2vec_191  node2vec_192  node2vec_193  node2vec_194  \\\n",
-       "0           0.073037       0.15891     -0.047578     -0.057776     -0.077222   \n",
-       "1           0.073037       0.15891     -0.047578     -0.057776     -0.077222   \n",
-       "2           0.073037       0.15891     -0.047578     -0.057776     -0.077222   \n",
-       "3           0.073037       0.15891     -0.047578     -0.057776     -0.077222   \n",
-       "4           0.073037       0.15891     -0.047578     -0.057776     -0.077222   \n",
-       "...              ...           ...           ...           ...           ...   \n",
-       "502499      0.073037       0.15891     -0.047578     -0.057776     -0.077222   \n",
-       "502500      0.073037       0.15891     -0.047578     -0.057776     -0.077222   \n",
-       "502501      0.073037       0.15891     -0.047578     -0.057776     -0.077222   \n",
-       "502502      0.073037       0.15891     -0.047578     -0.057776     -0.077222   \n",
-       "502503      0.073037       0.15891     -0.047578     -0.057776     -0.077222   \n",
-       "\n",
-       "        node2vec_195  node2vec_196  node2vec_197  node2vec_198  node2vec_199  \n",
-       "0           0.167877     -0.211251     -0.101427     -0.190028     -0.064002  \n",
-       "1           0.167877     -0.211251     -0.101427     -0.190028     -0.064002  \n",
-       "2           0.167877     -0.211251     -0.101427     -0.190028     -0.064002  \n",
-       "3           0.167877     -0.211251     -0.101427     -0.190028     -0.064002  \n",
-       "4           0.167877     -0.211251     -0.101427     -0.190028     -0.064002  \n",
-       "...              ...           ...           ...           ...           ...  \n",
-       "502499      0.167877     -0.211251     -0.101427     -0.190028     -0.064002  \n",
-       "502500      0.167877     -0.211251     -0.101427     -0.190028     -0.064002  \n",
-       "502501      0.167877     -0.211251     -0.101427     -0.190028     -0.064002  \n",
-       "502502      0.167877     -0.211251     -0.101427     -0.190028     -0.064002  \n",
-       "502503      0.167877     -0.211251     -0.101427     -0.190028     -0.064002  \n",
-       "\n",
-       "[502504 rows x 202 columns]"
-      ]
-     },
-     "execution_count": 79,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "diagnoses_emb"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "outputs": [
-    {
-     "ename": "FileNotFoundError",
-     "evalue": "[Errno 2] Failed to open local file '/data/analysis/ag-reils/ag-reils-shared/cardioRS/data/2_datasets_pre/cvd_massive_from_birth/temp_diagnoses_emb.feather'. Detail: [errno 2] No such file or directory",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-14-45107a9d4a69>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m#diagnoses_emb.to_feather(os.path.join(path, dataset_path, 'temp_diagnoses_emb.feather'))\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdiagnoses_emb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_feather\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdataset_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'temp_diagnoses_emb.feather'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m~/miniconda3/envs/python/lib/python3.7/site-packages/pandas/io/feather_format.py\u001b[0m in \u001b[0;36mread_feather\u001b[0;34m(path, columns, use_threads)\u001b[0m\n\u001b[1;32m    101\u001b[0m     \u001b[0mpath\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstringify_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    102\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 103\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mfeather\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_feather\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0muse_threads\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbool\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0muse_threads\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m~/miniconda3/envs/python/lib/python3.7/site-packages/pyarrow/feather.py\u001b[0m in \u001b[0;36mread_feather\u001b[0;34m(source, columns, use_threads, memory_map)\u001b[0m\n\u001b[1;32m    212\u001b[0m     \"\"\"\n\u001b[1;32m    213\u001b[0m     \u001b[0m_check_pandas_version\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 214\u001b[0;31m     return (read_table(source, columns=columns, memory_map=memory_map)\n\u001b[0m\u001b[1;32m    215\u001b[0m             .to_pandas(use_threads=use_threads))\n\u001b[1;32m    216\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/python/lib/python3.7/site-packages/pyarrow/feather.py\u001b[0m in \u001b[0;36mread_table\u001b[0;34m(source, columns, memory_map)\u001b[0m\n\u001b[1;32m    234\u001b[0m     \"\"\"\n\u001b[1;32m    235\u001b[0m     \u001b[0mreader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mFeatherReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 236\u001b[0;31m     \u001b[0mreader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msource\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0muse_memory_map\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmemory_map\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    237\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    238\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcolumns\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/python/lib/python3.7/site-packages/pyarrow/feather.pxi\u001b[0m in \u001b[0;36mpyarrow.lib.FeatherReader.open\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/python/lib/python3.7/site-packages/pyarrow/io.pxi\u001b[0m in \u001b[0;36mpyarrow.lib.get_reader\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/python/lib/python3.7/site-packages/pyarrow/io.pxi\u001b[0m in \u001b[0;36mpyarrow.lib._get_native_file\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/python/lib/python3.7/site-packages/pyarrow/io.pxi\u001b[0m in \u001b[0;36mpyarrow.lib.memory_map\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/python/lib/python3.7/site-packages/pyarrow/io.pxi\u001b[0m in \u001b[0;36mpyarrow.lib.MemoryMappedFile._open\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/python/lib/python3.7/site-packages/pyarrow/error.pxi\u001b[0m in \u001b[0;36mpyarrow.lib.pyarrow_internal_check_status\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/python/lib/python3.7/site-packages/pyarrow/error.pxi\u001b[0m in \u001b[0;36mpyarrow.lib.check_status\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] Failed to open local file '/data/analysis/ag-reils/ag-reils-shared/cardioRS/data/2_datasets_pre/cvd_massive_from_birth/temp_diagnoses_emb.feather'. Detail: [errno 2] No such file or directory"
-     ]
-    }
-   ],
-   "source": [
-    "diagnoses_emb.to_feather(os.path.join(path, dataset_path, 'temp_diagnoses_emb.feather'))\n",
-    "diagnoses_emb = pd.read_feather(os.path.join(path, dataset_path, 'temp_diagnoses_emb.feather'))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Endpoints"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "1+1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 89,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:39:55.628580Z",
-     "start_time": "2020-11-04T12:33:33.036Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "### define in snomed and get icd codes from there"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 1. Hospital admissions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:39:55.629459Z",
-     "start_time": "2020-11-04T12:33:34.764Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'myocardial_infarction': ['I21', 'I22', 'I23', 'I24', 'I25', 'I51'],\n",
-       " 'stroke': ['G45', 'G46', 'I60', 'I67', 'I68', 'I69'],\n",
-       " 'cancer_breast': ['C50'],\n",
-       " 'diabetes': ['E10', 'E11', 'E12', 'E13', 'E14'],\n",
-       " 'atrial_fibrillation': ['I47', 'I48'],\n",
-       " 'copd': ['J44'],\n",
-       " 'dementia': ['F00', 'F01', 'F02', 'F03', 'F09', 'G31', 'R54']}"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "endpoint_list = {\n",
-    "    \"myocardial_infarction\": [\"Myocardial infarction\"],\n",
-    "    \"stroke\": [\"Cerebrovascular disease\"],\n",
-    "    \"cancer_breast\" : [\"Breast Cancer\"],\n",
-    "    \"diabetes\" : [\"Diabetes\"],\n",
-    "    \"atrial_fibrillation\": [\"Atrial fibrillation\", \"Atrial flutter\", \"paroxysmal tachycardia\"],\n",
-    "    \"copd\": [\"COPD\"],\n",
-    "    \"dementia\":[\"dementia\"]\n",
-    "}\n",
-    "\n",
-    "endpoint_list = phenotype_children(phecodes, endpoint_list)\n",
-    "endpoint_list[\"cancer_breast\"] = [\"C50\"]\n",
-    "endpoint_list[\"copd\"] = [\"J44\"]\n",
-    "endpoint_list[\"diabetes\"] = [\"E10\", \"E11\", \"E12\", \"E13\", \"E14\"]\n",
-    "endpoint_list[\"atrial_fibrillation\"] = [\"I47\", \"I48\"]\n",
-    "\n",
-    "\n",
-    "with open(os.path.join(path, dataset_path, 'endpoint_list.yaml'), 'w') as file: yaml.dump(endpoint_list, file, default_flow_style=False)\n",
-    "endpoint_list"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from dateutil.relativedelta import relativedelta\n",
-    "\n",
-    "def extract_endpoints_tte(data, diagnoses_codes, endpoint_list, time0_col, level=None):\n",
-    "    if level is not None: diagnoses_codes = diagnoses_codes.query(\"level==@level\")\n",
-    "    diagnoses_codes_time0 = diagnoses_codes.merge(data[[\"eid\", time0_col]], how=\"left\", on=\"eid\")\n",
-    "    \n",
-    "    cens_time_right = min(diagnoses_codes.sort_values('date').groupby('origin').tail(1).date.to_list())\n",
-    "    print(f\"t_0: {time0_col}\")\n",
-    "    print(f\"t_cens: {cens_time_right}\")\n",
-    "    \n",
-    "    df_interval = diagnoses_codes_time0[(diagnoses_codes_time0.date > diagnoses_codes_time0[time0_col]) & \n",
-    "                                        (diagnoses_codes_time0.date < cens_time_right)]\n",
-    "    \n",
-    "    temp = data[[\"eid\", time0_col]].copy()\n",
-    "    for ph, ph_codes in tqdm(endpoint_list.items()):\n",
-    "        regex = \"|\".join(ph_codes)\n",
-    "        ph_df = df_interval[df_interval.meaning.str.contains(regex, case=False)] \\\n",
-    "            .sort_values('date').groupby('eid').head(1).assign(phenotype=1, date=lambda x: x.date)\n",
-    "        temp_ph = temp.merge(ph_df, how=\"left\", on=\"eid\").fillna(0)\n",
-    "        temp[ph+\"_event\"], temp[ph+\"_event_date\"] = temp_ph.phenotype, temp_ph.date\n",
-    "        \n",
-    "        fill_date = {ph+\"_event_date\" : lambda x: [cens_time_right if event==0 else event_date for event, event_date in zip(x[ph+\"_event\"], x[ph+\"_event_date\"])]}\n",
-    "        calc_tte = {ph+\"_event_time\" : lambda x: [(event_date-time0).days/365.25  for time0, event_date in zip(x[time0_col], x[ph+\"_event_date\"])]}\n",
-    "        \n",
-    "        temp = temp.assign(**fill_date).assign(**calc_tte).drop([ph+\"_event_date\"], axis=1)\n",
-    "        \n",
-    "    temp = temp.drop([time0_col], axis=1)     \n",
-    "    \n",
-    "    return temp.drop_duplicates()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "t_0: birth_date\n",
-      "t_cens: 2020-03-14\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6a02ec6d409c4e998c0f1deb9032b535",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(FloatProgress(value=0.0, max=7.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "502504\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>myocardial_infarction_event</th>\n",
-       "      <th>myocardial_infarction_event_time</th>\n",
-       "      <th>stroke_event</th>\n",
-       "      <th>stroke_event_time</th>\n",
-       "      <th>cancer_breast_event</th>\n",
-       "      <th>cancer_breast_event_time</th>\n",
-       "      <th>diabetes_event</th>\n",
-       "      <th>diabetes_event_time</th>\n",
-       "      <th>atrial_fibrillation_event</th>\n",
-       "      <th>atrial_fibrillation_event_time</th>\n",
-       "      <th>copd_event</th>\n",
-       "      <th>copd_event_time</th>\n",
-       "      <th>dementia_event</th>\n",
-       "      <th>dementia_event_time</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>59.334702</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>59.334702</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>59.334702</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>59.334702</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>59.334702</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>59.334702</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>59.334702</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1000020</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>71.063655</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>71.063655</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>71.063655</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>71.063655</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>71.063655</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>71.063655</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>71.063655</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1000037</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>70.338125</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>70.338125</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>70.338125</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>70.338125</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>70.338125</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>70.338125</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>70.338125</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000043</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>68.123203</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>73.779603</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>73.779603</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>73.779603</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>73.779603</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>63.293634</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>73.779603</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1000051</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>64.761123</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>64.761123</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>64.761123</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>45.062286</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>64.761123</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>21.062286</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>64.761123</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       eid  myocardial_infarction_event  myocardial_infarction_event_time  \\\n",
-       "0  1000018                          0.0                         59.334702   \n",
-       "1  1000020                          0.0                         71.063655   \n",
-       "2  1000037                          0.0                         70.338125   \n",
-       "3  1000043                          1.0                         68.123203   \n",
-       "4  1000051                          0.0                         64.761123   \n",
-       "\n",
-       "   stroke_event  stroke_event_time  cancer_breast_event  \\\n",
-       "0           0.0          59.334702                  0.0   \n",
-       "1           0.0          71.063655                  0.0   \n",
-       "2           0.0          70.338125                  0.0   \n",
-       "3           0.0          73.779603                  0.0   \n",
-       "4           0.0          64.761123                  0.0   \n",
-       "\n",
-       "   cancer_breast_event_time  diabetes_event  diabetes_event_time  \\\n",
-       "0                 59.334702             0.0            59.334702   \n",
-       "1                 71.063655             0.0            71.063655   \n",
-       "2                 70.338125             0.0            70.338125   \n",
-       "3                 73.779603             0.0            73.779603   \n",
-       "4                 64.761123             1.0            45.062286   \n",
-       "\n",
-       "   atrial_fibrillation_event  atrial_fibrillation_event_time  copd_event  \\\n",
-       "0                        0.0                       59.334702         0.0   \n",
-       "1                        0.0                       71.063655         0.0   \n",
-       "2                        0.0                       70.338125         0.0   \n",
-       "3                        0.0                       73.779603         1.0   \n",
-       "4                        0.0                       64.761123         1.0   \n",
-       "\n",
-       "   copd_event_time  dementia_event  dementia_event_time  \n",
-       "0        59.334702             0.0            59.334702  \n",
-       "1        71.063655             0.0            71.063655  \n",
-       "2        70.338125             0.0            70.338125  \n",
-       "3        63.293634             0.0            73.779603  \n",
-       "4        21.062286             0.0            64.761123  "
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "endpoints_hospital = extract_endpoints_tte(basics, diagnoses_codes, endpoint_list, time0_col)\n",
-    "print(len(endpoints_hospital))\n",
-    "endpoints_hospital.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 2. Death registry"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "death_list = {\n",
-    "    \"death_allcause\":[],\n",
-    "    \"death_cvd\":['I{:02}'.format(ID+1) for ID in range(0, 98)],\n",
-    "}\n",
-    "\n",
-    "death_codes = pd.read_feather(f\"{data_path}/1_decoded/codes_death_records.feather\")#.drop(\"level\", axis=1)\n",
-    "\n",
-    "with open(os.path.join(path, dataset_path, 'death_list.yaml'), 'w') as file: yaml.dump(death_list, file, default_flow_style=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "t_0: birth_date\n",
-      "t_cens: 2020-06-28\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9ca18723f9984f399b46bce781a3f2dd",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(FloatProgress(value=0.0, max=2.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "endpoints_death = extract_endpoints_tte(basics, death_codes, death_list, time0_col, level=\"1\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## SCORES"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "scores_list = {\n",
-    "    \"SCORE\":['I{:02}'.format(ID) for ID in [10, 11, 12, 13, 14, 15, 20, 21, 22, 23, 24, 25, 44, 45, 46, 47, 48, 49, 50, 51, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73]],\n",
-    "    \"ASCVD\":['I{:02}'.format(ID) for ID in [20, 21, 22, 23, 24, 25, 63]],\n",
-    "    \"QRISK3\":[\"G45\", \"I20\", \"I21\", \"I22\", \"I23\", \"I24\", \"I25\", \"I63\", \"I64\"],\n",
-    "    \"MACE\":[\"G45\", \"I21\", \"I22\", \"I23\", \"I24\", \"I25\", \"I63\", \"I64\"],    \n",
-    "}\n",
-    "with open(os.path.join(path, dataset_path, 'scores_list.yaml'), 'w') as file: yaml.dump(scores_list, file, default_flow_style=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "scores_list_hospital = {}\n",
-    "scores_list_death = {}\n",
-    "for score, score_codes in scores_list.items():\n",
-    "    scores_list_hospital[\"hospital_\"+score] = score_codes\n",
-    "    scores_list_death[\"death_\"+score] = score_codes"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "t_0: birth_date\n",
-      "t_cens: 2020-03-14\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "17ed4f235a6e4cc08cdc2789bfc62d73",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(FloatProgress(value=0.0, max=4.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "t_0: birth_date\n",
-      "t_cens: 2020-06-28\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2bd6de2f761a464da6f8c6c8ba22a1d8",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(FloatProgress(value=0.0, max=4.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "endpoints_scores = {\n",
-    "    \"hospital\": extract_endpoints_tte(basics, diagnoses_codes, scores_list_hospital, time0_col=time0_col),\n",
-    "    \"death\": extract_endpoints_tte(basics, death_codes, scores_list_death, time0_col=time0_col, level=1)}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "endpoints_scores_all = endpoints_scores[\"hospital\"].merge(endpoints_scores[\"death\"], on=\"eid\", how=\"left\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### ESC SCORE (Conroy 2003)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "5323\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>SCORE_event</th>\n",
-       "      <th>SCORE_event_time</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>45</th>\n",
-       "      <td>1000463</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>74.165640</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>83</th>\n",
-       "      <td>1000841</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>76.005476</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>102</th>\n",
-       "      <td>1001031</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>75.537303</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>122</th>\n",
-       "      <td>1001237</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>50.132786</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>176</th>\n",
-       "      <td>1001777</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>72.238193</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         eid  SCORE_event  SCORE_event_time\n",
-       "45   1000463          1.0         74.165640\n",
-       "83   1000841          1.0         76.005476\n",
-       "102  1001031          1.0         75.537303\n",
-       "122  1001237          1.0         50.132786\n",
-       "176  1001777          1.0         72.238193"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "score = \"SCORE\"\n",
-    "\n",
-    "temp = endpoints_scores_all.filter(regex=\"eid|\"+score).rename(columns={\"death_SCORE_event\":\"SCORE_event\", \"death_SCORE_event_time\":\"SCORE_event_time\"})\n",
-    "score_SCORE = temp = temp[[\"eid\", \"SCORE_event\", \"SCORE_event_time\"]]\n",
-    "print(len(temp.query(score+\"_event==1\")))\n",
-    "temp.query(score+\"_event==1\").head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### ASCVD (Goff 2014)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "61785\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>ASCVD_event</th>\n",
-       "      <th>ASCVD_event_time</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1000037</td>\n",
-       "      <td>1</td>\n",
-       "      <td>66.970568</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000043</td>\n",
-       "      <td>1</td>\n",
-       "      <td>68.123203</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>1000066</td>\n",
-       "      <td>1</td>\n",
-       "      <td>60.944559</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>1000079</td>\n",
-       "      <td>1</td>\n",
-       "      <td>61.054073</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>1000233</td>\n",
-       "      <td>1</td>\n",
-       "      <td>68.673511</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        eid  ASCVD_event  ASCVD_event_time\n",
-       "2   1000037            1         66.970568\n",
-       "3   1000043            1         68.123203\n",
-       "5   1000066            1         60.944559\n",
-       "6   1000079            1         61.054073\n",
-       "22  1000233            1         68.673511"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "score = \"ASCVD\"\n",
-    "temp = endpoints_scores_all.filter(regex=\"eid|\"+score)\n",
-    "\n",
-    "aggr_event = {score +\"_event\" : lambda x: [1 if (hospital_event==1) | (death_event == 1) else 0 \n",
-    "                                                    for hospital_event, death_event in zip(x[\"hospital_\"+score+\"_event\"], x[\"death_\"+score+\"_event\"])]}\n",
-    "aggr_date = {score +\"_event_time\" : lambda x: [min(hospital_event_time, death_event_time)\n",
-    "                                                        for hospital_event_time, death_event_time in zip(x[\"hospital_\"+score+\"_event_time\"], x[\"death_\"+score+\"_event_time\"])]}\n",
-    "\n",
-    "score_ASCVD = temp = temp.assign(**aggr_event).assign(**aggr_date)[[\"eid\", score +\"_event\", score +\"_event_time\"]]\n",
-    "print(len(temp.query(score+\"_event==1\")))\n",
-    "temp.query(score+\"_event==1\").head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### UK QRISK3 (2017)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "69344\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>QRISK3_event</th>\n",
-       "      <th>QRISK3_event_time</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1000037</td>\n",
-       "      <td>1</td>\n",
-       "      <td>66.970568</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000043</td>\n",
-       "      <td>1</td>\n",
-       "      <td>68.123203</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>1000066</td>\n",
-       "      <td>1</td>\n",
-       "      <td>60.944559</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>1000079</td>\n",
-       "      <td>1</td>\n",
-       "      <td>61.054073</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>1000233</td>\n",
-       "      <td>1</td>\n",
-       "      <td>68.673511</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        eid  QRISK3_event  QRISK3_event_time\n",
-       "2   1000037             1          66.970568\n",
-       "3   1000043             1          68.123203\n",
-       "5   1000066             1          60.944559\n",
-       "6   1000079             1          61.054073\n",
-       "22  1000233             1          68.673511"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "score = \"QRISK3\"\n",
-    "temp = endpoints_scores_all.filter(regex=\"eid|\"+score)\n",
-    "\n",
-    "aggr_event = {score +\"_event\" : lambda x: [1 if (hospital_event==1) | (death_event == 1) else 0 \n",
-    "                                                    for hospital_event, death_event in zip(x[\"hospital_\"+score+\"_event\"], x[\"death_\"+score+\"_event\"])]}\n",
-    "aggr_date = {score +\"_event_time\" : lambda x: [min(hospital_event_time, death_event_time)\n",
-    "                                                        for hospital_event_time, death_event_time in zip(x[\"hospital_\"+score+\"_event_time\"], x[\"death_\"+score+\"_event_time\"])]}\n",
-    "\n",
-    "score_QRISK3 = temp = temp.assign(**aggr_event).assign(**aggr_date)[[\"eid\", score +\"_event\", score +\"_event_time\"]]\n",
-    "print(len(temp.query(score+\"_event==1\")))\n",
-    "temp.query(score+\"_event==1\").head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### MACE (2020)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "62097\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>MACE_event</th>\n",
-       "      <th>MACE_event_time</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000043</td>\n",
-       "      <td>1</td>\n",
-       "      <td>68.123203</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>1000233</td>\n",
-       "      <td>1</td>\n",
-       "      <td>68.673511</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30</th>\n",
-       "      <td>1000319</td>\n",
-       "      <td>1</td>\n",
-       "      <td>56.922656</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>45</th>\n",
-       "      <td>1000463</td>\n",
-       "      <td>1</td>\n",
-       "      <td>74.069815</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>53</th>\n",
-       "      <td>1000548</td>\n",
-       "      <td>1</td>\n",
-       "      <td>50.255989</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        eid  MACE_event  MACE_event_time\n",
-       "3   1000043           1        68.123203\n",
-       "22  1000233           1        68.673511\n",
-       "30  1000319           1        56.922656\n",
-       "45  1000463           1        74.069815\n",
-       "53  1000548           1        50.255989"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "score = \"MACE\"\n",
-    "temp = endpoints_scores_all.filter(regex=\"eid|\"+score)\n",
-    "\n",
-    "aggr_event = {score +\"_event\" : lambda x: [1 if (hospital_event==1) | (death_event == 1) else 0 \n",
-    "                                                    for hospital_event, death_event in zip(x[\"hospital_\"+score+\"_event\"], x[\"death_\"+score+\"_event\"])]}\n",
-    "aggr_date = {score +\"_event_time\" : lambda x: [min(hospital_event_time, death_event_time)\n",
-    "                                                        for hospital_event_time, death_event_time in zip(x[\"hospital_\"+score+\"_event_time\"], x[\"death_\"+score+\"_event_time\"])]}\n",
-    "\n",
-    "score_MACE = temp = temp.assign(**aggr_event).assign(**aggr_date)[[\"eid\", score +\"_event\", score +\"_event_time\"]]\n",
-    "print(len(temp.query(score+\"_event==1\")))\n",
-    "temp.query(score+\"_event==1\").head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Merge Everything"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_dfs_dict = {\"basics\": pd.read_feather(os.path.join(path, dataset_path, 'temp_basics.feather')), \n",
-    "                 \"questionnaire\": pd.read_feather(os.path.join(path, dataset_path, 'temp_questionnaire.feather')), \n",
-    "                 \"measurements\": pd.read_feather(os.path.join(path, dataset_path, 'temp_measurements.feather')), \n",
-    "                 \"labs\": pd.read_feather(os.path.join(path, dataset_path, 'temp_labs.feather')), \n",
-    "                 \"family_history\": pd.read_feather(os.path.join(path, dataset_path, 'temp_family_history.feather')), \n",
-    "                 \"diagnoses\": pd.read_feather(os.path.join(path, dataset_path, 'temp_diagnoses.feather')),\n",
-    "                # \"diagnoses_emb\": pd.read_feather(os.path.join(path, dataset_path, 'temp_diagnoses_emb.feather')), \n",
-    "                 \"medications\": pd.read_feather(os.path.join(path, dataset_path, 'temp_medications.feather')), \n",
-    "                 \"endpoints_hospital\":endpoints_hospital, \n",
-    "                 \"endpoints_death\":endpoints_death, \n",
-    "                 \"score_SCORE\":score_SCORE, \n",
-    "                 \"score_ASCVD\":score_ASCVD, \n",
-    "                 \"score_QRISK3\":score_QRISK3,\n",
-    "                 \"score_MACE\":score_MACE}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_cols_clean(df):\n",
-    "    df.columns = df.columns.str.replace(r'_0_0$', '').str.replace(r'_f[0-9]+$', '').str.replace(\"_automated_reading\", '')\n",
-    "    return df.columns\n",
-    "\n",
-    "def clean_df(df):\n",
-    "    df.columns = get_cols_clean(df)\n",
-    "    return df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "from functools import reduce\n",
-    "\n",
-    "data_baseline = reduce(lambda x, y: pd.merge(x, y, on = 'eid'), list(data_dfs_dict.values()))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_baseline = clean_df(data_baseline)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for col in [col for col in list(data_baseline.columns) if (\"_event\" in col) & (\"_time\" not in col)]:\n",
-    "    data_baseline[col] = data_baseline[col].astype(int)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "covariates = [col for col in list(data_baseline.columns) if not \"_event\" in col]\n",
-    "targets = [col for col in list(data_baseline.columns) if \"_event\" in col]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Exporting"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>age_at_recruitment</th>\n",
-       "      <th>sex</th>\n",
-       "      <th>ethnic_background</th>\n",
-       "      <th>townsend_deprivation_index_at_recruitment</th>\n",
-       "      <th>date_of_attending_assessment_centre</th>\n",
-       "      <th>birth_date</th>\n",
-       "      <th>overall_health_rating</th>\n",
-       "      <th>smoking_status</th>\n",
-       "      <th>alcohol_intake_frequency</th>\n",
-       "      <th>...</th>\n",
-       "      <th>death_cvd_event</th>\n",
-       "      <th>death_cvd_event_time</th>\n",
-       "      <th>SCORE_event</th>\n",
-       "      <th>SCORE_event_time</th>\n",
-       "      <th>ASCVD_event</th>\n",
-       "      <th>ASCVD_event_time</th>\n",
-       "      <th>QRISK3_event</th>\n",
-       "      <th>QRISK3_event_time</th>\n",
-       "      <th>MACE_event</th>\n",
-       "      <th>MACE_event_time</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>49.0</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>White</td>\n",
-       "      <td>-1.852930</td>\n",
-       "      <td>2009-11-12</td>\n",
-       "      <td>1960-11-12</td>\n",
-       "      <td>Fair</td>\n",
-       "      <td>Current</td>\n",
-       "      <td>Once or twice a week</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>59.624914</td>\n",
-       "      <td>0</td>\n",
-       "      <td>59.624914</td>\n",
-       "      <td>0</td>\n",
-       "      <td>59.334702</td>\n",
-       "      <td>0</td>\n",
-       "      <td>59.334702</td>\n",
-       "      <td>0</td>\n",
-       "      <td>59.334702</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1000020</td>\n",
-       "      <td>59.0</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>White</td>\n",
-       "      <td>0.204248</td>\n",
-       "      <td>2008-02-19</td>\n",
-       "      <td>1949-02-19</td>\n",
-       "      <td>Good</td>\n",
-       "      <td>Current</td>\n",
-       "      <td>Once or twice a week</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>71.353867</td>\n",
-       "      <td>0</td>\n",
-       "      <td>71.353867</td>\n",
-       "      <td>0</td>\n",
-       "      <td>71.063655</td>\n",
-       "      <td>0</td>\n",
-       "      <td>71.063655</td>\n",
-       "      <td>0</td>\n",
-       "      <td>71.063655</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1000037</td>\n",
-       "      <td>59.0</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>White</td>\n",
-       "      <td>-3.498860</td>\n",
-       "      <td>2008-11-11</td>\n",
-       "      <td>1949-11-11</td>\n",
-       "      <td>Good</td>\n",
-       "      <td>Previous</td>\n",
-       "      <td>Once or twice a week</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>70.628337</td>\n",
-       "      <td>0</td>\n",
-       "      <td>70.628337</td>\n",
-       "      <td>1</td>\n",
-       "      <td>66.970568</td>\n",
-       "      <td>1</td>\n",
-       "      <td>66.970568</td>\n",
-       "      <td>0</td>\n",
-       "      <td>70.338125</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000043</td>\n",
-       "      <td>63.0</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>White</td>\n",
-       "      <td>-5.351150</td>\n",
-       "      <td>2009-06-03</td>\n",
-       "      <td>1946-06-03</td>\n",
-       "      <td>Fair</td>\n",
-       "      <td>Previous</td>\n",
-       "      <td>Three or four times a week</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>74.069815</td>\n",
-       "      <td>0</td>\n",
-       "      <td>74.069815</td>\n",
-       "      <td>1</td>\n",
-       "      <td>68.123203</td>\n",
-       "      <td>1</td>\n",
-       "      <td>68.123203</td>\n",
-       "      <td>1</td>\n",
-       "      <td>68.123203</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1000051</td>\n",
-       "      <td>51.0</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>White</td>\n",
-       "      <td>-1.799080</td>\n",
-       "      <td>2006-06-10</td>\n",
-       "      <td>1955-06-10</td>\n",
-       "      <td>Poor</td>\n",
-       "      <td>Never</td>\n",
-       "      <td>One to three times a month</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>65.051335</td>\n",
-       "      <td>0</td>\n",
-       "      <td>65.051335</td>\n",
-       "      <td>0</td>\n",
-       "      <td>64.761123</td>\n",
-       "      <td>0</td>\n",
-       "      <td>64.761123</td>\n",
-       "      <td>0</td>\n",
-       "      <td>64.761123</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 3746 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       eid  age_at_recruitment     sex ethnic_background  \\\n",
-       "0  1000018                49.0  Female             White   \n",
-       "1  1000020                59.0    Male             White   \n",
-       "2  1000037                59.0  Female             White   \n",
-       "3  1000043                63.0    Male             White   \n",
-       "4  1000051                51.0  Female             White   \n",
-       "\n",
-       "   townsend_deprivation_index_at_recruitment  \\\n",
-       "0                                  -1.852930   \n",
-       "1                                   0.204248   \n",
-       "2                                  -3.498860   \n",
-       "3                                  -5.351150   \n",
-       "4                                  -1.799080   \n",
-       "\n",
-       "  date_of_attending_assessment_centre  birth_date overall_health_rating  \\\n",
-       "0                          2009-11-12  1960-11-12                  Fair   \n",
-       "1                          2008-02-19  1949-02-19                  Good   \n",
-       "2                          2008-11-11  1949-11-11                  Good   \n",
-       "3                          2009-06-03  1946-06-03                  Fair   \n",
-       "4                          2006-06-10  1955-06-10                  Poor   \n",
-       "\n",
-       "  smoking_status    alcohol_intake_frequency  ...  death_cvd_event  \\\n",
-       "0        Current        Once or twice a week  ...                0   \n",
-       "1        Current        Once or twice a week  ...                0   \n",
-       "2       Previous        Once or twice a week  ...                0   \n",
-       "3       Previous  Three or four times a week  ...                0   \n",
-       "4          Never  One to three times a month  ...                0   \n",
-       "\n",
-       "   death_cvd_event_time  SCORE_event  SCORE_event_time  ASCVD_event  \\\n",
-       "0             59.624914            0         59.624914            0   \n",
-       "1             71.353867            0         71.353867            0   \n",
-       "2             70.628337            0         70.628337            1   \n",
-       "3             74.069815            0         74.069815            1   \n",
-       "4             65.051335            0         65.051335            0   \n",
-       "\n",
-       "   ASCVD_event_time  QRISK3_event  QRISK3_event_time  MACE_event  \\\n",
-       "0         59.334702             0          59.334702           0   \n",
-       "1         71.063655             0          71.063655           0   \n",
-       "2         66.970568             1          66.970568           0   \n",
-       "3         68.123203             1          68.123203           1   \n",
-       "4         64.761123             0          64.761123           0   \n",
-       "\n",
-       "   MACE_event_time  \n",
-       "0        59.334702  \n",
-       "1        71.063655  \n",
-       "2        70.338125  \n",
-       "3        68.123203  \n",
-       "4        64.761123  \n",
-       "\n",
-       "[5 rows x 3746 columns]"
-      ]
-     },
-     "execution_count": 38,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data_baseline.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_cols = {}\n",
-    "for topic, df in data_dfs_dict.items(): \n",
-    "    data_cols[\"eid\"] = [\"admin\"]\n",
-    "    data_cols[topic]=list(get_cols_clean(df))[1:]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_cols_single = {}\n",
-    "for topic, columns in data_cols.items():\n",
-    "    for col in columns:\n",
-    "        data_cols_single[col] = topic"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>covariate</th>\n",
-       "      <th>dtype</th>\n",
-       "      <th>isTarget</th>\n",
-       "      <th>based_on</th>\n",
-       "      <th>aggr_fn</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>eid</td>\n",
-       "      <td>integer</td>\n",
-       "      <td>False</td>\n",
-       "      <td>eid</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2</td>\n",
-       "      <td>age_at_recruitment</td>\n",
-       "      <td>numeric</td>\n",
-       "      <td>False</td>\n",
-       "      <td>basics</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>sex</td>\n",
-       "      <td>category</td>\n",
-       "      <td>False</td>\n",
-       "      <td>basics</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4</td>\n",
-       "      <td>ethnic_background</td>\n",
-       "      <td>category</td>\n",
-       "      <td>False</td>\n",
-       "      <td>basics</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5</td>\n",
-       "      <td>townsend_deprivation_index_at_recruitment</td>\n",
-       "      <td>numeric</td>\n",
-       "      <td>False</td>\n",
-       "      <td>basics</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3741</th>\n",
-       "      <td>3742</td>\n",
-       "      <td>ASCVD_event_time</td>\n",
-       "      <td>numeric</td>\n",
-       "      <td>True</td>\n",
-       "      <td>score_ASCVD</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3742</th>\n",
-       "      <td>3743</td>\n",
-       "      <td>QRISK3_event</td>\n",
-       "      <td>integer</td>\n",
-       "      <td>True</td>\n",
-       "      <td>score_QRISK3</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3743</th>\n",
-       "      <td>3744</td>\n",
-       "      <td>QRISK3_event_time</td>\n",
-       "      <td>numeric</td>\n",
-       "      <td>True</td>\n",
-       "      <td>score_QRISK3</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3744</th>\n",
-       "      <td>3745</td>\n",
-       "      <td>MACE_event</td>\n",
-       "      <td>integer</td>\n",
-       "      <td>True</td>\n",
-       "      <td>score_MACE</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3745</th>\n",
-       "      <td>3746</td>\n",
-       "      <td>MACE_event_time</td>\n",
-       "      <td>numeric</td>\n",
-       "      <td>True</td>\n",
-       "      <td>score_MACE</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>3746 rows × 6 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        id                                  covariate     dtype  isTarget  \\\n",
-       "0        1                                        eid   integer     False   \n",
-       "1        2                         age_at_recruitment   numeric     False   \n",
-       "2        3                                        sex  category     False   \n",
-       "3        4                          ethnic_background  category     False   \n",
-       "4        5  townsend_deprivation_index_at_recruitment   numeric     False   \n",
-       "...    ...                                        ...       ...       ...   \n",
-       "3741  3742                           ASCVD_event_time   numeric      True   \n",
-       "3742  3743                               QRISK3_event   integer      True   \n",
-       "3743  3744                          QRISK3_event_time   numeric      True   \n",
-       "3744  3745                                 MACE_event   integer      True   \n",
-       "3745  3746                            MACE_event_time   numeric      True   \n",
-       "\n",
-       "          based_on  aggr_fn  \n",
-       "0              eid      NaN  \n",
-       "1           basics      NaN  \n",
-       "2           basics      NaN  \n",
-       "3           basics      NaN  \n",
-       "4           basics      NaN  \n",
-       "...            ...      ...  \n",
-       "3741   score_ASCVD      NaN  \n",
-       "3742  score_QRISK3      NaN  \n",
-       "3743  score_QRISK3      NaN  \n",
-       "3744    score_MACE      NaN  \n",
-       "3745    score_MACE      NaN  \n",
-       "\n",
-       "[3746 rows x 6 columns]"
-      ]
-     },
-     "execution_count": 41,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dtypes = {\"int32\":\"integer\", \"int64\":\"integer\", \"float64\":\"numeric\", \"category\":\"category\", \"object\":\"category\", \"bool\":\"logical\"}\n",
-    "desc_dict = {\"id\": [*range(1, len(data_baseline.columns.to_list())+1)] , \n",
-    "             \"covariate\": data_baseline.columns.to_list(), \n",
-    "             \"dtype\":[dtypes[str(col)] for col in data_baseline.dtypes.to_list()], \n",
-    "             \"isTarget\":[True if col in targets else False for col in data_baseline.columns.to_list()],\n",
-    "            \"based_on\":[topic for col, topic in data_cols_single.items()],\n",
-    "            \"aggr_fn\": [np.nan for col in data_baseline.columns.to_list()]}\n",
-    "data_baseline_description = pd.DataFrame.from_dict(desc_dict)\n",
-    "data_baseline_description"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Exclusion Criteria"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_baseline_excl = data_baseline.query(\"myocardial_infarction == False & coronary_heart_disease == False & statins == False\").reset_index(drop=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>age_at_recruitment</th>\n",
-       "      <th>sex</th>\n",
-       "      <th>ethnic_background</th>\n",
-       "      <th>townsend_deprivation_index_at_recruitment</th>\n",
-       "      <th>date_of_attending_assessment_centre</th>\n",
-       "      <th>birth_date</th>\n",
-       "      <th>overall_health_rating</th>\n",
-       "      <th>smoking_status</th>\n",
-       "      <th>alcohol_intake_frequency</th>\n",
-       "      <th>...</th>\n",
-       "      <th>death_cvd_event</th>\n",
-       "      <th>death_cvd_event_time</th>\n",
-       "      <th>SCORE_event</th>\n",
-       "      <th>SCORE_event_time</th>\n",
-       "      <th>ASCVD_event</th>\n",
-       "      <th>ASCVD_event_time</th>\n",
-       "      <th>QRISK3_event</th>\n",
-       "      <th>QRISK3_event_time</th>\n",
-       "      <th>MACE_event</th>\n",
-       "      <th>MACE_event_time</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>49.0</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>White</td>\n",
-       "      <td>-1.852930</td>\n",
-       "      <td>2009-11-12</td>\n",
-       "      <td>1960-11-12</td>\n",
-       "      <td>Fair</td>\n",
-       "      <td>Current</td>\n",
-       "      <td>Once or twice a week</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>59.624914</td>\n",
-       "      <td>0</td>\n",
-       "      <td>59.624914</td>\n",
-       "      <td>0</td>\n",
-       "      <td>59.334702</td>\n",
-       "      <td>0</td>\n",
-       "      <td>59.334702</td>\n",
-       "      <td>0</td>\n",
-       "      <td>59.334702</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1000020</td>\n",
-       "      <td>59.0</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>White</td>\n",
-       "      <td>0.204248</td>\n",
-       "      <td>2008-02-19</td>\n",
-       "      <td>1949-02-19</td>\n",
-       "      <td>Good</td>\n",
-       "      <td>Current</td>\n",
-       "      <td>Once or twice a week</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>71.353867</td>\n",
-       "      <td>0</td>\n",
-       "      <td>71.353867</td>\n",
-       "      <td>0</td>\n",
-       "      <td>71.063655</td>\n",
-       "      <td>0</td>\n",
-       "      <td>71.063655</td>\n",
-       "      <td>0</td>\n",
-       "      <td>71.063655</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1000037</td>\n",
-       "      <td>59.0</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>White</td>\n",
-       "      <td>-3.498860</td>\n",
-       "      <td>2008-11-11</td>\n",
-       "      <td>1949-11-11</td>\n",
-       "      <td>Good</td>\n",
-       "      <td>Previous</td>\n",
-       "      <td>Once or twice a week</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>70.628337</td>\n",
-       "      <td>0</td>\n",
-       "      <td>70.628337</td>\n",
-       "      <td>1</td>\n",
-       "      <td>66.970568</td>\n",
-       "      <td>1</td>\n",
-       "      <td>66.970568</td>\n",
-       "      <td>0</td>\n",
-       "      <td>70.338125</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000043</td>\n",
-       "      <td>63.0</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>White</td>\n",
-       "      <td>-5.351150</td>\n",
-       "      <td>2009-06-03</td>\n",
-       "      <td>1946-06-03</td>\n",
-       "      <td>Fair</td>\n",
-       "      <td>Previous</td>\n",
-       "      <td>Three or four times a week</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>74.069815</td>\n",
-       "      <td>0</td>\n",
-       "      <td>74.069815</td>\n",
-       "      <td>1</td>\n",
-       "      <td>68.123203</td>\n",
-       "      <td>1</td>\n",
-       "      <td>68.123203</td>\n",
-       "      <td>1</td>\n",
-       "      <td>68.123203</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1000079</td>\n",
-       "      <td>60.0</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>White</td>\n",
-       "      <td>-2.708040</td>\n",
-       "      <td>2008-03-18</td>\n",
-       "      <td>1948-03-18</td>\n",
-       "      <td>Fair</td>\n",
-       "      <td>Never</td>\n",
-       "      <td>Once or twice a week</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>72.279261</td>\n",
-       "      <td>0</td>\n",
-       "      <td>72.279261</td>\n",
-       "      <td>1</td>\n",
-       "      <td>61.054073</td>\n",
-       "      <td>1</td>\n",
-       "      <td>61.054073</td>\n",
-       "      <td>0</td>\n",
-       "      <td>71.989049</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>402296</th>\n",
-       "      <td>6025150</td>\n",
-       "      <td>43.0</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>White</td>\n",
-       "      <td>0.046781</td>\n",
-       "      <td>2007-06-30</td>\n",
-       "      <td>1964-06-30</td>\n",
-       "      <td>Excellent</td>\n",
-       "      <td>Never</td>\n",
-       "      <td>Three or four times a week</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>55.994524</td>\n",
-       "      <td>0</td>\n",
-       "      <td>55.994524</td>\n",
-       "      <td>0</td>\n",
-       "      <td>55.704312</td>\n",
-       "      <td>0</td>\n",
-       "      <td>55.704312</td>\n",
-       "      <td>0</td>\n",
-       "      <td>55.704312</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>402297</th>\n",
-       "      <td>6025165</td>\n",
-       "      <td>45.0</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>White</td>\n",
-       "      <td>-2.107040</td>\n",
-       "      <td>2008-09-02</td>\n",
-       "      <td>1963-09-02</td>\n",
-       "      <td>Good</td>\n",
-       "      <td>Never</td>\n",
-       "      <td>Three or four times a week</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>56.821355</td>\n",
-       "      <td>0</td>\n",
-       "      <td>56.821355</td>\n",
-       "      <td>0</td>\n",
-       "      <td>56.531143</td>\n",
-       "      <td>0</td>\n",
-       "      <td>56.531143</td>\n",
-       "      <td>0</td>\n",
-       "      <td>56.531143</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>402298</th>\n",
-       "      <td>6025173</td>\n",
-       "      <td>57.0</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>White</td>\n",
-       "      <td>-1.827220</td>\n",
-       "      <td>2008-09-17</td>\n",
-       "      <td>1951-09-17</td>\n",
-       "      <td>Good</td>\n",
-       "      <td>Never</td>\n",
-       "      <td>Never</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>68.780287</td>\n",
-       "      <td>0</td>\n",
-       "      <td>68.780287</td>\n",
-       "      <td>0</td>\n",
-       "      <td>68.490075</td>\n",
-       "      <td>0</td>\n",
-       "      <td>68.490075</td>\n",
-       "      <td>0</td>\n",
-       "      <td>68.490075</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>402299</th>\n",
-       "      <td>6025182</td>\n",
-       "      <td>56.0</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>White</td>\n",
-       "      <td>-0.010764</td>\n",
-       "      <td>2010-07-01</td>\n",
-       "      <td>1954-07-01</td>\n",
-       "      <td>Excellent</td>\n",
-       "      <td>Previous</td>\n",
-       "      <td>Daily or almost daily</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>65.993155</td>\n",
-       "      <td>0</td>\n",
-       "      <td>65.993155</td>\n",
-       "      <td>0</td>\n",
-       "      <td>65.702943</td>\n",
-       "      <td>0</td>\n",
-       "      <td>65.702943</td>\n",
-       "      <td>0</td>\n",
-       "      <td>65.702943</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>402300</th>\n",
-       "      <td>6025198</td>\n",
-       "      <td>67.0</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>White</td>\n",
-       "      <td>-1.930650</td>\n",
-       "      <td>2010-01-26</td>\n",
-       "      <td>1943-01-26</td>\n",
-       "      <td>Good</td>\n",
-       "      <td>Current</td>\n",
-       "      <td>Daily or almost daily</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>77.420945</td>\n",
-       "      <td>0</td>\n",
-       "      <td>77.420945</td>\n",
-       "      <td>0</td>\n",
-       "      <td>77.130732</td>\n",
-       "      <td>0</td>\n",
-       "      <td>77.130732</td>\n",
-       "      <td>0</td>\n",
-       "      <td>77.130732</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>402301 rows × 3746 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            eid  age_at_recruitment     sex ethnic_background  \\\n",
-       "0       1000018                49.0  Female             White   \n",
-       "1       1000020                59.0    Male             White   \n",
-       "2       1000037                59.0  Female             White   \n",
-       "3       1000043                63.0    Male             White   \n",
-       "4       1000079                60.0  Female             White   \n",
-       "...         ...                 ...     ...               ...   \n",
-       "402296  6025150                43.0  Female             White   \n",
-       "402297  6025165                45.0  Female             White   \n",
-       "402298  6025173                57.0    Male             White   \n",
-       "402299  6025182                56.0    Male             White   \n",
-       "402300  6025198                67.0    Male             White   \n",
-       "\n",
-       "        townsend_deprivation_index_at_recruitment  \\\n",
-       "0                                       -1.852930   \n",
-       "1                                        0.204248   \n",
-       "2                                       -3.498860   \n",
-       "3                                       -5.351150   \n",
-       "4                                       -2.708040   \n",
-       "...                                           ...   \n",
-       "402296                                   0.046781   \n",
-       "402297                                  -2.107040   \n",
-       "402298                                  -1.827220   \n",
-       "402299                                  -0.010764   \n",
-       "402300                                  -1.930650   \n",
-       "\n",
-       "       date_of_attending_assessment_centre  birth_date overall_health_rating  \\\n",
-       "0                               2009-11-12  1960-11-12                  Fair   \n",
-       "1                               2008-02-19  1949-02-19                  Good   \n",
-       "2                               2008-11-11  1949-11-11                  Good   \n",
-       "3                               2009-06-03  1946-06-03                  Fair   \n",
-       "4                               2008-03-18  1948-03-18                  Fair   \n",
-       "...                                    ...         ...                   ...   \n",
-       "402296                          2007-06-30  1964-06-30             Excellent   \n",
-       "402297                          2008-09-02  1963-09-02                  Good   \n",
-       "402298                          2008-09-17  1951-09-17                  Good   \n",
-       "402299                          2010-07-01  1954-07-01             Excellent   \n",
-       "402300                          2010-01-26  1943-01-26                  Good   \n",
-       "\n",
-       "       smoking_status    alcohol_intake_frequency  ...  death_cvd_event  \\\n",
-       "0             Current        Once or twice a week  ...                0   \n",
-       "1             Current        Once or twice a week  ...                0   \n",
-       "2            Previous        Once or twice a week  ...                0   \n",
-       "3            Previous  Three or four times a week  ...                0   \n",
-       "4               Never        Once or twice a week  ...                0   \n",
-       "...               ...                         ...  ...              ...   \n",
-       "402296          Never  Three or four times a week  ...                0   \n",
-       "402297          Never  Three or four times a week  ...                0   \n",
-       "402298          Never                       Never  ...                0   \n",
-       "402299       Previous       Daily or almost daily  ...                0   \n",
-       "402300        Current       Daily or almost daily  ...                0   \n",
-       "\n",
-       "        death_cvd_event_time  SCORE_event  SCORE_event_time  ASCVD_event  \\\n",
-       "0                  59.624914            0         59.624914            0   \n",
-       "1                  71.353867            0         71.353867            0   \n",
-       "2                  70.628337            0         70.628337            1   \n",
-       "3                  74.069815            0         74.069815            1   \n",
-       "4                  72.279261            0         72.279261            1   \n",
-       "...                      ...          ...               ...          ...   \n",
-       "402296             55.994524            0         55.994524            0   \n",
-       "402297             56.821355            0         56.821355            0   \n",
-       "402298             68.780287            0         68.780287            0   \n",
-       "402299             65.993155            0         65.993155            0   \n",
-       "402300             77.420945            0         77.420945            0   \n",
-       "\n",
-       "        ASCVD_event_time  QRISK3_event  QRISK3_event_time  MACE_event  \\\n",
-       "0              59.334702             0          59.334702           0   \n",
-       "1              71.063655             0          71.063655           0   \n",
-       "2              66.970568             1          66.970568           0   \n",
-       "3              68.123203             1          68.123203           1   \n",
-       "4              61.054073             1          61.054073           0   \n",
-       "...                  ...           ...                ...         ...   \n",
-       "402296         55.704312             0          55.704312           0   \n",
-       "402297         56.531143             0          56.531143           0   \n",
-       "402298         68.490075             0          68.490075           0   \n",
-       "402299         65.702943             0          65.702943           0   \n",
-       "402300         77.130732             0          77.130732           0   \n",
-       "\n",
-       "        MACE_event_time  \n",
-       "0             59.334702  \n",
-       "1             71.063655  \n",
-       "2             70.338125  \n",
-       "3             68.123203  \n",
-       "4             71.989049  \n",
-       "...                 ...  \n",
-       "402296        55.704312  \n",
-       "402297        56.531143  \n",
-       "402298        68.490075  \n",
-       "402299        65.702943  \n",
-       "402300        77.130732  \n",
-       "\n",
-       "[402301 rows x 3746 columns]"
-      ]
-     },
-     "execution_count": 43,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data_baseline_excl"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "feature_dict = {}\n",
-    "for group in data_baseline_description.based_on.unique(): feature_dict[group] = data_baseline_description.query(\"based_on==@group\").covariate.to_list()\n",
-    "with open(os.path.join(path, dataset_path, 'feature_list.yaml'), 'w') as file: yaml.dump(feature_dict, file, default_flow_style=False, allow_unicode=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#feature_dict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "### WRITE FEATURES IN YAML!!!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_baseline.to_feather(os.path.join(path, dataset_path, 'baseline_clinical.feather'))\n",
-    "data_baseline_excl.to_feather(os.path.join(path, dataset_path, 'baseline_clinical_excl.feather'))\n",
-    "data_baseline_description.to_feather(os.path.join(path, dataset_path, 'baseline_clinical_description.feather'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#data_baseline.to_csv(os.path.join(path, dataset_path, 'baseline_clinical.csv'), index=False)\n",
-    "#data_baseline_description.to_csv(os.path.join(path, dataset_path, 'baseline_clinical_description.csv'), index=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# !!! REMEMBER IMPUTATION !!!"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python [conda env:python]",
-   "language": "python",
-   "name": "conda-env-python-py"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.6"
-  },
-  "toc-autonumbering": true,
-  "toc-showcode": false,
-  "toc-showmarkdowntxt": false
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/neuralcvd/preprocessing/ukbb_tabular/2_preprocessing_clinical.ipynb b/neuralcvd/preprocessing/ukbb_tabular/2_preprocessing_clinical.ipynb
deleted file mode 100644
index ad4d691..0000000
--- a/neuralcvd/preprocessing/ukbb_tabular/2_preprocessing_clinical.ipynb
+++ /dev/null
@@ -1,5250 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Preprocessing"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:31:49.436340Z",
-     "start_time": "2020-11-04T12:31:48.732042Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import os\n",
-    "import yaml\n",
-    "from tqdm.notebook import trange, tqdm\n",
-    "dataset_name = \"cvd_interactions\"\n",
-    "path = \"/data/analysis/ag-reils/steinfej/code/umbrella/pre/ukbb\"\n",
-    "data_path = \"/data/analysis/ag-reils/ag-reils-shared/cardioRS/data\"\n",
-    "dataset_path = f\"{data_path}/2_datasets_pre/{dataset_name}\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:31:49.895222Z",
-     "start_time": "2020-11-04T12:31:49.891332Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from pathlib import Path\n",
-    "Path(dataset_path).mkdir(parents=True, exist_ok=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:33:14.171198Z",
-     "start_time": "2020-11-04T12:31:50.204540Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "data = pd.read_feather(f\"{data_path}/1_decoded/ukb_data.feather\")\n",
-    "data_field = pd.read_feather(f\"{data_path}/1_decoded/ukb_data_field.feather\")\n",
-    "data_columns = data.columns.to_list()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Mappings + Vocabulary"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:34:05.867152Z",
-     "start_time": "2020-11-04T12:33:16.878773Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "502505\n",
-      "502504\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Drop obviouse missing data\n",
-    "print(len(data))\n",
-    "data = data.dropna(subset=[\"sex_f31_0_0\"], axis=0)\n",
-    "print(len(data))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Starting information"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:34:05.872216Z",
-     "start_time": "2020-11-04T12:34:05.869505Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "time0_col=\"birth_date\"\n",
-    "# time0_col=\"date_of_attending_assessment_centre_f53_0_0\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Baseline covariates"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:34:05.889725Z",
-     "start_time": "2020-11-04T12:34:05.874587Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "def get_fields(fields, data, data_field):\n",
-    "    f = data_field[data_field[\"field.showcase\"].isin(fields) & data_field[\"field.tab\"].str.contains(\"f\\\\.\\\\d+\\\\.0\\\\.\\\\d\")].copy()\n",
-    "    f[\"field\"] = pd.Categorical(f[\"field.showcase\"], categories=fields, ordered=True)\n",
-    "    f = f.sort_values(\"field\").reset_index().drop(\"field\", axis=1)\n",
-    "    return f\n",
-    "\n",
-    "def get_fields_all(fields, data, data_field):\n",
-    "    f = data_field[data_field[\"field.showcase\"].isin(fields)].copy()\n",
-    "    f[\"field\"] = pd.Categorical(f[\"field.showcase\"], categories=fields, ordered=True)\n",
-    "    f = f.sort_values(\"field\").reset_index().drop(\"field\", axis=1)\n",
-    "    return f\n",
-    "\n",
-    "def get_data_fields(fields, data, data_field):\n",
-    "    f = get_fields(fields, data, data_field)\n",
-    "    return data[[\"eid\"]+f[\"col.name\"].to_list()].copy()\n",
-    "\n",
-    "def get_data_fields_all(fields, data, data_field):\n",
-    "    f = get_fields_all(fields, data, data_field)\n",
-    "    return data[[\"eid\"]+f[\"col.name\"].to_list()].copy()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Basics"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:34:06.176411Z",
-     "start_time": "2020-11-04T12:34:05.891730Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "502504\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>age_at_recruitment_f21022_0_0</th>\n",
-       "      <th>sex_f31_0_0</th>\n",
-       "      <th>ethnic_background_f21000_0_0</th>\n",
-       "      <th>townsend_deprivation_index_at_recruitment_f189_0_0</th>\n",
-       "      <th>date_of_attending_assessment_centre_f53_0_0</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>49.0</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>White</td>\n",
-       "      <td>-1.852930</td>\n",
-       "      <td>2009-11-12</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1000020</td>\n",
-       "      <td>59.0</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>White</td>\n",
-       "      <td>0.204248</td>\n",
-       "      <td>2008-02-19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1000037</td>\n",
-       "      <td>59.0</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>White</td>\n",
-       "      <td>-3.498860</td>\n",
-       "      <td>2008-11-11</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000043</td>\n",
-       "      <td>63.0</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>White</td>\n",
-       "      <td>-5.351150</td>\n",
-       "      <td>2009-06-03</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1000051</td>\n",
-       "      <td>51.0</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>White</td>\n",
-       "      <td>-1.799080</td>\n",
-       "      <td>2006-06-10</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       eid  age_at_recruitment_f21022_0_0 sex_f31_0_0  \\\n",
-       "0  1000018                           49.0      Female   \n",
-       "1  1000020                           59.0        Male   \n",
-       "2  1000037                           59.0      Female   \n",
-       "3  1000043                           63.0        Male   \n",
-       "4  1000051                           51.0      Female   \n",
-       "\n",
-       "  ethnic_background_f21000_0_0  \\\n",
-       "0                        White   \n",
-       "1                        White   \n",
-       "2                        White   \n",
-       "3                        White   \n",
-       "4                        White   \n",
-       "\n",
-       "   townsend_deprivation_index_at_recruitment_f189_0_0  \\\n",
-       "0                                          -1.852930    \n",
-       "1                                           0.204248    \n",
-       "2                                          -3.498860    \n",
-       "3                                          -5.351150    \n",
-       "4                                          -1.799080    \n",
-       "\n",
-       "  date_of_attending_assessment_centre_f53_0_0  \n",
-       "0                                  2009-11-12  \n",
-       "1                                  2008-02-19  \n",
-       "2                                  2008-11-11  \n",
-       "3                                  2009-06-03  \n",
-       "4                                  2006-06-10  "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fields_basics = [\n",
-    "    \"21022\", # age at recruitment\n",
-    "    \"31\", # sex\n",
-    "    \"21000\", # ethnicity\n",
-    "    \"189\", # Townsend index\n",
-    "    \"53\", # date of baseline assessment\n",
-    "]\n",
-    "\n",
-    "temp = get_data_fields(fields_basics, data, data_field)\n",
-    "\n",
-    "temp[\"sex_f31_0_0\"] = temp[\"sex_f31_0_0\"].cat.set_categories([\"Female\", 'Male'], ordered=False)\n",
-    "\n",
-    "#temp[\"ethnic_background_f21000_0_0\"] = temp[\"ethnic_background_f21000_0_0\"].astype(\"string\")\n",
-    "\n",
-    "ethn_bg_def = {\"White\": [\"White\", \"British\", \"Irish\", \"Any other white background\"],\n",
-    "                \"Mixed\": [\"Mixed\", \"White and Black Caribbean\", \"White and Black African\", \"White and Asian\", \"Any other mixed background\"],  \n",
-    "                \"Asian\": [\"Asian or Asian British\", \"Indian\", \"Pakistani\", \"Bangladeshi\", \"Any other Asian background\"], \n",
-    "                \"Black\": [\"Black or Black British\", \"Caribbean\", \"African\", \"Any other Black background\"],\n",
-    "                \"Chinese\": [\"Chinese\"],  \n",
-    "                np.nan: [\"Other ethnic group\", \"Do not know\", \"Prefer not to answer\"]}\n",
-    "\n",
-    "ethn_bg_dict = {}\n",
-    "for key, values in ethn_bg_def.items(): \n",
-    "    for value in values:\n",
-    "        ethn_bg_dict[value]=key \n",
-    "        \n",
-    "temp[\"ethnic_background_f21000_0_0\"].replace(ethn_bg_dict, inplace=True)\n",
-    "temp[\"ethnic_background_f21000_0_0\"] = temp[\"ethnic_background_f21000_0_0\"].astype(\"category\")\n",
-    "\n",
-    "#\n",
-    "#temp[\"ethnic_background_f21000_0_0\"] = temp[\"ethnic_background_f21000_0_0\"].astype(\"category\").cat.set_categories(['White', 'Black', 'Asien', 'Mixed', 'Chinese'], ordered=False)\n",
-    "\n",
-    "basics = temp\n",
-    "print(len(temp))\n",
-    "display(temp.head())\n",
-    "\n",
-    "from dateutil.relativedelta import relativedelta\n",
-    "calc_birth_date = [date_of_attending_assessment_centre - relativedelta(years=age_at_recruitment) \n",
-    "                                                             for date_of_attending_assessment_centre, age_at_recruitment \n",
-    "                                                             in zip(basics[\"date_of_attending_assessment_centre_f53_0_0\"], basics[\"age_at_recruitment_f21022_0_0\"])]\n",
-    "basics = basics.assign(birth_date = calc_birth_date)\n",
-    "\n",
-    "\n",
-    "basics.to_feather(os.path.join(path, dataset_path, 'temp_basics.feather'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:34:06.186613Z",
-     "start_time": "2020-11-04T12:34:06.178111Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[White, Black, NaN, Asian, Mixed, Chinese]\n",
-      "Categories (5, object): [White, Black, Asian, Mixed, Chinese]\n"
-     ]
-    }
-   ],
-   "source": [
-    " print(temp[\"ethnic_background_f21000_0_0\"].unique())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Questionnaire"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:34:06.389467Z",
-     "start_time": "2020-11-04T12:34:06.188206Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "502504\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>overall_health_rating_f2178_0_0</th>\n",
-       "      <th>smoking_status_f20116_0_0</th>\n",
-       "      <th>alcohol_intake_frequency_f1558_0_0</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>Fair</td>\n",
-       "      <td>Current</td>\n",
-       "      <td>Once or twice a week</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1000020</td>\n",
-       "      <td>Good</td>\n",
-       "      <td>Current</td>\n",
-       "      <td>Once or twice a week</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1000037</td>\n",
-       "      <td>Good</td>\n",
-       "      <td>Previous</td>\n",
-       "      <td>Once or twice a week</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000043</td>\n",
-       "      <td>Fair</td>\n",
-       "      <td>Previous</td>\n",
-       "      <td>Three or four times a week</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1000051</td>\n",
-       "      <td>Poor</td>\n",
-       "      <td>Never</td>\n",
-       "      <td>One to three times a month</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       eid overall_health_rating_f2178_0_0 smoking_status_f20116_0_0  \\\n",
-       "0  1000018                            Fair                   Current   \n",
-       "1  1000020                            Good                   Current   \n",
-       "2  1000037                            Good                  Previous   \n",
-       "3  1000043                            Fair                  Previous   \n",
-       "4  1000051                            Poor                     Never   \n",
-       "\n",
-       "  alcohol_intake_frequency_f1558_0_0  \n",
-       "0               Once or twice a week  \n",
-       "1               Once or twice a week  \n",
-       "2               Once or twice a week  \n",
-       "3         Three or four times a week  \n",
-       "4         One to three times a month  "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fields_questionnaire = [\n",
-    "    \"2178\", # Overall health\n",
-    "    \"20116\", # Smoking status\n",
-    "    \"1558\",\n",
-    "]\n",
-    "\n",
-    "temp = get_data_fields(fields_questionnaire, data, data_field)\n",
-    "\n",
-    "temp[\"overall_health_rating_f2178_0_0\"] = temp[\"overall_health_rating_f2178_0_0\"]\\\n",
-    "    .replace({\"Do not know\": np.nan, \"Prefer not to answer\": np.nan})\\\n",
-    "    .astype(\"category\").cat.set_categories(['Poor', 'Fair', 'Good', 'Excellent'], ordered=True)\n",
-    "\n",
-    "\n",
-    "temp[\"smoking_status_f20116_0_0\"] = temp[\"smoking_status_f20116_0_0\"]\\\n",
-    "    .replace({\"Prefer not to answer\": np.nan}, inplace=False)\\\n",
-    "    .astype(\"category\").cat.set_categories(['Current', 'Previous', 'Never'], ordered=True)\n",
-    "\n",
-    "temp[\"alcohol_intake_frequency_f1558_0_0\"] = temp[\"alcohol_intake_frequency_f1558_0_0\"]\\\n",
-    "    .replace({\"Prefer not to answer\": np.nan}, inplace=False)\\\n",
-    "    .astype(\"category\").cat.set_categories([\n",
-    "        'Daily or almost daily', \n",
-    "        'Three or four times a week', \n",
-    "        'Once or twice a week',\n",
-    "        'One to three times a month',\n",
-    "        'Special occasions only', \n",
-    "        'Never'], ordered=True)\n",
-    "\n",
-    "questionnaire = temp\n",
-    "print(len(temp))\n",
-    "display(temp.head())\n",
-    "\n",
-    "questionnaire.to_feather(os.path.join(path, dataset_path, 'temp_questionnaire.feather'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:34:06.398168Z",
-     "start_time": "2020-11-04T12:34:06.391082Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[Once or twice a week, Three or four times a week, One to three times a month, Daily or almost daily, Special occasions only, Never, NaN]\n",
-      "Categories (6, object): [Daily or almost daily < Three or four times a week < Once or twice a week < One to three times a month < Special occasions only < Never]\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(temp[\"alcohol_intake_frequency_f1558_0_0\"].unique())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Physical measurements"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:34:07.052989Z",
-     "start_time": "2020-11-04T12:34:06.400858Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "502504\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>body_mass_index_bmi_f21001_0_0</th>\n",
-       "      <th>weight_f21002_0_0</th>\n",
-       "      <th>pulse_wave_arterial_stiffness_index_f21021_0_0</th>\n",
-       "      <th>pulse_wave_reflection_index_f4195_0_0</th>\n",
-       "      <th>waist_circumference_f48_0_0</th>\n",
-       "      <th>hip_circumference_f49_0_0</th>\n",
-       "      <th>standing_height_f50_0_0</th>\n",
-       "      <th>trunk_fat_percentage_f23127_0_0</th>\n",
-       "      <th>body_fat_percentage_f23099_0_0</th>\n",
-       "      <th>basal_metabolic_rate_f23105_0_0</th>\n",
-       "      <th>forced_vital_capacity_fvc_best_measure_f20151_0_0</th>\n",
-       "      <th>forced_expiratory_volume_in_1second_fev1_best_measure_f20150_0_0</th>\n",
-       "      <th>fev1_fvc_ratio_zscore_f20258_0_0</th>\n",
-       "      <th>peak_expiratory_flow_pef_f3064_0_2</th>\n",
-       "      <th>peak_expiratory_flow_pef_f3064_0_1</th>\n",
-       "      <th>peak_expiratory_flow_pef_f3064_0_0</th>\n",
-       "      <th>systolic_blood_pressure_automated_reading_f4080</th>\n",
-       "      <th>diastolic_blood_pressure_automated_reading_f4079</th>\n",
-       "      <th>pulse_rate_automated_reading_f102</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>26.5557</td>\n",
-       "      <td>63.8</td>\n",
-       "      <td>7.2770</td>\n",
-       "      <td>80.0</td>\n",
-       "      <td>85.0</td>\n",
-       "      <td>107.0</td>\n",
-       "      <td>155.0</td>\n",
-       "      <td>37.5</td>\n",
-       "      <td>39.5</td>\n",
-       "      <td>5012.0</td>\n",
-       "      <td>3.21</td>\n",
-       "      <td>2.16</td>\n",
-       "      <td>1.978</td>\n",
-       "      <td>317.0</td>\n",
-       "      <td>312.0</td>\n",
-       "      <td>339.0</td>\n",
-       "      <td>159.5</td>\n",
-       "      <td>88.0</td>\n",
-       "      <td>50.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1000020</td>\n",
-       "      <td>22.7465</td>\n",
-       "      <td>70.7</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>87.8</td>\n",
-       "      <td>94.4</td>\n",
-       "      <td>176.3</td>\n",
-       "      <td>33.4</td>\n",
-       "      <td>28.7</td>\n",
-       "      <td>6171.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.375</td>\n",
-       "      <td>301.0</td>\n",
-       "      <td>496.0</td>\n",
-       "      <td>504.0</td>\n",
-       "      <td>133.0</td>\n",
-       "      <td>81.0</td>\n",
-       "      <td>74.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1000037</td>\n",
-       "      <td>32.4211</td>\n",
-       "      <td>78.9</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>101.0</td>\n",
-       "      <td>112.0</td>\n",
-       "      <td>156.0</td>\n",
-       "      <td>47.5</td>\n",
-       "      <td>48.4</td>\n",
-       "      <td>5397.0</td>\n",
-       "      <td>1.61</td>\n",
-       "      <td>1.27</td>\n",
-       "      <td>0.138</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>185.0</td>\n",
-       "      <td>208.0</td>\n",
-       "      <td>118.5</td>\n",
-       "      <td>78.0</td>\n",
-       "      <td>62.5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000043</td>\n",
-       "      <td>29.5679</td>\n",
-       "      <td>95.8</td>\n",
-       "      <td>11.1111</td>\n",
-       "      <td>78.0</td>\n",
-       "      <td>98.0</td>\n",
-       "      <td>104.0</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>27.6</td>\n",
-       "      <td>25.6</td>\n",
-       "      <td>8711.0</td>\n",
-       "      <td>4.14</td>\n",
-       "      <td>2.84</td>\n",
-       "      <td>1.096</td>\n",
-       "      <td>557.0</td>\n",
-       "      <td>513.0</td>\n",
-       "      <td>530.0</td>\n",
-       "      <td>141.5</td>\n",
-       "      <td>93.5</td>\n",
-       "      <td>64.5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1000051</td>\n",
-       "      <td>41.0222</td>\n",
-       "      <td>92.3</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>123.0</td>\n",
-       "      <td>129.0</td>\n",
-       "      <td>150.0</td>\n",
-       "      <td>48.9</td>\n",
-       "      <td>50.4</td>\n",
-       "      <td>6100.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.518</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>117.0</td>\n",
-       "      <td>81.0</td>\n",
-       "      <td>79.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       eid  body_mass_index_bmi_f21001_0_0  weight_f21002_0_0  \\\n",
-       "0  1000018                         26.5557               63.8   \n",
-       "1  1000020                         22.7465               70.7   \n",
-       "2  1000037                         32.4211               78.9   \n",
-       "3  1000043                         29.5679               95.8   \n",
-       "4  1000051                         41.0222               92.3   \n",
-       "\n",
-       "   pulse_wave_arterial_stiffness_index_f21021_0_0  \\\n",
-       "0                                          7.2770   \n",
-       "1                                             NaN   \n",
-       "2                                             NaN   \n",
-       "3                                         11.1111   \n",
-       "4                                             NaN   \n",
-       "\n",
-       "   pulse_wave_reflection_index_f4195_0_0  waist_circumference_f48_0_0  \\\n",
-       "0                                   80.0                         85.0   \n",
-       "1                                    NaN                         87.8   \n",
-       "2                                    NaN                        101.0   \n",
-       "3                                   78.0                         98.0   \n",
-       "4                                    NaN                        123.0   \n",
-       "\n",
-       "   hip_circumference_f49_0_0  standing_height_f50_0_0  \\\n",
-       "0                      107.0                    155.0   \n",
-       "1                       94.4                    176.3   \n",
-       "2                      112.0                    156.0   \n",
-       "3                      104.0                    180.0   \n",
-       "4                      129.0                    150.0   \n",
-       "\n",
-       "   trunk_fat_percentage_f23127_0_0  body_fat_percentage_f23099_0_0  \\\n",
-       "0                             37.5                            39.5   \n",
-       "1                             33.4                            28.7   \n",
-       "2                             47.5                            48.4   \n",
-       "3                             27.6                            25.6   \n",
-       "4                             48.9                            50.4   \n",
-       "\n",
-       "   basal_metabolic_rate_f23105_0_0  \\\n",
-       "0                           5012.0   \n",
-       "1                           6171.0   \n",
-       "2                           5397.0   \n",
-       "3                           8711.0   \n",
-       "4                           6100.0   \n",
-       "\n",
-       "   forced_vital_capacity_fvc_best_measure_f20151_0_0  \\\n",
-       "0                                               3.21   \n",
-       "1                                                NaN   \n",
-       "2                                               1.61   \n",
-       "3                                               4.14   \n",
-       "4                                                NaN   \n",
-       "\n",
-       "   forced_expiratory_volume_in_1second_fev1_best_measure_f20150_0_0  \\\n",
-       "0                                               2.16                  \n",
-       "1                                                NaN                  \n",
-       "2                                               1.27                  \n",
-       "3                                               2.84                  \n",
-       "4                                                NaN                  \n",
-       "\n",
-       "   fev1_fvc_ratio_zscore_f20258_0_0  peak_expiratory_flow_pef_f3064_0_2  \\\n",
-       "0                             1.978                               317.0   \n",
-       "1                             1.375                               301.0   \n",
-       "2                             0.138                                 NaN   \n",
-       "3                             1.096                               557.0   \n",
-       "4                             0.518                                 NaN   \n",
-       "\n",
-       "   peak_expiratory_flow_pef_f3064_0_1  peak_expiratory_flow_pef_f3064_0_0  \\\n",
-       "0                               312.0                               339.0   \n",
-       "1                               496.0                               504.0   \n",
-       "2                               185.0                               208.0   \n",
-       "3                               513.0                               530.0   \n",
-       "4                                 NaN                                 NaN   \n",
-       "\n",
-       "   systolic_blood_pressure_automated_reading_f4080  \\\n",
-       "0                                            159.5   \n",
-       "1                                            133.0   \n",
-       "2                                            118.5   \n",
-       "3                                            141.5   \n",
-       "4                                            117.0   \n",
-       "\n",
-       "   diastolic_blood_pressure_automated_reading_f4079  \\\n",
-       "0                                              88.0   \n",
-       "1                                              81.0   \n",
-       "2                                              78.0   \n",
-       "3                                              93.5   \n",
-       "4                                              81.0   \n",
-       "\n",
-       "   pulse_rate_automated_reading_f102  \n",
-       "0                               50.0  \n",
-       "1                               74.0  \n",
-       "2                               62.5  \n",
-       "3                               64.5  \n",
-       "4                               79.0  "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from statistics import mean\n",
-    "\n",
-    "fields_measurements = [\n",
-    "#    \"100313\", # Walking speed !!! MISSING !!!\n",
-    "    \"21001\", # BMI\n",
-    "    \"21002\", # weight\n",
-    "    \"4080\", # Syst. BP\n",
-    "    \"4079\", # Diast. BP\n",
-    "    \"102\",\n",
-    "    \"21021\",\n",
-    "    \"4195\",\n",
-    "    \"48\",\n",
-    "    \"49\",\n",
-    "    \"50\",\n",
-    "    \"23127\",\n",
-    "    \"23099\",\n",
-    "    \"23105\",\n",
-    "    \"20151\",\n",
-    "    \"20150\",\n",
-    "    \"20258\",\n",
-    "    \"3064\",\n",
-    "    \n",
-    "]\n",
-    "temp = get_data_fields(fields_measurements, data, data_field)\n",
-    "\n",
-    "sbp_cols = [\"systolic_blood_pressure_automated_reading_f4080_0_0\", \"systolic_blood_pressure_automated_reading_f4080_0_1\"]\n",
-    "dbp_cols = [\"diastolic_blood_pressure_automated_reading_f4079_0_0\", \"diastolic_blood_pressure_automated_reading_f4079_0_1\"]\n",
-    "pr_cols = [\"pulse_rate_automated_reading_f102_0_0\", \"pulse_rate_automated_reading_f102_0_1\"]\n",
-    "\n",
-    "temp = temp.assign(systolic_blood_pressure_automated_reading_f4080 = temp[sbp_cols].mean(axis=1),\n",
-    "                   diastolic_blood_pressure_automated_reading_f4079 = temp[dbp_cols].mean(axis=1),\n",
-    "                   pulse_rate_automated_reading_f102 = temp[pr_cols].mean(axis=1))\\\n",
-    "    .drop(sbp_cols + dbp_cols + pr_cols, axis=1)\n",
-    "\n",
-    "measurements = temp\n",
-    "print(len(temp))\n",
-    "display(temp.head())\n",
-    "\n",
-    "measurements.to_feather(os.path.join(path, dataset_path, 'temp_measurements.feather'))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Lab measurements"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:34:07.647955Z",
-     "start_time": "2020-11-04T12:34:07.055242Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "502504\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>basophill_count_f30160_0_0</th>\n",
-       "      <th>basophill_percentage_f30220_0_0</th>\n",
-       "      <th>eosinophill_count_f30150_0_0</th>\n",
-       "      <th>eosinophill_percentage_f30210_0_0</th>\n",
-       "      <th>haematocrit_percentage_f30030_0_0</th>\n",
-       "      <th>haemoglobin_concentration_f30020_0_0</th>\n",
-       "      <th>high_light_scatter_reticulocyte_count_f30300_0_0</th>\n",
-       "      <th>high_light_scatter_reticulocyte_percentage_f30290_0_0</th>\n",
-       "      <th>immature_reticulocyte_fraction_f30280_0_0</th>\n",
-       "      <th>...</th>\n",
-       "      <th>phosphate_f30810_0_0</th>\n",
-       "      <th>rheumatoid_factor_f30820_0_0</th>\n",
-       "      <th>shbg_f30830_0_0</th>\n",
-       "      <th>testosterone_f30850_0_0</th>\n",
-       "      <th>total_bilirubin_f30840_0_0</th>\n",
-       "      <th>total_protein_f30860_0_0</th>\n",
-       "      <th>triglycerides_f30870_0_0</th>\n",
-       "      <th>urate_f30880_0_0</th>\n",
-       "      <th>urea_f30670_0_0</th>\n",
-       "      <th>vitamin_d_f30890_0_0</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>0.04</td>\n",
-       "      <td>0.26</td>\n",
-       "      <td>0.25</td>\n",
-       "      <td>1.75</td>\n",
-       "      <td>39.79</td>\n",
-       "      <td>13.90</td>\n",
-       "      <td>0.022</td>\n",
-       "      <td>0.464</td>\n",
-       "      <td>0.378</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.422</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>70.11</td>\n",
-       "      <td>1.560</td>\n",
-       "      <td>7.41</td>\n",
-       "      <td>71.97</td>\n",
-       "      <td>1.247</td>\n",
-       "      <td>221.3</td>\n",
-       "      <td>5.48</td>\n",
-       "      <td>70.7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1000020</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0.30</td>\n",
-       "      <td>0.30</td>\n",
-       "      <td>2.50</td>\n",
-       "      <td>45.00</td>\n",
-       "      <td>15.60</td>\n",
-       "      <td>0.014</td>\n",
-       "      <td>0.290</td>\n",
-       "      <td>0.300</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.264</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>55.31</td>\n",
-       "      <td>12.237</td>\n",
-       "      <td>8.07</td>\n",
-       "      <td>78.45</td>\n",
-       "      <td>1.906</td>\n",
-       "      <td>374.7</td>\n",
-       "      <td>5.28</td>\n",
-       "      <td>35.9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1000037</td>\n",
-       "      <td>0.04</td>\n",
-       "      <td>0.57</td>\n",
-       "      <td>0.10</td>\n",
-       "      <td>1.43</td>\n",
-       "      <td>39.48</td>\n",
-       "      <td>13.58</td>\n",
-       "      <td>0.031</td>\n",
-       "      <td>0.686</td>\n",
-       "      <td>0.380</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000043</td>\n",
-       "      <td>0.02</td>\n",
-       "      <td>0.32</td>\n",
-       "      <td>0.11</td>\n",
-       "      <td>1.80</td>\n",
-       "      <td>44.31</td>\n",
-       "      <td>14.99</td>\n",
-       "      <td>0.025</td>\n",
-       "      <td>0.508</td>\n",
-       "      <td>0.250</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.928</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>31.63</td>\n",
-       "      <td>11.398</td>\n",
-       "      <td>8.65</td>\n",
-       "      <td>69.70</td>\n",
-       "      <td>5.184</td>\n",
-       "      <td>322.8</td>\n",
-       "      <td>6.67</td>\n",
-       "      <td>63.6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1000051</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 62 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       eid  basophill_count_f30160_0_0  basophill_percentage_f30220_0_0  \\\n",
-       "0  1000018                        0.04                             0.26   \n",
-       "1  1000020                        0.00                             0.30   \n",
-       "2  1000037                        0.04                             0.57   \n",
-       "3  1000043                        0.02                             0.32   \n",
-       "4  1000051                         NaN                              NaN   \n",
-       "\n",
-       "   eosinophill_count_f30150_0_0  eosinophill_percentage_f30210_0_0  \\\n",
-       "0                          0.25                               1.75   \n",
-       "1                          0.30                               2.50   \n",
-       "2                          0.10                               1.43   \n",
-       "3                          0.11                               1.80   \n",
-       "4                           NaN                                NaN   \n",
-       "\n",
-       "   haematocrit_percentage_f30030_0_0  haemoglobin_concentration_f30020_0_0  \\\n",
-       "0                              39.79                                 13.90   \n",
-       "1                              45.00                                 15.60   \n",
-       "2                              39.48                                 13.58   \n",
-       "3                              44.31                                 14.99   \n",
-       "4                                NaN                                   NaN   \n",
-       "\n",
-       "   high_light_scatter_reticulocyte_count_f30300_0_0  \\\n",
-       "0                                             0.022   \n",
-       "1                                             0.014   \n",
-       "2                                             0.031   \n",
-       "3                                             0.025   \n",
-       "4                                               NaN   \n",
-       "\n",
-       "   high_light_scatter_reticulocyte_percentage_f30290_0_0  \\\n",
-       "0                                              0.464       \n",
-       "1                                              0.290       \n",
-       "2                                              0.686       \n",
-       "3                                              0.508       \n",
-       "4                                                NaN       \n",
-       "\n",
-       "   immature_reticulocyte_fraction_f30280_0_0  ...  phosphate_f30810_0_0  \\\n",
-       "0                                      0.378  ...                 1.422   \n",
-       "1                                      0.300  ...                 1.264   \n",
-       "2                                      0.380  ...                   NaN   \n",
-       "3                                      0.250  ...                 0.928   \n",
-       "4                                        NaN  ...                   NaN   \n",
-       "\n",
-       "   rheumatoid_factor_f30820_0_0  shbg_f30830_0_0  testosterone_f30850_0_0  \\\n",
-       "0                           NaN            70.11                    1.560   \n",
-       "1                           NaN            55.31                   12.237   \n",
-       "2                           NaN              NaN                      NaN   \n",
-       "3                           NaN            31.63                   11.398   \n",
-       "4                           NaN              NaN                      NaN   \n",
-       "\n",
-       "   total_bilirubin_f30840_0_0  total_protein_f30860_0_0  \\\n",
-       "0                        7.41                     71.97   \n",
-       "1                        8.07                     78.45   \n",
-       "2                         NaN                       NaN   \n",
-       "3                        8.65                     69.70   \n",
-       "4                         NaN                       NaN   \n",
-       "\n",
-       "   triglycerides_f30870_0_0  urate_f30880_0_0  urea_f30670_0_0  \\\n",
-       "0                     1.247             221.3             5.48   \n",
-       "1                     1.906             374.7             5.28   \n",
-       "2                       NaN               NaN              NaN   \n",
-       "3                     5.184             322.8             6.67   \n",
-       "4                       NaN               NaN              NaN   \n",
-       "\n",
-       "   vitamin_d_f30890_0_0  \n",
-       "0                  70.7  \n",
-       "1                  35.9  \n",
-       "2                   NaN  \n",
-       "3                  63.6  \n",
-       "4                   NaN  \n",
-       "\n",
-       "[5 rows x 62 columns]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fields_blood_count = [\n",
-    "    \"30160\", #\tBasophill count\n",
-    "    \"30220\", #\tBasophill percentage\n",
-    "    \"30150\", #\tEosinophill count\n",
-    "    \"30210\", #\tEosinophill percentage\n",
-    "    \"30030\", #\tHaematocrit percentage\n",
-    "    \"30020\", #\tHaemoglobin concentration\n",
-    "    \"30300\", #\tHigh light scatter reticulocyte count\n",
-    "    \"30290\", #\tHigh light scatter reticulocyte percentage\n",
-    "    \"30280\", #\tImmature reticulocyte fraction\n",
-    "    \"30120\", #\tLymphocyte count\n",
-    "    \"30180\", #\tLymphocyte percentage\n",
-    "    \"30050\", #\tMean corpuscular haemoglobin\n",
-    "    \"30060\", #\tMean corpuscular haemoglobin concentration\n",
-    "    \"30040\", #\tMean corpuscular volume\n",
-    "    \"30100\", #\tMean platelet (thrombocyte) volume\n",
-    "    \"30260\", #\tMean reticulocyte volume\n",
-    "    \"30270\", #\tMean sphered cell volume\n",
-    "    \"30130\", #\tMonocyte count\n",
-    "    \"30190\", #\tMonocyte percentage\n",
-    "    \"30140\", #\tNeutrophill count\n",
-    "    \"30200\", #\tNeutrophill percentage\n",
-    "    \"30170\", #\tNucleated red blood cell count\n",
-    "    \"30230\", #\tNucleated red blood cell percentage\n",
-    "    \"30080\", #\tPlatelet count\n",
-    "    \"30090\", #\tPlatelet crit\n",
-    "    \"30110\", #\tPlatelet distribution width\n",
-    "    \"30010\", #\tRed blood cell (erythrocyte) count\n",
-    "    \"30070\", #\tRed blood cell (erythrocyte) distribution width\n",
-    "    \"30250\", #\tReticulocyte count\n",
-    "    \"30240\", #\tReticulocyte percentage\n",
-    "    \"30000\", #\tWhite blood cell (leukocyte) count\n",
-    "]\n",
-    "\n",
-    "fields_blood_biochemistry = [\n",
-    "    \"30620\",#\tAlanine aminotransferase\n",
-    "    \"30600\",#\tAlbumin\n",
-    "    \"30610\",#\tAlkaline phosphatase\n",
-    "    \"30630\",#\tApolipoprotein A\n",
-    "    \"30640\",#\tApolipoprotein B\n",
-    "    \"30650\",#\tAspartate aminotransferase\n",
-    "    \"30710\",#\tC-reactive protein\n",
-    "    \"30680\",#\tCalcium\n",
-    "    \"30690\",#\tCholesterol\n",
-    "    \"30700\",#\tCreatinine\n",
-    "    \"30720\",#\tCystatin C\n",
-    "    \"30660\",#\tDirect bilirubin\n",
-    "    \"30730\",#\tGamma glutamyltransferase\n",
-    "    \"30740\",#\tGlucose\n",
-    "    \"30750\",#\tGlycated haemoglobin (HbA1c)\n",
-    "    \"30760\",#\tHDL cholesterol\n",
-    "    \"30770\",#\tIGF-1\n",
-    "    \"30780\",#\tLDL direct\n",
-    "    \"30790\",#\tLipoprotein A\n",
-    "    \"30800\",#\tOestradiol\n",
-    "    \"30810\",#\tPhosphate\n",
-    "    \"30820\",#\tRheumatoid factor\n",
-    "    \"30830\",#\tSHBG\n",
-    "    \"30850\",#\tTestosterone\n",
-    "    \"30840\",#\tTotal bilirubin\n",
-    "    \"30860\",#\tTotal protein\n",
-    "    \"30870\",#\tTriglycerides\n",
-    "    \"30880\",#\tUrate\n",
-    "    \"30670\",#\tUrea\n",
-    "    \"30890\",#\tVitamin D\n",
-    "]\n",
-    "\n",
-    "fields_blood_infectious = [\n",
-    "    \"23000\", #\t1gG antigen for Herpes Simplex virus-1\n",
-    "    \"23001\", #\t2mgG unique antigen for Herpes Simplex virus-2\n",
-    "    \"23049\", #\tAntigen assay QC indicator\n",
-    "    \"23048\", #\tAntigen assay date\n",
-    "    \"23026\", #\tBK VP1 antigen for Human Polyomavirus BKV\n",
-    "    \"23039\", #\tCagA antigen for Helicobacter pylori\n",
-    "    \"23043\", #\tCatalase antigen for Helicobacter pylori\n",
-    "    \"23018\", #\tCore antigen for Hepatitis C Virus\n",
-    "    \"23030\", #\tE6 antigen for Human Papillomavirus type-16\n",
-    "    \"23031\", #\tE7 antigen for Human Papillomavirus type-16\n",
-    "    \"23006\", #\tEA-D antigen for Epstein-Barr Virus\n",
-    "    \"23004\", #\tEBNA-1 antigen for Epstein-Barr Virus\n",
-    "    \"23042\", #\tGroEL antigen for Helicobacter pylori\n",
-    "    \"23016\", #\tHBc antigen for Hepatitis B Virus\n",
-    "    \"23017\", #\tHBe antigen for Hepatitis B Virus\n",
-    "    \"23025\", #\tHIV-1 env antigen for Human Immunodeficiency Virus\n",
-    "    \"23024\", #\tHIV-1 gag antigen for Human Immunodeficiency Virus\n",
-    "    \"23023\", #\tHTLV-1 env antigen for Human T-Lymphotropic Virus 1\n",
-    "    \"23022\", #\tHTLV-1 gag antigen for Human T-Lymphotropic Virus 1\n",
-    "    \"23010\", #\tIE1A antigen for Human Herpesvirus-6\n",
-    "    \"23011\", #\tIE1B antigen for Human Herpesvirus-6\n",
-    "    \"23027\", #\tJC VP1 antigen for Human Polyomavirus JCV\n",
-    "    \"23015\", #\tK8.1 antigen for Kaposi's Sarcoma-Associated Herpesvirus\n",
-    "    \"23029\", #\tL1 antigen for Human Papillomavirus type-16\n",
-    "    \"23032\", #\tL1 antigen for Human Papillomavirus type-18\n",
-    "    \"23014\", #\tLANA antigen for Kaposi's Sarcoma-Associated Herpesvirus\n",
-    "    \"23028\", #\tMC VP1 antigen for Merkel Cell Polyomavirus\n",
-    "    \"23019\", #\tNS3 antigen for Hepatitis C Virus\n",
-    "    \"23041\", #\tOMP antigen for Helicobacter pylori\n",
-    "    \"23037\", #\tPorB antigen for Chlamydia trachomatis\n",
-    "    \"23013\", #\tU14 antigen for Human Herpesvirus-7\n",
-    "    \"23044\", #\tUreA antigen for Helicobacter pylori\n",
-    "    \"23003\", #\tVCA p18 antigen for Epstein-Barr Virus\n",
-    "    \"23040\", #\tVacA antigen for Helicobacter pylori\n",
-    "    \"23005\", #\tZEBRA antigen for Epstein-Barr Virus\n",
-    "    \"23002\", #\tgE / gI antigen for Varicella Zoster Virus\n",
-    "    \"23034\", #\tmomp A antigen for Chlamydia trachomatis\n",
-    "    \"23033\", #\tmomp D antigen for Chlamydia trachomatis\n",
-    "    \"23012\", #\tp101 k antigen for Human Herpesvirus-6\n",
-    "    \"23020\", #\tp22 antigen for Toxoplasma gondii\n",
-    "    \"23038\", #\tpGP3 antigen for Chlamydia trachomatis\n",
-    "    \"23009\", #\tpp 28 antigen for Human Cytomegalovirus\n",
-    "    \"23008\", #\tpp 52 antigen for Human Cytomegalovirus\n",
-    "    \"23007\", #\tpp150 Nter antigen for Human Cytomegalovirus\n",
-    "    \"23021\", #\tsag1 antigen for Toxoplasma gondii\n",
-    "    \"23035\", #\ttarp-D F1 antigen for Chlamydia trachomatis\n",
-    "    \"23036\", #\ttarp-D F2 antigen for Chlamydia trachomatis\n",
-    "]\n",
-    "\n",
-    "labs = temp = get_data_fields(fields_blood_count+fields_blood_biochemistry+fields_blood_infectious, data, data_field)\n",
-    "print(len(temp))\n",
-    "display(temp.head())\n",
-    "\n",
-    "labs.to_feather(os.path.join(path, dataset_path, 'temp_labs.feather'))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Family History"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:34:25.838958Z",
-     "start_time": "2020-11-04T12:34:07.649920Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "502504\n"
-     ]
-    }
-   ],
-   "source": [
-    "fh_list=[\"Heart disease\", \"Stroke\", \"High blood pressure\",  \"Diabetes\", \"Lung cancer\", \"Severe depression\", \"Parkinson's disease\", \"Alzheimer's disease/dementia\", \"Chronic bronchitis/emphysema\", \"Breast cancer\", \"Bowel cancer\"]\n",
-    "with open(os.path.join(path, dataset_path, 'fh_list.yaml'), 'w') as file: yaml.dump(fh_list, file, default_flow_style=False)\n",
-    "\n",
-    "fields_family_history = [\n",
-    "    \"20107\", # Family history \n",
-    "    \"20110\" # Family history\n",
-    "]\n",
-    "\n",
-    "raw = get_data_fields(fields_family_history, data, data_field)\n",
-    "temp = pd.melt(raw, id_vars=[\"eid\"], value_vars=raw.drop(\"eid\", axis=1).columns.to_list(), var_name = \"field\", value_name=\"family_history\").drop(\"field\", axis=1)\n",
-    "temp = temp[temp.family_history.isin(fh_list)].assign(family_history=temp[\"family_history\"].str.lower().replace(\" \", \"_\", regex=True))\n",
-    "\n",
-    "temp = temp.drop_duplicates().sort_values(\"eid\").reset_index().drop(\"index\", axis=1).assign(n=True)\n",
-    "temp = pd.pivot_table(temp, index=\"eid\", columns=\"family_history\", values=\"n\", observed=True).add_prefix('fh_')\n",
-    "family_history = temp = data[[\"eid\"]].copy().merge(temp, how=\"left\", on=\"eid\").fillna(False)\n",
-    "\n",
-    "print(len(temp))\n",
-    "temp.head()\n",
-    "\n",
-    "family_history.to_feather(os.path.join(path, dataset_path, 'temp_family_history.feather'))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Medications"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:34:25.842601Z",
-     "start_time": "2020-11-04T12:34:25.840607Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# https://list.essentialmeds.org/?showRemoved=0\n",
-    "# essential medicines WHO?!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:34:32.471831Z",
-     "start_time": "2020-11-04T12:34:25.844339Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "atc_mapping = pd.read_csv(f\"{path}/mapping/atc/atc_matched_list.csv\")\n",
-    "athena_concepts = pd.read_csv(f\"{data_path}/athena_vocabulary/CONCEPT.csv\", sep=\"\\t\").assign(vocabulary_id = lambda x: x.vocabulary_id.astype(\"string\"), concept_class_id = lambda x: x.concept_class_id.astype(\"string\"))\n",
-    "atc_concepts = athena_concepts[athena_concepts.vocabulary_id==\"ATC\"]\n",
-    "atc2_concepts = atc_concepts[atc_concepts.concept_class_id==\"ATC 2nd\"].sort_values(\"concept_code\")\n",
-    "medication_list = dict(zip([x.lower().replace(\" \", \"_\") for x in atc2_concepts.concept_name.to_list()], [[x] for x in atc2_concepts.concept_code.to_list()]))\n",
-    "medication_list_extra = {\n",
-    "    \"antihypertensives\": [\"C02\"],\n",
-    "    \"statins\": [\"C10A\", \"C10B\"],\n",
-    "    \"ass\": [\"B01\"],\n",
-    "    \"atypical_antipsychotics\" : [\"N05\"],\n",
-    "    \"glucocorticoids\" : [\"H02\"]                        \n",
-    "}\n",
-    "medication_list.update(medication_list_extra)\n",
-    "\n",
-    "with open(os.path.join(path, dataset_path, 'medication_list.yaml'), 'w') as file: yaml.dump(medication_list, file, default_flow_style=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:34:32.481137Z",
-     "start_time": "2020-11-04T12:34:32.473698Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "def had_medication_before(data, data_field, medications, atc_mapping):\n",
-    "    fields = [\"20003\"]\n",
-    "    raw = get_data_fields(fields, data, data_field)\n",
-    "    temp = pd.melt(raw, id_vars=[\"eid\"], value_vars=raw.drop(\"eid\", axis=1).columns.to_list(), var_name = \"field\", value_name=\"UKBB_code\").drop(\"field\", axis=1).drop_duplicates()\n",
-    "\n",
-    "    temp.UKBB_code = temp.UKBB_code.astype(str)\n",
-    "    temp = temp[temp.UKBB_code!=\"None\"].copy()\n",
-    "    temp.UKBB_code = temp.UKBB_code.astype(int)\n",
-    "\n",
-    "    temp_atc = temp.merge(atc_mapping, how=\"left\", on=\"UKBB_code\").sort_values(\"eid\").reset_index(drop=True).dropna(subset=[\"ATC_code\"], axis=0)\n",
-    "    temp_atc.ATC_code = temp_atc.ATC_code.astype(\"string\")\n",
-    "    temp = data[[\"eid\"]].copy()\n",
-    "    for med, med_codes in tqdm(medication_list.items()):\n",
-    "        regex_str = \"^\"+\"|^\".join(med_codes)\n",
-    "        df = temp_atc[temp_atc.ATC_code.str.contains(regex_str, case=False)][[\"eid\"]]\\\n",
-    "            .drop_duplicates(subset=[\"eid\"])\\\n",
-    "            .assign(medication=True)\n",
-    "        temp[med] = temp.merge(df, how=\"left\", on=\"eid\").fillna(False).medication\n",
-    "        \n",
-    "    return temp.sort_values(\"eid\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:36:14.425612Z",
-     "start_time": "2020-11-04T12:34:32.482863Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "13ef8609a6b64b219928981b7752c608",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(FloatProgress(value=0.0, max=98.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "502504\n"
-     ]
-    }
-   ],
-   "source": [
-    "medications = had_medication_before(data, data_field, medication_list, atc_mapping)\n",
-    "print(len(medications))\n",
-    "medications.head(100)\n",
-    "\n",
-    "medications.to_feather(os.path.join(path, dataset_path, 'temp_medications.feather'))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Diagnoses and events"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:37:14.667281Z",
-     "start_time": "2020-11-04T12:36:14.427693Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/steinfej/miniconda3/envs/python/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3072: DtypeWarning: Columns (5,6,9) have mixed types.Specify dtype option on import or set low_memory=False.\n",
-      "  interactivity=interactivity, compiler=compiler, result=result)\n",
-      "/home/steinfej/miniconda3/envs/python/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3072: DtypeWarning: Columns (11) have mixed types.Specify dtype option on import or set low_memory=False.\n",
-      "  interactivity=interactivity, compiler=compiler, result=result)\n"
-     ]
-    }
-   ],
-   "source": [
-    "vocab_dir = f\"{data_path}/athena_vocabulary_covid\"\n",
-    "vocab = {\n",
-    "    \"concept\": pd.read_csv(f\"{vocab_dir}/CONCEPT.csv\", sep='\\t'),\n",
-    "    \"domain\": pd.read_csv(f\"{vocab_dir}/DOMAIN.csv\", sep='\\t'),\n",
-    "    \"class\": pd.read_csv(f\"{vocab_dir}/CONCEPT_CLASS.csv\", sep='\\t'),\n",
-    "    \"relationship\": pd.read_csv(f\"{vocab_dir}/RELATIONSHIP.csv\", sep='\\t'),\n",
-    "    \"drug_strength\": pd.read_csv(f\"{vocab_dir}/DRUG_STRENGTH.csv\", sep='\\t'),\n",
-    "    \"vocabulary\": pd.read_csv(f\"{vocab_dir}/VOCABULARY.csv\", sep='\\t'),\n",
-    "    \"concept_synonym\": pd.read_csv(f\"{vocab_dir}/CONCEPT_SYNONYM.csv\", sep='\\t'),\n",
-    "    \"concept_ancestor\": pd.read_csv(f\"{vocab_dir}/CONCEPT_ANCESTOR.csv\", sep='\\t'),\n",
-    "    \"concept_relationship\": pd.read_csv(f\"{vocab_dir}/CONCEPT_RELATIONSHIP.csv\", sep='\\t')                       \n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Definitions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:37:14.772869Z",
-     "start_time": "2020-11-04T12:37:14.669541Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "coding1836 = pd.read_csv(f\"{path}/mapping/codings/coding1836.tsv\", sep=\"\\t\").rename(columns={\"coding\":\"code\"})\n",
-    "phecodes = pd.read_csv(f\"{path}/mapping/phecodes/phecode_icd10.csv\")\n",
-    "def phenotype_children(phecodes, phenotype_list):\n",
-    "    l={}\n",
-    "    phecodes = phecodes.dropna(subset=[\"Phenotype\"], axis=0)\n",
-    "    for ph, ph_names in phenotype_list.items():\n",
-    "        regex = \"|\".join(ph_names)\n",
-    "        l[ph] = list(phecodes[phecodes.Phenotype.str.contains(regex, case=False)].ICD10.str.replace(\"\\\\.\", \"\").str.slice(0, 3).unique())\n",
-    "    return l"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:37:14.851438Z",
-     "start_time": "2020-11-04T12:37:14.774599Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "snomed_core = pd.read_csv(f\"{path}/mapping/snomed_core_list.txt\", sep=\"|\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:37:14.909023Z",
-     "start_time": "2020-11-04T12:37:14.853286Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "snomed_core = snomed_core.query(\"SNOMED_CONCEPT_STATUS == 'Current'\").copy()\n",
-    "new = snomed_core.SNOMED_FSN.str.split(\"(\", n=1, expand=True)\n",
-    "snomed_core[\"snomed_name\"] = new[0].str.rstrip(' ')\n",
-    "snomed_core[\"snomed_type\"] = new[1].str.rstrip(')')\n",
-    "snomed_core_data = snomed_core#.query(\"(snomed_type=='disorder' | snomed_type=='finding') & USAGE>0.01\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:37:14.914973Z",
-     "start_time": "2020-11-04T12:37:14.910857Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "snomed_names = snomed_core_data.snomed_name.to_list()\n",
-    "snomed_names = [str(item).lower().strip().replace(\" \", \"_\").replace(\";\", \"\").replace(\",\", \"\") for item in snomed_names]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:39:55.342937Z",
-     "start_time": "2020-11-04T12:37:14.916906Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "989d1908f8624958a92da3602ad125f1",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(FloatProgress(value=0.0, max=6181.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "phenotype_list_snomed = dict(zip(snomed_names, snomed_core_data.SNOMED_CID.to_list()))\n",
-    "snomed_ids = vocab[\"concept\"].query(\"vocabulary_id == 'SNOMED'\").concept_id.to_list()\n",
-    "icd10_ids = vocab[\"concept\"].query(\"vocabulary_id == 'ICD10CM'\").concept_id.to_list()\n",
-    "\n",
-    "ph_to_icd10_mapping = {}\n",
-    "\n",
-    "def map_snomed_to_icd10(ph, snomed_code, concept, concept_ancestor, concept_relationship):\n",
-    "    concept_ids = concept.query(\"vocabulary_id == 'SNOMED' & concept_code == @snomed_code\").concept_id.to_list()\n",
-    "    snomed_desc_ids = concept_ancestor.query(\"ancestor_concept_id== @concept_ids\").descendant_concept_id.to_list()\n",
-    "    ph_desc = concept.query(\"concept_id == @snomed_desc_ids\").query(\"vocabulary_id == 'SNOMED'\")\n",
-    "    l_ph_desc_ids = ph_desc.concept_id.to_list()\n",
-    "    ph_icd10_ids = list(concept_relationship.query(\"concept_id_1==@l_ph_desc_ids\").query(\"concept_id_2 == @icd10_ids\").concept_id_2.unique())\n",
-    "    #ph_icd10_ids = list(concept_relationship.set_index(\"concept_id_1\").query(\"index==@l_ph_desc_ids\").query(\"concept_id_2 == @icd10_ids\").query(\"relationship_id == 'Mapped from'\").concept_id_2.unique()\n",
-    "    \n",
-    "    #ph_icd10_ids = list(temp.concept_id_2.unique())\n",
-    "    df = concept.query(\"concept_id == @ph_icd10_ids & vocabulary_id == 'ICD10CM'\")\n",
-    "    icd10_list = list(df[~df.concept_code.str.contains(\"OMOP\", na=False)].concept_code.unique())\n",
-    "    icd10_list = sorted(list(set([e[:3] for e in icd10_list])))\n",
-    "    #print(f\"{ph}: {icd10_list}\")\n",
-    "    return {ph: sorted(list(dict.fromkeys([str(e) for e in icd10_list])))}\n",
-    "\n",
-    "from joblib import Parallel, delayed\n",
-    "concept_ids = vocab[\"concept\"].query(\"(vocabulary_id == 'SNOMED') | (vocabulary_id == 'ICD10CM')\")\n",
-    "vocab_concept_ids = concept_ids.concept_id.to_list()\n",
-    "concept_ancestor = vocab[\"concept_ancestor\"][[\"ancestor_concept_id\", \"descendant_concept_id\"]].query(\"ancestor_concept_id == @vocab_concept_ids\")\n",
-    "concept_rel = vocab[\"concept_relationship\"][[\"concept_id_1\", \"concept_id_2\", \"relationship_id\"]].query(\"(concept_id_1 == @vocab_concept_ids) & (concept_id_2 == @vocab_concept_ids) & (relationship_id == 'Mapped from')\")\n",
-    "icd10_codes = Parallel(n_jobs=10, require=\"sharedmem\")(delayed(map_snomed_to_icd10)(ph, snomed_code, \n",
-    "                                                              concept_ids, concept_ancestor, concept_rel) for ph, snomed_code in tqdm(phenotype_list_snomed.items()))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:39:55.348009Z",
-     "start_time": "2020-11-04T12:39:55.344704Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "l10_snomed = {}\n",
-    "for ph in icd10_codes: l10_snomed.update(ph)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:39:55.468545Z",
-     "start_time": "2020-11-04T12:39:55.349545Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "phenotype_list_basic = {\n",
-    "    \"coronary_heart_disease\": [\"Ischemic heart disease\"],\n",
-    "    \"myocardial_infarction\": [\"Myocardial infarction\"],\n",
-    "    \"stroke\": [\"Cerebrovascular disease\"],\n",
-    "    \"diabetes1\" : [\"Type 1 diabetes\"],\n",
-    "    \"diabetes2\" : [\"Diabetes mellitus\", \"Type 2 diabetes\"],\n",
-    "    \"chronic_kidney_disease\": [\"Chronic kidney disease\", \"chronic renal failure\"],\n",
-    "    \"atrial_fibrillation\": [\"Atrial fibrillation\", \"Atrial flutter\", \"paroxysmal tachycardia\"],\n",
-    "    \"migraine\": [\"Migraine\"],\n",
-    "    \"rheumatoid_arthritis\": [\"Rheumatoid arthritis\"],\n",
-    "    \"systemic_lupus_erythematosus\": [\"Systemic lupus erythematosus\"],\n",
-    "    \"severe_mental_illness\": [\"Schizophrenia\", \"bipolar\", \"Major depressive disorder\"],\n",
-    "    \"erectile_dysfunction\" : [\"Erectile dysfunction\"],  \n",
-    "}\n",
-    "\n",
-    "l10_basic = phenotype_children(phecodes, phenotype_list_basic)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:39:55.473556Z",
-     "start_time": "2020-11-04T12:39:55.471051Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "l10_all = l10_basic\n",
-    "for key, value in l10_snomed.items(): \n",
-    "    if key not in l10_basic: l10_all[key] = value"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:39:55.620916Z",
-     "start_time": "2020-11-04T12:39:55.475137Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "l10 = {k: v for k, v in l10_all.items() if len(v)!=0}\n",
-    "\n",
-    "#phenotype_list = {k: v for k, v in phenotype_list.items() if k in list(l10.keys())}\n",
-    "\n",
-    "with open(os.path.join(path, dataset_path, 'phenotype_list.yaml'), 'w') as file: yaml.dump(l10, file, default_flow_style=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 1. Self Reported"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:43:13.288198Z",
-     "start_time": "2020-11-04T12:43:13.264683Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "coding609 = pd.read_csv(f\"{path}/mapping/codings/coding609.tsv\", sep=\"\\t\").rename(columns={\"coding\":\"code\"})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:43:13.304423Z",
-     "start_time": "2020-11-04T12:43:13.289982Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from datetime import datetime, timedelta\n",
-    "\n",
-    "def datetime_from_dec_year(dec_year):\n",
-    "    start = dec_year\n",
-    "    year = int(start)\n",
-    "    rem = start - year\n",
-    "\n",
-    "    base = datetime(year, 1, 1)\n",
-    "    result = base + timedelta(seconds=(base.replace(year=base.year + 1) - base).total_seconds() * rem)\n",
-    "    #result.strftime(\"%Y-%m-%d\")\n",
-    "    return result.date()\n",
-    "\n",
-    "def extract_map_self_reported(data, data_field, code_map):\n",
-    "    pbar = tqdm(total=16)\n",
-    "    ### codes\n",
-    "    fields = [\"20002\"]; pbar.update(1)\n",
-    "    raw = get_data_fields_all(fields, data, data_field); pbar.update(1)\n",
-    "    col = \"noncancer_illness_code_selfreported_f20002\"; pbar.update(1)\n",
-    "    temp = pd.wide_to_long(raw, stubnames=[col], i=\"eid\", j=\"instance_index\", sep=\"_\", suffix=\"\\w+\").reset_index(); pbar.update(1)\n",
-    "    codes = temp.rename(columns={col:\"code\"})\\\n",
-    "        .assign(code=lambda x: x.code.astype(str))\\\n",
-    "        .replace(\"None\", np.nan) \\\n",
-    "        .dropna(subset=[\"code\"], axis=0)\\\n",
-    "        .assign(code=lambda x: x.code.astype(int)) \\\n",
-    "        .merge(code_map, how=\"left\",on=\"code\") \\\n",
-    "        .dropna(subset=[\"meaning\"], axis=0)\\\n",
-    "        .sort_values([\"eid\", \"instance_index\"]) \\\n",
-    "        .reset_index(drop=True); pbar.update(1)\n",
-    "    \n",
-    "    ### dates\n",
-    "    fields = [\"20008\"]; pbar.update(1)\n",
-    "    raw = get_data_fields_all(fields, data, data_field); pbar.update(1)\n",
-    "    col=\"interpolated_year_when_noncancer_illness_first_diagnosed_f20008\"; pbar.update(1)\n",
-    "    temp = pd.wide_to_long(raw, stubnames=[col], i=\"eid\", j=\"instance_index\", sep=\"_\", suffix=\"\\w+\").reset_index(); pbar.update(1)\n",
-    "    dates = temp.rename(columns={col:\"date\"})\\\n",
-    "        .dropna(subset=[\"date\"], axis=0)\\\n",
-    "        .sort_values([\"eid\", \"instance_index\"]) \\\n",
-    "        .reset_index(drop=True); pbar.update(1)\n",
-    "\n",
-    "    dates = dates[dates.date!=-1]; pbar.update(1)\n",
-    "    dates = dates[dates.date!=-3]; pbar.update(1)\n",
-    "    dates.date = dates.date.apply(datetime_from_dec_year); pbar.update(1)\n",
-    "    \n",
-    "    test = codes.merge(dates, how=\"left\", on=[\"eid\", \"instance_index\"]).assign(origin=\"self_reported\").copy(); pbar.update(1)\n",
-    "    \n",
-    "    test[\"instance_index\"] = test[\"instance_index\"].astype(\"string\"); pbar.update(1)\n",
-    "    test[['instance','n']] = test.instance_index.str.split(\"_\",expand=True); pbar.update(1)\n",
-    "    pbar.close()\n",
-    "    \n",
-    "    return test[[\"eid\", \"origin\", 'instance','n', \"code\", \"meaning\", \"date\"]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:46:00.098893Z",
-     "start_time": "2020-11-04T12:43:13.432479Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "7af3e55ecb424d5fbbd303f1f5b67a66",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(FloatProgress(value=0.0, max=16.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "codes_self_reported = extract_map_self_reported(data, data_field, coding609)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "### 2. Primary Care"
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% raw\n"
-    }
-   },
-   "source": [
-    "# 2. Primary Care\n",
-    "fields = c(\n",
-    "    42040 # GP clinical event records\n",
-    ")\n",
-    "# extract covariates at baseline (Instance 0)\n",
-    "def = data_field %>% filter((field.showcase %in% fields),ignore.case = TRUE)\n",
-    "diagnoses_primary_care = data[, append(\"eid\", def$col.name)]\n",
-    "head(diagnoses_primary_care)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 3. Hospital episode statistics"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:46:08.959617Z",
-     "start_time": "2020-11-04T12:46:00.100618Z"
-    },
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "codes_hospital_records = pd.read_feather(f\"{data_path}/1_decoded/codes_hospital_records.feather\").drop(\"level\", axis=1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Combine diagnoses and events"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:46:25.115010Z",
-     "start_time": "2020-11-04T12:46:08.961388Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>origin</th>\n",
-       "      <th>instance</th>\n",
-       "      <th>n</th>\n",
-       "      <th>code</th>\n",
-       "      <th>meaning</th>\n",
-       "      <th>date</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>hes_icd10</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>S0240</td>\n",
-       "      <td>S02</td>\n",
-       "      <td>2005-06-02</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>hes_icd10</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>W188</td>\n",
-       "      <td>W18</td>\n",
-       "      <td>2005-06-02</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>hes_icd10</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>K37</td>\n",
-       "      <td>K37</td>\n",
-       "      <td>1998-05-11</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>hes_icd10</td>\n",
-       "      <td>0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>K37</td>\n",
-       "      <td>K37</td>\n",
-       "      <td>1998-05-16</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>hes_icd10</td>\n",
-       "      <td>0</td>\n",
-       "      <td>5</td>\n",
-       "      <td>K37</td>\n",
-       "      <td>K37</td>\n",
-       "      <td>1998-06-01</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       eid     origin instance  n   code meaning        date\n",
-       "0  1000018  hes_icd10        0  1  S0240     S02  2005-06-02\n",
-       "1  1000018  hes_icd10        0  2   W188     W18  2005-06-02\n",
-       "2  1000018  hes_icd10        0  3    K37     K37  1998-05-11\n",
-       "3  1000018  hes_icd10        0  4    K37     K37  1998-05-16\n",
-       "4  1000018  hes_icd10        0  5    K37     K37  1998-06-01"
-      ]
-     },
-     "execution_count": 51,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "diagnoses_codes = codes_self_reported.append(codes_hospital_records).sort_values([\"eid\", \"instance\", \"n\"]).dropna(subset=[\"date\"], axis=0)\n",
-    "diagnoses_codes.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "RangeIndex: 13339466 entries, 0 to 13339465\n",
-      "Data columns (total 7 columns):\n",
-      " #   Column    Dtype \n",
-      "---  ------    ----- \n",
-      " 0   eid       int64 \n",
-      " 1   origin    object\n",
-      " 2   instance  object\n",
-      " 3   n         object\n",
-      " 4   code      object\n",
-      " 5   meaning   object\n",
-      " 6   date      object\n",
-      "dtypes: int64(1), object(6)\n",
-      "memory usage: 712.4+ MB\n"
-     ]
-    }
-   ],
-   "source": [
-    "diagnoses_codes.reset_index(drop=True).info()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "RangeIndex: 12547626 entries, 0 to 12547625\n",
-      "Data columns (total 7 columns):\n",
-      " #   Column    Dtype  \n",
-      "---  ------    -----  \n",
-      " 0   eid       int32  \n",
-      " 1   origin    object \n",
-      " 2   instance  float64\n",
-      " 3   n         int32  \n",
-      " 4   code      object \n",
-      " 5   meaning   object \n",
-      " 6   date      object \n",
-      "dtypes: float64(1), int32(2), object(4)\n",
-      "memory usage: 574.4+ MB\n"
-     ]
-    }
-   ],
-   "source": [
-    "codes_hospital_records.info()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "diagnoses_codes.reset_index(drop=True).assign(eid = lambda x: x.eid.astype(int),\n",
-    "                                              origin = lambda x: x.origin.astype(str),\n",
-    "                                              instance = lambda x: x.instance.astype(int),\n",
-    "                                              n = lambda x: x.n.astype(int),\n",
-    "                                              code = lambda x: x.code.astype(str), \n",
-    "                                              meaning = lambda x: x.meaning.astype(str))\\\n",
-    "    .to_feather(os.path.join(path, dataset_path, 'temp_diagnoses_codes.feather'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "diagnoses_codes = pd.read_feather(os.path.join(path, dataset_path, 'temp_diagnoses_codes.feather'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:46:26.189927Z",
-     "start_time": "2020-11-04T12:46:25.117069Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from joblib import Parallel, delayed\n",
-    "from functools import reduce\n",
-    "from numba import jit\n",
-    "\n",
-    "def had_diagnosis_before_per_ph(df_before, ph, ph_codes, temp):\n",
-    "   # regex = \"|\".join(ph_codes)\n",
-    "    #df_ph = df_before.set_index(\"meaning\").loc[ph_codes][[\"eid\"]]\\\n",
-    "    #        .drop_duplicates(subset=[\"eid\"])\\\n",
-    "    #        .assign(phenotype=True) \n",
-    "    df_ph = df_before[df_before.meaning.isin(ph_codes)][[\"eid\"]]\\\n",
-    "            .drop_duplicates(subset=[\"eid\"])\\\n",
-    "            .assign(phenotype=True) \n",
-    "    #df_ph = df_before[df_before.meaning.str.contains(regex, case=False)][[\"eid\"]]\\\n",
-    "    #        .drop_duplicates(subset=[\"eid\"])\\\n",
-    "    #        .assign(phenotype=True)   \n",
-    "    return temp.merge(df_ph, how=\"left\", on=\"eid\").fillna(False).phenotype\n",
-    "\n",
-    "def had_diagnosis_before(data, diagnoses_codes, phenotypes, time0=time0_col):\n",
-    "    diagnoses_codes_time = diagnoses_codes.merge(data[[\"eid\", time0]], how=\"left\", on=\"eid\")\n",
-    "    \n",
-    "    temp = data[[\"eid\"]].copy()\n",
-    "    df_before = diagnoses_codes_time[diagnoses_codes_time.date < diagnoses_codes_time[time0]]\n",
-    "                                                                                         \n",
-    "    df_phs = Parallel(n_jobs=20, require=\"sharedmem\")(delayed(had_diagnosis_before_per_ph)(df_before, ph, phenotypes[ph], temp) for ph in tqdm(list(phenotypes)))\n",
-    "    for ph, df_ph_series in zip(tqdm(list(phenotypes)), df_phs): temp[ph] = df_ph_series#temp.merge(df_ph, how=\"left\", on=\"eid\").fillna(False).phenotype\n",
-    "    \n",
-    "    return temp.sort_values(\"eid\")  #reduce(lambda left,right: pd.merge(left,right,on=['eid'], how='left'), df_phs)."
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "source": [
-    "from joblib import Parallel, delayed\n",
-    "from functools import reduce\n",
-    "from numba import jit\n",
-    "\n",
-    "def had_diagnosis_before_per_ph(df_before, ph, ph_codes, temp):\n",
-    "    regex = \"|\".join(ph_codes)\n",
-    "    df_ph = df_before[df_before.meaning.str.contains(regex, case=False)][[\"eid\"]]\\\n",
-    "            .drop_duplicates(subset=[\"eid\"])\\\n",
-    "            .assign(phenotype=True)   \n",
-    "    return temp.merge(df_ph, how=\"left\", on=\"eid\").fillna(False).phenotype\n",
-    "\n",
-    "def had_diagnosis_before(data, diagnoses_codes, phenotypes, time0=time0_col):\n",
-    "    diagnoses_codes_time = diagnoses_codes.merge(data[[\"eid\", time0]], how=\"left\", on=\"eid\")\n",
-    "    \n",
-    "    temp = data[[\"eid\"]].copy()\n",
-    "    df_before = diagnoses_codes_time[diagnoses_codes_time.date < diagnoses_codes_time[time0]]\n",
-    "                                                                                         \n",
-    "    df_phs = Parallel(n_jobs=30, require=\"sharedmem\")(delayed(had_diagnosis_before_per_ph)(df_before, ph, phenotypes[ph], temp) for ph in tqdm(list(phenotypes)))\n",
-    "    for ph, ph_series in zip(tqdm(list(phenotypes)), df_phs): temp[ph] = ph_series\n",
-    "    \n",
-    "    return temp.sort_values(\"eid\")  #reduce(lambda left,right: pd.merge(left,right,on=['eid'], how='left'), df_phs)."
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "source": [
-    "phenotypes = l10\n",
-    "time0 = time0_col\n",
-    "diagnoses_codes_time = diagnoses_codes.merge(data[[\"eid\", time0]], how=\"left\", on=\"eid\")\n",
-    "\n",
-    "temp = data[[\"eid\"]].copy()\n",
-    "df_before = diagnoses_codes_time[diagnoses_codes_time.date < diagnoses_codes_time[time0]]\n",
-    "\n",
-    "df_phs = Parallel(n_jobs=10, require=\"sharedmem\")(delayed(had_diagnosis_before_per_ph)(df_before, ph, phenotypes[ph], temp) for ph in tqdm(list(phenotypes)[0:100]))"
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "source": [
-    "def had_diagnosis_before(data, diagnoses_codes, phenotypes, time0=time0_col):\n",
-    "    diagnoses_codes_time = diagnoses_codes.merge(data[[\"eid\", time0]], how=\"left\", on=\"eid\")\n",
-    "    \n",
-    "    temp = data[[\"eid\"]].copy()\n",
-    "    df_before = diagnoses_codes_time[diagnoses_codes_time.date < diagnoses_codes_time[time0]]\n",
-    "    for ph, ph_codes in tqdm(phenotypes.items()):\n",
-    "        regex = \"|\".join(ph_codes)\n",
-    "        df_ph = df_before[df_before.meaning.str.contains(regex, case=False)][[\"eid\"]]\\\n",
-    "            .drop_duplicates(subset=[\"eid\"])\\\n",
-    "            .assign(phenotype=True)\n",
-    "        temp[ph] = temp.merge(df_ph, how=\"left\", on=\"eid\").fillna(False).phenotype\n",
-    "       \n",
-    "    return temp.sort_values(\"eid\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T13:17:52.922023Z",
-     "start_time": "2020-11-04T12:46:26.191314Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6bea6215cd7a462ea5e12e755d4a70ed",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(FloatProgress(value=0.0, max=3521.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n"
-     ]
-    },
-    {
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-      "application/vnd.jupyter.widget-view+json": {
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-      },
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-       "HBox(children=(FloatProgress(value=0.0, max=3521.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "502504\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>coronary_heart_disease</th>\n",
-       "      <th>myocardial_infarction</th>\n",
-       "      <th>stroke</th>\n",
-       "      <th>diabetes1</th>\n",
-       "      <th>diabetes2</th>\n",
-       "      <th>chronic_kidney_disease</th>\n",
-       "      <th>atrial_fibrillation</th>\n",
-       "      <th>migraine</th>\n",
-       "      <th>rheumatoid_arthritis</th>\n",
-       "      <th>...</th>\n",
-       "      <th>sleep_terror_disorder</th>\n",
-       "      <th>acute_frontal_sinusitis</th>\n",
-       "      <th>benign_neoplasm_of_pancreas</th>\n",
-       "      <th>primary_malignant_neoplasm_of_soft_tissues_of_lower_limb</th>\n",
-       "      <th>neoplasm_of_uncertain_behavior_of_neck</th>\n",
-       "      <th>injury_of_peroneal_nerve</th>\n",
-       "      <th>dupuytren's_disease</th>\n",
-       "      <th>stem_cell_donor</th>\n",
-       "      <th>endemic_goiter</th>\n",
-       "      <th>diplegic_cerebral_palsy</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1000020</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1000037</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000043</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1000051</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 3522 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       eid  coronary_heart_disease  myocardial_infarction  stroke  diabetes1  \\\n",
-       "0  1000018                   False                  False   False      False   \n",
-       "1  1000020                   False                  False   False      False   \n",
-       "2  1000037                   False                  False   False      False   \n",
-       "3  1000043                   False                  False   False      False   \n",
-       "4  1000051                   False                  False   False      False   \n",
-       "\n",
-       "   diabetes2  chronic_kidney_disease  atrial_fibrillation  migraine  \\\n",
-       "0      False                   False                False     False   \n",
-       "1      False                   False                False     False   \n",
-       "2      False                   False                False     False   \n",
-       "3      False                   False                False     False   \n",
-       "4      False                   False                False     False   \n",
-       "\n",
-       "   rheumatoid_arthritis  ...  sleep_terror_disorder  acute_frontal_sinusitis  \\\n",
-       "0                 False  ...                  False                    False   \n",
-       "1                 False  ...                  False                    False   \n",
-       "2                 False  ...                  False                    False   \n",
-       "3                 False  ...                  False                    False   \n",
-       "4                 False  ...                  False                    False   \n",
-       "\n",
-       "   benign_neoplasm_of_pancreas  \\\n",
-       "0                        False   \n",
-       "1                        False   \n",
-       "2                        False   \n",
-       "3                        False   \n",
-       "4                        False   \n",
-       "\n",
-       "   primary_malignant_neoplasm_of_soft_tissues_of_lower_limb  \\\n",
-       "0                                              False          \n",
-       "1                                              False          \n",
-       "2                                              False          \n",
-       "3                                              False          \n",
-       "4                                              False          \n",
-       "\n",
-       "   neoplasm_of_uncertain_behavior_of_neck  injury_of_peroneal_nerve  \\\n",
-       "0                                   False                     False   \n",
-       "1                                   False                     False   \n",
-       "2                                   False                     False   \n",
-       "3                                   False                     False   \n",
-       "4                                   False                     False   \n",
-       "\n",
-       "   dupuytren's_disease  stem_cell_donor  endemic_goiter  \\\n",
-       "0                False            False           False   \n",
-       "1                False            False           False   \n",
-       "2                False            False           False   \n",
-       "3                False            False           False   \n",
-       "4                False            False           False   \n",
-       "\n",
-       "   diplegic_cerebral_palsy  \n",
-       "0                    False  \n",
-       "1                    False  \n",
-       "2                    False  \n",
-       "3                    False  \n",
-       "4                    False  \n",
-       "\n",
-       "[5 rows x 3522 columns]"
-      ]
-     },
-     "execution_count": 61,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "diagnoses = had_diagnosis_before(basics, diagnoses_codes, l10, time0=time0_col)\n",
-    "print(len(diagnoses))\n",
-    "\n",
-    "diagnoses.to_feather(os.path.join(path, dataset_path, 'temp_diagnoses.feather'))\n",
-    "\n",
-    "diagnoses.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "diagnoses = pd.read_feather(os.path.join(path, dataset_path, 'temp_diagnoses.feather'))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "source": [
-    "# Add embeddings for Snomed Diagnoses"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "source": [
-    "#### Get SNOMED - node2vec dict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "outputs": [],
-   "source": [
-    "snomed_embeddings = pd.read_csv(\"/data/analysis/ag-reils/steinfej/data/snomed_embeddings/snomed.emb.p1.q1.w20.l40.e200.graph_format.txt\", sep=\" \", header=None, skiprows=1)\n",
-    "snomed_embeddings.columns = [\"snomed_id\"]+list(snomed_embeddings.columns)[:-1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "metadata": {
-    "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true,
-     "source_hidden": true
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>snomed_id</th>\n",
-       "      <th>0</th>\n",
-       "      <th>1</th>\n",
-       "      <th>2</th>\n",
-       "      <th>3</th>\n",
-       "      <th>4</th>\n",
-       "      <th>5</th>\n",
-       "      <th>6</th>\n",
-       "      <th>7</th>\n",
-       "      <th>8</th>\n",
-       "      <th>...</th>\n",
-       "      <th>190</th>\n",
-       "      <th>191</th>\n",
-       "      <th>192</th>\n",
-       "      <th>193</th>\n",
-       "      <th>194</th>\n",
-       "      <th>195</th>\n",
-       "      <th>196</th>\n",
-       "      <th>197</th>\n",
-       "      <th>198</th>\n",
-       "      <th>199</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>129265001</td>\n",
-       "      <td>0.027456</td>\n",
-       "      <td>0.171026</td>\n",
-       "      <td>-0.178822</td>\n",
-       "      <td>-0.038667</td>\n",
-       "      <td>0.299777</td>\n",
-       "      <td>0.082540</td>\n",
-       "      <td>-0.201284</td>\n",
-       "      <td>0.215164</td>\n",
-       "      <td>0.093241</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.211030</td>\n",
-       "      <td>0.488475</td>\n",
-       "      <td>0.082177</td>\n",
-       "      <td>-0.047102</td>\n",
-       "      <td>-0.086411</td>\n",
-       "      <td>-0.012141</td>\n",
-       "      <td>-0.258416</td>\n",
-       "      <td>-0.113295</td>\n",
-       "      <td>0.040249</td>\n",
-       "      <td>-0.116884</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>360224006</td>\n",
-       "      <td>-0.090187</td>\n",
-       "      <td>0.086986</td>\n",
-       "      <td>-0.184378</td>\n",
-       "      <td>-0.028722</td>\n",
-       "      <td>-0.010235</td>\n",
-       "      <td>0.242439</td>\n",
-       "      <td>-0.094009</td>\n",
-       "      <td>0.203015</td>\n",
-       "      <td>-0.097894</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.112750</td>\n",
-       "      <td>0.333906</td>\n",
-       "      <td>-0.018193</td>\n",
-       "      <td>0.020459</td>\n",
-       "      <td>-0.170226</td>\n",
-       "      <td>0.277866</td>\n",
-       "      <td>-0.007079</td>\n",
-       "      <td>-0.080419</td>\n",
-       "      <td>0.162456</td>\n",
-       "      <td>-0.101768</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>102272007</td>\n",
-       "      <td>0.553420</td>\n",
-       "      <td>0.460947</td>\n",
-       "      <td>0.143925</td>\n",
-       "      <td>-0.053785</td>\n",
-       "      <td>-0.849588</td>\n",
-       "      <td>0.467587</td>\n",
-       "      <td>-0.654922</td>\n",
-       "      <td>-0.010799</td>\n",
-       "      <td>0.510141</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.187371</td>\n",
-       "      <td>0.248607</td>\n",
-       "      <td>0.079687</td>\n",
-       "      <td>-0.354121</td>\n",
-       "      <td>0.412602</td>\n",
-       "      <td>0.582461</td>\n",
-       "      <td>-0.780365</td>\n",
-       "      <td>0.045450</td>\n",
-       "      <td>-0.196551</td>\n",
-       "      <td>0.045745</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>39937001</td>\n",
-       "      <td>0.123158</td>\n",
-       "      <td>0.019882</td>\n",
-       "      <td>0.050896</td>\n",
-       "      <td>0.037364</td>\n",
-       "      <td>0.200435</td>\n",
-       "      <td>0.312911</td>\n",
-       "      <td>-0.338977</td>\n",
-       "      <td>-0.092584</td>\n",
-       "      <td>-0.167741</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.057770</td>\n",
-       "      <td>-0.012834</td>\n",
-       "      <td>-0.139794</td>\n",
-       "      <td>0.180572</td>\n",
-       "      <td>0.190781</td>\n",
-       "      <td>0.104039</td>\n",
-       "      <td>-0.358235</td>\n",
-       "      <td>-0.137954</td>\n",
-       "      <td>-0.236551</td>\n",
-       "      <td>-0.206458</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>23583003</td>\n",
-       "      <td>0.423193</td>\n",
-       "      <td>0.384338</td>\n",
-       "      <td>-0.041503</td>\n",
-       "      <td>0.116848</td>\n",
-       "      <td>-0.055029</td>\n",
-       "      <td>0.149064</td>\n",
-       "      <td>-0.092810</td>\n",
-       "      <td>-0.050148</td>\n",
-       "      <td>0.113122</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.162375</td>\n",
-       "      <td>-0.076505</td>\n",
-       "      <td>-0.274352</td>\n",
-       "      <td>-0.099204</td>\n",
-       "      <td>-0.281887</td>\n",
-       "      <td>-0.266345</td>\n",
-       "      <td>-0.020257</td>\n",
-       "      <td>-0.003843</td>\n",
-       "      <td>-0.008804</td>\n",
-       "      <td>-0.286832</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 201 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   snomed_id         0         1         2         3         4         5  \\\n",
-       "0  129265001  0.027456  0.171026 -0.178822 -0.038667  0.299777  0.082540   \n",
-       "1  360224006 -0.090187  0.086986 -0.184378 -0.028722 -0.010235  0.242439   \n",
-       "2  102272007  0.553420  0.460947  0.143925 -0.053785 -0.849588  0.467587   \n",
-       "3   39937001  0.123158  0.019882  0.050896  0.037364  0.200435  0.312911   \n",
-       "4   23583003  0.423193  0.384338 -0.041503  0.116848 -0.055029  0.149064   \n",
-       "\n",
-       "          6         7         8  ...       190       191       192       193  \\\n",
-       "0 -0.201284  0.215164  0.093241  ...  0.211030  0.488475  0.082177 -0.047102   \n",
-       "1 -0.094009  0.203015 -0.097894  ...  0.112750  0.333906 -0.018193  0.020459   \n",
-       "2 -0.654922 -0.010799  0.510141  ...  0.187371  0.248607  0.079687 -0.354121   \n",
-       "3 -0.338977 -0.092584 -0.167741  ...  0.057770 -0.012834 -0.139794  0.180572   \n",
-       "4 -0.092810 -0.050148  0.113122  ...  0.162375 -0.076505 -0.274352 -0.099204   \n",
-       "\n",
-       "        194       195       196       197       198       199  \n",
-       "0 -0.086411 -0.012141 -0.258416 -0.113295  0.040249 -0.116884  \n",
-       "1 -0.170226  0.277866 -0.007079 -0.080419  0.162456 -0.101768  \n",
-       "2  0.412602  0.582461 -0.780365  0.045450 -0.196551  0.045745  \n",
-       "3  0.190781  0.104039 -0.358235 -0.137954 -0.236551 -0.206458  \n",
-       "4 -0.281887 -0.266345 -0.020257 -0.003843 -0.008804 -0.286832  \n",
-       "\n",
-       "[5 rows x 201 columns]"
-      ]
-     },
-     "execution_count": 64,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "snomed_embeddings.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "outputs": [],
-   "source": [
-    "diagnoses_snomed = diagnoses.columns[13:].to_list()\n",
-    "diagnoses_snomed_dict = {}\n",
-    "for d in diagnoses_snomed: diagnoses_snomed_dict[d] = phenotype_list_snomed[d]\n",
-    "    \n",
-    "snomed_codes_used = list(phenotype_list_snomed.values())\n",
-    "snomed_codes_emb = snomed_embeddings.snomed_id.to_list()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "outputs": [],
-   "source": [
-    "snomed_id_array = snomed_embeddings[[\"snomed_id\"]].values\n",
-    "node2vec_array = snomed_embeddings.iloc[:, 1:].values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "metadata": {
-    "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true,
-     "source_hidden": true
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "456f56d7481648e48b74e9e76b675f01",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(FloatProgress(value=0.0, max=373286.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "snomed_arrays = {}\n",
-    "for sid, row in zip(tqdm(snomed_id_array), node2vec_array):\n",
-    "    snomed_arrays[sid[0]] = row"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "source": [
-    "#### Get Patient -> Snomed dict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 68,
-   "metadata": {
-    "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true,
-     "source_hidden": true
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "05ac46541972426da8a5b5f7fe461c6e",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(FloatProgress(value=0.0, max=502504.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "diagnoses_array = diagnoses[diagnoses_snomed].values\n",
-    "eid_array = diagnoses[[\"eid\"]].values\n",
-    "\n",
-    "from numba import jit\n",
-    "import numpy as np\n",
-    "\n",
-    "patient_diagnoses = {}\n",
-    "for eid, row in zip(tqdm(eid_array), diagnoses_array):\n",
-    "    patient_diagnoses[eid[0]] = list(np.argwhere(row==True).flatten())\n",
-    "\n",
-    "#diagnoses.query(\"eid==1000092\")[diagnoses_snomed]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 69,
-   "metadata": {
-    "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true,
-     "source_hidden": true
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f4c90d91d08e48119abcf8426cddd37e",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(FloatProgress(value=0.0, max=502504.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "patient_diagnoses_sid = {}\n",
-    "snomed_codes_emb_set = set(snomed_codes_emb)\n",
-    "for eid, p_d_col in tqdm(patient_diagnoses.items()):\n",
-    "    diagnoses_list = [diagnoses_snomed[i] for i in p_d_col]\n",
-    "    sid_list = [phenotype_list_snomed[i] for i in diagnoses_list]\n",
-    "    sid_list = [sid for sid in sid_list if sid in snomed_codes_emb_set]\n",
-    "    patient_diagnoses_sid[eid] = sid_list"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "source": [
-    "#### Get Patient -> node2vec average"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 70,
-   "metadata": {
-    "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true,
-     "source_hidden": true
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "47e934dc64334c82893d23b458108fbf",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(FloatProgress(value=0.0, max=502504.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/steinfej/miniconda3/envs/python/lib/python3.7/site-packages/numpy/core/fromnumeric.py:3335: RuntimeWarning: Mean of empty slice.\n",
-      "  out=out, **kwargs)\n",
-      "/home/steinfej/miniconda3/envs/python/lib/python3.7/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars\n",
-      "  ret = ret.dtype.type(ret / rcount)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "patient_node2vec_dict = {}\n",
-    "for eid, sids in tqdm(patient_diagnoses_sid.items()):\n",
-    "    array_list =  [snomed_arrays[sid] for sid in sids]\n",
-    "    patient_node2vec_dict[eid] = np.mean(array_list, axis=0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "metadata": {
-    "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true,
-     "source_hidden": true
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e1395c2d1bfc41f39697a8925b20d597",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(FloatProgress(value=0.0, max=502504.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "array([ 0.37033895,  0.13321076, -0.10030267,  0.05625264, -0.08437331,\n",
-       "        0.11504787, -0.24833219, -0.11936529,  0.25239648, -0.32739932,\n",
-       "        0.06330914, -0.06493541, -0.28790415, -0.2885537 ,  0.02546298,\n",
-       "       -0.06123153, -0.17690672, -0.0867546 , -0.28337599, -0.08474496,\n",
-       "        0.31454532, -0.24158154,  0.15930864,  0.06124846,  0.07694602,\n",
-       "       -0.13752803,  0.13671128, -0.27988751, -0.03987635,  0.03632156,\n",
-       "       -0.13793361, -0.15857369, -0.16441636,  0.2412931 ,  0.20070578,\n",
-       "       -0.11412784,  0.02563965, -0.03560184,  0.17169744, -0.0153383 ,\n",
-       "        0.00117099,  0.1025362 , -0.06505568,  0.05646065, -0.02705149,\n",
-       "        0.04416442, -0.15798991, -0.10650637,  0.02082507, -0.21182802,\n",
-       "        0.13972325, -0.18089307, -0.12731068,  0.02907221, -0.19797107,\n",
-       "        0.19550177,  0.14941799,  0.21561857, -0.18085379,  0.10768238,\n",
-       "        0.12968045,  0.2082016 ,  0.03561408, -0.01122218, -0.27099816,\n",
-       "       -0.06029919, -0.18787618,  0.10084175, -0.07939234, -0.22951897,\n",
-       "       -0.36536359, -0.01050854, -0.21419807, -0.23562986,  0.02380316,\n",
-       "        0.1213157 , -0.1601396 ,  0.07218057, -0.04362593, -0.22303355,\n",
-       "       -0.23844839, -0.14799905,  0.22346404,  0.14218655,  0.39873181,\n",
-       "       -0.32965129,  0.19102432, -0.08894028, -0.20726567,  0.25343222,\n",
-       "        0.29939455,  0.20513029,  0.17430424, -0.05073184,  0.05827969,\n",
-       "        0.31626954,  0.0791522 , -0.21433214,  0.11027244,  0.0805151 ,\n",
-       "        0.08557279,  0.25260801, -0.00096969, -0.06177831,  0.0544524 ,\n",
-       "       -0.17340027,  0.09731447,  0.09982385, -0.074546  , -0.10350239,\n",
-       "       -0.25798929,  0.01222471,  0.1605315 ,  0.00191767,  0.11642527,\n",
-       "       -0.15387604, -0.01520803,  0.03220765, -0.13534233,  0.09154689,\n",
-       "        0.12562997, -0.01667786, -0.09286805,  0.14670483, -0.13097813,\n",
-       "       -0.38270284, -0.0129984 , -0.06352304,  0.12521014, -0.17354363,\n",
-       "       -0.16399127,  0.11488736,  0.39664734,  0.08557075, -0.24862126,\n",
-       "       -0.05882866,  0.23174289,  0.01765143, -0.10257617,  0.01208248,\n",
-       "       -0.06876405, -0.04180976,  0.07489962, -0.10724381,  0.29349823,\n",
-       "        0.17342743, -0.32044841, -0.01118798,  0.28508293,  0.16492201,\n",
-       "        0.03994952, -0.17309702,  0.0365077 ,  0.08619365, -0.21749571,\n",
-       "        0.01038752, -0.21414902, -0.18092813,  0.28433131, -0.12601774,\n",
-       "       -0.02147302,  0.51513453,  0.21506432, -0.12596341, -0.0413009 ,\n",
-       "        0.12595327,  0.08973215, -0.06285449, -0.03649041,  0.04217289,\n",
-       "       -0.02532634, -0.21734533, -0.08211848,  0.14014083,  0.13635479,\n",
-       "       -0.13163414, -0.08576438, -0.04016334,  0.23952563, -0.14895943,\n",
-       "        0.08564821, -0.04287149,  0.22135877, -0.06337237, -0.07189574,\n",
-       "       -0.1004494 ,  0.01225299, -0.13810014, -0.06814497, -0.16715941,\n",
-       "        0.07303671,  0.15891015, -0.04757821, -0.05777645, -0.0772216 ,\n",
-       "        0.16787738, -0.2112512 , -0.10142653, -0.19002809, -0.06400223])"
-      ]
-     },
-     "execution_count": 71,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# create imputation vector\n",
-    "arrays = [patient_node2vec_dict[key] for key in list(patient_node2vec_dict)]\n",
-    "arrays_ok = [array for array in tqdm(arrays) if ~np.isnan(array).any()]\n",
-    "imp_vector = np.mean(arrays_ok, axis=0)\n",
-    "imp_vector"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
-   "metadata": {
-    "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true,
-     "source_hidden": true
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-   },
-   "outputs": [
-    {
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-      "text/plain": [
-       "HBox(children=(FloatProgress(value=0.0, max=502504.0), HTML(value='')))"
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-     "metadata": {},
-     "output_type": "display_data"
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-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "for eid, array in tqdm(patient_node2vec_dict.items()):\n",
-    "    if np.isnan(array).any(): \n",
-    "        patient_node2vec_dict[eid] = imp_vector"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 73,
-   "metadata": {
-    "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true,
-     "source_hidden": true
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a82c47a8f87a4df1bc81b78496edee0a",
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-       "HBox(children=(FloatProgress(value=0.0, max=502504.0), HTML(value='')))"
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-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "array_eids = [key for key in tqdm(list(patient_node2vec_dict))]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 74,
-   "metadata": {
-    "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true,
-     "source_hidden": true
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
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-       "HBox(children=(FloatProgress(value=0.0, max=502504.0), HTML(value='')))"
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-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "ind_ok = [np.array([1]) if ~np.isnan(array).any() else np.array([0])  for array in tqdm(arrays)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 75,
-   "metadata": {
-    "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true,
-     "source_hidden": true
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a85aec2f3e7644dc99c40781f999eb3c",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(FloatProgress(value=0.0, max=502504.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "arrays_emb = [patient_node2vec_dict[key] for key in tqdm(list(patient_node2vec_dict))]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 76,
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "outputs": [],
-   "source": [
-    "arrays_eids = np.reshape(np.stack(array_eids, axis=0),(-1,1)) \n",
-    "arrays_ind = np.stack(ind_ok, axis=0)\n",
-    "arrays_c = np.stack(arrays_emb, axis=0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 77,
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "outputs": [],
-   "source": [
-    "arrays_complete = np.concatenate([arrays_eids, arrays_ind, arrays_c], axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 78,
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "outputs": [],
-   "source": [
-    "diagnoses_emb = pd.DataFrame(data=arrays_complete, columns=[\"eid\"]+[\"node2vec_available\"]+[f\"node2vec_{e}\" for e in list(range(0, 200))])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 79,
-   "metadata": {
-    "collapsed": true,
-    "jupyter": {
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-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>node2vec_available</th>\n",
-       "      <th>node2vec_0</th>\n",
-       "      <th>node2vec_1</th>\n",
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-       "      <th>node2vec_6</th>\n",
-       "      <th>node2vec_7</th>\n",
-       "      <th>...</th>\n",
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-       "      <th>node2vec_193</th>\n",
-       "      <th>node2vec_194</th>\n",
-       "      <th>node2vec_195</th>\n",
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-       "      <td>0.056253</td>\n",
-       "      <td>-0.084373</td>\n",
-       "      <td>0.115048</td>\n",
-       "      <td>-0.248332</td>\n",
-       "      <td>-0.119365</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.073037</td>\n",
-       "      <td>0.15891</td>\n",
-       "      <td>-0.047578</td>\n",
-       "      <td>-0.057776</td>\n",
-       "      <td>-0.077222</td>\n",
-       "      <td>0.167877</td>\n",
-       "      <td>-0.211251</td>\n",
-       "      <td>-0.101427</td>\n",
-       "      <td>-0.190028</td>\n",
-       "      <td>-0.064002</td>\n",
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-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1000020.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.370339</td>\n",
-       "      <td>0.133211</td>\n",
-       "      <td>-0.100303</td>\n",
-       "      <td>0.056253</td>\n",
-       "      <td>-0.084373</td>\n",
-       "      <td>0.115048</td>\n",
-       "      <td>-0.248332</td>\n",
-       "      <td>-0.119365</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.073037</td>\n",
-       "      <td>0.15891</td>\n",
-       "      <td>-0.047578</td>\n",
-       "      <td>-0.057776</td>\n",
-       "      <td>-0.077222</td>\n",
-       "      <td>0.167877</td>\n",
-       "      <td>-0.211251</td>\n",
-       "      <td>-0.101427</td>\n",
-       "      <td>-0.190028</td>\n",
-       "      <td>-0.064002</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1000037.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.370339</td>\n",
-       "      <td>0.133211</td>\n",
-       "      <td>-0.100303</td>\n",
-       "      <td>0.056253</td>\n",
-       "      <td>-0.084373</td>\n",
-       "      <td>0.115048</td>\n",
-       "      <td>-0.248332</td>\n",
-       "      <td>-0.119365</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.073037</td>\n",
-       "      <td>0.15891</td>\n",
-       "      <td>-0.047578</td>\n",
-       "      <td>-0.057776</td>\n",
-       "      <td>-0.077222</td>\n",
-       "      <td>0.167877</td>\n",
-       "      <td>-0.211251</td>\n",
-       "      <td>-0.101427</td>\n",
-       "      <td>-0.190028</td>\n",
-       "      <td>-0.064002</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000043.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.370339</td>\n",
-       "      <td>0.133211</td>\n",
-       "      <td>-0.100303</td>\n",
-       "      <td>0.056253</td>\n",
-       "      <td>-0.084373</td>\n",
-       "      <td>0.115048</td>\n",
-       "      <td>-0.248332</td>\n",
-       "      <td>-0.119365</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.073037</td>\n",
-       "      <td>0.15891</td>\n",
-       "      <td>-0.047578</td>\n",
-       "      <td>-0.057776</td>\n",
-       "      <td>-0.077222</td>\n",
-       "      <td>0.167877</td>\n",
-       "      <td>-0.211251</td>\n",
-       "      <td>-0.101427</td>\n",
-       "      <td>-0.190028</td>\n",
-       "      <td>-0.064002</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1000051.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.370339</td>\n",
-       "      <td>0.133211</td>\n",
-       "      <td>-0.100303</td>\n",
-       "      <td>0.056253</td>\n",
-       "      <td>-0.084373</td>\n",
-       "      <td>0.115048</td>\n",
-       "      <td>-0.248332</td>\n",
-       "      <td>-0.119365</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.073037</td>\n",
-       "      <td>0.15891</td>\n",
-       "      <td>-0.047578</td>\n",
-       "      <td>-0.057776</td>\n",
-       "      <td>-0.077222</td>\n",
-       "      <td>0.167877</td>\n",
-       "      <td>-0.211251</td>\n",
-       "      <td>-0.101427</td>\n",
-       "      <td>-0.190028</td>\n",
-       "      <td>-0.064002</td>\n",
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-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
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-       "    <tr>\n",
-       "      <th>502499</th>\n",
-       "      <td>6025150.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.370339</td>\n",
-       "      <td>0.133211</td>\n",
-       "      <td>-0.100303</td>\n",
-       "      <td>0.056253</td>\n",
-       "      <td>-0.084373</td>\n",
-       "      <td>0.115048</td>\n",
-       "      <td>-0.248332</td>\n",
-       "      <td>-0.119365</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.073037</td>\n",
-       "      <td>0.15891</td>\n",
-       "      <td>-0.047578</td>\n",
-       "      <td>-0.057776</td>\n",
-       "      <td>-0.077222</td>\n",
-       "      <td>0.167877</td>\n",
-       "      <td>-0.211251</td>\n",
-       "      <td>-0.101427</td>\n",
-       "      <td>-0.190028</td>\n",
-       "      <td>-0.064002</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>502500</th>\n",
-       "      <td>6025165.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.370339</td>\n",
-       "      <td>0.133211</td>\n",
-       "      <td>-0.100303</td>\n",
-       "      <td>0.056253</td>\n",
-       "      <td>-0.084373</td>\n",
-       "      <td>0.115048</td>\n",
-       "      <td>-0.248332</td>\n",
-       "      <td>-0.119365</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.073037</td>\n",
-       "      <td>0.15891</td>\n",
-       "      <td>-0.047578</td>\n",
-       "      <td>-0.057776</td>\n",
-       "      <td>-0.077222</td>\n",
-       "      <td>0.167877</td>\n",
-       "      <td>-0.211251</td>\n",
-       "      <td>-0.101427</td>\n",
-       "      <td>-0.190028</td>\n",
-       "      <td>-0.064002</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>502501</th>\n",
-       "      <td>6025173.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.370339</td>\n",
-       "      <td>0.133211</td>\n",
-       "      <td>-0.100303</td>\n",
-       "      <td>0.056253</td>\n",
-       "      <td>-0.084373</td>\n",
-       "      <td>0.115048</td>\n",
-       "      <td>-0.248332</td>\n",
-       "      <td>-0.119365</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.073037</td>\n",
-       "      <td>0.15891</td>\n",
-       "      <td>-0.047578</td>\n",
-       "      <td>-0.057776</td>\n",
-       "      <td>-0.077222</td>\n",
-       "      <td>0.167877</td>\n",
-       "      <td>-0.211251</td>\n",
-       "      <td>-0.101427</td>\n",
-       "      <td>-0.190028</td>\n",
-       "      <td>-0.064002</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>502502</th>\n",
-       "      <td>6025182.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.370339</td>\n",
-       "      <td>0.133211</td>\n",
-       "      <td>-0.100303</td>\n",
-       "      <td>0.056253</td>\n",
-       "      <td>-0.084373</td>\n",
-       "      <td>0.115048</td>\n",
-       "      <td>-0.248332</td>\n",
-       "      <td>-0.119365</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.073037</td>\n",
-       "      <td>0.15891</td>\n",
-       "      <td>-0.047578</td>\n",
-       "      <td>-0.057776</td>\n",
-       "      <td>-0.077222</td>\n",
-       "      <td>0.167877</td>\n",
-       "      <td>-0.211251</td>\n",
-       "      <td>-0.101427</td>\n",
-       "      <td>-0.190028</td>\n",
-       "      <td>-0.064002</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>502503</th>\n",
-       "      <td>6025198.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.370339</td>\n",
-       "      <td>0.133211</td>\n",
-       "      <td>-0.100303</td>\n",
-       "      <td>0.056253</td>\n",
-       "      <td>-0.084373</td>\n",
-       "      <td>0.115048</td>\n",
-       "      <td>-0.248332</td>\n",
-       "      <td>-0.119365</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.073037</td>\n",
-       "      <td>0.15891</td>\n",
-       "      <td>-0.047578</td>\n",
-       "      <td>-0.057776</td>\n",
-       "      <td>-0.077222</td>\n",
-       "      <td>0.167877</td>\n",
-       "      <td>-0.211251</td>\n",
-       "      <td>-0.101427</td>\n",
-       "      <td>-0.190028</td>\n",
-       "      <td>-0.064002</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>502504 rows × 202 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "              eid  node2vec_available  node2vec_0  node2vec_1  node2vec_2  \\\n",
-       "0       1000018.0                 0.0    0.370339    0.133211   -0.100303   \n",
-       "1       1000020.0                 0.0    0.370339    0.133211   -0.100303   \n",
-       "2       1000037.0                 0.0    0.370339    0.133211   -0.100303   \n",
-       "3       1000043.0                 0.0    0.370339    0.133211   -0.100303   \n",
-       "4       1000051.0                 0.0    0.370339    0.133211   -0.100303   \n",
-       "...           ...                 ...         ...         ...         ...   \n",
-       "502499  6025150.0                 0.0    0.370339    0.133211   -0.100303   \n",
-       "502500  6025165.0                 0.0    0.370339    0.133211   -0.100303   \n",
-       "502501  6025173.0                 0.0    0.370339    0.133211   -0.100303   \n",
-       "502502  6025182.0                 0.0    0.370339    0.133211   -0.100303   \n",
-       "502503  6025198.0                 0.0    0.370339    0.133211   -0.100303   \n",
-       "\n",
-       "        node2vec_3  node2vec_4  node2vec_5  node2vec_6  node2vec_7  ...  \\\n",
-       "0         0.056253   -0.084373    0.115048   -0.248332   -0.119365  ...   \n",
-       "1         0.056253   -0.084373    0.115048   -0.248332   -0.119365  ...   \n",
-       "2         0.056253   -0.084373    0.115048   -0.248332   -0.119365  ...   \n",
-       "3         0.056253   -0.084373    0.115048   -0.248332   -0.119365  ...   \n",
-       "4         0.056253   -0.084373    0.115048   -0.248332   -0.119365  ...   \n",
-       "...            ...         ...         ...         ...         ...  ...   \n",
-       "502499    0.056253   -0.084373    0.115048   -0.248332   -0.119365  ...   \n",
-       "502500    0.056253   -0.084373    0.115048   -0.248332   -0.119365  ...   \n",
-       "502501    0.056253   -0.084373    0.115048   -0.248332   -0.119365  ...   \n",
-       "502502    0.056253   -0.084373    0.115048   -0.248332   -0.119365  ...   \n",
-       "502503    0.056253   -0.084373    0.115048   -0.248332   -0.119365  ...   \n",
-       "\n",
-       "        node2vec_190  node2vec_191  node2vec_192  node2vec_193  node2vec_194  \\\n",
-       "0           0.073037       0.15891     -0.047578     -0.057776     -0.077222   \n",
-       "1           0.073037       0.15891     -0.047578     -0.057776     -0.077222   \n",
-       "2           0.073037       0.15891     -0.047578     -0.057776     -0.077222   \n",
-       "3           0.073037       0.15891     -0.047578     -0.057776     -0.077222   \n",
-       "4           0.073037       0.15891     -0.047578     -0.057776     -0.077222   \n",
-       "...              ...           ...           ...           ...           ...   \n",
-       "502499      0.073037       0.15891     -0.047578     -0.057776     -0.077222   \n",
-       "502500      0.073037       0.15891     -0.047578     -0.057776     -0.077222   \n",
-       "502501      0.073037       0.15891     -0.047578     -0.057776     -0.077222   \n",
-       "502502      0.073037       0.15891     -0.047578     -0.057776     -0.077222   \n",
-       "502503      0.073037       0.15891     -0.047578     -0.057776     -0.077222   \n",
-       "\n",
-       "        node2vec_195  node2vec_196  node2vec_197  node2vec_198  node2vec_199  \n",
-       "0           0.167877     -0.211251     -0.101427     -0.190028     -0.064002  \n",
-       "1           0.167877     -0.211251     -0.101427     -0.190028     -0.064002  \n",
-       "2           0.167877     -0.211251     -0.101427     -0.190028     -0.064002  \n",
-       "3           0.167877     -0.211251     -0.101427     -0.190028     -0.064002  \n",
-       "4           0.167877     -0.211251     -0.101427     -0.190028     -0.064002  \n",
-       "...              ...           ...           ...           ...           ...  \n",
-       "502499      0.167877     -0.211251     -0.101427     -0.190028     -0.064002  \n",
-       "502500      0.167877     -0.211251     -0.101427     -0.190028     -0.064002  \n",
-       "502501      0.167877     -0.211251     -0.101427     -0.190028     -0.064002  \n",
-       "502502      0.167877     -0.211251     -0.101427     -0.190028     -0.064002  \n",
-       "502503      0.167877     -0.211251     -0.101427     -0.190028     -0.064002  \n",
-       "\n",
-       "[502504 rows x 202 columns]"
-      ]
-     },
-     "execution_count": 79,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "diagnoses_emb"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "outputs": [
-    {
-     "ename": "FileNotFoundError",
-     "evalue": "[Errno 2] Failed to open local file '/data/analysis/ag-reils/ag-reils-shared/cardioRS/data/2_datasets_pre/cvd_massive_from_birth/temp_diagnoses_emb.feather'. Detail: [errno 2] No such file or directory",
-     "output_type": "error",
-     "traceback": [
-      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
-      "\u001B[0;31mFileNotFoundError\u001B[0m                         Traceback (most recent call last)",
-      "\u001B[0;32m<ipython-input-14-45107a9d4a69>\u001B[0m in \u001B[0;36m<module>\u001B[0;34m\u001B[0m\n\u001B[1;32m      1\u001B[0m \u001B[0;31m#diagnoses_emb.to_feather(os.path.join(path, dataset_path, 'temp_diagnoses_emb.feather'))\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m----> 2\u001B[0;31m \u001B[0mdiagnoses_emb\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mpd\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mread_feather\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mos\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mpath\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mjoin\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mpath\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mdataset_path\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m'temp_diagnoses_emb.feather'\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m",
-      "\u001B[0;32m~/miniconda3/envs/python/lib/python3.7/site-packages/pandas/io/feather_format.py\u001B[0m in \u001B[0;36mread_feather\u001B[0;34m(path, columns, use_threads)\u001B[0m\n\u001B[1;32m    101\u001B[0m     \u001B[0mpath\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mstringify_path\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mpath\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    102\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 103\u001B[0;31m     \u001B[0;32mreturn\u001B[0m \u001B[0mfeather\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mread_feather\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mpath\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mcolumns\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0mcolumns\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0muse_threads\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0mbool\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0muse_threads\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m",
-      "\u001B[0;32m~/miniconda3/envs/python/lib/python3.7/site-packages/pyarrow/feather.py\u001B[0m in \u001B[0;36mread_feather\u001B[0;34m(source, columns, use_threads, memory_map)\u001B[0m\n\u001B[1;32m    212\u001B[0m     \"\"\"\n\u001B[1;32m    213\u001B[0m     \u001B[0m_check_pandas_version\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 214\u001B[0;31m     return (read_table(source, columns=columns, memory_map=memory_map)\n\u001B[0m\u001B[1;32m    215\u001B[0m             .to_pandas(use_threads=use_threads))\n\u001B[1;32m    216\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n",
-      "\u001B[0;32m~/miniconda3/envs/python/lib/python3.7/site-packages/pyarrow/feather.py\u001B[0m in \u001B[0;36mread_table\u001B[0;34m(source, columns, memory_map)\u001B[0m\n\u001B[1;32m    234\u001B[0m     \"\"\"\n\u001B[1;32m    235\u001B[0m     \u001B[0mreader\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mext\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mFeatherReader\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 236\u001B[0;31m     \u001B[0mreader\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mopen\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0msource\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0muse_memory_map\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0mmemory_map\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    237\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    238\u001B[0m     \u001B[0;32mif\u001B[0m \u001B[0mcolumns\u001B[0m \u001B[0;32mis\u001B[0m \u001B[0;32mNone\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
-      "\u001B[0;32m~/miniconda3/envs/python/lib/python3.7/site-packages/pyarrow/feather.pxi\u001B[0m in \u001B[0;36mpyarrow.lib.FeatherReader.open\u001B[0;34m()\u001B[0m\n",
-      "\u001B[0;32m~/miniconda3/envs/python/lib/python3.7/site-packages/pyarrow/io.pxi\u001B[0m in \u001B[0;36mpyarrow.lib.get_reader\u001B[0;34m()\u001B[0m\n",
-      "\u001B[0;32m~/miniconda3/envs/python/lib/python3.7/site-packages/pyarrow/io.pxi\u001B[0m in \u001B[0;36mpyarrow.lib._get_native_file\u001B[0;34m()\u001B[0m\n",
-      "\u001B[0;32m~/miniconda3/envs/python/lib/python3.7/site-packages/pyarrow/io.pxi\u001B[0m in \u001B[0;36mpyarrow.lib.memory_map\u001B[0;34m()\u001B[0m\n",
-      "\u001B[0;32m~/miniconda3/envs/python/lib/python3.7/site-packages/pyarrow/io.pxi\u001B[0m in \u001B[0;36mpyarrow.lib.MemoryMappedFile._open\u001B[0;34m()\u001B[0m\n",
-      "\u001B[0;32m~/miniconda3/envs/python/lib/python3.7/site-packages/pyarrow/error.pxi\u001B[0m in \u001B[0;36mpyarrow.lib.pyarrow_internal_check_status\u001B[0;34m()\u001B[0m\n",
-      "\u001B[0;32m~/miniconda3/envs/python/lib/python3.7/site-packages/pyarrow/error.pxi\u001B[0m in \u001B[0;36mpyarrow.lib.check_status\u001B[0;34m()\u001B[0m\n",
-      "\u001B[0;31mFileNotFoundError\u001B[0m: [Errno 2] Failed to open local file '/data/analysis/ag-reils/ag-reils-shared/cardioRS/data/2_datasets_pre/cvd_massive_from_birth/temp_diagnoses_emb.feather'. Detail: [errno 2] No such file or directory"
-     ]
-    }
-   ],
-   "source": [
-    "diagnoses_emb.to_feather(os.path.join(path, dataset_path, 'temp_diagnoses_emb.feather'))\n",
-    "diagnoses_emb = pd.read_feather(os.path.join(path, dataset_path, 'temp_diagnoses_emb.feather'))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Endpoints"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "1+1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 89,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:39:55.628580Z",
-     "start_time": "2020-11-04T12:33:33.036Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "### define in snomed and get icd codes from there"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 1. Hospital admissions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:39:55.629459Z",
-     "start_time": "2020-11-04T12:33:34.764Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'myocardial_infarction': ['I21', 'I22', 'I23', 'I24', 'I25', 'I51'],\n",
-       " 'stroke': ['G45', 'G46', 'I60', 'I67', 'I68', 'I69'],\n",
-       " 'cancer_breast': ['C50'],\n",
-       " 'diabetes': ['E10', 'E11', 'E12', 'E13', 'E14'],\n",
-       " 'atrial_fibrillation': ['I47', 'I48'],\n",
-       " 'copd': ['J44'],\n",
-       " 'dementia': ['F00', 'F01', 'F02', 'F03', 'F09', 'G31', 'R54']}"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "endpoint_list = {\n",
-    "    \"myocardial_infarction\": [\"Myocardial infarction\"],\n",
-    "    \"stroke\": [\"Cerebrovascular disease\"],\n",
-    "    \"cancer_breast\" : [\"Breast Cancer\"],\n",
-    "    \"diabetes\" : [\"Diabetes\"],\n",
-    "    \"atrial_fibrillation\": [\"Atrial fibrillation\", \"Atrial flutter\", \"paroxysmal tachycardia\"],\n",
-    "    \"copd\": [\"COPD\"],\n",
-    "    \"dementia\":[\"dementia\"]\n",
-    "}\n",
-    "\n",
-    "endpoint_list = phenotype_children(phecodes, endpoint_list)\n",
-    "endpoint_list[\"cancer_breast\"] = [\"C50\"]\n",
-    "endpoint_list[\"copd\"] = [\"J44\"]\n",
-    "endpoint_list[\"diabetes\"] = [\"E10\", \"E11\", \"E12\", \"E13\", \"E14\"]\n",
-    "endpoint_list[\"atrial_fibrillation\"] = [\"I47\", \"I48\"]\n",
-    "\n",
-    "\n",
-    "with open(os.path.join(path, dataset_path, 'endpoint_list.yaml'), 'w') as file: yaml.dump(endpoint_list, file, default_flow_style=False)\n",
-    "endpoint_list"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from dateutil.relativedelta import relativedelta\n",
-    "\n",
-    "def extract_endpoints_tte(data, diagnoses_codes, endpoint_list, time0_col, level=None):\n",
-    "    if level is not None: diagnoses_codes = diagnoses_codes.query(\"level==@level\")\n",
-    "    diagnoses_codes_time0 = diagnoses_codes.merge(data[[\"eid\", time0_col]], how=\"left\", on=\"eid\")\n",
-    "    \n",
-    "    cens_time_right = min(diagnoses_codes.sort_values('date').groupby('origin').tail(1).date.to_list())\n",
-    "    print(f\"t_0: {time0_col}\")\n",
-    "    print(f\"t_cens: {cens_time_right}\")\n",
-    "    \n",
-    "    df_interval = diagnoses_codes_time0[(diagnoses_codes_time0.date > diagnoses_codes_time0[time0_col]) & \n",
-    "                                        (diagnoses_codes_time0.date < cens_time_right)]\n",
-    "    \n",
-    "    temp = data[[\"eid\", time0_col]].copy()\n",
-    "    for ph, ph_codes in tqdm(endpoint_list.items()):\n",
-    "        regex = \"|\".join(ph_codes)\n",
-    "        ph_df = df_interval[df_interval.meaning.str.contains(regex, case=False)] \\\n",
-    "            .sort_values('date').groupby('eid').head(1).assign(phenotype=1, date=lambda x: x.date)\n",
-    "        temp_ph = temp.merge(ph_df, how=\"left\", on=\"eid\").fillna(0)\n",
-    "        temp[ph+\"_event\"], temp[ph+\"_event_date\"] = temp_ph.phenotype, temp_ph.date\n",
-    "        \n",
-    "        fill_date = {ph+\"_event_date\" : lambda x: [cens_time_right if event==0 else event_date for event, event_date in zip(x[ph+\"_event\"], x[ph+\"_event_date\"])]}\n",
-    "        calc_tte = {ph+\"_event_time\" : lambda x: [(event_date-time0).days/365.25  for time0, event_date in zip(x[time0_col], x[ph+\"_event_date\"])]}\n",
-    "        \n",
-    "        temp = temp.assign(**fill_date).assign(**calc_tte).drop([ph+\"_event_date\"], axis=1)\n",
-    "        \n",
-    "    temp = temp.drop([time0_col], axis=1)     \n",
-    "    \n",
-    "    return temp.drop_duplicates()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "t_0: birth_date\n",
-      "t_cens: 2020-03-14\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6a02ec6d409c4e998c0f1deb9032b535",
-       "version_major": 2,
-       "version_minor": 0
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-      "text/plain": [
-       "HBox(children=(FloatProgress(value=0.0, max=7.0), HTML(value='')))"
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-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "502504\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "    .dataframe thead th {\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>myocardial_infarction_event</th>\n",
-       "      <th>myocardial_infarction_event_time</th>\n",
-       "      <th>stroke_event</th>\n",
-       "      <th>stroke_event_time</th>\n",
-       "      <th>cancer_breast_event</th>\n",
-       "      <th>cancer_breast_event_time</th>\n",
-       "      <th>diabetes_event</th>\n",
-       "      <th>diabetes_event_time</th>\n",
-       "      <th>atrial_fibrillation_event</th>\n",
-       "      <th>atrial_fibrillation_event_time</th>\n",
-       "      <th>copd_event</th>\n",
-       "      <th>copd_event_time</th>\n",
-       "      <th>dementia_event</th>\n",
-       "      <th>dementia_event_time</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
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-       "      <td>71.063655</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>71.063655</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>71.063655</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>71.063655</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>71.063655</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>71.063655</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>71.063655</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1000037</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>70.338125</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>70.338125</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>70.338125</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>70.338125</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>70.338125</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>70.338125</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>70.338125</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000043</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>68.123203</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>73.779603</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>73.779603</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>73.779603</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>73.779603</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>63.293634</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>73.779603</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1000051</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>64.761123</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>64.761123</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>64.761123</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>45.062286</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>64.761123</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>21.062286</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>64.761123</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       eid  myocardial_infarction_event  myocardial_infarction_event_time  \\\n",
-       "0  1000018                          0.0                         59.334702   \n",
-       "1  1000020                          0.0                         71.063655   \n",
-       "2  1000037                          0.0                         70.338125   \n",
-       "3  1000043                          1.0                         68.123203   \n",
-       "4  1000051                          0.0                         64.761123   \n",
-       "\n",
-       "   stroke_event  stroke_event_time  cancer_breast_event  \\\n",
-       "0           0.0          59.334702                  0.0   \n",
-       "1           0.0          71.063655                  0.0   \n",
-       "2           0.0          70.338125                  0.0   \n",
-       "3           0.0          73.779603                  0.0   \n",
-       "4           0.0          64.761123                  0.0   \n",
-       "\n",
-       "   cancer_breast_event_time  diabetes_event  diabetes_event_time  \\\n",
-       "0                 59.334702             0.0            59.334702   \n",
-       "1                 71.063655             0.0            71.063655   \n",
-       "2                 70.338125             0.0            70.338125   \n",
-       "3                 73.779603             0.0            73.779603   \n",
-       "4                 64.761123             1.0            45.062286   \n",
-       "\n",
-       "   atrial_fibrillation_event  atrial_fibrillation_event_time  copd_event  \\\n",
-       "0                        0.0                       59.334702         0.0   \n",
-       "1                        0.0                       71.063655         0.0   \n",
-       "2                        0.0                       70.338125         0.0   \n",
-       "3                        0.0                       73.779603         1.0   \n",
-       "4                        0.0                       64.761123         1.0   \n",
-       "\n",
-       "   copd_event_time  dementia_event  dementia_event_time  \n",
-       "0        59.334702             0.0            59.334702  \n",
-       "1        71.063655             0.0            71.063655  \n",
-       "2        70.338125             0.0            70.338125  \n",
-       "3        63.293634             0.0            73.779603  \n",
-       "4        21.062286             0.0            64.761123  "
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "endpoints_hospital = extract_endpoints_tte(basics, diagnoses_codes, endpoint_list, time0_col)\n",
-    "print(len(endpoints_hospital))\n",
-    "endpoints_hospital.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 2. Death registry"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "death_list = {\n",
-    "    \"death_allcause\":[],\n",
-    "    \"death_cvd\":['I{:02}'.format(ID+1) for ID in range(0, 98)],\n",
-    "}\n",
-    "\n",
-    "death_codes = pd.read_feather(f\"{data_path}/1_decoded/codes_death_records.feather\")#.drop(\"level\", axis=1)\n",
-    "\n",
-    "with open(os.path.join(path, dataset_path, 'death_list.yaml'), 'w') as file: yaml.dump(death_list, file, default_flow_style=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "t_0: birth_date\n",
-      "t_cens: 2020-06-28\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9ca18723f9984f399b46bce781a3f2dd",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(FloatProgress(value=0.0, max=2.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "endpoints_death = extract_endpoints_tte(basics, death_codes, death_list, time0_col, level=\"1\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## SCORES"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "scores_list = {\n",
-    "    \"SCORE\":['I{:02}'.format(ID) for ID in [10, 11, 12, 13, 14, 15, 20, 21, 22, 23, 24, 25, 44, 45, 46, 47, 48, 49, 50, 51, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73]],\n",
-    "    \"ASCVD\":['I{:02}'.format(ID) for ID in [20, 21, 22, 23, 24, 25, 63]],\n",
-    "    \"QRISK3\":[\"G45\", \"I20\", \"I21\", \"I22\", \"I23\", \"I24\", \"I25\", \"I63\", \"I64\"],\n",
-    "    \"MACE\":[\"G45\", \"I21\", \"I22\", \"I23\", \"I24\", \"I25\", \"I63\", \"I64\"],    \n",
-    "}\n",
-    "with open(os.path.join(path, dataset_path, 'scores_list.yaml'), 'w') as file: yaml.dump(scores_list, file, default_flow_style=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "scores_list_hospital = {}\n",
-    "scores_list_death = {}\n",
-    "for score, score_codes in scores_list.items():\n",
-    "    scores_list_hospital[\"hospital_\"+score] = score_codes\n",
-    "    scores_list_death[\"death_\"+score] = score_codes"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "t_0: birth_date\n",
-      "t_cens: 2020-03-14\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "17ed4f235a6e4cc08cdc2789bfc62d73",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(FloatProgress(value=0.0, max=4.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "t_0: birth_date\n",
-      "t_cens: 2020-06-28\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2bd6de2f761a464da6f8c6c8ba22a1d8",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(FloatProgress(value=0.0, max=4.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "endpoints_scores = {\n",
-    "    \"hospital\": extract_endpoints_tte(basics, diagnoses_codes, scores_list_hospital, time0_col=time0_col),\n",
-    "    \"death\": extract_endpoints_tte(basics, death_codes, scores_list_death, time0_col=time0_col, level=1)}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "endpoints_scores_all = endpoints_scores[\"hospital\"].merge(endpoints_scores[\"death\"], on=\"eid\", how=\"left\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### ESC SCORE (Conroy 2003)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "5323\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>SCORE_event</th>\n",
-       "      <th>SCORE_event_time</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>45</th>\n",
-       "      <td>1000463</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>74.165640</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>83</th>\n",
-       "      <td>1000841</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>76.005476</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>102</th>\n",
-       "      <td>1001031</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>75.537303</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>122</th>\n",
-       "      <td>1001237</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>50.132786</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>176</th>\n",
-       "      <td>1001777</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>72.238193</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         eid  SCORE_event  SCORE_event_time\n",
-       "45   1000463          1.0         74.165640\n",
-       "83   1000841          1.0         76.005476\n",
-       "102  1001031          1.0         75.537303\n",
-       "122  1001237          1.0         50.132786\n",
-       "176  1001777          1.0         72.238193"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "score = \"SCORE\"\n",
-    "\n",
-    "temp = endpoints_scores_all.filter(regex=\"eid|\"+score).rename(columns={\"death_SCORE_event\":\"SCORE_event\", \"death_SCORE_event_time\":\"SCORE_event_time\"})\n",
-    "score_SCORE = temp = temp[[\"eid\", \"SCORE_event\", \"SCORE_event_time\"]]\n",
-    "print(len(temp.query(score+\"_event==1\")))\n",
-    "temp.query(score+\"_event==1\").head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### ASCVD (Goff 2014)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "61785\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>ASCVD_event</th>\n",
-       "      <th>ASCVD_event_time</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1000037</td>\n",
-       "      <td>1</td>\n",
-       "      <td>66.970568</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000043</td>\n",
-       "      <td>1</td>\n",
-       "      <td>68.123203</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>1000066</td>\n",
-       "      <td>1</td>\n",
-       "      <td>60.944559</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>1000079</td>\n",
-       "      <td>1</td>\n",
-       "      <td>61.054073</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>1000233</td>\n",
-       "      <td>1</td>\n",
-       "      <td>68.673511</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        eid  ASCVD_event  ASCVD_event_time\n",
-       "2   1000037            1         66.970568\n",
-       "3   1000043            1         68.123203\n",
-       "5   1000066            1         60.944559\n",
-       "6   1000079            1         61.054073\n",
-       "22  1000233            1         68.673511"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "score = \"ASCVD\"\n",
-    "temp = endpoints_scores_all.filter(regex=\"eid|\"+score)\n",
-    "\n",
-    "aggr_event = {score +\"_event\" : lambda x: [1 if (hospital_event==1) | (death_event == 1) else 0 \n",
-    "                                                    for hospital_event, death_event in zip(x[\"hospital_\"+score+\"_event\"], x[\"death_\"+score+\"_event\"])]}\n",
-    "aggr_date = {score +\"_event_time\" : lambda x: [min(hospital_event_time, death_event_time)\n",
-    "                                                        for hospital_event_time, death_event_time in zip(x[\"hospital_\"+score+\"_event_time\"], x[\"death_\"+score+\"_event_time\"])]}\n",
-    "\n",
-    "score_ASCVD = temp = temp.assign(**aggr_event).assign(**aggr_date)[[\"eid\", score +\"_event\", score +\"_event_time\"]]\n",
-    "print(len(temp.query(score+\"_event==1\")))\n",
-    "temp.query(score+\"_event==1\").head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### UK QRISK3 (2017)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "69344\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>QRISK3_event</th>\n",
-       "      <th>QRISK3_event_time</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1000037</td>\n",
-       "      <td>1</td>\n",
-       "      <td>66.970568</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000043</td>\n",
-       "      <td>1</td>\n",
-       "      <td>68.123203</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>1000066</td>\n",
-       "      <td>1</td>\n",
-       "      <td>60.944559</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>1000079</td>\n",
-       "      <td>1</td>\n",
-       "      <td>61.054073</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>1000233</td>\n",
-       "      <td>1</td>\n",
-       "      <td>68.673511</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        eid  QRISK3_event  QRISK3_event_time\n",
-       "2   1000037             1          66.970568\n",
-       "3   1000043             1          68.123203\n",
-       "5   1000066             1          60.944559\n",
-       "6   1000079             1          61.054073\n",
-       "22  1000233             1          68.673511"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "score = \"QRISK3\"\n",
-    "temp = endpoints_scores_all.filter(regex=\"eid|\"+score)\n",
-    "\n",
-    "aggr_event = {score +\"_event\" : lambda x: [1 if (hospital_event==1) | (death_event == 1) else 0 \n",
-    "                                                    for hospital_event, death_event in zip(x[\"hospital_\"+score+\"_event\"], x[\"death_\"+score+\"_event\"])]}\n",
-    "aggr_date = {score +\"_event_time\" : lambda x: [min(hospital_event_time, death_event_time)\n",
-    "                                                        for hospital_event_time, death_event_time in zip(x[\"hospital_\"+score+\"_event_time\"], x[\"death_\"+score+\"_event_time\"])]}\n",
-    "\n",
-    "score_QRISK3 = temp = temp.assign(**aggr_event).assign(**aggr_date)[[\"eid\", score +\"_event\", score +\"_event_time\"]]\n",
-    "print(len(temp.query(score+\"_event==1\")))\n",
-    "temp.query(score+\"_event==1\").head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### MACE (2020)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "62097\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>MACE_event</th>\n",
-       "      <th>MACE_event_time</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000043</td>\n",
-       "      <td>1</td>\n",
-       "      <td>68.123203</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>1000233</td>\n",
-       "      <td>1</td>\n",
-       "      <td>68.673511</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30</th>\n",
-       "      <td>1000319</td>\n",
-       "      <td>1</td>\n",
-       "      <td>56.922656</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>45</th>\n",
-       "      <td>1000463</td>\n",
-       "      <td>1</td>\n",
-       "      <td>74.069815</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>53</th>\n",
-       "      <td>1000548</td>\n",
-       "      <td>1</td>\n",
-       "      <td>50.255989</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        eid  MACE_event  MACE_event_time\n",
-       "3   1000043           1        68.123203\n",
-       "22  1000233           1        68.673511\n",
-       "30  1000319           1        56.922656\n",
-       "45  1000463           1        74.069815\n",
-       "53  1000548           1        50.255989"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "score = \"MACE\"\n",
-    "temp = endpoints_scores_all.filter(regex=\"eid|\"+score)\n",
-    "\n",
-    "aggr_event = {score +\"_event\" : lambda x: [1 if (hospital_event==1) | (death_event == 1) else 0 \n",
-    "                                                    for hospital_event, death_event in zip(x[\"hospital_\"+score+\"_event\"], x[\"death_\"+score+\"_event\"])]}\n",
-    "aggr_date = {score +\"_event_time\" : lambda x: [min(hospital_event_time, death_event_time)\n",
-    "                                                        for hospital_event_time, death_event_time in zip(x[\"hospital_\"+score+\"_event_time\"], x[\"death_\"+score+\"_event_time\"])]}\n",
-    "\n",
-    "score_MACE = temp = temp.assign(**aggr_event).assign(**aggr_date)[[\"eid\", score +\"_event\", score +\"_event_time\"]]\n",
-    "print(len(temp.query(score+\"_event==1\")))\n",
-    "temp.query(score+\"_event==1\").head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Merge Everything"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_dfs_dict = {\"basics\": pd.read_feather(os.path.join(path, dataset_path, 'temp_basics.feather')), \n",
-    "                 \"questionnaire\": pd.read_feather(os.path.join(path, dataset_path, 'temp_questionnaire.feather')), \n",
-    "                 \"measurements\": pd.read_feather(os.path.join(path, dataset_path, 'temp_measurements.feather')), \n",
-    "                 \"labs\": pd.read_feather(os.path.join(path, dataset_path, 'temp_labs.feather')), \n",
-    "                 \"family_history\": pd.read_feather(os.path.join(path, dataset_path, 'temp_family_history.feather')), \n",
-    "                 \"diagnoses\": pd.read_feather(os.path.join(path, dataset_path, 'temp_diagnoses.feather')),\n",
-    "                # \"diagnoses_emb\": pd.read_feather(os.path.join(path, dataset_path, 'temp_diagnoses_emb.feather')), \n",
-    "                 \"medications\": pd.read_feather(os.path.join(path, dataset_path, 'temp_medications.feather')), \n",
-    "                 \"endpoints_hospital\":endpoints_hospital, \n",
-    "                 \"endpoints_death\":endpoints_death, \n",
-    "                 \"score_SCORE\":score_SCORE, \n",
-    "                 \"score_ASCVD\":score_ASCVD, \n",
-    "                 \"score_QRISK3\":score_QRISK3,\n",
-    "                 \"score_MACE\":score_MACE}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_cols_clean(df):\n",
-    "    df.columns = df.columns.str.replace(r'_0_0$', '').str.replace(r'_f[0-9]+$', '').str.replace(\"_automated_reading\", '')\n",
-    "    return df.columns\n",
-    "\n",
-    "def clean_df(df):\n",
-    "    df.columns = get_cols_clean(df)\n",
-    "    return df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "from functools import reduce\n",
-    "\n",
-    "data_baseline = reduce(lambda x, y: pd.merge(x, y, on = 'eid'), list(data_dfs_dict.values()))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_baseline = clean_df(data_baseline)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for col in [col for col in list(data_baseline.columns) if (\"_event\" in col) & (\"_time\" not in col)]:\n",
-    "    data_baseline[col] = data_baseline[col].astype(int)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "covariates = [col for col in list(data_baseline.columns) if not \"_event\" in col]\n",
-    "targets = [col for col in list(data_baseline.columns) if \"_event\" in col]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Exporting"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>age_at_recruitment</th>\n",
-       "      <th>sex</th>\n",
-       "      <th>ethnic_background</th>\n",
-       "      <th>townsend_deprivation_index_at_recruitment</th>\n",
-       "      <th>date_of_attending_assessment_centre</th>\n",
-       "      <th>birth_date</th>\n",
-       "      <th>overall_health_rating</th>\n",
-       "      <th>smoking_status</th>\n",
-       "      <th>alcohol_intake_frequency</th>\n",
-       "      <th>...</th>\n",
-       "      <th>death_cvd_event</th>\n",
-       "      <th>death_cvd_event_time</th>\n",
-       "      <th>SCORE_event</th>\n",
-       "      <th>SCORE_event_time</th>\n",
-       "      <th>ASCVD_event</th>\n",
-       "      <th>ASCVD_event_time</th>\n",
-       "      <th>QRISK3_event</th>\n",
-       "      <th>QRISK3_event_time</th>\n",
-       "      <th>MACE_event</th>\n",
-       "      <th>MACE_event_time</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>49.0</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>White</td>\n",
-       "      <td>-1.852930</td>\n",
-       "      <td>2009-11-12</td>\n",
-       "      <td>1960-11-12</td>\n",
-       "      <td>Fair</td>\n",
-       "      <td>Current</td>\n",
-       "      <td>Once or twice a week</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>59.624914</td>\n",
-       "      <td>0</td>\n",
-       "      <td>59.624914</td>\n",
-       "      <td>0</td>\n",
-       "      <td>59.334702</td>\n",
-       "      <td>0</td>\n",
-       "      <td>59.334702</td>\n",
-       "      <td>0</td>\n",
-       "      <td>59.334702</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1000020</td>\n",
-       "      <td>59.0</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>White</td>\n",
-       "      <td>0.204248</td>\n",
-       "      <td>2008-02-19</td>\n",
-       "      <td>1949-02-19</td>\n",
-       "      <td>Good</td>\n",
-       "      <td>Current</td>\n",
-       "      <td>Once or twice a week</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>71.353867</td>\n",
-       "      <td>0</td>\n",
-       "      <td>71.353867</td>\n",
-       "      <td>0</td>\n",
-       "      <td>71.063655</td>\n",
-       "      <td>0</td>\n",
-       "      <td>71.063655</td>\n",
-       "      <td>0</td>\n",
-       "      <td>71.063655</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1000037</td>\n",
-       "      <td>59.0</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>White</td>\n",
-       "      <td>-3.498860</td>\n",
-       "      <td>2008-11-11</td>\n",
-       "      <td>1949-11-11</td>\n",
-       "      <td>Good</td>\n",
-       "      <td>Previous</td>\n",
-       "      <td>Once or twice a week</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>70.628337</td>\n",
-       "      <td>0</td>\n",
-       "      <td>70.628337</td>\n",
-       "      <td>1</td>\n",
-       "      <td>66.970568</td>\n",
-       "      <td>1</td>\n",
-       "      <td>66.970568</td>\n",
-       "      <td>0</td>\n",
-       "      <td>70.338125</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000043</td>\n",
-       "      <td>63.0</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>White</td>\n",
-       "      <td>-5.351150</td>\n",
-       "      <td>2009-06-03</td>\n",
-       "      <td>1946-06-03</td>\n",
-       "      <td>Fair</td>\n",
-       "      <td>Previous</td>\n",
-       "      <td>Three or four times a week</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>74.069815</td>\n",
-       "      <td>0</td>\n",
-       "      <td>74.069815</td>\n",
-       "      <td>1</td>\n",
-       "      <td>68.123203</td>\n",
-       "      <td>1</td>\n",
-       "      <td>68.123203</td>\n",
-       "      <td>1</td>\n",
-       "      <td>68.123203</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1000051</td>\n",
-       "      <td>51.0</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>White</td>\n",
-       "      <td>-1.799080</td>\n",
-       "      <td>2006-06-10</td>\n",
-       "      <td>1955-06-10</td>\n",
-       "      <td>Poor</td>\n",
-       "      <td>Never</td>\n",
-       "      <td>One to three times a month</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>65.051335</td>\n",
-       "      <td>0</td>\n",
-       "      <td>65.051335</td>\n",
-       "      <td>0</td>\n",
-       "      <td>64.761123</td>\n",
-       "      <td>0</td>\n",
-       "      <td>64.761123</td>\n",
-       "      <td>0</td>\n",
-       "      <td>64.761123</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 3746 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       eid  age_at_recruitment     sex ethnic_background  \\\n",
-       "0  1000018                49.0  Female             White   \n",
-       "1  1000020                59.0    Male             White   \n",
-       "2  1000037                59.0  Female             White   \n",
-       "3  1000043                63.0    Male             White   \n",
-       "4  1000051                51.0  Female             White   \n",
-       "\n",
-       "   townsend_deprivation_index_at_recruitment  \\\n",
-       "0                                  -1.852930   \n",
-       "1                                   0.204248   \n",
-       "2                                  -3.498860   \n",
-       "3                                  -5.351150   \n",
-       "4                                  -1.799080   \n",
-       "\n",
-       "  date_of_attending_assessment_centre  birth_date overall_health_rating  \\\n",
-       "0                          2009-11-12  1960-11-12                  Fair   \n",
-       "1                          2008-02-19  1949-02-19                  Good   \n",
-       "2                          2008-11-11  1949-11-11                  Good   \n",
-       "3                          2009-06-03  1946-06-03                  Fair   \n",
-       "4                          2006-06-10  1955-06-10                  Poor   \n",
-       "\n",
-       "  smoking_status    alcohol_intake_frequency  ...  death_cvd_event  \\\n",
-       "0        Current        Once or twice a week  ...                0   \n",
-       "1        Current        Once or twice a week  ...                0   \n",
-       "2       Previous        Once or twice a week  ...                0   \n",
-       "3       Previous  Three or four times a week  ...                0   \n",
-       "4          Never  One to three times a month  ...                0   \n",
-       "\n",
-       "   death_cvd_event_time  SCORE_event  SCORE_event_time  ASCVD_event  \\\n",
-       "0             59.624914            0         59.624914            0   \n",
-       "1             71.353867            0         71.353867            0   \n",
-       "2             70.628337            0         70.628337            1   \n",
-       "3             74.069815            0         74.069815            1   \n",
-       "4             65.051335            0         65.051335            0   \n",
-       "\n",
-       "   ASCVD_event_time  QRISK3_event  QRISK3_event_time  MACE_event  \\\n",
-       "0         59.334702             0          59.334702           0   \n",
-       "1         71.063655             0          71.063655           0   \n",
-       "2         66.970568             1          66.970568           0   \n",
-       "3         68.123203             1          68.123203           1   \n",
-       "4         64.761123             0          64.761123           0   \n",
-       "\n",
-       "   MACE_event_time  \n",
-       "0        59.334702  \n",
-       "1        71.063655  \n",
-       "2        70.338125  \n",
-       "3        68.123203  \n",
-       "4        64.761123  \n",
-       "\n",
-       "[5 rows x 3746 columns]"
-      ]
-     },
-     "execution_count": 38,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data_baseline.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_cols = {}\n",
-    "for topic, df in data_dfs_dict.items(): \n",
-    "    data_cols[\"eid\"] = [\"admin\"]\n",
-    "    data_cols[topic]=list(get_cols_clean(df))[1:]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_cols_single = {}\n",
-    "for topic, columns in data_cols.items():\n",
-    "    for col in columns:\n",
-    "        data_cols_single[col] = topic"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>covariate</th>\n",
-       "      <th>dtype</th>\n",
-       "      <th>isTarget</th>\n",
-       "      <th>based_on</th>\n",
-       "      <th>aggr_fn</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>eid</td>\n",
-       "      <td>integer</td>\n",
-       "      <td>False</td>\n",
-       "      <td>eid</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2</td>\n",
-       "      <td>age_at_recruitment</td>\n",
-       "      <td>numeric</td>\n",
-       "      <td>False</td>\n",
-       "      <td>basics</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>sex</td>\n",
-       "      <td>category</td>\n",
-       "      <td>False</td>\n",
-       "      <td>basics</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4</td>\n",
-       "      <td>ethnic_background</td>\n",
-       "      <td>category</td>\n",
-       "      <td>False</td>\n",
-       "      <td>basics</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5</td>\n",
-       "      <td>townsend_deprivation_index_at_recruitment</td>\n",
-       "      <td>numeric</td>\n",
-       "      <td>False</td>\n",
-       "      <td>basics</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3741</th>\n",
-       "      <td>3742</td>\n",
-       "      <td>ASCVD_event_time</td>\n",
-       "      <td>numeric</td>\n",
-       "      <td>True</td>\n",
-       "      <td>score_ASCVD</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3742</th>\n",
-       "      <td>3743</td>\n",
-       "      <td>QRISK3_event</td>\n",
-       "      <td>integer</td>\n",
-       "      <td>True</td>\n",
-       "      <td>score_QRISK3</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3743</th>\n",
-       "      <td>3744</td>\n",
-       "      <td>QRISK3_event_time</td>\n",
-       "      <td>numeric</td>\n",
-       "      <td>True</td>\n",
-       "      <td>score_QRISK3</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3744</th>\n",
-       "      <td>3745</td>\n",
-       "      <td>MACE_event</td>\n",
-       "      <td>integer</td>\n",
-       "      <td>True</td>\n",
-       "      <td>score_MACE</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3745</th>\n",
-       "      <td>3746</td>\n",
-       "      <td>MACE_event_time</td>\n",
-       "      <td>numeric</td>\n",
-       "      <td>True</td>\n",
-       "      <td>score_MACE</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>3746 rows × 6 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        id                                  covariate     dtype  isTarget  \\\n",
-       "0        1                                        eid   integer     False   \n",
-       "1        2                         age_at_recruitment   numeric     False   \n",
-       "2        3                                        sex  category     False   \n",
-       "3        4                          ethnic_background  category     False   \n",
-       "4        5  townsend_deprivation_index_at_recruitment   numeric     False   \n",
-       "...    ...                                        ...       ...       ...   \n",
-       "3741  3742                           ASCVD_event_time   numeric      True   \n",
-       "3742  3743                               QRISK3_event   integer      True   \n",
-       "3743  3744                          QRISK3_event_time   numeric      True   \n",
-       "3744  3745                                 MACE_event   integer      True   \n",
-       "3745  3746                            MACE_event_time   numeric      True   \n",
-       "\n",
-       "          based_on  aggr_fn  \n",
-       "0              eid      NaN  \n",
-       "1           basics      NaN  \n",
-       "2           basics      NaN  \n",
-       "3           basics      NaN  \n",
-       "4           basics      NaN  \n",
-       "...            ...      ...  \n",
-       "3741   score_ASCVD      NaN  \n",
-       "3742  score_QRISK3      NaN  \n",
-       "3743  score_QRISK3      NaN  \n",
-       "3744    score_MACE      NaN  \n",
-       "3745    score_MACE      NaN  \n",
-       "\n",
-       "[3746 rows x 6 columns]"
-      ]
-     },
-     "execution_count": 41,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dtypes = {\"int32\":\"integer\", \"int64\":\"integer\", \"float64\":\"numeric\", \"category\":\"category\", \"object\":\"category\", \"bool\":\"logical\"}\n",
-    "desc_dict = {\"id\": [*range(1, len(data_baseline.columns.to_list())+1)] , \n",
-    "             \"covariate\": data_baseline.columns.to_list(), \n",
-    "             \"dtype\":[dtypes[str(col)] for col in data_baseline.dtypes.to_list()], \n",
-    "             \"isTarget\":[True if col in targets else False for col in data_baseline.columns.to_list()],\n",
-    "            \"based_on\":[topic for col, topic in data_cols_single.items()],\n",
-    "            \"aggr_fn\": [np.nan for col in data_baseline.columns.to_list()]}\n",
-    "data_baseline_description = pd.DataFrame.from_dict(desc_dict)\n",
-    "data_baseline_description"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "# Exclusion Criteria"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "data_baseline_excl = data_baseline.query(\"myocardial_infarction == False & coronary_heart_disease == False & statins == False\").reset_index(drop=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_baseline_excl"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "### WRITE FEATURES IN YAML!!!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_baseline.to_feather(os.path.join(path, dataset_path, 'baseline_clinical.feather'))\n",
-    "data_baseline_excl.to_feather(os.path.join(path, dataset_path, 'baseline_clinical_excl.feather'))\n",
-    "data_baseline_description.to_feather(os.path.join(path, dataset_path, 'baseline_clinical_description.feather'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#data_baseline.to_csv(os.path.join(path, dataset_path, 'baseline_clinical.csv'), index=False)\n",
-    "#data_baseline_description.to_csv(os.path.join(path, dataset_path, 'baseline_clinical_description.csv'), index=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# !!! REMEMBER IMPUTATION !!!"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python [conda env:python]",
-   "language": "python",
-   "name": "conda-env-python-py"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.6"
-  },
-  "toc-autonumbering": true,
-  "toc-showcode": false,
-  "toc-showmarkdowntxt": false
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
\ No newline at end of file
diff --git a/neuralcvd/preprocessing/ukbb_tabular/2_preprocessing_clinical_covariates.ipynb b/neuralcvd/preprocessing/ukbb_tabular/2_preprocessing_clinical_covariates.ipynb
deleted file mode 100644
index a6ddee5..0000000
--- a/neuralcvd/preprocessing/ukbb_tabular/2_preprocessing_clinical_covariates.ipynb
+++ /dev/null
@@ -1,4439 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Preprocessing"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:31:49.436340Z",
-     "start_time": "2020-11-04T12:31:48.732042Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import os\n",
-    "import yaml\n",
-    "from tqdm.notebook import trange, tqdm\n",
-    "dataset_name = \"210212_cvd_gp\"\n",
-    "path = \"/data/analysis/ag-reils/steinfej/code/umbrella/pre/ukbb\"\n",
-    "data_path = \"/data/analysis/ag-reils/ag-reils-shared/cardioRS/data\"\n",
-    "dataset_path = f\"{data_path}/2_datasets_pre/{dataset_name}\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:31:49.895222Z",
-     "start_time": "2020-11-04T12:31:49.891332Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from pathlib import Path\n",
-    "Path(dataset_path).mkdir(parents=True, exist_ok=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:33:14.171198Z",
-     "start_time": "2020-11-04T12:31:50.204540Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "data = pd.read_feather(f\"{data_path}/1_decoded/ukb_data.feather\")\n",
-    "data_field = pd.read_feather(f\"{data_path}/1_decoded/ukb_data_field.feather\")\n",
-    "data_columns = data.columns.to_list()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Mappings + Vocabulary"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:34:05.867152Z",
-     "start_time": "2020-11-04T12:33:16.878773Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "502505\n",
-      "502504\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Drop obviouse missing data\n",
-    "print(len(data))\n",
-    "data = data.dropna(subset=[\"sex_f31_0_0\"], axis=0)\n",
-    "print(len(data))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Starting information"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:34:05.872216Z",
-     "start_time": "2020-11-04T12:34:05.869505Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "#time0_col=\"birth_date\"\n",
-    "time0_col=\"date_of_attending_assessment_centre_f53_0_0\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Baseline covariates"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:34:05.889725Z",
-     "start_time": "2020-11-04T12:34:05.874587Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "def get_fields(fields, data, data_field):\n",
-    "    f = data_field[data_field[\"field.showcase\"].isin(fields) & data_field[\"field.tab\"].str.contains(\"f\\\\.\\\\d+\\\\.0\\\\.\\\\d\")].copy()\n",
-    "    f[\"field\"] = pd.Categorical(f[\"field.showcase\"], categories=fields, ordered=True)\n",
-    "    f = f.sort_values(\"field\").reset_index().drop(\"field\", axis=1)\n",
-    "    return f\n",
-    "\n",
-    "def get_fields_all(fields, data, data_field):\n",
-    "    f = data_field[data_field[\"field.showcase\"].isin(fields)].copy()\n",
-    "    f[\"field\"] = pd.Categorical(f[\"field.showcase\"], categories=fields, ordered=True)\n",
-    "    f = f.sort_values(\"field\").reset_index().drop(\"field\", axis=1)\n",
-    "    return f\n",
-    "\n",
-    "def get_data_fields(fields, data, data_field):\n",
-    "    f = get_fields(fields, data, data_field)\n",
-    "    return data[[\"eid\"]+f[\"col.name\"].to_list()].copy()\n",
-    "\n",
-    "def get_data_fields_all(fields, data, data_field):\n",
-    "    f = get_fields_all(fields, data, data_field)\n",
-    "    return data[[\"eid\"]+f[\"col.name\"].to_list()].copy()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Basics"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "coding10 = pd.read_csv(f\"{path}/mapping/codings/coding10.tsv\", sep=\"\\t\").assign(coding = lambda x: x.coding.astype(\"int\")).rename(columns={\"coding\":\"uk_biobank_assessment_centre_f54_0_0\"})\n",
-    "coding10[\"uk_biobank_assessment_centre_f54_0_0\"] = coding10[\"uk_biobank_assessment_centre_f54_0_0\"].astype(\"int\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:34:06.176411Z",
-     "start_time": "2020-11-04T12:34:05.891730Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "502504\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>age_at_recruitment_f21022_0_0</th>\n",
-       "      <th>sex_f31_0_0</th>\n",
-       "      <th>ethnic_background_f21000_0_0</th>\n",
-       "      <th>townsend_deprivation_index_at_recruitment_f189_0_0</th>\n",
-       "      <th>date_of_attending_assessment_centre_f53_0_0</th>\n",
-       "      <th>uk_biobank_assessment_centre_f54_0_0</th>\n",
-       "      <th>birth_date</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>49.0</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>White</td>\n",
-       "      <td>-1.852930</td>\n",
-       "      <td>2009-11-12</td>\n",
-       "      <td>Sheffield</td>\n",
-       "      <td>1960-11-12</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1000020</td>\n",
-       "      <td>59.0</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>White</td>\n",
-       "      <td>0.204248</td>\n",
-       "      <td>2008-02-19</td>\n",
-       "      <td>Sheffield</td>\n",
-       "      <td>1949-02-19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1000037</td>\n",
-       "      <td>59.0</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>White</td>\n",
-       "      <td>-3.498860</td>\n",
-       "      <td>2008-11-11</td>\n",
-       "      <td>Sheffield</td>\n",
-       "      <td>1949-11-11</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000043</td>\n",
-       "      <td>63.0</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>White</td>\n",
-       "      <td>-5.351150</td>\n",
-       "      <td>2009-06-03</td>\n",
-       "      <td>Sheffield</td>\n",
-       "      <td>1946-06-03</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1000051</td>\n",
-       "      <td>51.0</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>White</td>\n",
-       "      <td>-1.799080</td>\n",
-       "      <td>2006-06-10</td>\n",
-       "      <td>Sheffield</td>\n",
-       "      <td>1955-06-10</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       eid  age_at_recruitment_f21022_0_0 sex_f31_0_0  \\\n",
-       "0  1000018                           49.0      Female   \n",
-       "1  1000020                           59.0        Male   \n",
-       "2  1000037                           59.0      Female   \n",
-       "3  1000043                           63.0        Male   \n",
-       "4  1000051                           51.0      Female   \n",
-       "\n",
-       "  ethnic_background_f21000_0_0  \\\n",
-       "0                        White   \n",
-       "1                        White   \n",
-       "2                        White   \n",
-       "3                        White   \n",
-       "4                        White   \n",
-       "\n",
-       "   townsend_deprivation_index_at_recruitment_f189_0_0  \\\n",
-       "0                                          -1.852930    \n",
-       "1                                           0.204248    \n",
-       "2                                          -3.498860    \n",
-       "3                                          -5.351150    \n",
-       "4                                          -1.799080    \n",
-       "\n",
-       "  date_of_attending_assessment_centre_f53_0_0  \\\n",
-       "0                                  2009-11-12   \n",
-       "1                                  2008-02-19   \n",
-       "2                                  2008-11-11   \n",
-       "3                                  2009-06-03   \n",
-       "4                                  2006-06-10   \n",
-       "\n",
-       "  uk_biobank_assessment_centre_f54_0_0  birth_date  \n",
-       "0                            Sheffield  1960-11-12  \n",
-       "1                            Sheffield  1949-02-19  \n",
-       "2                            Sheffield  1949-11-11  \n",
-       "3                            Sheffield  1946-06-03  \n",
-       "4                            Sheffield  1955-06-10  "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fields_basics = [\n",
-    "    \"21022\", # age at recruitment\n",
-    "    \"31\", # sex\n",
-    "    \"21000\", # ethnicity\n",
-    "    \"189\", # Townsend index\n",
-    "    \"53\", # date of baseline assessment\n",
-    "    \"54\", # assessment center\n",
-    "]\n",
-    "\n",
-    "temp = get_data_fields(fields_basics, data, data_field)\n",
-    "\n",
-    "temp[\"sex_f31_0_0\"] = temp[\"sex_f31_0_0\"].cat.set_categories([\"Female\", 'Male'], ordered=False)\n",
-    "\n",
-    "#temp[\"ethnic_background_f21000_0_0\"] = temp[\"ethnic_background_f21000_0_0\"].astype(\"string\")\n",
-    "\n",
-    "ethn_bg_def = {\"White\": [\"White\", \"British\", \"Irish\", \"Any other white background\"],\n",
-    "                \"Mixed\": [\"Mixed\", \"White and Black Caribbean\", \"White and Black African\", \"White and Asian\", \"Any other mixed background\"],  \n",
-    "                \"Asian\": [\"Asian or Asian British\", \"Indian\", \"Pakistani\", \"Bangladeshi\", \"Any other Asian background\"], \n",
-    "                \"Black\": [\"Black or Black British\", \"Caribbean\", \"African\", \"Any other Black background\"],\n",
-    "                \"Chinese\": [\"Chinese\"],  \n",
-    "                np.nan: [\"Other ethnic group\", \"Do not know\", \"Prefer not to answer\"]}\n",
-    "\n",
-    "ethn_bg_dict = {}\n",
-    "for key, values in ethn_bg_def.items(): \n",
-    "    for value in values:\n",
-    "        ethn_bg_dict[value]=key \n",
-    "        \n",
-    "temp[\"ethnic_background_f21000_0_0\"].replace(ethn_bg_dict, inplace=True)\n",
-    "temp[\"ethnic_background_f21000_0_0\"] = temp[\"ethnic_background_f21000_0_0\"].astype(\"category\")\n",
-    "\n",
-    "#\n",
-    "#temp[\"ethnic_background_f21000_0_0\"] = temp[\"ethnic_background_f21000_0_0\"].astype(\"category\").cat.set_categories(['White', 'Black', 'Asien', 'Mixed', 'Chinese'], ordered=False)\n",
-    "\n",
-    "basics = temp\n",
-    "print(len(temp))\n",
-    "\n",
-    "from dateutil.relativedelta import relativedelta\n",
-    "calc_birth_date = [date_of_attending_assessment_centre - relativedelta(years=age_at_recruitment) \n",
-    "                                                             for date_of_attending_assessment_centre, age_at_recruitment \n",
-    "                                                             in zip(basics[\"date_of_attending_assessment_centre_f53_0_0\"], basics[\"age_at_recruitment_f21022_0_0\"])]\n",
-    "\n",
-    "basics = basics.assign(birth_date = calc_birth_date)\n",
-    "basics[\"uk_biobank_assessment_centre_f54_0_0\"] = basics.assign(uk_biobank_assessment_centre_f54_0_0 = lambda x: x.uk_biobank_assessment_centre_f54_0_0.astype(\"int\")).merge(coding10, on=\"uk_biobank_assessment_centre_f54_0_0\")[\"meaning\"]\n",
-    "\n",
-    "\n",
-    "display(basics.head())\n",
-    "basics.to_feather(os.path.join(path, dataset_path, 'temp_basics.feather'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:34:06.186613Z",
-     "start_time": "2020-11-04T12:34:06.178111Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['White', 'Black', NaN, 'Asian', 'Mixed', 'Chinese']\n",
-      "Categories (5, object): ['White', 'Black', 'Asian', 'Mixed', 'Chinese']\n"
-     ]
-    }
-   ],
-   "source": [
-    " print(temp[\"ethnic_background_f21000_0_0\"].unique())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Questionnaire"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:34:06.389467Z",
-     "start_time": "2020-11-04T12:34:06.188206Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "502504\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>overall_health_rating_f2178_0_0</th>\n",
-       "      <th>smoking_status_f20116_0_0</th>\n",
-       "      <th>alcohol_intake_frequency_f1558_0_0</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>Fair</td>\n",
-       "      <td>Current</td>\n",
-       "      <td>Once or twice a week</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1000020</td>\n",
-       "      <td>Good</td>\n",
-       "      <td>Current</td>\n",
-       "      <td>Once or twice a week</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1000037</td>\n",
-       "      <td>Good</td>\n",
-       "      <td>Previous</td>\n",
-       "      <td>Once or twice a week</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000043</td>\n",
-       "      <td>Fair</td>\n",
-       "      <td>Previous</td>\n",
-       "      <td>Three or four times a week</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1000051</td>\n",
-       "      <td>Poor</td>\n",
-       "      <td>Never</td>\n",
-       "      <td>One to three times a month</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       eid overall_health_rating_f2178_0_0 smoking_status_f20116_0_0  \\\n",
-       "0  1000018                            Fair                   Current   \n",
-       "1  1000020                            Good                   Current   \n",
-       "2  1000037                            Good                  Previous   \n",
-       "3  1000043                            Fair                  Previous   \n",
-       "4  1000051                            Poor                     Never   \n",
-       "\n",
-       "  alcohol_intake_frequency_f1558_0_0  \n",
-       "0               Once or twice a week  \n",
-       "1               Once or twice a week  \n",
-       "2               Once or twice a week  \n",
-       "3         Three or four times a week  \n",
-       "4         One to three times a month  "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fields_questionnaire = [\n",
-    "    \"2178\", # Overall health\n",
-    "    \"20116\", # Smoking status\n",
-    "    \"1558\",\n",
-    "]\n",
-    "\n",
-    "temp = get_data_fields(fields_questionnaire, data, data_field)\n",
-    "\n",
-    "temp[\"overall_health_rating_f2178_0_0\"] = temp[\"overall_health_rating_f2178_0_0\"]\\\n",
-    "    .replace({\"Do not know\": np.nan, \"Prefer not to answer\": np.nan})\\\n",
-    "    .astype(\"category\").cat.set_categories(['Poor', 'Fair', 'Good', 'Excellent'], ordered=True)\n",
-    "\n",
-    "\n",
-    "temp[\"smoking_status_f20116_0_0\"] = temp[\"smoking_status_f20116_0_0\"]\\\n",
-    "    .replace({\"Prefer not to answer\": np.nan}, inplace=False)\\\n",
-    "    .astype(\"category\").cat.set_categories(['Current', 'Previous', 'Never'], ordered=True)\n",
-    "\n",
-    "temp[\"alcohol_intake_frequency_f1558_0_0\"] = temp[\"alcohol_intake_frequency_f1558_0_0\"]\\\n",
-    "    .replace({\"Prefer not to answer\": np.nan}, inplace=False)\\\n",
-    "    .astype(\"category\").cat.set_categories([\n",
-    "        'Daily or almost daily', \n",
-    "        'Three or four times a week', \n",
-    "        'Once or twice a week',\n",
-    "        'One to three times a month',\n",
-    "        'Special occasions only', \n",
-    "        'Never'], ordered=True)\n",
-    "\n",
-    "questionnaire = temp\n",
-    "print(len(temp))\n",
-    "display(temp.head())\n",
-    "\n",
-    "questionnaire.to_feather(os.path.join(path, dataset_path, 'temp_questionnaire.feather'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:34:06.398168Z",
-     "start_time": "2020-11-04T12:34:06.391082Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['Once or twice a week', 'Three or four times a week', 'One to three times a month', 'Daily or almost daily', 'Special occasions only', 'Never', NaN]\n",
-      "Categories (6, object): ['Daily or almost daily' < 'Three or four times a week' < 'Once or twice a week' < 'One to three times a month' < 'Special occasions only' < 'Never']\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(temp[\"alcohol_intake_frequency_f1558_0_0\"].unique())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Physical measurements"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:34:07.052989Z",
-     "start_time": "2020-11-04T12:34:06.400858Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "502504\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>body_mass_index_bmi_f21001_0_0</th>\n",
-       "      <th>weight_f21002_0_0</th>\n",
-       "      <th>pulse_wave_arterial_stiffness_index_f21021_0_0</th>\n",
-       "      <th>pulse_wave_reflection_index_f4195_0_0</th>\n",
-       "      <th>waist_circumference_f48_0_0</th>\n",
-       "      <th>hip_circumference_f49_0_0</th>\n",
-       "      <th>standing_height_f50_0_0</th>\n",
-       "      <th>trunk_fat_percentage_f23127_0_0</th>\n",
-       "      <th>body_fat_percentage_f23099_0_0</th>\n",
-       "      <th>basal_metabolic_rate_f23105_0_0</th>\n",
-       "      <th>forced_vital_capacity_fvc_best_measure_f20151_0_0</th>\n",
-       "      <th>forced_expiratory_volume_in_1second_fev1_best_measure_f20150_0_0</th>\n",
-       "      <th>fev1_fvc_ratio_zscore_f20258_0_0</th>\n",
-       "      <th>peak_expiratory_flow_pef_f3064_0_2</th>\n",
-       "      <th>peak_expiratory_flow_pef_f3064_0_1</th>\n",
-       "      <th>peak_expiratory_flow_pef_f3064_0_0</th>\n",
-       "      <th>systolic_blood_pressure_automated_reading_f4080</th>\n",
-       "      <th>diastolic_blood_pressure_automated_reading_f4079</th>\n",
-       "      <th>pulse_rate_automated_reading_f102</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>26.5557</td>\n",
-       "      <td>63.8</td>\n",
-       "      <td>7.2770</td>\n",
-       "      <td>80.0</td>\n",
-       "      <td>85.0</td>\n",
-       "      <td>107.0</td>\n",
-       "      <td>155.0</td>\n",
-       "      <td>37.5</td>\n",
-       "      <td>39.5</td>\n",
-       "      <td>5012.0</td>\n",
-       "      <td>3.21</td>\n",
-       "      <td>2.16</td>\n",
-       "      <td>1.978</td>\n",
-       "      <td>317.0</td>\n",
-       "      <td>312.0</td>\n",
-       "      <td>339.0</td>\n",
-       "      <td>159.5</td>\n",
-       "      <td>88.0</td>\n",
-       "      <td>50.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1000020</td>\n",
-       "      <td>22.7465</td>\n",
-       "      <td>70.7</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>87.8</td>\n",
-       "      <td>94.4</td>\n",
-       "      <td>176.3</td>\n",
-       "      <td>33.4</td>\n",
-       "      <td>28.7</td>\n",
-       "      <td>6171.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.375</td>\n",
-       "      <td>301.0</td>\n",
-       "      <td>496.0</td>\n",
-       "      <td>504.0</td>\n",
-       "      <td>133.0</td>\n",
-       "      <td>81.0</td>\n",
-       "      <td>74.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1000037</td>\n",
-       "      <td>32.4211</td>\n",
-       "      <td>78.9</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>101.0</td>\n",
-       "      <td>112.0</td>\n",
-       "      <td>156.0</td>\n",
-       "      <td>47.5</td>\n",
-       "      <td>48.4</td>\n",
-       "      <td>5397.0</td>\n",
-       "      <td>1.61</td>\n",
-       "      <td>1.27</td>\n",
-       "      <td>0.138</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>185.0</td>\n",
-       "      <td>208.0</td>\n",
-       "      <td>118.5</td>\n",
-       "      <td>78.0</td>\n",
-       "      <td>62.5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000043</td>\n",
-       "      <td>29.5679</td>\n",
-       "      <td>95.8</td>\n",
-       "      <td>11.1111</td>\n",
-       "      <td>78.0</td>\n",
-       "      <td>98.0</td>\n",
-       "      <td>104.0</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>27.6</td>\n",
-       "      <td>25.6</td>\n",
-       "      <td>8711.0</td>\n",
-       "      <td>4.14</td>\n",
-       "      <td>2.84</td>\n",
-       "      <td>1.096</td>\n",
-       "      <td>557.0</td>\n",
-       "      <td>513.0</td>\n",
-       "      <td>530.0</td>\n",
-       "      <td>141.5</td>\n",
-       "      <td>93.5</td>\n",
-       "      <td>64.5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1000051</td>\n",
-       "      <td>41.0222</td>\n",
-       "      <td>92.3</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>123.0</td>\n",
-       "      <td>129.0</td>\n",
-       "      <td>150.0</td>\n",
-       "      <td>48.9</td>\n",
-       "      <td>50.4</td>\n",
-       "      <td>6100.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.518</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>117.0</td>\n",
-       "      <td>81.0</td>\n",
-       "      <td>79.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       eid  body_mass_index_bmi_f21001_0_0  weight_f21002_0_0  \\\n",
-       "0  1000018                         26.5557               63.8   \n",
-       "1  1000020                         22.7465               70.7   \n",
-       "2  1000037                         32.4211               78.9   \n",
-       "3  1000043                         29.5679               95.8   \n",
-       "4  1000051                         41.0222               92.3   \n",
-       "\n",
-       "   pulse_wave_arterial_stiffness_index_f21021_0_0  \\\n",
-       "0                                          7.2770   \n",
-       "1                                             NaN   \n",
-       "2                                             NaN   \n",
-       "3                                         11.1111   \n",
-       "4                                             NaN   \n",
-       "\n",
-       "   pulse_wave_reflection_index_f4195_0_0  waist_circumference_f48_0_0  \\\n",
-       "0                                   80.0                         85.0   \n",
-       "1                                    NaN                         87.8   \n",
-       "2                                    NaN                        101.0   \n",
-       "3                                   78.0                         98.0   \n",
-       "4                                    NaN                        123.0   \n",
-       "\n",
-       "   hip_circumference_f49_0_0  standing_height_f50_0_0  \\\n",
-       "0                      107.0                    155.0   \n",
-       "1                       94.4                    176.3   \n",
-       "2                      112.0                    156.0   \n",
-       "3                      104.0                    180.0   \n",
-       "4                      129.0                    150.0   \n",
-       "\n",
-       "   trunk_fat_percentage_f23127_0_0  body_fat_percentage_f23099_0_0  \\\n",
-       "0                             37.5                            39.5   \n",
-       "1                             33.4                            28.7   \n",
-       "2                             47.5                            48.4   \n",
-       "3                             27.6                            25.6   \n",
-       "4                             48.9                            50.4   \n",
-       "\n",
-       "   basal_metabolic_rate_f23105_0_0  \\\n",
-       "0                           5012.0   \n",
-       "1                           6171.0   \n",
-       "2                           5397.0   \n",
-       "3                           8711.0   \n",
-       "4                           6100.0   \n",
-       "\n",
-       "   forced_vital_capacity_fvc_best_measure_f20151_0_0  \\\n",
-       "0                                               3.21   \n",
-       "1                                                NaN   \n",
-       "2                                               1.61   \n",
-       "3                                               4.14   \n",
-       "4                                                NaN   \n",
-       "\n",
-       "   forced_expiratory_volume_in_1second_fev1_best_measure_f20150_0_0  \\\n",
-       "0                                               2.16                  \n",
-       "1                                                NaN                  \n",
-       "2                                               1.27                  \n",
-       "3                                               2.84                  \n",
-       "4                                                NaN                  \n",
-       "\n",
-       "   fev1_fvc_ratio_zscore_f20258_0_0  peak_expiratory_flow_pef_f3064_0_2  \\\n",
-       "0                             1.978                               317.0   \n",
-       "1                             1.375                               301.0   \n",
-       "2                             0.138                                 NaN   \n",
-       "3                             1.096                               557.0   \n",
-       "4                             0.518                                 NaN   \n",
-       "\n",
-       "   peak_expiratory_flow_pef_f3064_0_1  peak_expiratory_flow_pef_f3064_0_0  \\\n",
-       "0                               312.0                               339.0   \n",
-       "1                               496.0                               504.0   \n",
-       "2                               185.0                               208.0   \n",
-       "3                               513.0                               530.0   \n",
-       "4                                 NaN                                 NaN   \n",
-       "\n",
-       "   systolic_blood_pressure_automated_reading_f4080  \\\n",
-       "0                                            159.5   \n",
-       "1                                            133.0   \n",
-       "2                                            118.5   \n",
-       "3                                            141.5   \n",
-       "4                                            117.0   \n",
-       "\n",
-       "   diastolic_blood_pressure_automated_reading_f4079  \\\n",
-       "0                                              88.0   \n",
-       "1                                              81.0   \n",
-       "2                                              78.0   \n",
-       "3                                              93.5   \n",
-       "4                                              81.0   \n",
-       "\n",
-       "   pulse_rate_automated_reading_f102  \n",
-       "0                               50.0  \n",
-       "1                               74.0  \n",
-       "2                               62.5  \n",
-       "3                               64.5  \n",
-       "4                               79.0  "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from statistics import mean\n",
-    "\n",
-    "fields_measurements = [\n",
-    "#    \"100313\", # Walking speed !!! MISSING !!!\n",
-    "    \"21001\", # BMI\n",
-    "    \"21002\", # weight\n",
-    "    \"4080\", # Syst. BP\n",
-    "    \"4079\", # Diast. BP\n",
-    "    \"102\",\n",
-    "    \"21021\",\n",
-    "    \"4195\",\n",
-    "    \"48\",\n",
-    "    \"49\",\n",
-    "    \"50\",\n",
-    "    \"23127\",\n",
-    "    \"23099\",\n",
-    "    \"23105\",\n",
-    "    \"20151\",\n",
-    "    \"20150\",\n",
-    "    \"20258\",\n",
-    "    \"3064\",\n",
-    "    \n",
-    "]\n",
-    "temp = get_data_fields(fields_measurements, data, data_field)\n",
-    "\n",
-    "sbp_cols = [\"systolic_blood_pressure_automated_reading_f4080_0_0\", \"systolic_blood_pressure_automated_reading_f4080_0_1\"]\n",
-    "dbp_cols = [\"diastolic_blood_pressure_automated_reading_f4079_0_0\", \"diastolic_blood_pressure_automated_reading_f4079_0_1\"]\n",
-    "pr_cols = [\"pulse_rate_automated_reading_f102_0_0\", \"pulse_rate_automated_reading_f102_0_1\"]\n",
-    "\n",
-    "temp = temp.assign(systolic_blood_pressure_automated_reading_f4080 = temp[sbp_cols].mean(axis=1),\n",
-    "                   diastolic_blood_pressure_automated_reading_f4079 = temp[dbp_cols].mean(axis=1),\n",
-    "                   pulse_rate_automated_reading_f102 = temp[pr_cols].mean(axis=1))\\\n",
-    "    .drop(sbp_cols + dbp_cols + pr_cols, axis=1)\n",
-    "\n",
-    "measurements = temp\n",
-    "print(len(temp))\n",
-    "display(temp.head())\n",
-    "\n",
-    "measurements.to_feather(os.path.join(path, dataset_path, 'temp_measurements.feather'))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Lab measurements"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:34:07.647955Z",
-     "start_time": "2020-11-04T12:34:07.055242Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "502504\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>basophill_count_f30160_0_0</th>\n",
-       "      <th>basophill_percentage_f30220_0_0</th>\n",
-       "      <th>eosinophill_count_f30150_0_0</th>\n",
-       "      <th>eosinophill_percentage_f30210_0_0</th>\n",
-       "      <th>haematocrit_percentage_f30030_0_0</th>\n",
-       "      <th>haemoglobin_concentration_f30020_0_0</th>\n",
-       "      <th>high_light_scatter_reticulocyte_count_f30300_0_0</th>\n",
-       "      <th>high_light_scatter_reticulocyte_percentage_f30290_0_0</th>\n",
-       "      <th>immature_reticulocyte_fraction_f30280_0_0</th>\n",
-       "      <th>...</th>\n",
-       "      <th>phosphate_f30810_0_0</th>\n",
-       "      <th>rheumatoid_factor_f30820_0_0</th>\n",
-       "      <th>shbg_f30830_0_0</th>\n",
-       "      <th>testosterone_f30850_0_0</th>\n",
-       "      <th>total_bilirubin_f30840_0_0</th>\n",
-       "      <th>total_protein_f30860_0_0</th>\n",
-       "      <th>triglycerides_f30870_0_0</th>\n",
-       "      <th>urate_f30880_0_0</th>\n",
-       "      <th>urea_f30670_0_0</th>\n",
-       "      <th>vitamin_d_f30890_0_0</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>0.04</td>\n",
-       "      <td>0.26</td>\n",
-       "      <td>0.25</td>\n",
-       "      <td>1.75</td>\n",
-       "      <td>39.79</td>\n",
-       "      <td>13.90</td>\n",
-       "      <td>0.022</td>\n",
-       "      <td>0.464</td>\n",
-       "      <td>0.378</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.422</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>70.11</td>\n",
-       "      <td>1.560</td>\n",
-       "      <td>7.41</td>\n",
-       "      <td>71.97</td>\n",
-       "      <td>1.247</td>\n",
-       "      <td>221.3</td>\n",
-       "      <td>5.48</td>\n",
-       "      <td>70.7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1000020</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0.30</td>\n",
-       "      <td>0.30</td>\n",
-       "      <td>2.50</td>\n",
-       "      <td>45.00</td>\n",
-       "      <td>15.60</td>\n",
-       "      <td>0.014</td>\n",
-       "      <td>0.290</td>\n",
-       "      <td>0.300</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.264</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>55.31</td>\n",
-       "      <td>12.237</td>\n",
-       "      <td>8.07</td>\n",
-       "      <td>78.45</td>\n",
-       "      <td>1.906</td>\n",
-       "      <td>374.7</td>\n",
-       "      <td>5.28</td>\n",
-       "      <td>35.9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1000037</td>\n",
-       "      <td>0.04</td>\n",
-       "      <td>0.57</td>\n",
-       "      <td>0.10</td>\n",
-       "      <td>1.43</td>\n",
-       "      <td>39.48</td>\n",
-       "      <td>13.58</td>\n",
-       "      <td>0.031</td>\n",
-       "      <td>0.686</td>\n",
-       "      <td>0.380</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000043</td>\n",
-       "      <td>0.02</td>\n",
-       "      <td>0.32</td>\n",
-       "      <td>0.11</td>\n",
-       "      <td>1.80</td>\n",
-       "      <td>44.31</td>\n",
-       "      <td>14.99</td>\n",
-       "      <td>0.025</td>\n",
-       "      <td>0.508</td>\n",
-       "      <td>0.250</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.928</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>31.63</td>\n",
-       "      <td>11.398</td>\n",
-       "      <td>8.65</td>\n",
-       "      <td>69.70</td>\n",
-       "      <td>5.184</td>\n",
-       "      <td>322.8</td>\n",
-       "      <td>6.67</td>\n",
-       "      <td>63.6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1000051</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 62 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       eid  basophill_count_f30160_0_0  basophill_percentage_f30220_0_0  \\\n",
-       "0  1000018                        0.04                             0.26   \n",
-       "1  1000020                        0.00                             0.30   \n",
-       "2  1000037                        0.04                             0.57   \n",
-       "3  1000043                        0.02                             0.32   \n",
-       "4  1000051                         NaN                              NaN   \n",
-       "\n",
-       "   eosinophill_count_f30150_0_0  eosinophill_percentage_f30210_0_0  \\\n",
-       "0                          0.25                               1.75   \n",
-       "1                          0.30                               2.50   \n",
-       "2                          0.10                               1.43   \n",
-       "3                          0.11                               1.80   \n",
-       "4                           NaN                                NaN   \n",
-       "\n",
-       "   haematocrit_percentage_f30030_0_0  haemoglobin_concentration_f30020_0_0  \\\n",
-       "0                              39.79                                 13.90   \n",
-       "1                              45.00                                 15.60   \n",
-       "2                              39.48                                 13.58   \n",
-       "3                              44.31                                 14.99   \n",
-       "4                                NaN                                   NaN   \n",
-       "\n",
-       "   high_light_scatter_reticulocyte_count_f30300_0_0  \\\n",
-       "0                                             0.022   \n",
-       "1                                             0.014   \n",
-       "2                                             0.031   \n",
-       "3                                             0.025   \n",
-       "4                                               NaN   \n",
-       "\n",
-       "   high_light_scatter_reticulocyte_percentage_f30290_0_0  \\\n",
-       "0                                              0.464       \n",
-       "1                                              0.290       \n",
-       "2                                              0.686       \n",
-       "3                                              0.508       \n",
-       "4                                                NaN       \n",
-       "\n",
-       "   immature_reticulocyte_fraction_f30280_0_0  ...  phosphate_f30810_0_0  \\\n",
-       "0                                      0.378  ...                 1.422   \n",
-       "1                                      0.300  ...                 1.264   \n",
-       "2                                      0.380  ...                   NaN   \n",
-       "3                                      0.250  ...                 0.928   \n",
-       "4                                        NaN  ...                   NaN   \n",
-       "\n",
-       "   rheumatoid_factor_f30820_0_0  shbg_f30830_0_0  testosterone_f30850_0_0  \\\n",
-       "0                           NaN            70.11                    1.560   \n",
-       "1                           NaN            55.31                   12.237   \n",
-       "2                           NaN              NaN                      NaN   \n",
-       "3                           NaN            31.63                   11.398   \n",
-       "4                           NaN              NaN                      NaN   \n",
-       "\n",
-       "   total_bilirubin_f30840_0_0  total_protein_f30860_0_0  \\\n",
-       "0                        7.41                     71.97   \n",
-       "1                        8.07                     78.45   \n",
-       "2                         NaN                       NaN   \n",
-       "3                        8.65                     69.70   \n",
-       "4                         NaN                       NaN   \n",
-       "\n",
-       "   triglycerides_f30870_0_0  urate_f30880_0_0  urea_f30670_0_0  \\\n",
-       "0                     1.247             221.3             5.48   \n",
-       "1                     1.906             374.7             5.28   \n",
-       "2                       NaN               NaN              NaN   \n",
-       "3                     5.184             322.8             6.67   \n",
-       "4                       NaN               NaN              NaN   \n",
-       "\n",
-       "   vitamin_d_f30890_0_0  \n",
-       "0                  70.7  \n",
-       "1                  35.9  \n",
-       "2                   NaN  \n",
-       "3                  63.6  \n",
-       "4                   NaN  \n",
-       "\n",
-       "[5 rows x 62 columns]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fields_blood_count = [\n",
-    "    \"30160\", #\tBasophill count\n",
-    "    \"30220\", #\tBasophill percentage\n",
-    "    \"30150\", #\tEosinophill count\n",
-    "    \"30210\", #\tEosinophill percentage\n",
-    "    \"30030\", #\tHaematocrit percentage\n",
-    "    \"30020\", #\tHaemoglobin concentration\n",
-    "    \"30300\", #\tHigh light scatter reticulocyte count\n",
-    "    \"30290\", #\tHigh light scatter reticulocyte percentage\n",
-    "    \"30280\", #\tImmature reticulocyte fraction\n",
-    "    \"30120\", #\tLymphocyte count\n",
-    "    \"30180\", #\tLymphocyte percentage\n",
-    "    \"30050\", #\tMean corpuscular haemoglobin\n",
-    "    \"30060\", #\tMean corpuscular haemoglobin concentration\n",
-    "    \"30040\", #\tMean corpuscular volume\n",
-    "    \"30100\", #\tMean platelet (thrombocyte) volume\n",
-    "    \"30260\", #\tMean reticulocyte volume\n",
-    "    \"30270\", #\tMean sphered cell volume\n",
-    "    \"30130\", #\tMonocyte count\n",
-    "    \"30190\", #\tMonocyte percentage\n",
-    "    \"30140\", #\tNeutrophill count\n",
-    "    \"30200\", #\tNeutrophill percentage\n",
-    "    \"30170\", #\tNucleated red blood cell count\n",
-    "    \"30230\", #\tNucleated red blood cell percentage\n",
-    "    \"30080\", #\tPlatelet count\n",
-    "    \"30090\", #\tPlatelet crit\n",
-    "    \"30110\", #\tPlatelet distribution width\n",
-    "    \"30010\", #\tRed blood cell (erythrocyte) count\n",
-    "    \"30070\", #\tRed blood cell (erythrocyte) distribution width\n",
-    "    \"30250\", #\tReticulocyte count\n",
-    "    \"30240\", #\tReticulocyte percentage\n",
-    "    \"30000\", #\tWhite blood cell (leukocyte) count\n",
-    "]\n",
-    "\n",
-    "fields_blood_biochemistry = [\n",
-    "    \"30620\",#\tAlanine aminotransferase\n",
-    "    \"30600\",#\tAlbumin\n",
-    "    \"30610\",#\tAlkaline phosphatase\n",
-    "    \"30630\",#\tApolipoprotein A\n",
-    "    \"30640\",#\tApolipoprotein B\n",
-    "    \"30650\",#\tAspartate aminotransferase\n",
-    "    \"30710\",#\tC-reactive protein\n",
-    "    \"30680\",#\tCalcium\n",
-    "    \"30690\",#\tCholesterol\n",
-    "    \"30700\",#\tCreatinine\n",
-    "    \"30720\",#\tCystatin C\n",
-    "    \"30660\",#\tDirect bilirubin\n",
-    "    \"30730\",#\tGamma glutamyltransferase\n",
-    "    \"30740\",#\tGlucose\n",
-    "    \"30750\",#\tGlycated haemoglobin (HbA1c)\n",
-    "    \"30760\",#\tHDL cholesterol\n",
-    "    \"30770\",#\tIGF-1\n",
-    "    \"30780\",#\tLDL direct\n",
-    "    \"30790\",#\tLipoprotein A\n",
-    "    \"30800\",#\tOestradiol\n",
-    "    \"30810\",#\tPhosphate\n",
-    "    \"30820\",#\tRheumatoid factor\n",
-    "    \"30830\",#\tSHBG\n",
-    "    \"30850\",#\tTestosterone\n",
-    "    \"30840\",#\tTotal bilirubin\n",
-    "    \"30860\",#\tTotal protein\n",
-    "    \"30870\",#\tTriglycerides\n",
-    "    \"30880\",#\tUrate\n",
-    "    \"30670\",#\tUrea\n",
-    "    \"30890\",#\tVitamin D\n",
-    "]\n",
-    "\n",
-    "fields_blood_infectious = [\n",
-    "    \"23000\", #\t1gG antigen for Herpes Simplex virus-1\n",
-    "    \"23001\", #\t2mgG unique antigen for Herpes Simplex virus-2\n",
-    "    \"23049\", #\tAntigen assay QC indicator\n",
-    "    \"23048\", #\tAntigen assay date\n",
-    "    \"23026\", #\tBK VP1 antigen for Human Polyomavirus BKV\n",
-    "    \"23039\", #\tCagA antigen for Helicobacter pylori\n",
-    "    \"23043\", #\tCatalase antigen for Helicobacter pylori\n",
-    "    \"23018\", #\tCore antigen for Hepatitis C Virus\n",
-    "    \"23030\", #\tE6 antigen for Human Papillomavirus type-16\n",
-    "    \"23031\", #\tE7 antigen for Human Papillomavirus type-16\n",
-    "    \"23006\", #\tEA-D antigen for Epstein-Barr Virus\n",
-    "    \"23004\", #\tEBNA-1 antigen for Epstein-Barr Virus\n",
-    "    \"23042\", #\tGroEL antigen for Helicobacter pylori\n",
-    "    \"23016\", #\tHBc antigen for Hepatitis B Virus\n",
-    "    \"23017\", #\tHBe antigen for Hepatitis B Virus\n",
-    "    \"23025\", #\tHIV-1 env antigen for Human Immunodeficiency Virus\n",
-    "    \"23024\", #\tHIV-1 gag antigen for Human Immunodeficiency Virus\n",
-    "    \"23023\", #\tHTLV-1 env antigen for Human T-Lymphotropic Virus 1\n",
-    "    \"23022\", #\tHTLV-1 gag antigen for Human T-Lymphotropic Virus 1\n",
-    "    \"23010\", #\tIE1A antigen for Human Herpesvirus-6\n",
-    "    \"23011\", #\tIE1B antigen for Human Herpesvirus-6\n",
-    "    \"23027\", #\tJC VP1 antigen for Human Polyomavirus JCV\n",
-    "    \"23015\", #\tK8.1 antigen for Kaposi's Sarcoma-Associated Herpesvirus\n",
-    "    \"23029\", #\tL1 antigen for Human Papillomavirus type-16\n",
-    "    \"23032\", #\tL1 antigen for Human Papillomavirus type-18\n",
-    "    \"23014\", #\tLANA antigen for Kaposi's Sarcoma-Associated Herpesvirus\n",
-    "    \"23028\", #\tMC VP1 antigen for Merkel Cell Polyomavirus\n",
-    "    \"23019\", #\tNS3 antigen for Hepatitis C Virus\n",
-    "    \"23041\", #\tOMP antigen for Helicobacter pylori\n",
-    "    \"23037\", #\tPorB antigen for Chlamydia trachomatis\n",
-    "    \"23013\", #\tU14 antigen for Human Herpesvirus-7\n",
-    "    \"23044\", #\tUreA antigen for Helicobacter pylori\n",
-    "    \"23003\", #\tVCA p18 antigen for Epstein-Barr Virus\n",
-    "    \"23040\", #\tVacA antigen for Helicobacter pylori\n",
-    "    \"23005\", #\tZEBRA antigen for Epstein-Barr Virus\n",
-    "    \"23002\", #\tgE / gI antigen for Varicella Zoster Virus\n",
-    "    \"23034\", #\tmomp A antigen for Chlamydia trachomatis\n",
-    "    \"23033\", #\tmomp D antigen for Chlamydia trachomatis\n",
-    "    \"23012\", #\tp101 k antigen for Human Herpesvirus-6\n",
-    "    \"23020\", #\tp22 antigen for Toxoplasma gondii\n",
-    "    \"23038\", #\tpGP3 antigen for Chlamydia trachomatis\n",
-    "    \"23009\", #\tpp 28 antigen for Human Cytomegalovirus\n",
-    "    \"23008\", #\tpp 52 antigen for Human Cytomegalovirus\n",
-    "    \"23007\", #\tpp150 Nter antigen for Human Cytomegalovirus\n",
-    "    \"23021\", #\tsag1 antigen for Toxoplasma gondii\n",
-    "    \"23035\", #\ttarp-D F1 antigen for Chlamydia trachomatis\n",
-    "    \"23036\", #\ttarp-D F2 antigen for Chlamydia trachomatis\n",
-    "]\n",
-    "\n",
-    "labs = temp = get_data_fields(fields_blood_count+fields_blood_biochemistry+fields_blood_infectious, data, data_field)\n",
-    "print(len(temp))\n",
-    "display(temp.head())\n",
-    "\n",
-    "labs.to_feather(os.path.join(path, dataset_path, 'temp_labs.feather'))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Family History"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:34:25.838958Z",
-     "start_time": "2020-11-04T12:34:07.649920Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "502504\n"
-     ]
-    }
-   ],
-   "source": [
-    "fh_list=[\"Heart disease\", \"Stroke\", \"High blood pressure\",  \"Diabetes\", \"Lung cancer\", \"Severe depression\", \"Parkinson's disease\", \"Alzheimer's disease/dementia\", \"Chronic bronchitis/emphysema\", \"Breast cancer\", \"Bowel cancer\"]\n",
-    "with open(os.path.join(path, dataset_path, 'fh_list.yaml'), 'w') as file: yaml.dump(fh_list, file, default_flow_style=False)\n",
-    "\n",
-    "fields_family_history = [\n",
-    "    \"20107\", # Family history \n",
-    "    \"20110\" # Family history\n",
-    "]\n",
-    "\n",
-    "raw = get_data_fields(fields_family_history, data, data_field)\n",
-    "temp = pd.melt(raw, id_vars=[\"eid\"], value_vars=raw.drop(\"eid\", axis=1).columns.to_list(), var_name = \"field\", value_name=\"family_history\").drop(\"field\", axis=1)\n",
-    "temp = temp[temp.family_history.isin(fh_list)].assign(family_history=temp[\"family_history\"].str.lower().replace(\" \", \"_\", regex=True))\n",
-    "\n",
-    "temp = temp.drop_duplicates().sort_values(\"eid\").reset_index().drop(\"index\", axis=1).assign(n=True)\n",
-    "temp = pd.pivot_table(temp, index=\"eid\", columns=\"family_history\", values=\"n\", observed=True).add_prefix('fh_')\n",
-    "family_history = temp = data[[\"eid\"]].copy().merge(temp, how=\"left\", on=\"eid\").fillna(False)\n",
-    "\n",
-    "print(len(temp))\n",
-    "temp.head()\n",
-    "\n",
-    "family_history.to_feather(os.path.join(path, dataset_path, 'temp_family_history.feather'))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Medications"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:34:25.842601Z",
-     "start_time": "2020-11-04T12:34:25.840607Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# https://list.essentialmeds.org/?showRemoved=0\n",
-    "# essential medicines WHO?!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:34:32.471831Z",
-     "start_time": "2020-11-04T12:34:25.844339Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "atc_mapping = pd.read_csv(f\"{path}/mapping/atc/atc_matched_list.csv\")\n",
-    "athena_concepts = pd.read_csv(f\"{data_path}/athena_vocabulary/CONCEPT.csv\", sep=\"\\t\").assign(vocabulary_id = lambda x: x.vocabulary_id.astype(\"string\"), concept_class_id = lambda x: x.concept_class_id.astype(\"string\"))\n",
-    "atc_concepts = athena_concepts[athena_concepts.vocabulary_id==\"ATC\"]\n",
-    "atc2_concepts = atc_concepts[atc_concepts.concept_class_id==\"ATC 2nd\"].sort_values(\"concept_code\")\n",
-    "medication_list = dict(zip([x.lower().replace(\" \", \"_\") for x in atc2_concepts.concept_name.to_list()], [[x] for x in atc2_concepts.concept_code.to_list()]))\n",
-    "medication_list_extra = {\n",
-    "    \"antihypertensives\": [\"C02\"],\n",
-    "    \"statins\": [\"C10A\", \"C10B\"],\n",
-    "    \"ass\": [\"B01\"],\n",
-    "    \"atypical_antipsychotics\" : [\"N05\"],\n",
-    "    \"glucocorticoids\" : [\"H02\"]                        \n",
-    "}\n",
-    "medication_list.update(medication_list_extra)\n",
-    "\n",
-    "with open(os.path.join(path, dataset_path, 'medication_list.yaml'), 'w') as file: yaml.dump(medication_list, file, default_flow_style=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:34:32.481137Z",
-     "start_time": "2020-11-04T12:34:32.473698Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "def had_medication_before(data, data_field, medications, atc_mapping):\n",
-    "    fields = [\"20003\"]\n",
-    "    raw = get_data_fields(fields, data, data_field)\n",
-    "    temp = pd.melt(raw, id_vars=[\"eid\"], value_vars=raw.drop(\"eid\", axis=1).columns.to_list(), var_name = \"field\", value_name=\"UKBB_code\").drop(\"field\", axis=1).drop_duplicates()\n",
-    "\n",
-    "    temp.UKBB_code = temp.UKBB_code.astype(str)\n",
-    "    temp = temp[temp.UKBB_code!=\"None\"].copy()\n",
-    "    temp = temp[temp.UKBB_code!=\"nan\"].copy()\n",
-    "    temp.UKBB_code = temp.UKBB_code.astype(int)\n",
-    "\n",
-    "    temp_atc = temp.merge(atc_mapping, how=\"left\", on=\"UKBB_code\").sort_values(\"eid\").reset_index(drop=True).dropna(subset=[\"ATC_code\"], axis=0)\n",
-    "    temp_atc.ATC_code = temp_atc.ATC_code.astype(\"string\")\n",
-    "    temp = data[[\"eid\"]].copy()\n",
-    "    for med, med_codes in tqdm(medication_list.items()):\n",
-    "        regex_str = \"^\"+\"|^\".join(med_codes)\n",
-    "        df = temp_atc[temp_atc.ATC_code.str.contains(regex_str, case=False)][[\"eid\"]]\\\n",
-    "            .drop_duplicates(subset=[\"eid\"])\\\n",
-    "            .assign(medication=True)\n",
-    "        temp[med] = temp.merge(df, how=\"left\", on=\"eid\").fillna(False).medication\n",
-    "        \n",
-    "    return temp.sort_values(\"eid\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:36:14.425612Z",
-     "start_time": "2020-11-04T12:34:32.482863Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4ea3a79139e04f94a00e3a35c527fa25",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(HTML(value=''), FloatProgress(value=0.0, max=98.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "502504\n"
-     ]
-    }
-   ],
-   "source": [
-    "medications = had_medication_before(data, data_field, medication_list, atc_mapping)\n",
-    "print(len(medications))\n",
-    "medications.head(10)\n",
-    "\n",
-    "medications.to_feather(os.path.join(path, dataset_path, 'temp_medications.feather'))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Diagnoses and events"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:37:14.667281Z",
-     "start_time": "2020-11-04T12:36:14.427693Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3146: DtypeWarning: Columns (5,6,9) have mixed types.Specify dtype option on import or set low_memory=False.\n",
-      "  interactivity=interactivity, compiler=compiler, result=result)\n",
-      "/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3146: DtypeWarning: Columns (11) have mixed types.Specify dtype option on import or set low_memory=False.\n",
-      "  interactivity=interactivity, compiler=compiler, result=result)\n"
-     ]
-    }
-   ],
-   "source": [
-    "vocab_dir = f\"{data_path}/athena_vocabulary_covid\"\n",
-    "vocab = {\n",
-    "    \"concept\": pd.read_csv(f\"{vocab_dir}/CONCEPT.csv\", sep='\\t'),\n",
-    "    \"domain\": pd.read_csv(f\"{vocab_dir}/DOMAIN.csv\", sep='\\t'),\n",
-    "    \"class\": pd.read_csv(f\"{vocab_dir}/CONCEPT_CLASS.csv\", sep='\\t'),\n",
-    "    \"relationship\": pd.read_csv(f\"{vocab_dir}/RELATIONSHIP.csv\", sep='\\t'),\n",
-    "    \"drug_strength\": pd.read_csv(f\"{vocab_dir}/DRUG_STRENGTH.csv\", sep='\\t'),\n",
-    "    \"vocabulary\": pd.read_csv(f\"{vocab_dir}/VOCABULARY.csv\", sep='\\t'),\n",
-    "    \"concept_synonym\": pd.read_csv(f\"{vocab_dir}/CONCEPT_SYNONYM.csv\", sep='\\t'),\n",
-    "    \"concept_ancestor\": pd.read_csv(f\"{vocab_dir}/CONCEPT_ANCESTOR.csv\", sep='\\t'),\n",
-    "    \"concept_relationship\": pd.read_csv(f\"{vocab_dir}/CONCEPT_RELATIONSHIP.csv\", sep='\\t')                       \n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Definitions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:37:14.772869Z",
-     "start_time": "2020-11-04T12:37:14.669541Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "coding1836 = pd.read_csv(f\"{path}/mapping/codings/coding1836.tsv\", sep=\"\\t\").rename(columns={\"coding\":\"code\"})\n",
-    "phecodes = pd.read_csv(f\"{path}/mapping/phecodes/phecode_icd10.csv\")\n",
-    "def phenotype_children(phecodes, phenotype_list):\n",
-    "    l={}\n",
-    "    phecodes = phecodes.dropna(subset=[\"Phenotype\"], axis=0)\n",
-    "    for ph, ph_names in phenotype_list.items():\n",
-    "        regex = \"|\".join(ph_names)\n",
-    "        l[ph] = list(phecodes[phecodes.Phenotype.str.contains(regex, case=False)].ICD10.str.replace(\"\\\\.\", \"\").str.slice(0, 3).unique())\n",
-    "    return l"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:37:14.851438Z",
-     "start_time": "2020-11-04T12:37:14.774599Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "snomed_core = pd.read_csv(f\"{path}/mapping/snomed_core_list.txt\", sep=\"|\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:37:14.909023Z",
-     "start_time": "2020-11-04T12:37:14.853286Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "snomed_core = snomed_core.query(\"SNOMED_CONCEPT_STATUS == 'Current'\").copy()\n",
-    "new = snomed_core.SNOMED_FSN.str.split(\"(\", n=1, expand=True)\n",
-    "snomed_core[\"snomed_name\"] = new[0].str.rstrip(' ')\n",
-    "snomed_core[\"snomed_type\"] = new[1].str.rstrip(')')\n",
-    "snomed_core_data = snomed_core#.query(\"(snomed_type=='disorder' | snomed_type=='finding') & USAGE>0.01\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:37:14.914973Z",
-     "start_time": "2020-11-04T12:37:14.910857Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "snomed_names = snomed_core_data.snomed_name.to_list()\n",
-    "snomed_names = [str(item).lower().strip().replace(\" \", \"_\").replace(\";\", \"\").replace(\",\", \"\") for item in snomed_names]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "phenotype_list_snomed = dict(zip(snomed_names, snomed_core_data.SNOMED_CID.to_list()))\n",
-    "snomed_df = pd.DataFrame.from_dict(phenotype_list_snomed, orient='index').reset_index()\n",
-    "snomed_df.columns = [\"diagnosis\", \"concept_code\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>ancestor_concept_id</th>\n",
-       "      <th>descendant_concept_id</th>\n",
-       "      <th>min_levels_of_separation</th>\n",
-       "      <th>max_levels_of_separation</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>375415</td>\n",
-       "      <td>4335743</td>\n",
-       "      <td>4</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>735979</td>\n",
-       "      <td>41070383</td>\n",
-       "      <td>3</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>529411</td>\n",
-       "      <td>43269406</td>\n",
-       "      <td>3</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>141960</td>\n",
-       "      <td>200164</td>\n",
-       "      <td>3</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>441840</td>\n",
-       "      <td>4096781</td>\n",
-       "      <td>4</td>\n",
-       "      <td>12</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>63586490</th>\n",
-       "      <td>45893522</td>\n",
-       "      <td>35412270</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>63586491</th>\n",
-       "      <td>45893522</td>\n",
-       "      <td>46275678</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>63586492</th>\n",
-       "      <td>45893522</td>\n",
-       "      <td>46275680</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>63586493</th>\n",
-       "      <td>45893522</td>\n",
-       "      <td>46275683</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>63586494</th>\n",
-       "      <td>45893522</td>\n",
-       "      <td>46275684</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>63586495 rows × 4 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          ancestor_concept_id  descendant_concept_id  \\\n",
-       "0                      375415                4335743   \n",
-       "1                      735979               41070383   \n",
-       "2                      529411               43269406   \n",
-       "3                      141960                 200164   \n",
-       "4                      441840                4096781   \n",
-       "...                       ...                    ...   \n",
-       "63586490             45893522               35412270   \n",
-       "63586491             45893522               46275678   \n",
-       "63586492             45893522               46275680   \n",
-       "63586493             45893522               46275683   \n",
-       "63586494             45893522               46275684   \n",
-       "\n",
-       "          min_levels_of_separation  max_levels_of_separation  \n",
-       "0                                4                         4  \n",
-       "1                                3                         5  \n",
-       "2                                3                         3  \n",
-       "3                                3                         6  \n",
-       "4                                4                        12  \n",
-       "...                            ...                       ...  \n",
-       "63586490                         2                         2  \n",
-       "63586491                         1                         1  \n",
-       "63586492                         2                         3  \n",
-       "63586493                         1                         2  \n",
-       "63586494                         2                         3  \n",
-       "\n",
-       "[63586495 rows x 4 columns]"
-      ]
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "vocab[\"concept_ancestor\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "concept_ids = vocab[\"concept\"].query(\"(vocabulary_id == 'SNOMED') | (vocabulary_id == 'ICD10CM')\")\n",
-    "concept_ids_icd10 = vocab[\"concept\"].query(\"vocabulary_id == 'ICD10CM'\").concept_id.to_list()\n",
-    "vocab_concept_ids = concept_ids.concept_id.to_list()\n",
-    "concept_ancestor = vocab[\"concept_ancestor\"][[\"ancestor_concept_id\", \"descendant_concept_id\"]].query(\"ancestor_concept_id == @vocab_concept_ids\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "concept_rel = vocab[\"concept_relationship\"][[\"concept_id_1\", \"concept_id_2\", \"relationship_id\"]].query(\"(concept_id_1 == @vocab_concept_ids) & (concept_id_2 == @concept_ids_icd10) & (relationship_id == 'Mapped from')\")\n",
-    "concept_mapping = concept_rel.rename(columns={\"concept_id_2\":\"concept_id\"}).merge(concept_ids, on=\"concept_id\").query(\"vocabulary_id == 'ICD10CM'\")[[\"concept_id_1\", \"concept_code\"]].rename(columns={\"concept_id_1\":\"concept_id_desc\",\"concept_code\":\"icd10\"})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_snomed_concept_id = snomed_df.merge(concept_ids[[\"concept_code\", \"concept_id\"]], on=\"concept_code\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#df_desc = df_snomed_concept_id.merge(concept_ancestor.rename(columns={\"ancestor_concept_id\":\"concept_id\"})[[\"concept_id\", \"descendant_concept_id\"]], on=\"concept_id\")\n",
-    "#df_desc = df_desc[[\"diagnosis\", \"concept_code\", \"concept_id\", \"descendant_concept_id\"]].rename(columns={\"descendant_concept_id\":\"concept_id_desc\"})\n",
-    "#df_desc_codes = df_desc.merge(concept_ids[[\"concept_id\", \"concept_code\"]].rename(columns={\"concept_id\":\"concept_id_desc\", \"concept_code\":\"concept_codes_desc\"}), on=\"concept_id_desc\").drop_duplicates().sort_values(\"concept_code\")\n",
-    "#df_desc_icd = df_desc_codes.merge(concept_mapping, on=\"concept_id_desc\")#.rename(columns={\"concept_id\":\"concept_id_desc\", \"concept_code\":\"concept_codes_desc\"}))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_icd = df_snomed_concept_id.merge(concept_mapping.rename(columns={\"concept_id_desc\":\"concept_id\"}), on=\"concept_id\")\n",
-    "df_mapped = df_icd[[\"diagnosis\", \"concept_code\", \"icd10\"]].drop_duplicates()\n",
-    "df_mapped[\"icd10\"] = df_mapped[\"icd10\"].str.replace(\".\", \"\")\n",
-    "df_mapped[\"meaning\"] = [e[:3] for e in df_mapped[\"icd10\"].to_list()]\n",
-    "icd10_codes = dict(df_mapped[[\"diagnosis\", \"meaning\"]].drop_duplicates().groupby(\"diagnosis\")[\"meaning\"].apply(list).to_dict())#set_index(\"diagnosis\", drop=True).to_dict()[\"meaning\"]#.sort_values(\"diagnosis\")"
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:39:55.342937Z",
-     "start_time": "2020-11-04T12:37:14.916906Z"
-    },
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "source": [
-    "phenotype_list_snomed = dict(zip(snomed_names, snomed_core_data.SNOMED_CID.to_list()))\n",
-    "snomed_ids = vocab[\"concept\"].query(\"vocabulary_id == 'SNOMED'\").concept_id.to_list()\n",
-    "icd10_ids = vocab[\"concept\"].query(\"vocabulary_id == 'ICD10CM'\").concept_id.to_list()\n",
-    "\n",
-    "ph_to_icd10_mapping = {}\n",
-    "\n",
-    "def map_snomed_to_icd10(ph, snomed_code, concept, concept_ancestor, concept_relationship):\n",
-    "    concept_ids = concept.query(\"vocabulary_id == 'SNOMED' & concept_code == @snomed_code\").concept_id.to_list()\n",
-    "    snomed_desc_ids = concept_ancestor.query(\"ancestor_concept_id== @concept_ids\").descendant_concept_id.to_list()\n",
-    "    ph_desc = concept.query(\"concept_id == @snomed_desc_ids\").query(\"vocabulary_id == 'SNOMED'\")\n",
-    "    l_ph_desc_ids = ph_desc.concept_id.to_list()\n",
-    "    ph_icd10_ids = list(concept_relationship.query(\"concept_id_1==@l_ph_desc_ids\").query(\"concept_id_2 == @icd10_ids\").concept_id_2.unique())\n",
-    "    #ph_icd10_ids = list(concept_relationship.set_index(\"concept_id_1\").query(\"index==@l_ph_desc_ids\").query(\"concept_id_2 == @icd10_ids\").query(\"relationship_id == 'Mapped from'\").concept_id_2.unique()\n",
-    "    \n",
-    "    #ph_icd10_ids = list(temp.concept_id_2.unique())\n",
-    "    df = concept.query(\"concept_id == @ph_icd10_ids & vocabulary_id == 'ICD10CM'\")\n",
-    "    icd10_list = list(df[~df.concept_code.str.contains(\"OMOP\", na=False)].concept_code.unique())\n",
-    "    icd10_list = sorted(list(set([e[:3] for e in icd10_list])))\n",
-    "    #print(f\"{ph}: {icd10_list}\")\n",
-    "    return {ph: sorted(list(dict.fromkeys([str(e) for e in icd10_list])))}\n",
-    "\n",
-    "from joblib import Parallel, delayed\n",
-    "concept_ids = vocab[\"concept\"].query(\"(vocabulary_id == 'SNOMED') | (vocabulary_id == 'ICD10CM')\")\n",
-    "vocab_concept_ids = concept_ids.concept_id.to_list()\n",
-    "concept_ancestor = vocab[\"concept_ancestor\"][[\"ancestor_concept_id\", \"descendant_concept_id\"]].query(\"ancestor_concept_id == @vocab_concept_ids\")\n",
-    "concept_rel = vocab[\"concept_relationship\"][[\"concept_id_1\", \"concept_id_2\", \"relationship_id\"]].query(\"(concept_id_1 == @vocab_concept_ids) & (concept_id_2 == @vocab_concept_ids) & (relationship_id == 'Mapped from')\")\n",
-    "icd10_codes = Parallel(n_jobs=10, require=\"sharedmem\")(delayed(map_snomed_to_icd10)(ph, snomed_code, \n",
-    "                                                              concept_ids, concept_ancestor, concept_rel) for ph, snomed_code in tqdm(phenotype_list_snomed.items()))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:39:55.348009Z",
-     "start_time": "2020-11-04T12:39:55.344704Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "l10_snomed = {}\n",
-    "for ph in icd10_codes: l10_snomed.update({ph:icd10_codes[ph]})"
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:39:55.468545Z",
-     "start_time": "2020-11-04T12:39:55.349545Z"
-    }
-   },
-   "source": [
-    "phenotype_list_basic = {\n",
-    "    \"myocardial_infarction\": [\"Myocardial infarction\"],\n",
-    "    \"stroke\": [\"Cerebrovascular disease\"],\n",
-    "    \"diabetes1\" : [\"Type 1 diabetes\"],\n",
-    "    \"diabetes2\" : [\"Diabetes mellitus\", \"Type 2 diabetes\"],\n",
-    "    \"chronic_kidney_disease\": [\"Chronic kidney disease\", \"chronic renal failure\"],\n",
-    "    \"atrial_fibrillation\": [\"Atrial fibrillation\", \"Atrial flutter\", \"paroxysmal tachycardia\"],\n",
-    "    \"migraine\": [\"Migraine\"],\n",
-    "    \"rheumatoid_arthritis\": [\"Rheumatoid arthritis\"],\n",
-    "    \"systemic_lupus_erythematosus\": [\"Systemic lupus erythematosus\"],\n",
-    "    \"severe_mental_illness\": [\"Schizophrenia\", \"bipolar\", \"Major depressive disorder\"],\n",
-    "    \"erectile_dysfunction\" : [\"Erectile dysfunction\"],  \n",
-    "}\n",
-    "\n",
-    "l10_basic = phenotype_children(phecodes, phenotype_list_basic)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "l10_basic = {\n",
-    "    \"myocardial_infarction\": ['I21', 'I22', 'I23', 'I24', 'I25'],\n",
-    "    \"stroke\": ['G45', \"I63\", \"I64\"],\n",
-    "    \"diabetes1\" : ['E10'],\n",
-    "    \"diabetes2\" : ['E11', 'E12', 'E13', 'E14'],\n",
-    "    \"chronic_kidney_disease\": [\"I12\", \"N18\", \"N19\"],\n",
-    "    'atrial_fibrillation': ['I47', 'I48'],\n",
-    "    'migraine': ['G43', 'G44'],\n",
-    "    'rheumatoid_arthritis': ['J99', 'M05', 'M06', 'M08', 'M12', 'M13'],\n",
-    "    \"systemic_lupus_erythematosus\": ['M32'],\n",
-    "    'severe_mental_illness': ['F20', 'F25', 'F30', 'F31', 'F32', 'F33', 'F44'],\n",
-    "    \"erectile_dysfunction\" : ['F52', 'N48'],  \n",
-    "    \"liver_disease\":[\"K70\", \"K71\", \"K72\", \"K73\", \"K74\", \"K75\", \"K76\", \"K77\"],\n",
-    "    \"dementia\":['F00', 'F01', 'F02', 'F03'],\n",
-    "    \"copd\": ['J44']\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'myocardial_infarction': ['I21', 'I22', 'I23', 'I24', 'I25'],\n",
-       " 'stroke': ['G45', 'I63', 'I64'],\n",
-       " 'diabetes1': ['E10'],\n",
-       " 'diabetes2': ['E11', 'E12', 'E13', 'E14'],\n",
-       " 'chronic_kidney_disease': ['I12', 'N18', 'N19'],\n",
-       " 'atrial_fibrillation': ['I47', 'I48'],\n",
-       " 'migraine': ['G43', 'G44'],\n",
-       " 'rheumatoid_arthritis': ['J99', 'M05', 'M06', 'M08', 'M12', 'M13'],\n",
-       " 'systemic_lupus_erythematosus': ['M32'],\n",
-       " 'severe_mental_illness': ['F20', 'F25', 'F30', 'F31', 'F32', 'F33', 'F44'],\n",
-       " 'erectile_dysfunction': ['F52', 'N48'],\n",
-       " 'liver_disease': ['K70', 'K71', 'K72', 'K73', 'K74', 'K75', 'K76', 'K77'],\n",
-       " 'dementia': ['F00', 'F01', 'F02', 'F03'],\n",
-       " 'copd': ['J44']}"
-      ]
-     },
-     "execution_count": 49,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "l10_basic"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:39:55.473556Z",
-     "start_time": "2020-11-04T12:39:55.471051Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "l10_all = l10_basic\n",
-    "for key, value in l10_snomed.items(): \n",
-    "    if key not in l10_basic: l10_all[key] = value"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:39:55.620916Z",
-     "start_time": "2020-11-04T12:39:55.475137Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "l10 = {k: v for k, v in l10_all.items() if len(v)!=0}\n",
-    "\n",
-    "#phenotype_list = {k: v for k, v in phenotype_list.items() if k in list(l10.keys())}\n",
-    "\n",
-    "with open(os.path.join(path, dataset_path, 'phenotype_list.yaml'), 'w') as file: yaml.dump(l10, file, default_flow_style=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 1. Self Reported"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:43:13.288198Z",
-     "start_time": "2020-11-04T12:43:13.264683Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "coding609 = pd.read_csv(f\"{path}/mapping/codings/coding609.tsv\", sep=\"\\t\").rename(columns={\"coding\":\"code\"})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datetime import datetime, timedelta\n",
-    "\n",
-    "def datetime_from_dec_year(dec_year):\n",
-    "    start = dec_year\n",
-    "    year = int(start)\n",
-    "    rem = start - year\n",
-    "\n",
-    "    base = datetime(year, 1, 1)\n",
-    "    result = base + timedelta(seconds=(base.replace(year=base.year + 1) - base).total_seconds() * rem)\n",
-    "    #result.strftime(\"%Y-%m-%d\")\n",
-    "    return result.date()\n",
-    "\n",
-    "def extract_map_self_reported(data, data_field, code_map):\n",
-    "    pbar = tqdm(total=16)\n",
-    "    ### codes\n",
-    "    fields = [\"20002\"]; pbar.update(1)\n",
-    "    raw = get_data_fields_all(fields, data, data_field); pbar.update(1)\n",
-    "    col = \"noncancer_illness_code_selfreported_f20002\"; pbar.update(1)\n",
-    "    temp = pd.wide_to_long(raw, stubnames=[col], i=\"eid\", j=\"instance_index\", sep=\"_\", suffix=\"\\w+\").reset_index(); pbar.update(1)\n",
-    "    codes = temp.rename(columns={col:\"code\"})\\\n",
-    "        .assign(code=lambda x: x.code.astype(str))\\\n",
-    "        .replace(\"None\", np.nan) \\\n",
-    "        .replace(\"nan\", np.nan) \\\n",
-    "        .dropna(subset=[\"code\"], axis=0)\\\n",
-    "        .assign(code=lambda x: x.code.astype(int)) \\\n",
-    "        .merge(code_map, how=\"left\",on=\"code\") \\\n",
-    "        .dropna(subset=[\"meaning\"], axis=0)\\\n",
-    "        .sort_values([\"eid\", \"instance_index\"]) \\\n",
-    "        .reset_index(drop=True); pbar.update(1)\n",
-    "    \n",
-    "    ### dates\n",
-    "    fields = [\"20008\"]; pbar.update(1)\n",
-    "    raw = get_data_fields_all(fields, data, data_field); pbar.update(1)\n",
-    "    col=\"interpolated_year_when_noncancer_illness_first_diagnosed_f20008\"; pbar.update(1)\n",
-    "    temp = pd.wide_to_long(raw, stubnames=[col], i=\"eid\", j=\"instance_index\", sep=\"_\", suffix=\"\\w+\").reset_index(); pbar.update(1)\n",
-    "    dates = temp.rename(columns={col:\"date\"})\\\n",
-    "        .dropna(subset=[\"date\"], axis=0)\\\n",
-    "        .sort_values([\"eid\", \"instance_index\"]) \\\n",
-    "        .reset_index(drop=True); pbar.update(1)\n",
-    "\n",
-    "    dates = dates[dates.date!=-1]; pbar.update(1)\n",
-    "    dates = dates[dates.date!=-3]; pbar.update(1)\n",
-    "    dates.date = dates.date.apply(datetime_from_dec_year); pbar.update(1)\n",
-    "    \n",
-    "    test = codes.merge(dates, how=\"left\", on=[\"eid\", \"instance_index\"]).assign(origin=\"self_reported\").copy(); pbar.update(1)\n",
-    "    \n",
-    "    test[\"instance_index\"] = test[\"instance_index\"].astype(\"string\"); pbar.update(1)\n",
-    "    test[['instance','n']] = test.instance_index.str.split(\"_\",expand=True); pbar.update(1)\n",
-    "    pbar.close()\n",
-    "    \n",
-    "    return test[[\"eid\", \"origin\", 'instance','n', \"code\", \"meaning\", \"date\"]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tqdm.auto import tqdm"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:46:00.098893Z",
-     "start_time": "2020-11-04T12:43:13.432479Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4a6bf94594574b48b26a251987e4baf0",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(HTML(value=''), FloatProgress(value=0.0, max=16.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "codes_self_reported = extract_map_self_reported(data, data_field, coding609)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Load records"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "codes_gp_records = pd.read_feather(f\"{data_path}/1_decoded/codes_gp_diagnoses_210119.feather\").drop(\"level\", axis=1)\n",
-    "codes_hospital_records = pd.read_feather(f\"{data_path}/1_decoded/codes_hes_diagnoses_210120.feather\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Combine diagnoses and events"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:46:25.115010Z",
-     "start_time": "2020-11-04T12:46:08.961388Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>origin</th>\n",
-       "      <th>instance</th>\n",
-       "      <th>n</th>\n",
-       "      <th>code</th>\n",
-       "      <th>meaning</th>\n",
-       "      <th>date</th>\n",
-       "      <th>level</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>gp_read3</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>F171</td>\n",
-       "      <td>F17</td>\n",
-       "      <td>1976-01-01</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>gp_read3</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>O800</td>\n",
-       "      <td>O80</td>\n",
-       "      <td>1986-03-23</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>gp_read3</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>O800</td>\n",
-       "      <td>O80</td>\n",
-       "      <td>1989-05-25</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>gp_read3</td>\n",
-       "      <td>0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>Z824</td>\n",
-       "      <td>Z82</td>\n",
-       "      <td>1994-09-13</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>gp_read3</td>\n",
-       "      <td>0</td>\n",
-       "      <td>5</td>\n",
-       "      <td>Z867</td>\n",
-       "      <td>Z86</td>\n",
-       "      <td>1994-09-13</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       eid    origin instance  n  code meaning        date  level\n",
-       "0  1000018  gp_read3        0  1  F171     F17  1976-01-01    NaN\n",
-       "1  1000018  gp_read3        0  2  O800     O80  1986-03-23    NaN\n",
-       "2  1000018  gp_read3        0  3  O800     O80  1989-05-25    NaN\n",
-       "3  1000018  gp_read3        0  4  Z824     Z82  1994-09-13    NaN\n",
-       "4  1000018  gp_read3        0  5  Z867     Z86  1994-09-13    NaN"
-      ]
-     },
-     "execution_count": 61,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "diagnoses_codes = codes_self_reported.append(codes_hospital_records).append(codes_gp_records).sort_values([\"eid\", \"date\"]).dropna(subset=[\"date\"], axis=0).reset_index(drop=True)\n",
-    "diagnoses_codes.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "diagnoses_codes.reset_index(drop=True).assign(eid = lambda x: x.eid.astype(int),\n",
-    "                                              origin = lambda x: x.origin.astype(str),\n",
-    "                                              instance = lambda x: x.instance.astype(int),\n",
-    "                                              n = lambda x: x.n.astype(int),\n",
-    "                                              code = lambda x: x.code.astype(str), \n",
-    "                                              meaning = lambda x: x.meaning.astype(str))\\\n",
-    "    .to_feather(os.path.join(path, dataset_path, 'temp_diagnoses_codes.feather'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "diagnoses_codes = pd.read_feather(os.path.join(path, dataset_path, 'temp_diagnoses_codes.feather'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>origin</th>\n",
-       "      <th>instance</th>\n",
-       "      <th>n</th>\n",
-       "      <th>code</th>\n",
-       "      <th>meaning</th>\n",
-       "      <th>date</th>\n",
-       "      <th>level</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>gp_read3</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>F171</td>\n",
-       "      <td>F17</td>\n",
-       "      <td>1976-01-01</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>gp_read3</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>O800</td>\n",
-       "      <td>O80</td>\n",
-       "      <td>1986-03-23</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>gp_read3</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>O800</td>\n",
-       "      <td>O80</td>\n",
-       "      <td>1989-05-25</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>gp_read3</td>\n",
-       "      <td>0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>Z824</td>\n",
-       "      <td>Z82</td>\n",
-       "      <td>1994-09-13</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>gp_read3</td>\n",
-       "      <td>0</td>\n",
-       "      <td>5</td>\n",
-       "      <td>Z867</td>\n",
-       "      <td>Z86</td>\n",
-       "      <td>1994-09-13</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>32075621</th>\n",
-       "      <td>6025198</td>\n",
-       "      <td>hes_icd10</td>\n",
-       "      <td>6</td>\n",
-       "      <td>13</td>\n",
-       "      <td>['R945']</td>\n",
-       "      <td>R94</td>\n",
-       "      <td>2018-12-09</td>\n",
-       "      <td>2.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>32075622</th>\n",
-       "      <td>6025198</td>\n",
-       "      <td>hes_icd10</td>\n",
-       "      <td>6</td>\n",
-       "      <td>14</td>\n",
-       "      <td>['F171']</td>\n",
-       "      <td>F17</td>\n",
-       "      <td>2018-12-09</td>\n",
-       "      <td>2.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>32075623</th>\n",
-       "      <td>6025198</td>\n",
-       "      <td>hes_icd10</td>\n",
-       "      <td>6</td>\n",
-       "      <td>15</td>\n",
-       "      <td>['I10']</td>\n",
-       "      <td>I10</td>\n",
-       "      <td>2018-12-09</td>\n",
-       "      <td>2.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>32075624</th>\n",
-       "      <td>6025198</td>\n",
-       "      <td>hes_icd10</td>\n",
-       "      <td>6</td>\n",
-       "      <td>16</td>\n",
-       "      <td>['E780']</td>\n",
-       "      <td>E78</td>\n",
-       "      <td>2018-12-09</td>\n",
-       "      <td>2.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>32075625</th>\n",
-       "      <td>6025198</td>\n",
-       "      <td>hes_icd10</td>\n",
-       "      <td>6</td>\n",
-       "      <td>17</td>\n",
-       "      <td>['W802']</td>\n",
-       "      <td>W80</td>\n",
-       "      <td>2018-12-09</td>\n",
-       "      <td>3.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>32075626 rows × 8 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "              eid     origin  instance   n      code meaning        date  \\\n",
-       "0         1000018   gp_read3         0   1      F171     F17  1976-01-01   \n",
-       "1         1000018   gp_read3         0   2      O800     O80  1986-03-23   \n",
-       "2         1000018   gp_read3         0   3      O800     O80  1989-05-25   \n",
-       "3         1000018   gp_read3         0   4      Z824     Z82  1994-09-13   \n",
-       "4         1000018   gp_read3         0   5      Z867     Z86  1994-09-13   \n",
-       "...           ...        ...       ...  ..       ...     ...         ...   \n",
-       "32075621  6025198  hes_icd10         6  13  ['R945']     R94  2018-12-09   \n",
-       "32075622  6025198  hes_icd10         6  14  ['F171']     F17  2018-12-09   \n",
-       "32075623  6025198  hes_icd10         6  15   ['I10']     I10  2018-12-09   \n",
-       "32075624  6025198  hes_icd10         6  16  ['E780']     E78  2018-12-09   \n",
-       "32075625  6025198  hes_icd10         6  17  ['W802']     W80  2018-12-09   \n",
-       "\n",
-       "          level  \n",
-       "0           NaN  \n",
-       "1           NaN  \n",
-       "2           NaN  \n",
-       "3           NaN  \n",
-       "4           NaN  \n",
-       "...         ...  \n",
-       "32075621    2.0  \n",
-       "32075622    2.0  \n",
-       "32075623    2.0  \n",
-       "32075624    2.0  \n",
-       "32075625    3.0  \n",
-       "\n",
-       "[32075626 rows x 8 columns]"
-      ]
-     },
-     "execution_count": 64,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "diagnoses_codes"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:46:26.189927Z",
-     "start_time": "2020-11-04T12:46:25.117069Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from joblib import Parallel, delayed\n",
-    "from functools import reduce\n",
-    "from numba import jit\n",
-    "\n",
-    "def had_diagnosis_before_per_ph(df_before, ph, ph_codes, temp):\n",
-    "    df_ph = df_before[df_before.meaning.isin(ph_codes)][[\"eid\"]]\\\n",
-    "            .drop_duplicates(subset=[\"eid\"])\\\n",
-    "            .assign(phenotype=True) \n",
-    "    return temp.merge(df_ph, how=\"left\", on=\"eid\").fillna(False).phenotype\n",
-    "\n",
-    "def had_diagnosis_before(data, diagnoses_codes, phenotypes, time0=time0_col):\n",
-    "    diagnoses_codes_time = diagnoses_codes.merge(data[[\"eid\", time0]], how=\"left\", on=\"eid\")\n",
-    "    \n",
-    "    temp = data[[\"eid\"]].copy()\n",
-    "    df_before = diagnoses_codes_time[diagnoses_codes_time.date < diagnoses_codes_time[time0]]\n",
-    "                                                                                         \n",
-    "    df_phs = Parallel(n_jobs=20, require=\"sharedmem\")(delayed(had_diagnosis_before_per_ph)(df_before, ph, phenotypes[ph], temp) for ph in tqdm(list(phenotypes)))\n",
-    "    for ph, df_ph_series in zip(tqdm(list(phenotypes)), df_phs): temp[ph] = df_ph_series#temp.merge(df_ph, how=\"left\", on=\"eid\").fillna(False).phenotype\n",
-    "    \n",
-    "    return temp.sort_values(\"eid\")  #reduce(lambda left,right: pd.merge(left,right,on=['eid'], how='left'), df_phs)."
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "source": [
-    "from joblib import Parallel, delayed\n",
-    "from functools import reduce\n",
-    "from numba import jit\n",
-    "\n",
-    "def had_diagnosis_before_per_ph(df_before, ph, ph_codes, temp):\n",
-    "    regex = \"|\".join(ph_codes)\n",
-    "    df_ph = df_before[df_before.meaning.str.contains(regex, case=False)][[\"eid\"]]\\\n",
-    "            .drop_duplicates(subset=[\"eid\"])\\\n",
-    "            .assign(phenotype=True)   \n",
-    "    return temp.merge(df_ph, how=\"left\", on=\"eid\").fillna(False).phenotype\n",
-    "\n",
-    "def had_diagnosis_before(data, diagnoses_codes, phenotypes, time0=time0_col):\n",
-    "    diagnoses_codes_time = diagnoses_codes.merge(data[[\"eid\", time0]], how=\"left\", on=\"eid\")\n",
-    "    \n",
-    "    temp = data[[\"eid\"]].copy()\n",
-    "    df_before = diagnoses_codes_time[diagnoses_codes_time.date < diagnoses_codes_time[time0]]\n",
-    "                                                                                         \n",
-    "    df_phs = Parallel(n_jobs=30, require=\"sharedmem\")(delayed(had_diagnosis_before_per_ph)(df_before, ph, phenotypes[ph], temp) for ph in tqdm(list(phenotypes)))\n",
-    "    for ph, ph_series in zip(tqdm(list(phenotypes)), df_phs): temp[ph] = ph_series\n",
-    "    \n",
-    "    return temp.sort_values(\"eid\")  #reduce(lambda left,right: pd.merge(left,right,on=['eid'], how='left'), df_phs)."
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "source": [
-    "phenotypes = l10\n",
-    "time0 = time0_col\n",
-    "diagnoses_codes_time = diagnoses_codes.merge(data[[\"eid\", time0]], how=\"left\", on=\"eid\")\n",
-    "\n",
-    "temp = data[[\"eid\"]].copy()\n",
-    "df_before = diagnoses_codes_time[diagnoses_codes_time.date < diagnoses_codes_time[time0]]\n",
-    "\n",
-    "df_phs = Parallel(n_jobs=10, require=\"sharedmem\")(delayed(had_diagnosis_before_per_ph)(df_before, ph, phenotypes[ph], temp) for ph in tqdm(list(phenotypes)[0:100]))"
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "source": [
-    "def had_diagnosis_before(data, diagnoses_codes, phenotypes, time0=time0_col):\n",
-    "    diagnoses_codes_time = diagnoses_codes.merge(data[[\"eid\", time0]], how=\"left\", on=\"eid\")\n",
-    "    \n",
-    "    temp = data[[\"eid\"]].copy()\n",
-    "    df_before = diagnoses_codes_time[diagnoses_codes_time.date < diagnoses_codes_time[time0]]\n",
-    "    for ph, ph_codes in tqdm(phenotypes.items()):\n",
-    "        regex = \"|\".join(ph_codes)\n",
-    "        df_ph = df_before[df_before.meaning.str.contains(regex, case=False)][[\"eid\"]]\\\n",
-    "            .drop_duplicates(subset=[\"eid\"])\\\n",
-    "            .assign(phenotype=True)\n",
-    "        temp[ph] = temp.merge(df_ph, how=\"left\", on=\"eid\").fillna(False).phenotype\n",
-    "       \n",
-    "    return temp.sort_values(\"eid\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 68,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T13:17:52.922023Z",
-     "start_time": "2020-11-04T12:46:26.191314Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
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-      "text/plain": [
-       "HBox(children=(HTML(value=''), FloatProgress(value=0.0, max=2662.0), HTML(value='')))"
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-     "metadata": {},
-     "output_type": "display_data"
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-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    },
-    {
-     "data": {
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-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "502504\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>myocardial_infarction</th>\n",
-       "      <th>stroke</th>\n",
-       "      <th>diabetes1</th>\n",
-       "      <th>diabetes2</th>\n",
-       "      <th>chronic_kidney_disease</th>\n",
-       "      <th>atrial_fibrillation</th>\n",
-       "      <th>migraine</th>\n",
-       "      <th>rheumatoid_arthritis</th>\n",
-       "      <th>systemic_lupus_erythematosus</th>\n",
-       "      <th>...</th>\n",
-       "      <th>vulvitis</th>\n",
-       "      <th>vulvodynia</th>\n",
-       "      <th>vulvovaginitis</th>\n",
-       "      <th>waldenström_macroglobulinemia</th>\n",
-       "      <th>wheezing</th>\n",
-       "      <th>white_blood_cell_disorder</th>\n",
-       "      <th>worried_well</th>\n",
-       "      <th>wound_dehiscence</th>\n",
-       "      <th>wrist_joint_pain</th>\n",
-       "      <th>xerostomia</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1000020</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1000037</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000043</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1000051</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 2663 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       eid  myocardial_infarction  stroke  diabetes1  diabetes2  \\\n",
-       "0  1000018                  False   False      False      False   \n",
-       "1  1000020                  False   False      False      False   \n",
-       "2  1000037                  False   False      False      False   \n",
-       "3  1000043                  False   False      False      False   \n",
-       "4  1000051                  False   False      False       True   \n",
-       "\n",
-       "   chronic_kidney_disease  atrial_fibrillation  migraine  \\\n",
-       "0                    True                False     False   \n",
-       "1                   False                False     False   \n",
-       "2                   False                False      True   \n",
-       "3                   False                False     False   \n",
-       "4                   False                False     False   \n",
-       "\n",
-       "   rheumatoid_arthritis  systemic_lupus_erythematosus  ...  vulvitis  \\\n",
-       "0                 False                         False  ...     False   \n",
-       "1                 False                         False  ...     False   \n",
-       "2                  True                         False  ...     False   \n",
-       "3                 False                         False  ...     False   \n",
-       "4                 False                         False  ...     False   \n",
-       "\n",
-       "   vulvodynia  vulvovaginitis  waldenström_macroglobulinemia  wheezing  \\\n",
-       "0       False           False                          False     False   \n",
-       "1       False           False                          False     False   \n",
-       "2       False           False                          False     False   \n",
-       "3       False           False                          False      True   \n",
-       "4       False           False                          False     False   \n",
-       "\n",
-       "   white_blood_cell_disorder  worried_well  wound_dehiscence  \\\n",
-       "0                      False          True             False   \n",
-       "1                      False         False             False   \n",
-       "2                      False          True             False   \n",
-       "3                      False         False             False   \n",
-       "4                      False         False             False   \n",
-       "\n",
-       "   wrist_joint_pain  xerostomia  \n",
-       "0             False       False  \n",
-       "1             False       False  \n",
-       "2              True       False  \n",
-       "3             False       False  \n",
-       "4             False       False  \n",
-       "\n",
-       "[5 rows x 2663 columns]"
-      ]
-     },
-     "execution_count": 68,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "diagnoses = had_diagnosis_before(basics, diagnoses_codes, l10, time0=time0_col)\n",
-    "print(len(diagnoses))\n",
-    "\n",
-    "diagnoses.to_feather(os.path.join(path, dataset_path, 'temp_diagnoses.feather'))\n",
-    "\n",
-    "diagnoses.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 147,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "diagnoses = pd.read_feather(os.path.join(path, dataset_path, 'temp_diagnoses.feather'))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "source": [
-    "# Add embeddings for Snomed Diagnoses"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "source": [
-    "#### Get SNOMED - node2vec dict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "outputs": [],
-   "source": [
-    "snomed_embeddings = pd.read_csv(\"/data/analysis/ag-reils/steinfej/data/snomed_embeddings/snomed.emb.p1.q1.w20.l40.e200.graph_format.txt\", sep=\" \", header=None, skiprows=1)\n",
-    "snomed_embeddings.columns = [\"snomed_id\"]+list(snomed_embeddings.columns)[:-1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "metadata": {
-    "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true,
-     "source_hidden": true
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "\n",
-       "    .dataframe tbody tr th {\n",
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-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>snomed_id</th>\n",
-       "      <th>0</th>\n",
-       "      <th>1</th>\n",
-       "      <th>2</th>\n",
-       "      <th>3</th>\n",
-       "      <th>4</th>\n",
-       "      <th>5</th>\n",
-       "      <th>6</th>\n",
-       "      <th>7</th>\n",
-       "      <th>8</th>\n",
-       "      <th>...</th>\n",
-       "      <th>190</th>\n",
-       "      <th>191</th>\n",
-       "      <th>192</th>\n",
-       "      <th>193</th>\n",
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-       "      <th>195</th>\n",
-       "      <th>196</th>\n",
-       "      <th>197</th>\n",
-       "      <th>198</th>\n",
-       "      <th>199</th>\n",
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-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
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-       "      <td>0.027456</td>\n",
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-       "      <td>-0.178822</td>\n",
-       "      <td>-0.038667</td>\n",
-       "      <td>0.299777</td>\n",
-       "      <td>0.082540</td>\n",
-       "      <td>-0.201284</td>\n",
-       "      <td>0.215164</td>\n",
-       "      <td>0.093241</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.211030</td>\n",
-       "      <td>0.488475</td>\n",
-       "      <td>0.082177</td>\n",
-       "      <td>-0.047102</td>\n",
-       "      <td>-0.086411</td>\n",
-       "      <td>-0.012141</td>\n",
-       "      <td>-0.258416</td>\n",
-       "      <td>-0.113295</td>\n",
-       "      <td>0.040249</td>\n",
-       "      <td>-0.116884</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>360224006</td>\n",
-       "      <td>-0.090187</td>\n",
-       "      <td>0.086986</td>\n",
-       "      <td>-0.184378</td>\n",
-       "      <td>-0.028722</td>\n",
-       "      <td>-0.010235</td>\n",
-       "      <td>0.242439</td>\n",
-       "      <td>-0.094009</td>\n",
-       "      <td>0.203015</td>\n",
-       "      <td>-0.097894</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.112750</td>\n",
-       "      <td>0.333906</td>\n",
-       "      <td>-0.018193</td>\n",
-       "      <td>0.020459</td>\n",
-       "      <td>-0.170226</td>\n",
-       "      <td>0.277866</td>\n",
-       "      <td>-0.007079</td>\n",
-       "      <td>-0.080419</td>\n",
-       "      <td>0.162456</td>\n",
-       "      <td>-0.101768</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>102272007</td>\n",
-       "      <td>0.553420</td>\n",
-       "      <td>0.460947</td>\n",
-       "      <td>0.143925</td>\n",
-       "      <td>-0.053785</td>\n",
-       "      <td>-0.849588</td>\n",
-       "      <td>0.467587</td>\n",
-       "      <td>-0.654922</td>\n",
-       "      <td>-0.010799</td>\n",
-       "      <td>0.510141</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.187371</td>\n",
-       "      <td>0.248607</td>\n",
-       "      <td>0.079687</td>\n",
-       "      <td>-0.354121</td>\n",
-       "      <td>0.412602</td>\n",
-       "      <td>0.582461</td>\n",
-       "      <td>-0.780365</td>\n",
-       "      <td>0.045450</td>\n",
-       "      <td>-0.196551</td>\n",
-       "      <td>0.045745</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>39937001</td>\n",
-       "      <td>0.123158</td>\n",
-       "      <td>0.019882</td>\n",
-       "      <td>0.050896</td>\n",
-       "      <td>0.037364</td>\n",
-       "      <td>0.200435</td>\n",
-       "      <td>0.312911</td>\n",
-       "      <td>-0.338977</td>\n",
-       "      <td>-0.092584</td>\n",
-       "      <td>-0.167741</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.057770</td>\n",
-       "      <td>-0.012834</td>\n",
-       "      <td>-0.139794</td>\n",
-       "      <td>0.180572</td>\n",
-       "      <td>0.190781</td>\n",
-       "      <td>0.104039</td>\n",
-       "      <td>-0.358235</td>\n",
-       "      <td>-0.137954</td>\n",
-       "      <td>-0.236551</td>\n",
-       "      <td>-0.206458</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>23583003</td>\n",
-       "      <td>0.423193</td>\n",
-       "      <td>0.384338</td>\n",
-       "      <td>-0.041503</td>\n",
-       "      <td>0.116848</td>\n",
-       "      <td>-0.055029</td>\n",
-       "      <td>0.149064</td>\n",
-       "      <td>-0.092810</td>\n",
-       "      <td>-0.050148</td>\n",
-       "      <td>0.113122</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.162375</td>\n",
-       "      <td>-0.076505</td>\n",
-       "      <td>-0.274352</td>\n",
-       "      <td>-0.099204</td>\n",
-       "      <td>-0.281887</td>\n",
-       "      <td>-0.266345</td>\n",
-       "      <td>-0.020257</td>\n",
-       "      <td>-0.003843</td>\n",
-       "      <td>-0.008804</td>\n",
-       "      <td>-0.286832</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 201 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   snomed_id         0         1         2         3         4         5  \\\n",
-       "0  129265001  0.027456  0.171026 -0.178822 -0.038667  0.299777  0.082540   \n",
-       "1  360224006 -0.090187  0.086986 -0.184378 -0.028722 -0.010235  0.242439   \n",
-       "2  102272007  0.553420  0.460947  0.143925 -0.053785 -0.849588  0.467587   \n",
-       "3   39937001  0.123158  0.019882  0.050896  0.037364  0.200435  0.312911   \n",
-       "4   23583003  0.423193  0.384338 -0.041503  0.116848 -0.055029  0.149064   \n",
-       "\n",
-       "          6         7         8  ...       190       191       192       193  \\\n",
-       "0 -0.201284  0.215164  0.093241  ...  0.211030  0.488475  0.082177 -0.047102   \n",
-       "1 -0.094009  0.203015 -0.097894  ...  0.112750  0.333906 -0.018193  0.020459   \n",
-       "2 -0.654922 -0.010799  0.510141  ...  0.187371  0.248607  0.079687 -0.354121   \n",
-       "3 -0.338977 -0.092584 -0.167741  ...  0.057770 -0.012834 -0.139794  0.180572   \n",
-       "4 -0.092810 -0.050148  0.113122  ...  0.162375 -0.076505 -0.274352 -0.099204   \n",
-       "\n",
-       "        194       195       196       197       198       199  \n",
-       "0 -0.086411 -0.012141 -0.258416 -0.113295  0.040249 -0.116884  \n",
-       "1 -0.170226  0.277866 -0.007079 -0.080419  0.162456 -0.101768  \n",
-       "2  0.412602  0.582461 -0.780365  0.045450 -0.196551  0.045745  \n",
-       "3  0.190781  0.104039 -0.358235 -0.137954 -0.236551 -0.206458  \n",
-       "4 -0.281887 -0.266345 -0.020257 -0.003843 -0.008804 -0.286832  \n",
-       "\n",
-       "[5 rows x 201 columns]"
-      ]
-     },
-     "execution_count": 64,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "snomed_embeddings.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "outputs": [],
-   "source": [
-    "diagnoses_snomed = diagnoses.columns[13:].to_list()\n",
-    "diagnoses_snomed_dict = {}\n",
-    "for d in diagnoses_snomed: diagnoses_snomed_dict[d] = phenotype_list_snomed[d]\n",
-    "    \n",
-    "snomed_codes_used = list(phenotype_list_snomed.values())\n",
-    "snomed_codes_emb = snomed_embeddings.snomed_id.to_list()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "outputs": [],
-   "source": [
-    "snomed_id_array = snomed_embeddings[[\"snomed_id\"]].values\n",
-    "node2vec_array = snomed_embeddings.iloc[:, 1:].values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "metadata": {
-    "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true,
-     "source_hidden": true
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "456f56d7481648e48b74e9e76b675f01",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(FloatProgress(value=0.0, max=373286.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "snomed_arrays = {}\n",
-    "for sid, row in zip(tqdm(snomed_id_array), node2vec_array):\n",
-    "    snomed_arrays[sid[0]] = row"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "source": [
-    "#### Get Patient -> Snomed dict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 68,
-   "metadata": {
-    "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true,
-     "source_hidden": true
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "05ac46541972426da8a5b5f7fe461c6e",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(FloatProgress(value=0.0, max=502504.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "diagnoses_array = diagnoses[diagnoses_snomed].values\n",
-    "eid_array = diagnoses[[\"eid\"]].values\n",
-    "\n",
-    "from numba import jit\n",
-    "import numpy as np\n",
-    "\n",
-    "patient_diagnoses = {}\n",
-    "for eid, row in zip(tqdm(eid_array), diagnoses_array):\n",
-    "    patient_diagnoses[eid[0]] = list(np.argwhere(row==True).flatten())\n",
-    "\n",
-    "#diagnoses.query(\"eid==1000092\")[diagnoses_snomed]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 69,
-   "metadata": {
-    "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true,
-     "source_hidden": true
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f4c90d91d08e48119abcf8426cddd37e",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(FloatProgress(value=0.0, max=502504.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "patient_diagnoses_sid = {}\n",
-    "snomed_codes_emb_set = set(snomed_codes_emb)\n",
-    "for eid, p_d_col in tqdm(patient_diagnoses.items()):\n",
-    "    diagnoses_list = [diagnoses_snomed[i] for i in p_d_col]\n",
-    "    sid_list = [phenotype_list_snomed[i] for i in diagnoses_list]\n",
-    "    sid_list = [sid for sid in sid_list if sid in snomed_codes_emb_set]\n",
-    "    patient_diagnoses_sid[eid] = sid_list"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "source": [
-    "#### Get Patient -> node2vec average"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 70,
-   "metadata": {
-    "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true,
-     "source_hidden": true
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "47e934dc64334c82893d23b458108fbf",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(FloatProgress(value=0.0, max=502504.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/steinfej/miniconda3/envs/python/lib/python3.7/site-packages/numpy/core/fromnumeric.py:3335: RuntimeWarning: Mean of empty slice.\n",
-      "  out=out, **kwargs)\n",
-      "/home/steinfej/miniconda3/envs/python/lib/python3.7/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars\n",
-      "  ret = ret.dtype.type(ret / rcount)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "patient_node2vec_dict = {}\n",
-    "for eid, sids in tqdm(patient_diagnoses_sid.items()):\n",
-    "    array_list =  [snomed_arrays[sid] for sid in sids]\n",
-    "    patient_node2vec_dict[eid] = np.mean(array_list, axis=0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "metadata": {
-    "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true,
-     "source_hidden": true
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e1395c2d1bfc41f39697a8925b20d597",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(FloatProgress(value=0.0, max=502504.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "array([ 0.37033895,  0.13321076, -0.10030267,  0.05625264, -0.08437331,\n",
-       "        0.11504787, -0.24833219, -0.11936529,  0.25239648, -0.32739932,\n",
-       "        0.06330914, -0.06493541, -0.28790415, -0.2885537 ,  0.02546298,\n",
-       "       -0.06123153, -0.17690672, -0.0867546 , -0.28337599, -0.08474496,\n",
-       "        0.31454532, -0.24158154,  0.15930864,  0.06124846,  0.07694602,\n",
-       "       -0.13752803,  0.13671128, -0.27988751, -0.03987635,  0.03632156,\n",
-       "       -0.13793361, -0.15857369, -0.16441636,  0.2412931 ,  0.20070578,\n",
-       "       -0.11412784,  0.02563965, -0.03560184,  0.17169744, -0.0153383 ,\n",
-       "        0.00117099,  0.1025362 , -0.06505568,  0.05646065, -0.02705149,\n",
-       "        0.04416442, -0.15798991, -0.10650637,  0.02082507, -0.21182802,\n",
-       "        0.13972325, -0.18089307, -0.12731068,  0.02907221, -0.19797107,\n",
-       "        0.19550177,  0.14941799,  0.21561857, -0.18085379,  0.10768238,\n",
-       "        0.12968045,  0.2082016 ,  0.03561408, -0.01122218, -0.27099816,\n",
-       "       -0.06029919, -0.18787618,  0.10084175, -0.07939234, -0.22951897,\n",
-       "       -0.36536359, -0.01050854, -0.21419807, -0.23562986,  0.02380316,\n",
-       "        0.1213157 , -0.1601396 ,  0.07218057, -0.04362593, -0.22303355,\n",
-       "       -0.23844839, -0.14799905,  0.22346404,  0.14218655,  0.39873181,\n",
-       "       -0.32965129,  0.19102432, -0.08894028, -0.20726567,  0.25343222,\n",
-       "        0.29939455,  0.20513029,  0.17430424, -0.05073184,  0.05827969,\n",
-       "        0.31626954,  0.0791522 , -0.21433214,  0.11027244,  0.0805151 ,\n",
-       "        0.08557279,  0.25260801, -0.00096969, -0.06177831,  0.0544524 ,\n",
-       "       -0.17340027,  0.09731447,  0.09982385, -0.074546  , -0.10350239,\n",
-       "       -0.25798929,  0.01222471,  0.1605315 ,  0.00191767,  0.11642527,\n",
-       "       -0.15387604, -0.01520803,  0.03220765, -0.13534233,  0.09154689,\n",
-       "        0.12562997, -0.01667786, -0.09286805,  0.14670483, -0.13097813,\n",
-       "       -0.38270284, -0.0129984 , -0.06352304,  0.12521014, -0.17354363,\n",
-       "       -0.16399127,  0.11488736,  0.39664734,  0.08557075, -0.24862126,\n",
-       "       -0.05882866,  0.23174289,  0.01765143, -0.10257617,  0.01208248,\n",
-       "       -0.06876405, -0.04180976,  0.07489962, -0.10724381,  0.29349823,\n",
-       "        0.17342743, -0.32044841, -0.01118798,  0.28508293,  0.16492201,\n",
-       "        0.03994952, -0.17309702,  0.0365077 ,  0.08619365, -0.21749571,\n",
-       "        0.01038752, -0.21414902, -0.18092813,  0.28433131, -0.12601774,\n",
-       "       -0.02147302,  0.51513453,  0.21506432, -0.12596341, -0.0413009 ,\n",
-       "        0.12595327,  0.08973215, -0.06285449, -0.03649041,  0.04217289,\n",
-       "       -0.02532634, -0.21734533, -0.08211848,  0.14014083,  0.13635479,\n",
-       "       -0.13163414, -0.08576438, -0.04016334,  0.23952563, -0.14895943,\n",
-       "        0.08564821, -0.04287149,  0.22135877, -0.06337237, -0.07189574,\n",
-       "       -0.1004494 ,  0.01225299, -0.13810014, -0.06814497, -0.16715941,\n",
-       "        0.07303671,  0.15891015, -0.04757821, -0.05777645, -0.0772216 ,\n",
-       "        0.16787738, -0.2112512 , -0.10142653, -0.19002809, -0.06400223])"
-      ]
-     },
-     "execution_count": 71,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# create imputation vector\n",
-    "arrays = [patient_node2vec_dict[key] for key in list(patient_node2vec_dict)]\n",
-    "arrays_ok = [array for array in tqdm(arrays) if ~np.isnan(array).any()]\n",
-    "imp_vector = np.mean(arrays_ok, axis=0)\n",
-    "imp_vector"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
-   "metadata": {
-    "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true,
-     "source_hidden": true
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e2dd803c2e2a4cb68e71111979d235c2",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(FloatProgress(value=0.0, max=502504.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "for eid, array in tqdm(patient_node2vec_dict.items()):\n",
-    "    if np.isnan(array).any(): \n",
-    "        patient_node2vec_dict[eid] = imp_vector"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 73,
-   "metadata": {
-    "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true,
-     "source_hidden": true
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a82c47a8f87a4df1bc81b78496edee0a",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(FloatProgress(value=0.0, max=502504.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "array_eids = [key for key in tqdm(list(patient_node2vec_dict))]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 74,
-   "metadata": {
-    "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true,
-     "source_hidden": true
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "510554b8bed8456cbfd694b764286acb",
-       "version_major": 2,
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-      "text/plain": [
-       "HBox(children=(FloatProgress(value=0.0, max=502504.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "ind_ok = [np.array([1]) if ~np.isnan(array).any() else np.array([0])  for array in tqdm(arrays)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 75,
-   "metadata": {
-    "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true,
-     "source_hidden": true
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a85aec2f3e7644dc99c40781f999eb3c",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(FloatProgress(value=0.0, max=502504.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "arrays_emb = [patient_node2vec_dict[key] for key in tqdm(list(patient_node2vec_dict))]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 76,
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "outputs": [],
-   "source": [
-    "arrays_eids = np.reshape(np.stack(array_eids, axis=0),(-1,1)) \n",
-    "arrays_ind = np.stack(ind_ok, axis=0)\n",
-    "arrays_c = np.stack(arrays_emb, axis=0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 77,
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "outputs": [],
-   "source": [
-    "arrays_complete = np.concatenate([arrays_eids, arrays_ind, arrays_c], axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 78,
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "outputs": [],
-   "source": [
-    "diagnoses_emb = pd.DataFrame(data=arrays_complete, columns=[\"eid\"]+[\"node2vec_available\"]+[f\"node2vec_{e}\" for e in list(range(0, 200))])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 79,
-   "metadata": {
-    "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true,
-     "source_hidden": true
-    }
-   },
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-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>node2vec_available</th>\n",
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-       "      <td>-0.211251</td>\n",
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-       "      <td>0.133211</td>\n",
-       "      <td>-0.100303</td>\n",
-       "      <td>0.056253</td>\n",
-       "      <td>-0.084373</td>\n",
-       "      <td>0.115048</td>\n",
-       "      <td>-0.248332</td>\n",
-       "      <td>-0.119365</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.073037</td>\n",
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-       "      <td>-0.047578</td>\n",
-       "      <td>-0.057776</td>\n",
-       "      <td>-0.077222</td>\n",
-       "      <td>0.167877</td>\n",
-       "      <td>-0.211251</td>\n",
-       "      <td>-0.101427</td>\n",
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-       "      <td>-0.057776</td>\n",
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-       "      <td>-0.211251</td>\n",
-       "      <td>-0.101427</td>\n",
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-       "      <td>-0.119365</td>\n",
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-       "      <td>-0.057776</td>\n",
-       "      <td>-0.077222</td>\n",
-       "      <td>0.167877</td>\n",
-       "      <td>-0.211251</td>\n",
-       "      <td>-0.101427</td>\n",
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-       "      <td>0.0</td>\n",
-       "      <td>0.370339</td>\n",
-       "      <td>0.133211</td>\n",
-       "      <td>-0.100303</td>\n",
-       "      <td>0.056253</td>\n",
-       "      <td>-0.084373</td>\n",
-       "      <td>0.115048</td>\n",
-       "      <td>-0.248332</td>\n",
-       "      <td>-0.119365</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.073037</td>\n",
-       "      <td>0.15891</td>\n",
-       "      <td>-0.047578</td>\n",
-       "      <td>-0.057776</td>\n",
-       "      <td>-0.077222</td>\n",
-       "      <td>0.167877</td>\n",
-       "      <td>-0.211251</td>\n",
-       "      <td>-0.101427</td>\n",
-       "      <td>-0.190028</td>\n",
-       "      <td>-0.064002</td>\n",
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-       "      <th>502500</th>\n",
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-       "      <td>...</td>\n",
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-       "      <td>-0.119365</td>\n",
-       "      <td>...</td>\n",
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-       "      <th>502502</th>\n",
-       "      <td>6025182.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.370339</td>\n",
-       "      <td>0.133211</td>\n",
-       "      <td>-0.100303</td>\n",
-       "      <td>0.056253</td>\n",
-       "      <td>-0.084373</td>\n",
-       "      <td>0.115048</td>\n",
-       "      <td>-0.248332</td>\n",
-       "      <td>-0.119365</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.073037</td>\n",
-       "      <td>0.15891</td>\n",
-       "      <td>-0.047578</td>\n",
-       "      <td>-0.057776</td>\n",
-       "      <td>-0.077222</td>\n",
-       "      <td>0.167877</td>\n",
-       "      <td>-0.211251</td>\n",
-       "      <td>-0.101427</td>\n",
-       "      <td>-0.190028</td>\n",
-       "      <td>-0.064002</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>502503</th>\n",
-       "      <td>6025198.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.370339</td>\n",
-       "      <td>0.133211</td>\n",
-       "      <td>-0.100303</td>\n",
-       "      <td>0.056253</td>\n",
-       "      <td>-0.084373</td>\n",
-       "      <td>0.115048</td>\n",
-       "      <td>-0.248332</td>\n",
-       "      <td>-0.119365</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.073037</td>\n",
-       "      <td>0.15891</td>\n",
-       "      <td>-0.047578</td>\n",
-       "      <td>-0.057776</td>\n",
-       "      <td>-0.077222</td>\n",
-       "      <td>0.167877</td>\n",
-       "      <td>-0.211251</td>\n",
-       "      <td>-0.101427</td>\n",
-       "      <td>-0.190028</td>\n",
-       "      <td>-0.064002</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>502504 rows × 202 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "              eid  node2vec_available  node2vec_0  node2vec_1  node2vec_2  \\\n",
-       "0       1000018.0                 0.0    0.370339    0.133211   -0.100303   \n",
-       "1       1000020.0                 0.0    0.370339    0.133211   -0.100303   \n",
-       "2       1000037.0                 0.0    0.370339    0.133211   -0.100303   \n",
-       "3       1000043.0                 0.0    0.370339    0.133211   -0.100303   \n",
-       "4       1000051.0                 0.0    0.370339    0.133211   -0.100303   \n",
-       "...           ...                 ...         ...         ...         ...   \n",
-       "502499  6025150.0                 0.0    0.370339    0.133211   -0.100303   \n",
-       "502500  6025165.0                 0.0    0.370339    0.133211   -0.100303   \n",
-       "502501  6025173.0                 0.0    0.370339    0.133211   -0.100303   \n",
-       "502502  6025182.0                 0.0    0.370339    0.133211   -0.100303   \n",
-       "502503  6025198.0                 0.0    0.370339    0.133211   -0.100303   \n",
-       "\n",
-       "        node2vec_3  node2vec_4  node2vec_5  node2vec_6  node2vec_7  ...  \\\n",
-       "0         0.056253   -0.084373    0.115048   -0.248332   -0.119365  ...   \n",
-       "1         0.056253   -0.084373    0.115048   -0.248332   -0.119365  ...   \n",
-       "2         0.056253   -0.084373    0.115048   -0.248332   -0.119365  ...   \n",
-       "3         0.056253   -0.084373    0.115048   -0.248332   -0.119365  ...   \n",
-       "4         0.056253   -0.084373    0.115048   -0.248332   -0.119365  ...   \n",
-       "...            ...         ...         ...         ...         ...  ...   \n",
-       "502499    0.056253   -0.084373    0.115048   -0.248332   -0.119365  ...   \n",
-       "502500    0.056253   -0.084373    0.115048   -0.248332   -0.119365  ...   \n",
-       "502501    0.056253   -0.084373    0.115048   -0.248332   -0.119365  ...   \n",
-       "502502    0.056253   -0.084373    0.115048   -0.248332   -0.119365  ...   \n",
-       "502503    0.056253   -0.084373    0.115048   -0.248332   -0.119365  ...   \n",
-       "\n",
-       "        node2vec_190  node2vec_191  node2vec_192  node2vec_193  node2vec_194  \\\n",
-       "0           0.073037       0.15891     -0.047578     -0.057776     -0.077222   \n",
-       "1           0.073037       0.15891     -0.047578     -0.057776     -0.077222   \n",
-       "2           0.073037       0.15891     -0.047578     -0.057776     -0.077222   \n",
-       "3           0.073037       0.15891     -0.047578     -0.057776     -0.077222   \n",
-       "4           0.073037       0.15891     -0.047578     -0.057776     -0.077222   \n",
-       "...              ...           ...           ...           ...           ...   \n",
-       "502499      0.073037       0.15891     -0.047578     -0.057776     -0.077222   \n",
-       "502500      0.073037       0.15891     -0.047578     -0.057776     -0.077222   \n",
-       "502501      0.073037       0.15891     -0.047578     -0.057776     -0.077222   \n",
-       "502502      0.073037       0.15891     -0.047578     -0.057776     -0.077222   \n",
-       "502503      0.073037       0.15891     -0.047578     -0.057776     -0.077222   \n",
-       "\n",
-       "        node2vec_195  node2vec_196  node2vec_197  node2vec_198  node2vec_199  \n",
-       "0           0.167877     -0.211251     -0.101427     -0.190028     -0.064002  \n",
-       "1           0.167877     -0.211251     -0.101427     -0.190028     -0.064002  \n",
-       "2           0.167877     -0.211251     -0.101427     -0.190028     -0.064002  \n",
-       "3           0.167877     -0.211251     -0.101427     -0.190028     -0.064002  \n",
-       "4           0.167877     -0.211251     -0.101427     -0.190028     -0.064002  \n",
-       "...              ...           ...           ...           ...           ...  \n",
-       "502499      0.167877     -0.211251     -0.101427     -0.190028     -0.064002  \n",
-       "502500      0.167877     -0.211251     -0.101427     -0.190028     -0.064002  \n",
-       "502501      0.167877     -0.211251     -0.101427     -0.190028     -0.064002  \n",
-       "502502      0.167877     -0.211251     -0.101427     -0.190028     -0.064002  \n",
-       "502503      0.167877     -0.211251     -0.101427     -0.190028     -0.064002  \n",
-       "\n",
-       "[502504 rows x 202 columns]"
-      ]
-     },
-     "execution_count": 79,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "diagnoses_emb"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "outputs": [],
-   "source": [
-    "diagnoses_emb.to_feather(os.path.join(path, dataset_path, 'temp_diagnoses_emb.feather'))\n",
-    "diagnoses_emb = pd.read_feather(os.path.join(path, dataset_path, 'temp_diagnoses_emb.feather'))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Merge Everything"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 69,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_dfs_dict = {\"basics\": pd.read_feather(os.path.join(path, dataset_path, 'temp_basics.feather')), \n",
-    "                 \"questionnaire\": pd.read_feather(os.path.join(path, dataset_path, 'temp_questionnaire.feather')), \n",
-    "                 \"measurements\": pd.read_feather(os.path.join(path, dataset_path, 'temp_measurements.feather')), \n",
-    "                 \"labs\": pd.read_feather(os.path.join(path, dataset_path, 'temp_labs.feather')), \n",
-    "                 \"family_history\": pd.read_feather(os.path.join(path, dataset_path, 'temp_family_history.feather')), \n",
-    "                 \"diagnoses\": pd.read_feather(os.path.join(path, dataset_path, 'temp_diagnoses.feather')),\n",
-    "                # \"diagnoses_emb\": pd.read_feather(os.path.join(path, dataset_path, 'temp_diagnoses_emb.feather')), \n",
-    "                 \"medications\": pd.read_feather(os.path.join(path, dataset_path, 'temp_medications.feather'))}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 70,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_cols_clean(df):\n",
-    "    df.columns = df.columns.str.replace(r'_0_0$', '').str.replace(r'_f[0-9]+$', '').str.replace(\"_automated_reading\", '')\n",
-    "    return df.columns\n",
-    "\n",
-    "def clean_df(df):\n",
-    "    df.columns = get_cols_clean(df)\n",
-    "    return df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "from functools import reduce\n",
-    "\n",
-    "data_baseline = reduce(lambda x, y: pd.merge(x, y, on = 'eid'), list(data_dfs_dict.values()))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_baseline = clean_df(data_baseline)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 73,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for col in [col for col in list(data_baseline.columns) if (\"_event\" in col) & (\"_time\" not in col)]:\n",
-    "    data_baseline[col] = data_baseline[col].astype(int)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 74,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "covariates = [col for col in list(data_baseline.columns) if not \"_event\" in col]\n",
-    "targets = [col for col in list(data_baseline.columns) if \"_event\" in col]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Exporting"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 75,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_cols = {}\n",
-    "for topic, df in data_dfs_dict.items(): \n",
-    "    data_cols[\"eid\"] = [\"admin\"]\n",
-    "    data_cols[topic]=list(get_cols_clean(df))[1:]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 76,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_cols_single = {}\n",
-    "for topic, columns in data_cols.items():\n",
-    "    for col in columns:\n",
-    "        data_cols_single[col] = topic"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 86,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>covariate</th>\n",
-       "      <th>dtype</th>\n",
-       "      <th>isTarget</th>\n",
-       "      <th>based_on</th>\n",
-       "      <th>aggr_fn</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>eid</td>\n",
-       "      <td>int</td>\n",
-       "      <td>False</td>\n",
-       "      <td>eid</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2</td>\n",
-       "      <td>age_at_recruitment</td>\n",
-       "      <td>float</td>\n",
-       "      <td>False</td>\n",
-       "      <td>basics</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>sex</td>\n",
-       "      <td>category</td>\n",
-       "      <td>False</td>\n",
-       "      <td>basics</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4</td>\n",
-       "      <td>ethnic_background</td>\n",
-       "      <td>category</td>\n",
-       "      <td>False</td>\n",
-       "      <td>basics</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5</td>\n",
-       "      <td>townsend_deprivation_index_at_recruitment</td>\n",
-       "      <td>float</td>\n",
-       "      <td>False</td>\n",
-       "      <td>basics</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2857</th>\n",
-       "      <td>2858</td>\n",
-       "      <td>surgical_dressings</td>\n",
-       "      <td>bool</td>\n",
-       "      <td>False</td>\n",
-       "      <td>medications</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2858</th>\n",
-       "      <td>2859</td>\n",
-       "      <td>statins</td>\n",
-       "      <td>bool</td>\n",
-       "      <td>False</td>\n",
-       "      <td>medications</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2859</th>\n",
-       "      <td>2860</td>\n",
-       "      <td>ass</td>\n",
-       "      <td>bool</td>\n",
-       "      <td>False</td>\n",
-       "      <td>medications</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2860</th>\n",
-       "      <td>2861</td>\n",
-       "      <td>atypical_antipsychotics</td>\n",
-       "      <td>bool</td>\n",
-       "      <td>False</td>\n",
-       "      <td>medications</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2861</th>\n",
-       "      <td>2862</td>\n",
-       "      <td>glucocorticoids</td>\n",
-       "      <td>bool</td>\n",
-       "      <td>False</td>\n",
-       "      <td>medications</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>2862 rows × 6 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        id                                  covariate     dtype  isTarget  \\\n",
-       "0        1                                        eid       int     False   \n",
-       "1        2                         age_at_recruitment     float     False   \n",
-       "2        3                                        sex  category     False   \n",
-       "3        4                          ethnic_background  category     False   \n",
-       "4        5  townsend_deprivation_index_at_recruitment     float     False   \n",
-       "...    ...                                        ...       ...       ...   \n",
-       "2857  2858                         surgical_dressings      bool     False   \n",
-       "2858  2859                                    statins      bool     False   \n",
-       "2859  2860                                        ass      bool     False   \n",
-       "2860  2861                    atypical_antipsychotics      bool     False   \n",
-       "2861  2862                            glucocorticoids      bool     False   \n",
-       "\n",
-       "         based_on  aggr_fn  \n",
-       "0             eid      NaN  \n",
-       "1          basics      NaN  \n",
-       "2          basics      NaN  \n",
-       "3          basics      NaN  \n",
-       "4          basics      NaN  \n",
-       "...           ...      ...  \n",
-       "2857  medications      NaN  \n",
-       "2858  medications      NaN  \n",
-       "2859  medications      NaN  \n",
-       "2860  medications      NaN  \n",
-       "2861  medications      NaN  \n",
-       "\n",
-       "[2862 rows x 6 columns]"
-      ]
-     },
-     "execution_count": 86,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dtypes = {\"int32\":\"int\", \"int64\":\"int\", \"float64\":\"float\", \"category\":\"category\", \"object\":\"category\", \"bool\":\"bool\"}\n",
-    "desc_dict = {\"id\": [*range(1, len(data_baseline.columns.to_list())+1)] , \n",
-    "             \"covariate\": data_baseline.columns.to_list(), \n",
-    "             \"dtype\":[dtypes[str(col)] for col in data_baseline.dtypes.to_list()], \n",
-    "             \"isTarget\":[True if col in targets else False for col in data_baseline.columns.to_list()],\n",
-    "            \"based_on\":[topic for col, topic in data_cols_single.items()],\n",
-    "            \"aggr_fn\": [np.nan for col in data_baseline.columns.to_list()]}\n",
-    "data_baseline_description = pd.DataFrame.from_dict(desc_dict)\n",
-    "data_baseline_description"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Exclusion Criteria"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 78,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#data_baseline_excl = data_baseline.copy().query(\"myocardial_infarction == False & coronary_heart_disease == False & statins == False\").reset_index(drop=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 79,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#data_baseline"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 87,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "feature_dict = {}\n",
-    "for group in data_baseline_description.based_on.unique(): feature_dict[group] = data_baseline_description.query(\"based_on==@group\").covariate.to_list()\n",
-    "with open(os.path.join(path, dataset_path, 'feature_list.yaml'), 'w') as file: yaml.dump(feature_dict, file, default_flow_style=False, allow_unicode=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 88,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#feature_dict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 89,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "### WRITE FEATURES IN YAML!!!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 90,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_baseline.to_feather(os.path.join(path, dataset_path, 'baseline_covariates.feather'))\n",
-    "#data_baseline_excl.to_feather(os.path.join(path, dataset_path, 'baseline_clinical_excl.feather'))\n",
-    "data_baseline_description.to_feather(os.path.join(path, dataset_path, 'baseline_covariates_description.feather'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#data_baseline.to_csv(os.path.join(path, dataset_path, 'baseline_clinical.csv'), index=False)\n",
-    "#data_baseline_description.to_csv(os.path.join(path, dataset_path, 'baseline_clinical_description.csv'), index=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# !!! REMEMBER IMPUTATION !!!"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python [conda env:miniconda3-pl1.x]",
-   "language": "python",
-   "name": "conda-env-miniconda3-pl1.x-py"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.8"
-  },
-  "toc-autonumbering": true,
-  "toc-showcode": false,
-  "toc-showmarkdowntxt": false
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/neuralcvd/preprocessing/ukbb_tabular/2_preprocessing_clinical_endpoints.ipynb b/neuralcvd/preprocessing/ukbb_tabular/2_preprocessing_clinical_endpoints.ipynb
deleted file mode 100644
index 1d47044..0000000
--- a/neuralcvd/preprocessing/ukbb_tabular/2_preprocessing_clinical_endpoints.ipynb
+++ /dev/null
@@ -1,3022 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Preprocessing"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:31:49.436340Z",
-     "start_time": "2020-11-04T12:31:48.732042Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import os\n",
-    "import yaml\n",
-    "from tqdm.notebook import trange, tqdm\n",
-    "dataset_name = \"210212_cvd_gp\"\n",
-    "path = \"/data/analysis/ag-reils/steinfej/code/umbrella/pre/ukbb\"\n",
-    "data_path = \"/data/analysis/ag-reils/ag-reils-shared/cardioRS/data\"\n",
-    "dataset_path = f\"{data_path}/2_datasets_pre/{dataset_name}\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:31:49.895222Z",
-     "start_time": "2020-11-04T12:31:49.891332Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from pathlib import Path\n",
-    "Path(dataset_path).mkdir(parents=True, exist_ok=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:33:14.171198Z",
-     "start_time": "2020-11-04T12:31:50.204540Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "data = pd.read_feather(f\"{data_path}/1_decoded/ukb_data.feather\")\n",
-    "data_field = pd.read_feather(f\"{data_path}/1_decoded/ukb_data_field.feather\")\n",
-    "data_columns = data.columns.to_list()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Mappings + Vocabulary"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:34:05.867152Z",
-     "start_time": "2020-11-04T12:33:16.878773Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "502505\n",
-      "502504\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Drop obviouse missing data\n",
-    "print(len(data))\n",
-    "data = data.dropna(subset=[\"sex_f31_0_0\"], axis=0)\n",
-    "print(len(data))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Starting information"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:34:05.872216Z",
-     "start_time": "2020-11-04T12:34:05.869505Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "#time0_col=\"birth_date\"\n",
-    "time0_col=\"date_of_attending_assessment_centre_f53_0_0\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Baseline covariates"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:34:05.889725Z",
-     "start_time": "2020-11-04T12:34:05.874587Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "def get_fields(fields, data, data_field):\n",
-    "    f = data_field[data_field[\"field.showcase\"].isin(fields) & data_field[\"field.tab\"].str.contains(\"f\\\\.\\\\d+\\\\.0\\\\.\\\\d\")].copy()\n",
-    "    f[\"field\"] = pd.Categorical(f[\"field.showcase\"], categories=fields, ordered=True)\n",
-    "    f = f.sort_values(\"field\").reset_index().drop(\"field\", axis=1)\n",
-    "    return f\n",
-    "\n",
-    "def get_fields_all(fields, data, data_field):\n",
-    "    f = data_field[data_field[\"field.showcase\"].isin(fields)].copy()\n",
-    "    f[\"field\"] = pd.Categorical(f[\"field.showcase\"], categories=fields, ordered=True)\n",
-    "    f = f.sort_values(\"field\").reset_index().drop(\"field\", axis=1)\n",
-    "    return f\n",
-    "\n",
-    "def get_data_fields(fields, data, data_field):\n",
-    "    f = get_fields(fields, data, data_field)\n",
-    "    return data[[\"eid\"]+f[\"col.name\"].to_list()].copy()\n",
-    "\n",
-    "def get_data_fields_all(fields, data, data_field):\n",
-    "    f = get_fields_all(fields, data, data_field)\n",
-    "    return data[[\"eid\"]+f[\"col.name\"].to_list()].copy()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Diagnoses and events"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:37:14.667281Z",
-     "start_time": "2020-11-04T12:36:14.427693Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3146: DtypeWarning: Columns (5,6,9) have mixed types.Specify dtype option on import or set low_memory=False.\n",
-      "  interactivity=interactivity, compiler=compiler, result=result)\n",
-      "/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3146: DtypeWarning: Columns (11) have mixed types.Specify dtype option on import or set low_memory=False.\n",
-      "  interactivity=interactivity, compiler=compiler, result=result)\n"
-     ]
-    }
-   ],
-   "source": [
-    "vocab_dir = f\"{data_path}/athena_vocabulary_covid\"\n",
-    "vocab = {\n",
-    "    \"concept\": pd.read_csv(f\"{vocab_dir}/CONCEPT.csv\", sep='\\t'),\n",
-    "    \"domain\": pd.read_csv(f\"{vocab_dir}/DOMAIN.csv\", sep='\\t'),\n",
-    "    \"class\": pd.read_csv(f\"{vocab_dir}/CONCEPT_CLASS.csv\", sep='\\t'),\n",
-    "    \"relationship\": pd.read_csv(f\"{vocab_dir}/RELATIONSHIP.csv\", sep='\\t'),\n",
-    "    \"drug_strength\": pd.read_csv(f\"{vocab_dir}/DRUG_STRENGTH.csv\", sep='\\t'),\n",
-    "    \"vocabulary\": pd.read_csv(f\"{vocab_dir}/VOCABULARY.csv\", sep='\\t'),\n",
-    "    \"concept_synonym\": pd.read_csv(f\"{vocab_dir}/CONCEPT_SYNONYM.csv\", sep='\\t'),\n",
-    "    \"concept_ancestor\": pd.read_csv(f\"{vocab_dir}/CONCEPT_ANCESTOR.csv\", sep='\\t'),\n",
-    "    \"concept_relationship\": pd.read_csv(f\"{vocab_dir}/CONCEPT_RELATIONSHIP.csv\", sep='\\t')                       \n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Definitions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:37:14.772869Z",
-     "start_time": "2020-11-04T12:37:14.669541Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "coding1836 = pd.read_csv(f\"{path}/mapping/codings/coding1836.tsv\", sep=\"\\t\").rename(columns={\"coding\":\"code\"})\n",
-    "phecodes = pd.read_csv(f\"{path}/mapping/phecodes/phecode_icd10.csv\")\n",
-    "def phenotype_children(phecodes, phenotype_list):\n",
-    "    l={}\n",
-    "    phecodes = phecodes.dropna(subset=[\"Phenotype\"], axis=0)\n",
-    "    for ph, ph_names in phenotype_list.items():\n",
-    "        regex = \"|\".join(ph_names)\n",
-    "        l[ph] = list(phecodes[phecodes.Phenotype.str.contains(regex, case=False)].ICD10.str.replace(\"\\\\.\", \"\").str.slice(0, 3).unique())\n",
-    "    return l"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "diagnoses_codes = pd.read_feather(os.path.join(path, dataset_path, 'temp_diagnoses_codes.feather')).drop(\"level\", axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "death_codes = pd.read_feather(f\"{data_path}/1_decoded/codes_death_records_210115.feather\").query(\"level==1\").drop(\"level\", axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "endpoint_codes = pd.concat([diagnoses_codes, death_codes[diagnoses_codes.columns]])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Endpoints"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:39:55.628580Z",
-     "start_time": "2020-11-04T12:33:33.036Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "### define in snomed and get icd codes from there"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 1. Hospital admissions"
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:39:55.629459Z",
-     "start_time": "2020-11-04T12:33:34.764Z"
-    },
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "source": [
-    "endpoint_list = {\n",
-    "    \"myocardial_infarction\": [\"Myocardial infarction\"],\n",
-    "    \"stroke\": [\"Cerebrovascular disease\"],\n",
-    "    \"cancer_breast\" : [\"Breast Cancer\"],\n",
-    "    \"diabetes\" : [\"Diabetes\"],\n",
-    "    \"atrial_fibrillation\": [\"Atrial fibrillation\", \"Atrial flutter\", \"paroxysmal tachycardia\"],\n",
-    "    \"copd\": [\"COPD\"],\n",
-    "    \"dementia\":[\"dementia\"]\n",
-    "}\n",
-    "\n",
-    "endpoint_list = phenotype_children(phecodes, endpoint_list)\n",
-    "\n",
-    "\n",
-    "\n",
-    "endpoint_list[\"cancer_breast\"] = [\"C50\"]\n",
-    "endpoint_list[\"copd\"] = [\"J44\"]\n",
-    "endpoint_list[\"diabetes\"] = [\"E10\", \"E11\", \"E12\", \"E13\", \"E14\"]\n",
-    "endpoint_list[\"atrial_fibrillation\"] = [\"I47\", \"I48\"]\n",
-    "\n",
-    "\n",
-    "with open(os.path.join(path, dataset_path, 'endpoint_list.yaml'), 'w') as file: yaml.dump(endpoint_list, file, default_flow_style=False)\n",
-    "endpoint_list"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'myocardial_infarction': ['I21', 'I22', 'I23', 'I24', 'I25'],\n",
-       " 'stroke': ['G45', 'I63', 'I64'],\n",
-       " 'diabetes': ['E10', 'E11', 'E12', 'E13', 'E14'],\n",
-       " 'diabetes1': ['E10'],\n",
-       " 'diabetes2': ['E11', 'E12', 'E13', 'E14'],\n",
-       " 'atrial_fibrillation': ['I47', 'I48'],\n",
-       " 'migraine': ['G43', 'G44'],\n",
-       " 'rheumatoid_arthritis': ['J99', 'M05', 'M06', 'M08', 'M12', 'M13'],\n",
-       " 'systemic_lupus_erythematosus': ['M32'],\n",
-       " 'severe_mental_illness': ['F20', 'F25', 'F30', 'F31', 'F32', 'F33', 'F44'],\n",
-       " 'erectile_dysfunction': ['F52', 'N48'],\n",
-       " 'chronic_kidney_disease': ['I12', 'N18', 'N19'],\n",
-       " 'liver_disease': ['K70', 'K71', 'K72', 'K73', 'K74', 'K75', 'K76', 'K77'],\n",
-       " 'dementia': ['F00', 'F01', 'F02', 'F03'],\n",
-       " 'copd': ['J44']}"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "endpoint_list = {\n",
-    "    \"myocardial_infarction\": ['I21', 'I22', 'I23', 'I24', 'I25'],\n",
-    "    \"stroke\": ['G45', \"I63\", \"I64\"],\n",
-    "    \"diabetes\" : ['E10', 'E11', 'E12', 'E13', 'E14'],\n",
-    "    \"diabetes1\" : ['E10'],\n",
-    "    \"diabetes2\" : ['E11', 'E12', 'E13', 'E14'],\n",
-    "    \"atrial_fibrillation\": ['I47', 'I48'],\n",
-    "    'migraine': ['G43', 'G44'],\n",
-    "    'rheumatoid_arthritis': ['J99', 'M05', 'M06', 'M08', 'M12', 'M13'],\n",
-    "    \"systemic_lupus_erythematosus\": ['M32'],\n",
-    "    'severe_mental_illness': ['F20', 'F25', 'F30', 'F31', 'F32', 'F33', 'F44'],\n",
-    "    \"erectile_dysfunction\" : ['F52', 'N48'],  \n",
-    "    \"chronic_kidney_disease\": [\"I12\", \"N18\", \"N19\"],\n",
-    "    \"liver_disease\":[\"K70\", \"K71\", \"K72\", \"K73\", \"K74\", \"K75\", \"K76\", \"K77\"],\n",
-    "    \"dementia\":['F00', 'F01', 'F02', 'F03'],\n",
-    "    \"copd\": ['J44']}\n",
-    "\n",
-    "with open(os.path.join(path, dataset_path, 'endpoint_list.yaml'), 'w') as file: yaml.dump(endpoint_list, file, default_flow_style=False)\n",
-    "endpoint_list"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from dateutil.relativedelta import relativedelta\n",
-    "import datetime\n",
-    "\n",
-    "def extract_endpoints_tte(data, diagnoses_codes, endpoint_list, time0_col, level=None):\n",
-    "    if level is not None: diagnoses_codes = diagnoses_codes.query(\"level==@level\")\n",
-    "    diagnoses_codes_time0 = diagnoses_codes.merge(data[[\"eid\", time0_col]], how=\"left\", on=\"eid\")\n",
-    "    \n",
-    "    #cens_time_right = max(diagnoses_codes.sort_values('date').groupby('origin').tail(1).date.to_list())\n",
-    "    cens_time_right = datetime.date(2020, 9, 30)\n",
-    "    print(f\"t_0: {time0_col}\")\n",
-    "    print(f\"t_cens: {cens_time_right}\")\n",
-    "    \n",
-    "    df_interval = diagnoses_codes_time0[(diagnoses_codes_time0.date > diagnoses_codes_time0[time0_col]) & \n",
-    "                                        (diagnoses_codes_time0.date < cens_time_right)]\n",
-    "    \n",
-    "    temp = data[[\"eid\", time0_col]].copy()\n",
-    "    for ph, ph_codes in tqdm(endpoint_list.items()):\n",
-    "        regex = \"|\".join(ph_codes)\n",
-    "        ph_df = df_interval[df_interval.meaning.str.contains(regex, case=False)] \\\n",
-    "            .sort_values('date').groupby('eid').head(1).assign(phenotype=1, date=lambda x: x.date)\n",
-    "        temp_ph = temp.merge(ph_df, how=\"left\", on=\"eid\").fillna(0)\n",
-    "        temp[ph+\"_event\"], temp[ph+\"_event_date\"] = temp_ph.phenotype, temp_ph.date\n",
-    "        \n",
-    "        fill_date = {ph+\"_event_date\" : lambda x: [cens_time_right if event==0 else event_date for event, event_date in zip(x[ph+\"_event\"], x[ph+\"_event_date\"])]}\n",
-    "        calc_tte = {ph+\"_event_time\" : lambda x: [(event_date-time0).days/365.25  for time0, event_date in zip(x[time0_col], x[ph+\"_event_date\"])]}\n",
-    "        \n",
-    "        temp = temp.assign(**fill_date).assign(**calc_tte).drop([ph+\"_event_date\"], axis=1)\n",
-    "        \n",
-    "    temp = temp.drop([time0_col], axis=1)     \n",
-    "    \n",
-    "    return temp.drop_duplicates()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "t_0: date_of_attending_assessment_centre_f53_0_0\n",
-      "t_cens: 2020-09-30\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "18bf96a8c26844d3ae00147005bacfc3",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(HTML(value=''), FloatProgress(value=0.0, max=15.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "502504\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>myocardial_infarction_event</th>\n",
-       "      <th>myocardial_infarction_event_time</th>\n",
-       "      <th>stroke_event</th>\n",
-       "      <th>stroke_event_time</th>\n",
-       "      <th>diabetes_event</th>\n",
-       "      <th>diabetes_event_time</th>\n",
-       "      <th>diabetes1_event</th>\n",
-       "      <th>diabetes1_event_time</th>\n",
-       "      <th>diabetes2_event</th>\n",
-       "      <th>...</th>\n",
-       "      <th>erectile_dysfunction_event</th>\n",
-       "      <th>erectile_dysfunction_event_time</th>\n",
-       "      <th>chronic_kidney_disease_event</th>\n",
-       "      <th>chronic_kidney_disease_event_time</th>\n",
-       "      <th>liver_disease_event</th>\n",
-       "      <th>liver_disease_event_time</th>\n",
-       "      <th>dementia_event</th>\n",
-       "      <th>dementia_event_time</th>\n",
-       "      <th>copd_event</th>\n",
-       "      <th>copd_event_time</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>10.882957</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>10.882957</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>10.882957</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>10.882957</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>10.882957</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>10.882957</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>10.882957</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.305955</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>10.882957</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1000020</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>12.613279</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>12.613279</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>12.613279</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>12.613279</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>12.613279</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>12.613279</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>12.613279</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>12.613279</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>12.613279</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1000037</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>11.885010</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>11.885010</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>11.885010</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>11.885010</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>11.885010</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>11.885010</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>11.885010</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>11.885010</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>11.885010</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000043</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>5.122519</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>11.326489</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>11.326489</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>11.326489</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>11.326489</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>11.326489</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>11.326489</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>11.326489</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.292950</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1000051</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>14.308008</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>14.308008</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>4.722793</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>14.308008</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>14.308008</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>4.722793</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>14.308008</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>14.308008</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>4.840520</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 31 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       eid  myocardial_infarction_event  myocardial_infarction_event_time  \\\n",
-       "0  1000018                          0.0                         10.882957   \n",
-       "1  1000020                          0.0                         12.613279   \n",
-       "2  1000037                          0.0                         11.885010   \n",
-       "3  1000043                          1.0                          5.122519   \n",
-       "4  1000051                          0.0                         14.308008   \n",
-       "\n",
-       "   stroke_event  stroke_event_time  diabetes_event  diabetes_event_time  \\\n",
-       "0           0.0          10.882957             0.0            10.882957   \n",
-       "1           0.0          12.613279             0.0            12.613279   \n",
-       "2           0.0          11.885010             0.0            11.885010   \n",
-       "3           0.0          11.326489             0.0            11.326489   \n",
-       "4           0.0          14.308008             1.0             4.722793   \n",
-       "\n",
-       "   diabetes1_event  diabetes1_event_time  diabetes2_event  ...  \\\n",
-       "0              0.0             10.882957              0.0  ...   \n",
-       "1              0.0             12.613279              0.0  ...   \n",
-       "2              0.0             11.885010              0.0  ...   \n",
-       "3              0.0             11.326489              0.0  ...   \n",
-       "4              0.0             14.308008              1.0  ...   \n",
-       "\n",
-       "   erectile_dysfunction_event  erectile_dysfunction_event_time  \\\n",
-       "0                         0.0                        10.882957   \n",
-       "1                         0.0                        12.613279   \n",
-       "2                         0.0                        11.885010   \n",
-       "3                         0.0                        11.326489   \n",
-       "4                         0.0                        14.308008   \n",
-       "\n",
-       "   chronic_kidney_disease_event  chronic_kidney_disease_event_time  \\\n",
-       "0                           0.0                          10.882957   \n",
-       "1                           0.0                          12.613279   \n",
-       "2                           0.0                          11.885010   \n",
-       "3                           0.0                          11.326489   \n",
-       "4                           1.0                           4.722793   \n",
-       "\n",
-       "   liver_disease_event  liver_disease_event_time  dementia_event  \\\n",
-       "0                  0.0                 10.882957             1.0   \n",
-       "1                  0.0                 12.613279             0.0   \n",
-       "2                  0.0                 11.885010             0.0   \n",
-       "3                  0.0                 11.326489             0.0   \n",
-       "4                  0.0                 14.308008             0.0   \n",
-       "\n",
-       "   dementia_event_time  copd_event  copd_event_time  \n",
-       "0             1.305955         0.0        10.882957  \n",
-       "1            12.613279         0.0        12.613279  \n",
-       "2            11.885010         0.0        11.885010  \n",
-       "3            11.326489         1.0         0.292950  \n",
-       "4            14.308008         1.0         4.840520  \n",
-       "\n",
-       "[5 rows x 31 columns]"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "basics = pd.read_feather(os.path.join(path, dataset_path, 'temp_basics.feather'))\n",
-    "endpoints_diagnoses = extract_endpoints_tte(basics, endpoint_codes, endpoint_list, time0_col)\n",
-    "print(len(endpoints_diagnoses))\n",
-    "endpoints_diagnoses.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 2. Death registry"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "death_list = {\n",
-    "    \"death_allcause\":[],\n",
-    "    \"death_cvd\":['I{:02}'.format(ID+1) for ID in range(0, 98)],\n",
-    "}\n",
-    "\n",
-    "with open(os.path.join(path, dataset_path, 'death_list.yaml'), 'w') as file: yaml.dump(death_list, file, default_flow_style=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "t_0: date_of_attending_assessment_centre_f53_0_0\n",
-      "t_cens: 2020-09-30\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1f3a468059214c8d83558a52ab31f2c3",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(HTML(value=''), FloatProgress(value=0.0, max=2.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "endpoints_death = extract_endpoints_tte(basics, death_codes, death_list, time0_col)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## SCORES"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "scores_list = {\n",
-    "    \"SCORE\":['I{:02}'.format(ID) for ID in [10, 11, 12, 13, 14, 15, 20, 21, 22, 23, 24, 25, 44, 45, 46, 47, 48, 49, 50, 51, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73]],\n",
-    "    \"ASCVD\":['I{:02}'.format(ID) for ID in [20, 21, 22, 23, 24, 25, 63]],\n",
-    "    \"QRISK3\":[\"G45\", \"I20\", \"I21\", \"I22\", \"I23\", \"I24\", \"I25\", \"I63\", \"I64\"],\n",
-    "    \"MACE\":[\"G45\", \"I21\", \"I22\", \"I23\", \"I24\", \"I25\", \"I63\", \"I64\"],    \n",
-    "}\n",
-    "with open(os.path.join(path, dataset_path, 'scores_list.yaml'), 'w') as file: yaml.dump(scores_list, file, default_flow_style=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "t_0: date_of_attending_assessment_centre_f53_0_0\n",
-      "t_cens: 2020-09-30\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b97320f9aebb49f281e716a58eb5ff37",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(HTML(value=''), FloatProgress(value=0.0, max=4.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "t_0: date_of_attending_assessment_centre_f53_0_0\n",
-      "t_cens: 2020-09-30\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "265cac92d16046638a27ae9a295848f6",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(HTML(value=''), FloatProgress(value=0.0, max=4.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "death_scores =  extract_endpoints_tte(basics, death_codes, scores_list, time0_col=time0_col)\n",
-    "endpoint_scores = extract_endpoints_tte(basics, endpoint_codes, scores_list, time0_col=time0_col)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "endpoints_scores_all = death_scores[[\"eid\", \"SCORE_event\", \"SCORE_event_time\"]].merge(endpoint_scores[[\"eid\", \"ASCVD_event\", \"ASCVD_event_time\", \"QRISK3_event\", \"QRISK3_event_time\", \"MACE_event\", \"MACE_event_time\"]], on=\"eid\")\n",
-    "endpoints_scores_all.to_feather(os.path.join(path, dataset_path, 'temp_endpoints_scores_all.feather'))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### ESC SCORE (Conroy 2003)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "5540\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>SCORE_event</th>\n",
-       "      <th>SCORE_event_time</th>\n",
-       "      <th>ASCVD_event</th>\n",
-       "      <th>ASCVD_event_time</th>\n",
-       "      <th>QRISK3_event</th>\n",
-       "      <th>QRISK3_event_time</th>\n",
-       "      <th>MACE_event</th>\n",
-       "      <th>MACE_event_time</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>45</th>\n",
-       "      <td>1000463</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>6.165640</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>6.069815</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>6.069815</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>6.069815</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>83</th>\n",
-       "      <td>1000841</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>11.003422</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>12.076660</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>10.978782</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>10.978782</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>102</th>\n",
-       "      <td>1001031</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>6.535250</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>5.401780</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>5.401780</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>5.401780</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>122</th>\n",
-       "      <td>1001237</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2.132786</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2.132786</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2.132786</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2.132786</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>176</th>\n",
-       "      <td>1001777</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>7.238877</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>7.222450</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>7.222450</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>11.364819</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         eid  SCORE_event  SCORE_event_time  ASCVD_event  ASCVD_event_time  \\\n",
-       "45   1000463          1.0          6.165640          1.0          6.069815   \n",
-       "83   1000841          1.0         11.003422          0.0         12.076660   \n",
-       "102  1001031          1.0          6.535250          1.0          5.401780   \n",
-       "122  1001237          1.0          2.132786          1.0          2.132786   \n",
-       "176  1001777          1.0          7.238877          1.0          7.222450   \n",
-       "\n",
-       "     QRISK3_event  QRISK3_event_time  MACE_event  MACE_event_time  \n",
-       "45            1.0           6.069815         1.0         6.069815  \n",
-       "83            1.0          10.978782         1.0        10.978782  \n",
-       "102           1.0           5.401780         1.0         5.401780  \n",
-       "122           1.0           2.132786         1.0         2.132786  \n",
-       "176           1.0           7.222450         0.0        11.364819  "
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "score = \"SCORE\"\n",
-    "print(len(endpoints_scores_all.query(score+\"_event==1\")))\n",
-    "endpoints_scores_all.query(score+\"_event==1\").head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### ASCVD (Goff 2014)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "62937\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>SCORE_event</th>\n",
-       "      <th>SCORE_event_time</th>\n",
-       "      <th>ASCVD_event</th>\n",
-       "      <th>ASCVD_event_time</th>\n",
-       "      <th>QRISK3_event</th>\n",
-       "      <th>QRISK3_event_time</th>\n",
-       "      <th>MACE_event</th>\n",
-       "      <th>MACE_event_time</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1000037</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>11.885010</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>7.969884</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>7.969884</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>11.885010</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000043</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>11.326489</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>5.122519</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>5.122519</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>5.122519</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>1000079</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>12.536619</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.054073</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.054073</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>12.536619</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>1000233</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>12.153320</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>3.671458</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>3.671458</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>3.671458</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30</th>\n",
-       "      <td>1000319</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>10.795346</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>10.203970</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>10.203970</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>10.203970</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        eid  SCORE_event  SCORE_event_time  ASCVD_event  ASCVD_event_time  \\\n",
-       "2   1000037          0.0         11.885010          1.0          7.969884   \n",
-       "3   1000043          0.0         11.326489          1.0          5.122519   \n",
-       "6   1000079          0.0         12.536619          1.0          1.054073   \n",
-       "22  1000233          0.0         12.153320          1.0          3.671458   \n",
-       "30  1000319          0.0         10.795346          1.0         10.203970   \n",
-       "\n",
-       "    QRISK3_event  QRISK3_event_time  MACE_event  MACE_event_time  \n",
-       "2            1.0           7.969884         0.0        11.885010  \n",
-       "3            1.0           5.122519         1.0         5.122519  \n",
-       "6            1.0           1.054073         0.0        12.536619  \n",
-       "22           1.0           3.671458         1.0         3.671458  \n",
-       "30           1.0          10.203970         1.0        10.203970  "
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "score = \"ASCVD\"\n",
-    "print(len(endpoints_scores_all.query(score+\"_event==1\")))\n",
-    "endpoints_scores_all.query(score+\"_event==1\").head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### UK QRISK3 (2017)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "68413\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>SCORE_event</th>\n",
-       "      <th>SCORE_event_time</th>\n",
-       "      <th>ASCVD_event</th>\n",
-       "      <th>ASCVD_event_time</th>\n",
-       "      <th>QRISK3_event</th>\n",
-       "      <th>QRISK3_event_time</th>\n",
-       "      <th>MACE_event</th>\n",
-       "      <th>MACE_event_time</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1000037</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>11.885010</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>7.969884</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>7.969884</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>11.885010</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000043</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>11.326489</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>5.122519</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>5.122519</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>5.122519</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>1000079</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>12.536619</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.054073</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.054073</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>12.536619</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>1000233</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>12.153320</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>3.671458</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>3.671458</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>3.671458</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30</th>\n",
-       "      <td>1000319</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>10.795346</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>10.203970</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>10.203970</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>10.203970</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        eid  SCORE_event  SCORE_event_time  ASCVD_event  ASCVD_event_time  \\\n",
-       "2   1000037          0.0         11.885010          1.0          7.969884   \n",
-       "3   1000043          0.0         11.326489          1.0          5.122519   \n",
-       "6   1000079          0.0         12.536619          1.0          1.054073   \n",
-       "22  1000233          0.0         12.153320          1.0          3.671458   \n",
-       "30  1000319          0.0         10.795346          1.0         10.203970   \n",
-       "\n",
-       "    QRISK3_event  QRISK3_event_time  MACE_event  MACE_event_time  \n",
-       "2            1.0           7.969884         0.0        11.885010  \n",
-       "3            1.0           5.122519         1.0         5.122519  \n",
-       "6            1.0           1.054073         0.0        12.536619  \n",
-       "22           1.0           3.671458         1.0         3.671458  \n",
-       "30           1.0          10.203970         1.0        10.203970  "
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "score = \"QRISK3\"\n",
-    "print(len(endpoints_scores_all.query(score+\"_event==1\")))\n",
-    "endpoints_scores_all.query(score+\"_event==1\").head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### MACE (2020)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "57869\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>SCORE_event</th>\n",
-       "      <th>SCORE_event_time</th>\n",
-       "      <th>ASCVD_event</th>\n",
-       "      <th>ASCVD_event_time</th>\n",
-       "      <th>QRISK3_event</th>\n",
-       "      <th>QRISK3_event_time</th>\n",
-       "      <th>MACE_event</th>\n",
-       "      <th>MACE_event_time</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000043</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>11.326489</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>5.122519</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>5.122519</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>5.122519</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>1000233</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>12.153320</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>3.671458</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>3.671458</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>3.671458</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30</th>\n",
-       "      <td>1000319</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>10.795346</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>10.203970</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>10.203970</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>10.203970</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>45</th>\n",
-       "      <td>1000463</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>6.165640</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>6.069815</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>6.069815</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>6.069815</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>72</th>\n",
-       "      <td>1000731</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>11.321013</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>11.249829</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>11.249829</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>11.249829</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        eid  SCORE_event  SCORE_event_time  ASCVD_event  ASCVD_event_time  \\\n",
-       "3   1000043          0.0         11.326489          1.0          5.122519   \n",
-       "22  1000233          0.0         12.153320          1.0          3.671458   \n",
-       "30  1000319          0.0         10.795346          1.0         10.203970   \n",
-       "45  1000463          1.0          6.165640          1.0          6.069815   \n",
-       "72  1000731          0.0         11.321013          1.0         11.249829   \n",
-       "\n",
-       "    QRISK3_event  QRISK3_event_time  MACE_event  MACE_event_time  \n",
-       "3            1.0           5.122519         1.0         5.122519  \n",
-       "22           1.0           3.671458         1.0         3.671458  \n",
-       "30           1.0          10.203970         1.0        10.203970  \n",
-       "45           1.0           6.069815         1.0         6.069815  \n",
-       "72           1.0          11.249829         1.0        11.249829  "
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "score = \"MACE\"\n",
-    "print(len(endpoints_scores_all.query(score+\"_event==1\")))\n",
-    "endpoints_scores_all.query(score+\"_event==1\").head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Merge Everything"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_dfs_dict = {\"endpoints_diagnoses\":endpoints_diagnoses, \n",
-    "                 \"endpoints_death\":endpoints_death, \n",
-    "                 \"endpoints_scores_all\":endpoints_scores_all}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_cols_clean(df):\n",
-    "    df.columns = df.columns.str.replace(r'_0_0$', '').str.replace(r'_f[0-9]+$', '').str.replace(\"_automated_reading\", '')\n",
-    "    return df.columns\n",
-    "\n",
-    "def clean_df(df):\n",
-    "    df.columns = get_cols_clean(df)\n",
-    "    return df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 124,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['myocardial_infarction', 'stroke', 'diabetes', 'diabetes1', 'diabetes2', 'atrial_fibrillation', 'migraine', 'rheumatoid_arthritis', 'systemic_lupus_erythematosus', 'severe_mental_illness', 'erectile_dysfunction', 'chronic_kidney_disease', 'liver_disease', 'dementia', 'copd', 'death_allcause', 'death_cvd', 'SCORE', 'ASCVD', 'QRISK3', 'MACE']\n"
-     ]
-    }
-   ],
-   "source": [
-    "import pandas as pd\n",
-    "from functools import reduce\n",
-    "\n",
-    "data_baseline = reduce(lambda x, y: pd.merge(x, y, on = 'eid'), list(data_dfs_dict.values()))\n",
-    "endpoint_columns = [c[:-11] for c in data_baseline.columns.tolist() if \"_event_time\" in c]\n",
-    "print(endpoint_columns)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Competing Events"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 125,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2b97c16289c6405c87f89134f7e1f106",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(HTML(value=''), FloatProgress(value=0.0, max=21.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "# endpoint < death -> 1\n",
-    "# death < endpoint -> 2\n",
-    "# time min(endpoint_time, death_time) -> time\n",
-    "def event_calc(endpoint, endpoint_time, death, death_time):\n",
-    "    endpoint = int(endpoint)\n",
-    "    death = int(death)\n",
-    "    if (endpoint==0) and (death==0): \n",
-    "        return 0.0\n",
-    "    if (endpoint==1) and (death==0): \n",
-    "        return 1.0\n",
-    "    elif (endpoint==0) and (death==1): \n",
-    "        return 2.0\n",
-    "    elif (endpoint==1) and (death==1) and (endpoint_time<=death_time):\n",
-    "        return float(1)\n",
-    "    elif (endpoint==1) and (death==1) and (death_time<endpoint_time):\n",
-    "        return float(2)\n",
-    "    else: return np.nan\n",
-    "\n",
-    "for c in tqdm(endpoint_columns): \n",
-    "    if c!=\"death_allcause\":\n",
-    "        data_baseline[f\"{c}_comp_event\"] = [event_calc(endpoint, endpoint_time, death, death_time) for endpoint, endpoint_time, death, death_time \n",
-    "                                            in zip(data_baseline[f\"{c}_event\"], data_baseline[f\"{c}_event_time\"], data_baseline[\"death_allcause_event\"], data_baseline[\"death_allcause_event_time\"])]\n",
-    "        \n",
-    "        data_baseline[f\"{c}_comp_event_time\"] = [min(endpoint_time, death_time)\n",
-    "                                                 for endpoint, endpoint_time, death, death_time\n",
-    "                                                 in zip(data_baseline[f\"{c}_event\"], data_baseline[f\"{c}_event_time\"],\n",
-    "                                                        data_baseline[\"death_allcause_event\"], data_baseline[\"death_allcause_event_time\"])]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 126,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>ASCVD_event</th>\n",
-       "      <th>ASCVD_comp_event</th>\n",
-       "      <th>MACE_event</th>\n",
-       "      <th>MACE_comp_event</th>\n",
-       "      <th>QRISK3_event</th>\n",
-       "      <th>QRISK3_comp_event</th>\n",
-       "      <th>SCORE_event</th>\n",
-       "      <th>SCORE_comp_event</th>\n",
-       "      <th>atrial_fibrillation_event</th>\n",
-       "      <th>atrial_fibrillation_comp_event</th>\n",
-       "      <th>...</th>\n",
-       "      <th>myocardial_infarction_event</th>\n",
-       "      <th>myocardial_infarction_comp_event</th>\n",
-       "      <th>rheumatoid_arthritis_event</th>\n",
-       "      <th>rheumatoid_arthritis_comp_event</th>\n",
-       "      <th>severe_mental_illness_event</th>\n",
-       "      <th>severe_mental_illness_comp_event</th>\n",
-       "      <th>stroke_event</th>\n",
-       "      <th>stroke_comp_event</th>\n",
-       "      <th>systemic_lupus_erythematosus_event</th>\n",
-       "      <th>systemic_lupus_erythematosus_comp_event</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
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-       "      <th>0</th>\n",
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-       "      <td>...</td>\n",
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-       "  </tbody>\n",
-       "</table>\n",
-       "<p>502504 rows × 41 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        ASCVD_event  ASCVD_comp_event  MACE_event  MACE_comp_event  \\\n",
-       "0               0.0               0.0         0.0              0.0   \n",
-       "1               0.0               0.0         0.0              0.0   \n",
-       "2               1.0               1.0         0.0              0.0   \n",
-       "3               1.0               1.0         1.0              1.0   \n",
-       "4               0.0               0.0         0.0              0.0   \n",
-       "...             ...               ...         ...              ...   \n",
-       "502499          0.0               0.0         0.0              0.0   \n",
-       "502500          0.0               0.0         0.0              0.0   \n",
-       "502501          0.0               0.0         0.0              0.0   \n",
-       "502502          0.0               0.0         0.0              0.0   \n",
-       "502503          0.0               2.0         0.0              2.0   \n",
-       "\n",
-       "        QRISK3_event  QRISK3_comp_event  SCORE_event  SCORE_comp_event  \\\n",
-       "0                0.0                0.0          0.0               0.0   \n",
-       "1                0.0                0.0          0.0               0.0   \n",
-       "2                1.0                1.0          0.0               0.0   \n",
-       "3                1.0                1.0          0.0               0.0   \n",
-       "4                0.0                0.0          0.0               0.0   \n",
-       "...              ...                ...          ...               ...   \n",
-       "502499           0.0                0.0          0.0               0.0   \n",
-       "502500           0.0                0.0          0.0               0.0   \n",
-       "502501           0.0                0.0          0.0               0.0   \n",
-       "502502           0.0                0.0          0.0               0.0   \n",
-       "502503           0.0                2.0          0.0               2.0   \n",
-       "\n",
-       "        atrial_fibrillation_event  atrial_fibrillation_comp_event  ...  \\\n",
-       "0                             0.0                             0.0  ...   \n",
-       "1                             0.0                             0.0  ...   \n",
-       "2                             0.0                             0.0  ...   \n",
-       "3                             0.0                             0.0  ...   \n",
-       "4                             0.0                             0.0  ...   \n",
-       "...                           ...                             ...  ...   \n",
-       "502499                        0.0                             0.0  ...   \n",
-       "502500                        0.0                             0.0  ...   \n",
-       "502501                        0.0                             0.0  ...   \n",
-       "502502                        0.0                             0.0  ...   \n",
-       "502503                        0.0                             2.0  ...   \n",
-       "\n",
-       "        myocardial_infarction_event  myocardial_infarction_comp_event  \\\n",
-       "0                               0.0                               0.0   \n",
-       "1                               0.0                               0.0   \n",
-       "2                               0.0                               0.0   \n",
-       "3                               1.0                               1.0   \n",
-       "4                               0.0                               0.0   \n",
-       "...                             ...                               ...   \n",
-       "502499                          0.0                               0.0   \n",
-       "502500                          0.0                               0.0   \n",
-       "502501                          0.0                               0.0   \n",
-       "502502                          0.0                               0.0   \n",
-       "502503                          0.0                               2.0   \n",
-       "\n",
-       "        rheumatoid_arthritis_event  rheumatoid_arthritis_comp_event  \\\n",
-       "0                              0.0                              0.0   \n",
-       "1                              0.0                              0.0   \n",
-       "2                              0.0                              0.0   \n",
-       "3                              0.0                              0.0   \n",
-       "4                              1.0                              1.0   \n",
-       "...                            ...                              ...   \n",
-       "502499                         0.0                              0.0   \n",
-       "502500                         0.0                              0.0   \n",
-       "502501                         0.0                              0.0   \n",
-       "502502                         1.0                              1.0   \n",
-       "502503                         0.0                              2.0   \n",
-       "\n",
-       "        severe_mental_illness_event  severe_mental_illness_comp_event  \\\n",
-       "0                               1.0                               1.0   \n",
-       "1                               0.0                               0.0   \n",
-       "2                               0.0                               0.0   \n",
-       "3                               0.0                               0.0   \n",
-       "4                               0.0                               0.0   \n",
-       "...                             ...                               ...   \n",
-       "502499                          0.0                               0.0   \n",
-       "502500                          0.0                               0.0   \n",
-       "502501                          0.0                               0.0   \n",
-       "502502                          0.0                               0.0   \n",
-       "502503                          0.0                               2.0   \n",
-       "\n",
-       "        stroke_event  stroke_comp_event  systemic_lupus_erythematosus_event  \\\n",
-       "0                0.0                0.0                                 0.0   \n",
-       "1                0.0                0.0                                 0.0   \n",
-       "2                0.0                0.0                                 0.0   \n",
-       "3                0.0                0.0                                 0.0   \n",
-       "4                0.0                0.0                                 0.0   \n",
-       "...              ...                ...                                 ...   \n",
-       "502499           0.0                0.0                                 0.0   \n",
-       "502500           0.0                0.0                                 0.0   \n",
-       "502501           0.0                0.0                                 0.0   \n",
-       "502502           0.0                0.0                                 0.0   \n",
-       "502503           0.0                2.0                                 0.0   \n",
-       "\n",
-       "        systemic_lupus_erythematosus_comp_event  \n",
-       "0                                           0.0  \n",
-       "1                                           0.0  \n",
-       "2                                           0.0  \n",
-       "3                                           0.0  \n",
-       "4                                           0.0  \n",
-       "...                                         ...  \n",
-       "502499                                      0.0  \n",
-       "502500                                      0.0  \n",
-       "502501                                      0.0  \n",
-       "502502                                      0.0  \n",
-       "502503                                      2.0  \n",
-       "\n",
-       "[502504 rows x 41 columns]"
-      ]
-     },
-     "execution_count": 126,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data_baseline[[f\"{c}_event\" for c in sorted([c[:-11] for c in data_baseline.columns.tolist() if \"_event_time\" in c])]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 127,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "    <tr>\n",
-       "      <th>502430</th>\n",
-       "      <td>6024468</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>11.540041</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>7.252567</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>502440</th>\n",
-       "      <td>6024566</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>10.658453</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>2.329911</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>502445</th>\n",
-       "      <td>6024611</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>10.324435</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.577687</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>502450</th>\n",
-       "      <td>6024667</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>10.316222</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>2.431211</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>502503</th>\n",
-       "      <td>6025198</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>10.677618</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>8.867899</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>21407 rows × 5 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            eid  MACE_event  MACE_event_time  MACE_comp_event  \\\n",
-       "13      1000144         0.0        12.041068              2.0   \n",
-       "21      1000221         0.0        10.948665              2.0   \n",
-       "49      1000500         0.0        14.450376              2.0   \n",
-       "59      1000608         0.0        11.477070              2.0   \n",
-       "67      1000686         0.0        13.054073              2.0   \n",
-       "...         ...         ...              ...              ...   \n",
-       "502430  6024468         0.0        11.540041              2.0   \n",
-       "502440  6024566         0.0        10.658453              2.0   \n",
-       "502445  6024611         0.0        10.324435              2.0   \n",
-       "502450  6024667         0.0        10.316222              2.0   \n",
-       "502503  6025198         0.0        10.677618              2.0   \n",
-       "\n",
-       "        MACE_comp_event_time  \n",
-       "13                  3.523614  \n",
-       "21                  6.891170  \n",
-       "49                 12.279261  \n",
-       "59                  2.507871  \n",
-       "67                 10.318960  \n",
-       "...                      ...  \n",
-       "502430              7.252567  \n",
-       "502440              2.329911  \n",
-       "502445              0.577687  \n",
-       "502450              2.431211  \n",
-       "502503              8.867899  \n",
-       "\n",
-       "[21407 rows x 5 columns]"
-      ]
-     },
-     "execution_count": 127,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data_baseline[data_baseline.death_allcause_event==1][[\"eid\", \"MACE_event\", \"MACE_event_time\", \"MACE_comp_event\", \"MACE_comp_event_time\"]].query(\"MACE_event==0\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 128,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
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-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>myocardial_infarction_event</th>\n",
-       "      <th>myocardial_infarction_event_time</th>\n",
-       "      <th>stroke_event</th>\n",
-       "      <th>stroke_event_time</th>\n",
-       "      <th>diabetes_event</th>\n",
-       "      <th>diabetes_event_time</th>\n",
-       "      <th>diabetes1_event</th>\n",
-       "      <th>diabetes1_event_time</th>\n",
-       "      <th>diabetes2_event</th>\n",
-       "      <th>...</th>\n",
-       "      <th>death_cvd_comp_event</th>\n",
-       "      <th>death_cvd_comp_event_time</th>\n",
-       "      <th>SCORE_comp_event</th>\n",
-       "      <th>SCORE_comp_event_time</th>\n",
-       "      <th>ASCVD_comp_event</th>\n",
-       "      <th>ASCVD_comp_event_time</th>\n",
-       "      <th>QRISK3_comp_event</th>\n",
-       "      <th>QRISK3_comp_event_time</th>\n",
-       "      <th>MACE_comp_event</th>\n",
-       "      <th>MACE_comp_event_time</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>count</th>\n",
-       "      <td>5.025040e+05</td>\n",
-       "      <td>502504.000000</td>\n",
-       "      <td>502504.000000</td>\n",
-       "      <td>502504.000000</td>\n",
-       "      <td>502504.000000</td>\n",
-       "      <td>502504.000000</td>\n",
-       "      <td>502504.000000</td>\n",
-       "      <td>502504.000000</td>\n",
-       "      <td>502504.000000</td>\n",
-       "      <td>502504.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>502504.000000</td>\n",
-       "      <td>502504.000000</td>\n",
-       "      <td>502504.000000</td>\n",
-       "      <td>502504.000000</td>\n",
-       "      <td>502504.000000</td>\n",
-       "      <td>502504.000000</td>\n",
-       "      <td>502504.000000</td>\n",
-       "      <td>502504.000000</td>\n",
-       "      <td>502504.000000</td>\n",
-       "      <td>502504.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mean</th>\n",
-       "      <td>3.512606e+06</td>\n",
-       "      <td>0.092865</td>\n",
-       "      <td>11.095097</td>\n",
-       "      <td>0.030394</td>\n",
-       "      <td>11.504350</td>\n",
-       "      <td>0.088111</td>\n",
-       "      <td>11.048187</td>\n",
-       "      <td>0.019781</td>\n",
-       "      <td>11.525729</td>\n",
-       "      <td>0.086529</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.114708</td>\n",
-       "      <td>11.390389</td>\n",
-       "      <td>0.116879</td>\n",
-       "      <td>11.390389</td>\n",
-       "      <td>0.210277</td>\n",
-       "      <td>10.641807</td>\n",
-       "      <td>0.218651</td>\n",
-       "      <td>10.567688</td>\n",
-       "      <td>0.200367</td>\n",
-       "      <td>10.747562</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>1.450653e+06</td>\n",
-       "      <td>0.290243</td>\n",
-       "      <td>2.274576</td>\n",
-       "      <td>0.171668</td>\n",
-       "      <td>1.450134</td>\n",
-       "      <td>0.283456</td>\n",
-       "      <td>2.430626</td>\n",
-       "      <td>0.139247</td>\n",
-       "      <td>1.480295</td>\n",
-       "      <td>0.281143</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.450624</td>\n",
-       "      <td>1.599884</td>\n",
-       "      <td>0.457244</td>\n",
-       "      <td>1.599884</td>\n",
-       "      <td>0.501094</td>\n",
-       "      <td>2.831019</td>\n",
-       "      <td>0.503343</td>\n",
-       "      <td>2.914048</td>\n",
-       "      <td>0.495408</td>\n",
-       "      <td>2.669474</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>min</th>\n",
-       "      <td>1.000018e+06</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.002738</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.002738</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.002738</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.002738</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.010951</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.010951</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.002738</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.002738</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.002738</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25%</th>\n",
-       "      <td>2.256298e+06</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>10.789870</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>10.924025</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>10.800821</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>10.943190</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>10.866530</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>10.866530</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>10.532512</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>10.505133</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>10.559890</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50%</th>\n",
-       "      <td>3.512620e+06</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>11.556468</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>11.633128</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>11.561944</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>11.652293</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>11.592060</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>11.592060</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>11.427789</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>11.405886</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>11.444216</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75%</th>\n",
-       "      <td>4.768908e+06</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>12.290212</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>12.336756</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>12.295688</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>12.344969</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>12.303901</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>12.303901</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>12.219028</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>12.208077</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>12.221766</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>max</th>\n",
-       "      <td>6.025198e+06</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>14.551677</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>14.551677</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>14.551677</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>14.551677</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>14.551677</td>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>14.551677</td>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>14.551677</td>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>14.551677</td>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>14.551677</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>8 rows × 83 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                eid  myocardial_infarction_event  \\\n",
-       "count  5.025040e+05                502504.000000   \n",
-       "mean   3.512606e+06                     0.092865   \n",
-       "std    1.450653e+06                     0.290243   \n",
-       "min    1.000018e+06                     0.000000   \n",
-       "25%    2.256298e+06                     0.000000   \n",
-       "50%    3.512620e+06                     0.000000   \n",
-       "75%    4.768908e+06                     0.000000   \n",
-       "max    6.025198e+06                     1.000000   \n",
-       "\n",
-       "       myocardial_infarction_event_time   stroke_event  stroke_event_time  \\\n",
-       "count                     502504.000000  502504.000000      502504.000000   \n",
-       "mean                          11.095097       0.030394          11.504350   \n",
-       "std                            2.274576       0.171668           1.450134   \n",
-       "min                            0.002738       0.000000           0.002738   \n",
-       "25%                           10.789870       0.000000          10.924025   \n",
-       "50%                           11.556468       0.000000          11.633128   \n",
-       "75%                           12.290212       0.000000          12.336756   \n",
-       "max                           14.551677       1.000000          14.551677   \n",
-       "\n",
-       "       diabetes_event  diabetes_event_time  diabetes1_event  \\\n",
-       "count   502504.000000        502504.000000    502504.000000   \n",
-       "mean         0.088111            11.048187         0.019781   \n",
-       "std          0.283456             2.430626         0.139247   \n",
-       "min          0.000000             0.002738         0.000000   \n",
-       "25%          0.000000            10.800821         0.000000   \n",
-       "50%          0.000000            11.561944         0.000000   \n",
-       "75%          0.000000            12.295688         0.000000   \n",
-       "max          1.000000            14.551677         1.000000   \n",
-       "\n",
-       "       diabetes1_event_time  diabetes2_event  ...  death_cvd_comp_event  \\\n",
-       "count         502504.000000    502504.000000  ...         502504.000000   \n",
-       "mean              11.525729         0.086529  ...              0.114708   \n",
-       "std                1.480295         0.281143  ...              0.450624   \n",
-       "min                0.002738         0.000000  ...              0.000000   \n",
-       "25%               10.943190         0.000000  ...              0.000000   \n",
-       "50%               11.652293         0.000000  ...              0.000000   \n",
-       "75%               12.344969         0.000000  ...              0.000000   \n",
-       "max               14.551677         1.000000  ...              2.000000   \n",
-       "\n",
-       "       death_cvd_comp_event_time  SCORE_comp_event  SCORE_comp_event_time  \\\n",
-       "count              502504.000000     502504.000000          502504.000000   \n",
-       "mean                   11.390389          0.116879              11.390389   \n",
-       "std                     1.599884          0.457244               1.599884   \n",
-       "min                     0.010951          0.000000               0.010951   \n",
-       "25%                    10.866530          0.000000              10.866530   \n",
-       "50%                    11.592060          0.000000              11.592060   \n",
-       "75%                    12.303901          0.000000              12.303901   \n",
-       "max                    14.551677          2.000000              14.551677   \n",
-       "\n",
-       "       ASCVD_comp_event  ASCVD_comp_event_time  QRISK3_comp_event  \\\n",
-       "count     502504.000000          502504.000000      502504.000000   \n",
-       "mean           0.210277              10.641807           0.218651   \n",
-       "std            0.501094               2.831019           0.503343   \n",
-       "min            0.000000               0.002738           0.000000   \n",
-       "25%            0.000000              10.532512           0.000000   \n",
-       "50%            0.000000              11.427789           0.000000   \n",
-       "75%            0.000000              12.219028           0.000000   \n",
-       "max            2.000000              14.551677           2.000000   \n",
-       "\n",
-       "       QRISK3_comp_event_time  MACE_comp_event  MACE_comp_event_time  \n",
-       "count           502504.000000    502504.000000         502504.000000  \n",
-       "mean                10.567688         0.200367             10.747562  \n",
-       "std                  2.914048         0.495408              2.669474  \n",
-       "min                  0.002738         0.000000              0.002738  \n",
-       "25%                 10.505133         0.000000             10.559890  \n",
-       "50%                 11.405886         0.000000             11.444216  \n",
-       "75%                 12.208077         0.000000             12.221766  \n",
-       "max                 14.551677         2.000000             14.551677  \n",
-       "\n",
-       "[8 rows x 83 columns]"
-      ]
-     },
-     "execution_count": 128,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data_baseline.describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 129,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_baseline = clean_df(data_baseline)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 130,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for col in [col for col in list(data_baseline.columns) if (\"_event\" in col) & (\"_time\" not in col)]:\n",
-    "    data_baseline[col] = data_baseline[col].astype(int)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 131,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "covariates = [col for col in list(data_baseline.columns) if not \"_event\" in col]\n",
-    "targets = [col for col in list(data_baseline.columns) if \"_event\" in col]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Exporting"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 132,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>myocardial_infarction_event</th>\n",
-       "      <th>myocardial_infarction_event_time</th>\n",
-       "      <th>stroke_event</th>\n",
-       "      <th>stroke_event_time</th>\n",
-       "      <th>diabetes_event</th>\n",
-       "      <th>diabetes_event_time</th>\n",
-       "      <th>diabetes1_event</th>\n",
-       "      <th>diabetes1_event_time</th>\n",
-       "      <th>diabetes2_event</th>\n",
-       "      <th>...</th>\n",
-       "      <th>death_cvd_comp_event</th>\n",
-       "      <th>death_cvd_comp_event_time</th>\n",
-       "      <th>SCORE_comp_event</th>\n",
-       "      <th>SCORE_comp_event_time</th>\n",
-       "      <th>ASCVD_comp_event</th>\n",
-       "      <th>ASCVD_comp_event_time</th>\n",
-       "      <th>QRISK3_comp_event</th>\n",
-       "      <th>QRISK3_comp_event_time</th>\n",
-       "      <th>MACE_comp_event</th>\n",
-       "      <th>MACE_comp_event_time</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>0</td>\n",
-       "      <td>10.882957</td>\n",
-       "      <td>0</td>\n",
-       "      <td>10.882957</td>\n",
-       "      <td>0</td>\n",
-       "      <td>10.882957</td>\n",
-       "      <td>0</td>\n",
-       "      <td>10.882957</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>10.882957</td>\n",
-       "      <td>0</td>\n",
-       "      <td>10.882957</td>\n",
-       "      <td>0</td>\n",
-       "      <td>10.882957</td>\n",
-       "      <td>0</td>\n",
-       "      <td>10.882957</td>\n",
-       "      <td>0</td>\n",
-       "      <td>10.882957</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1000020</td>\n",
-       "      <td>0</td>\n",
-       "      <td>12.613279</td>\n",
-       "      <td>0</td>\n",
-       "      <td>12.613279</td>\n",
-       "      <td>0</td>\n",
-       "      <td>12.613279</td>\n",
-       "      <td>0</td>\n",
-       "      <td>12.613279</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>12.613279</td>\n",
-       "      <td>0</td>\n",
-       "      <td>12.613279</td>\n",
-       "      <td>0</td>\n",
-       "      <td>12.613279</td>\n",
-       "      <td>0</td>\n",
-       "      <td>12.613279</td>\n",
-       "      <td>0</td>\n",
-       "      <td>12.613279</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1000037</td>\n",
-       "      <td>0</td>\n",
-       "      <td>11.885010</td>\n",
-       "      <td>0</td>\n",
-       "      <td>11.885010</td>\n",
-       "      <td>0</td>\n",
-       "      <td>11.885010</td>\n",
-       "      <td>0</td>\n",
-       "      <td>11.885010</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>11.885010</td>\n",
-       "      <td>0</td>\n",
-       "      <td>11.885010</td>\n",
-       "      <td>1</td>\n",
-       "      <td>7.969884</td>\n",
-       "      <td>1</td>\n",
-       "      <td>7.969884</td>\n",
-       "      <td>0</td>\n",
-       "      <td>11.885010</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000043</td>\n",
-       "      <td>1</td>\n",
-       "      <td>5.122519</td>\n",
-       "      <td>0</td>\n",
-       "      <td>11.326489</td>\n",
-       "      <td>0</td>\n",
-       "      <td>11.326489</td>\n",
-       "      <td>0</td>\n",
-       "      <td>11.326489</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>11.326489</td>\n",
-       "      <td>0</td>\n",
-       "      <td>11.326489</td>\n",
-       "      <td>1</td>\n",
-       "      <td>5.122519</td>\n",
-       "      <td>1</td>\n",
-       "      <td>5.122519</td>\n",
-       "      <td>1</td>\n",
-       "      <td>5.122519</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1000051</td>\n",
-       "      <td>0</td>\n",
-       "      <td>14.308008</td>\n",
-       "      <td>0</td>\n",
-       "      <td>14.308008</td>\n",
-       "      <td>1</td>\n",
-       "      <td>4.722793</td>\n",
-       "      <td>0</td>\n",
-       "      <td>14.308008</td>\n",
-       "      <td>1</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>14.308008</td>\n",
-       "      <td>0</td>\n",
-       "      <td>14.308008</td>\n",
-       "      <td>0</td>\n",
-       "      <td>14.308008</td>\n",
-       "      <td>0</td>\n",
-       "      <td>14.308008</td>\n",
-       "      <td>0</td>\n",
-       "      <td>14.308008</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 83 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       eid  myocardial_infarction_event  myocardial_infarction_event_time  \\\n",
-       "0  1000018                            0                         10.882957   \n",
-       "1  1000020                            0                         12.613279   \n",
-       "2  1000037                            0                         11.885010   \n",
-       "3  1000043                            1                          5.122519   \n",
-       "4  1000051                            0                         14.308008   \n",
-       "\n",
-       "   stroke_event  stroke_event_time  diabetes_event  diabetes_event_time  \\\n",
-       "0             0          10.882957               0            10.882957   \n",
-       "1             0          12.613279               0            12.613279   \n",
-       "2             0          11.885010               0            11.885010   \n",
-       "3             0          11.326489               0            11.326489   \n",
-       "4             0          14.308008               1             4.722793   \n",
-       "\n",
-       "   diabetes1_event  diabetes1_event_time  diabetes2_event  ...  \\\n",
-       "0                0             10.882957                0  ...   \n",
-       "1                0             12.613279                0  ...   \n",
-       "2                0             11.885010                0  ...   \n",
-       "3                0             11.326489                0  ...   \n",
-       "4                0             14.308008                1  ...   \n",
-       "\n",
-       "   death_cvd_comp_event  death_cvd_comp_event_time  SCORE_comp_event  \\\n",
-       "0                     0                  10.882957                 0   \n",
-       "1                     0                  12.613279                 0   \n",
-       "2                     0                  11.885010                 0   \n",
-       "3                     0                  11.326489                 0   \n",
-       "4                     0                  14.308008                 0   \n",
-       "\n",
-       "   SCORE_comp_event_time  ASCVD_comp_event  ASCVD_comp_event_time  \\\n",
-       "0              10.882957                 0              10.882957   \n",
-       "1              12.613279                 0              12.613279   \n",
-       "2              11.885010                 1               7.969884   \n",
-       "3              11.326489                 1               5.122519   \n",
-       "4              14.308008                 0              14.308008   \n",
-       "\n",
-       "   QRISK3_comp_event  QRISK3_comp_event_time  MACE_comp_event  \\\n",
-       "0                  0               10.882957                0   \n",
-       "1                  0               12.613279                0   \n",
-       "2                  1                7.969884                0   \n",
-       "3                  1                5.122519                1   \n",
-       "4                  0               14.308008                0   \n",
-       "\n",
-       "   MACE_comp_event_time  \n",
-       "0             10.882957  \n",
-       "1             12.613279  \n",
-       "2             11.885010  \n",
-       "3              5.122519  \n",
-       "4             14.308008  \n",
-       "\n",
-       "[5 rows x 83 columns]"
-      ]
-     },
-     "execution_count": 132,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data_baseline.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 133,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_cols = {}\n",
-    "for topic, df in data_dfs_dict.items(): \n",
-    "    data_cols[\"eid\"] = [\"admin\"]\n",
-    "    data_cols[topic]=list(get_cols_clean(df))[1:]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 134,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_cols_single = {}\n",
-    "for topic, columns in data_cols.items():\n",
-    "    for col in columns:\n",
-    "        data_cols_single[col] = topic"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 144,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for c in [c for c in data_baseline.columns.tolist() if \"comp\" in c]:\n",
-    "    data_cols_single.update({c:\"endpoints_competing\"})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 151,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>covariate</th>\n",
-       "      <th>dtype</th>\n",
-       "      <th>isTarget</th>\n",
-       "      <th>based_on</th>\n",
-       "      <th>aggr_fn</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>eid</td>\n",
-       "      <td>int</td>\n",
-       "      <td>False</td>\n",
-       "      <td>eid</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2</td>\n",
-       "      <td>myocardial_infarction_event</td>\n",
-       "      <td>int</td>\n",
-       "      <td>True</td>\n",
-       "      <td>endpoints_diagnoses</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>myocardial_infarction_event_time</td>\n",
-       "      <td>float</td>\n",
-       "      <td>True</td>\n",
-       "      <td>endpoints_diagnoses</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4</td>\n",
-       "      <td>stroke_event</td>\n",
-       "      <td>int</td>\n",
-       "      <td>True</td>\n",
-       "      <td>endpoints_diagnoses</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5</td>\n",
-       "      <td>stroke_event_time</td>\n",
-       "      <td>float</td>\n",
-       "      <td>True</td>\n",
-       "      <td>endpoints_diagnoses</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>78</th>\n",
-       "      <td>79</td>\n",
-       "      <td>ASCVD_comp_event_time</td>\n",
-       "      <td>float</td>\n",
-       "      <td>True</td>\n",
-       "      <td>endpoints_competing</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>79</th>\n",
-       "      <td>80</td>\n",
-       "      <td>QRISK3_comp_event</td>\n",
-       "      <td>int</td>\n",
-       "      <td>True</td>\n",
-       "      <td>endpoints_competing</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>80</th>\n",
-       "      <td>81</td>\n",
-       "      <td>QRISK3_comp_event_time</td>\n",
-       "      <td>float</td>\n",
-       "      <td>True</td>\n",
-       "      <td>endpoints_competing</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>81</th>\n",
-       "      <td>82</td>\n",
-       "      <td>MACE_comp_event</td>\n",
-       "      <td>int</td>\n",
-       "      <td>True</td>\n",
-       "      <td>endpoints_competing</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>82</th>\n",
-       "      <td>83</td>\n",
-       "      <td>MACE_comp_event_time</td>\n",
-       "      <td>float</td>\n",
-       "      <td>True</td>\n",
-       "      <td>endpoints_competing</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>83 rows × 6 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    id                         covariate  dtype  isTarget  \\\n",
-       "0    1                               eid    int     False   \n",
-       "1    2       myocardial_infarction_event    int      True   \n",
-       "2    3  myocardial_infarction_event_time  float      True   \n",
-       "3    4                      stroke_event    int      True   \n",
-       "4    5                 stroke_event_time  float      True   \n",
-       "..  ..                               ...    ...       ...   \n",
-       "78  79             ASCVD_comp_event_time  float      True   \n",
-       "79  80                 QRISK3_comp_event    int      True   \n",
-       "80  81            QRISK3_comp_event_time  float      True   \n",
-       "81  82                   MACE_comp_event    int      True   \n",
-       "82  83              MACE_comp_event_time  float      True   \n",
-       "\n",
-       "               based_on  aggr_fn  \n",
-       "0                   eid      NaN  \n",
-       "1   endpoints_diagnoses      NaN  \n",
-       "2   endpoints_diagnoses      NaN  \n",
-       "3   endpoints_diagnoses      NaN  \n",
-       "4   endpoints_diagnoses      NaN  \n",
-       "..                  ...      ...  \n",
-       "78  endpoints_competing      NaN  \n",
-       "79  endpoints_competing      NaN  \n",
-       "80  endpoints_competing      NaN  \n",
-       "81  endpoints_competing      NaN  \n",
-       "82  endpoints_competing      NaN  \n",
-       "\n",
-       "[83 rows x 6 columns]"
-      ]
-     },
-     "execution_count": 151,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dtypes = {\"int32\":\"int\", \"int64\":\"int\", \"float64\":\"float\", \"category\":\"category\", \"object\":\"category\", \"bool\":\"bool\"}\n",
-    "desc_dict = {\"id\": [*range(1, len(data_baseline.columns.to_list())+1)] , \n",
-    "             \"covariate\": data_baseline.columns.to_list(), \n",
-    "             \"dtype\":[dtypes[str(col)] for col in data_baseline.dtypes.to_list()], \n",
-    "             \"isTarget\":[True if col in targets else False for col in data_baseline.columns.to_list()],\n",
-    "            \"based_on\":[topic for col, topic in data_cols_single.items()],\n",
-    "            \"aggr_fn\": [np.nan for col in data_baseline.columns.to_list()]}\n",
-    "data_baseline_description = pd.DataFrame.from_dict(desc_dict)\n",
-    "data_baseline_description"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 152,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#feature_dict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 153,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "### WRITE FEATURES IN YAML!!!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 154,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_baseline.to_feather(os.path.join(path, dataset_path, 'baseline_endpoints.feather'))\n",
-    "data_baseline_description.to_feather(os.path.join(path, dataset_path, 'baseline_endpoints_description.feather'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#data_baseline.to_csv(os.path.join(path, dataset_path, 'baseline_clinical.csv'), index=False)\n",
-    "#data_baseline_description.to_csv(os.path.join(path, dataset_path, 'baseline_clinical_description.csv'), index=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# !!! REMEMBER IMPUTATION !!!"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python [conda env:miniconda3-pl1.x]",
-   "language": "python",
-   "name": "conda-env-miniconda3-pl1.x-py"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.8"
-  },
-  "toc-autonumbering": true,
-  "toc-showcode": false,
-  "toc-showmarkdowntxt": false
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/neuralcvd/preprocessing/ukbb_tabular/2_preprocessing_clinical_from_birth.ipynb b/neuralcvd/preprocessing/ukbb_tabular/2_preprocessing_clinical_from_birth.ipynb
deleted file mode 100644
index 29e9830..0000000
--- a/neuralcvd/preprocessing/ukbb_tabular/2_preprocessing_clinical_from_birth.ipynb
+++ /dev/null
@@ -1,5692 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Preprocessing"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:31:49.436340Z",
-     "start_time": "2020-11-04T12:31:48.732042Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import os\n",
-    "import yaml\n",
-    "from tqdm.notebook import trange, tqdm\n",
-    "dataset_name = \"cvd_interactions\"\n",
-    "path = \"/data/analysis/ag-reils/steinfej/code/umbrella/pre/ukbb\"\n",
-    "data_path = \"/data/analysis/ag-reils/ag-reils-shared/cardioRS/data\"\n",
-    "dataset_path = f\"{data_path}/2_datasets_pre/{dataset_name}\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:31:49.895222Z",
-     "start_time": "2020-11-04T12:31:49.891332Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from pathlib import Path\n",
-    "Path(dataset_path).mkdir(parents=True, exist_ok=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:33:14.171198Z",
-     "start_time": "2020-11-04T12:31:50.204540Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "data = pd.read_feather(f\"{data_path}/1_decoded/ukb_data.feather\")\n",
-    "data_field = pd.read_feather(f\"{data_path}/1_decoded/ukb_data_field.feather\")\n",
-    "data_columns = data.columns.to_list()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Mappings + Vocabulary"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:34:05.867152Z",
-     "start_time": "2020-11-04T12:33:16.878773Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "502505\n",
-      "502504\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Drop obviouse missing data\n",
-    "print(len(data))\n",
-    "data = data.dropna(subset=[\"sex_f31_0_0\"], axis=0)\n",
-    "print(len(data))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Starting information"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:34:05.872216Z",
-     "start_time": "2020-11-04T12:34:05.869505Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "time0_col=\"birth_date\"\n",
-    "# time0_col=\"date_of_attending_assessment_centre_f53_0_0\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Baseline covariates"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:34:05.889725Z",
-     "start_time": "2020-11-04T12:34:05.874587Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "def get_fields(fields, data, data_field):\n",
-    "    f = data_field[data_field[\"field.showcase\"].isin(fields) & data_field[\"field.tab\"].str.contains(\"f\\\\.\\\\d+\\\\.0\\\\.\\\\d\")].copy()\n",
-    "    f[\"field\"] = pd.Categorical(f[\"field.showcase\"], categories=fields, ordered=True)\n",
-    "    f = f.sort_values(\"field\").reset_index().drop(\"field\", axis=1)\n",
-    "    return f\n",
-    "\n",
-    "def get_fields_all(fields, data, data_field):\n",
-    "    f = data_field[data_field[\"field.showcase\"].isin(fields)].copy()\n",
-    "    f[\"field\"] = pd.Categorical(f[\"field.showcase\"], categories=fields, ordered=True)\n",
-    "    f = f.sort_values(\"field\").reset_index().drop(\"field\", axis=1)\n",
-    "    return f\n",
-    "\n",
-    "def get_data_fields(fields, data, data_field):\n",
-    "    f = get_fields(fields, data, data_field)\n",
-    "    return data[[\"eid\"]+f[\"col.name\"].to_list()].copy()\n",
-    "\n",
-    "def get_data_fields_all(fields, data, data_field):\n",
-    "    f = get_fields_all(fields, data, data_field)\n",
-    "    return data[[\"eid\"]+f[\"col.name\"].to_list()].copy()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Basics"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:34:06.176411Z",
-     "start_time": "2020-11-04T12:34:05.891730Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "502504\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>age_at_recruitment_f21022_0_0</th>\n",
-       "      <th>sex_f31_0_0</th>\n",
-       "      <th>ethnic_background_f21000_0_0</th>\n",
-       "      <th>townsend_deprivation_index_at_recruitment_f189_0_0</th>\n",
-       "      <th>date_of_attending_assessment_centre_f53_0_0</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>49.0</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>White</td>\n",
-       "      <td>-1.852930</td>\n",
-       "      <td>2009-11-12</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1000020</td>\n",
-       "      <td>59.0</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>White</td>\n",
-       "      <td>0.204248</td>\n",
-       "      <td>2008-02-19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1000037</td>\n",
-       "      <td>59.0</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>White</td>\n",
-       "      <td>-3.498860</td>\n",
-       "      <td>2008-11-11</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000043</td>\n",
-       "      <td>63.0</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>White</td>\n",
-       "      <td>-5.351150</td>\n",
-       "      <td>2009-06-03</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1000051</td>\n",
-       "      <td>51.0</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>White</td>\n",
-       "      <td>-1.799080</td>\n",
-       "      <td>2006-06-10</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       eid  age_at_recruitment_f21022_0_0 sex_f31_0_0  \\\n",
-       "0  1000018                           49.0      Female   \n",
-       "1  1000020                           59.0        Male   \n",
-       "2  1000037                           59.0      Female   \n",
-       "3  1000043                           63.0        Male   \n",
-       "4  1000051                           51.0      Female   \n",
-       "\n",
-       "  ethnic_background_f21000_0_0  \\\n",
-       "0                        White   \n",
-       "1                        White   \n",
-       "2                        White   \n",
-       "3                        White   \n",
-       "4                        White   \n",
-       "\n",
-       "   townsend_deprivation_index_at_recruitment_f189_0_0  \\\n",
-       "0                                          -1.852930    \n",
-       "1                                           0.204248    \n",
-       "2                                          -3.498860    \n",
-       "3                                          -5.351150    \n",
-       "4                                          -1.799080    \n",
-       "\n",
-       "  date_of_attending_assessment_centre_f53_0_0  \n",
-       "0                                  2009-11-12  \n",
-       "1                                  2008-02-19  \n",
-       "2                                  2008-11-11  \n",
-       "3                                  2009-06-03  \n",
-       "4                                  2006-06-10  "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fields_basics = [\n",
-    "    \"21022\", # age at recruitment\n",
-    "    \"31\", # sex\n",
-    "    \"21000\", # ethnicity\n",
-    "    \"189\", # Townsend index\n",
-    "    \"53\", # date of baseline assessment\n",
-    "]\n",
-    "\n",
-    "temp = get_data_fields(fields_basics, data, data_field)\n",
-    "\n",
-    "temp[\"sex_f31_0_0\"] = temp[\"sex_f31_0_0\"].cat.set_categories([\"Female\", 'Male'], ordered=False)\n",
-    "\n",
-    "#temp[\"ethnic_background_f21000_0_0\"] = temp[\"ethnic_background_f21000_0_0\"].astype(\"string\")\n",
-    "\n",
-    "ethn_bg_def = {\"White\": [\"White\", \"British\", \"Irish\", \"Any other white background\"],\n",
-    "                \"Mixed\": [\"Mixed\", \"White and Black Caribbean\", \"White and Black African\", \"White and Asian\", \"Any other mixed background\"],  \n",
-    "                \"Asian\": [\"Asian or Asian British\", \"Indian\", \"Pakistani\", \"Bangladeshi\", \"Any other Asian background\"], \n",
-    "                \"Black\": [\"Black or Black British\", \"Caribbean\", \"African\", \"Any other Black background\"],\n",
-    "                \"Chinese\": [\"Chinese\"],  \n",
-    "                np.nan: [\"Other ethnic group\", \"Do not know\", \"Prefer not to answer\"]}\n",
-    "\n",
-    "ethn_bg_dict = {}\n",
-    "for key, values in ethn_bg_def.items(): \n",
-    "    for value in values:\n",
-    "        ethn_bg_dict[value]=key \n",
-    "        \n",
-    "temp[\"ethnic_background_f21000_0_0\"].replace(ethn_bg_dict, inplace=True)\n",
-    "temp[\"ethnic_background_f21000_0_0\"] = temp[\"ethnic_background_f21000_0_0\"].astype(\"category\")\n",
-    "\n",
-    "#\n",
-    "#temp[\"ethnic_background_f21000_0_0\"] = temp[\"ethnic_background_f21000_0_0\"].astype(\"category\").cat.set_categories(['White', 'Black', 'Asien', 'Mixed', 'Chinese'], ordered=False)\n",
-    "\n",
-    "basics = temp\n",
-    "print(len(temp))\n",
-    "display(temp.head())\n",
-    "\n",
-    "from dateutil.relativedelta import relativedelta\n",
-    "calc_birth_date = [date_of_attending_assessment_centre - relativedelta(years=age_at_recruitment) \n",
-    "                                                             for date_of_attending_assessment_centre, age_at_recruitment \n",
-    "                                                             in zip(basics[\"date_of_attending_assessment_centre_f53_0_0\"], basics[\"age_at_recruitment_f21022_0_0\"])]\n",
-    "basics = basics.assign(birth_date = calc_birth_date)\n",
-    "\n",
-    "\n",
-    "basics.to_feather(os.path.join(path, dataset_path, 'temp_basics.feather'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:34:06.186613Z",
-     "start_time": "2020-11-04T12:34:06.178111Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[White, Black, NaN, Asian, Mixed, Chinese]\n",
-      "Categories (5, object): [White, Black, Asian, Mixed, Chinese]\n"
-     ]
-    }
-   ],
-   "source": [
-    " print(temp[\"ethnic_background_f21000_0_0\"].unique())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Questionnaire"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:34:06.389467Z",
-     "start_time": "2020-11-04T12:34:06.188206Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "502504\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>overall_health_rating_f2178_0_0</th>\n",
-       "      <th>smoking_status_f20116_0_0</th>\n",
-       "      <th>alcohol_intake_frequency_f1558_0_0</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>Fair</td>\n",
-       "      <td>Current</td>\n",
-       "      <td>Once or twice a week</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1000020</td>\n",
-       "      <td>Good</td>\n",
-       "      <td>Current</td>\n",
-       "      <td>Once or twice a week</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1000037</td>\n",
-       "      <td>Good</td>\n",
-       "      <td>Previous</td>\n",
-       "      <td>Once or twice a week</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000043</td>\n",
-       "      <td>Fair</td>\n",
-       "      <td>Previous</td>\n",
-       "      <td>Three or four times a week</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1000051</td>\n",
-       "      <td>Poor</td>\n",
-       "      <td>Never</td>\n",
-       "      <td>One to three times a month</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       eid overall_health_rating_f2178_0_0 smoking_status_f20116_0_0  \\\n",
-       "0  1000018                            Fair                   Current   \n",
-       "1  1000020                            Good                   Current   \n",
-       "2  1000037                            Good                  Previous   \n",
-       "3  1000043                            Fair                  Previous   \n",
-       "4  1000051                            Poor                     Never   \n",
-       "\n",
-       "  alcohol_intake_frequency_f1558_0_0  \n",
-       "0               Once or twice a week  \n",
-       "1               Once or twice a week  \n",
-       "2               Once or twice a week  \n",
-       "3         Three or four times a week  \n",
-       "4         One to three times a month  "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fields_questionnaire = [\n",
-    "    \"2178\", # Overall health\n",
-    "    \"20116\", # Smoking status\n",
-    "    \"1558\",\n",
-    "]\n",
-    "\n",
-    "temp = get_data_fields(fields_questionnaire, data, data_field)\n",
-    "\n",
-    "temp[\"overall_health_rating_f2178_0_0\"] = temp[\"overall_health_rating_f2178_0_0\"]\\\n",
-    "    .replace({\"Do not know\": np.nan, \"Prefer not to answer\": np.nan})\\\n",
-    "    .astype(\"category\").cat.set_categories(['Poor', 'Fair', 'Good', 'Excellent'], ordered=True)\n",
-    "\n",
-    "\n",
-    "temp[\"smoking_status_f20116_0_0\"] = temp[\"smoking_status_f20116_0_0\"]\\\n",
-    "    .replace({\"Prefer not to answer\": np.nan}, inplace=False)\\\n",
-    "    .astype(\"category\").cat.set_categories(['Current', 'Previous', 'Never'], ordered=True)\n",
-    "\n",
-    "temp[\"alcohol_intake_frequency_f1558_0_0\"] = temp[\"alcohol_intake_frequency_f1558_0_0\"]\\\n",
-    "    .replace({\"Prefer not to answer\": np.nan}, inplace=False)\\\n",
-    "    .astype(\"category\").cat.set_categories([\n",
-    "        'Daily or almost daily', \n",
-    "        'Three or four times a week', \n",
-    "        'Once or twice a week',\n",
-    "        'One to three times a month',\n",
-    "        'Special occasions only', \n",
-    "        'Never'], ordered=True)\n",
-    "\n",
-    "questionnaire = temp\n",
-    "print(len(temp))\n",
-    "display(temp.head())\n",
-    "\n",
-    "questionnaire.to_feather(os.path.join(path, dataset_path, 'temp_questionnaire.feather'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:34:06.398168Z",
-     "start_time": "2020-11-04T12:34:06.391082Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[Once or twice a week, Three or four times a week, One to three times a month, Daily or almost daily, Special occasions only, Never, NaN]\n",
-      "Categories (6, object): [Daily or almost daily < Three or four times a week < Once or twice a week < One to three times a month < Special occasions only < Never]\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(temp[\"alcohol_intake_frequency_f1558_0_0\"].unique())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Physical measurements"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:34:07.052989Z",
-     "start_time": "2020-11-04T12:34:06.400858Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "502504\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>body_mass_index_bmi_f21001_0_0</th>\n",
-       "      <th>weight_f21002_0_0</th>\n",
-       "      <th>pulse_wave_arterial_stiffness_index_f21021_0_0</th>\n",
-       "      <th>pulse_wave_reflection_index_f4195_0_0</th>\n",
-       "      <th>waist_circumference_f48_0_0</th>\n",
-       "      <th>hip_circumference_f49_0_0</th>\n",
-       "      <th>standing_height_f50_0_0</th>\n",
-       "      <th>trunk_fat_percentage_f23127_0_0</th>\n",
-       "      <th>body_fat_percentage_f23099_0_0</th>\n",
-       "      <th>basal_metabolic_rate_f23105_0_0</th>\n",
-       "      <th>forced_vital_capacity_fvc_best_measure_f20151_0_0</th>\n",
-       "      <th>forced_expiratory_volume_in_1second_fev1_best_measure_f20150_0_0</th>\n",
-       "      <th>fev1_fvc_ratio_zscore_f20258_0_0</th>\n",
-       "      <th>peak_expiratory_flow_pef_f3064_0_2</th>\n",
-       "      <th>peak_expiratory_flow_pef_f3064_0_1</th>\n",
-       "      <th>peak_expiratory_flow_pef_f3064_0_0</th>\n",
-       "      <th>systolic_blood_pressure_automated_reading_f4080</th>\n",
-       "      <th>diastolic_blood_pressure_automated_reading_f4079</th>\n",
-       "      <th>pulse_rate_automated_reading_f102</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>26.5557</td>\n",
-       "      <td>63.8</td>\n",
-       "      <td>7.2770</td>\n",
-       "      <td>80.0</td>\n",
-       "      <td>85.0</td>\n",
-       "      <td>107.0</td>\n",
-       "      <td>155.0</td>\n",
-       "      <td>37.5</td>\n",
-       "      <td>39.5</td>\n",
-       "      <td>5012.0</td>\n",
-       "      <td>3.21</td>\n",
-       "      <td>2.16</td>\n",
-       "      <td>1.978</td>\n",
-       "      <td>317.0</td>\n",
-       "      <td>312.0</td>\n",
-       "      <td>339.0</td>\n",
-       "      <td>159.5</td>\n",
-       "      <td>88.0</td>\n",
-       "      <td>50.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1000020</td>\n",
-       "      <td>22.7465</td>\n",
-       "      <td>70.7</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>87.8</td>\n",
-       "      <td>94.4</td>\n",
-       "      <td>176.3</td>\n",
-       "      <td>33.4</td>\n",
-       "      <td>28.7</td>\n",
-       "      <td>6171.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.375</td>\n",
-       "      <td>301.0</td>\n",
-       "      <td>496.0</td>\n",
-       "      <td>504.0</td>\n",
-       "      <td>133.0</td>\n",
-       "      <td>81.0</td>\n",
-       "      <td>74.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1000037</td>\n",
-       "      <td>32.4211</td>\n",
-       "      <td>78.9</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>101.0</td>\n",
-       "      <td>112.0</td>\n",
-       "      <td>156.0</td>\n",
-       "      <td>47.5</td>\n",
-       "      <td>48.4</td>\n",
-       "      <td>5397.0</td>\n",
-       "      <td>1.61</td>\n",
-       "      <td>1.27</td>\n",
-       "      <td>0.138</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>185.0</td>\n",
-       "      <td>208.0</td>\n",
-       "      <td>118.5</td>\n",
-       "      <td>78.0</td>\n",
-       "      <td>62.5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000043</td>\n",
-       "      <td>29.5679</td>\n",
-       "      <td>95.8</td>\n",
-       "      <td>11.1111</td>\n",
-       "      <td>78.0</td>\n",
-       "      <td>98.0</td>\n",
-       "      <td>104.0</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>27.6</td>\n",
-       "      <td>25.6</td>\n",
-       "      <td>8711.0</td>\n",
-       "      <td>4.14</td>\n",
-       "      <td>2.84</td>\n",
-       "      <td>1.096</td>\n",
-       "      <td>557.0</td>\n",
-       "      <td>513.0</td>\n",
-       "      <td>530.0</td>\n",
-       "      <td>141.5</td>\n",
-       "      <td>93.5</td>\n",
-       "      <td>64.5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1000051</td>\n",
-       "      <td>41.0222</td>\n",
-       "      <td>92.3</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>123.0</td>\n",
-       "      <td>129.0</td>\n",
-       "      <td>150.0</td>\n",
-       "      <td>48.9</td>\n",
-       "      <td>50.4</td>\n",
-       "      <td>6100.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.518</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>117.0</td>\n",
-       "      <td>81.0</td>\n",
-       "      <td>79.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       eid  body_mass_index_bmi_f21001_0_0  weight_f21002_0_0  \\\n",
-       "0  1000018                         26.5557               63.8   \n",
-       "1  1000020                         22.7465               70.7   \n",
-       "2  1000037                         32.4211               78.9   \n",
-       "3  1000043                         29.5679               95.8   \n",
-       "4  1000051                         41.0222               92.3   \n",
-       "\n",
-       "   pulse_wave_arterial_stiffness_index_f21021_0_0  \\\n",
-       "0                                          7.2770   \n",
-       "1                                             NaN   \n",
-       "2                                             NaN   \n",
-       "3                                         11.1111   \n",
-       "4                                             NaN   \n",
-       "\n",
-       "   pulse_wave_reflection_index_f4195_0_0  waist_circumference_f48_0_0  \\\n",
-       "0                                   80.0                         85.0   \n",
-       "1                                    NaN                         87.8   \n",
-       "2                                    NaN                        101.0   \n",
-       "3                                   78.0                         98.0   \n",
-       "4                                    NaN                        123.0   \n",
-       "\n",
-       "   hip_circumference_f49_0_0  standing_height_f50_0_0  \\\n",
-       "0                      107.0                    155.0   \n",
-       "1                       94.4                    176.3   \n",
-       "2                      112.0                    156.0   \n",
-       "3                      104.0                    180.0   \n",
-       "4                      129.0                    150.0   \n",
-       "\n",
-       "   trunk_fat_percentage_f23127_0_0  body_fat_percentage_f23099_0_0  \\\n",
-       "0                             37.5                            39.5   \n",
-       "1                             33.4                            28.7   \n",
-       "2                             47.5                            48.4   \n",
-       "3                             27.6                            25.6   \n",
-       "4                             48.9                            50.4   \n",
-       "\n",
-       "   basal_metabolic_rate_f23105_0_0  \\\n",
-       "0                           5012.0   \n",
-       "1                           6171.0   \n",
-       "2                           5397.0   \n",
-       "3                           8711.0   \n",
-       "4                           6100.0   \n",
-       "\n",
-       "   forced_vital_capacity_fvc_best_measure_f20151_0_0  \\\n",
-       "0                                               3.21   \n",
-       "1                                                NaN   \n",
-       "2                                               1.61   \n",
-       "3                                               4.14   \n",
-       "4                                                NaN   \n",
-       "\n",
-       "   forced_expiratory_volume_in_1second_fev1_best_measure_f20150_0_0  \\\n",
-       "0                                               2.16                  \n",
-       "1                                                NaN                  \n",
-       "2                                               1.27                  \n",
-       "3                                               2.84                  \n",
-       "4                                                NaN                  \n",
-       "\n",
-       "   fev1_fvc_ratio_zscore_f20258_0_0  peak_expiratory_flow_pef_f3064_0_2  \\\n",
-       "0                             1.978                               317.0   \n",
-       "1                             1.375                               301.0   \n",
-       "2                             0.138                                 NaN   \n",
-       "3                             1.096                               557.0   \n",
-       "4                             0.518                                 NaN   \n",
-       "\n",
-       "   peak_expiratory_flow_pef_f3064_0_1  peak_expiratory_flow_pef_f3064_0_0  \\\n",
-       "0                               312.0                               339.0   \n",
-       "1                               496.0                               504.0   \n",
-       "2                               185.0                               208.0   \n",
-       "3                               513.0                               530.0   \n",
-       "4                                 NaN                                 NaN   \n",
-       "\n",
-       "   systolic_blood_pressure_automated_reading_f4080  \\\n",
-       "0                                            159.5   \n",
-       "1                                            133.0   \n",
-       "2                                            118.5   \n",
-       "3                                            141.5   \n",
-       "4                                            117.0   \n",
-       "\n",
-       "   diastolic_blood_pressure_automated_reading_f4079  \\\n",
-       "0                                              88.0   \n",
-       "1                                              81.0   \n",
-       "2                                              78.0   \n",
-       "3                                              93.5   \n",
-       "4                                              81.0   \n",
-       "\n",
-       "   pulse_rate_automated_reading_f102  \n",
-       "0                               50.0  \n",
-       "1                               74.0  \n",
-       "2                               62.5  \n",
-       "3                               64.5  \n",
-       "4                               79.0  "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from statistics import mean\n",
-    "\n",
-    "fields_measurements = [\n",
-    "#    \"100313\", # Walking speed !!! MISSING !!!\n",
-    "    \"21001\", # BMI\n",
-    "    \"21002\", # weight\n",
-    "    \"4080\", # Syst. BP\n",
-    "    \"4079\", # Diast. BP\n",
-    "    \"102\",\n",
-    "    \"21021\",\n",
-    "    \"4195\",\n",
-    "    \"48\",\n",
-    "    \"49\",\n",
-    "    \"50\",\n",
-    "    \"23127\",\n",
-    "    \"23099\",\n",
-    "    \"23105\",\n",
-    "    \"20151\",\n",
-    "    \"20150\",\n",
-    "    \"20258\",\n",
-    "    \"3064\",\n",
-    "    \n",
-    "]\n",
-    "temp = get_data_fields(fields_measurements, data, data_field)\n",
-    "\n",
-    "sbp_cols = [\"systolic_blood_pressure_automated_reading_f4080_0_0\", \"systolic_blood_pressure_automated_reading_f4080_0_1\"]\n",
-    "dbp_cols = [\"diastolic_blood_pressure_automated_reading_f4079_0_0\", \"diastolic_blood_pressure_automated_reading_f4079_0_1\"]\n",
-    "pr_cols = [\"pulse_rate_automated_reading_f102_0_0\", \"pulse_rate_automated_reading_f102_0_1\"]\n",
-    "\n",
-    "temp = temp.assign(systolic_blood_pressure_automated_reading_f4080 = temp[sbp_cols].mean(axis=1),\n",
-    "                   diastolic_blood_pressure_automated_reading_f4079 = temp[dbp_cols].mean(axis=1),\n",
-    "                   pulse_rate_automated_reading_f102 = temp[pr_cols].mean(axis=1))\\\n",
-    "    .drop(sbp_cols + dbp_cols + pr_cols, axis=1)\n",
-    "\n",
-    "measurements = temp\n",
-    "print(len(temp))\n",
-    "display(temp.head())\n",
-    "\n",
-    "measurements.to_feather(os.path.join(path, dataset_path, 'temp_measurements.feather'))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Lab measurements"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:34:07.647955Z",
-     "start_time": "2020-11-04T12:34:07.055242Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "502504\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>basophill_count_f30160_0_0</th>\n",
-       "      <th>basophill_percentage_f30220_0_0</th>\n",
-       "      <th>eosinophill_count_f30150_0_0</th>\n",
-       "      <th>eosinophill_percentage_f30210_0_0</th>\n",
-       "      <th>haematocrit_percentage_f30030_0_0</th>\n",
-       "      <th>haemoglobin_concentration_f30020_0_0</th>\n",
-       "      <th>high_light_scatter_reticulocyte_count_f30300_0_0</th>\n",
-       "      <th>high_light_scatter_reticulocyte_percentage_f30290_0_0</th>\n",
-       "      <th>immature_reticulocyte_fraction_f30280_0_0</th>\n",
-       "      <th>...</th>\n",
-       "      <th>phosphate_f30810_0_0</th>\n",
-       "      <th>rheumatoid_factor_f30820_0_0</th>\n",
-       "      <th>shbg_f30830_0_0</th>\n",
-       "      <th>testosterone_f30850_0_0</th>\n",
-       "      <th>total_bilirubin_f30840_0_0</th>\n",
-       "      <th>total_protein_f30860_0_0</th>\n",
-       "      <th>triglycerides_f30870_0_0</th>\n",
-       "      <th>urate_f30880_0_0</th>\n",
-       "      <th>urea_f30670_0_0</th>\n",
-       "      <th>vitamin_d_f30890_0_0</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>0.04</td>\n",
-       "      <td>0.26</td>\n",
-       "      <td>0.25</td>\n",
-       "      <td>1.75</td>\n",
-       "      <td>39.79</td>\n",
-       "      <td>13.90</td>\n",
-       "      <td>0.022</td>\n",
-       "      <td>0.464</td>\n",
-       "      <td>0.378</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.422</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>70.11</td>\n",
-       "      <td>1.560</td>\n",
-       "      <td>7.41</td>\n",
-       "      <td>71.97</td>\n",
-       "      <td>1.247</td>\n",
-       "      <td>221.3</td>\n",
-       "      <td>5.48</td>\n",
-       "      <td>70.7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1000020</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0.30</td>\n",
-       "      <td>0.30</td>\n",
-       "      <td>2.50</td>\n",
-       "      <td>45.00</td>\n",
-       "      <td>15.60</td>\n",
-       "      <td>0.014</td>\n",
-       "      <td>0.290</td>\n",
-       "      <td>0.300</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.264</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>55.31</td>\n",
-       "      <td>12.237</td>\n",
-       "      <td>8.07</td>\n",
-       "      <td>78.45</td>\n",
-       "      <td>1.906</td>\n",
-       "      <td>374.7</td>\n",
-       "      <td>5.28</td>\n",
-       "      <td>35.9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1000037</td>\n",
-       "      <td>0.04</td>\n",
-       "      <td>0.57</td>\n",
-       "      <td>0.10</td>\n",
-       "      <td>1.43</td>\n",
-       "      <td>39.48</td>\n",
-       "      <td>13.58</td>\n",
-       "      <td>0.031</td>\n",
-       "      <td>0.686</td>\n",
-       "      <td>0.380</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000043</td>\n",
-       "      <td>0.02</td>\n",
-       "      <td>0.32</td>\n",
-       "      <td>0.11</td>\n",
-       "      <td>1.80</td>\n",
-       "      <td>44.31</td>\n",
-       "      <td>14.99</td>\n",
-       "      <td>0.025</td>\n",
-       "      <td>0.508</td>\n",
-       "      <td>0.250</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.928</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>31.63</td>\n",
-       "      <td>11.398</td>\n",
-       "      <td>8.65</td>\n",
-       "      <td>69.70</td>\n",
-       "      <td>5.184</td>\n",
-       "      <td>322.8</td>\n",
-       "      <td>6.67</td>\n",
-       "      <td>63.6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1000051</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 62 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       eid  basophill_count_f30160_0_0  basophill_percentage_f30220_0_0  \\\n",
-       "0  1000018                        0.04                             0.26   \n",
-       "1  1000020                        0.00                             0.30   \n",
-       "2  1000037                        0.04                             0.57   \n",
-       "3  1000043                        0.02                             0.32   \n",
-       "4  1000051                         NaN                              NaN   \n",
-       "\n",
-       "   eosinophill_count_f30150_0_0  eosinophill_percentage_f30210_0_0  \\\n",
-       "0                          0.25                               1.75   \n",
-       "1                          0.30                               2.50   \n",
-       "2                          0.10                               1.43   \n",
-       "3                          0.11                               1.80   \n",
-       "4                           NaN                                NaN   \n",
-       "\n",
-       "   haematocrit_percentage_f30030_0_0  haemoglobin_concentration_f30020_0_0  \\\n",
-       "0                              39.79                                 13.90   \n",
-       "1                              45.00                                 15.60   \n",
-       "2                              39.48                                 13.58   \n",
-       "3                              44.31                                 14.99   \n",
-       "4                                NaN                                   NaN   \n",
-       "\n",
-       "   high_light_scatter_reticulocyte_count_f30300_0_0  \\\n",
-       "0                                             0.022   \n",
-       "1                                             0.014   \n",
-       "2                                             0.031   \n",
-       "3                                             0.025   \n",
-       "4                                               NaN   \n",
-       "\n",
-       "   high_light_scatter_reticulocyte_percentage_f30290_0_0  \\\n",
-       "0                                              0.464       \n",
-       "1                                              0.290       \n",
-       "2                                              0.686       \n",
-       "3                                              0.508       \n",
-       "4                                                NaN       \n",
-       "\n",
-       "   immature_reticulocyte_fraction_f30280_0_0  ...  phosphate_f30810_0_0  \\\n",
-       "0                                      0.378  ...                 1.422   \n",
-       "1                                      0.300  ...                 1.264   \n",
-       "2                                      0.380  ...                   NaN   \n",
-       "3                                      0.250  ...                 0.928   \n",
-       "4                                        NaN  ...                   NaN   \n",
-       "\n",
-       "   rheumatoid_factor_f30820_0_0  shbg_f30830_0_0  testosterone_f30850_0_0  \\\n",
-       "0                           NaN            70.11                    1.560   \n",
-       "1                           NaN            55.31                   12.237   \n",
-       "2                           NaN              NaN                      NaN   \n",
-       "3                           NaN            31.63                   11.398   \n",
-       "4                           NaN              NaN                      NaN   \n",
-       "\n",
-       "   total_bilirubin_f30840_0_0  total_protein_f30860_0_0  \\\n",
-       "0                        7.41                     71.97   \n",
-       "1                        8.07                     78.45   \n",
-       "2                         NaN                       NaN   \n",
-       "3                        8.65                     69.70   \n",
-       "4                         NaN                       NaN   \n",
-       "\n",
-       "   triglycerides_f30870_0_0  urate_f30880_0_0  urea_f30670_0_0  \\\n",
-       "0                     1.247             221.3             5.48   \n",
-       "1                     1.906             374.7             5.28   \n",
-       "2                       NaN               NaN              NaN   \n",
-       "3                     5.184             322.8             6.67   \n",
-       "4                       NaN               NaN              NaN   \n",
-       "\n",
-       "   vitamin_d_f30890_0_0  \n",
-       "0                  70.7  \n",
-       "1                  35.9  \n",
-       "2                   NaN  \n",
-       "3                  63.6  \n",
-       "4                   NaN  \n",
-       "\n",
-       "[5 rows x 62 columns]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fields_blood_count = [\n",
-    "    \"30160\", #\tBasophill count\n",
-    "    \"30220\", #\tBasophill percentage\n",
-    "    \"30150\", #\tEosinophill count\n",
-    "    \"30210\", #\tEosinophill percentage\n",
-    "    \"30030\", #\tHaematocrit percentage\n",
-    "    \"30020\", #\tHaemoglobin concentration\n",
-    "    \"30300\", #\tHigh light scatter reticulocyte count\n",
-    "    \"30290\", #\tHigh light scatter reticulocyte percentage\n",
-    "    \"30280\", #\tImmature reticulocyte fraction\n",
-    "    \"30120\", #\tLymphocyte count\n",
-    "    \"30180\", #\tLymphocyte percentage\n",
-    "    \"30050\", #\tMean corpuscular haemoglobin\n",
-    "    \"30060\", #\tMean corpuscular haemoglobin concentration\n",
-    "    \"30040\", #\tMean corpuscular volume\n",
-    "    \"30100\", #\tMean platelet (thrombocyte) volume\n",
-    "    \"30260\", #\tMean reticulocyte volume\n",
-    "    \"30270\", #\tMean sphered cell volume\n",
-    "    \"30130\", #\tMonocyte count\n",
-    "    \"30190\", #\tMonocyte percentage\n",
-    "    \"30140\", #\tNeutrophill count\n",
-    "    \"30200\", #\tNeutrophill percentage\n",
-    "    \"30170\", #\tNucleated red blood cell count\n",
-    "    \"30230\", #\tNucleated red blood cell percentage\n",
-    "    \"30080\", #\tPlatelet count\n",
-    "    \"30090\", #\tPlatelet crit\n",
-    "    \"30110\", #\tPlatelet distribution width\n",
-    "    \"30010\", #\tRed blood cell (erythrocyte) count\n",
-    "    \"30070\", #\tRed blood cell (erythrocyte) distribution width\n",
-    "    \"30250\", #\tReticulocyte count\n",
-    "    \"30240\", #\tReticulocyte percentage\n",
-    "    \"30000\", #\tWhite blood cell (leukocyte) count\n",
-    "]\n",
-    "\n",
-    "fields_blood_biochemistry = [\n",
-    "    \"30620\",#\tAlanine aminotransferase\n",
-    "    \"30600\",#\tAlbumin\n",
-    "    \"30610\",#\tAlkaline phosphatase\n",
-    "    \"30630\",#\tApolipoprotein A\n",
-    "    \"30640\",#\tApolipoprotein B\n",
-    "    \"30650\",#\tAspartate aminotransferase\n",
-    "    \"30710\",#\tC-reactive protein\n",
-    "    \"30680\",#\tCalcium\n",
-    "    \"30690\",#\tCholesterol\n",
-    "    \"30700\",#\tCreatinine\n",
-    "    \"30720\",#\tCystatin C\n",
-    "    \"30660\",#\tDirect bilirubin\n",
-    "    \"30730\",#\tGamma glutamyltransferase\n",
-    "    \"30740\",#\tGlucose\n",
-    "    \"30750\",#\tGlycated haemoglobin (HbA1c)\n",
-    "    \"30760\",#\tHDL cholesterol\n",
-    "    \"30770\",#\tIGF-1\n",
-    "    \"30780\",#\tLDL direct\n",
-    "    \"30790\",#\tLipoprotein A\n",
-    "    \"30800\",#\tOestradiol\n",
-    "    \"30810\",#\tPhosphate\n",
-    "    \"30820\",#\tRheumatoid factor\n",
-    "    \"30830\",#\tSHBG\n",
-    "    \"30850\",#\tTestosterone\n",
-    "    \"30840\",#\tTotal bilirubin\n",
-    "    \"30860\",#\tTotal protein\n",
-    "    \"30870\",#\tTriglycerides\n",
-    "    \"30880\",#\tUrate\n",
-    "    \"30670\",#\tUrea\n",
-    "    \"30890\",#\tVitamin D\n",
-    "]\n",
-    "\n",
-    "fields_blood_infectious = [\n",
-    "    \"23000\", #\t1gG antigen for Herpes Simplex virus-1\n",
-    "    \"23001\", #\t2mgG unique antigen for Herpes Simplex virus-2\n",
-    "    \"23049\", #\tAntigen assay QC indicator\n",
-    "    \"23048\", #\tAntigen assay date\n",
-    "    \"23026\", #\tBK VP1 antigen for Human Polyomavirus BKV\n",
-    "    \"23039\", #\tCagA antigen for Helicobacter pylori\n",
-    "    \"23043\", #\tCatalase antigen for Helicobacter pylori\n",
-    "    \"23018\", #\tCore antigen for Hepatitis C Virus\n",
-    "    \"23030\", #\tE6 antigen for Human Papillomavirus type-16\n",
-    "    \"23031\", #\tE7 antigen for Human Papillomavirus type-16\n",
-    "    \"23006\", #\tEA-D antigen for Epstein-Barr Virus\n",
-    "    \"23004\", #\tEBNA-1 antigen for Epstein-Barr Virus\n",
-    "    \"23042\", #\tGroEL antigen for Helicobacter pylori\n",
-    "    \"23016\", #\tHBc antigen for Hepatitis B Virus\n",
-    "    \"23017\", #\tHBe antigen for Hepatitis B Virus\n",
-    "    \"23025\", #\tHIV-1 env antigen for Human Immunodeficiency Virus\n",
-    "    \"23024\", #\tHIV-1 gag antigen for Human Immunodeficiency Virus\n",
-    "    \"23023\", #\tHTLV-1 env antigen for Human T-Lymphotropic Virus 1\n",
-    "    \"23022\", #\tHTLV-1 gag antigen for Human T-Lymphotropic Virus 1\n",
-    "    \"23010\", #\tIE1A antigen for Human Herpesvirus-6\n",
-    "    \"23011\", #\tIE1B antigen for Human Herpesvirus-6\n",
-    "    \"23027\", #\tJC VP1 antigen for Human Polyomavirus JCV\n",
-    "    \"23015\", #\tK8.1 antigen for Kaposi's Sarcoma-Associated Herpesvirus\n",
-    "    \"23029\", #\tL1 antigen for Human Papillomavirus type-16\n",
-    "    \"23032\", #\tL1 antigen for Human Papillomavirus type-18\n",
-    "    \"23014\", #\tLANA antigen for Kaposi's Sarcoma-Associated Herpesvirus\n",
-    "    \"23028\", #\tMC VP1 antigen for Merkel Cell Polyomavirus\n",
-    "    \"23019\", #\tNS3 antigen for Hepatitis C Virus\n",
-    "    \"23041\", #\tOMP antigen for Helicobacter pylori\n",
-    "    \"23037\", #\tPorB antigen for Chlamydia trachomatis\n",
-    "    \"23013\", #\tU14 antigen for Human Herpesvirus-7\n",
-    "    \"23044\", #\tUreA antigen for Helicobacter pylori\n",
-    "    \"23003\", #\tVCA p18 antigen for Epstein-Barr Virus\n",
-    "    \"23040\", #\tVacA antigen for Helicobacter pylori\n",
-    "    \"23005\", #\tZEBRA antigen for Epstein-Barr Virus\n",
-    "    \"23002\", #\tgE / gI antigen for Varicella Zoster Virus\n",
-    "    \"23034\", #\tmomp A antigen for Chlamydia trachomatis\n",
-    "    \"23033\", #\tmomp D antigen for Chlamydia trachomatis\n",
-    "    \"23012\", #\tp101 k antigen for Human Herpesvirus-6\n",
-    "    \"23020\", #\tp22 antigen for Toxoplasma gondii\n",
-    "    \"23038\", #\tpGP3 antigen for Chlamydia trachomatis\n",
-    "    \"23009\", #\tpp 28 antigen for Human Cytomegalovirus\n",
-    "    \"23008\", #\tpp 52 antigen for Human Cytomegalovirus\n",
-    "    \"23007\", #\tpp150 Nter antigen for Human Cytomegalovirus\n",
-    "    \"23021\", #\tsag1 antigen for Toxoplasma gondii\n",
-    "    \"23035\", #\ttarp-D F1 antigen for Chlamydia trachomatis\n",
-    "    \"23036\", #\ttarp-D F2 antigen for Chlamydia trachomatis\n",
-    "]\n",
-    "\n",
-    "labs = temp = get_data_fields(fields_blood_count+fields_blood_biochemistry+fields_blood_infectious, data, data_field)\n",
-    "print(len(temp))\n",
-    "display(temp.head())\n",
-    "\n",
-    "labs.to_feather(os.path.join(path, dataset_path, 'temp_labs.feather'))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Family History"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:34:25.838958Z",
-     "start_time": "2020-11-04T12:34:07.649920Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "502504\n"
-     ]
-    }
-   ],
-   "source": [
-    "fh_list=[\"Heart disease\", \"Stroke\", \"High blood pressure\",  \"Diabetes\", \"Lung cancer\", \"Severe depression\", \"Parkinson's disease\", \"Alzheimer's disease/dementia\", \"Chronic bronchitis/emphysema\", \"Breast cancer\", \"Bowel cancer\"]\n",
-    "with open(os.path.join(path, dataset_path, 'fh_list.yaml'), 'w') as file: yaml.dump(fh_list, file, default_flow_style=False)\n",
-    "\n",
-    "fields_family_history = [\n",
-    "    \"20107\", # Family history \n",
-    "    \"20110\" # Family history\n",
-    "]\n",
-    "\n",
-    "raw = get_data_fields(fields_family_history, data, data_field)\n",
-    "temp = pd.melt(raw, id_vars=[\"eid\"], value_vars=raw.drop(\"eid\", axis=1).columns.to_list(), var_name = \"field\", value_name=\"family_history\").drop(\"field\", axis=1)\n",
-    "temp = temp[temp.family_history.isin(fh_list)].assign(family_history=temp[\"family_history\"].str.lower().replace(\" \", \"_\", regex=True))\n",
-    "\n",
-    "temp = temp.drop_duplicates().sort_values(\"eid\").reset_index().drop(\"index\", axis=1).assign(n=True)\n",
-    "temp = pd.pivot_table(temp, index=\"eid\", columns=\"family_history\", values=\"n\", observed=True).add_prefix('fh_')\n",
-    "family_history = temp = data[[\"eid\"]].copy().merge(temp, how=\"left\", on=\"eid\").fillna(False)\n",
-    "\n",
-    "print(len(temp))\n",
-    "temp.head()\n",
-    "\n",
-    "family_history.to_feather(os.path.join(path, dataset_path, 'temp_family_history.feather'))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Medications"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:34:25.842601Z",
-     "start_time": "2020-11-04T12:34:25.840607Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# https://list.essentialmeds.org/?showRemoved=0\n",
-    "# essential medicines WHO?!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:34:32.471831Z",
-     "start_time": "2020-11-04T12:34:25.844339Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "atc_mapping = pd.read_csv(f\"{path}/mapping/atc/atc_matched_list.csv\")\n",
-    "athena_concepts = pd.read_csv(f\"{data_path}/athena_vocabulary/CONCEPT.csv\", sep=\"\\t\").assign(vocabulary_id = lambda x: x.vocabulary_id.astype(\"string\"), concept_class_id = lambda x: x.concept_class_id.astype(\"string\"))\n",
-    "atc_concepts = athena_concepts[athena_concepts.vocabulary_id==\"ATC\"]\n",
-    "atc2_concepts = atc_concepts[atc_concepts.concept_class_id==\"ATC 2nd\"].sort_values(\"concept_code\")\n",
-    "medication_list = dict(zip([x.lower().replace(\" \", \"_\") for x in atc2_concepts.concept_name.to_list()], [[x] for x in atc2_concepts.concept_code.to_list()]))\n",
-    "medication_list_extra = {\n",
-    "    \"antihypertensives\": [\"C02\"],\n",
-    "    \"statins\": [\"C10A\", \"C10B\"],\n",
-    "    \"ass\": [\"B01\"],\n",
-    "    \"atypical_antipsychotics\" : [\"N05\"],\n",
-    "    \"glucocorticoids\" : [\"H02\"]                        \n",
-    "}\n",
-    "medication_list.update(medication_list_extra)\n",
-    "\n",
-    "with open(os.path.join(path, dataset_path, 'medication_list.yaml'), 'w') as file: yaml.dump(medication_list, file, default_flow_style=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def had_medication_before(data, data_field, medications, atc_mapping):\n",
-    "    fields = [\"20003\"]\n",
-    "    raw = get_data_fields(fields, data, data_field)\n",
-    "    temp = pd.melt(raw, id_vars=[\"eid\"], value_vars=raw.drop(\"eid\", axis=1).columns.to_list(), var_name = \"field\", value_name=\"UKBB_code\").drop(\"field\", axis=1).drop_duplicates()\n",
-    "\n",
-    "    temp.UKBB_code = temp.UKBB_code.astype(str)\n",
-    "    temp = temp[temp.UKBB_code!=\"None\"].copy()\n",
-    "    temp.UKBB_code = temp.UKBB_code.astype(int)\n",
-    "\n",
-    "    temp_atc = temp.merge(atc_mapping, how=\"left\", on=\"UKBB_code\").sort_values(\"eid\").reset_index(drop=True).dropna(subset=[\"ATC_code\"], axis=0)\n",
-    "    temp_atc.ATC_code = temp_atc.ATC_code.astype(\"string\")\n",
-    "    temp = data[[\"eid\"]].copy()\n",
-    "    for med, med_codes in tqdm(medication_list.items()):\n",
-    "        regex_str = \"^\"+\"|^\".join(med_codes)\n",
-    "        df = temp_atc[temp_atc.ATC_code.str.contains(regex_str, case=False)][[\"eid\"]]\\\n",
-    "            .drop_duplicates(subset=[\"eid\"])\\\n",
-    "            .assign(medication=True)\n",
-    "        temp[med] = temp.merge(df, how=\"left\", on=\"eid\").fillna(False).medication\n",
-    "        \n",
-    "    return temp.sort_values(\"eid\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:34:32.481137Z",
-     "start_time": "2020-11-04T12:34:32.473698Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "def had_diagnosis_before_per_ph(df_before, ph, ph_codes, temp):\n",
-    "   # regex = \"|\".join(ph_codes)\n",
-    "    #df_ph = df_before.set_index(\"meaning\").loc[ph_codes][[\"eid\"]]\\\n",
-    "    #        .drop_duplicates(subset=[\"eid\"])\\\n",
-    "    #        .assign(phenotype=True) \n",
-    "    df_ph = df_before[df_before.meaning.isin(ph_codes)][[\"eid\"]]\\\n",
-    "            .drop_duplicates(subset=[\"eid\"])\\\n",
-    "            .assign(phenotype=True) \n",
-    "    #df_ph = df_before[df_before.meaning.str.contains(regex, case=False)][[\"eid\"]]\\\n",
-    "    #        .drop_duplicates(subset=[\"eid\"])\\\n",
-    "    #        .assign(phenotype=True)   \n",
-    "    return temp.merge(df_ph, how=\"left\", on=\"eid\").fillna(False).phenotype\n",
-    "\n",
-    "def had_diagnosis_before(data, diagnoses_codes, phenotypes, time0=time0_col):\n",
-    "    diagnoses_codes_time = diagnoses_codes.merge(data[[\"eid\", time0]], how=\"left\", on=\"eid\")\n",
-    "    \n",
-    "    temp = data[[\"eid\"]].copy()\n",
-    "    df_before = diagnoses_codes_time[diagnoses_codes_time.date < diagnoses_codes_time[time0]]\n",
-    "                                                                                         \n",
-    "    df_phs = Parallel(n_jobs=20, require=\"sharedmem\")(delayed(had_diagnosis_before_per_ph)(df_before, ph, phenotypes[ph], temp) for ph in tqdm(list(phenotypes)))\n",
-    "    for ph, df_ph_series in zip(tqdm(list(phenotypes)), df_phs): temp[ph] = df_ph_series#temp.merge(df_ph, how=\"left\", on=\"eid\").fillna(False).phenotype\n",
-    "    \n",
-    "    return temp.sort_values(\"eid\")  #reduce(lambda left,right: pd.merge(left,right,on=['eid'], how='left'), df_phs)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:36:14.425612Z",
-     "start_time": "2020-11-04T12:34:32.482863Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "13ef8609a6b64b219928981b7752c608",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(FloatProgress(value=0.0, max=98.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "502504\n"
-     ]
-    }
-   ],
-   "source": [
-    "medications = had_medication_before(data, data_field, medication_list, atc_mapping)\n",
-    "print(len(medications))\n",
-    "medications.head(100)\n",
-    "\n",
-    "medications.to_feather(os.path.join(path, dataset_path, 'temp_medications.feather'))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Diagnoses and events"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:37:14.667281Z",
-     "start_time": "2020-11-04T12:36:14.427693Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/steinfej/miniconda3/envs/python/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3072: DtypeWarning: Columns (5,6,9) have mixed types.Specify dtype option on import or set low_memory=False.\n",
-      "  interactivity=interactivity, compiler=compiler, result=result)\n",
-      "/home/steinfej/miniconda3/envs/python/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3072: DtypeWarning: Columns (11) have mixed types.Specify dtype option on import or set low_memory=False.\n",
-      "  interactivity=interactivity, compiler=compiler, result=result)\n"
-     ]
-    }
-   ],
-   "source": [
-    "vocab_dir = f\"{data_path}/athena_vocabulary_covid\"\n",
-    "vocab = {\n",
-    "    \"concept\": pd.read_csv(f\"{vocab_dir}/CONCEPT.csv\", sep='\\t'),\n",
-    "    \"domain\": pd.read_csv(f\"{vocab_dir}/DOMAIN.csv\", sep='\\t'),\n",
-    "    \"class\": pd.read_csv(f\"{vocab_dir}/CONCEPT_CLASS.csv\", sep='\\t'),\n",
-    "    \"relationship\": pd.read_csv(f\"{vocab_dir}/RELATIONSHIP.csv\", sep='\\t'),\n",
-    "    \"drug_strength\": pd.read_csv(f\"{vocab_dir}/DRUG_STRENGTH.csv\", sep='\\t'),\n",
-    "    \"vocabulary\": pd.read_csv(f\"{vocab_dir}/VOCABULARY.csv\", sep='\\t'),\n",
-    "    \"concept_synonym\": pd.read_csv(f\"{vocab_dir}/CONCEPT_SYNONYM.csv\", sep='\\t'),\n",
-    "    \"concept_ancestor\": pd.read_csv(f\"{vocab_dir}/CONCEPT_ANCESTOR.csv\", sep='\\t'),\n",
-    "    \"concept_relationship\": pd.read_csv(f\"{vocab_dir}/CONCEPT_RELATIONSHIP.csv\", sep='\\t')                       \n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Definitions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:37:14.772869Z",
-     "start_time": "2020-11-04T12:37:14.669541Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "coding1836 = pd.read_csv(f\"{path}/mapping/codings/coding1836.tsv\", sep=\"\\t\").rename(columns={\"coding\":\"code\"})\n",
-    "phecodes = pd.read_csv(f\"{path}/mapping/phecodes/phecode_icd10.csv\")\n",
-    "def phenotype_children(phecodes, phenotype_list):\n",
-    "    l={}\n",
-    "    phecodes = phecodes.dropna(subset=[\"Phenotype\"], axis=0)\n",
-    "    for ph, ph_names in phenotype_list.items():\n",
-    "        regex = \"|\".join(ph_names)\n",
-    "        l[ph] = list(phecodes[phecodes.Phenotype.str.contains(regex, case=False)].ICD10.str.replace(\"\\\\.\", \"\").str.slice(0, 3).unique())\n",
-    "    return l"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:37:14.851438Z",
-     "start_time": "2020-11-04T12:37:14.774599Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "snomed_core = pd.read_csv(f\"{path}/mapping/snomed_core_list.txt\", sep=\"|\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:37:14.909023Z",
-     "start_time": "2020-11-04T12:37:14.853286Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "snomed_core = snomed_core.query(\"SNOMED_CONCEPT_STATUS == 'Current'\").copy()\n",
-    "new = snomed_core.SNOMED_FSN.str.split(\"(\", n=1, expand=True)\n",
-    "snomed_core[\"snomed_name\"] = new[0].str.rstrip(' ')\n",
-    "snomed_core[\"snomed_type\"] = new[1].str.rstrip(')')\n",
-    "snomed_core_data = snomed_core#.query(\"(snomed_type=='disorder' | snomed_type=='finding') & USAGE>0.01\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:37:14.914973Z",
-     "start_time": "2020-11-04T12:37:14.910857Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "snomed_names = snomed_core_data.snomed_name.to_list()\n",
-    "snomed_names = [str(item).lower().strip().replace(\" \", \"_\").replace(\";\", \"\").replace(\",\", \"\") for item in snomed_names]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:39:55.342937Z",
-     "start_time": "2020-11-04T12:37:14.916906Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "989d1908f8624958a92da3602ad125f1",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(FloatProgress(value=0.0, max=6181.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "phenotype_list_snomed = dict(zip(snomed_names, snomed_core_data.SNOMED_CID.to_list()))\n",
-    "snomed_ids = vocab[\"concept\"].query(\"vocabulary_id == 'SNOMED'\").concept_id.to_list()\n",
-    "icd10_ids = vocab[\"concept\"].query(\"vocabulary_id == 'ICD10CM'\").concept_id.to_list()\n",
-    "\n",
-    "ph_to_icd10_mapping = {}\n",
-    "\n",
-    "def map_snomed_to_icd10(ph, snomed_code, concept, concept_ancestor, concept_relationship):\n",
-    "    concept_ids = concept.query(\"vocabulary_id == 'SNOMED' & concept_code == @snomed_code\").concept_id.to_list()\n",
-    "    snomed_desc_ids = concept_ancestor.query(\"ancestor_concept_id== @concept_ids\").descendant_concept_id.to_list()\n",
-    "    ph_desc = concept.query(\"concept_id == @snomed_desc_ids\").query(\"vocabulary_id == 'SNOMED'\")\n",
-    "    l_ph_desc_ids = ph_desc.concept_id.to_list()\n",
-    "    ph_icd10_ids = list(concept_relationship.query(\"concept_id_1==@l_ph_desc_ids\").query(\"concept_id_2 == @icd10_ids\").concept_id_2.unique())\n",
-    "    #ph_icd10_ids = list(concept_relationship.set_index(\"concept_id_1\").query(\"index==@l_ph_desc_ids\").query(\"concept_id_2 == @icd10_ids\").query(\"relationship_id == 'Mapped from'\").concept_id_2.unique()\n",
-    "    \n",
-    "    #ph_icd10_ids = list(temp.concept_id_2.unique())\n",
-    "    df = concept.query(\"concept_id == @ph_icd10_ids & vocabulary_id == 'ICD10CM'\")\n",
-    "    icd10_list = list(df[~df.concept_code.str.contains(\"OMOP\", na=False)].concept_code.unique())\n",
-    "    icd10_list = sorted(list(set([e[:3] for e in icd10_list])))\n",
-    "    #print(f\"{ph}: {icd10_list}\")\n",
-    "    return {ph: sorted(list(dict.fromkeys([str(e) for e in icd10_list])))}\n",
-    "\n",
-    "from joblib import Parallel, delayed\n",
-    "concept_ids = vocab[\"concept\"].query(\"(vocabulary_id == 'SNOMED') | (vocabulary_id == 'ICD10CM')\")\n",
-    "vocab_concept_ids = concept_ids.concept_id.to_list()\n",
-    "concept_ancestor = vocab[\"concept_ancestor\"][[\"ancestor_concept_id\", \"descendant_concept_id\"]].query(\"ancestor_concept_id == @vocab_concept_ids\")\n",
-    "concept_rel = vocab[\"concept_relationship\"][[\"concept_id_1\", \"concept_id_2\", \"relationship_id\"]].query(\"(concept_id_1 == @vocab_concept_ids) & (concept_id_2 == @vocab_concept_ids) & (relationship_id == 'Mapped from')\")\n",
-    "icd10_codes = Parallel(n_jobs=10, require=\"sharedmem\")(delayed(map_snomed_to_icd10)(ph, snomed_code, \n",
-    "                                                              concept_ids, concept_ancestor, concept_rel) for ph, snomed_code in tqdm(phenotype_list_snomed.items()))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:39:55.348009Z",
-     "start_time": "2020-11-04T12:39:55.344704Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "l10_snomed = {}\n",
-    "for ph in icd10_codes: l10_snomed.update(ph)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:39:55.468545Z",
-     "start_time": "2020-11-04T12:39:55.349545Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "phenotype_list_basic = {\n",
-    "    \"coronary_heart_disease\": [\"Ischemic heart disease\"],\n",
-    "    \"myocardial_infarction\": [\"Myocardial infarction\"],\n",
-    "    \"stroke\": [\"Cerebrovascular disease\"],\n",
-    "    \"diabetes1\" : [\"Type 1 diabetes\"],\n",
-    "    \"diabetes2\" : [\"Diabetes mellitus\", \"Type 2 diabetes\"],\n",
-    "    \"chronic_kidney_disease\": [\"Chronic kidney disease\", \"chronic renal failure\"],\n",
-    "    \"atrial_fibrillation\": [\"Atrial fibrillation\", \"Atrial flutter\", \"paroxysmal tachycardia\"],\n",
-    "    \"migraine\": [\"Migraine\"],\n",
-    "    \"rheumatoid_arthritis\": [\"Rheumatoid arthritis\"],\n",
-    "    \"systemic_lupus_erythematosus\": [\"Systemic lupus erythematosus\"],\n",
-    "    \"severe_mental_illness\": [\"Schizophrenia\", \"bipolar\", \"Major depressive disorder\"],\n",
-    "    \"erectile_dysfunction\" : [\"Erectile dysfunction\"],  \n",
-    "}\n",
-    "\n",
-    "l10_basic = phenotype_children(phecodes, phenotype_list_basic)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:39:55.473556Z",
-     "start_time": "2020-11-04T12:39:55.471051Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "l10_all = l10_basic\n",
-    "for key, value in l10_snomed.items(): \n",
-    "    if key not in l10_basic: l10_all[key] = value"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:39:55.620916Z",
-     "start_time": "2020-11-04T12:39:55.475137Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "l10 = {k: v for k, v in l10_all.items() if len(v)!=0}\n",
-    "\n",
-    "#phenotype_list = {k: v for k, v in phenotype_list.items() if k in list(l10.keys())}\n",
-    "\n",
-    "with open(os.path.join(path, dataset_path, 'phenotype_list.yaml'), 'w') as file: yaml.dump(l10, file, default_flow_style=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 1. Self Reported"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:43:13.288198Z",
-     "start_time": "2020-11-04T12:43:13.264683Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "coding609 = pd.read_csv(f\"{path}/mapping/codings/coding609.tsv\", sep=\"\\t\").rename(columns={\"coding\":\"code\"})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:43:13.304423Z",
-     "start_time": "2020-11-04T12:43:13.289982Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from datetime import datetime, timedelta\n",
-    "\n",
-    "def datetime_from_dec_year(dec_year):\n",
-    "    start = dec_year\n",
-    "    year = int(start)\n",
-    "    rem = start - year\n",
-    "\n",
-    "    base = datetime(year, 1, 1)\n",
-    "    result = base + timedelta(seconds=(base.replace(year=base.year + 1) - base).total_seconds() * rem)\n",
-    "    #result.strftime(\"%Y-%m-%d\")\n",
-    "    return result.date()\n",
-    "\n",
-    "def extract_map_self_reported(data, data_field, code_map):\n",
-    "    pbar = tqdm(total=16)\n",
-    "    ### codes\n",
-    "    fields = [\"20002\"]; pbar.update(1)\n",
-    "    raw = get_data_fields_all(fields, data, data_field); pbar.update(1)\n",
-    "    col = \"noncancer_illness_code_selfreported_f20002\"; pbar.update(1)\n",
-    "    temp = pd.wide_to_long(raw, stubnames=[col], i=\"eid\", j=\"instance_index\", sep=\"_\", suffix=\"\\w+\").reset_index(); pbar.update(1)\n",
-    "    codes = temp.rename(columns={col:\"code\"})\\\n",
-    "        .assign(code=lambda x: x.code.astype(str))\\\n",
-    "        .replace(\"None\", np.nan) \\\n",
-    "        .dropna(subset=[\"code\"], axis=0)\\\n",
-    "        .assign(code=lambda x: x.code.astype(int)) \\\n",
-    "        .merge(code_map, how=\"left\",on=\"code\") \\\n",
-    "        .dropna(subset=[\"meaning\"], axis=0)\\\n",
-    "        .sort_values([\"eid\", \"instance_index\"]) \\\n",
-    "        .reset_index(drop=True); pbar.update(1)\n",
-    "    \n",
-    "    ### dates\n",
-    "    fields = [\"20008\"]; pbar.update(1)\n",
-    "    raw = get_data_fields_all(fields, data, data_field); pbar.update(1)\n",
-    "    col=\"interpolated_year_when_noncancer_illness_first_diagnosed_f20008\"; pbar.update(1)\n",
-    "    temp = pd.wide_to_long(raw, stubnames=[col], i=\"eid\", j=\"instance_index\", sep=\"_\", suffix=\"\\w+\").reset_index(); pbar.update(1)\n",
-    "    dates = temp.rename(columns={col:\"date\"})\\\n",
-    "        .dropna(subset=[\"date\"], axis=0)\\\n",
-    "        .sort_values([\"eid\", \"instance_index\"]) \\\n",
-    "        .reset_index(drop=True); pbar.update(1)\n",
-    "\n",
-    "    dates = dates[dates.date!=-1]; pbar.update(1)\n",
-    "    dates = dates[dates.date!=-3]; pbar.update(1)\n",
-    "    dates.date = dates.date.apply(datetime_from_dec_year); pbar.update(1)\n",
-    "    \n",
-    "    test = codes.merge(dates, how=\"left\", on=[\"eid\", \"instance_index\"]).assign(origin=\"self_reported\").copy(); pbar.update(1)\n",
-    "    \n",
-    "    test[\"instance_index\"] = test[\"instance_index\"].astype(\"string\"); pbar.update(1)\n",
-    "    test[['instance','n']] = test.instance_index.str.split(\"_\",expand=True); pbar.update(1)\n",
-    "    pbar.close()\n",
-    "    \n",
-    "    return test[[\"eid\", \"origin\", 'instance','n', \"code\", \"meaning\", \"date\"]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:46:00.098893Z",
-     "start_time": "2020-11-04T12:43:13.432479Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "7af3e55ecb424d5fbbd303f1f5b67a66",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(FloatProgress(value=0.0, max=16.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "codes_self_reported = extract_map_self_reported(data, data_field, coding609)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 2. Primary Care"
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {},
-   "source": [
-    "# 2. Primary Care\n",
-    "fields = c(\n",
-    "    42040 # GP clinical event records\n",
-    ")\n",
-    "# extract covariates at baseline (Instance 0)\n",
-    "def = data_field %>% filter((field.showcase %in% fields),ignore.case = TRUE)\n",
-    "diagnoses_primary_care = data[, append(\"eid\", def$col.name)]\n",
-    "head(diagnoses_primary_care)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 3. Hospital episode statistics"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:46:08.959617Z",
-     "start_time": "2020-11-04T12:46:00.100618Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "codes_hospital_records = pd.read_feather(f\"{data_path}/1_decoded/codes_hospital_records.feather\").drop(\"level\", axis=1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Combine diagnoses and events"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:46:25.115010Z",
-     "start_time": "2020-11-04T12:46:08.961388Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>origin</th>\n",
-       "      <th>instance</th>\n",
-       "      <th>n</th>\n",
-       "      <th>code</th>\n",
-       "      <th>meaning</th>\n",
-       "      <th>date</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>hes_icd10</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>S0240</td>\n",
-       "      <td>S02</td>\n",
-       "      <td>2005-06-02</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>hes_icd10</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>W188</td>\n",
-       "      <td>W18</td>\n",
-       "      <td>2005-06-02</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>hes_icd10</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>K37</td>\n",
-       "      <td>K37</td>\n",
-       "      <td>1998-05-11</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>hes_icd10</td>\n",
-       "      <td>0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>K37</td>\n",
-       "      <td>K37</td>\n",
-       "      <td>1998-05-16</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>hes_icd10</td>\n",
-       "      <td>0</td>\n",
-       "      <td>5</td>\n",
-       "      <td>K37</td>\n",
-       "      <td>K37</td>\n",
-       "      <td>1998-06-01</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       eid     origin instance  n   code meaning        date\n",
-       "0  1000018  hes_icd10        0  1  S0240     S02  2005-06-02\n",
-       "1  1000018  hes_icd10        0  2   W188     W18  2005-06-02\n",
-       "2  1000018  hes_icd10        0  3    K37     K37  1998-05-11\n",
-       "3  1000018  hes_icd10        0  4    K37     K37  1998-05-16\n",
-       "4  1000018  hes_icd10        0  5    K37     K37  1998-06-01"
-      ]
-     },
-     "execution_count": 51,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "diagnoses_codes = codes_self_reported.append(codes_hospital_records).sort_values([\"eid\", \"instance\", \"n\"]).dropna(subset=[\"date\"], axis=0)\n",
-    "diagnoses_codes.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "RangeIndex: 13339466 entries, 0 to 13339465\n",
-      "Data columns (total 7 columns):\n",
-      " #   Column    Dtype \n",
-      "---  ------    ----- \n",
-      " 0   eid       int64 \n",
-      " 1   origin    object\n",
-      " 2   instance  object\n",
-      " 3   n         object\n",
-      " 4   code      object\n",
-      " 5   meaning   object\n",
-      " 6   date      object\n",
-      "dtypes: int64(1), object(6)\n",
-      "memory usage: 712.4+ MB\n"
-     ]
-    }
-   ],
-   "source": [
-    "diagnoses_codes.reset_index(drop=True).info()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "RangeIndex: 12547626 entries, 0 to 12547625\n",
-      "Data columns (total 7 columns):\n",
-      " #   Column    Dtype  \n",
-      "---  ------    -----  \n",
-      " 0   eid       int32  \n",
-      " 1   origin    object \n",
-      " 2   instance  float64\n",
-      " 3   n         int32  \n",
-      " 4   code      object \n",
-      " 5   meaning   object \n",
-      " 6   date      object \n",
-      "dtypes: float64(1), int32(2), object(4)\n",
-      "memory usage: 574.4+ MB\n"
-     ]
-    }
-   ],
-   "source": [
-    "codes_hospital_records.info()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "diagnoses_codes.reset_index(drop=True).assign(eid = lambda x: x.eid.astype(int),\n",
-    "                                              origin = lambda x: x.origin.astype(str),\n",
-    "                                              instance = lambda x: x.instance.astype(int),\n",
-    "                                              n = lambda x: x.n.astype(int),\n",
-    "                                              code = lambda x: x.code.astype(str), \n",
-    "                                              meaning = lambda x: x.meaning.astype(str))\\\n",
-    "    .to_feather(os.path.join(path, dataset_path, 'temp_diagnoses_codes.feather'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "diagnoses_codes = pd.read_feather(os.path.join(path, dataset_path, 'temp_diagnoses_codes.feather'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:46:26.189927Z",
-     "start_time": "2020-11-04T12:46:25.117069Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from joblib import Parallel, delayed\n",
-    "from functools import reduce\n",
-    "from numba import jit\n",
-    "\n",
-    "def had_diagnosis_before_per_ph(df_before, ph, ph_codes, temp):\n",
-    "   # regex = \"|\".join(ph_codes)\n",
-    "    #df_ph = df_before.set_index(\"meaning\").loc[ph_codes][[\"eid\"]]\\\n",
-    "    #        .drop_duplicates(subset=[\"eid\"])\\\n",
-    "    #        .assign(phenotype=True) \n",
-    "    df_ph = df_before[df_before.meaning.isin(ph_codes)][[\"eid\"]]\\\n",
-    "            .drop_duplicates(subset=[\"eid\"])\\\n",
-    "            .assign(phenotype=True) \n",
-    "    #df_ph = df_before[df_before.meaning.str.contains(regex, case=False)][[\"eid\"]]\\\n",
-    "    #        .drop_duplicates(subset=[\"eid\"])\\\n",
-    "    #        .assign(phenotype=True)   \n",
-    "    return temp.merge(df_ph, how=\"left\", on=\"eid\").fillna(False).phenotype\n",
-    "\n",
-    "def had_diagnosis_before(data, diagnoses_codes, phenotypes, time0=time0_col):\n",
-    "    diagnoses_codes_time = diagnoses_codes.merge(data[[\"eid\", time0]], how=\"left\", on=\"eid\")\n",
-    "    \n",
-    "    temp = data[[\"eid\"]].copy()\n",
-    "    df_before = diagnoses_codes_time[diagnoses_codes_time.date < diagnoses_codes_time[time0]]\n",
-    "                                                                                         \n",
-    "    df_phs = Parallel(n_jobs=20, require=\"sharedmem\")(delayed(had_diagnosis_before_per_ph)(df_before, ph, phenotypes[ph], temp) for ph in tqdm(list(phenotypes)))\n",
-    "    for ph, df_ph_series in zip(tqdm(list(phenotypes)), df_phs): temp[ph] = df_ph_series#temp.merge(df_ph, how=\"left\", on=\"eid\").fillna(False).phenotype\n",
-    "    \n",
-    "    return temp.sort_values(\"eid\")  #reduce(lambda left,right: pd.merge(left,right,on=['eid'], how='left'), df_phs)."
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "source": [
-    "from joblib import Parallel, delayed\n",
-    "from functools import reduce\n",
-    "from numba import jit\n",
-    "\n",
-    "def had_diagnosis_before_per_ph(df_before, ph, ph_codes, temp):\n",
-    "    regex = \"|\".join(ph_codes)\n",
-    "    df_ph = df_before[df_before.meaning.str.contains(regex, case=False)][[\"eid\"]]\\\n",
-    "            .drop_duplicates(subset=[\"eid\"])\\\n",
-    "            .assign(phenotype=True)   \n",
-    "    return temp.merge(df_ph, how=\"left\", on=\"eid\").fillna(False).phenotype\n",
-    "\n",
-    "def had_diagnosis_before(data, diagnoses_codes, phenotypes, time0=time0_col):\n",
-    "    diagnoses_codes_time = diagnoses_codes.merge(data[[\"eid\", time0]], how=\"left\", on=\"eid\")\n",
-    "    \n",
-    "    temp = data[[\"eid\"]].copy()\n",
-    "    df_before = diagnoses_codes_time[diagnoses_codes_time.date < diagnoses_codes_time[time0]]\n",
-    "                                                                                         \n",
-    "    df_phs = Parallel(n_jobs=30, require=\"sharedmem\")(delayed(had_diagnosis_before_per_ph)(df_before, ph, phenotypes[ph], temp) for ph in tqdm(list(phenotypes)))\n",
-    "    for ph, ph_series in zip(tqdm(list(phenotypes)), df_phs): temp[ph] = ph_series\n",
-    "    \n",
-    "    return temp.sort_values(\"eid\")  #reduce(lambda left,right: pd.merge(left,right,on=['eid'], how='left'), df_phs)."
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "source": [
-    "phenotypes = l10\n",
-    "time0 = time0_col\n",
-    "diagnoses_codes_time = diagnoses_codes.merge(data[[\"eid\", time0]], how=\"left\", on=\"eid\")\n",
-    "\n",
-    "temp = data[[\"eid\"]].copy()\n",
-    "df_before = diagnoses_codes_time[diagnoses_codes_time.date < diagnoses_codes_time[time0]]\n",
-    "\n",
-    "df_phs = Parallel(n_jobs=10, require=\"sharedmem\")(delayed(had_diagnosis_before_per_ph)(df_before, ph, phenotypes[ph], temp) for ph in tqdm(list(phenotypes)[0:100]))"
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "source": [
-    "def had_diagnosis_before(data, diagnoses_codes, phenotypes, time0=time0_col):\n",
-    "    diagnoses_codes_time = diagnoses_codes.merge(data[[\"eid\", time0]], how=\"left\", on=\"eid\")\n",
-    "    \n",
-    "    temp = data[[\"eid\"]].copy()\n",
-    "    df_before = diagnoses_codes_time[diagnoses_codes_time.date < diagnoses_codes_time[time0]]\n",
-    "    for ph, ph_codes in tqdm(phenotypes.items()):\n",
-    "        regex = \"|\".join(ph_codes)\n",
-    "        df_ph = df_before[df_before.meaning.str.contains(regex, case=False)][[\"eid\"]]\\\n",
-    "            .drop_duplicates(subset=[\"eid\"])\\\n",
-    "            .assign(phenotype=True)\n",
-    "        temp[ph] = temp.merge(df_ph, how=\"left\", on=\"eid\").fillna(False).phenotype\n",
-    "       \n",
-    "    return temp.sort_values(\"eid\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T13:17:52.922023Z",
-     "start_time": "2020-11-04T12:46:26.191314Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
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-       "model_id": "6bea6215cd7a462ea5e12e755d4a70ed",
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-      "text/plain": [
-       "HBox(children=(FloatProgress(value=0.0, max=3521.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
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-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
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-     ]
-    },
-    {
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-       "HBox(children=(FloatProgress(value=0.0, max=3521.0), HTML(value='')))"
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-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "502504\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
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-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>coronary_heart_disease</th>\n",
-       "      <th>myocardial_infarction</th>\n",
-       "      <th>stroke</th>\n",
-       "      <th>diabetes1</th>\n",
-       "      <th>diabetes2</th>\n",
-       "      <th>chronic_kidney_disease</th>\n",
-       "      <th>atrial_fibrillation</th>\n",
-       "      <th>migraine</th>\n",
-       "      <th>rheumatoid_arthritis</th>\n",
-       "      <th>...</th>\n",
-       "      <th>sleep_terror_disorder</th>\n",
-       "      <th>acute_frontal_sinusitis</th>\n",
-       "      <th>benign_neoplasm_of_pancreas</th>\n",
-       "      <th>primary_malignant_neoplasm_of_soft_tissues_of_lower_limb</th>\n",
-       "      <th>neoplasm_of_uncertain_behavior_of_neck</th>\n",
-       "      <th>injury_of_peroneal_nerve</th>\n",
-       "      <th>dupuytren's_disease</th>\n",
-       "      <th>stem_cell_donor</th>\n",
-       "      <th>endemic_goiter</th>\n",
-       "      <th>diplegic_cerebral_palsy</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1000020</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1000037</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000043</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1000051</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 3522 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       eid  coronary_heart_disease  myocardial_infarction  stroke  diabetes1  \\\n",
-       "0  1000018                   False                  False   False      False   \n",
-       "1  1000020                   False                  False   False      False   \n",
-       "2  1000037                   False                  False   False      False   \n",
-       "3  1000043                   False                  False   False      False   \n",
-       "4  1000051                   False                  False   False      False   \n",
-       "\n",
-       "   diabetes2  chronic_kidney_disease  atrial_fibrillation  migraine  \\\n",
-       "0      False                   False                False     False   \n",
-       "1      False                   False                False     False   \n",
-       "2      False                   False                False     False   \n",
-       "3      False                   False                False     False   \n",
-       "4      False                   False                False     False   \n",
-       "\n",
-       "   rheumatoid_arthritis  ...  sleep_terror_disorder  acute_frontal_sinusitis  \\\n",
-       "0                 False  ...                  False                    False   \n",
-       "1                 False  ...                  False                    False   \n",
-       "2                 False  ...                  False                    False   \n",
-       "3                 False  ...                  False                    False   \n",
-       "4                 False  ...                  False                    False   \n",
-       "\n",
-       "   benign_neoplasm_of_pancreas  \\\n",
-       "0                        False   \n",
-       "1                        False   \n",
-       "2                        False   \n",
-       "3                        False   \n",
-       "4                        False   \n",
-       "\n",
-       "   primary_malignant_neoplasm_of_soft_tissues_of_lower_limb  \\\n",
-       "0                                              False          \n",
-       "1                                              False          \n",
-       "2                                              False          \n",
-       "3                                              False          \n",
-       "4                                              False          \n",
-       "\n",
-       "   neoplasm_of_uncertain_behavior_of_neck  injury_of_peroneal_nerve  \\\n",
-       "0                                   False                     False   \n",
-       "1                                   False                     False   \n",
-       "2                                   False                     False   \n",
-       "3                                   False                     False   \n",
-       "4                                   False                     False   \n",
-       "\n",
-       "   dupuytren's_disease  stem_cell_donor  endemic_goiter  \\\n",
-       "0                False            False           False   \n",
-       "1                False            False           False   \n",
-       "2                False            False           False   \n",
-       "3                False            False           False   \n",
-       "4                False            False           False   \n",
-       "\n",
-       "   diplegic_cerebral_palsy  \n",
-       "0                    False  \n",
-       "1                    False  \n",
-       "2                    False  \n",
-       "3                    False  \n",
-       "4                    False  \n",
-       "\n",
-       "[5 rows x 3522 columns]"
-      ]
-     },
-     "execution_count": 61,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "diagnoses = had_diagnosis_before(basics, diagnoses_codes, l10, time0=time0_col)\n",
-    "print(len(diagnoses))\n",
-    "\n",
-    "diagnoses.to_feather(os.path.join(path, dataset_path, 'temp_diagnoses.feather'))\n",
-    "\n",
-    "diagnoses.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "diagnoses = pd.read_feather(os.path.join(path, dataset_path, 'temp_diagnoses.feather'))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "source": [
-    "# Add embeddings for Snomed Diagnoses"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "source": [
-    "#### Get SNOMED - node2vec dict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "outputs": [],
-   "source": [
-    "snomed_embeddings = pd.read_csv(\"/data/analysis/ag-reils/steinfej/data/snomed_embeddings/snomed.emb.p1.q1.w20.l40.e200.graph_format.txt\", sep=\" \", header=None, skiprows=1)\n",
-    "snomed_embeddings.columns = [\"snomed_id\"]+list(snomed_embeddings.columns)[:-1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "metadata": {
-    "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true,
-     "source_hidden": true
-    }
-   },
-   "outputs": [
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-       "      <td>...</td>\n",
-       "      <td>0.211030</td>\n",
-       "      <td>0.488475</td>\n",
-       "      <td>0.082177</td>\n",
-       "      <td>-0.047102</td>\n",
-       "      <td>-0.086411</td>\n",
-       "      <td>-0.012141</td>\n",
-       "      <td>-0.258416</td>\n",
-       "      <td>-0.113295</td>\n",
-       "      <td>0.040249</td>\n",
-       "      <td>-0.116884</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>360224006</td>\n",
-       "      <td>-0.090187</td>\n",
-       "      <td>0.086986</td>\n",
-       "      <td>-0.184378</td>\n",
-       "      <td>-0.028722</td>\n",
-       "      <td>-0.010235</td>\n",
-       "      <td>0.242439</td>\n",
-       "      <td>-0.094009</td>\n",
-       "      <td>0.203015</td>\n",
-       "      <td>-0.097894</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.112750</td>\n",
-       "      <td>0.333906</td>\n",
-       "      <td>-0.018193</td>\n",
-       "      <td>0.020459</td>\n",
-       "      <td>-0.170226</td>\n",
-       "      <td>0.277866</td>\n",
-       "      <td>-0.007079</td>\n",
-       "      <td>-0.080419</td>\n",
-       "      <td>0.162456</td>\n",
-       "      <td>-0.101768</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>102272007</td>\n",
-       "      <td>0.553420</td>\n",
-       "      <td>0.460947</td>\n",
-       "      <td>0.143925</td>\n",
-       "      <td>-0.053785</td>\n",
-       "      <td>-0.849588</td>\n",
-       "      <td>0.467587</td>\n",
-       "      <td>-0.654922</td>\n",
-       "      <td>-0.010799</td>\n",
-       "      <td>0.510141</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.187371</td>\n",
-       "      <td>0.248607</td>\n",
-       "      <td>0.079687</td>\n",
-       "      <td>-0.354121</td>\n",
-       "      <td>0.412602</td>\n",
-       "      <td>0.582461</td>\n",
-       "      <td>-0.780365</td>\n",
-       "      <td>0.045450</td>\n",
-       "      <td>-0.196551</td>\n",
-       "      <td>0.045745</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>39937001</td>\n",
-       "      <td>0.123158</td>\n",
-       "      <td>0.019882</td>\n",
-       "      <td>0.050896</td>\n",
-       "      <td>0.037364</td>\n",
-       "      <td>0.200435</td>\n",
-       "      <td>0.312911</td>\n",
-       "      <td>-0.338977</td>\n",
-       "      <td>-0.092584</td>\n",
-       "      <td>-0.167741</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.057770</td>\n",
-       "      <td>-0.012834</td>\n",
-       "      <td>-0.139794</td>\n",
-       "      <td>0.180572</td>\n",
-       "      <td>0.190781</td>\n",
-       "      <td>0.104039</td>\n",
-       "      <td>-0.358235</td>\n",
-       "      <td>-0.137954</td>\n",
-       "      <td>-0.236551</td>\n",
-       "      <td>-0.206458</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>23583003</td>\n",
-       "      <td>0.423193</td>\n",
-       "      <td>0.384338</td>\n",
-       "      <td>-0.041503</td>\n",
-       "      <td>0.116848</td>\n",
-       "      <td>-0.055029</td>\n",
-       "      <td>0.149064</td>\n",
-       "      <td>-0.092810</td>\n",
-       "      <td>-0.050148</td>\n",
-       "      <td>0.113122</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.162375</td>\n",
-       "      <td>-0.076505</td>\n",
-       "      <td>-0.274352</td>\n",
-       "      <td>-0.099204</td>\n",
-       "      <td>-0.281887</td>\n",
-       "      <td>-0.266345</td>\n",
-       "      <td>-0.020257</td>\n",
-       "      <td>-0.003843</td>\n",
-       "      <td>-0.008804</td>\n",
-       "      <td>-0.286832</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 201 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   snomed_id         0         1         2         3         4         5  \\\n",
-       "0  129265001  0.027456  0.171026 -0.178822 -0.038667  0.299777  0.082540   \n",
-       "1  360224006 -0.090187  0.086986 -0.184378 -0.028722 -0.010235  0.242439   \n",
-       "2  102272007  0.553420  0.460947  0.143925 -0.053785 -0.849588  0.467587   \n",
-       "3   39937001  0.123158  0.019882  0.050896  0.037364  0.200435  0.312911   \n",
-       "4   23583003  0.423193  0.384338 -0.041503  0.116848 -0.055029  0.149064   \n",
-       "\n",
-       "          6         7         8  ...       190       191       192       193  \\\n",
-       "0 -0.201284  0.215164  0.093241  ...  0.211030  0.488475  0.082177 -0.047102   \n",
-       "1 -0.094009  0.203015 -0.097894  ...  0.112750  0.333906 -0.018193  0.020459   \n",
-       "2 -0.654922 -0.010799  0.510141  ...  0.187371  0.248607  0.079687 -0.354121   \n",
-       "3 -0.338977 -0.092584 -0.167741  ...  0.057770 -0.012834 -0.139794  0.180572   \n",
-       "4 -0.092810 -0.050148  0.113122  ...  0.162375 -0.076505 -0.274352 -0.099204   \n",
-       "\n",
-       "        194       195       196       197       198       199  \n",
-       "0 -0.086411 -0.012141 -0.258416 -0.113295  0.040249 -0.116884  \n",
-       "1 -0.170226  0.277866 -0.007079 -0.080419  0.162456 -0.101768  \n",
-       "2  0.412602  0.582461 -0.780365  0.045450 -0.196551  0.045745  \n",
-       "3  0.190781  0.104039 -0.358235 -0.137954 -0.236551 -0.206458  \n",
-       "4 -0.281887 -0.266345 -0.020257 -0.003843 -0.008804 -0.286832  \n",
-       "\n",
-       "[5 rows x 201 columns]"
-      ]
-     },
-     "execution_count": 64,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "snomed_embeddings.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "outputs": [],
-   "source": [
-    "diagnoses_snomed = diagnoses.columns[13:].to_list()\n",
-    "diagnoses_snomed_dict = {}\n",
-    "for d in diagnoses_snomed: diagnoses_snomed_dict[d] = phenotype_list_snomed[d]\n",
-    "    \n",
-    "snomed_codes_used = list(phenotype_list_snomed.values())\n",
-    "snomed_codes_emb = snomed_embeddings.snomed_id.to_list()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "outputs": [],
-   "source": [
-    "snomed_id_array = snomed_embeddings[[\"snomed_id\"]].values\n",
-    "node2vec_array = snomed_embeddings.iloc[:, 1:].values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "metadata": {
-    "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true,
-     "source_hidden": true
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "456f56d7481648e48b74e9e76b675f01",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(FloatProgress(value=0.0, max=373286.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "snomed_arrays = {}\n",
-    "for sid, row in zip(tqdm(snomed_id_array), node2vec_array):\n",
-    "    snomed_arrays[sid[0]] = row"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "source": [
-    "#### Get Patient -> Snomed dict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 68,
-   "metadata": {
-    "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true,
-     "source_hidden": true
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "05ac46541972426da8a5b5f7fe461c6e",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(FloatProgress(value=0.0, max=502504.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "diagnoses_array = diagnoses[diagnoses_snomed].values\n",
-    "eid_array = diagnoses[[\"eid\"]].values\n",
-    "\n",
-    "from numba import jit\n",
-    "import numpy as np\n",
-    "\n",
-    "patient_diagnoses = {}\n",
-    "for eid, row in zip(tqdm(eid_array), diagnoses_array):\n",
-    "    patient_diagnoses[eid[0]] = list(np.argwhere(row==True).flatten())\n",
-    "\n",
-    "#diagnoses.query(\"eid==1000092\")[diagnoses_snomed]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 69,
-   "metadata": {
-    "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true,
-     "source_hidden": true
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f4c90d91d08e48119abcf8426cddd37e",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(FloatProgress(value=0.0, max=502504.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "patient_diagnoses_sid = {}\n",
-    "snomed_codes_emb_set = set(snomed_codes_emb)\n",
-    "for eid, p_d_col in tqdm(patient_diagnoses.items()):\n",
-    "    diagnoses_list = [diagnoses_snomed[i] for i in p_d_col]\n",
-    "    sid_list = [phenotype_list_snomed[i] for i in diagnoses_list]\n",
-    "    sid_list = [sid for sid in sid_list if sid in snomed_codes_emb_set]\n",
-    "    patient_diagnoses_sid[eid] = sid_list"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "source": [
-    "#### Get Patient -> node2vec average"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 70,
-   "metadata": {
-    "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true,
-     "source_hidden": true
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "47e934dc64334c82893d23b458108fbf",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(FloatProgress(value=0.0, max=502504.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/steinfej/miniconda3/envs/python/lib/python3.7/site-packages/numpy/core/fromnumeric.py:3335: RuntimeWarning: Mean of empty slice.\n",
-      "  out=out, **kwargs)\n",
-      "/home/steinfej/miniconda3/envs/python/lib/python3.7/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars\n",
-      "  ret = ret.dtype.type(ret / rcount)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "patient_node2vec_dict = {}\n",
-    "for eid, sids in tqdm(patient_diagnoses_sid.items()):\n",
-    "    array_list =  [snomed_arrays[sid] for sid in sids]\n",
-    "    patient_node2vec_dict[eid] = np.mean(array_list, axis=0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "metadata": {
-    "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true,
-     "source_hidden": true
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e1395c2d1bfc41f39697a8925b20d597",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(FloatProgress(value=0.0, max=502504.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "array([ 0.37033895,  0.13321076, -0.10030267,  0.05625264, -0.08437331,\n",
-       "        0.11504787, -0.24833219, -0.11936529,  0.25239648, -0.32739932,\n",
-       "        0.06330914, -0.06493541, -0.28790415, -0.2885537 ,  0.02546298,\n",
-       "       -0.06123153, -0.17690672, -0.0867546 , -0.28337599, -0.08474496,\n",
-       "        0.31454532, -0.24158154,  0.15930864,  0.06124846,  0.07694602,\n",
-       "       -0.13752803,  0.13671128, -0.27988751, -0.03987635,  0.03632156,\n",
-       "       -0.13793361, -0.15857369, -0.16441636,  0.2412931 ,  0.20070578,\n",
-       "       -0.11412784,  0.02563965, -0.03560184,  0.17169744, -0.0153383 ,\n",
-       "        0.00117099,  0.1025362 , -0.06505568,  0.05646065, -0.02705149,\n",
-       "        0.04416442, -0.15798991, -0.10650637,  0.02082507, -0.21182802,\n",
-       "        0.13972325, -0.18089307, -0.12731068,  0.02907221, -0.19797107,\n",
-       "        0.19550177,  0.14941799,  0.21561857, -0.18085379,  0.10768238,\n",
-       "        0.12968045,  0.2082016 ,  0.03561408, -0.01122218, -0.27099816,\n",
-       "       -0.06029919, -0.18787618,  0.10084175, -0.07939234, -0.22951897,\n",
-       "       -0.36536359, -0.01050854, -0.21419807, -0.23562986,  0.02380316,\n",
-       "        0.1213157 , -0.1601396 ,  0.07218057, -0.04362593, -0.22303355,\n",
-       "       -0.23844839, -0.14799905,  0.22346404,  0.14218655,  0.39873181,\n",
-       "       -0.32965129,  0.19102432, -0.08894028, -0.20726567,  0.25343222,\n",
-       "        0.29939455,  0.20513029,  0.17430424, -0.05073184,  0.05827969,\n",
-       "        0.31626954,  0.0791522 , -0.21433214,  0.11027244,  0.0805151 ,\n",
-       "        0.08557279,  0.25260801, -0.00096969, -0.06177831,  0.0544524 ,\n",
-       "       -0.17340027,  0.09731447,  0.09982385, -0.074546  , -0.10350239,\n",
-       "       -0.25798929,  0.01222471,  0.1605315 ,  0.00191767,  0.11642527,\n",
-       "       -0.15387604, -0.01520803,  0.03220765, -0.13534233,  0.09154689,\n",
-       "        0.12562997, -0.01667786, -0.09286805,  0.14670483, -0.13097813,\n",
-       "       -0.38270284, -0.0129984 , -0.06352304,  0.12521014, -0.17354363,\n",
-       "       -0.16399127,  0.11488736,  0.39664734,  0.08557075, -0.24862126,\n",
-       "       -0.05882866,  0.23174289,  0.01765143, -0.10257617,  0.01208248,\n",
-       "       -0.06876405, -0.04180976,  0.07489962, -0.10724381,  0.29349823,\n",
-       "        0.17342743, -0.32044841, -0.01118798,  0.28508293,  0.16492201,\n",
-       "        0.03994952, -0.17309702,  0.0365077 ,  0.08619365, -0.21749571,\n",
-       "        0.01038752, -0.21414902, -0.18092813,  0.28433131, -0.12601774,\n",
-       "       -0.02147302,  0.51513453,  0.21506432, -0.12596341, -0.0413009 ,\n",
-       "        0.12595327,  0.08973215, -0.06285449, -0.03649041,  0.04217289,\n",
-       "       -0.02532634, -0.21734533, -0.08211848,  0.14014083,  0.13635479,\n",
-       "       -0.13163414, -0.08576438, -0.04016334,  0.23952563, -0.14895943,\n",
-       "        0.08564821, -0.04287149,  0.22135877, -0.06337237, -0.07189574,\n",
-       "       -0.1004494 ,  0.01225299, -0.13810014, -0.06814497, -0.16715941,\n",
-       "        0.07303671,  0.15891015, -0.04757821, -0.05777645, -0.0772216 ,\n",
-       "        0.16787738, -0.2112512 , -0.10142653, -0.19002809, -0.06400223])"
-      ]
-     },
-     "execution_count": 71,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# create imputation vector\n",
-    "arrays = [patient_node2vec_dict[key] for key in list(patient_node2vec_dict)]\n",
-    "arrays_ok = [array for array in tqdm(arrays) if ~np.isnan(array).any()]\n",
-    "imp_vector = np.mean(arrays_ok, axis=0)\n",
-    "imp_vector"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
-   "metadata": {
-    "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true,
-     "source_hidden": true
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e2dd803c2e2a4cb68e71111979d235c2",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(FloatProgress(value=0.0, max=502504.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "for eid, array in tqdm(patient_node2vec_dict.items()):\n",
-    "    if np.isnan(array).any(): \n",
-    "        patient_node2vec_dict[eid] = imp_vector"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 73,
-   "metadata": {
-    "collapsed": true,
-    "jupyter": {
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-   "outputs": [
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-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
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-   ],
-   "source": [
-    "array_eids = [key for key in tqdm(list(patient_node2vec_dict))]"
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-  {
-   "cell_type": "code",
-   "execution_count": 74,
-   "metadata": {
-    "collapsed": true,
-    "jupyter": {
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-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
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-   ],
-   "source": [
-    "ind_ok = [np.array([1]) if ~np.isnan(array).any() else np.array([0])  for array in tqdm(arrays)]"
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-  {
-   "cell_type": "code",
-   "execution_count": 75,
-   "metadata": {
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-     "metadata": {},
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-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
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-   ],
-   "source": [
-    "arrays_emb = [patient_node2vec_dict[key] for key in tqdm(list(patient_node2vec_dict))]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 76,
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "outputs": [],
-   "source": [
-    "arrays_eids = np.reshape(np.stack(array_eids, axis=0),(-1,1)) \n",
-    "arrays_ind = np.stack(ind_ok, axis=0)\n",
-    "arrays_c = np.stack(arrays_emb, axis=0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 77,
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "outputs": [],
-   "source": [
-    "arrays_complete = np.concatenate([arrays_eids, arrays_ind, arrays_c], axis=1)"
-   ]
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-  {
-   "cell_type": "code",
-   "execution_count": 78,
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
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-   },
-   "outputs": [],
-   "source": [
-    "diagnoses_emb = pd.DataFrame(data=arrays_complete, columns=[\"eid\"]+[\"node2vec_available\"]+[f\"node2vec_{e}\" for e in list(range(0, 200))])"
-   ]
-  },
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-    "jupyter": {
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-       "      <th></th>\n",
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-       "      <td>1000020.0</td>\n",
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-       "      <td>0.133211</td>\n",
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-       "      <td>0.115048</td>\n",
-       "      <td>-0.248332</td>\n",
-       "      <td>-0.119365</td>\n",
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-       "      <td>0.073037</td>\n",
-       "      <td>0.15891</td>\n",
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-       "      <td>-0.057776</td>\n",
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-       "      <td>-0.211251</td>\n",
-       "      <td>-0.101427</td>\n",
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-       "      <td>-0.064002</td>\n",
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-       "      <td>1000037.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.370339</td>\n",
-       "      <td>0.133211</td>\n",
-       "      <td>-0.100303</td>\n",
-       "      <td>0.056253</td>\n",
-       "      <td>-0.084373</td>\n",
-       "      <td>0.115048</td>\n",
-       "      <td>-0.248332</td>\n",
-       "      <td>-0.119365</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.073037</td>\n",
-       "      <td>0.15891</td>\n",
-       "      <td>-0.047578</td>\n",
-       "      <td>-0.057776</td>\n",
-       "      <td>-0.077222</td>\n",
-       "      <td>0.167877</td>\n",
-       "      <td>-0.211251</td>\n",
-       "      <td>-0.101427</td>\n",
-       "      <td>-0.190028</td>\n",
-       "      <td>-0.064002</td>\n",
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-       "      <th>3</th>\n",
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-       "      <td>0.0</td>\n",
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-       "      <td>0.133211</td>\n",
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-       "      <td>0.056253</td>\n",
-       "      <td>-0.084373</td>\n",
-       "      <td>0.115048</td>\n",
-       "      <td>-0.248332</td>\n",
-       "      <td>-0.119365</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.073037</td>\n",
-       "      <td>0.15891</td>\n",
-       "      <td>-0.047578</td>\n",
-       "      <td>-0.057776</td>\n",
-       "      <td>-0.077222</td>\n",
-       "      <td>0.167877</td>\n",
-       "      <td>-0.211251</td>\n",
-       "      <td>-0.101427</td>\n",
-       "      <td>-0.190028</td>\n",
-       "      <td>-0.064002</td>\n",
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-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1000051.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.370339</td>\n",
-       "      <td>0.133211</td>\n",
-       "      <td>-0.100303</td>\n",
-       "      <td>0.056253</td>\n",
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-       "      <td>-0.119365</td>\n",
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-       "      <td>0.073037</td>\n",
-       "      <td>0.15891</td>\n",
-       "      <td>-0.047578</td>\n",
-       "      <td>-0.057776</td>\n",
-       "      <td>-0.077222</td>\n",
-       "      <td>0.167877</td>\n",
-       "      <td>-0.211251</td>\n",
-       "      <td>-0.101427</td>\n",
-       "      <td>-0.190028</td>\n",
-       "      <td>-0.064002</td>\n",
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-       "      <td>...</td>\n",
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-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
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-       "    <tr>\n",
-       "      <th>502499</th>\n",
-       "      <td>6025150.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.370339</td>\n",
-       "      <td>0.133211</td>\n",
-       "      <td>-0.100303</td>\n",
-       "      <td>0.056253</td>\n",
-       "      <td>-0.084373</td>\n",
-       "      <td>0.115048</td>\n",
-       "      <td>-0.248332</td>\n",
-       "      <td>-0.119365</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.073037</td>\n",
-       "      <td>0.15891</td>\n",
-       "      <td>-0.047578</td>\n",
-       "      <td>-0.057776</td>\n",
-       "      <td>-0.077222</td>\n",
-       "      <td>0.167877</td>\n",
-       "      <td>-0.211251</td>\n",
-       "      <td>-0.101427</td>\n",
-       "      <td>-0.190028</td>\n",
-       "      <td>-0.064002</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>502500</th>\n",
-       "      <td>6025165.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.370339</td>\n",
-       "      <td>0.133211</td>\n",
-       "      <td>-0.100303</td>\n",
-       "      <td>0.056253</td>\n",
-       "      <td>-0.084373</td>\n",
-       "      <td>0.115048</td>\n",
-       "      <td>-0.248332</td>\n",
-       "      <td>-0.119365</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.073037</td>\n",
-       "      <td>0.15891</td>\n",
-       "      <td>-0.047578</td>\n",
-       "      <td>-0.057776</td>\n",
-       "      <td>-0.077222</td>\n",
-       "      <td>0.167877</td>\n",
-       "      <td>-0.211251</td>\n",
-       "      <td>-0.101427</td>\n",
-       "      <td>-0.190028</td>\n",
-       "      <td>-0.064002</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>502501</th>\n",
-       "      <td>6025173.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.370339</td>\n",
-       "      <td>0.133211</td>\n",
-       "      <td>-0.100303</td>\n",
-       "      <td>0.056253</td>\n",
-       "      <td>-0.084373</td>\n",
-       "      <td>0.115048</td>\n",
-       "      <td>-0.248332</td>\n",
-       "      <td>-0.119365</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.073037</td>\n",
-       "      <td>0.15891</td>\n",
-       "      <td>-0.047578</td>\n",
-       "      <td>-0.057776</td>\n",
-       "      <td>-0.077222</td>\n",
-       "      <td>0.167877</td>\n",
-       "      <td>-0.211251</td>\n",
-       "      <td>-0.101427</td>\n",
-       "      <td>-0.190028</td>\n",
-       "      <td>-0.064002</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>502502</th>\n",
-       "      <td>6025182.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.370339</td>\n",
-       "      <td>0.133211</td>\n",
-       "      <td>-0.100303</td>\n",
-       "      <td>0.056253</td>\n",
-       "      <td>-0.084373</td>\n",
-       "      <td>0.115048</td>\n",
-       "      <td>-0.248332</td>\n",
-       "      <td>-0.119365</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.073037</td>\n",
-       "      <td>0.15891</td>\n",
-       "      <td>-0.047578</td>\n",
-       "      <td>-0.057776</td>\n",
-       "      <td>-0.077222</td>\n",
-       "      <td>0.167877</td>\n",
-       "      <td>-0.211251</td>\n",
-       "      <td>-0.101427</td>\n",
-       "      <td>-0.190028</td>\n",
-       "      <td>-0.064002</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>502503</th>\n",
-       "      <td>6025198.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.370339</td>\n",
-       "      <td>0.133211</td>\n",
-       "      <td>-0.100303</td>\n",
-       "      <td>0.056253</td>\n",
-       "      <td>-0.084373</td>\n",
-       "      <td>0.115048</td>\n",
-       "      <td>-0.248332</td>\n",
-       "      <td>-0.119365</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.073037</td>\n",
-       "      <td>0.15891</td>\n",
-       "      <td>-0.047578</td>\n",
-       "      <td>-0.057776</td>\n",
-       "      <td>-0.077222</td>\n",
-       "      <td>0.167877</td>\n",
-       "      <td>-0.211251</td>\n",
-       "      <td>-0.101427</td>\n",
-       "      <td>-0.190028</td>\n",
-       "      <td>-0.064002</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>502504 rows × 202 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "              eid  node2vec_available  node2vec_0  node2vec_1  node2vec_2  \\\n",
-       "0       1000018.0                 0.0    0.370339    0.133211   -0.100303   \n",
-       "1       1000020.0                 0.0    0.370339    0.133211   -0.100303   \n",
-       "2       1000037.0                 0.0    0.370339    0.133211   -0.100303   \n",
-       "3       1000043.0                 0.0    0.370339    0.133211   -0.100303   \n",
-       "4       1000051.0                 0.0    0.370339    0.133211   -0.100303   \n",
-       "...           ...                 ...         ...         ...         ...   \n",
-       "502499  6025150.0                 0.0    0.370339    0.133211   -0.100303   \n",
-       "502500  6025165.0                 0.0    0.370339    0.133211   -0.100303   \n",
-       "502501  6025173.0                 0.0    0.370339    0.133211   -0.100303   \n",
-       "502502  6025182.0                 0.0    0.370339    0.133211   -0.100303   \n",
-       "502503  6025198.0                 0.0    0.370339    0.133211   -0.100303   \n",
-       "\n",
-       "        node2vec_3  node2vec_4  node2vec_5  node2vec_6  node2vec_7  ...  \\\n",
-       "0         0.056253   -0.084373    0.115048   -0.248332   -0.119365  ...   \n",
-       "1         0.056253   -0.084373    0.115048   -0.248332   -0.119365  ...   \n",
-       "2         0.056253   -0.084373    0.115048   -0.248332   -0.119365  ...   \n",
-       "3         0.056253   -0.084373    0.115048   -0.248332   -0.119365  ...   \n",
-       "4         0.056253   -0.084373    0.115048   -0.248332   -0.119365  ...   \n",
-       "...            ...         ...         ...         ...         ...  ...   \n",
-       "502499    0.056253   -0.084373    0.115048   -0.248332   -0.119365  ...   \n",
-       "502500    0.056253   -0.084373    0.115048   -0.248332   -0.119365  ...   \n",
-       "502501    0.056253   -0.084373    0.115048   -0.248332   -0.119365  ...   \n",
-       "502502    0.056253   -0.084373    0.115048   -0.248332   -0.119365  ...   \n",
-       "502503    0.056253   -0.084373    0.115048   -0.248332   -0.119365  ...   \n",
-       "\n",
-       "        node2vec_190  node2vec_191  node2vec_192  node2vec_193  node2vec_194  \\\n",
-       "0           0.073037       0.15891     -0.047578     -0.057776     -0.077222   \n",
-       "1           0.073037       0.15891     -0.047578     -0.057776     -0.077222   \n",
-       "2           0.073037       0.15891     -0.047578     -0.057776     -0.077222   \n",
-       "3           0.073037       0.15891     -0.047578     -0.057776     -0.077222   \n",
-       "4           0.073037       0.15891     -0.047578     -0.057776     -0.077222   \n",
-       "...              ...           ...           ...           ...           ...   \n",
-       "502499      0.073037       0.15891     -0.047578     -0.057776     -0.077222   \n",
-       "502500      0.073037       0.15891     -0.047578     -0.057776     -0.077222   \n",
-       "502501      0.073037       0.15891     -0.047578     -0.057776     -0.077222   \n",
-       "502502      0.073037       0.15891     -0.047578     -0.057776     -0.077222   \n",
-       "502503      0.073037       0.15891     -0.047578     -0.057776     -0.077222   \n",
-       "\n",
-       "        node2vec_195  node2vec_196  node2vec_197  node2vec_198  node2vec_199  \n",
-       "0           0.167877     -0.211251     -0.101427     -0.190028     -0.064002  \n",
-       "1           0.167877     -0.211251     -0.101427     -0.190028     -0.064002  \n",
-       "2           0.167877     -0.211251     -0.101427     -0.190028     -0.064002  \n",
-       "3           0.167877     -0.211251     -0.101427     -0.190028     -0.064002  \n",
-       "4           0.167877     -0.211251     -0.101427     -0.190028     -0.064002  \n",
-       "...              ...           ...           ...           ...           ...  \n",
-       "502499      0.167877     -0.211251     -0.101427     -0.190028     -0.064002  \n",
-       "502500      0.167877     -0.211251     -0.101427     -0.190028     -0.064002  \n",
-       "502501      0.167877     -0.211251     -0.101427     -0.190028     -0.064002  \n",
-       "502502      0.167877     -0.211251     -0.101427     -0.190028     -0.064002  \n",
-       "502503      0.167877     -0.211251     -0.101427     -0.190028     -0.064002  \n",
-       "\n",
-       "[502504 rows x 202 columns]"
-      ]
-     },
-     "execution_count": 79,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "diagnoses_emb"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "outputs": [
-    {
-     "ename": "FileNotFoundError",
-     "evalue": "[Errno 2] Failed to open local file '/data/analysis/ag-reils/ag-reils-shared/cardioRS/data/2_datasets_pre/cvd_massive_from_birth/temp_diagnoses_emb.feather'. Detail: [errno 2] No such file or directory",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-14-45107a9d4a69>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m#diagnoses_emb.to_feather(os.path.join(path, dataset_path, 'temp_diagnoses_emb.feather'))\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdiagnoses_emb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_feather\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdataset_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'temp_diagnoses_emb.feather'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m~/miniconda3/envs/python/lib/python3.7/site-packages/pandas/io/feather_format.py\u001b[0m in \u001b[0;36mread_feather\u001b[0;34m(path, columns, use_threads)\u001b[0m\n\u001b[1;32m    101\u001b[0m     \u001b[0mpath\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstringify_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    102\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 103\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mfeather\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_feather\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0muse_threads\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbool\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0muse_threads\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m~/miniconda3/envs/python/lib/python3.7/site-packages/pyarrow/feather.py\u001b[0m in \u001b[0;36mread_feather\u001b[0;34m(source, columns, use_threads, memory_map)\u001b[0m\n\u001b[1;32m    212\u001b[0m     \"\"\"\n\u001b[1;32m    213\u001b[0m     \u001b[0m_check_pandas_version\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 214\u001b[0;31m     return (read_table(source, columns=columns, memory_map=memory_map)\n\u001b[0m\u001b[1;32m    215\u001b[0m             .to_pandas(use_threads=use_threads))\n\u001b[1;32m    216\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/python/lib/python3.7/site-packages/pyarrow/feather.py\u001b[0m in \u001b[0;36mread_table\u001b[0;34m(source, columns, memory_map)\u001b[0m\n\u001b[1;32m    234\u001b[0m     \"\"\"\n\u001b[1;32m    235\u001b[0m     \u001b[0mreader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mFeatherReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 236\u001b[0;31m     \u001b[0mreader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msource\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0muse_memory_map\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmemory_map\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    237\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    238\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcolumns\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/python/lib/python3.7/site-packages/pyarrow/feather.pxi\u001b[0m in \u001b[0;36mpyarrow.lib.FeatherReader.open\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/python/lib/python3.7/site-packages/pyarrow/io.pxi\u001b[0m in \u001b[0;36mpyarrow.lib.get_reader\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/python/lib/python3.7/site-packages/pyarrow/io.pxi\u001b[0m in \u001b[0;36mpyarrow.lib._get_native_file\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/python/lib/python3.7/site-packages/pyarrow/io.pxi\u001b[0m in \u001b[0;36mpyarrow.lib.memory_map\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/python/lib/python3.7/site-packages/pyarrow/io.pxi\u001b[0m in \u001b[0;36mpyarrow.lib.MemoryMappedFile._open\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/python/lib/python3.7/site-packages/pyarrow/error.pxi\u001b[0m in \u001b[0;36mpyarrow.lib.pyarrow_internal_check_status\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/python/lib/python3.7/site-packages/pyarrow/error.pxi\u001b[0m in \u001b[0;36mpyarrow.lib.check_status\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] Failed to open local file '/data/analysis/ag-reils/ag-reils-shared/cardioRS/data/2_datasets_pre/cvd_massive_from_birth/temp_diagnoses_emb.feather'. Detail: [errno 2] No such file or directory"
-     ]
-    }
-   ],
-   "source": [
-    "diagnoses_emb.to_feather(os.path.join(path, dataset_path, 'temp_diagnoses_emb.feather'))\n",
-    "diagnoses_emb = pd.read_feather(os.path.join(path, dataset_path, 'temp_diagnoses_emb.feather'))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Endpoints"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "1+1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 89,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:39:55.628580Z",
-     "start_time": "2020-11-04T12:33:33.036Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "### define in snomed and get icd codes from there"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 1. Hospital admissions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:39:55.629459Z",
-     "start_time": "2020-11-04T12:33:34.764Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'myocardial_infarction': ['I21', 'I22', 'I23', 'I24', 'I25', 'I51'],\n",
-       " 'stroke': ['G45', 'G46', 'I60', 'I67', 'I68', 'I69'],\n",
-       " 'cancer_breast': ['C50'],\n",
-       " 'diabetes': ['E10', 'E11', 'E12', 'E13', 'E14'],\n",
-       " 'atrial_fibrillation': ['I47', 'I48'],\n",
-       " 'copd': ['J44'],\n",
-       " 'dementia': ['F00', 'F01', 'F02', 'F03', 'F09', 'G31', 'R54']}"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "endpoint_list = {\n",
-    "    \"myocardial_infarction\": [\"Myocardial infarction\"],\n",
-    "    \"stroke\": [\"Cerebrovascular disease\"],\n",
-    "    \"cancer_breast\" : [\"Breast Cancer\"],\n",
-    "    \"diabetes\" : [\"Diabetes\"],\n",
-    "    \"atrial_fibrillation\": [\"Atrial fibrillation\", \"Atrial flutter\", \"paroxysmal tachycardia\"],\n",
-    "    \"copd\": [\"COPD\"],\n",
-    "    \"dementia\":[\"dementia\"]\n",
-    "}\n",
-    "\n",
-    "endpoint_list = phenotype_children(phecodes, endpoint_list)\n",
-    "endpoint_list[\"cancer_breast\"] = [\"C50\"]\n",
-    "endpoint_list[\"copd\"] = [\"J44\"]\n",
-    "endpoint_list[\"diabetes\"] = [\"E10\", \"E11\", \"E12\", \"E13\", \"E14\"]\n",
-    "endpoint_list[\"atrial_fibrillation\"] = [\"I47\", \"I48\"]\n",
-    "\n",
-    "\n",
-    "with open(os.path.join(path, dataset_path, 'endpoint_list.yaml'), 'w') as file: yaml.dump(endpoint_list, file, default_flow_style=False)\n",
-    "endpoint_list"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from dateutil.relativedelta import relativedelta\n",
-    "\n",
-    "def extract_endpoints_tte(data, diagnoses_codes, endpoint_list, time0_col, level=None):\n",
-    "    if level is not None: diagnoses_codes = diagnoses_codes.query(\"level==@level\")\n",
-    "    diagnoses_codes_time0 = diagnoses_codes.merge(data[[\"eid\", time0_col]], how=\"left\", on=\"eid\")\n",
-    "    \n",
-    "    cens_time_right = min(diagnoses_codes.sort_values('date').groupby('origin').tail(1).date.to_list())\n",
-    "    print(f\"t_0: {time0_col}\")\n",
-    "    print(f\"t_cens: {cens_time_right}\")\n",
-    "    \n",
-    "    df_interval = diagnoses_codes_time0[(diagnoses_codes_time0.date > diagnoses_codes_time0[time0_col]) & \n",
-    "                                        (diagnoses_codes_time0.date < cens_time_right)]\n",
-    "    \n",
-    "    temp = data[[\"eid\", time0_col]].copy()\n",
-    "    for ph, ph_codes in tqdm(endpoint_list.items()):\n",
-    "        regex = \"|\".join(ph_codes)\n",
-    "        ph_df = df_interval[df_interval.meaning.str.contains(regex, case=False)] \\\n",
-    "            .sort_values('date').groupby('eid').head(1).assign(phenotype=1, date=lambda x: x.date)\n",
-    "        temp_ph = temp.merge(ph_df, how=\"left\", on=\"eid\").fillna(0)\n",
-    "        temp[ph+\"_event\"], temp[ph+\"_event_date\"] = temp_ph.phenotype, temp_ph.date\n",
-    "        \n",
-    "        fill_date = {ph+\"_event_date\" : lambda x: [cens_time_right if event==0 else event_date for event, event_date in zip(x[ph+\"_event\"], x[ph+\"_event_date\"])]}\n",
-    "        calc_tte = {ph+\"_event_time\" : lambda x: [(event_date-time0).days/365.25  for time0, event_date in zip(x[time0_col], x[ph+\"_event_date\"])]}\n",
-    "        \n",
-    "        temp = temp.assign(**fill_date).assign(**calc_tte).drop([ph+\"_event_date\"], axis=1)\n",
-    "        \n",
-    "    temp = temp.drop([time0_col], axis=1)     \n",
-    "    \n",
-    "    return temp.drop_duplicates()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "t_0: birth_date\n",
-      "t_cens: 2020-03-14\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6a02ec6d409c4e998c0f1deb9032b535",
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-      "text/plain": [
-       "HBox(children=(FloatProgress(value=0.0, max=7.0), HTML(value='')))"
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-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "502504\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>myocardial_infarction_event</th>\n",
-       "      <th>myocardial_infarction_event_time</th>\n",
-       "      <th>stroke_event</th>\n",
-       "      <th>stroke_event_time</th>\n",
-       "      <th>cancer_breast_event</th>\n",
-       "      <th>cancer_breast_event_time</th>\n",
-       "      <th>diabetes_event</th>\n",
-       "      <th>diabetes_event_time</th>\n",
-       "      <th>atrial_fibrillation_event</th>\n",
-       "      <th>atrial_fibrillation_event_time</th>\n",
-       "      <th>copd_event</th>\n",
-       "      <th>copd_event_time</th>\n",
-       "      <th>dementia_event</th>\n",
-       "      <th>dementia_event_time</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
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-       "      <td>0.0</td>\n",
-       "      <td>70.338125</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000043</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>68.123203</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>73.779603</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>73.779603</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>73.779603</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>73.779603</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>63.293634</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>73.779603</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1000051</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>64.761123</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>64.761123</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>64.761123</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>45.062286</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>64.761123</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>21.062286</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>64.761123</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       eid  myocardial_infarction_event  myocardial_infarction_event_time  \\\n",
-       "0  1000018                          0.0                         59.334702   \n",
-       "1  1000020                          0.0                         71.063655   \n",
-       "2  1000037                          0.0                         70.338125   \n",
-       "3  1000043                          1.0                         68.123203   \n",
-       "4  1000051                          0.0                         64.761123   \n",
-       "\n",
-       "   stroke_event  stroke_event_time  cancer_breast_event  \\\n",
-       "0           0.0          59.334702                  0.0   \n",
-       "1           0.0          71.063655                  0.0   \n",
-       "2           0.0          70.338125                  0.0   \n",
-       "3           0.0          73.779603                  0.0   \n",
-       "4           0.0          64.761123                  0.0   \n",
-       "\n",
-       "   cancer_breast_event_time  diabetes_event  diabetes_event_time  \\\n",
-       "0                 59.334702             0.0            59.334702   \n",
-       "1                 71.063655             0.0            71.063655   \n",
-       "2                 70.338125             0.0            70.338125   \n",
-       "3                 73.779603             0.0            73.779603   \n",
-       "4                 64.761123             1.0            45.062286   \n",
-       "\n",
-       "   atrial_fibrillation_event  atrial_fibrillation_event_time  copd_event  \\\n",
-       "0                        0.0                       59.334702         0.0   \n",
-       "1                        0.0                       71.063655         0.0   \n",
-       "2                        0.0                       70.338125         0.0   \n",
-       "3                        0.0                       73.779603         1.0   \n",
-       "4                        0.0                       64.761123         1.0   \n",
-       "\n",
-       "   copd_event_time  dementia_event  dementia_event_time  \n",
-       "0        59.334702             0.0            59.334702  \n",
-       "1        71.063655             0.0            71.063655  \n",
-       "2        70.338125             0.0            70.338125  \n",
-       "3        63.293634             0.0            73.779603  \n",
-       "4        21.062286             0.0            64.761123  "
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "endpoints_hospital = extract_endpoints_tte(basics, diagnoses_codes, endpoint_list, time0_col)\n",
-    "print(len(endpoints_hospital))\n",
-    "endpoints_hospital.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 2. Death registry"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "death_list = {\n",
-    "    \"death_allcause\":[],\n",
-    "    \"death_cvd\":['I{:02}'.format(ID+1) for ID in range(0, 98)],\n",
-    "}\n",
-    "\n",
-    "death_codes = pd.read_feather(f\"{data_path}/1_decoded/codes_death_records.feather\")#.drop(\"level\", axis=1)\n",
-    "\n",
-    "with open(os.path.join(path, dataset_path, 'death_list.yaml'), 'w') as file: yaml.dump(death_list, file, default_flow_style=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "t_0: birth_date\n",
-      "t_cens: 2020-06-28\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9ca18723f9984f399b46bce781a3f2dd",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(FloatProgress(value=0.0, max=2.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "endpoints_death = extract_endpoints_tte(basics, death_codes, death_list, time0_col, level=\"1\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## SCORES"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "scores_list = {\n",
-    "    \"SCORE\":['I{:02}'.format(ID) for ID in [10, 11, 12, 13, 14, 15, 20, 21, 22, 23, 24, 25, 44, 45, 46, 47, 48, 49, 50, 51, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73]],\n",
-    "    \"ASCVD\":['I{:02}'.format(ID) for ID in [20, 21, 22, 23, 24, 25, 63]],\n",
-    "    \"QRISK3\":[\"G45\", \"I20\", \"I21\", \"I22\", \"I23\", \"I24\", \"I25\", \"I63\", \"I64\"],\n",
-    "    \"MACE\":[\"G45\", \"I21\", \"I22\", \"I23\", \"I24\", \"I25\", \"I63\", \"I64\"],    \n",
-    "}\n",
-    "with open(os.path.join(path, dataset_path, 'scores_list.yaml'), 'w') as file: yaml.dump(scores_list, file, default_flow_style=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "scores_list_hospital = {}\n",
-    "scores_list_death = {}\n",
-    "for score, score_codes in scores_list.items():\n",
-    "    scores_list_hospital[\"hospital_\"+score] = score_codes\n",
-    "    scores_list_death[\"death_\"+score] = score_codes"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "t_0: birth_date\n",
-      "t_cens: 2020-03-14\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "17ed4f235a6e4cc08cdc2789bfc62d73",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(FloatProgress(value=0.0, max=4.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "t_0: birth_date\n",
-      "t_cens: 2020-06-28\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2bd6de2f761a464da6f8c6c8ba22a1d8",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(FloatProgress(value=0.0, max=4.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "endpoints_scores = {\n",
-    "    \"hospital\": extract_endpoints_tte(basics, diagnoses_codes, scores_list_hospital, time0_col=time0_col),\n",
-    "    \"death\": extract_endpoints_tte(basics, death_codes, scores_list_death, time0_col=time0_col, level=1)}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "endpoints_scores_all = endpoints_scores[\"hospital\"].merge(endpoints_scores[\"death\"], on=\"eid\", how=\"left\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### ESC SCORE (Conroy 2003)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "5323\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>SCORE_event</th>\n",
-       "      <th>SCORE_event_time</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>45</th>\n",
-       "      <td>1000463</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>74.165640</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>83</th>\n",
-       "      <td>1000841</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>76.005476</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>102</th>\n",
-       "      <td>1001031</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>75.537303</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>122</th>\n",
-       "      <td>1001237</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>50.132786</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>176</th>\n",
-       "      <td>1001777</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>72.238193</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         eid  SCORE_event  SCORE_event_time\n",
-       "45   1000463          1.0         74.165640\n",
-       "83   1000841          1.0         76.005476\n",
-       "102  1001031          1.0         75.537303\n",
-       "122  1001237          1.0         50.132786\n",
-       "176  1001777          1.0         72.238193"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "score = \"SCORE\"\n",
-    "\n",
-    "temp = endpoints_scores_all.filter(regex=\"eid|\"+score).rename(columns={\"death_SCORE_event\":\"SCORE_event\", \"death_SCORE_event_time\":\"SCORE_event_time\"})\n",
-    "score_SCORE = temp = temp[[\"eid\", \"SCORE_event\", \"SCORE_event_time\"]]\n",
-    "print(len(temp.query(score+\"_event==1\")))\n",
-    "temp.query(score+\"_event==1\").head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### ASCVD (Goff 2014)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "61785\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>ASCVD_event</th>\n",
-       "      <th>ASCVD_event_time</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1000037</td>\n",
-       "      <td>1</td>\n",
-       "      <td>66.970568</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000043</td>\n",
-       "      <td>1</td>\n",
-       "      <td>68.123203</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>1000066</td>\n",
-       "      <td>1</td>\n",
-       "      <td>60.944559</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>1000079</td>\n",
-       "      <td>1</td>\n",
-       "      <td>61.054073</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>1000233</td>\n",
-       "      <td>1</td>\n",
-       "      <td>68.673511</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        eid  ASCVD_event  ASCVD_event_time\n",
-       "2   1000037            1         66.970568\n",
-       "3   1000043            1         68.123203\n",
-       "5   1000066            1         60.944559\n",
-       "6   1000079            1         61.054073\n",
-       "22  1000233            1         68.673511"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "score = \"ASCVD\"\n",
-    "temp = endpoints_scores_all.filter(regex=\"eid|\"+score)\n",
-    "\n",
-    "aggr_event = {score +\"_event\" : lambda x: [1 if (hospital_event==1) | (death_event == 1) else 0 \n",
-    "                                                    for hospital_event, death_event in zip(x[\"hospital_\"+score+\"_event\"], x[\"death_\"+score+\"_event\"])]}\n",
-    "aggr_date = {score +\"_event_time\" : lambda x: [min(hospital_event_time, death_event_time)\n",
-    "                                                        for hospital_event_time, death_event_time in zip(x[\"hospital_\"+score+\"_event_time\"], x[\"death_\"+score+\"_event_time\"])]}\n",
-    "\n",
-    "score_ASCVD = temp = temp.assign(**aggr_event).assign(**aggr_date)[[\"eid\", score +\"_event\", score +\"_event_time\"]]\n",
-    "print(len(temp.query(score+\"_event==1\")))\n",
-    "temp.query(score+\"_event==1\").head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### UK QRISK3 (2017)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "69344\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>QRISK3_event</th>\n",
-       "      <th>QRISK3_event_time</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1000037</td>\n",
-       "      <td>1</td>\n",
-       "      <td>66.970568</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000043</td>\n",
-       "      <td>1</td>\n",
-       "      <td>68.123203</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>1000066</td>\n",
-       "      <td>1</td>\n",
-       "      <td>60.944559</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>1000079</td>\n",
-       "      <td>1</td>\n",
-       "      <td>61.054073</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>1000233</td>\n",
-       "      <td>1</td>\n",
-       "      <td>68.673511</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        eid  QRISK3_event  QRISK3_event_time\n",
-       "2   1000037             1          66.970568\n",
-       "3   1000043             1          68.123203\n",
-       "5   1000066             1          60.944559\n",
-       "6   1000079             1          61.054073\n",
-       "22  1000233             1          68.673511"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "score = \"QRISK3\"\n",
-    "temp = endpoints_scores_all.filter(regex=\"eid|\"+score)\n",
-    "\n",
-    "aggr_event = {score +\"_event\" : lambda x: [1 if (hospital_event==1) | (death_event == 1) else 0 \n",
-    "                                                    for hospital_event, death_event in zip(x[\"hospital_\"+score+\"_event\"], x[\"death_\"+score+\"_event\"])]}\n",
-    "aggr_date = {score +\"_event_time\" : lambda x: [min(hospital_event_time, death_event_time)\n",
-    "                                                        for hospital_event_time, death_event_time in zip(x[\"hospital_\"+score+\"_event_time\"], x[\"death_\"+score+\"_event_time\"])]}\n",
-    "\n",
-    "score_QRISK3 = temp = temp.assign(**aggr_event).assign(**aggr_date)[[\"eid\", score +\"_event\", score +\"_event_time\"]]\n",
-    "print(len(temp.query(score+\"_event==1\")))\n",
-    "temp.query(score+\"_event==1\").head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### MACE (2020)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "62097\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>MACE_event</th>\n",
-       "      <th>MACE_event_time</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000043</td>\n",
-       "      <td>1</td>\n",
-       "      <td>68.123203</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>1000233</td>\n",
-       "      <td>1</td>\n",
-       "      <td>68.673511</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30</th>\n",
-       "      <td>1000319</td>\n",
-       "      <td>1</td>\n",
-       "      <td>56.922656</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>45</th>\n",
-       "      <td>1000463</td>\n",
-       "      <td>1</td>\n",
-       "      <td>74.069815</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>53</th>\n",
-       "      <td>1000548</td>\n",
-       "      <td>1</td>\n",
-       "      <td>50.255989</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        eid  MACE_event  MACE_event_time\n",
-       "3   1000043           1        68.123203\n",
-       "22  1000233           1        68.673511\n",
-       "30  1000319           1        56.922656\n",
-       "45  1000463           1        74.069815\n",
-       "53  1000548           1        50.255989"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "score = \"MACE\"\n",
-    "temp = endpoints_scores_all.filter(regex=\"eid|\"+score)\n",
-    "\n",
-    "aggr_event = {score +\"_event\" : lambda x: [1 if (hospital_event==1) | (death_event == 1) else 0 \n",
-    "                                                    for hospital_event, death_event in zip(x[\"hospital_\"+score+\"_event\"], x[\"death_\"+score+\"_event\"])]}\n",
-    "aggr_date = {score +\"_event_time\" : lambda x: [min(hospital_event_time, death_event_time)\n",
-    "                                                        for hospital_event_time, death_event_time in zip(x[\"hospital_\"+score+\"_event_time\"], x[\"death_\"+score+\"_event_time\"])]}\n",
-    "\n",
-    "score_MACE = temp = temp.assign(**aggr_event).assign(**aggr_date)[[\"eid\", score +\"_event\", score +\"_event_time\"]]\n",
-    "print(len(temp.query(score+\"_event==1\")))\n",
-    "temp.query(score+\"_event==1\").head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Merge Everything"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_dfs_dict = {\"basics\": pd.read_feather(os.path.join(path, dataset_path, 'temp_basics.feather')), \n",
-    "                 \"questionnaire\": pd.read_feather(os.path.join(path, dataset_path, 'temp_questionnaire.feather')), \n",
-    "                 \"measurements\": pd.read_feather(os.path.join(path, dataset_path, 'temp_measurements.feather')), \n",
-    "                 \"labs\": pd.read_feather(os.path.join(path, dataset_path, 'temp_labs.feather')), \n",
-    "                 \"family_history\": pd.read_feather(os.path.join(path, dataset_path, 'temp_family_history.feather')), \n",
-    "                 \"diagnoses\": pd.read_feather(os.path.join(path, dataset_path, 'temp_diagnoses.feather')),\n",
-    "                # \"diagnoses_emb\": pd.read_feather(os.path.join(path, dataset_path, 'temp_diagnoses_emb.feather')), \n",
-    "                 \"medications\": pd.read_feather(os.path.join(path, dataset_path, 'temp_medications.feather')), \n",
-    "                 \"endpoints_hospital\":endpoints_hospital, \n",
-    "                 \"endpoints_death\":endpoints_death, \n",
-    "                 \"score_SCORE\":score_SCORE, \n",
-    "                 \"score_ASCVD\":score_ASCVD, \n",
-    "                 \"score_QRISK3\":score_QRISK3,\n",
-    "                 \"score_MACE\":score_MACE}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_cols_clean(df):\n",
-    "    df.columns = df.columns.str.replace(r'_0_0$', '').str.replace(r'_f[0-9]+$', '').str.replace(\"_automated_reading\", '')\n",
-    "    return df.columns\n",
-    "\n",
-    "def clean_df(df):\n",
-    "    df.columns = get_cols_clean(df)\n",
-    "    return df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "from functools import reduce\n",
-    "\n",
-    "data_baseline = reduce(lambda x, y: pd.merge(x, y, on = 'eid'), list(data_dfs_dict.values()))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_baseline = clean_df(data_baseline)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for col in [col for col in list(data_baseline.columns) if (\"_event\" in col) & (\"_time\" not in col)]:\n",
-    "    data_baseline[col] = data_baseline[col].astype(int)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "covariates = [col for col in list(data_baseline.columns) if not \"_event\" in col]\n",
-    "targets = [col for col in list(data_baseline.columns) if \"_event\" in col]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Exporting"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>age_at_recruitment</th>\n",
-       "      <th>sex</th>\n",
-       "      <th>ethnic_background</th>\n",
-       "      <th>townsend_deprivation_index_at_recruitment</th>\n",
-       "      <th>date_of_attending_assessment_centre</th>\n",
-       "      <th>birth_date</th>\n",
-       "      <th>overall_health_rating</th>\n",
-       "      <th>smoking_status</th>\n",
-       "      <th>alcohol_intake_frequency</th>\n",
-       "      <th>...</th>\n",
-       "      <th>death_cvd_event</th>\n",
-       "      <th>death_cvd_event_time</th>\n",
-       "      <th>SCORE_event</th>\n",
-       "      <th>SCORE_event_time</th>\n",
-       "      <th>ASCVD_event</th>\n",
-       "      <th>ASCVD_event_time</th>\n",
-       "      <th>QRISK3_event</th>\n",
-       "      <th>QRISK3_event_time</th>\n",
-       "      <th>MACE_event</th>\n",
-       "      <th>MACE_event_time</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>49.0</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>White</td>\n",
-       "      <td>-1.852930</td>\n",
-       "      <td>2009-11-12</td>\n",
-       "      <td>1960-11-12</td>\n",
-       "      <td>Fair</td>\n",
-       "      <td>Current</td>\n",
-       "      <td>Once or twice a week</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>59.624914</td>\n",
-       "      <td>0</td>\n",
-       "      <td>59.624914</td>\n",
-       "      <td>0</td>\n",
-       "      <td>59.334702</td>\n",
-       "      <td>0</td>\n",
-       "      <td>59.334702</td>\n",
-       "      <td>0</td>\n",
-       "      <td>59.334702</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1000020</td>\n",
-       "      <td>59.0</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>White</td>\n",
-       "      <td>0.204248</td>\n",
-       "      <td>2008-02-19</td>\n",
-       "      <td>1949-02-19</td>\n",
-       "      <td>Good</td>\n",
-       "      <td>Current</td>\n",
-       "      <td>Once or twice a week</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>71.353867</td>\n",
-       "      <td>0</td>\n",
-       "      <td>71.353867</td>\n",
-       "      <td>0</td>\n",
-       "      <td>71.063655</td>\n",
-       "      <td>0</td>\n",
-       "      <td>71.063655</td>\n",
-       "      <td>0</td>\n",
-       "      <td>71.063655</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1000037</td>\n",
-       "      <td>59.0</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>White</td>\n",
-       "      <td>-3.498860</td>\n",
-       "      <td>2008-11-11</td>\n",
-       "      <td>1949-11-11</td>\n",
-       "      <td>Good</td>\n",
-       "      <td>Previous</td>\n",
-       "      <td>Once or twice a week</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>70.628337</td>\n",
-       "      <td>0</td>\n",
-       "      <td>70.628337</td>\n",
-       "      <td>1</td>\n",
-       "      <td>66.970568</td>\n",
-       "      <td>1</td>\n",
-       "      <td>66.970568</td>\n",
-       "      <td>0</td>\n",
-       "      <td>70.338125</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000043</td>\n",
-       "      <td>63.0</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>White</td>\n",
-       "      <td>-5.351150</td>\n",
-       "      <td>2009-06-03</td>\n",
-       "      <td>1946-06-03</td>\n",
-       "      <td>Fair</td>\n",
-       "      <td>Previous</td>\n",
-       "      <td>Three or four times a week</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>74.069815</td>\n",
-       "      <td>0</td>\n",
-       "      <td>74.069815</td>\n",
-       "      <td>1</td>\n",
-       "      <td>68.123203</td>\n",
-       "      <td>1</td>\n",
-       "      <td>68.123203</td>\n",
-       "      <td>1</td>\n",
-       "      <td>68.123203</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1000051</td>\n",
-       "      <td>51.0</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>White</td>\n",
-       "      <td>-1.799080</td>\n",
-       "      <td>2006-06-10</td>\n",
-       "      <td>1955-06-10</td>\n",
-       "      <td>Poor</td>\n",
-       "      <td>Never</td>\n",
-       "      <td>One to three times a month</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>65.051335</td>\n",
-       "      <td>0</td>\n",
-       "      <td>65.051335</td>\n",
-       "      <td>0</td>\n",
-       "      <td>64.761123</td>\n",
-       "      <td>0</td>\n",
-       "      <td>64.761123</td>\n",
-       "      <td>0</td>\n",
-       "      <td>64.761123</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 3746 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       eid  age_at_recruitment     sex ethnic_background  \\\n",
-       "0  1000018                49.0  Female             White   \n",
-       "1  1000020                59.0    Male             White   \n",
-       "2  1000037                59.0  Female             White   \n",
-       "3  1000043                63.0    Male             White   \n",
-       "4  1000051                51.0  Female             White   \n",
-       "\n",
-       "   townsend_deprivation_index_at_recruitment  \\\n",
-       "0                                  -1.852930   \n",
-       "1                                   0.204248   \n",
-       "2                                  -3.498860   \n",
-       "3                                  -5.351150   \n",
-       "4                                  -1.799080   \n",
-       "\n",
-       "  date_of_attending_assessment_centre  birth_date overall_health_rating  \\\n",
-       "0                          2009-11-12  1960-11-12                  Fair   \n",
-       "1                          2008-02-19  1949-02-19                  Good   \n",
-       "2                          2008-11-11  1949-11-11                  Good   \n",
-       "3                          2009-06-03  1946-06-03                  Fair   \n",
-       "4                          2006-06-10  1955-06-10                  Poor   \n",
-       "\n",
-       "  smoking_status    alcohol_intake_frequency  ...  death_cvd_event  \\\n",
-       "0        Current        Once or twice a week  ...                0   \n",
-       "1        Current        Once or twice a week  ...                0   \n",
-       "2       Previous        Once or twice a week  ...                0   \n",
-       "3       Previous  Three or four times a week  ...                0   \n",
-       "4          Never  One to three times a month  ...                0   \n",
-       "\n",
-       "   death_cvd_event_time  SCORE_event  SCORE_event_time  ASCVD_event  \\\n",
-       "0             59.624914            0         59.624914            0   \n",
-       "1             71.353867            0         71.353867            0   \n",
-       "2             70.628337            0         70.628337            1   \n",
-       "3             74.069815            0         74.069815            1   \n",
-       "4             65.051335            0         65.051335            0   \n",
-       "\n",
-       "   ASCVD_event_time  QRISK3_event  QRISK3_event_time  MACE_event  \\\n",
-       "0         59.334702             0          59.334702           0   \n",
-       "1         71.063655             0          71.063655           0   \n",
-       "2         66.970568             1          66.970568           0   \n",
-       "3         68.123203             1          68.123203           1   \n",
-       "4         64.761123             0          64.761123           0   \n",
-       "\n",
-       "   MACE_event_time  \n",
-       "0        59.334702  \n",
-       "1        71.063655  \n",
-       "2        70.338125  \n",
-       "3        68.123203  \n",
-       "4        64.761123  \n",
-       "\n",
-       "[5 rows x 3746 columns]"
-      ]
-     },
-     "execution_count": 38,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data_baseline.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_cols = {}\n",
-    "for topic, df in data_dfs_dict.items(): \n",
-    "    data_cols[\"eid\"] = [\"admin\"]\n",
-    "    data_cols[topic]=list(get_cols_clean(df))[1:]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_cols_single = {}\n",
-    "for topic, columns in data_cols.items():\n",
-    "    for col in columns:\n",
-    "        data_cols_single[col] = topic"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>covariate</th>\n",
-       "      <th>dtype</th>\n",
-       "      <th>isTarget</th>\n",
-       "      <th>based_on</th>\n",
-       "      <th>aggr_fn</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>eid</td>\n",
-       "      <td>integer</td>\n",
-       "      <td>False</td>\n",
-       "      <td>eid</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2</td>\n",
-       "      <td>age_at_recruitment</td>\n",
-       "      <td>numeric</td>\n",
-       "      <td>False</td>\n",
-       "      <td>basics</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>sex</td>\n",
-       "      <td>category</td>\n",
-       "      <td>False</td>\n",
-       "      <td>basics</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4</td>\n",
-       "      <td>ethnic_background</td>\n",
-       "      <td>category</td>\n",
-       "      <td>False</td>\n",
-       "      <td>basics</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5</td>\n",
-       "      <td>townsend_deprivation_index_at_recruitment</td>\n",
-       "      <td>numeric</td>\n",
-       "      <td>False</td>\n",
-       "      <td>basics</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3741</th>\n",
-       "      <td>3742</td>\n",
-       "      <td>ASCVD_event_time</td>\n",
-       "      <td>numeric</td>\n",
-       "      <td>True</td>\n",
-       "      <td>score_ASCVD</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3742</th>\n",
-       "      <td>3743</td>\n",
-       "      <td>QRISK3_event</td>\n",
-       "      <td>integer</td>\n",
-       "      <td>True</td>\n",
-       "      <td>score_QRISK3</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3743</th>\n",
-       "      <td>3744</td>\n",
-       "      <td>QRISK3_event_time</td>\n",
-       "      <td>numeric</td>\n",
-       "      <td>True</td>\n",
-       "      <td>score_QRISK3</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3744</th>\n",
-       "      <td>3745</td>\n",
-       "      <td>MACE_event</td>\n",
-       "      <td>integer</td>\n",
-       "      <td>True</td>\n",
-       "      <td>score_MACE</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3745</th>\n",
-       "      <td>3746</td>\n",
-       "      <td>MACE_event_time</td>\n",
-       "      <td>numeric</td>\n",
-       "      <td>True</td>\n",
-       "      <td>score_MACE</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>3746 rows × 6 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        id                                  covariate     dtype  isTarget  \\\n",
-       "0        1                                        eid   integer     False   \n",
-       "1        2                         age_at_recruitment   numeric     False   \n",
-       "2        3                                        sex  category     False   \n",
-       "3        4                          ethnic_background  category     False   \n",
-       "4        5  townsend_deprivation_index_at_recruitment   numeric     False   \n",
-       "...    ...                                        ...       ...       ...   \n",
-       "3741  3742                           ASCVD_event_time   numeric      True   \n",
-       "3742  3743                               QRISK3_event   integer      True   \n",
-       "3743  3744                          QRISK3_event_time   numeric      True   \n",
-       "3744  3745                                 MACE_event   integer      True   \n",
-       "3745  3746                            MACE_event_time   numeric      True   \n",
-       "\n",
-       "          based_on  aggr_fn  \n",
-       "0              eid      NaN  \n",
-       "1           basics      NaN  \n",
-       "2           basics      NaN  \n",
-       "3           basics      NaN  \n",
-       "4           basics      NaN  \n",
-       "...            ...      ...  \n",
-       "3741   score_ASCVD      NaN  \n",
-       "3742  score_QRISK3      NaN  \n",
-       "3743  score_QRISK3      NaN  \n",
-       "3744    score_MACE      NaN  \n",
-       "3745    score_MACE      NaN  \n",
-       "\n",
-       "[3746 rows x 6 columns]"
-      ]
-     },
-     "execution_count": 41,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dtypes = {\"int32\":\"integer\", \"int64\":\"integer\", \"float64\":\"numeric\", \"category\":\"category\", \"object\":\"category\", \"bool\":\"logical\"}\n",
-    "desc_dict = {\"id\": [*range(1, len(data_baseline.columns.to_list())+1)] , \n",
-    "             \"covariate\": data_baseline.columns.to_list(), \n",
-    "             \"dtype\":[dtypes[str(col)] for col in data_baseline.dtypes.to_list()], \n",
-    "             \"isTarget\":[True if col in targets else False for col in data_baseline.columns.to_list()],\n",
-    "            \"based_on\":[topic for col, topic in data_cols_single.items()],\n",
-    "            \"aggr_fn\": [np.nan for col in data_baseline.columns.to_list()]}\n",
-    "data_baseline_description = pd.DataFrame.from_dict(desc_dict)\n",
-    "data_baseline_description"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Exclusion Criteria"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_baseline_excl = data_baseline.query(\"myocardial_infarction == False & coronary_heart_disease == False & statins == False\").reset_index(drop=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>age_at_recruitment</th>\n",
-       "      <th>sex</th>\n",
-       "      <th>ethnic_background</th>\n",
-       "      <th>townsend_deprivation_index_at_recruitment</th>\n",
-       "      <th>date_of_attending_assessment_centre</th>\n",
-       "      <th>birth_date</th>\n",
-       "      <th>overall_health_rating</th>\n",
-       "      <th>smoking_status</th>\n",
-       "      <th>alcohol_intake_frequency</th>\n",
-       "      <th>...</th>\n",
-       "      <th>death_cvd_event</th>\n",
-       "      <th>death_cvd_event_time</th>\n",
-       "      <th>SCORE_event</th>\n",
-       "      <th>SCORE_event_time</th>\n",
-       "      <th>ASCVD_event</th>\n",
-       "      <th>ASCVD_event_time</th>\n",
-       "      <th>QRISK3_event</th>\n",
-       "      <th>QRISK3_event_time</th>\n",
-       "      <th>MACE_event</th>\n",
-       "      <th>MACE_event_time</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>49.0</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>White</td>\n",
-       "      <td>-1.852930</td>\n",
-       "      <td>2009-11-12</td>\n",
-       "      <td>1960-11-12</td>\n",
-       "      <td>Fair</td>\n",
-       "      <td>Current</td>\n",
-       "      <td>Once or twice a week</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>59.624914</td>\n",
-       "      <td>0</td>\n",
-       "      <td>59.624914</td>\n",
-       "      <td>0</td>\n",
-       "      <td>59.334702</td>\n",
-       "      <td>0</td>\n",
-       "      <td>59.334702</td>\n",
-       "      <td>0</td>\n",
-       "      <td>59.334702</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1000020</td>\n",
-       "      <td>59.0</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>White</td>\n",
-       "      <td>0.204248</td>\n",
-       "      <td>2008-02-19</td>\n",
-       "      <td>1949-02-19</td>\n",
-       "      <td>Good</td>\n",
-       "      <td>Current</td>\n",
-       "      <td>Once or twice a week</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>71.353867</td>\n",
-       "      <td>0</td>\n",
-       "      <td>71.353867</td>\n",
-       "      <td>0</td>\n",
-       "      <td>71.063655</td>\n",
-       "      <td>0</td>\n",
-       "      <td>71.063655</td>\n",
-       "      <td>0</td>\n",
-       "      <td>71.063655</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1000037</td>\n",
-       "      <td>59.0</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>White</td>\n",
-       "      <td>-3.498860</td>\n",
-       "      <td>2008-11-11</td>\n",
-       "      <td>1949-11-11</td>\n",
-       "      <td>Good</td>\n",
-       "      <td>Previous</td>\n",
-       "      <td>Once or twice a week</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>70.628337</td>\n",
-       "      <td>0</td>\n",
-       "      <td>70.628337</td>\n",
-       "      <td>1</td>\n",
-       "      <td>66.970568</td>\n",
-       "      <td>1</td>\n",
-       "      <td>66.970568</td>\n",
-       "      <td>0</td>\n",
-       "      <td>70.338125</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000043</td>\n",
-       "      <td>63.0</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>White</td>\n",
-       "      <td>-5.351150</td>\n",
-       "      <td>2009-06-03</td>\n",
-       "      <td>1946-06-03</td>\n",
-       "      <td>Fair</td>\n",
-       "      <td>Previous</td>\n",
-       "      <td>Three or four times a week</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>74.069815</td>\n",
-       "      <td>0</td>\n",
-       "      <td>74.069815</td>\n",
-       "      <td>1</td>\n",
-       "      <td>68.123203</td>\n",
-       "      <td>1</td>\n",
-       "      <td>68.123203</td>\n",
-       "      <td>1</td>\n",
-       "      <td>68.123203</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1000079</td>\n",
-       "      <td>60.0</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>White</td>\n",
-       "      <td>-2.708040</td>\n",
-       "      <td>2008-03-18</td>\n",
-       "      <td>1948-03-18</td>\n",
-       "      <td>Fair</td>\n",
-       "      <td>Never</td>\n",
-       "      <td>Once or twice a week</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>72.279261</td>\n",
-       "      <td>0</td>\n",
-       "      <td>72.279261</td>\n",
-       "      <td>1</td>\n",
-       "      <td>61.054073</td>\n",
-       "      <td>1</td>\n",
-       "      <td>61.054073</td>\n",
-       "      <td>0</td>\n",
-       "      <td>71.989049</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>402296</th>\n",
-       "      <td>6025150</td>\n",
-       "      <td>43.0</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>White</td>\n",
-       "      <td>0.046781</td>\n",
-       "      <td>2007-06-30</td>\n",
-       "      <td>1964-06-30</td>\n",
-       "      <td>Excellent</td>\n",
-       "      <td>Never</td>\n",
-       "      <td>Three or four times a week</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>55.994524</td>\n",
-       "      <td>0</td>\n",
-       "      <td>55.994524</td>\n",
-       "      <td>0</td>\n",
-       "      <td>55.704312</td>\n",
-       "      <td>0</td>\n",
-       "      <td>55.704312</td>\n",
-       "      <td>0</td>\n",
-       "      <td>55.704312</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>402297</th>\n",
-       "      <td>6025165</td>\n",
-       "      <td>45.0</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>White</td>\n",
-       "      <td>-2.107040</td>\n",
-       "      <td>2008-09-02</td>\n",
-       "      <td>1963-09-02</td>\n",
-       "      <td>Good</td>\n",
-       "      <td>Never</td>\n",
-       "      <td>Three or four times a week</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>56.821355</td>\n",
-       "      <td>0</td>\n",
-       "      <td>56.821355</td>\n",
-       "      <td>0</td>\n",
-       "      <td>56.531143</td>\n",
-       "      <td>0</td>\n",
-       "      <td>56.531143</td>\n",
-       "      <td>0</td>\n",
-       "      <td>56.531143</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>402298</th>\n",
-       "      <td>6025173</td>\n",
-       "      <td>57.0</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>White</td>\n",
-       "      <td>-1.827220</td>\n",
-       "      <td>2008-09-17</td>\n",
-       "      <td>1951-09-17</td>\n",
-       "      <td>Good</td>\n",
-       "      <td>Never</td>\n",
-       "      <td>Never</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>68.780287</td>\n",
-       "      <td>0</td>\n",
-       "      <td>68.780287</td>\n",
-       "      <td>0</td>\n",
-       "      <td>68.490075</td>\n",
-       "      <td>0</td>\n",
-       "      <td>68.490075</td>\n",
-       "      <td>0</td>\n",
-       "      <td>68.490075</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>402299</th>\n",
-       "      <td>6025182</td>\n",
-       "      <td>56.0</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>White</td>\n",
-       "      <td>-0.010764</td>\n",
-       "      <td>2010-07-01</td>\n",
-       "      <td>1954-07-01</td>\n",
-       "      <td>Excellent</td>\n",
-       "      <td>Previous</td>\n",
-       "      <td>Daily or almost daily</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>65.993155</td>\n",
-       "      <td>0</td>\n",
-       "      <td>65.993155</td>\n",
-       "      <td>0</td>\n",
-       "      <td>65.702943</td>\n",
-       "      <td>0</td>\n",
-       "      <td>65.702943</td>\n",
-       "      <td>0</td>\n",
-       "      <td>65.702943</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>402300</th>\n",
-       "      <td>6025198</td>\n",
-       "      <td>67.0</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>White</td>\n",
-       "      <td>-1.930650</td>\n",
-       "      <td>2010-01-26</td>\n",
-       "      <td>1943-01-26</td>\n",
-       "      <td>Good</td>\n",
-       "      <td>Current</td>\n",
-       "      <td>Daily or almost daily</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>77.420945</td>\n",
-       "      <td>0</td>\n",
-       "      <td>77.420945</td>\n",
-       "      <td>0</td>\n",
-       "      <td>77.130732</td>\n",
-       "      <td>0</td>\n",
-       "      <td>77.130732</td>\n",
-       "      <td>0</td>\n",
-       "      <td>77.130732</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>402301 rows × 3746 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            eid  age_at_recruitment     sex ethnic_background  \\\n",
-       "0       1000018                49.0  Female             White   \n",
-       "1       1000020                59.0    Male             White   \n",
-       "2       1000037                59.0  Female             White   \n",
-       "3       1000043                63.0    Male             White   \n",
-       "4       1000079                60.0  Female             White   \n",
-       "...         ...                 ...     ...               ...   \n",
-       "402296  6025150                43.0  Female             White   \n",
-       "402297  6025165                45.0  Female             White   \n",
-       "402298  6025173                57.0    Male             White   \n",
-       "402299  6025182                56.0    Male             White   \n",
-       "402300  6025198                67.0    Male             White   \n",
-       "\n",
-       "        townsend_deprivation_index_at_recruitment  \\\n",
-       "0                                       -1.852930   \n",
-       "1                                        0.204248   \n",
-       "2                                       -3.498860   \n",
-       "3                                       -5.351150   \n",
-       "4                                       -2.708040   \n",
-       "...                                           ...   \n",
-       "402296                                   0.046781   \n",
-       "402297                                  -2.107040   \n",
-       "402298                                  -1.827220   \n",
-       "402299                                  -0.010764   \n",
-       "402300                                  -1.930650   \n",
-       "\n",
-       "       date_of_attending_assessment_centre  birth_date overall_health_rating  \\\n",
-       "0                               2009-11-12  1960-11-12                  Fair   \n",
-       "1                               2008-02-19  1949-02-19                  Good   \n",
-       "2                               2008-11-11  1949-11-11                  Good   \n",
-       "3                               2009-06-03  1946-06-03                  Fair   \n",
-       "4                               2008-03-18  1948-03-18                  Fair   \n",
-       "...                                    ...         ...                   ...   \n",
-       "402296                          2007-06-30  1964-06-30             Excellent   \n",
-       "402297                          2008-09-02  1963-09-02                  Good   \n",
-       "402298                          2008-09-17  1951-09-17                  Good   \n",
-       "402299                          2010-07-01  1954-07-01             Excellent   \n",
-       "402300                          2010-01-26  1943-01-26                  Good   \n",
-       "\n",
-       "       smoking_status    alcohol_intake_frequency  ...  death_cvd_event  \\\n",
-       "0             Current        Once or twice a week  ...                0   \n",
-       "1             Current        Once or twice a week  ...                0   \n",
-       "2            Previous        Once or twice a week  ...                0   \n",
-       "3            Previous  Three or four times a week  ...                0   \n",
-       "4               Never        Once or twice a week  ...                0   \n",
-       "...               ...                         ...  ...              ...   \n",
-       "402296          Never  Three or four times a week  ...                0   \n",
-       "402297          Never  Three or four times a week  ...                0   \n",
-       "402298          Never                       Never  ...                0   \n",
-       "402299       Previous       Daily or almost daily  ...                0   \n",
-       "402300        Current       Daily or almost daily  ...                0   \n",
-       "\n",
-       "        death_cvd_event_time  SCORE_event  SCORE_event_time  ASCVD_event  \\\n",
-       "0                  59.624914            0         59.624914            0   \n",
-       "1                  71.353867            0         71.353867            0   \n",
-       "2                  70.628337            0         70.628337            1   \n",
-       "3                  74.069815            0         74.069815            1   \n",
-       "4                  72.279261            0         72.279261            1   \n",
-       "...                      ...          ...               ...          ...   \n",
-       "402296             55.994524            0         55.994524            0   \n",
-       "402297             56.821355            0         56.821355            0   \n",
-       "402298             68.780287            0         68.780287            0   \n",
-       "402299             65.993155            0         65.993155            0   \n",
-       "402300             77.420945            0         77.420945            0   \n",
-       "\n",
-       "        ASCVD_event_time  QRISK3_event  QRISK3_event_time  MACE_event  \\\n",
-       "0              59.334702             0          59.334702           0   \n",
-       "1              71.063655             0          71.063655           0   \n",
-       "2              66.970568             1          66.970568           0   \n",
-       "3              68.123203             1          68.123203           1   \n",
-       "4              61.054073             1          61.054073           0   \n",
-       "...                  ...           ...                ...         ...   \n",
-       "402296         55.704312             0          55.704312           0   \n",
-       "402297         56.531143             0          56.531143           0   \n",
-       "402298         68.490075             0          68.490075           0   \n",
-       "402299         65.702943             0          65.702943           0   \n",
-       "402300         77.130732             0          77.130732           0   \n",
-       "\n",
-       "        MACE_event_time  \n",
-       "0             59.334702  \n",
-       "1             71.063655  \n",
-       "2             70.338125  \n",
-       "3             68.123203  \n",
-       "4             71.989049  \n",
-       "...                 ...  \n",
-       "402296        55.704312  \n",
-       "402297        56.531143  \n",
-       "402298        68.490075  \n",
-       "402299        65.702943  \n",
-       "402300        77.130732  \n",
-       "\n",
-       "[402301 rows x 3746 columns]"
-      ]
-     },
-     "execution_count": 43,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data_baseline_excl"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "feature_dict = {}\n",
-    "for group in data_baseline_description.based_on.unique(): feature_dict[group] = data_baseline_description.query(\"based_on==@group\").covariate.to_list()\n",
-    "with open(os.path.join(path, dataset_path, 'feature_list.yaml'), 'w') as file: yaml.dump(feature_dict, file, default_flow_style=False, allow_unicode=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#feature_dict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "### WRITE FEATURES IN YAML!!!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_baseline.to_feather(os.path.join(path, dataset_path, 'baseline_clinical.feather'))\n",
-    "data_baseline_excl.to_feather(os.path.join(path, dataset_path, 'baseline_clinical_excl.feather'))\n",
-    "data_baseline_description.to_feather(os.path.join(path, dataset_path, 'baseline_clinical_description.feather'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#data_baseline.to_csv(os.path.join(path, dataset_path, 'baseline_clinical.csv'), index=False)\n",
-    "#data_baseline_description.to_csv(os.path.join(path, dataset_path, 'baseline_clinical_description.csv'), index=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# !!! REMEMBER IMPUTATION !!!"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python [conda env:python]",
-   "language": "python",
-   "name": "conda-env-python-py"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.9"
-  },
-  "toc-autonumbering": true,
-  "toc-showcode": false,
-  "toc-showmarkdowntxt": false
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/neuralcvd/preprocessing/ukbb_tabular/2_preprocessing_clinical_from_birth_PxMxT.ipynb b/neuralcvd/preprocessing/ukbb_tabular/2_preprocessing_clinical_from_birth_PxMxT.ipynb
deleted file mode 100644
index bb5e7e3..0000000
--- a/neuralcvd/preprocessing/ukbb_tabular/2_preprocessing_clinical_from_birth_PxMxT.ipynb
+++ /dev/null
@@ -1,10333 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Preprocessing"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:31:49.436340Z",
-     "start_time": "2020-11-04T12:31:48.732042Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import os\n",
-    "import yaml\n",
-    "from tqdm.notebook import trange, tqdm\n",
-    "dataset_name = \"cvd_lifetime_time_series\"\n",
-    "path = \"/data/analysis/ag-reils/steinfej/code/umbrella/pre/ukbb\"\n",
-    "data_path = \"/data/analysis/ag-reils/ag-reils-shared/cardioRS/data\"\n",
-    "dataset_path = f\"{data_path}/2_datasets_pre/{dataset_name}\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:31:49.895222Z",
-     "start_time": "2020-11-04T12:31:49.891332Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from pathlib import Path\n",
-    "Path(dataset_path).mkdir(parents=True, exist_ok=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:33:14.171198Z",
-     "start_time": "2020-11-04T12:31:50.204540Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 39.3 s, sys: 1min 12s, total: 1min 52s\n",
-      "Wall time: 1min 17s\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "\n",
-    "data = pd.read_feather(f\"{data_path}/1_decoded/ukb_data.feather\")\n",
-    "data_field = pd.read_feather(f\"{data_path}/1_decoded/ukb_data_field.feather\")\n",
-    "data_columns = data.columns.to_list()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
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-       "      <td>Female</td>\n",
-       "      <td>...</td>\n",
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-       "      <td>Male</td>\n",
-       "      <td>...</td>\n",
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-       "      <td>Female</td>\n",
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-       "      <td>None</td>\n",
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-       "      <td>None</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000043</td>\n",
-       "      <td>Direct entry</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Direct entry</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Direct entry</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Direct entry</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>...</td>\n",
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-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
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-       "      <td>None</td>\n",
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-       "      <td>Direct entry</td>\n",
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-       "      <td>2012-08-16</td>\n",
-       "      <td>Primary care only</td>\n",
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-       "    <tr>\n",
-       "      <th>502503</th>\n",
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-       "      <td>Direct entry</td>\n",
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-       "    <tr>\n",
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-       "      <td>...</td>\n",
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-       "      <td>None</td>\n",
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-       "      <td>None</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>502505 rows × 7162 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            eid weight_method_f21_0_0 weight_method_f21_1_0  \\\n",
-       "0       1000018          Direct entry                   NaN   \n",
-       "1       1000020          Direct entry                   NaN   \n",
-       "2       1000037          Direct entry                   NaN   \n",
-       "3       1000043          Direct entry                   NaN   \n",
-       "4       1000051          Direct entry                   NaN   \n",
-       "...         ...                   ...                   ...   \n",
-       "502500  6025165          Direct entry                   NaN   \n",
-       "502501  6025173          Direct entry                   NaN   \n",
-       "502502  6025182          Direct entry                   NaN   \n",
-       "502503  6025198          Direct entry                   NaN   \n",
-       "502504  6025200                   NaN                   NaN   \n",
-       "\n",
-       "       weight_method_f21_2_0 weight_method_f21_3_0 spirometry_method_f23_0_0  \\\n",
-       "0                        NaN                   NaN              Direct entry   \n",
-       "1                        NaN                   NaN              Direct entry   \n",
-       "2                        NaN                   NaN              Direct entry   \n",
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-       "4                        NaN                   NaN                       NaN   \n",
-       "...                      ...                   ...                       ...   \n",
-       "502500                   NaN                   NaN              Direct entry   \n",
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-       "502502                   NaN                   NaN              Direct entry   \n",
-       "502503                   NaN                   NaN              Direct entry   \n",
-       "502504                   NaN                   NaN                       NaN   \n",
-       "\n",
-       "       spirometry_method_f23_1_0 spirometry_method_f23_2_0  \\\n",
-       "0                            NaN                       NaN   \n",
-       "1                            NaN                       NaN   \n",
-       "2                            NaN                       NaN   \n",
-       "3                            NaN              Direct entry   \n",
-       "4                            NaN                       NaN   \n",
-       "...                          ...                       ...   \n",
-       "502500                       NaN                       NaN   \n",
-       "502501                       NaN                       NaN   \n",
-       "502502                       NaN                       NaN   \n",
-       "502503                       NaN                       NaN   \n",
-       "502504                       NaN                       NaN   \n",
-       "\n",
-       "       spirometry_method_f23_3_0 sex_f31_0_0  ...  \\\n",
-       "0                            NaN      Female  ...   \n",
-       "1                            NaN        Male  ...   \n",
-       "2                            NaN      Female  ...   \n",
-       "3                            NaN        Male  ...   \n",
-       "4                            NaN      Female  ...   \n",
-       "...                          ...         ...  ...   \n",
-       "502500                       NaN      Female  ...   \n",
-       "502501                       NaN        Male  ...   \n",
-       "502502                       NaN        Male  ...   \n",
-       "502503                       NaN        Male  ...   \n",
-       "502504                       NaN         NaN  ...   \n",
-       "\n",
-       "        source_of_report_of_i85_oesophageal_varices_f131407_0_0  \\\n",
-       "0                                                     NaN         \n",
-       "1                                                     NaN         \n",
-       "2                                                     NaN         \n",
-       "3                                                     NaN         \n",
-       "4                                                     NaN         \n",
-       "...                                                   ...         \n",
-       "502500                                                NaN         \n",
-       "502501                                                NaN         \n",
-       "502502                                                NaN         \n",
-       "502503                                                NaN         \n",
-       "502504                                                NaN         \n",
-       "\n",
-       "       source_of_report_of_i89_other_noninfective_disorders_of_lymphatic_vessels_and_lymph_nodes_f131415_0_0  \\\n",
-       "0                                                     NaN                                                      \n",
-       "1                                                     NaN                                                      \n",
-       "2                                                     NaN                                                      \n",
-       "3                                                     NaN                                                      \n",
-       "4                                                     NaN                                                      \n",
-       "...                                                   ...                                                      \n",
-       "502500                                                NaN                                                      \n",
-       "502501                                                NaN                                                      \n",
-       "502502                                                NaN                                                      \n",
-       "502503                                                NaN                                                      \n",
-       "502504                                                NaN                                                      \n",
-       "\n",
-       "       date_i95_first_reported_hypotension_f131416_0_0  \\\n",
-       "0                                                 None   \n",
-       "1                                                 None   \n",
-       "2                                                 None   \n",
-       "3                                                 None   \n",
-       "4                                                 None   \n",
-       "...                                                ...   \n",
-       "502500                                            None   \n",
-       "502501                                            None   \n",
-       "502502                                            None   \n",
-       "502503                                            None   \n",
-       "502504                                            None   \n",
-       "\n",
-       "       source_of_report_of_i95_hypotension_f131417_0_0  \\\n",
-       "0                                                  NaN   \n",
-       "1                                                  NaN   \n",
-       "2                                                  NaN   \n",
-       "3                                                  NaN   \n",
-       "4                                                  NaN   \n",
-       "...                                                ...   \n",
-       "502500                                             NaN   \n",
-       "502501                                             NaN   \n",
-       "502502                                             NaN   \n",
-       "502503                                             NaN   \n",
-       "502504                                             NaN   \n",
-       "\n",
-       "       date_i97_first_reported_postprocedural_disorders_of_circulatory_system_not_elsewhere_classified_f131418_0_0  \\\n",
-       "0                                                    None                                                            \n",
-       "1                                                    None                                                            \n",
-       "2                                                    None                                                            \n",
-       "3                                                    None                                                            \n",
-       "4                                                    None                                                            \n",
-       "...                                                   ...                                                            \n",
-       "502500                                               None                                                            \n",
-       "502501                                               None                                                            \n",
-       "502502                                               None                                                            \n",
-       "502503                                               None                                                            \n",
-       "502504                                               None                                                            \n",
-       "\n",
-       "       source_of_report_of_i97_postprocedural_disorders_of_circulatory_system_not_elsewhere_classified_f131419_0_0  \\\n",
-       "0                                                     NaN                                                            \n",
-       "1                                                     NaN                                                            \n",
-       "2                                                     NaN                                                            \n",
-       "3                                                     NaN                                                            \n",
-       "4                                                     NaN                                                            \n",
-       "...                                                   ...                                                            \n",
-       "502500                                                NaN                                                            \n",
-       "502501                                                NaN                                                            \n",
-       "502502                                                NaN                                                            \n",
-       "502503                                                NaN                                                            \n",
-       "502504                                                NaN                                                            \n",
-       "\n",
-       "       date_i98_first_reported_other_disorders_of_circulatory_system_in_diseases_classified_elsewhere_f131420_0_0  \\\n",
-       "0                                                    None                                                           \n",
-       "1                                                    None                                                           \n",
-       "2                                                    None                                                           \n",
-       "3                                                    None                                                           \n",
-       "4                                                    None                                                           \n",
-       "...                                                   ...                                                           \n",
-       "502500                                               None                                                           \n",
-       "502501                                               None                                                           \n",
-       "502502                                               None                                                           \n",
-       "502503                                               None                                                           \n",
-       "502504                                               None                                                           \n",
-       "\n",
-       "       source_of_report_of_i98_other_disorders_of_circulatory_system_in_diseases_classified_elsewhere_f131421_0_0  \\\n",
-       "0                                                     NaN                                                           \n",
-       "1                                                     NaN                                                           \n",
-       "2                                                     NaN                                                           \n",
-       "3                                                     NaN                                                           \n",
-       "4                                                     NaN                                                           \n",
-       "...                                                   ...                                                           \n",
-       "502500                                                NaN                                                           \n",
-       "502501                                                NaN                                                           \n",
-       "502502                                                NaN                                                           \n",
-       "502503                                                NaN                                                           \n",
-       "502504                                                NaN                                                           \n",
-       "\n",
-       "       date_i99_first_reported_other_and_unspecified_disorders_of_circulatory_system_f131422_0_0  \\\n",
-       "0                                                    None                                          \n",
-       "1                                                    None                                          \n",
-       "2                                                    None                                          \n",
-       "3                                                    None                                          \n",
-       "4                                                    None                                          \n",
-       "...                                                   ...                                          \n",
-       "502500                                               None                                          \n",
-       "502501                                               None                                          \n",
-       "502502                                         2012-08-16                                          \n",
-       "502503                                               None                                          \n",
-       "502504                                               None                                          \n",
-       "\n",
-       "       source_of_report_of_i99_other_and_unspecified_disorders_of_circulatory_system_f131423_0_0  \n",
-       "0                                                     NaN                                         \n",
-       "1                                                     NaN                                         \n",
-       "2                                                     NaN                                         \n",
-       "3                                                     NaN                                         \n",
-       "4                                                     NaN                                         \n",
-       "...                                                   ...                                         \n",
-       "502500                                                NaN                                         \n",
-       "502501                                                NaN                                         \n",
-       "502502                                  Primary care only                                         \n",
-       "502503                                                NaN                                         \n",
-       "502504                                                NaN                                         \n",
-       "\n",
-       "[502505 rows x 7162 columns]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Mappings + Vocabulary"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:34:05.867152Z",
-     "start_time": "2020-11-04T12:33:16.878773Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "502504\n",
-      "502504\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Drop obviouse missing data\n",
-    "print(len(data))\n",
-    "data = data.dropna(subset=[\"sex_f31_0_0\"], axis=0)\n",
-    "print(len(data))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Starting information"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:34:05.872216Z",
-     "start_time": "2020-11-04T12:34:05.869505Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "time0_col=\"birthdate\"\n",
-    "# time0_col=\"date_of_attending_assessment_centre_f53_0_0\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Basic Covariates"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:34:05.889725Z",
-     "start_time": "2020-11-04T12:34:05.874587Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "def get_fields(fields, data, data_field):\n",
-    "    f = data_field[data_field[\"field.showcase\"].isin(fields) & data_field[\"field.tab\"].str.contains(\"f\\\\.\\\\d+\\\\.0\\\\.\\\\d\")].copy()\n",
-    "    f[\"field\"] = pd.Categorical(f[\"field.showcase\"], categories=fields, ordered=True)\n",
-    "    f = f.sort_values(\"field\").reset_index().drop(\"field\", axis=1)\n",
-    "    return f\n",
-    "\n",
-    "def get_fields_all(fields, data, data_field):\n",
-    "    f = data_field[data_field[\"field.showcase\"].isin(fields)].copy()\n",
-    "    f[\"field\"] = pd.Categorical(f[\"field.showcase\"], categories=fields, ordered=True)\n",
-    "    f = f.sort_values(\"field\").reset_index().drop(\"field\", axis=1)\n",
-    "    return f\n",
-    "\n",
-    "def get_data_fields(fields, data, data_field):\n",
-    "    f = get_fields(fields, data, data_field)\n",
-    "    return data[[\"eid\"]+f[\"col.name\"].to_list()].copy()\n",
-    "\n",
-    "def get_data_fields_all(fields, data, data_field):\n",
-    "    f = get_fields_all(fields, data, data_field)\n",
-    "    return data[[\"eid\"]+f[\"col.name\"].to_list()].copy()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fields_basics = [\n",
-    "    \"21022\", # age at recruitment\n",
-    "    \"31\", # sex\n",
-    "    \"21000\", # ethnicity\n",
-    "    \"189\", # Townsend index\n",
-    "    \"53\", # date of baseline assessment\n",
-    "]\n",
-    "\n",
-    "temp = get_data_fields_all(fields_basics, data, data_field)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Basics"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:34:06.176411Z",
-     "start_time": "2020-11-04T12:34:05.891730Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "502504\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>age_at_recruitment_f21022_0_0</th>\n",
-       "      <th>sex_f31_0_0</th>\n",
-       "      <th>ethnic_background_f21000_0_0</th>\n",
-       "      <th>ethnic_background_f21000_1_0</th>\n",
-       "      <th>ethnic_background_f21000_2_0</th>\n",
-       "      <th>townsend_deprivation_index_at_recruitment_f189_0_0</th>\n",
-       "      <th>date_of_attending_assessment_centre_f53_0_0</th>\n",
-       "      <th>date_of_attending_assessment_centre_f53_1_0</th>\n",
-       "      <th>date_of_attending_assessment_centre_f53_2_0</th>\n",
-       "      <th>date_of_attending_assessment_centre_f53_3_0</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>49.0</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>British</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-1.852930</td>\n",
-       "      <td>2009-11-12</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1000020</td>\n",
-       "      <td>59.0</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>British</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.204248</td>\n",
-       "      <td>2008-02-19</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1000037</td>\n",
-       "      <td>59.0</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>British</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-3.498860</td>\n",
-       "      <td>2008-11-11</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000043</td>\n",
-       "      <td>63.0</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>British</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-5.351150</td>\n",
-       "      <td>2009-06-03</td>\n",
-       "      <td>None</td>\n",
-       "      <td>2018-06-08</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1000051</td>\n",
-       "      <td>51.0</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>British</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-1.799080</td>\n",
-       "      <td>2006-06-10</td>\n",
-       "      <td>None</td>\n",
-       "      <td>2019-09-15</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       eid  age_at_recruitment_f21022_0_0 sex_f31_0_0  \\\n",
-       "0  1000018                           49.0      Female   \n",
-       "1  1000020                           59.0        Male   \n",
-       "2  1000037                           59.0      Female   \n",
-       "3  1000043                           63.0        Male   \n",
-       "4  1000051                           51.0      Female   \n",
-       "\n",
-       "  ethnic_background_f21000_0_0 ethnic_background_f21000_1_0  \\\n",
-       "0                      British                          NaN   \n",
-       "1                      British                          NaN   \n",
-       "2                      British                          NaN   \n",
-       "3                      British                          NaN   \n",
-       "4                      British                          NaN   \n",
-       "\n",
-       "  ethnic_background_f21000_2_0  \\\n",
-       "0                          NaN   \n",
-       "1                          NaN   \n",
-       "2                          NaN   \n",
-       "3                          NaN   \n",
-       "4                          NaN   \n",
-       "\n",
-       "   townsend_deprivation_index_at_recruitment_f189_0_0  \\\n",
-       "0                                          -1.852930    \n",
-       "1                                           0.204248    \n",
-       "2                                          -3.498860    \n",
-       "3                                          -5.351150    \n",
-       "4                                          -1.799080    \n",
-       "\n",
-       "  date_of_attending_assessment_centre_f53_0_0  \\\n",
-       "0                                  2009-11-12   \n",
-       "1                                  2008-02-19   \n",
-       "2                                  2008-11-11   \n",
-       "3                                  2009-06-03   \n",
-       "4                                  2006-06-10   \n",
-       "\n",
-       "  date_of_attending_assessment_centre_f53_1_0  \\\n",
-       "0                                        None   \n",
-       "1                                        None   \n",
-       "2                                        None   \n",
-       "3                                        None   \n",
-       "4                                        None   \n",
-       "\n",
-       "  date_of_attending_assessment_centre_f53_2_0  \\\n",
-       "0                                        None   \n",
-       "1                                        None   \n",
-       "2                                        None   \n",
-       "3                                  2018-06-08   \n",
-       "4                                  2019-09-15   \n",
-       "\n",
-       "  date_of_attending_assessment_centre_f53_3_0  \n",
-       "0                                        None  \n",
-       "1                                        None  \n",
-       "2                                        None  \n",
-       "3                                        None  \n",
-       "4                                        None  "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fields_basics = [\n",
-    "    \"21022\", # age at recruitment\n",
-    "    \"31\", # sex\n",
-    "    \"21000\", # ethnicity\n",
-    "    \"189\", # Townsend index\n",
-    "    \"53\", # date of baseline assessment\n",
-    "]\n",
-    "\n",
-    "temp = get_data_fields_all(fields_basics, data, data_field)\n",
-    "\n",
-    "temp[\"sex_f31_0_0\"] = temp[\"sex_f31_0_0\"].cat.set_categories([\"Female\", 'Male'], ordered=False)\n",
-    "temp[\"ethnic_background_f21000_0_0\"] = temp[\"ethnic_background_f21000_0_0\"].astype(\"category\")\n",
-    "\n",
-    "basics = temp\n",
-    "print(len(temp))\n",
-    "display(temp.head())\n",
-    "\n",
-    "from dateutil.relativedelta import relativedelta\n",
-    "calc_birth_date = [date_of_attending_assessment_centre - relativedelta(years=age_at_recruitment) \n",
-    "                                                             for date_of_attending_assessment_centre, age_at_recruitment \n",
-    "                                                             in zip(basics[\"date_of_attending_assessment_centre_f53_0_0\"], basics[\"age_at_recruitment_f21022_0_0\"])]\n",
-    "#basics = basics.assign(birth_date = calc_birth_date)\n",
-    "\n",
-    "\n",
-    "basics.to_feather(os.path.join(path, dataset_path, 'temp_basics.feather'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#basics[\"t\"] = (basics.date_of_attending_assessment_centre_f53_0_0-basics.birth_date).dt.days/365.2425"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:34:06.186613Z",
-     "start_time": "2020-11-04T12:34:06.178111Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['British', 'Caribbean', 'Other ethnic group', 'Irish', 'Indian', ..., 'White and Black African', 'Any other Black background', 'Asian or Asian British', 'Mixed', 'Black or Black British']\n",
-      "Length: 23\n",
-      "Categories (22, object): ['Prefer not to answer' < 'Do not know' < 'White' < 'Mixed' ... 'Any other Asian background' < 'Caribbean' < 'African' < 'Any other Black background']\n"
-     ]
-    }
-   ],
-   "source": [
-    " print(temp[\"ethnic_background_f21000_0_0\"].unique())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Questionnaire"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:34:06.389467Z",
-     "start_time": "2020-11-04T12:34:06.188206Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "502504\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>overall_health_rating_f2178_0_0</th>\n",
-       "      <th>overall_health_rating_f2178_1_0</th>\n",
-       "      <th>overall_health_rating_f2178_2_0</th>\n",
-       "      <th>overall_health_rating_f2178_3_0</th>\n",
-       "      <th>smoking_status_f20116_0_0</th>\n",
-       "      <th>smoking_status_f20116_1_0</th>\n",
-       "      <th>smoking_status_f20116_2_0</th>\n",
-       "      <th>smoking_status_f20116_3_0</th>\n",
-       "      <th>alcohol_intake_frequency_f1558_0_0</th>\n",
-       "      <th>alcohol_intake_frequency_f1558_1_0</th>\n",
-       "      <th>alcohol_intake_frequency_f1558_2_0</th>\n",
-       "      <th>alcohol_intake_frequency_f1558_3_0</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
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-       "      <td>1000018</td>\n",
-       "      <td>Fair</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Current</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Once or twice a week</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
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-       "      <td>1000020</td>\n",
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-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1000037</td>\n",
-       "      <td>Good</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Previous</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Once or twice a week</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000043</td>\n",
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-       "      <td>Fair</td>\n",
-       "      <td>NaN</td>\n",
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-       "      <td>NaN</td>\n",
-       "      <td>Previous</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Three or four times a week</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Three or four times a week</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1000051</td>\n",
-       "      <td>Poor</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Fair</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Never</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Never</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>One to three times a month</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>One to three times a month</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       eid overall_health_rating_f2178_0_0 overall_health_rating_f2178_1_0  \\\n",
-       "0  1000018                            Fair                             NaN   \n",
-       "1  1000020                            Good                             NaN   \n",
-       "2  1000037                            Good                             NaN   \n",
-       "3  1000043                            Fair                             NaN   \n",
-       "4  1000051                            Poor                             NaN   \n",
-       "\n",
-       "  overall_health_rating_f2178_2_0 overall_health_rating_f2178_3_0  \\\n",
-       "0                             NaN                             NaN   \n",
-       "1                             NaN                             NaN   \n",
-       "2                             NaN                             NaN   \n",
-       "3                            Fair                             NaN   \n",
-       "4                            Fair                             NaN   \n",
-       "\n",
-       "  smoking_status_f20116_0_0 smoking_status_f20116_1_0  \\\n",
-       "0                   Current                       NaN   \n",
-       "1                   Current                       NaN   \n",
-       "2                  Previous                       NaN   \n",
-       "3                  Previous                       NaN   \n",
-       "4                     Never                       NaN   \n",
-       "\n",
-       "  smoking_status_f20116_2_0 smoking_status_f20116_3_0  \\\n",
-       "0                       NaN                       NaN   \n",
-       "1                       NaN                       NaN   \n",
-       "2                       NaN                       NaN   \n",
-       "3                  Previous                       NaN   \n",
-       "4                     Never                       NaN   \n",
-       "\n",
-       "  alcohol_intake_frequency_f1558_0_0 alcohol_intake_frequency_f1558_1_0  \\\n",
-       "0               Once or twice a week                                NaN   \n",
-       "1               Once or twice a week                                NaN   \n",
-       "2               Once or twice a week                                NaN   \n",
-       "3         Three or four times a week                                NaN   \n",
-       "4         One to three times a month                                NaN   \n",
-       "\n",
-       "  alcohol_intake_frequency_f1558_2_0 alcohol_intake_frequency_f1558_3_0  \n",
-       "0                                NaN                                NaN  \n",
-       "1                                NaN                                NaN  \n",
-       "2                                NaN                                NaN  \n",
-       "3         Three or four times a week                                NaN  \n",
-       "4         One to three times a month                                NaN  "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fields_questionnaire = [\n",
-    "    \"2178\", # Overall health\n",
-    "    \"20116\", # Smoking status\n",
-    "    \"1558\",\n",
-    "]\n",
-    "\n",
-    "temp = get_data_fields_all(fields_questionnaire, data, data_field)\n",
-    "\n",
-    "temp[\"overall_health_rating_f2178_0_0\"] = temp[\"overall_health_rating_f2178_0_0\"]\\\n",
-    "    .replace({\"Do not know\": np.nan, \"Prefer not to answer\": np.nan})\\\n",
-    "    .astype(\"category\").cat.set_categories(['Poor', 'Fair', 'Good', 'Excellent'], ordered=True)\n",
-    "\n",
-    "\n",
-    "temp[\"smoking_status_f20116_0_0\"] = temp[\"smoking_status_f20116_0_0\"]\\\n",
-    "    .replace({\"Prefer not to answer\": np.nan}, inplace=False)\\\n",
-    "    .astype(\"category\").cat.set_categories(['Current', 'Previous', 'Never'], ordered=True)\n",
-    "\n",
-    "temp[\"alcohol_intake_frequency_f1558_0_0\"] = temp[\"alcohol_intake_frequency_f1558_0_0\"]\\\n",
-    "    .replace({\"Prefer not to answer\": np.nan}, inplace=False)\\\n",
-    "    .astype(\"category\").cat.set_categories([\n",
-    "        'Daily or almost daily', \n",
-    "        'Three or four times a week', \n",
-    "        'Once or twice a week',\n",
-    "        'One to three times a month',\n",
-    "        'Special occasions only', \n",
-    "        'Never'], ordered=True)\n",
-    "\n",
-    "questionnaire = temp\n",
-    "print(len(temp))\n",
-    "display(temp.head())\n",
-    "\n",
-    "questionnaire.to_feather(os.path.join(path, dataset_path, 'temp_questionnaire.feather'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:34:06.398168Z",
-     "start_time": "2020-11-04T12:34:06.391082Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['Once or twice a week', 'Three or four times a week', 'One to three times a month', 'Daily or almost daily', 'Special occasions only', 'Never', NaN]\n",
-      "Categories (6, object): ['Daily or almost daily' < 'Three or four times a week' < 'Once or twice a week' < 'One to three times a month' < 'Special occasions only' < 'Never']\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(temp[\"alcohol_intake_frequency_f1558_0_0\"].unique())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Physical measurements"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:34:07.052989Z",
-     "start_time": "2020-11-04T12:34:06.400858Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "502504\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>body_mass_index_bmi_f21001_2_0</th>\n",
-       "      <th>body_mass_index_bmi_f21001_3_0</th>\n",
-       "      <th>body_mass_index_bmi_f21001_0_0</th>\n",
-       "      <th>body_mass_index_bmi_f21001_1_0</th>\n",
-       "      <th>weight_f21002_3_0</th>\n",
-       "      <th>weight_f21002_2_0</th>\n",
-       "      <th>weight_f21002_1_0</th>\n",
-       "      <th>weight_f21002_0_0</th>\n",
-       "      <th>systolic_blood_pressure_automated_reading_f4080_3_0</th>\n",
-       "      <th>...</th>\n",
-       "      <th>peak_expiratory_flow_pef_f3064_2_0</th>\n",
-       "      <th>peak_expiratory_flow_pef_f3064_1_2</th>\n",
-       "      <th>peak_expiratory_flow_pef_f3064_1_1</th>\n",
-       "      <th>peak_expiratory_flow_pef_f3064_1_0</th>\n",
-       "      <th>peak_expiratory_flow_pef_f3064_0_2</th>\n",
-       "      <th>peak_expiratory_flow_pef_f3064_0_1</th>\n",
-       "      <th>peak_expiratory_flow_pef_f3064_3_0</th>\n",
-       "      <th>peak_expiratory_flow_pef_f3064_3_1</th>\n",
-       "      <th>peak_expiratory_flow_pef_f3064_2_2</th>\n",
-       "      <th>peak_expiratory_flow_pef_f3064_3_2</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>26.5557</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>63.8</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>317.0</td>\n",
-       "      <td>312.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1000020</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>22.7465</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>70.7</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>301.0</td>\n",
-       "      <td>496.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1000037</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>32.4211</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>78.9</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>185.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000043</td>\n",
-       "      <td>28.4349</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>29.5679</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>90.6</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>95.8</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>476.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>557.0</td>\n",
-       "      <td>513.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>390.0</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1000051</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>41.0222</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>92.3</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 80 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       eid  body_mass_index_bmi_f21001_2_0  body_mass_index_bmi_f21001_3_0  \\\n",
-       "0  1000018                             NaN                             NaN   \n",
-       "1  1000020                             NaN                             NaN   \n",
-       "2  1000037                             NaN                             NaN   \n",
-       "3  1000043                         28.4349                             NaN   \n",
-       "4  1000051                             NaN                             NaN   \n",
-       "\n",
-       "   body_mass_index_bmi_f21001_0_0  body_mass_index_bmi_f21001_1_0  \\\n",
-       "0                         26.5557                             NaN   \n",
-       "1                         22.7465                             NaN   \n",
-       "2                         32.4211                             NaN   \n",
-       "3                         29.5679                             NaN   \n",
-       "4                         41.0222                             NaN   \n",
-       "\n",
-       "   weight_f21002_3_0  weight_f21002_2_0  weight_f21002_1_0  weight_f21002_0_0  \\\n",
-       "0                NaN                NaN                NaN               63.8   \n",
-       "1                NaN                NaN                NaN               70.7   \n",
-       "2                NaN                NaN                NaN               78.9   \n",
-       "3                NaN               90.6                NaN               95.8   \n",
-       "4                NaN                NaN                NaN               92.3   \n",
-       "\n",
-       "   systolic_blood_pressure_automated_reading_f4080_3_0  ...  \\\n",
-       "0                                                NaN    ...   \n",
-       "1                                                NaN    ...   \n",
-       "2                                                NaN    ...   \n",
-       "3                                                NaN    ...   \n",
-       "4                                                NaN    ...   \n",
-       "\n",
-       "   peak_expiratory_flow_pef_f3064_2_0  peak_expiratory_flow_pef_f3064_1_2  \\\n",
-       "0                                 NaN                                 NaN   \n",
-       "1                                 NaN                                 NaN   \n",
-       "2                                 NaN                                 NaN   \n",
-       "3                               476.0                                 NaN   \n",
-       "4                                 NaN                                 NaN   \n",
-       "\n",
-       "   peak_expiratory_flow_pef_f3064_1_1  peak_expiratory_flow_pef_f3064_1_0  \\\n",
-       "0                                 NaN                                 NaN   \n",
-       "1                                 NaN                                 NaN   \n",
-       "2                                 NaN                                 NaN   \n",
-       "3                                 NaN                                 NaN   \n",
-       "4                                 NaN                                 NaN   \n",
-       "\n",
-       "   peak_expiratory_flow_pef_f3064_0_2  peak_expiratory_flow_pef_f3064_0_1  \\\n",
-       "0                               317.0                               312.0   \n",
-       "1                               301.0                               496.0   \n",
-       "2                                 NaN                               185.0   \n",
-       "3                               557.0                               513.0   \n",
-       "4                                 NaN                                 NaN   \n",
-       "\n",
-       "   peak_expiratory_flow_pef_f3064_3_0  peak_expiratory_flow_pef_f3064_3_1  \\\n",
-       "0                                 NaN                                 NaN   \n",
-       "1                                 NaN                                 NaN   \n",
-       "2                                 NaN                                 NaN   \n",
-       "3                                 NaN                                 NaN   \n",
-       "4                                 NaN                                 NaN   \n",
-       "\n",
-       "   peak_expiratory_flow_pef_f3064_2_2  peak_expiratory_flow_pef_f3064_3_2  \n",
-       "0                                 NaN                                 NaN  \n",
-       "1                                 NaN                                 NaN  \n",
-       "2                                 NaN                                 NaN  \n",
-       "3                               390.0                                 NaN  \n",
-       "4                                 NaN                                 NaN  \n",
-       "\n",
-       "[5 rows x 80 columns]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from statistics import mean\n",
-    "\n",
-    "fields_measurements = [\n",
-    "#    \"100313\", # Walking speed !!! MISSING !!!\n",
-    "    \"21001\", # BMI\n",
-    "    \"21002\", # weight\n",
-    "    \"4080\", # Syst. BP\n",
-    "    \"4079\", # Diast. BP\n",
-    "    \"102\",\n",
-    "    \"21021\",\n",
-    "    \"4195\",\n",
-    "    \"48\",\n",
-    "    \"49\",\n",
-    "    \"50\",\n",
-    "    \"23127\",\n",
-    "    \"23099\",\n",
-    "    \"23105\",\n",
-    "    \"20151\",\n",
-    "    \"20150\",\n",
-    "    \"20258\",\n",
-    "    \"3064\",\n",
-    "    \n",
-    "]\n",
-    "temp = get_data_fields_all(fields_measurements, data, data_field)\n",
-    "\n",
-    "#sbp_cols = [\"systolic_blood_pressure_automated_reading_f4080_0_0\", \"systolic_blood_pressure_automated_reading_f4080_0_1\"]\n",
-    "#dbp_cols = [\"diastolic_blood_pressure_automated_reading_f4079_0_0\", \"diastolic_blood_pressure_automated_reading_f4079_0_1\"]\n",
-    "#pr_cols = [\"pulse_rate_automated_reading_f102_0_0\", \"pulse_rate_automated_reading_f102_0_1\"]\n",
-    "\n",
-    "#temp = temp.assign(systolic_blood_pressure_automated_reading_f4080 = temp[sbp_cols].mean(axis=1),\n",
-    "#                   diastolic_blood_pressure_automated_reading_f4079 = temp[dbp_cols].mean(axis=1),\n",
-    "#                   pulse_rate_automated_reading_f102 = temp[pr_cols].mean(axis=1))\\\n",
-    "#    .drop(sbp_cols + dbp_cols + pr_cols, axis=1)\n",
-    "\n",
-    "measurements = temp\n",
-    "print(len(temp))\n",
-    "display(temp.head())\n",
-    "\n",
-    "measurements.to_feather(os.path.join(path, dataset_path, 'temp_measurements.feather'))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Lab measurements"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:34:07.647955Z",
-     "start_time": "2020-11-04T12:34:07.055242Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "502504\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>basophill_count_f30160_2_0</th>\n",
-       "      <th>basophill_count_f30160_1_0</th>\n",
-       "      <th>basophill_count_f30160_0_0</th>\n",
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-       "      <th>basophill_percentage_f30220_0_0</th>\n",
-       "      <th>basophill_percentage_f30220_1_0</th>\n",
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-       "      <th>eosinophill_count_f30150_1_0</th>\n",
-       "      <th>eosinophill_count_f30150_2_0</th>\n",
-       "      <th>...</th>\n",
-       "      <th>total_protein_f30860_0_0</th>\n",
-       "      <th>total_protein_f30860_1_0</th>\n",
-       "      <th>triglycerides_f30870_0_0</th>\n",
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-       "      <th>vitamin_d_f30890_1_0</th>\n",
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-       "      <td>0.04</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.26</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.25</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
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-       "      <td>71.97</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.247</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>221.3</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>5.48</td>\n",
-       "      <td>70.7</td>\n",
-       "      <td>NaN</td>\n",
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-       "    <tr>\n",
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-       "      <td>1000020</td>\n",
-       "      <td>NaN</td>\n",
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-       "      <td>NaN</td>\n",
-       "      <td>0.30</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.30</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>78.45</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.906</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>374.7</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>5.28</td>\n",
-       "      <td>35.9</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1000037</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.04</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.57</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.10</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000043</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.02</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.32</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.11</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>69.70</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>5.184</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>322.8</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>6.67</td>\n",
-       "      <td>63.6</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1000051</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 154 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       eid  basophill_count_f30160_2_0  basophill_count_f30160_1_0  \\\n",
-       "0  1000018                         NaN                         NaN   \n",
-       "1  1000020                         NaN                         NaN   \n",
-       "2  1000037                         NaN                         NaN   \n",
-       "3  1000043                         NaN                         NaN   \n",
-       "4  1000051                         NaN                         NaN   \n",
-       "\n",
-       "   basophill_count_f30160_0_0  basophill_percentage_f30220_2_0  \\\n",
-       "0                        0.04                              NaN   \n",
-       "1                        0.00                              NaN   \n",
-       "2                        0.04                              NaN   \n",
-       "3                        0.02                              NaN   \n",
-       "4                         NaN                              NaN   \n",
-       "\n",
-       "   basophill_percentage_f30220_0_0  basophill_percentage_f30220_1_0  \\\n",
-       "0                             0.26                              NaN   \n",
-       "1                             0.30                              NaN   \n",
-       "2                             0.57                              NaN   \n",
-       "3                             0.32                              NaN   \n",
-       "4                              NaN                              NaN   \n",
-       "\n",
-       "   eosinophill_count_f30150_0_0  eosinophill_count_f30150_1_0  \\\n",
-       "0                          0.25                           NaN   \n",
-       "1                          0.30                           NaN   \n",
-       "2                          0.10                           NaN   \n",
-       "3                          0.11                           NaN   \n",
-       "4                           NaN                           NaN   \n",
-       "\n",
-       "   eosinophill_count_f30150_2_0  ...  total_protein_f30860_0_0  \\\n",
-       "0                           NaN  ...                     71.97   \n",
-       "1                           NaN  ...                     78.45   \n",
-       "2                           NaN  ...                       NaN   \n",
-       "3                           NaN  ...                     69.70   \n",
-       "4                           NaN  ...                       NaN   \n",
-       "\n",
-       "   total_protein_f30860_1_0  triglycerides_f30870_0_0  \\\n",
-       "0                       NaN                     1.247   \n",
-       "1                       NaN                     1.906   \n",
-       "2                       NaN                       NaN   \n",
-       "3                       NaN                     5.184   \n",
-       "4                       NaN                       NaN   \n",
-       "\n",
-       "   triglycerides_f30870_1_0  urate_f30880_0_0  urate_f30880_1_0  \\\n",
-       "0                       NaN             221.3               NaN   \n",
-       "1                       NaN             374.7               NaN   \n",
-       "2                       NaN               NaN               NaN   \n",
-       "3                       NaN             322.8               NaN   \n",
-       "4                       NaN               NaN               NaN   \n",
-       "\n",
-       "   urea_f30670_1_0  urea_f30670_0_0  vitamin_d_f30890_0_0  \\\n",
-       "0              NaN             5.48                  70.7   \n",
-       "1              NaN             5.28                  35.9   \n",
-       "2              NaN              NaN                   NaN   \n",
-       "3              NaN             6.67                  63.6   \n",
-       "4              NaN              NaN                   NaN   \n",
-       "\n",
-       "   vitamin_d_f30890_1_0  \n",
-       "0                   NaN  \n",
-       "1                   NaN  \n",
-       "2                   NaN  \n",
-       "3                   NaN  \n",
-       "4                   NaN  \n",
-       "\n",
-       "[5 rows x 154 columns]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fields_blood_count = [\n",
-    "    \"30160\", #\tBasophill count\n",
-    "    \"30220\", #\tBasophill percentage\n",
-    "    \"30150\", #\tEosinophill count\n",
-    "    \"30210\", #\tEosinophill percentage\n",
-    "    \"30030\", #\tHaematocrit percentage\n",
-    "    \"30020\", #\tHaemoglobin concentration\n",
-    "    \"30300\", #\tHigh light scatter reticulocyte count\n",
-    "    \"30290\", #\tHigh light scatter reticulocyte percentage\n",
-    "    \"30280\", #\tImmature reticulocyte fraction\n",
-    "    \"30120\", #\tLymphocyte count\n",
-    "    \"30180\", #\tLymphocyte percentage\n",
-    "    \"30050\", #\tMean corpuscular haemoglobin\n",
-    "    \"30060\", #\tMean corpuscular haemoglobin concentration\n",
-    "    \"30040\", #\tMean corpuscular volume\n",
-    "    \"30100\", #\tMean platelet (thrombocyte) volume\n",
-    "    \"30260\", #\tMean reticulocyte volume\n",
-    "    \"30270\", #\tMean sphered cell volume\n",
-    "    \"30130\", #\tMonocyte count\n",
-    "    \"30190\", #\tMonocyte percentage\n",
-    "    \"30140\", #\tNeutrophill count\n",
-    "    \"30200\", #\tNeutrophill percentage\n",
-    "    \"30170\", #\tNucleated red blood cell count\n",
-    "    \"30230\", #\tNucleated red blood cell percentage\n",
-    "    \"30080\", #\tPlatelet count\n",
-    "    \"30090\", #\tPlatelet crit\n",
-    "    \"30110\", #\tPlatelet distribution width\n",
-    "    \"30010\", #\tRed blood cell (erythrocyte) count\n",
-    "    \"30070\", #\tRed blood cell (erythrocyte) distribution width\n",
-    "    \"30250\", #\tReticulocyte count\n",
-    "    \"30240\", #\tReticulocyte percentage\n",
-    "    \"30000\", #\tWhite blood cell (leukocyte) count\n",
-    "]\n",
-    "\n",
-    "fields_blood_biochemistry = [\n",
-    "    \"30620\",#\tAlanine aminotransferase\n",
-    "    \"30600\",#\tAlbumin\n",
-    "    \"30610\",#\tAlkaline phosphatase\n",
-    "    \"30630\",#\tApolipoprotein A\n",
-    "    \"30640\",#\tApolipoprotein B\n",
-    "    \"30650\",#\tAspartate aminotransferase\n",
-    "    \"30710\",#\tC-reactive protein\n",
-    "    \"30680\",#\tCalcium\n",
-    "    \"30690\",#\tCholesterol\n",
-    "    \"30700\",#\tCreatinine\n",
-    "    \"30720\",#\tCystatin C\n",
-    "    \"30660\",#\tDirect bilirubin\n",
-    "    \"30730\",#\tGamma glutamyltransferase\n",
-    "    \"30740\",#\tGlucose\n",
-    "    \"30750\",#\tGlycated haemoglobin (HbA1c)\n",
-    "    \"30760\",#\tHDL cholesterol\n",
-    "    \"30770\",#\tIGF-1\n",
-    "    \"30780\",#\tLDL direct\n",
-    "    \"30790\",#\tLipoprotein A\n",
-    "    \"30800\",#\tOestradiol\n",
-    "    \"30810\",#\tPhosphate\n",
-    "    \"30820\",#\tRheumatoid factor\n",
-    "    \"30830\",#\tSHBG\n",
-    "    \"30850\",#\tTestosterone\n",
-    "    \"30840\",#\tTotal bilirubin\n",
-    "    \"30860\",#\tTotal protein\n",
-    "    \"30870\",#\tTriglycerides\n",
-    "    \"30880\",#\tUrate\n",
-    "    \"30670\",#\tUrea\n",
-    "    \"30890\",#\tVitamin D\n",
-    "]\n",
-    "\n",
-    "fields_blood_infectious = [\n",
-    "    \"23000\", #\t1gG antigen for Herpes Simplex virus-1\n",
-    "    \"23001\", #\t2mgG unique antigen for Herpes Simplex virus-2\n",
-    "    \"23049\", #\tAntigen assay QC indicator\n",
-    "    \"23048\", #\tAntigen assay date\n",
-    "    \"23026\", #\tBK VP1 antigen for Human Polyomavirus BKV\n",
-    "    \"23039\", #\tCagA antigen for Helicobacter pylori\n",
-    "    \"23043\", #\tCatalase antigen for Helicobacter pylori\n",
-    "    \"23018\", #\tCore antigen for Hepatitis C Virus\n",
-    "    \"23030\", #\tE6 antigen for Human Papillomavirus type-16\n",
-    "    \"23031\", #\tE7 antigen for Human Papillomavirus type-16\n",
-    "    \"23006\", #\tEA-D antigen for Epstein-Barr Virus\n",
-    "    \"23004\", #\tEBNA-1 antigen for Epstein-Barr Virus\n",
-    "    \"23042\", #\tGroEL antigen for Helicobacter pylori\n",
-    "    \"23016\", #\tHBc antigen for Hepatitis B Virus\n",
-    "    \"23017\", #\tHBe antigen for Hepatitis B Virus\n",
-    "    \"23025\", #\tHIV-1 env antigen for Human Immunodeficiency Virus\n",
-    "    \"23024\", #\tHIV-1 gag antigen for Human Immunodeficiency Virus\n",
-    "    \"23023\", #\tHTLV-1 env antigen for Human T-Lymphotropic Virus 1\n",
-    "    \"23022\", #\tHTLV-1 gag antigen for Human T-Lymphotropic Virus 1\n",
-    "    \"23010\", #\tIE1A antigen for Human Herpesvirus-6\n",
-    "    \"23011\", #\tIE1B antigen for Human Herpesvirus-6\n",
-    "    \"23027\", #\tJC VP1 antigen for Human Polyomavirus JCV\n",
-    "    \"23015\", #\tK8.1 antigen for Kaposi's Sarcoma-Associated Herpesvirus\n",
-    "    \"23029\", #\tL1 antigen for Human Papillomavirus type-16\n",
-    "    \"23032\", #\tL1 antigen for Human Papillomavirus type-18\n",
-    "    \"23014\", #\tLANA antigen for Kaposi's Sarcoma-Associated Herpesvirus\n",
-    "    \"23028\", #\tMC VP1 antigen for Merkel Cell Polyomavirus\n",
-    "    \"23019\", #\tNS3 antigen for Hepatitis C Virus\n",
-    "    \"23041\", #\tOMP antigen for Helicobacter pylori\n",
-    "    \"23037\", #\tPorB antigen for Chlamydia trachomatis\n",
-    "    \"23013\", #\tU14 antigen for Human Herpesvirus-7\n",
-    "    \"23044\", #\tUreA antigen for Helicobacter pylori\n",
-    "    \"23003\", #\tVCA p18 antigen for Epstein-Barr Virus\n",
-    "    \"23040\", #\tVacA antigen for Helicobacter pylori\n",
-    "    \"23005\", #\tZEBRA antigen for Epstein-Barr Virus\n",
-    "    \"23002\", #\tgE / gI antigen for Varicella Zoster Virus\n",
-    "    \"23034\", #\tmomp A antigen for Chlamydia trachomatis\n",
-    "    \"23033\", #\tmomp D antigen for Chlamydia trachomatis\n",
-    "    \"23012\", #\tp101 k antigen for Human Herpesvirus-6\n",
-    "    \"23020\", #\tp22 antigen for Toxoplasma gondii\n",
-    "    \"23038\", #\tpGP3 antigen for Chlamydia trachomatis\n",
-    "    \"23009\", #\tpp 28 antigen for Human Cytomegalovirus\n",
-    "    \"23008\", #\tpp 52 antigen for Human Cytomegalovirus\n",
-    "    \"23007\", #\tpp150 Nter antigen for Human Cytomegalovirus\n",
-    "    \"23021\", #\tsag1 antigen for Toxoplasma gondii\n",
-    "    \"23035\", #\ttarp-D F1 antigen for Chlamydia trachomatis\n",
-    "    \"23036\", #\ttarp-D F2 antigen for Chlamydia trachomatis\n",
-    "]\n",
-    "\n",
-    "labs = temp = get_data_fields_all(fields_blood_count+fields_blood_biochemistry+fields_blood_infectious, data, data_field)\n",
-    "print(len(temp))\n",
-    "display(temp.head())\n",
-    "\n",
-    "labs.to_feather(os.path.join(path, dataset_path, 'temp_labs.feather'))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Get Demographic Data with times"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "standard = pd.concat([basics.set_index(\"eid\"), questionnaire.set_index(\"eid\"), measurements.set_index(\"eid\"), labs.set_index(\"eid\")], axis=1).reset_index()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cols_raw = [c[:-4] for c in standard.drop(\"eid\", axis=1).columns.to_list()]\n",
-    "cols = list(dict.fromkeys(cols_raw))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>age_at_recruitment_f21022_0_0</th>\n",
-       "      <th>sex_f31_0_0</th>\n",
-       "      <th>ethnic_background_f21000_0_0</th>\n",
-       "      <th>ethnic_background_f21000_1_0</th>\n",
-       "      <th>ethnic_background_f21000_2_0</th>\n",
-       "      <th>townsend_deprivation_index_at_recruitment_f189_0_0</th>\n",
-       "      <th>date_of_attending_assessment_centre_f53_0_0</th>\n",
-       "      <th>date_of_attending_assessment_centre_f53_1_0</th>\n",
-       "      <th>date_of_attending_assessment_centre_f53_2_0</th>\n",
-       "      <th>...</th>\n",
-       "      <th>total_protein_f30860_0_0</th>\n",
-       "      <th>total_protein_f30860_1_0</th>\n",
-       "      <th>triglycerides_f30870_0_0</th>\n",
-       "      <th>triglycerides_f30870_1_0</th>\n",
-       "      <th>urate_f30880_0_0</th>\n",
-       "      <th>urate_f30880_1_0</th>\n",
-       "      <th>urea_f30670_1_0</th>\n",
-       "      <th>urea_f30670_0_0</th>\n",
-       "      <th>vitamin_d_f30890_0_0</th>\n",
-       "      <th>vitamin_d_f30890_1_0</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>49.0</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>British</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-1.852930</td>\n",
-       "      <td>2009-11-12</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>...</td>\n",
-       "      <td>71.97</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.247</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>221.3</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>5.48</td>\n",
-       "      <td>70.7</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1000020</td>\n",
-       "      <td>59.0</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>British</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.204248</td>\n",
-       "      <td>2008-02-19</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>...</td>\n",
-       "      <td>78.45</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.906</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>374.7</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>5.28</td>\n",
-       "      <td>35.9</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1000037</td>\n",
-       "      <td>59.0</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>British</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-3.498860</td>\n",
-       "      <td>2008-11-11</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000043</td>\n",
-       "      <td>63.0</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>British</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-5.351150</td>\n",
-       "      <td>2009-06-03</td>\n",
-       "      <td>None</td>\n",
-       "      <td>2018-06-08</td>\n",
-       "      <td>...</td>\n",
-       "      <td>69.70</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>5.184</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>322.8</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>6.67</td>\n",
-       "      <td>63.6</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1000051</td>\n",
-       "      <td>51.0</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>British</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-1.799080</td>\n",
-       "      <td>2006-06-10</td>\n",
-       "      <td>None</td>\n",
-       "      <td>2019-09-15</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>502499</th>\n",
-       "      <td>6025150</td>\n",
-       "      <td>43.0</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>British</td>\n",
-       "      <td>British</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.046781</td>\n",
-       "      <td>2007-06-30</td>\n",
-       "      <td>2012-11-17</td>\n",
-       "      <td>2017-08-12</td>\n",
-       "      <td>...</td>\n",
-       "      <td>72.10</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.730</td>\n",
-       "      <td>2.285</td>\n",
-       "      <td>298.8</td>\n",
-       "      <td>356.1</td>\n",
-       "      <td>5.65</td>\n",
-       "      <td>4.21</td>\n",
-       "      <td>41.6</td>\n",
-       "      <td>17.9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>502500</th>\n",
-       "      <td>6025165</td>\n",
-       "      <td>45.0</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>British</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-2.107040</td>\n",
-       "      <td>2008-09-02</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>...</td>\n",
-       "      <td>74.20</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.442</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>220.2</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4.01</td>\n",
-       "      <td>72.7</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>502501</th>\n",
-       "      <td>6025173</td>\n",
-       "      <td>57.0</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>British</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-1.827220</td>\n",
-       "      <td>2008-09-17</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>...</td>\n",
-       "      <td>72.03</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.136</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>255.5</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>5.25</td>\n",
-       "      <td>41.6</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>502502</th>\n",
-       "      <td>6025182</td>\n",
-       "      <td>56.0</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>British</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-0.010764</td>\n",
-       "      <td>2010-07-01</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>...</td>\n",
-       "      <td>70.65</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>5.756</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>353.6</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4.42</td>\n",
-       "      <td>45.9</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>502503</th>\n",
-       "      <td>6025198</td>\n",
-       "      <td>67.0</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>British</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-1.930650</td>\n",
-       "      <td>2010-01-26</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>...</td>\n",
-       "      <td>70.62</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2.327</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>454.8</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>5.14</td>\n",
-       "      <td>20.2</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>502504 rows × 255 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            eid  age_at_recruitment_f21022_0_0 sex_f31_0_0  \\\n",
-       "0       1000018                           49.0      Female   \n",
-       "1       1000020                           59.0        Male   \n",
-       "2       1000037                           59.0      Female   \n",
-       "3       1000043                           63.0        Male   \n",
-       "4       1000051                           51.0      Female   \n",
-       "...         ...                            ...         ...   \n",
-       "502499  6025150                           43.0      Female   \n",
-       "502500  6025165                           45.0      Female   \n",
-       "502501  6025173                           57.0        Male   \n",
-       "502502  6025182                           56.0        Male   \n",
-       "502503  6025198                           67.0        Male   \n",
-       "\n",
-       "       ethnic_background_f21000_0_0 ethnic_background_f21000_1_0  \\\n",
-       "0                           British                          NaN   \n",
-       "1                           British                          NaN   \n",
-       "2                           British                          NaN   \n",
-       "3                           British                          NaN   \n",
-       "4                           British                          NaN   \n",
-       "...                             ...                          ...   \n",
-       "502499                      British                      British   \n",
-       "502500                      British                          NaN   \n",
-       "502501                      British                          NaN   \n",
-       "502502                      British                          NaN   \n",
-       "502503                      British                          NaN   \n",
-       "\n",
-       "       ethnic_background_f21000_2_0  \\\n",
-       "0                               NaN   \n",
-       "1                               NaN   \n",
-       "2                               NaN   \n",
-       "3                               NaN   \n",
-       "4                               NaN   \n",
-       "...                             ...   \n",
-       "502499                          NaN   \n",
-       "502500                          NaN   \n",
-       "502501                          NaN   \n",
-       "502502                          NaN   \n",
-       "502503                          NaN   \n",
-       "\n",
-       "        townsend_deprivation_index_at_recruitment_f189_0_0  \\\n",
-       "0                                               -1.852930    \n",
-       "1                                                0.204248    \n",
-       "2                                               -3.498860    \n",
-       "3                                               -5.351150    \n",
-       "4                                               -1.799080    \n",
-       "...                                                   ...    \n",
-       "502499                                           0.046781    \n",
-       "502500                                          -2.107040    \n",
-       "502501                                          -1.827220    \n",
-       "502502                                          -0.010764    \n",
-       "502503                                          -1.930650    \n",
-       "\n",
-       "       date_of_attending_assessment_centre_f53_0_0  \\\n",
-       "0                                       2009-11-12   \n",
-       "1                                       2008-02-19   \n",
-       "2                                       2008-11-11   \n",
-       "3                                       2009-06-03   \n",
-       "4                                       2006-06-10   \n",
-       "...                                            ...   \n",
-       "502499                                  2007-06-30   \n",
-       "502500                                  2008-09-02   \n",
-       "502501                                  2008-09-17   \n",
-       "502502                                  2010-07-01   \n",
-       "502503                                  2010-01-26   \n",
-       "\n",
-       "       date_of_attending_assessment_centre_f53_1_0  \\\n",
-       "0                                             None   \n",
-       "1                                             None   \n",
-       "2                                             None   \n",
-       "3                                             None   \n",
-       "4                                             None   \n",
-       "...                                            ...   \n",
-       "502499                                  2012-11-17   \n",
-       "502500                                        None   \n",
-       "502501                                        None   \n",
-       "502502                                        None   \n",
-       "502503                                        None   \n",
-       "\n",
-       "       date_of_attending_assessment_centre_f53_2_0  ...  \\\n",
-       "0                                             None  ...   \n",
-       "1                                             None  ...   \n",
-       "2                                             None  ...   \n",
-       "3                                       2018-06-08  ...   \n",
-       "4                                       2019-09-15  ...   \n",
-       "...                                            ...  ...   \n",
-       "502499                                  2017-08-12  ...   \n",
-       "502500                                        None  ...   \n",
-       "502501                                        None  ...   \n",
-       "502502                                        None  ...   \n",
-       "502503                                        None  ...   \n",
-       "\n",
-       "       total_protein_f30860_0_0 total_protein_f30860_1_0  \\\n",
-       "0                         71.97                      NaN   \n",
-       "1                         78.45                      NaN   \n",
-       "2                           NaN                      NaN   \n",
-       "3                         69.70                      NaN   \n",
-       "4                           NaN                      NaN   \n",
-       "...                         ...                      ...   \n",
-       "502499                    72.10                      NaN   \n",
-       "502500                    74.20                      NaN   \n",
-       "502501                    72.03                      NaN   \n",
-       "502502                    70.65                      NaN   \n",
-       "502503                    70.62                      NaN   \n",
-       "\n",
-       "       triglycerides_f30870_0_0 triglycerides_f30870_1_0 urate_f30880_0_0  \\\n",
-       "0                         1.247                      NaN            221.3   \n",
-       "1                         1.906                      NaN            374.7   \n",
-       "2                           NaN                      NaN              NaN   \n",
-       "3                         5.184                      NaN            322.8   \n",
-       "4                           NaN                      NaN              NaN   \n",
-       "...                         ...                      ...              ...   \n",
-       "502499                    0.730                    2.285            298.8   \n",
-       "502500                    1.442                      NaN            220.2   \n",
-       "502501                    1.136                      NaN            255.5   \n",
-       "502502                    5.756                      NaN            353.6   \n",
-       "502503                    2.327                      NaN            454.8   \n",
-       "\n",
-       "       urate_f30880_1_0 urea_f30670_1_0 urea_f30670_0_0 vitamin_d_f30890_0_0  \\\n",
-       "0                   NaN             NaN            5.48                 70.7   \n",
-       "1                   NaN             NaN            5.28                 35.9   \n",
-       "2                   NaN             NaN             NaN                  NaN   \n",
-       "3                   NaN             NaN            6.67                 63.6   \n",
-       "4                   NaN             NaN             NaN                  NaN   \n",
-       "...                 ...             ...             ...                  ...   \n",
-       "502499            356.1            5.65            4.21                 41.6   \n",
-       "502500              NaN             NaN            4.01                 72.7   \n",
-       "502501              NaN             NaN            5.25                 41.6   \n",
-       "502502              NaN             NaN            4.42                 45.9   \n",
-       "502503              NaN             NaN            5.14                 20.2   \n",
-       "\n",
-       "       vitamin_d_f30890_1_0  \n",
-       "0                       NaN  \n",
-       "1                       NaN  \n",
-       "2                       NaN  \n",
-       "3                       NaN  \n",
-       "4                       NaN  \n",
-       "...                     ...  \n",
-       "502499                 17.9  \n",
-       "502500                  NaN  \n",
-       "502501                  NaN  \n",
-       "502502                  NaN  \n",
-       "502503                  NaN  \n",
-       "\n",
-       "[502504 rows x 255 columns]"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "standard"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "4"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "2+2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "ModuleNotFoundError",
-     "evalue": "No module named 'cudf'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-35-e13365c50bc4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mcudf\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'cudf'"
-     ]
-    }
-   ],
-   "source": [
-    "import cudf"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d3b78428eda44d26b055c31254877601",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(HTML(value=''), FloatProgress(value=0.0, max=51.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "ename": "TerminatedWorkerError",
-     "evalue": "A worker process managed by the executor was unexpectedly terminated. This could be caused by a segmentation fault while calling the function or by an excessive memory usage causing the Operating System to kill the worker.\n\nThe exit codes of the workers are {SIGKILL(-9)}",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTerminatedWorkerError\u001b[0m                     Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-34-1953fd3b9ff7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mlist_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mdf_input\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdf_input\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0mdf_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mParallel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdelayed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_wide_to_long\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mdf\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtqdm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlist_df\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m \u001b[0mdf_concat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreset_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m   1059\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1060\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretrieval_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1061\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretrieve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1062\u001b[0m             \u001b[0;31m# Make sure that we get a last message telling us we are done\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1063\u001b[0m             \u001b[0melapsed_time\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_start_time\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36mretrieve\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    938\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    939\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'supports_timeout'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 940\u001b[0;31m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_output\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    941\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    942\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_output\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36mwrap_future_result\u001b[0;34m(future, timeout)\u001b[0m\n\u001b[1;32m    540\u001b[0m         AsyncResults.get from multiprocessing.\"\"\"\n\u001b[1;32m    541\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 542\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfuture\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    543\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mCfTimeoutError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    544\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mTimeoutError\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/concurrent/futures/_base.py\u001b[0m in \u001b[0;36mresult\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    433\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mCancelledError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    434\u001b[0m             \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_state\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mFINISHED\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 435\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__get_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    436\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    437\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mTimeoutError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/concurrent/futures/_base.py\u001b[0m in \u001b[0;36m__get_result\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    382\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__get_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    383\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_exception\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 384\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_exception\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    385\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    386\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_result\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mTerminatedWorkerError\u001b[0m: A worker process managed by the executor was unexpectedly terminated. This could be caused by a segmentation fault while calling the function or by an excessive memory usage causing the Operating System to kill the worker.\n\nThe exit codes of the workers are {SIGKILL(-9)}"
-     ]
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "\n",
-    "from joblib import Parallel, delayed\n",
-    "\n",
-    "def df_wide_to_long(df): return pd.wide_to_long(standard, cols, i=\"eid\", j=\"t\", sep=\"_\", suffix='\\w+').reset_index()\n",
-    "\n",
-    "df_input = standard\n",
-    "n = 10000 \n",
-    "list_df = [df_input[i:i+n] for i in range(0,df_input.shape[0],n)]\n",
-    "\n",
-    "df_list = Parallel(n_jobs=10)(delayed(df_wide_to_long)(df) for df in tqdm(list_df))\n",
-    "df_concat = pd.concat(df_list, axis=0).reset_index(drop=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ee25fa009d6e4e858f160756345d9518",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(HTML(value=''), FloatProgress(value=0.0, max=503.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36mretrieve\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    939\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'supports_timeout'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 940\u001b[0;31m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_output\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    941\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36mwrap_future_result\u001b[0;34m(future, timeout)\u001b[0m\n\u001b[1;32m    541\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 542\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfuture\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    543\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mCfTimeoutError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/concurrent/futures/_base.py\u001b[0m in \u001b[0;36mresult\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    429\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 430\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_condition\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    431\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/threading.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    295\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 296\u001b[0;31m                 \u001b[0mwaiter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    297\u001b[0m                 \u001b[0mgotit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m: ",
-      "\nDuring handling of the above exception, another exception occurred:\n",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-31-5b9916c88d67>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mjoblib\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mParallel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdelayed\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdf_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mParallel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdelayed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_wide_to_long\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mdf\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtqdm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlist_df\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m   1059\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1060\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretrieval_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1061\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretrieve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1062\u001b[0m             \u001b[0;31m# Make sure that we get a last message telling us we are done\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1063\u001b[0m             \u001b[0melapsed_time\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_start_time\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36mretrieve\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    960\u001b[0m                     \u001b[0;31m# scheduling.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    961\u001b[0m                     \u001b[0mensure_ready\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_managed_backend\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 962\u001b[0;31m                     \u001b[0mbackend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mabort_everything\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mensure_ready\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mensure_ready\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    963\u001b[0m                 \u001b[0;32mraise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    964\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36mabort_everything\u001b[0;34m(self, ensure_ready)\u001b[0m\n\u001b[1;32m    559\u001b[0m         \"\"\"Shutdown the workers and restart a new one with the same parameters\n\u001b[1;32m    560\u001b[0m         \"\"\"\n\u001b[0;32m--> 561\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_workers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mterminate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkill_workers\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    562\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_workers\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    563\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/joblib/executor.py\u001b[0m in \u001b[0;36mterminate\u001b[0;34m(self, kill_workers)\u001b[0m\n\u001b[1;32m     72\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     73\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mterminate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkill_workers\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 74\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshutdown\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkill_workers\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkill_workers\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     75\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mkill_workers\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     76\u001b[0m             \u001b[0;31m# When workers are killed in such a brutal manner, they cannot\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/joblib/externals/loky/process_executor.py\u001b[0m in \u001b[0;36mshutdown\u001b[0;34m(self, wait, kill_workers)\u001b[0m\n\u001b[1;32m   1169\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1170\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mexecutor_manager_thread\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mwait\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1171\u001b[0;31m             \u001b[0mexecutor_manager_thread\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1172\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1173\u001b[0m         \u001b[0;31m# To reduce the risk of opening too many files, remove references to\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/threading.py\u001b[0m in \u001b[0;36mjoin\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m   1042\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1043\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1044\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_wait_for_tstate_lock\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1045\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1046\u001b[0m             \u001b[0;31m# the behavior of a negative timeout isn't documented, but\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/threading.py\u001b[0m in \u001b[0;36m_wait_for_tstate_lock\u001b[0;34m(self, block, timeout)\u001b[0m\n\u001b[1;32m   1058\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlock\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# already determined that the C code is done\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1059\u001b[0m             \u001b[0;32massert\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_is_stopped\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1060\u001b[0;31m         \u001b[0;32melif\u001b[0m \u001b[0mlock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mblock\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1061\u001b[0m             \u001b[0mlock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrelease\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1062\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
-     ]
-    }
-   ],
-   "source": [
-    "from joblib import Parallel, delayed\n",
-    "df_list = Parallel(n_jobs=50)(delayed(df_wide_to_long)(df) for df in tqdm(list_df))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 37min 49s, sys: 6min 53s, total: 44min 43s\n",
-      "Wall time: 45min 6s\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "\n",
-    "standard_long = pd.wide_to_long(standard, cols, i=\"eid\", j=\"t\", sep=\"_\", suffix='\\w+').reset_index()#.set_index(\"eid\")\n",
-    "standard_long = standard_long.dropna(how=\"all\", subset=cols, axis=0).reset_index(drop=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 297,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def split_ukb_index(df, idx_col=\"t\"):\n",
-    "    new = df[idx_col].str.split(\"_\", n = 1, expand = True) \n",
-    "    df[\"visit\"] = new[0]\n",
-    "    df[\"measurement\"]= new[1]\n",
-    "    return df.drop(columns =[idx_col]) \n",
-    "df_raw = split_ukb_index(standard_long, idx_col=\"t\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 298,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def process_multiple_measurements(df):\n",
-    "    df_nonfloat = df.set_index([\"eid\", \"visit\"]).select_dtypes(exclude=np.number)\n",
-    "    nonfloat_columns = [c for c in df_nonfloat.columns if c not in [\"measurement\"]]\n",
-    "    df_nonfloat = df_nonfloat.dropna(subset=nonfloat_columns, how=\"all\")[nonfloat_columns]\n",
-    "    df_float = df.set_index([\"eid\", \"visit\"]).select_dtypes(include=np.number).groupby([\"eid\", \"visit\"]).mean(numeric_only=True)\n",
-    "    df_complete = pd.concat([df_nonfloat, df_float], axis=1).reset_index()\n",
-    "    return df_complete\n",
-    "\n",
-    "def df_sort_cols(df, cols): return df[start_cols+[c for c in df.columns.to_list() if c not in start_cols]]\n",
-    "\n",
-    "start_cols = [\"eid\", \"visit\", \"date_of_attending_assessment_centre_f53\", \"age_at_recruitment_f21022\", \"sex_f31\", \"ethnic_background_f21000\"]\n",
-    "df_agg_measurement = df_sort_cols(process_multiple_measurements(df_raw), start_cols)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 299,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_birthdate(df_complete):\n",
-    "    from dateutil.relativedelta import relativedelta\n",
-    "    df = df_complete[df_complete.visit==\"0\"].copy()#.reset_index()\n",
-    "    df[\"birthdate\"] =  [date - relativedelta(years=age) for date, age in zip(df.date_of_attending_assessment_centre_f53, df.age_at_recruitment_f21022)]\n",
-    "    df_birthdate = df.set_index(\"eid\")[[\"birthdate\"]]\n",
-    "    return df_birthdate\n",
-    "\n",
-    "def convert_dates_to_timedelta(df_birthdate, df_complete):\n",
-    "    df_complete_bd = pd.concat([df_birthdate, df_complete.set_index([\"eid\"])], axis=1).reset_index()\n",
-    "\n",
-    "    start_cols = [\"eid\", \"birthdate\",  \"sex_f31\", \"ethnic_background_f21000\", \"visit\", \"date_of_attending_assessment_centre_f53\"]\n",
-    "    df_complete_bd = df_complete_bd[start_cols+[c for c in df_complete_bd.columns.to_list() if c not in start_cols]]\n",
-    "\n",
-    "    df_complete_bd = df_complete_bd.rename(columns={\"visit\":\"t\"}).assign(t= lambda x: (x.date_of_attending_assessment_centre_f53-x.birthdate).dt.days/365.2425)\n",
-    "    df_complete_bd = df_complete_bd.set_index([\"eid\", \"t\"]).drop([\"date_of_attending_assessment_centre_f53\", \"age_at_recruitment_f21022\"], axis=1)\n",
-    "    return df_complete_bd.reset_index()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 312,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>birthdate</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>eid</th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1000018</th>\n",
-       "      <td>1960-11-12</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1000020</th>\n",
-       "      <td>1949-02-19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1000037</th>\n",
-       "      <td>1949-11-11</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1000043</th>\n",
-       "      <td>1946-06-03</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1000051</th>\n",
-       "      <td>1955-06-10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6025150</th>\n",
-       "      <td>1964-06-30</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6025165</th>\n",
-       "      <td>1963-09-02</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6025173</th>\n",
-       "      <td>1951-09-17</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6025182</th>\n",
-       "      <td>1954-07-01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6025198</th>\n",
-       "      <td>1943-01-26</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>502504 rows × 1 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          birthdate\n",
-       "eid                \n",
-       "1000018  1960-11-12\n",
-       "1000020  1949-02-19\n",
-       "1000037  1949-11-11\n",
-       "1000043  1946-06-03\n",
-       "1000051  1955-06-10\n",
-       "...             ...\n",
-       "6025150  1964-06-30\n",
-       "6025165  1963-09-02\n",
-       "6025173  1951-09-17\n",
-       "6025182  1954-07-01\n",
-       "6025198  1943-01-26\n",
-       "\n",
-       "[502504 rows x 1 columns]"
-      ]
-     },
-     "execution_count": 312,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_birthdate"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 300,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_birthdate = get_birthdate(df_agg_measurement)\n",
-    "df_baseline_time = convert_dates_to_timedelta(df_birthdate, df_agg_measurement)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 301,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>t</th>\n",
-       "      <th>birthdate</th>\n",
-       "      <th>sex_f31</th>\n",
-       "      <th>ethnic_background_f21000</th>\n",
-       "      <th>overall_health_rating_f2178</th>\n",
-       "      <th>smoking_status_f20116</th>\n",
-       "      <th>alcohol_intake_frequency_f1558</th>\n",
-       "      <th>townsend_deprivation_index_at_recruitment_f189</th>\n",
-       "      <th>body_mass_index_bmi_f21001</th>\n",
-       "      <th>...</th>\n",
-       "      <th>phosphate_f30810</th>\n",
-       "      <th>rheumatoid_factor_f30820</th>\n",
-       "      <th>shbg_f30830</th>\n",
-       "      <th>testosterone_f30850</th>\n",
-       "      <th>total_bilirubin_f30840</th>\n",
-       "      <th>total_protein_f30860</th>\n",
-       "      <th>triglycerides_f30870</th>\n",
-       "      <th>urate_f30880</th>\n",
-       "      <th>urea_f30670</th>\n",
-       "      <th>vitamin_d_f30890</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>49.000322</td>\n",
-       "      <td>1960-11-12</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>British</td>\n",
-       "      <td>Fair</td>\n",
-       "      <td>Current</td>\n",
-       "      <td>Once or twice a week</td>\n",
-       "      <td>-1.852930</td>\n",
-       "      <td>26.5557</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.422</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>70.11</td>\n",
-       "      <td>1.560</td>\n",
-       "      <td>7.41</td>\n",
-       "      <td>71.97</td>\n",
-       "      <td>1.247</td>\n",
-       "      <td>221.3</td>\n",
-       "      <td>5.48</td>\n",
-       "      <td>70.7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1000020</td>\n",
-       "      <td>58.999158</td>\n",
-       "      <td>1949-02-19</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>British</td>\n",
-       "      <td>Good</td>\n",
-       "      <td>Current</td>\n",
-       "      <td>Once or twice a week</td>\n",
-       "      <td>0.204248</td>\n",
-       "      <td>22.7465</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.264</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>55.31</td>\n",
-       "      <td>12.237</td>\n",
-       "      <td>8.07</td>\n",
-       "      <td>78.45</td>\n",
-       "      <td>1.906</td>\n",
-       "      <td>374.7</td>\n",
-       "      <td>5.28</td>\n",
-       "      <td>35.9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1000037</td>\n",
-       "      <td>59.001896</td>\n",
-       "      <td>1949-11-11</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>British</td>\n",
-       "      <td>Good</td>\n",
-       "      <td>Previous</td>\n",
-       "      <td>Once or twice a week</td>\n",
-       "      <td>-3.498860</td>\n",
-       "      <td>32.4211</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000043</td>\n",
-       "      <td>63.001978</td>\n",
-       "      <td>1946-06-03</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>British</td>\n",
-       "      <td>Fair</td>\n",
-       "      <td>Previous</td>\n",
-       "      <td>Three or four times a week</td>\n",
-       "      <td>-5.351150</td>\n",
-       "      <td>29.5679</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.928</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>31.63</td>\n",
-       "      <td>11.398</td>\n",
-       "      <td>8.65</td>\n",
-       "      <td>69.70</td>\n",
-       "      <td>5.184</td>\n",
-       "      <td>322.8</td>\n",
-       "      <td>6.67</td>\n",
-       "      <td>63.6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1000043</td>\n",
-       "      <td>72.015168</td>\n",
-       "      <td>1946-06-03</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Fair</td>\n",
-       "      <td>Previous</td>\n",
-       "      <td>Three or four times a week</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>28.4349</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>575055</th>\n",
-       "      <td>6025150</td>\n",
-       "      <td>55.305722</td>\n",
-       "      <td>1964-06-30</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Good</td>\n",
-       "      <td>Never</td>\n",
-       "      <td>Three or four times a week</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>33.5072</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>575056</th>\n",
-       "      <td>6025165</td>\n",
-       "      <td>45.002977</td>\n",
-       "      <td>1963-09-02</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>British</td>\n",
-       "      <td>Good</td>\n",
-       "      <td>Never</td>\n",
-       "      <td>Three or four times a week</td>\n",
-       "      <td>-2.107040</td>\n",
-       "      <td>24.2275</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.996</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>73.38</td>\n",
-       "      <td>0.652</td>\n",
-       "      <td>11.19</td>\n",
-       "      <td>74.20</td>\n",
-       "      <td>1.442</td>\n",
-       "      <td>220.2</td>\n",
-       "      <td>4.01</td>\n",
-       "      <td>72.7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>575057</th>\n",
-       "      <td>6025173</td>\n",
-       "      <td>57.003224</td>\n",
-       "      <td>1951-09-17</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>British</td>\n",
-       "      <td>Good</td>\n",
-       "      <td>Never</td>\n",
-       "      <td>Never</td>\n",
-       "      <td>-1.827220</td>\n",
-       "      <td>25.9504</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.119</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>50.13</td>\n",
-       "      <td>13.517</td>\n",
-       "      <td>6.31</td>\n",
-       "      <td>72.03</td>\n",
-       "      <td>1.136</td>\n",
-       "      <td>255.5</td>\n",
-       "      <td>5.25</td>\n",
-       "      <td>41.6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>575058</th>\n",
-       "      <td>6025182</td>\n",
-       "      <td>56.001150</td>\n",
-       "      <td>1954-07-01</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>British</td>\n",
-       "      <td>Excellent</td>\n",
-       "      <td>Previous</td>\n",
-       "      <td>Daily or almost daily</td>\n",
-       "      <td>-0.010764</td>\n",
-       "      <td>29.1425</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.986</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>24.48</td>\n",
-       "      <td>10.951</td>\n",
-       "      <td>9.95</td>\n",
-       "      <td>70.65</td>\n",
-       "      <td>5.756</td>\n",
-       "      <td>353.6</td>\n",
-       "      <td>4.42</td>\n",
-       "      <td>45.9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>575059</th>\n",
-       "      <td>6025198</td>\n",
-       "      <td>67.002060</td>\n",
-       "      <td>1943-01-26</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>British</td>\n",
-       "      <td>Good</td>\n",
-       "      <td>Current</td>\n",
-       "      <td>Daily or almost daily</td>\n",
-       "      <td>-1.930650</td>\n",
-       "      <td>29.5988</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.163</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>45.09</td>\n",
-       "      <td>15.030</td>\n",
-       "      <td>11.85</td>\n",
-       "      <td>70.62</td>\n",
-       "      <td>2.327</td>\n",
-       "      <td>454.8</td>\n",
-       "      <td>5.14</td>\n",
-       "      <td>20.2</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>575060 rows × 87 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            eid          t   birthdate sex_f31 ethnic_background_f21000  \\\n",
-       "0       1000018  49.000322  1960-11-12  Female                  British   \n",
-       "1       1000020  58.999158  1949-02-19    Male                  British   \n",
-       "2       1000037  59.001896  1949-11-11  Female                  British   \n",
-       "3       1000043  63.001978  1946-06-03    Male                  British   \n",
-       "4       1000043  72.015168  1946-06-03     NaN                      NaN   \n",
-       "...         ...        ...         ...     ...                      ...   \n",
-       "575055  6025150  55.305722  1964-06-30     NaN                      NaN   \n",
-       "575056  6025165  45.002977  1963-09-02  Female                  British   \n",
-       "575057  6025173  57.003224  1951-09-17    Male                  British   \n",
-       "575058  6025182  56.001150  1954-07-01    Male                  British   \n",
-       "575059  6025198  67.002060  1943-01-26    Male                  British   \n",
-       "\n",
-       "       overall_health_rating_f2178 smoking_status_f20116  \\\n",
-       "0                             Fair               Current   \n",
-       "1                             Good               Current   \n",
-       "2                             Good              Previous   \n",
-       "3                             Fair              Previous   \n",
-       "4                             Fair              Previous   \n",
-       "...                            ...                   ...   \n",
-       "575055                        Good                 Never   \n",
-       "575056                        Good                 Never   \n",
-       "575057                        Good                 Never   \n",
-       "575058                   Excellent              Previous   \n",
-       "575059                        Good               Current   \n",
-       "\n",
-       "       alcohol_intake_frequency_f1558  \\\n",
-       "0                Once or twice a week   \n",
-       "1                Once or twice a week   \n",
-       "2                Once or twice a week   \n",
-       "3          Three or four times a week   \n",
-       "4          Three or four times a week   \n",
-       "...                               ...   \n",
-       "575055     Three or four times a week   \n",
-       "575056     Three or four times a week   \n",
-       "575057                          Never   \n",
-       "575058          Daily or almost daily   \n",
-       "575059          Daily or almost daily   \n",
-       "\n",
-       "        townsend_deprivation_index_at_recruitment_f189  \\\n",
-       "0                                            -1.852930   \n",
-       "1                                             0.204248   \n",
-       "2                                            -3.498860   \n",
-       "3                                            -5.351150   \n",
-       "4                                                  NaN   \n",
-       "...                                                ...   \n",
-       "575055                                             NaN   \n",
-       "575056                                       -2.107040   \n",
-       "575057                                       -1.827220   \n",
-       "575058                                       -0.010764   \n",
-       "575059                                       -1.930650   \n",
-       "\n",
-       "        body_mass_index_bmi_f21001  ...  phosphate_f30810  \\\n",
-       "0                          26.5557  ...             1.422   \n",
-       "1                          22.7465  ...             1.264   \n",
-       "2                          32.4211  ...               NaN   \n",
-       "3                          29.5679  ...             0.928   \n",
-       "4                          28.4349  ...               NaN   \n",
-       "...                            ...  ...               ...   \n",
-       "575055                     33.5072  ...               NaN   \n",
-       "575056                     24.2275  ...             0.996   \n",
-       "575057                     25.9504  ...             1.119   \n",
-       "575058                     29.1425  ...             0.986   \n",
-       "575059                     29.5988  ...             1.163   \n",
-       "\n",
-       "        rheumatoid_factor_f30820  shbg_f30830  testosterone_f30850  \\\n",
-       "0                            NaN        70.11                1.560   \n",
-       "1                            NaN        55.31               12.237   \n",
-       "2                            NaN          NaN                  NaN   \n",
-       "3                            NaN        31.63               11.398   \n",
-       "4                            NaN          NaN                  NaN   \n",
-       "...                          ...          ...                  ...   \n",
-       "575055                       NaN          NaN                  NaN   \n",
-       "575056                       NaN        73.38                0.652   \n",
-       "575057                       NaN        50.13               13.517   \n",
-       "575058                       NaN        24.48               10.951   \n",
-       "575059                       NaN        45.09               15.030   \n",
-       "\n",
-       "        total_bilirubin_f30840  total_protein_f30860  triglycerides_f30870  \\\n",
-       "0                         7.41                 71.97                 1.247   \n",
-       "1                         8.07                 78.45                 1.906   \n",
-       "2                          NaN                   NaN                   NaN   \n",
-       "3                         8.65                 69.70                 5.184   \n",
-       "4                          NaN                   NaN                   NaN   \n",
-       "...                        ...                   ...                   ...   \n",
-       "575055                     NaN                   NaN                   NaN   \n",
-       "575056                   11.19                 74.20                 1.442   \n",
-       "575057                    6.31                 72.03                 1.136   \n",
-       "575058                    9.95                 70.65                 5.756   \n",
-       "575059                   11.85                 70.62                 2.327   \n",
-       "\n",
-       "        urate_f30880  urea_f30670  vitamin_d_f30890  \n",
-       "0              221.3         5.48              70.7  \n",
-       "1              374.7         5.28              35.9  \n",
-       "2                NaN          NaN               NaN  \n",
-       "3              322.8         6.67              63.6  \n",
-       "4                NaN          NaN               NaN  \n",
-       "...              ...          ...               ...  \n",
-       "575055           NaN          NaN               NaN  \n",
-       "575056         220.2         4.01              72.7  \n",
-       "575057         255.5         5.25              41.6  \n",
-       "575058         353.6         4.42              45.9  \n",
-       "575059         454.8         5.14              20.2  \n",
-       "\n",
-       "[575060 rows x 87 columns]"
-      ]
-     },
-     "execution_count": 301,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_baseline_time"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Medications"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:34:25.842601Z",
-     "start_time": "2020-11-04T12:34:25.840607Z"
-    },
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# https://list.essentialmeds.org/?showRemoved=0\n",
-    "# essential medicines WHO?!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:34:32.471831Z",
-     "start_time": "2020-11-04T12:34:25.844339Z"
-    },
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "outputs": [],
-   "source": [
-    "atc_mapping = pd.read_csv(f\"{path}/mapping/atc/atc_matched_list.csv\")\n",
-    "athena_concepts = pd.read_csv(f\"{data_path}/athena_vocabulary/CONCEPT.csv\", sep=\"\\t\").assign(vocabulary_id = lambda x: x.vocabulary_id.astype(\"string\"), concept_class_id = lambda x: x.concept_class_id.astype(\"string\"))\n",
-    "atc_concepts = athena_concepts[athena_concepts.vocabulary_id==\"ATC\"]\n",
-    "atc2_concepts = atc_concepts[atc_concepts.concept_class_id==\"ATC 2nd\"].sort_values(\"concept_code\")\n",
-    "medication_list = dict(zip([x.lower().replace(\" \", \"_\") for x in atc2_concepts.concept_name.to_list()], [[x] for x in atc2_concepts.concept_code.to_list()]))\n",
-    "medication_list_extra = {\n",
-    "    \"antihypertensives\": [\"C02\"],\n",
-    "    \"statins\": [\"C10A\", \"C10B\"],\n",
-    "    \"ass\": [\"B01\"],\n",
-    "    \"atypical_antipsychotics\" : [\"N05\"],\n",
-    "    \"glucocorticoids\" : [\"H02\"]                        \n",
-    "}\n",
-    "medication_list.update(medication_list_extra)\n",
-    "\n",
-    "with open(os.path.join(path, dataset_path, 'medication_list.yaml'), 'w') as file: yaml.dump(medication_list, file, default_flow_style=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "outputs": [],
-   "source": [
-    "def had_medication_before(data, data_field, medications, atc_mapping):\n",
-    "    fields = [\"20003\"]\n",
-    "    raw = get_data_fields(fields, data, data_field)\n",
-    "    temp = pd.melt(raw, id_vars=[\"eid\"], value_vars=raw.drop(\"eid\", axis=1).columns.to_list(), var_name = \"field\", value_name=\"UKBB_code\").drop(\"field\", axis=1).drop_duplicates()\n",
-    "\n",
-    "    temp.UKBB_code = temp.UKBB_code.astype(str)\n",
-    "    temp = temp[temp.UKBB_code!=\"None\"].copy()\n",
-    "    temp.UKBB_code = temp.UKBB_code.astype(int)\n",
-    "\n",
-    "    temp_atc = temp.merge(atc_mapping, how=\"left\", on=\"UKBB_code\").sort_values(\"eid\").reset_index(drop=True).dropna(subset=[\"ATC_code\"], axis=0)\n",
-    "    temp_atc.ATC_code = temp_atc.ATC_code.astype(\"string\")\n",
-    "    temp = data[[\"eid\"]].copy()\n",
-    "    for med, med_codes in tqdm(medication_list.items()):\n",
-    "        regex_str = \"^\"+\"|^\".join(med_codes)\n",
-    "        df = temp_atc[temp_atc.ATC_code.str.contains(regex_str, case=False)][[\"eid\"]]\\\n",
-    "            .drop_duplicates(subset=[\"eid\"])\\\n",
-    "            .assign(medication=True)\n",
-    "        temp[med] = temp.merge(df, how=\"left\", on=\"eid\").fillna(False).medication\n",
-    "        \n",
-    "    return temp.sort_values(\"eid\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:34:32.481137Z",
-     "start_time": "2020-11-04T12:34:32.473698Z"
-    },
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "outputs": [],
-   "source": [
-    "def had_diagnosis_before_per_ph(df_before, ph, ph_codes, temp):\n",
-    "   # regex = \"|\".join(ph_codes)\n",
-    "    #df_ph = df_before.set_index(\"meaning\").loc[ph_codes][[\"eid\"]]\\\n",
-    "    #        .drop_duplicates(subset=[\"eid\"])\\\n",
-    "    #        .assign(phenotype=True) \n",
-    "    df_ph = df_before[df_before.meaning.isin(ph_codes)][[\"eid\"]]\\\n",
-    "            .drop_duplicates(subset=[\"eid\"])\\\n",
-    "            .assign(phenotype=True) \n",
-    "    #df_ph = df_before[df_before.meaning.str.contains(regex, case=False)][[\"eid\"]]\\\n",
-    "    #        .drop_duplicates(subset=[\"eid\"])\\\n",
-    "    #        .assign(phenotype=True)   \n",
-    "    return temp.merge(df_ph, how=\"left\", on=\"eid\").fillna(False).phenotype\n",
-    "\n",
-    "def had_diagnosis_before(data, diagnoses_codes, phenotypes, time0=time0_col):\n",
-    "    diagnoses_codes_time = diagnoses_codes.merge(data[[\"eid\", time0]], how=\"left\", on=\"eid\")\n",
-    "    \n",
-    "    temp = data[[\"eid\"]].copy()\n",
-    "    df_before = diagnoses_codes_time[diagnoses_codes_time.date < diagnoses_codes_time[time0]]\n",
-    "                                                                                         \n",
-    "    df_phs = Parallel(n_jobs=20, require=\"sharedmem\")(delayed(had_diagnosis_before_per_ph)(df_before, ph, phenotypes[ph], temp) for ph in tqdm(list(phenotypes)))\n",
-    "    for ph, df_ph_series in zip(tqdm(list(phenotypes)), df_phs): temp[ph] = df_ph_series#temp.merge(df_ph, how=\"left\", on=\"eid\").fillna(False).phenotype\n",
-    "    \n",
-    "    return temp.sort_values(\"eid\")  #reduce(lambda left,right: pd.merge(left,right,on=['eid'], how='left'), df_phs)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:36:14.425612Z",
-     "start_time": "2020-11-04T12:34:32.482863Z"
-    },
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "13ef8609a6b64b219928981b7752c608",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(FloatProgress(value=0.0, max=98.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "502504\n"
-     ]
-    }
-   ],
-   "source": [
-    "medications = had_medication_before(data, data_field, medication_list, atc_mapping)\n",
-    "print(len(medications))\n",
-    "medications.head(100)\n",
-    "\n",
-    "medications.to_feather(os.path.join(path, dataset_path, 'temp_medications.feather'))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Diagnoses and events"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 106,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:37:14.667281Z",
-     "start_time": "2020-11-04T12:36:14.427693Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3146: DtypeWarning: Columns (5,6,9) have mixed types.Specify dtype option on import or set low_memory=False.\n",
-      "  interactivity=interactivity, compiler=compiler, result=result)\n",
-      "/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3146: DtypeWarning: Columns (11) have mixed types.Specify dtype option on import or set low_memory=False.\n",
-      "  interactivity=interactivity, compiler=compiler, result=result)\n"
-     ]
-    }
-   ],
-   "source": [
-    "vocab_dir = f\"{data_path}/athena_vocabulary_covid\"\n",
-    "vocab = {\n",
-    "    \"concept\": pd.read_csv(f\"{vocab_dir}/CONCEPT.csv\", sep='\\t'),\n",
-    "    \"domain\": pd.read_csv(f\"{vocab_dir}/DOMAIN.csv\", sep='\\t'),\n",
-    "    \"class\": pd.read_csv(f\"{vocab_dir}/CONCEPT_CLASS.csv\", sep='\\t'),\n",
-    "    \"relationship\": pd.read_csv(f\"{vocab_dir}/RELATIONSHIP.csv\", sep='\\t'),\n",
-    "    \"drug_strength\": pd.read_csv(f\"{vocab_dir}/DRUG_STRENGTH.csv\", sep='\\t'),\n",
-    "    \"vocabulary\": pd.read_csv(f\"{vocab_dir}/VOCABULARY.csv\", sep='\\t'),\n",
-    "    \"concept_synonym\": pd.read_csv(f\"{vocab_dir}/CONCEPT_SYNONYM.csv\", sep='\\t'),\n",
-    "    \"concept_ancestor\": pd.read_csv(f\"{vocab_dir}/CONCEPT_ANCESTOR.csv\", sep='\\t'),\n",
-    "    \"concept_relationship\": pd.read_csv(f\"{vocab_dir}/CONCEPT_RELATIONSHIP.csv\", sep='\\t')                       \n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Definitions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 107,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:37:14.772869Z",
-     "start_time": "2020-11-04T12:37:14.669541Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "coding1836 = pd.read_csv(f\"{path}/mapping/codings/coding1836.tsv\", sep=\"\\t\").rename(columns={\"coding\":\"code\"})\n",
-    "phecodes = pd.read_csv(f\"{path}/mapping/phecodes/phecode_icd10.csv\")\n",
-    "def phenotype_children(phecodes, phenotype_list):\n",
-    "    l={}\n",
-    "    phecodes = phecodes.dropna(subset=[\"Phenotype\"], axis=0)\n",
-    "    for ph, ph_names in phenotype_list.items():\n",
-    "        regex = \"|\".join(ph_names)\n",
-    "        l[ph] = list(phecodes[phecodes.Phenotype.str.contains(regex, case=False)].ICD10.str.replace(\"\\\\.\", \"\").str.slice(0, 3).unique())\n",
-    "    return l"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 108,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:37:14.851438Z",
-     "start_time": "2020-11-04T12:37:14.774599Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "snomed_core = pd.read_csv(f\"{path}/mapping/snomed_core_list.txt\", sep=\"|\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 279,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:37:14.909023Z",
-     "start_time": "2020-11-04T12:37:14.853286Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "snomed_core = snomed_core.query(\"SNOMED_CONCEPT_STATUS == 'Current'\").copy()\n",
-    "new = snomed_core.SNOMED_FSN.str.split(\"(\", n=1, expand=True)\n",
-    "snomed_core[\"snomed_name\"] = new[0].str.rstrip(' ')\n",
-    "snomed_core[\"snomed_type\"] = new[1].str.rstrip(')')\n",
-    "snomed_core_data = snomed_core.query(\"(snomed_type=='disorder' | snomed_type=='finding') & USAGE>0.01\").head(1000).reset_index(drop=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 280,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>SNOMED_CID</th>\n",
-       "      <th>SNOMED_FSN</th>\n",
-       "      <th>SNOMED_CONCEPT_STATUS</th>\n",
-       "      <th>UMLS_CUI</th>\n",
-       "      <th>OCCURRENCE</th>\n",
-       "      <th>USAGE</th>\n",
-       "      <th>FIRST_IN_SUBSET</th>\n",
-       "      <th>IS_RETIRED_FROM_SUBSET</th>\n",
-       "      <th>LAST_IN_SUBSET</th>\n",
-       "      <th>REPLACED_BY_SNOMED_CID</th>\n",
-       "      <th>snomed_name</th>\n",
-       "      <th>snomed_type</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>38341003</td>\n",
-       "      <td>Hypertensive disorder, systemic arterial (diso...</td>\n",
-       "      <td>Current</td>\n",
-       "      <td>C0020538</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>3.2242</td>\n",
-       "      <td>200907</td>\n",
-       "      <td>False</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Hypertensive disorder, systemic arterial</td>\n",
-       "      <td>disorder</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>55822004</td>\n",
-       "      <td>Hyperlipidemia (disorder)</td>\n",
-       "      <td>Current</td>\n",
-       "      <td>C0020473</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>2.1369</td>\n",
-       "      <td>200907</td>\n",
-       "      <td>False</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Hyperlipidemia</td>\n",
-       "      <td>disorder</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>35489007</td>\n",
-       "      <td>Depressive disorder (disorder)</td>\n",
-       "      <td>Current</td>\n",
-       "      <td>C0011581</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>1.5077</td>\n",
-       "      <td>200907</td>\n",
-       "      <td>False</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Depressive disorder</td>\n",
-       "      <td>disorder</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>235595009</td>\n",
-       "      <td>Gastroesophageal reflux disease (disorder)</td>\n",
-       "      <td>Current</td>\n",
-       "      <td>C0017168</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>1.3691</td>\n",
-       "      <td>200907</td>\n",
-       "      <td>False</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Gastroesophageal reflux disease</td>\n",
-       "      <td>disorder</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>44054006</td>\n",
-       "      <td>Diabetes mellitus type 2 (disorder)</td>\n",
-       "      <td>Current</td>\n",
-       "      <td>C0011860</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>1.0432</td>\n",
-       "      <td>200907</td>\n",
-       "      <td>False</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Diabetes mellitus type 2</td>\n",
-       "      <td>disorder</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>995</th>\n",
-       "      <td>191187006</td>\n",
-       "      <td>Alpha trait thalassemia (disorder)</td>\n",
-       "      <td>Current</td>\n",
-       "      <td>C0472762</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.0104</td>\n",
-       "      <td>200907</td>\n",
-       "      <td>False</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Alpha trait thalassemia</td>\n",
-       "      <td>disorder</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>996</th>\n",
-       "      <td>429305003</td>\n",
-       "      <td>Nonvenomous insect bite (disorder)</td>\n",
-       "      <td>Current</td>\n",
-       "      <td>C0332815</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0104</td>\n",
-       "      <td>200907</td>\n",
-       "      <td>False</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Nonvenomous insect bite</td>\n",
-       "      <td>disorder</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>997</th>\n",
-       "      <td>444613000</td>\n",
-       "      <td>Adult attention deficit hyperactivity disorder...</td>\n",
-       "      <td>Current</td>\n",
-       "      <td>C0865424</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0104</td>\n",
-       "      <td>201008</td>\n",
-       "      <td>False</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Adult attention deficit hyperactivity disorder</td>\n",
-       "      <td>disorder</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>998</th>\n",
-       "      <td>274152003</td>\n",
-       "      <td>Spondylolisthesis (disorder)</td>\n",
-       "      <td>Current</td>\n",
-       "      <td>C0038016</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>0.0103</td>\n",
-       "      <td>200907</td>\n",
-       "      <td>False</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Spondylolisthesis</td>\n",
-       "      <td>disorder</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>999</th>\n",
-       "      <td>363402007</td>\n",
-       "      <td>Malignant tumor of esophagus (disorder)</td>\n",
-       "      <td>Current</td>\n",
-       "      <td>C0546837</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>0.0103</td>\n",
-       "      <td>200907</td>\n",
-       "      <td>False</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Malignant tumor of esophagus</td>\n",
-       "      <td>disorder</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1000 rows × 12 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     SNOMED_CID                                         SNOMED_FSN  \\\n",
-       "0      38341003  Hypertensive disorder, systemic arterial (diso...   \n",
-       "1      55822004                          Hyperlipidemia (disorder)   \n",
-       "2      35489007                     Depressive disorder (disorder)   \n",
-       "3     235595009         Gastroesophageal reflux disease (disorder)   \n",
-       "4      44054006                Diabetes mellitus type 2 (disorder)   \n",
-       "..          ...                                                ...   \n",
-       "995   191187006                 Alpha trait thalassemia (disorder)   \n",
-       "996   429305003                 Nonvenomous insect bite (disorder)   \n",
-       "997   444613000  Adult attention deficit hyperactivity disorder...   \n",
-       "998   274152003                       Spondylolisthesis (disorder)   \n",
-       "999   363402007            Malignant tumor of esophagus (disorder)   \n",
-       "\n",
-       "    SNOMED_CONCEPT_STATUS  UMLS_CUI  OCCURRENCE   USAGE  FIRST_IN_SUBSET  \\\n",
-       "0                 Current  C0020538         8.0  3.2242           200907   \n",
-       "1                 Current  C0020473         8.0  2.1369           200907   \n",
-       "2                 Current  C0011581         8.0  1.5077           200907   \n",
-       "3                 Current  C0017168         8.0  1.3691           200907   \n",
-       "4                 Current  C0011860         8.0  1.0432           200907   \n",
-       "..                    ...       ...         ...     ...              ...   \n",
-       "995               Current  C0472762         2.0  0.0104           200907   \n",
-       "996               Current  C0332815         1.0  0.0104           200907   \n",
-       "997               Current  C0865424         1.0  0.0104           201008   \n",
-       "998               Current  C0038016         5.0  0.0103           200907   \n",
-       "999               Current  C0546837         5.0  0.0103           200907   \n",
-       "\n",
-       "     IS_RETIRED_FROM_SUBSET  LAST_IN_SUBSET REPLACED_BY_SNOMED_CID  \\\n",
-       "0                     False             NaN                    NaN   \n",
-       "1                     False             NaN                    NaN   \n",
-       "2                     False             NaN                    NaN   \n",
-       "3                     False             NaN                    NaN   \n",
-       "4                     False             NaN                    NaN   \n",
-       "..                      ...             ...                    ...   \n",
-       "995                   False             NaN                    NaN   \n",
-       "996                   False             NaN                    NaN   \n",
-       "997                   False             NaN                    NaN   \n",
-       "998                   False             NaN                    NaN   \n",
-       "999                   False             NaN                    NaN   \n",
-       "\n",
-       "                                        snomed_name snomed_type  \n",
-       "0          Hypertensive disorder, systemic arterial    disorder  \n",
-       "1                                    Hyperlipidemia    disorder  \n",
-       "2                               Depressive disorder    disorder  \n",
-       "3                   Gastroesophageal reflux disease    disorder  \n",
-       "4                          Diabetes mellitus type 2    disorder  \n",
-       "..                                              ...         ...  \n",
-       "995                         Alpha trait thalassemia    disorder  \n",
-       "996                         Nonvenomous insect bite    disorder  \n",
-       "997  Adult attention deficit hyperactivity disorder    disorder  \n",
-       "998                               Spondylolisthesis    disorder  \n",
-       "999                    Malignant tumor of esophagus    disorder  \n",
-       "\n",
-       "[1000 rows x 12 columns]"
-      ]
-     },
-     "execution_count": 280,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "snomed_core_data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 281,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:37:14.914973Z",
-     "start_time": "2020-11-04T12:37:14.910857Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "snomed_names = snomed_core_data.snomed_name.to_list()\n",
-    "snomed_names = [str(item).lower().strip().replace(\" \", \"_\").replace(\";\", \"\").replace(\",\", \"\") for item in snomed_names]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 282,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:39:55.342937Z",
-     "start_time": "2020-11-04T12:37:14.916906Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "154e9b5dcc614db88b9db1ac8fc646e1",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(HTML(value=''), FloatProgress(value=0.0, max=1000.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "phenotype_list_snomed = dict(zip(snomed_names, snomed_core_data.SNOMED_CID.to_list()))\n",
-    "snomed_ids = vocab[\"concept\"].query(\"vocabulary_id == 'SNOMED'\").concept_id.to_list()\n",
-    "icd10_ids = vocab[\"concept\"].query(\"vocabulary_id == 'ICD10CM'\").concept_id.to_list()\n",
-    "\n",
-    "ph_to_icd10_mapping = {}\n",
-    "\n",
-    "def map_snomed_to_icd10(ph, snomed_code, concept, concept_ancestor, concept_relationship):\n",
-    "    concept_ids = concept.query(\"vocabulary_id == 'SNOMED' & concept_code == @snomed_code\").concept_id.to_list()\n",
-    "    snomed_desc_ids = concept_ancestor.query(\"ancestor_concept_id== @concept_ids\").descendant_concept_id.to_list()\n",
-    "    ph_desc = concept.query(\"concept_id == @snomed_desc_ids\").query(\"vocabulary_id == 'SNOMED'\")\n",
-    "    l_ph_desc_ids = ph_desc.concept_id.to_list()\n",
-    "    ph_icd10_ids = list(concept_relationship.query(\"concept_id_1==@l_ph_desc_ids\").query(\"concept_id_2 == @icd10_ids\").concept_id_2.unique())\n",
-    "    #ph_icd10_ids = list(concept_relationship.set_index(\"concept_id_1\").query(\"index==@l_ph_desc_ids\").query(\"concept_id_2 == @icd10_ids\").query(\"relationship_id == 'Mapped from'\").concept_id_2.unique()\n",
-    "    \n",
-    "    #ph_icd10_ids = list(temp.concept_id_2.unique())\n",
-    "    df = concept.query(\"concept_id == @ph_icd10_ids & vocabulary_id == 'ICD10CM'\")\n",
-    "    icd10_list = list(df[~df.concept_code.str.contains(\"OMOP\", na=False)].concept_code.unique())\n",
-    "    icd10_list = sorted(list(set([e[:3] for e in icd10_list])))\n",
-    "    #print(f\"{ph}: {icd10_list}\")\n",
-    "    return {ph: sorted(list(dict.fromkeys([str(e) for e in icd10_list])))}\n",
-    "\n",
-    "from joblib import Parallel, delayed\n",
-    "concept_ids = vocab[\"concept\"].query(\"(vocabulary_id == 'SNOMED') | (vocabulary_id == 'ICD10CM')\")\n",
-    "vocab_concept_ids = concept_ids.concept_id.to_list()\n",
-    "concept_ancestor = vocab[\"concept_ancestor\"][[\"ancestor_concept_id\", \"descendant_concept_id\"]].query(\"ancestor_concept_id == @vocab_concept_ids\")\n",
-    "concept_rel = vocab[\"concept_relationship\"][[\"concept_id_1\", \"concept_id_2\", \"relationship_id\"]].query(\"(concept_id_1 == @vocab_concept_ids) & (concept_id_2 == @vocab_concept_ids) & (relationship_id == 'Mapped from')\")\n",
-    "icd10_codes = Parallel(n_jobs=10, require=\"sharedmem\")(delayed(map_snomed_to_icd10)(ph, snomed_code, \n",
-    "                                                              concept_ids, concept_ancestor, concept_rel) for ph, snomed_code in tqdm(phenotype_list_snomed.items()))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 283,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:39:55.348009Z",
-     "start_time": "2020-11-04T12:39:55.344704Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "l10_snomed = {}\n",
-    "for ph in icd10_codes: l10_snomed.update(ph)"
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:39:55.468545Z",
-     "start_time": "2020-11-04T12:39:55.349545Z"
-    },
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "source": [
-    "phenotype_list_basic = {\n",
-    "    \"coronary_heart_disease\": [\"Ischemic heart disease\"],\n",
-    "    \"myocardial_infarction\": [\"Myocardial infarction\"],\n",
-    "    \"stroke\": [\"Cerebrovascular disease\"],\n",
-    "    \"diabetes1\" : [\"Type 1 diabetes\"],\n",
-    "    \"diabetes2\" : [\"Diabetes mellitus\", \"Type 2 diabetes\"],\n",
-    "    \"chronic_kidney_disease\": [\"Chronic kidney disease\", \"chronic renal failure\"],\n",
-    "    \"atrial_fibrillation\": [\"Atrial fibrillation\", \"Atrial flutter\", \"paroxysmal tachycardia\"],\n",
-    "    \"migraine\": [\"Migraine\"],\n",
-    "    \"rheumatoid_arthritis\": [\"Rheumatoid arthritis\"],\n",
-    "    \"systemic_lupus_erythematosus\": [\"Systemic lupus erythematosus\"],\n",
-    "    \"severe_mental_illness\": [\"Schizophrenia\", \"bipolar\", \"Major depressive disorder\"],\n",
-    "    \"erectile_dysfunction\" : [\"Erectile dysfunction\"],  \n",
-    "}\n",
-    "\n",
-    "l10_basic = phenotype_children(phecodes, phenotype_list_basic)\n",
-    "l10_all = l10_basic\n",
-    "for key, value in l10_snomed.items(): \n",
-    "    if key not in l10_basic: l10_all[key] = value"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 284,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:39:55.620916Z",
-     "start_time": "2020-11-04T12:39:55.475137Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "l10 = {k: v for k, v in l10_snomed.items() if len(v)!=0}\n",
-    "\n",
-    "#phenotype_list = {k: v for k, v in phenotype_list.items() if k in list(l10.keys())}\n",
-    "\n",
-    "with open(os.path.join(path, dataset_path, 'phenotype_list.yaml'), 'w') as file: yaml.dump(l10, file, default_flow_style=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "source": [
-    "### 1. Self Reported"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 122,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:43:13.288198Z",
-     "start_time": "2020-11-04T12:43:13.264683Z"
-    },
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "outputs": [],
-   "source": [
-    "coding609 = pd.read_csv(f\"{path}/mapping/codings/coding609.tsv\", sep=\"\\t\").rename(columns={\"coding\":\"code\"})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 125,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:43:13.304423Z",
-     "start_time": "2020-11-04T12:43:13.289982Z"
-    },
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from datetime import datetime, timedelta\n",
-    "\n",
-    "def datetime_from_dec_year(dec_year):\n",
-    "    start = dec_year\n",
-    "    year = int(start)\n",
-    "    rem = start - year\n",
-    "\n",
-    "    base = datetime(year, 1, 1)\n",
-    "    result = base + timedelta(seconds=(base.replace(year=base.year + 1) - base).total_seconds() * rem)\n",
-    "    #result.strftime(\"%Y-%m-%d\")\n",
-    "    return result.date()\n",
-    "\n",
-    "def extract_map_self_reported(data, data_field, code_map):\n",
-    "    pbar = tqdm(total=16)\n",
-    "    ### codes\n",
-    "    fields = [\"20002\"]; pbar.update(1)\n",
-    "    raw = get_data_fields_all(fields, data, data_field); pbar.update(1)\n",
-    "    col = \"noncancer_illness_code_selfreported_f20002\"; pbar.update(1)\n",
-    "    temp = pd.wide_to_long(raw, stubnames=[col], i=\"eid\", j=\"instance_index\", sep=\"_\", suffix=\"\\w+\").reset_index(); pbar.update(1)\n",
-    "    codes = temp.rename(columns={col:\"code\"})\\\n",
-    "        .assign(code=lambda x: x.code.astype(str))\\\n",
-    "        .replace(\"None\", np.nan) \\\n",
-    "        .dropna(subset=[\"code\"], axis=0)\\\n",
-    "        .assign(code=lambda x: x.code.astype(int)) \\\n",
-    "        .merge(code_map, how=\"left\",on=\"code\") \\\n",
-    "        .dropna(subset=[\"meaning\"], axis=0)\\\n",
-    "        .sort_values([\"eid\", \"instance_index\"]) \\\n",
-    "        .reset_index(drop=True); pbar.update(1)\n",
-    "    \n",
-    "    ### dates\n",
-    "    fields = [\"20008\"]; pbar.update(1)\n",
-    "    raw = get_data_fields_all(fields, data, data_field); pbar.update(1)\n",
-    "    col=\"interpolated_year_when_noncancer_illness_first_diagnosed_f20008\"; pbar.update(1)\n",
-    "    temp = pd.wide_to_long(raw, stubnames=[col], i=\"eid\", j=\"instance_index\", sep=\"_\", suffix=\"\\w+\").reset_index(); pbar.update(1)\n",
-    "    dates = temp.rename(columns={col:\"date\"})\\\n",
-    "        .dropna(subset=[\"date\"], axis=0)\\\n",
-    "        .sort_values([\"eid\", \"instance_index\"]) \\\n",
-    "        .reset_index(drop=True); pbar.update(1)\n",
-    "\n",
-    "    dates = dates[dates.date!=-1]; pbar.update(1)\n",
-    "    dates = dates[dates.date!=-3]; pbar.update(1)\n",
-    "    dates.date = dates.date.apply(datetime_from_dec_year); pbar.update(1)\n",
-    "    \n",
-    "    test = codes.merge(dates, how=\"left\", on=[\"eid\", \"instance_index\"]).assign(origin=\"self_reported\").copy(); pbar.update(1)\n",
-    "    \n",
-    "    test[\"instance_index\"] = test[\"instance_index\"].astype(\"string\"); pbar.update(1)\n",
-    "    test[['instance','n']] = test.instance_index.str.split(\"_\",expand=True); pbar.update(1)\n",
-    "    pbar.close()\n",
-    "    \n",
-    "    return test[[\"eid\", \"origin\", 'instance','n', \"code\", \"meaning\", \"date\"]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 126,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:46:00.098893Z",
-     "start_time": "2020-11-04T12:43:13.432479Z"
-    },
-    "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true,
-     "source_hidden": true
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "aaecac4f22c148f09d00f7036aef37ff",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(HTML(value=''), FloatProgress(value=0.0, max=16.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "ename": "ValueError",
-     "evalue": "invalid literal for int() with base 10: 'nan'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-126-8b0ceffca143>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcodes_self_reported\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mextract_map_self_reported\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata_field\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcoding609\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
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-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36massign\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m   3693\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3694\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3695\u001b[0;31m             \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_if_callable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3696\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3697\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/pandas/core/common.py\u001b[0m in \u001b[0;36mapply_if_callable\u001b[0;34m(maybe_callable, obj, **kwargs)\u001b[0m\n\u001b[1;32m    339\u001b[0m     \"\"\"\n\u001b[1;32m    340\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmaybe_callable\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 341\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mmaybe_callable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    342\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    343\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mmaybe_callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m   5544\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5545\u001b[0m             \u001b[0;31m# else, only a single dtype is given\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5546\u001b[0;31m             \u001b[0mnew_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5547\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_constructor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__finalize__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"astype\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5548\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m    593\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"raise\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    594\u001b[0m     ) -> \"BlockManager\":\n\u001b[0;32m--> 595\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"astype\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    596\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    597\u001b[0m     def convert(\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, f, align_keys, **kwargs)\u001b[0m\n\u001b[1;32m    404\u001b[0m                 \u001b[0mapplied\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    405\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 406\u001b[0;31m                 \u001b[0mapplied\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    407\u001b[0m             \u001b[0mresult_blocks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_extend_blocks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mapplied\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult_blocks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    408\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/pandas/core/internals/blocks.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m    593\u001b[0m             \u001b[0mvals1d\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    594\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 595\u001b[0;31m                 \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mastype_nansafe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvals1d\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    596\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mValueError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    597\u001b[0m                 \u001b[0;31m# e.g. astype_nansafe can fail on object-dtype of strings\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/pandas/core/dtypes/cast.py\u001b[0m in \u001b[0;36mastype_nansafe\u001b[0;34m(arr, dtype, copy, skipna)\u001b[0m\n\u001b[1;32m    970\u001b[0m         \u001b[0;31m# work around NumPy brokenness, #1987\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    971\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0missubdtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minteger\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 972\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype_intsafe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    973\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    974\u001b[0m         \u001b[0;31m# if we have a datetime/timedelta array of objects\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32mpandas/_libs/lib.pyx\u001b[0m in \u001b[0;36mpandas._libs.lib.astype_intsafe\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;31mValueError\u001b[0m: invalid literal for int() with base 10: 'nan'"
-     ]
-    }
-   ],
-   "source": [
-    "codes_self_reported = extract_map_self_reported(data, data_field, coding609)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 2. Primary Care"
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {},
-   "source": [
-    "# 2. Primary Care\n",
-    "fields = c(\n",
-    "    42040 # GP clinical event records\n",
-    ")\n",
-    "# extract covariates at baseline (Instance 0)\n",
-    "def = data_field %>% filter((field.showcase %in% fields),ignore.case = TRUE)\n",
-    "diagnoses_primary_care = data[, append(\"eid\", def$col.name)]\n",
-    "head(diagnoses_primary_care)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 3. Hospital episode statistics"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 325,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:46:08.959617Z",
-     "start_time": "2020-11-04T12:46:00.100618Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>origin</th>\n",
-       "      <th>instance</th>\n",
-       "      <th>n</th>\n",
-       "      <th>code</th>\n",
-       "      <th>meaning</th>\n",
-       "      <th>date</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>hes_icd10</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>S0240</td>\n",
-       "      <td>S02</td>\n",
-       "      <td>2005-06-02</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>hes_icd10</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>W188</td>\n",
-       "      <td>W18</td>\n",
-       "      <td>2005-06-02</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>hes_icd10</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>K37</td>\n",
-       "      <td>K37</td>\n",
-       "      <td>1998-05-11</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>hes_icd10</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>K37</td>\n",
-       "      <td>K37</td>\n",
-       "      <td>1998-05-16</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>hes_icd10</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>5</td>\n",
-       "      <td>K37</td>\n",
-       "      <td>K37</td>\n",
-       "      <td>1998-06-01</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       eid     origin  instance  n   code meaning        date\n",
-       "0  1000018  hes_icd10       0.0  1  S0240     S02  2005-06-02\n",
-       "1  1000018  hes_icd10       0.0  2   W188     W18  2005-06-02\n",
-       "2  1000018  hes_icd10       0.0  3    K37     K37  1998-05-11\n",
-       "3  1000018  hes_icd10       0.0  4    K37     K37  1998-05-16\n",
-       "4  1000018  hes_icd10       0.0  5    K37     K37  1998-06-01"
-      ]
-     },
-     "execution_count": 325,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "codes_hospital_records = pd.read_feather(f\"{data_path}/1_decoded/codes_hospital_records.feather\").drop(\"level\", axis=1)\n",
-    "# self reported bypass\n",
-    "diagnoses_codes = codes_hospital_records \n",
-    "diagnoses_codes.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Combine diagnoses and events"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 146,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:46:25.115010Z",
-     "start_time": "2020-11-04T12:46:08.961388Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "#diagnoses_codes = codes_self_reported.append(codes_hospital_records).sort_values([\"eid\", \"instance\", \"n\"]).dropna(subset=[\"date\"], axis=0)\n",
-    "#diagnoses_codes.head()\n",
-    "#diagnoses_codes.reset_index(drop=True).info()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 326,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "fa4a11bb545e4a65b22151769f697ba2",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(HTML(value=''), FloatProgress(value=0.0, max=1167.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "l_test = l10\n",
-    "icd_list = [item for sublist in l_test.values() for item in sublist]\n",
-    "icd_list = sorted(list(dict.fromkeys(icd_list)))\n",
-    "\n",
-    "icd_dict = {}\n",
-    "for code in tqdm(icd_list):\n",
-    "    diag_list = []\n",
-    "    for key in l_test:\n",
-    "        if code in l_test[key]: diag_list.append(key)\n",
-    "    icd_dict[code] = diag_list"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 327,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "time0=time0_col\n",
-    "diagnoses_codes_eid = diagnoses_codes[diagnoses_codes.eid.isin(df_birthdate.reset_index().eid.to_list())].reset_index(drop=True)\n",
-    "diagnoses_codes_eid_icd = diagnoses_codes_eid[diagnoses_codes_eid.meaning.isin(icd_dict)]\n",
-    "diagnoses_codes_time = diagnoses_codes_eid_icd.merge(df_birthdate.reset_index()[[\"eid\", time0]], how=\"left\", on=\"eid\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 328,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>origin</th>\n",
-       "      <th>instance</th>\n",
-       "      <th>n</th>\n",
-       "      <th>code</th>\n",
-       "      <th>meaning</th>\n",
-       "      <th>date</th>\n",
-       "      <th>birthdate</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>hes_icd10</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>S0240</td>\n",
-       "      <td>S02</td>\n",
-       "      <td>2005-06-02</td>\n",
-       "      <td>1960-11-12</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>hes_icd10</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>K37</td>\n",
-       "      <td>K37</td>\n",
-       "      <td>1998-05-11</td>\n",
-       "      <td>1960-11-12</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>hes_icd10</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>K37</td>\n",
-       "      <td>K37</td>\n",
-       "      <td>1998-05-16</td>\n",
-       "      <td>1960-11-12</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>hes_icd10</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>5</td>\n",
-       "      <td>K37</td>\n",
-       "      <td>K37</td>\n",
-       "      <td>1998-06-01</td>\n",
-       "      <td>1960-11-12</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>hes_icd10</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>6</td>\n",
-       "      <td>N950</td>\n",
-       "      <td>N95</td>\n",
-       "      <td>2016-11-08</td>\n",
-       "      <td>1960-11-12</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9438551</th>\n",
-       "      <td>6025198</td>\n",
-       "      <td>hes_icd10</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>77</td>\n",
-       "      <td>D649</td>\n",
-       "      <td>D64</td>\n",
-       "      <td>2018-12-09</td>\n",
-       "      <td>1943-01-26</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9438552</th>\n",
-       "      <td>6025198</td>\n",
-       "      <td>hes_icd10</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>78</td>\n",
-       "      <td>R945</td>\n",
-       "      <td>R94</td>\n",
-       "      <td>2018-12-09</td>\n",
-       "      <td>1943-01-26</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9438553</th>\n",
-       "      <td>6025198</td>\n",
-       "      <td>hes_icd10</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>79</td>\n",
-       "      <td>F171</td>\n",
-       "      <td>F17</td>\n",
-       "      <td>2018-12-09</td>\n",
-       "      <td>1943-01-26</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9438554</th>\n",
-       "      <td>6025198</td>\n",
-       "      <td>hes_icd10</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>80</td>\n",
-       "      <td>I10</td>\n",
-       "      <td>I10</td>\n",
-       "      <td>2018-12-09</td>\n",
-       "      <td>1943-01-26</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9438555</th>\n",
-       "      <td>6025198</td>\n",
-       "      <td>hes_icd10</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>81</td>\n",
-       "      <td>E780</td>\n",
-       "      <td>E78</td>\n",
-       "      <td>2018-12-09</td>\n",
-       "      <td>1943-01-26</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>9438556 rows × 8 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "             eid     origin  instance   n   code meaning        date  \\\n",
-       "0        1000018  hes_icd10       0.0   1  S0240     S02  2005-06-02   \n",
-       "1        1000018  hes_icd10       0.0   3    K37     K37  1998-05-11   \n",
-       "2        1000018  hes_icd10       0.0   4    K37     K37  1998-05-16   \n",
-       "3        1000018  hes_icd10       0.0   5    K37     K37  1998-06-01   \n",
-       "4        1000018  hes_icd10       0.0   6   N950     N95  2016-11-08   \n",
-       "...          ...        ...       ...  ..    ...     ...         ...   \n",
-       "9438551  6025198  hes_icd10       0.0  77   D649     D64  2018-12-09   \n",
-       "9438552  6025198  hes_icd10       0.0  78   R945     R94  2018-12-09   \n",
-       "9438553  6025198  hes_icd10       0.0  79   F171     F17  2018-12-09   \n",
-       "9438554  6025198  hes_icd10       0.0  80    I10     I10  2018-12-09   \n",
-       "9438555  6025198  hes_icd10       0.0  81   E780     E78  2018-12-09   \n",
-       "\n",
-       "          birthdate  \n",
-       "0        1960-11-12  \n",
-       "1        1960-11-12  \n",
-       "2        1960-11-12  \n",
-       "3        1960-11-12  \n",
-       "4        1960-11-12  \n",
-       "...             ...  \n",
-       "9438551  1943-01-26  \n",
-       "9438552  1943-01-26  \n",
-       "9438553  1943-01-26  \n",
-       "9438554  1943-01-26  \n",
-       "9438555  1943-01-26  \n",
-       "\n",
-       "[9438556 rows x 8 columns]"
-      ]
-     },
-     "execution_count": 328,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "diagnoses_codes_time"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 329,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dct_simple = diagnoses_codes_time.assign(t= lambda x: (x.date-x.birthdate).dt.days/365.2425)[[\"eid\", \"t\", \"meaning\"]]\n",
-    "dct_simple.t= dct_simple.t.round(1)\n",
-    "dct_simple[\"diagnosis\"] = [icd_dict[code] for code in dct_simple.meaning]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 330,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>t</th>\n",
-       "      <th>meaning</th>\n",
-       "      <th>diagnosis</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>44.6</td>\n",
-       "      <td>S02</td>\n",
-       "      <td>[injury_of_head, fracture_of_bone]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>37.5</td>\n",
-       "      <td>K37</td>\n",
-       "      <td>[appendicitis]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>37.5</td>\n",
-       "      <td>K37</td>\n",
-       "      <td>[appendicitis]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>37.6</td>\n",
-       "      <td>K37</td>\n",
-       "      <td>[appendicitis]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>56.0</td>\n",
-       "      <td>N95</td>\n",
-       "      <td>[vaginitis, postmenopausal_bleeding, bleeding_...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9438551</th>\n",
-       "      <td>6025198</td>\n",
-       "      <td>75.9</td>\n",
-       "      <td>D64</td>\n",
-       "      <td>[anemia]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9438552</th>\n",
-       "      <td>6025198</td>\n",
-       "      <td>75.9</td>\n",
-       "      <td>R94</td>\n",
-       "      <td>[liver_function_tests_abnormal]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9438553</th>\n",
-       "      <td>6025198</td>\n",
-       "      <td>75.9</td>\n",
-       "      <td>F17</td>\n",
-       "      <td>[tobacco_dependence_syndrome, tobacco_user, sm...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9438554</th>\n",
-       "      <td>6025198</td>\n",
-       "      <td>75.9</td>\n",
-       "      <td>I10</td>\n",
-       "      <td>[hypertensive_disorder_systemic_arterial, esse...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9438555</th>\n",
-       "      <td>6025198</td>\n",
-       "      <td>75.9</td>\n",
-       "      <td>E78</td>\n",
-       "      <td>[hyperlipidemia, hypercholesterolemia, arthrit...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>9438556 rows × 4 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "             eid     t meaning  \\\n",
-       "0        1000018  44.6     S02   \n",
-       "1        1000018  37.5     K37   \n",
-       "2        1000018  37.5     K37   \n",
-       "3        1000018  37.6     K37   \n",
-       "4        1000018  56.0     N95   \n",
-       "...          ...   ...     ...   \n",
-       "9438551  6025198  75.9     D64   \n",
-       "9438552  6025198  75.9     R94   \n",
-       "9438553  6025198  75.9     F17   \n",
-       "9438554  6025198  75.9     I10   \n",
-       "9438555  6025198  75.9     E78   \n",
-       "\n",
-       "                                                 diagnosis  \n",
-       "0                       [injury_of_head, fracture_of_bone]  \n",
-       "1                                           [appendicitis]  \n",
-       "2                                           [appendicitis]  \n",
-       "3                                           [appendicitis]  \n",
-       "4        [vaginitis, postmenopausal_bleeding, bleeding_...  \n",
-       "...                                                    ...  \n",
-       "9438551                                           [anemia]  \n",
-       "9438552                    [liver_function_tests_abnormal]  \n",
-       "9438553  [tobacco_dependence_syndrome, tobacco_user, sm...  \n",
-       "9438554  [hypertensive_disorder_systemic_arterial, esse...  \n",
-       "9438555  [hyperlipidemia, hypercholesterolemia, arthrit...  \n",
-       "\n",
-       "[9438556 rows x 4 columns]"
-      ]
-     },
-     "execution_count": 330,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dct_simple"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 331,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#for col in list(l10.keys()): dct_simple[col]=False"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 332,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dct_simple_eids = df_birthdate.reset_index()[[\"eid\"]].merge(dct_simple, how=\"left\", on=\"eid\").drop([\"meaning\"], axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 333,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>t</th>\n",
-       "      <th>diagnosis</th>\n",
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-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1000018</td>\n",
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-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1000018</td>\n",
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-       "      <td>[appendicitis]</td>\n",
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-       "      <th>2</th>\n",
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-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000018</td>\n",
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-       "      <td>[appendicitis]</td>\n",
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-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>56.0</td>\n",
-       "      <td>[vaginitis, postmenopausal_bleeding, bleeding_...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9517540</th>\n",
-       "      <td>6025198</td>\n",
-       "      <td>75.9</td>\n",
-       "      <td>[anemia]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9517541</th>\n",
-       "      <td>6025198</td>\n",
-       "      <td>75.9</td>\n",
-       "      <td>[liver_function_tests_abnormal]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9517542</th>\n",
-       "      <td>6025198</td>\n",
-       "      <td>75.9</td>\n",
-       "      <td>[tobacco_dependence_syndrome, tobacco_user, sm...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9517543</th>\n",
-       "      <td>6025198</td>\n",
-       "      <td>75.9</td>\n",
-       "      <td>[hypertensive_disorder_systemic_arterial, esse...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9517544</th>\n",
-       "      <td>6025198</td>\n",
-       "      <td>75.9</td>\n",
-       "      <td>[hyperlipidemia, hypercholesterolemia, arthrit...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>9517545 rows × 3 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "             eid     t                                          diagnosis\n",
-       "0        1000018  44.6                 [injury_of_head, fracture_of_bone]\n",
-       "1        1000018  37.5                                     [appendicitis]\n",
-       "2        1000018  37.5                                     [appendicitis]\n",
-       "3        1000018  37.6                                     [appendicitis]\n",
-       "4        1000018  56.0  [vaginitis, postmenopausal_bleeding, bleeding_...\n",
-       "...          ...   ...                                                ...\n",
-       "9517540  6025198  75.9                                           [anemia]\n",
-       "9517541  6025198  75.9                    [liver_function_tests_abnormal]\n",
-       "9517542  6025198  75.9  [tobacco_dependence_syndrome, tobacco_user, sm...\n",
-       "9517543  6025198  75.9  [hypertensive_disorder_systemic_arterial, esse...\n",
-       "9517544  6025198  75.9  [hyperlipidemia, hypercholesterolemia, arthrit...\n",
-       "\n",
-       "[9517545 rows x 3 columns]"
-      ]
-     },
-     "execution_count": 333,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dct_simple_eids"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 334,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dct = dct_simple_eids.groupby([\"eid\", \"t\"]).agg({'diagnosis': \"sum\"})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 335,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th>diagnosis</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>eid</th>\n",
-       "      <th>t</th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"5\" valign=\"top\">1000018</th>\n",
-       "      <th>37.5</th>\n",
-       "      <td>[appendicitis, appendicitis]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>37.6</th>\n",
-       "      <td>[appendicitis]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>44.6</th>\n",
-       "      <td>[injury_of_head, fracture_of_bone]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>56.0</th>\n",
-       "      <td>[vaginitis, postmenopausal_bleeding, bleeding_...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>58.3</th>\n",
-       "      <td>[melanocytic_nevus, hypertensive_disorder_syst...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6025173</th>\n",
-       "      <th>66.5</th>\n",
-       "      <td>[neutropenic_disorder, leukopenia]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"2\" valign=\"top\">6025182</th>\n",
-       "      <th>44.2</th>\n",
-       "      <td>[urinary_tract_infectious_disease, urinary_inc...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50.3</th>\n",
-       "      <td>[headache, pain]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"2\" valign=\"top\">6025198</th>\n",
-       "      <th>75.8</th>\n",
-       "      <td>[sepsis, methicillin_resistant_staphylococcus_...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75.9</th>\n",
-       "      <td>[cardiac_arrest, chronic_obstructive_lung_dise...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>2413496 rows × 1 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                      diagnosis\n",
-       "eid     t                                                      \n",
-       "1000018 37.5                       [appendicitis, appendicitis]\n",
-       "        37.6                                     [appendicitis]\n",
-       "        44.6                 [injury_of_head, fracture_of_bone]\n",
-       "        56.0  [vaginitis, postmenopausal_bleeding, bleeding_...\n",
-       "        58.3  [melanocytic_nevus, hypertensive_disorder_syst...\n",
-       "...                                                         ...\n",
-       "6025173 66.5                 [neutropenic_disorder, leukopenia]\n",
-       "6025182 44.2  [urinary_tract_infectious_disease, urinary_inc...\n",
-       "        50.3                                   [headache, pain]\n",
-       "6025198 75.8  [sepsis, methicillin_resistant_staphylococcus_...\n",
-       "        75.9  [cardiac_arrest, chronic_obstructive_lung_dise...\n",
-       "\n",
-       "[2413496 rows x 1 columns]"
-      ]
-     },
-     "execution_count": 335,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dct"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 336,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "aa7a9a7015a64772b9997990b5232e50",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(HTML(value=''), FloatProgress(value=0.0, max=2413496.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    },
-    {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-336-c1ce43766891>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mdss_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0md_list\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtqdm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdct\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"diagnosis\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0mdss_list\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;32mTrue\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0me\u001b[0m \u001b[0;32min\u001b[0m \u001b[0md_list\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;32mFalse\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0me\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ml10\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m<ipython-input-336-c1ce43766891>\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mdss_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0md_list\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtqdm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdct\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"diagnosis\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0mdss_list\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;32mTrue\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0me\u001b[0m \u001b[0;32min\u001b[0m \u001b[0md_list\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;32mFalse\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0me\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ml10\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
-     ]
-    }
-   ],
-   "source": [
-    "dss_list = []\n",
-    "for d_list in tqdm(dct[\"diagnosis\"].values):\n",
-    "    dss_list.append([True if e in d_list else False for e in list(l10.keys())])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 348,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%load_ext Cython"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_list(d_list, keys): \n",
-    "    return [True if e in d_list else False for e in list(keys)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_diagnoses_wide(diagnoses_array, keys): \n",
-    "    dss_list = []\n",
-    "    for d_list in diagnoses_array:\n",
-    "        dss_list.append([True if e in d_list else False for e in keys])  \n",
-    "    return dss_list"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "diagnoses_array = dct[\"diagnosis\"].values\n",
-    "keys = list(l10.keys())\n",
-    "dss_list = get_diagnoses_wide(diagnoses_array, keys)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "diagnoses_array"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "2+2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 350,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "037ae435dd7f42819666e382865da845",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(HTML(value=''), FloatProgress(value=0.0, max=2413496.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "ERROR:root:Internal Python error in the inspect module.\n",
-      "Below is the traceback from this internal error.\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Traceback (most recent call last):\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/IPython/core/interactiveshell.py\", line 3417, in run_code\n",
-      "    exec(code_obj, self.user_global_ns, self.user_ns)\n",
-      "  File \"<ipython-input-350-f1e2d854b5dc>\", line 4, in <module>\n",
-      "    dss_list = [get_list(d_list, keys) for d_list in tqdm(dct[\"diagnosis\"].values)]\n",
-      "  File \"<ipython-input-350-f1e2d854b5dc>\", line 4, in <listcomp>\n",
-      "    dss_list = [get_list(d_list, keys) for d_list in tqdm(dct[\"diagnosis\"].values)]\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/tqdm/notebook.py\", line 234, in __iter__\n",
-      "    for obj in super(tqdm_notebook, self).__iter__(*args, **kwargs):\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/tqdm/std.py\", line 1190, in __iter__\n",
-      "    self.refresh(lock_args=self.lock_args)\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/tqdm/std.py\", line 1386, in refresh\n",
-      "    self.display()\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/tqdm/notebook.py\", line 152, in display\n",
-      "    pbar.value = self.n\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/traitlets/traitlets.py\", line 604, in __set__\n",
-      "    self.set(obj, value)\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/traitlets/traitlets.py\", line 593, in set\n",
-      "    obj._notify_trait(self.name, old_value, new_value)\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/traitlets/traitlets.py\", line 1222, in _notify_trait\n",
-      "    type='change',\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/ipywidgets/widgets/widget.py\", line 605, in notify_change\n",
-      "    self.send_state(key=name)\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/ipywidgets/widgets/widget.py\", line 489, in send_state\n",
-      "    self._send(msg, buffers=buffers)\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/ipywidgets/widgets/widget.py\", line 737, in _send\n",
-      "    self.comm.send(data=msg, buffers=buffers)\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/ipykernel/comm/comm.py\", line 123, in send\n",
-      "    data=data, metadata=metadata, buffers=buffers,\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/ipykernel/comm/comm.py\", line 71, in _publish_msg\n",
-      "    buffers=buffers,\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/jupyter_client/session.py\", line 740, in send\n",
-      "    to_send = self.serialize(msg, ident)\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/jupyter_client/session.py\", line 653, in serialize\n",
-      "    signature = self.sign(real_message)\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/jupyter_client/session.py\", line 598, in sign\n",
-      "    h.update(m)\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/hmac.py\", line 102, in update\n",
-      "    self.inner.update(msg)\n",
-      "KeyboardInterrupt\n",
-      "\n",
-      "During handling of the above exception, another exception occurred:\n",
-      "\n",
-      "Traceback (most recent call last):\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/IPython/core/interactiveshell.py\", line 2044, in showtraceback\n",
-      "    stb = value._render_traceback_()\n",
-      "AttributeError: 'KeyboardInterrupt' object has no attribute '_render_traceback_'\n",
-      "\n",
-      "During handling of the above exception, another exception occurred:\n",
-      "\n",
-      "Traceback (most recent call last):\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/IPython/core/ultratb.py\", line 1169, in get_records\n",
-      "    return _fixed_getinnerframes(etb, number_of_lines_of_context, tb_offset)\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/IPython/core/ultratb.py\", line 316, in wrapped\n",
-      "    return f(*args, **kwargs)\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/IPython/core/ultratb.py\", line 350, in _fixed_getinnerframes\n",
-      "    records = fix_frame_records_filenames(inspect.getinnerframes(etb, context))\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/inspect.py\", line 1502, in getinnerframes\n",
-      "    frameinfo = (tb.tb_frame,) + getframeinfo(tb, context)\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/inspect.py\", line 1460, in getframeinfo\n",
-      "    filename = getsourcefile(frame) or getfile(frame)\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/inspect.py\", line 696, in getsourcefile\n",
-      "    if getattr(getmodule(object, filename), '__loader__', None) is not None:\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/inspect.py\", line 742, in getmodule\n",
-      "    os.path.realpath(f)] = module.__name__\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/posixpath.py\", line 395, in realpath\n",
-      "    path, ok = _joinrealpath(filename[:0], filename, {})\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/posixpath.py\", line 429, in _joinrealpath\n",
-      "    if not islink(newpath):\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/posixpath.py\", line 171, in islink\n",
-      "    st = os.lstat(path)\n",
-      "KeyboardInterrupt\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "KeyboardInterrupt\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "#def get_list(d_list, keys): return [True if e in d_list else False for e in list(keys)]\n",
-    "\n",
-    "keys = list(l10.keys())\n",
-    "dss_list = [get_list(d_list, keys) for d_list in tqdm(dct[\"diagnosis\"].values)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "terminate()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 342,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "353ed425b015405c951725db4d9ed20a",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(HTML(value=''), FloatProgress(value=0.0, max=2413496.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Exception ignored in: <function _releaseLock at 0x7fa759b983b0>\n",
-      "Traceback (most recent call last):\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/logging/__init__.py\", line 221, in _releaseLock\n",
-      "    def _releaseLock():\n",
-      "KeyboardInterrupt\n",
-      "ERROR:root:Internal Python error in the inspect module.\n",
-      "Below is the traceback from this internal error.\n",
-      "\n",
-      "ERROR:root:Internal Python error in the inspect module.\n",
-      "Below is the traceback from this internal error.\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "joblib.externals.loky.process_executor._RemoteTraceback: \n",
-      "\"\"\"\n",
-      "Traceback (most recent call last):\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/joblib/externals/loky/process_executor.py\", line 404, in _process_worker\n",
-      "    call_item = call_queue.get(block=True, timeout=timeout)\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/multiprocessing/queues.py\", line 99, in get\n",
-      "    if not self._rlock.acquire(block, timeout):\n",
-      "KeyboardInterrupt\n",
-      "\"\"\"\n",
-      "\n",
-      "The above exception was the direct cause of the following exception:\n",
-      "\n",
-      "Traceback (most recent call last):\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/joblib/parallel.py\", line 940, in retrieve\n",
-      "    self._output.extend(job.get(timeout=self.timeout))\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/joblib/_parallel_backends.py\", line 542, in wrap_future_result\n",
-      "    return future.result(timeout=timeout)\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/concurrent/futures/_base.py\", line 435, in result\n",
-      "    return self.__get_result()\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/concurrent/futures/_base.py\", line 384, in __get_result\n",
-      "    raise self._exception\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/joblib/externals/loky/_base.py\", line 625, in _invoke_callbacks\n",
-      "    callback(self)\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/joblib/parallel.py\", line 366, in __call__\n",
-      "    self.parallel.dispatch_next()\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/joblib/parallel.py\", line 799, in dispatch_next\n",
-      "    if not self.dispatch_one_batch(self._original_iterator):\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/joblib/parallel.py\", line 866, in dispatch_one_batch\n",
-      "    self._dispatch(tasks)\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/joblib/parallel.py\", line 784, in _dispatch\n",
-      "    job = self._backend.apply_async(batch, callback=cb)\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/joblib/_parallel_backends.py\", line 531, in apply_async\n",
-      "    future = self._workers.submit(SafeFunction(func))\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/joblib/externals/loky/reusable_executor.py\", line 178, in submit\n",
-      "    fn, *args, **kwargs)\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/joblib/externals/loky/process_executor.py\", line 1102, in submit\n",
-      "    raise self._flags.broken\n",
-      "joblib.externals.loky.process_executor.BrokenProcessPool: A task has failed to un-serialize. Please ensure that the arguments of the function are all picklable.\n",
-      "\n",
-      "During handling of the above exception, another exception occurred:\n",
-      "\n",
-      "Traceback (most recent call last):\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/IPython/core/interactiveshell.py\", line 3417, in run_code\n",
-      "    exec(code_obj, self.user_global_ns, self.user_ns)\n",
-      "  File \"<ipython-input-343-38cbc2942fcd>\", line 2, in <module>\n",
-      "    dss_list = Parallel(n_jobs=10)(delayed(get_list)(d_list, l10.keys()) for d_list in tqdm(dct[\"diagnosis\"].values))\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/joblib/parallel.py\", line 1061, in __call__\n",
-      "    self.retrieve()\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/joblib/parallel.py\", line 962, in retrieve\n",
-      "    backend.abort_everything(ensure_ready=ensure_ready)\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/joblib/_parallel_backends.py\", line 561, in abort_everything\n",
-      "    self._workers.terminate(kill_workers=True)\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/joblib/executor.py\", line 74, in terminate\n",
-      "    self.shutdown(kill_workers=kill_workers)\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/joblib/externals/loky/process_executor.py\", line 1171, in shutdown\n",
-      "    executor_manager_thread.join()\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/threading.py\", line 1044, in join\n",
-      "    self._wait_for_tstate_lock()\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/threading.py\", line 1060, in _wait_for_tstate_lock\n",
-      "    elif lock.acquire(block, timeout):\n",
-      "KeyboardInterrupt\n",
-      "\n",
-      "During handling of the above exception, another exception occurred:\n",
-      "\n",
-      "Traceback (most recent call last):\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/IPython/core/interactiveshell.py\", line 2044, in showtraceback\n",
-      "    stb = value._render_traceback_()\n",
-      "AttributeError: 'KeyboardInterrupt' object has no attribute '_render_traceback_'\n",
-      "\n",
-      "During handling of the above exception, another exception occurred:\n",
-      "\n",
-      "Traceback (most recent call last):\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/IPython/core/ultratb.py\", line 1169, in get_records\n",
-      "    return _fixed_getinnerframes(etb, number_of_lines_of_context, tb_offset)\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/IPython/core/ultratb.py\", line 316, in wrapped\n",
-      "    return f(*args, **kwargs)\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/IPython/core/ultratb.py\", line 350, in _fixed_getinnerframes\n",
-      "    records = fix_frame_records_filenames(inspect.getinnerframes(etb, context))\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/inspect.py\", line 1502, in getinnerframes\n",
-      "    frameinfo = (tb.tb_frame,) + getframeinfo(tb, context)\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/inspect.py\", line 1460, in getframeinfo\n",
-      "    filename = getsourcefile(frame) or getfile(frame)\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/inspect.py\", line 696, in getsourcefile\n",
-      "    if getattr(getmodule(object, filename), '__loader__', None) is not None:\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/inspect.py\", line 742, in getmodule\n",
-      "    os.path.realpath(f)] = module.__name__\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/posixpath.py\", line 395, in realpath\n",
-      "    path, ok = _joinrealpath(filename[:0], filename, {})\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/posixpath.py\", line 428, in _joinrealpath\n",
-      "    newpath = join(path, name)\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/posixpath.py\", line 86, in join\n",
-      "    for b in map(os.fspath, p):\n",
-      "KeyboardInterrupt\n",
-      "\n",
-      "During handling of the above exception, another exception occurred:\n",
-      "\n",
-      "Traceback (most recent call last):\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/IPython/core/interactiveshell.py\", line 2047, in showtraceback\n",
-      "    value, tb, tb_offset=tb_offset)\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/IPython/core/ultratb.py\", line 1436, in structured_traceback\n",
-      "    self, etype, value, tb, tb_offset, number_of_lines_of_context)\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/IPython/core/ultratb.py\", line 1336, in structured_traceback\n",
-      "    self, etype, value, tb, tb_offset, number_of_lines_of_context\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/IPython/core/ultratb.py\", line 1193, in structured_traceback\n",
-      "    tb_offset)\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/IPython/core/ultratb.py\", line 1147, in format_exception_as_a_whole\n",
-      "    records = self.get_records(etb, number_of_lines_of_context, tb_offset)\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/IPython/core/ultratb.py\", line 1184, in get_records\n",
-      "    traceback.print_exc(file=self.ostream)\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/traceback.py\", line 163, in print_exc\n",
-      "    print_exception(*sys.exc_info(), limit=limit, file=file, chain=chain)\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/traceback.py\", line 104, in print_exception\n",
-      "    type(value), value, tb, limit=limit).format(chain=chain):\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/traceback.py\", line 508, in __init__\n",
-      "    capture_locals=capture_locals)\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/traceback.py\", line 363, in extract\n",
-      "    f.line\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/traceback.py\", line 285, in line\n",
-      "    self._line = linecache.getline(self.filename, self.lineno).strip()\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/linecache.py\", line 16, in getline\n",
-      "    lines = getlines(filename, module_globals)\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/linecache.py\", line 47, in getlines\n",
-      "    return updatecache(filename, module_globals)\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/linecache.py\", line 136, in updatecache\n",
-      "    with tokenize.open(fullname) as fp:\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/tokenize.py\", line 449, in open\n",
-      "    encoding, lines = detect_encoding(buffer.readline)\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/tokenize.py\", line 418, in detect_encoding\n",
-      "    first = read_or_stop()\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/tokenize.py\", line 376, in read_or_stop\n",
-      "    return readline()\n",
-      "KeyboardInterrupt\n",
-      "\n",
-      "During handling of the above exception, another exception occurred:\n",
-      "\n",
-      "Traceback (most recent call last):\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/IPython/core/interactiveshell.py\", line 3337, in run_ast_nodes\n",
-      "    if (await self.run_code(code, result,  async_=asy)):\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/IPython/core/interactiveshell.py\", line 3434, in run_code\n",
-      "    self.showtraceback(running_compiled_code=True)\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/IPython/core/interactiveshell.py\", line 2059, in showtraceback\n",
-      "    print('\\n' + self.get_exception_only(), file=sys.stderr)\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/IPython/core/interactiveshell.py\", line 2004, in get_exception_only\n",
-      "    msg = traceback.format_exception_only(etype, value)\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/traceback.py\", line 140, in format_exception_only\n",
-      "    return list(TracebackException(etype, value, None).format_exception_only())\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/traceback.py\", line 521, in __init__\n",
-      "    self._load_lines()\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/traceback.py\", line 533, in _load_lines\n",
-      "    self.__context__._load_lines()\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/traceback.py\", line 531, in _load_lines\n",
-      "    frame.line\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/traceback.py\", line 285, in line\n",
-      "    self._line = linecache.getline(self.filename, self.lineno).strip()\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/linecache.py\", line 16, in getline\n",
-      "    lines = getlines(filename, module_globals)\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/linecache.py\", line 47, in getlines\n",
-      "    return updatecache(filename, module_globals)\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/linecache.py\", line 136, in updatecache\n",
-      "    with tokenize.open(fullname) as fp:\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/tokenize.py\", line 449, in open\n",
-      "    encoding, lines = detect_encoding(buffer.readline)\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/tokenize.py\", line 418, in detect_encoding\n",
-      "    first = read_or_stop()\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/tokenize.py\", line 376, in read_or_stop\n",
-      "    return readline()\n",
-      "KeyboardInterrupt\n",
-      "\n",
-      "During handling of the above exception, another exception occurred:\n",
-      "\n",
-      "Traceback (most recent call last):\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/IPython/core/interactiveshell.py\", line 2044, in showtraceback\n",
-      "    stb = value._render_traceback_()\n",
-      "AttributeError: 'KeyboardInterrupt' object has no attribute '_render_traceback_'\n",
-      "\n",
-      "During handling of the above exception, another exception occurred:\n",
-      "\n",
-      "Traceback (most recent call last):\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/IPython/core/ultratb.py\", line 1169, in get_records\n",
-      "    return _fixed_getinnerframes(etb, number_of_lines_of_context, tb_offset)\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/IPython/core/ultratb.py\", line 316, in wrapped\n",
-      "    return f(*args, **kwargs)\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/IPython/core/ultratb.py\", line 350, in _fixed_getinnerframes\n",
-      "    records = fix_frame_records_filenames(inspect.getinnerframes(etb, context))\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/inspect.py\", line 1502, in getinnerframes\n",
-      "    frameinfo = (tb.tb_frame,) + getframeinfo(tb, context)\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/inspect.py\", line 1464, in getframeinfo\n",
-      "    lines, lnum = findsource(frame)\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/IPython/core/ultratb.py\", line 182, in findsource\n",
-      "    lines = linecache.getlines(file, globals_dict)\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/linecache.py\", line 47, in getlines\n",
-      "    return updatecache(filename, module_globals)\n",
-      "  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/linecache.py\", line 137, in updatecache\n",
-      "    lines = fp.readlines()\n",
-      "KeyboardInterrupt\n"
-     ]
-    },
-    {
-     "ename": "TypeError",
-     "evalue": "object of type 'NoneType' has no len()",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31m_RemoteTraceback\u001b[0m                          Traceback (most recent call last)",
-      "\u001b[0;31m_RemoteTraceback\u001b[0m: \n\"\"\"\nTraceback (most recent call last):\n  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/joblib/externals/loky/process_executor.py\", line 404, in _process_worker\n    call_item = call_queue.get(block=True, timeout=timeout)\n  File \"/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/multiprocessing/queues.py\", line 99, in get\n    if not self._rlock.acquire(block, timeout):\nKeyboardInterrupt\n\"\"\"",
-      "\nThe above exception was the direct cause of the following exception:\n",
-      "\u001b[0;31mBrokenProcessPool\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36mretrieve\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    939\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'supports_timeout'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 940\u001b[0;31m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_output\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    941\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36mwrap_future_result\u001b[0;34m(future, timeout)\u001b[0m\n\u001b[1;32m    541\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 542\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfuture\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    543\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mCfTimeoutError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/concurrent/futures/_base.py\u001b[0m in \u001b[0;36mresult\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    434\u001b[0m             \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_state\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mFINISHED\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 435\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__get_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    436\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/concurrent/futures/_base.py\u001b[0m in \u001b[0;36m__get_result\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    383\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_exception\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 384\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_exception\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    385\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/joblib/externals/loky/_base.py\u001b[0m in \u001b[0;36m_invoke_callbacks\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    624\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 625\u001b[0;31m                 \u001b[0mcallback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    626\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mBaseException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, out)\u001b[0m\n\u001b[1;32m    365\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparallel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_original_iterator\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 366\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparallel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdispatch_next\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    367\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36mdispatch_next\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    798\u001b[0m         \"\"\"\n\u001b[0;32m--> 799\u001b[0;31m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdispatch_one_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_original_iterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    800\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36mdispatch_one_batch\u001b[0;34m(self, iterator)\u001b[0m\n\u001b[1;32m    865\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 866\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dispatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtasks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    867\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m_dispatch\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    783\u001b[0m             \u001b[0mjob_idx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 784\u001b[0;31m             \u001b[0mjob\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    785\u001b[0m             \u001b[0;31m# A job can complete so quickly than its callback is\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36mapply_async\u001b[0;34m(self, func, callback)\u001b[0m\n\u001b[1;32m    530\u001b[0m         \u001b[0;34m\"\"\"Schedule a func to be run\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 531\u001b[0;31m         \u001b[0mfuture\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_workers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubmit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mSafeFunction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    532\u001b[0m         \u001b[0mfuture\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunctools\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpartial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrap_future_result\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfuture\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/joblib/externals/loky/reusable_executor.py\u001b[0m in \u001b[0;36msubmit\u001b[0;34m(self, fn, *args, **kwargs)\u001b[0m\n\u001b[1;32m    177\u001b[0m             return super(_ReusablePoolExecutor, self).submit(\n\u001b[0;32m--> 178\u001b[0;31m                 fn, *args, **kwargs)\n\u001b[0m\u001b[1;32m    179\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/joblib/externals/loky/process_executor.py\u001b[0m in \u001b[0;36msubmit\u001b[0;34m(self, fn, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1101\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_flags\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbroken\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1102\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_flags\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbroken\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1103\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_flags\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshutdown\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mBrokenProcessPool\u001b[0m: A task has failed to un-serialize. Please ensure that the arguments of the function are all picklable.",
-      "\nDuring handling of the above exception, another exception occurred:\n",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "    \u001b[0;31m[... skipping hidden 1 frame]\u001b[0m\n",
-      "\u001b[0;32m<ipython-input-343-38cbc2942fcd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mget_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0md_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeys\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;32mTrue\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0me\u001b[0m \u001b[0;32min\u001b[0m \u001b[0md_list\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;32mFalse\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0me\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdss_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mParallel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdelayed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mget_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0md_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ml10\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0md_list\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtqdm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdct\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"diagnosis\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m   1060\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretrieval_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1061\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretrieve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1062\u001b[0m             \u001b[0;31m# Make sure that we get a last message telling us we are done\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36mretrieve\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    961\u001b[0m                     \u001b[0mensure_ready\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_managed_backend\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 962\u001b[0;31m                     \u001b[0mbackend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mabort_everything\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mensure_ready\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mensure_ready\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    963\u001b[0m                 \u001b[0;32mraise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36mabort_everything\u001b[0;34m(self, ensure_ready)\u001b[0m\n\u001b[1;32m    560\u001b[0m         \"\"\"\n\u001b[0;32m--> 561\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_workers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mterminate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkill_workers\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    562\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_workers\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/joblib/executor.py\u001b[0m in \u001b[0;36mterminate\u001b[0;34m(self, kill_workers)\u001b[0m\n\u001b[1;32m     73\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mterminate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkill_workers\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 74\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshutdown\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkill_workers\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkill_workers\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     75\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mkill_workers\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/joblib/externals/loky/process_executor.py\u001b[0m in \u001b[0;36mshutdown\u001b[0;34m(self, wait, kill_workers)\u001b[0m\n\u001b[1;32m   1170\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mexecutor_manager_thread\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mwait\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1171\u001b[0;31m             \u001b[0mexecutor_manager_thread\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1172\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/threading.py\u001b[0m in \u001b[0;36mjoin\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m   1043\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1044\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_wait_for_tstate_lock\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1045\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/threading.py\u001b[0m in \u001b[0;36m_wait_for_tstate_lock\u001b[0;34m(self, block, timeout)\u001b[0m\n\u001b[1;32m   1059\u001b[0m             \u001b[0;32massert\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_is_stopped\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1060\u001b[0;31m         \u001b[0;32melif\u001b[0m \u001b[0mlock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mblock\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1061\u001b[0m             \u001b[0mlock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrelease\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m: ",
-      "\nDuring handling of the above exception, another exception occurred:\n",
-      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mshowtraceback\u001b[0;34m(self, exc_tuple, filename, tb_offset, exception_only, running_compiled_code)\u001b[0m\n\u001b[1;32m   2043\u001b[0m                         \u001b[0;31m# in the engines. This should return a list of strings.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2044\u001b[0;31m                         \u001b[0mstb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_render_traceback_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2045\u001b[0m                     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mAttributeError\u001b[0m: 'KeyboardInterrupt' object has no attribute '_render_traceback_'",
-      "\nDuring handling of the above exception, another exception occurred:\n",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/IPython/core/ultratb.py\u001b[0m in \u001b[0;36mget_records\u001b[0;34m(self, etb, number_of_lines_of_context, tb_offset)\u001b[0m\n\u001b[1;32m   1168\u001b[0m             \u001b[0;31m# (5 blanks lines) where none should be returned.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1169\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0m_fixed_getinnerframes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0metb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumber_of_lines_of_context\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtb_offset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1170\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mUnicodeDecodeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/IPython/core/ultratb.py\u001b[0m in \u001b[0;36mwrapped\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    315\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 316\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    317\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/IPython/core/ultratb.py\u001b[0m in \u001b[0;36m_fixed_getinnerframes\u001b[0;34m(etb, context, tb_offset)\u001b[0m\n\u001b[1;32m    349\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 350\u001b[0;31m     \u001b[0mrecords\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfix_frame_records_filenames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minspect\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetinnerframes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0metb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    351\u001b[0m     \u001b[0;31m# If the error is at the console, don't build any context, since it would\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/inspect.py\u001b[0m in \u001b[0;36mgetinnerframes\u001b[0;34m(tb, context)\u001b[0m\n\u001b[1;32m   1501\u001b[0m     \u001b[0;32mwhile\u001b[0m \u001b[0mtb\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1502\u001b[0;31m         \u001b[0mframeinfo\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mtb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtb_frame\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mgetframeinfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1503\u001b[0m         \u001b[0mframelist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFrameInfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mframeinfo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/inspect.py\u001b[0m in \u001b[0;36mgetframeinfo\u001b[0;34m(frame, context)\u001b[0m\n\u001b[1;32m   1459\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1460\u001b[0;31m     \u001b[0mfilename\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetsourcefile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mframe\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mgetfile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mframe\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1461\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcontext\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/inspect.py\u001b[0m in \u001b[0;36mgetsourcefile\u001b[0;34m(object)\u001b[0m\n\u001b[1;32m    695\u001b[0m     \u001b[0;31m# only return a non-existent filename if the module has a PEP 302 loader\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 696\u001b[0;31m     \u001b[0;32mif\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgetmodule\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobject\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'__loader__'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    697\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mfilename\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/inspect.py\u001b[0m in \u001b[0;36mgetmodule\u001b[0;34m(object, _filename)\u001b[0m\n\u001b[1;32m    741\u001b[0m             modulesbyfile[f] = modulesbyfile[\n\u001b[0;32m--> 742\u001b[0;31m                 os.path.realpath(f)] = module.__name__\n\u001b[0m\u001b[1;32m    743\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfile\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmodulesbyfile\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/posixpath.py\u001b[0m in \u001b[0;36mrealpath\u001b[0;34m(filename)\u001b[0m\n\u001b[1;32m    394\u001b[0m     \u001b[0mfilename\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfspath\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 395\u001b[0;31m     \u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mok\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_joinrealpath\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    396\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mabspath\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/posixpath.py\u001b[0m in \u001b[0;36m_joinrealpath\u001b[0;34m(path, rest, seen)\u001b[0m\n\u001b[1;32m    427\u001b[0m             \u001b[0;32mcontinue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 428\u001b[0;31m         \u001b[0mnewpath\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    429\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mislink\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnewpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/posixpath.py\u001b[0m in \u001b[0;36mjoin\u001b[0;34m(a, *p)\u001b[0m\n\u001b[1;32m     85\u001b[0m             \u001b[0mpath\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0msep\u001b[0m  \u001b[0;31m#23780: Ensure compatible data type even if p is null.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 86\u001b[0;31m         \u001b[0;32mfor\u001b[0m \u001b[0mb\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfspath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     87\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstartswith\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m: ",
-      "\nDuring handling of the above exception, another exception occurred:\n",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mshowtraceback\u001b[0;34m(self, exc_tuple, filename, tb_offset, exception_only, running_compiled_code)\u001b[0m\n\u001b[1;32m   2046\u001b[0m                         stb = self.InteractiveTB.structured_traceback(etype,\n\u001b[0;32m-> 2047\u001b[0;31m                                             value, tb, tb_offset=tb_offset)\n\u001b[0m\u001b[1;32m   2048\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/IPython/core/ultratb.py\u001b[0m in \u001b[0;36mstructured_traceback\u001b[0;34m(self, etype, value, tb, tb_offset, number_of_lines_of_context)\u001b[0m\n\u001b[1;32m   1435\u001b[0m         return FormattedTB.structured_traceback(\n\u001b[0;32m-> 1436\u001b[0;31m             self, etype, value, tb, tb_offset, number_of_lines_of_context)\n\u001b[0m\u001b[1;32m   1437\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/IPython/core/ultratb.py\u001b[0m in \u001b[0;36mstructured_traceback\u001b[0;34m(self, etype, value, tb, tb_offset, number_of_lines_of_context)\u001b[0m\n\u001b[1;32m   1335\u001b[0m             return VerboseTB.structured_traceback(\n\u001b[0;32m-> 1336\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0metype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtb_offset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumber_of_lines_of_context\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1337\u001b[0m             )\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/IPython/core/ultratb.py\u001b[0m in \u001b[0;36mstructured_traceback\u001b[0;34m(self, etype, evalue, etb, tb_offset, number_of_lines_of_context)\u001b[0m\n\u001b[1;32m   1192\u001b[0m         formatted_exception = self.format_exception_as_a_whole(etype, evalue, etb, number_of_lines_of_context,\n\u001b[0;32m-> 1193\u001b[0;31m                                                                tb_offset)\n\u001b[0m\u001b[1;32m   1194\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/IPython/core/ultratb.py\u001b[0m in \u001b[0;36mformat_exception_as_a_whole\u001b[0;34m(self, etype, evalue, etb, number_of_lines_of_context, tb_offset)\u001b[0m\n\u001b[1;32m   1146\u001b[0m         \u001b[0mhead\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprepare_header\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0metype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlong_header\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1147\u001b[0;31m         \u001b[0mrecords\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_records\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0metb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumber_of_lines_of_context\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtb_offset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1148\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/IPython/core/ultratb.py\u001b[0m in \u001b[0;36mget_records\u001b[0;34m(self, etb, number_of_lines_of_context, tb_offset)\u001b[0m\n\u001b[1;32m   1183\u001b[0m             \u001b[0minspect_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1184\u001b[0;31m             \u001b[0mtraceback\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_exc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mostream\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1185\u001b[0m             \u001b[0minfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'\\nUnfortunately, your original traceback can not be constructed.\\n'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/traceback.py\u001b[0m in \u001b[0;36mprint_exc\u001b[0;34m(limit, file, chain)\u001b[0m\n\u001b[1;32m    162\u001b[0m     \u001b[0;34m\"\"\"Shorthand for 'print_exception(*sys.exc_info(), limit, file)'.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 163\u001b[0;31m     \u001b[0mprint_exception\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexc_info\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlimit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlimit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfile\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfile\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mchain\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mchain\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    164\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/traceback.py\u001b[0m in \u001b[0;36mprint_exception\u001b[0;34m(etype, value, tb, limit, file, chain)\u001b[0m\n\u001b[1;32m    103\u001b[0m     for line in TracebackException(\n\u001b[0;32m--> 104\u001b[0;31m             type(value), value, tb, limit=limit).format(chain=chain):\n\u001b[0m\u001b[1;32m    105\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfile\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfile\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/traceback.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, exc_type, exc_value, exc_traceback, limit, lookup_lines, capture_locals, _seen)\u001b[0m\n\u001b[1;32m    507\u001b[0m             \u001b[0mwalk_tb\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexc_traceback\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlimit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlimit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlookup_lines\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlookup_lines\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 508\u001b[0;31m             capture_locals=capture_locals)\n\u001b[0m\u001b[1;32m    509\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexc_type\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mexc_type\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/traceback.py\u001b[0m in \u001b[0;36mextract\u001b[0;34m(klass, frame_gen, limit, lookup_lines, capture_locals)\u001b[0m\n\u001b[1;32m    362\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mf\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 363\u001b[0;31m                 \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    364\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/traceback.py\u001b[0m in \u001b[0;36mline\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    284\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_line\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 285\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_line\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlinecache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlineno\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    286\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_line\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/linecache.py\u001b[0m in \u001b[0;36mgetline\u001b[0;34m(filename, lineno, module_globals)\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mgetline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlineno\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodule_globals\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m     \u001b[0mlines\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetlines\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodule_globals\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     17\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0mlineno\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlines\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/linecache.py\u001b[0m in \u001b[0;36mgetlines\u001b[0;34m(filename, module_globals)\u001b[0m\n\u001b[1;32m     46\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 47\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mupdatecache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodule_globals\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     48\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mMemoryError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/linecache.py\u001b[0m in \u001b[0;36mupdatecache\u001b[0;34m(filename, module_globals)\u001b[0m\n\u001b[1;32m    135\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 136\u001b[0;31m         \u001b[0;32mwith\u001b[0m \u001b[0mtokenize\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfullname\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mfp\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    137\u001b[0m             \u001b[0mlines\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreadlines\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/tokenize.py\u001b[0m in \u001b[0;36mopen\u001b[0;34m(filename)\u001b[0m\n\u001b[1;32m    448\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 449\u001b[0;31m         \u001b[0mencoding\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlines\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdetect_encoding\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbuffer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreadline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    450\u001b[0m         \u001b[0mbuffer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mseek\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/tokenize.py\u001b[0m in \u001b[0;36mdetect_encoding\u001b[0;34m(readline)\u001b[0m\n\u001b[1;32m    417\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 418\u001b[0;31m     \u001b[0mfirst\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mread_or_stop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    419\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfirst\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstartswith\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mBOM_UTF8\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/tokenize.py\u001b[0m in \u001b[0;36mread_or_stop\u001b[0;34m()\u001b[0m\n\u001b[1;32m    375\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 376\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mreadline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    377\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mStopIteration\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m: ",
-      "\nDuring handling of the above exception, another exception occurred:\n",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mrun_ast_nodes\u001b[0;34m(self, nodelist, cell_name, interactivity, compiler, result)\u001b[0m\n\u001b[1;32m   3336\u001b[0m                         \u001b[0masy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompare\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3337\u001b[0;31m                     \u001b[0;32mif\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;32mawait\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_code\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m  \u001b[0masync_\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0masy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3338\u001b[0m                         \u001b[0;32mreturn\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "    \u001b[0;31m[... skipping hidden 1 frame]\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mshowtraceback\u001b[0;34m(self, exc_tuple, filename, tb_offset, exception_only, running_compiled_code)\u001b[0m\n\u001b[1;32m   2058\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mKeyboardInterrupt\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2059\u001b[0;31m             \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'\\n'\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_exception_only\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfile\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstderr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2060\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mget_exception_only\u001b[0;34m(self, exc_tuple)\u001b[0m\n\u001b[1;32m   2003\u001b[0m         \u001b[0metype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_exc_info\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexc_tuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2004\u001b[0;31m         \u001b[0mmsg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtraceback\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat_exception_only\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0metype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2005\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0;34m''\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/traceback.py\u001b[0m in \u001b[0;36mformat_exception_only\u001b[0;34m(etype, value)\u001b[0m\n\u001b[1;32m    139\u001b[0m     \"\"\"\n\u001b[0;32m--> 140\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mTracebackException\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0metype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat_exception_only\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    141\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/traceback.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, exc_type, exc_value, exc_traceback, limit, lookup_lines, capture_locals, _seen)\u001b[0m\n\u001b[1;32m    520\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlookup_lines\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 521\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_load_lines\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    522\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/traceback.py\u001b[0m in \u001b[0;36m_load_lines\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    532\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__context__\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 533\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__context__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_load_lines\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    534\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__cause__\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/traceback.py\u001b[0m in \u001b[0;36m_load_lines\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    530\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mframe\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstack\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 531\u001b[0;31m             \u001b[0mframe\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    532\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__context__\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/traceback.py\u001b[0m in \u001b[0;36mline\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    284\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_line\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 285\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_line\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlinecache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlineno\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    286\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_line\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/linecache.py\u001b[0m in \u001b[0;36mgetline\u001b[0;34m(filename, lineno, module_globals)\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mgetline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlineno\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodule_globals\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m     \u001b[0mlines\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetlines\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodule_globals\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     17\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0mlineno\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlines\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/linecache.py\u001b[0m in \u001b[0;36mgetlines\u001b[0;34m(filename, module_globals)\u001b[0m\n\u001b[1;32m     46\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 47\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mupdatecache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodule_globals\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     48\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mMemoryError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/linecache.py\u001b[0m in \u001b[0;36mupdatecache\u001b[0;34m(filename, module_globals)\u001b[0m\n\u001b[1;32m    135\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 136\u001b[0;31m         \u001b[0;32mwith\u001b[0m \u001b[0mtokenize\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfullname\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mfp\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    137\u001b[0m             \u001b[0mlines\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreadlines\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/tokenize.py\u001b[0m in \u001b[0;36mopen\u001b[0;34m(filename)\u001b[0m\n\u001b[1;32m    448\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 449\u001b[0;31m         \u001b[0mencoding\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlines\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdetect_encoding\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbuffer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreadline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    450\u001b[0m         \u001b[0mbuffer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mseek\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/tokenize.py\u001b[0m in \u001b[0;36mdetect_encoding\u001b[0;34m(readline)\u001b[0m\n\u001b[1;32m    417\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 418\u001b[0;31m     \u001b[0mfirst\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mread_or_stop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    419\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfirst\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstartswith\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mBOM_UTF8\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/tokenize.py\u001b[0m in \u001b[0;36mread_or_stop\u001b[0;34m()\u001b[0m\n\u001b[1;32m    375\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 376\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mreadline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    377\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mStopIteration\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m: ",
-      "\nDuring handling of the above exception, another exception occurred:\n",
-      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mshowtraceback\u001b[0;34m(self, exc_tuple, filename, tb_offset, exception_only, running_compiled_code)\u001b[0m\n\u001b[1;32m   2043\u001b[0m                         \u001b[0;31m# in the engines. This should return a list of strings.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2044\u001b[0;31m                         \u001b[0mstb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_render_traceback_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2045\u001b[0m                     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mAttributeError\u001b[0m: 'KeyboardInterrupt' object has no attribute '_render_traceback_'",
-      "\nDuring handling of the above exception, another exception occurred:\n",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/IPython/core/async_helpers.py\u001b[0m in \u001b[0;36m_pseudo_sync_runner\u001b[0;34m(coro)\u001b[0m\n\u001b[1;32m     66\u001b[0m     \"\"\"\n\u001b[1;32m     67\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 68\u001b[0;31m         \u001b[0mcoro\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     69\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mStopIteration\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     70\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mrun_cell_async\u001b[0;34m(self, raw_cell, store_history, silent, shell_futures, transformed_cell, preprocessing_exc_tuple)\u001b[0m\n\u001b[1;32m   3144\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3145\u001b[0m                 has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n\u001b[0;32m-> 3146\u001b[0;31m                        interactivity=interactivity, compiler=compiler, result=result)\n\u001b[0m\u001b[1;32m   3147\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3148\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlast_execution_succeeded\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_raised\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mrun_ast_nodes\u001b[0;34m(self, nodelist, cell_name, interactivity, compiler, result)\u001b[0m\n\u001b[1;32m   3354\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3355\u001b[0m                 \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merror_before_exec\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexc_info\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3356\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshowtraceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3357\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3358\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mshowtraceback\u001b[0;34m(self, exc_tuple, filename, tb_offset, exception_only, running_compiled_code)\u001b[0m\n\u001b[1;32m   2045\u001b[0m                     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2046\u001b[0m                         stb = self.InteractiveTB.structured_traceback(etype,\n\u001b[0;32m-> 2047\u001b[0;31m                                             value, tb, tb_offset=tb_offset)\n\u001b[0m\u001b[1;32m   2048\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2049\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_showtraceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0metype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/IPython/core/ultratb.py\u001b[0m in \u001b[0;36mstructured_traceback\u001b[0;34m(self, etype, value, tb, tb_offset, number_of_lines_of_context)\u001b[0m\n\u001b[1;32m   1434\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1435\u001b[0m         return FormattedTB.structured_traceback(\n\u001b[0;32m-> 1436\u001b[0;31m             self, etype, value, tb, tb_offset, number_of_lines_of_context)\n\u001b[0m\u001b[1;32m   1437\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1438\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/IPython/core/ultratb.py\u001b[0m in \u001b[0;36mstructured_traceback\u001b[0;34m(self, etype, value, tb, tb_offset, number_of_lines_of_context)\u001b[0m\n\u001b[1;32m   1334\u001b[0m             \u001b[0;31m# Verbose modes need a full traceback\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1335\u001b[0m             return VerboseTB.structured_traceback(\n\u001b[0;32m-> 1336\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0metype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtb_offset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumber_of_lines_of_context\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1337\u001b[0m             )\n\u001b[1;32m   1338\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mmode\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'Minimal'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/IPython/core/ultratb.py\u001b[0m in \u001b[0;36mstructured_traceback\u001b[0;34m(self, etype, evalue, etb, tb_offset, number_of_lines_of_context)\u001b[0m\n\u001b[1;32m   1191\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1192\u001b[0m         formatted_exception = self.format_exception_as_a_whole(etype, evalue, etb, number_of_lines_of_context,\n\u001b[0;32m-> 1193\u001b[0;31m                                                                tb_offset)\n\u001b[0m\u001b[1;32m   1194\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1195\u001b[0m         \u001b[0mcolors\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mColors\u001b[0m  \u001b[0;31m# just a shorthand + quicker name lookup\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/IPython/core/ultratb.py\u001b[0m in \u001b[0;36mformat_exception_as_a_whole\u001b[0;34m(self, etype, evalue, etb, number_of_lines_of_context, tb_offset)\u001b[0m\n\u001b[1;32m   1148\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1149\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1150\u001b[0;31m         \u001b[0mlast_unique\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrecursion_repeat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfind_recursion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0morig_etype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrecords\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1151\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1152\u001b[0m         \u001b[0mframes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat_records\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrecords\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlast_unique\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrecursion_repeat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/IPython/core/ultratb.py\u001b[0m in \u001b[0;36mfind_recursion\u001b[0;34m(etype, value, records)\u001b[0m\n\u001b[1;32m    449\u001b[0m     \u001b[0;31m# first frame (from in to out) that looks different.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    450\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_recursion_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0metype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrecords\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 451\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrecords\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    452\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    453\u001b[0m     \u001b[0;31m# Select filename, lineno, func_name to track frames with\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mTypeError\u001b[0m: object of type 'NoneType' has no len()"
-     ]
-    }
-   ],
-   "source": [
-    "\n",
-    "dss_list = Parallel(n_jobs=10)(delayed(get_list)(d_list, l10.keys()) for d_list in tqdm(dct[\"diagnosis\"].values))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 309,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "diagnoses_df = pd.DataFrame(data=np.array(dss_list), columns=list(l10.keys()))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 310,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "diagnoses = pd.concat([dct.reset_index(), diagnoses_df], axis=1).set_index([\"eid\", \"t\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 311,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
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-       "\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th>diagnosis</th>\n",
-       "      <th>hypertensive_disorder_systemic_arterial</th>\n",
-       "      <th>hyperlipidemia</th>\n",
-       "      <th>depressive_disorder</th>\n",
-       "      <th>gastroesophageal_reflux_disease</th>\n",
-       "      <th>diabetes_mellitus_type_2</th>\n",
-       "      <th>essential_hypertension</th>\n",
-       "      <th>obesity</th>\n",
-       "      <th>diabetes_mellitus</th>\n",
-       "      <th>asthma</th>\n",
-       "      <th>...</th>\n",
-       "      <th>chronic_tonsillitis</th>\n",
-       "      <th>acute_duodenal_ulcer_with_hemorrhage</th>\n",
-       "      <th>hammer_toe</th>\n",
-       "      <th>malignant_tumor_of_cervix</th>\n",
-       "      <th>prolapsed_lumbar_intervertebral_disc</th>\n",
-       "      <th>hematemesis</th>\n",
-       "      <th>perianal_abscess</th>\n",
-       "      <th>nonvenomous_insect_bite</th>\n",
-       "      <th>spondylolisthesis</th>\n",
-       "      <th>malignant_tumor_of_esophagus</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>eid</th>\n",
-       "      <th>t</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"3\" valign=\"top\">1000018</th>\n",
-       "      <th>56.0</th>\n",
-       "      <td>[hypertensive_disorder_systemic_arterial, esse...</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>58.3</th>\n",
-       "      <td>[hypertensive_disorder_systemic_arterial, esse...</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>58.9</th>\n",
-       "      <td>[disorder_of_the_peripheral_nervous_system, vi...</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"2\" valign=\"top\">1000037</th>\n",
-       "      <th>53.3</th>\n",
-       "      <td>[osteoarthritis, arthritis, degenerative_joint...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>54.3</th>\n",
-       "      <td>[osteoarthritis, arthritis, degenerative_joint...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"3\" valign=\"top\">1099996</th>\n",
-       "      <th>54.3</th>\n",
-       "      <td>[cataract, injury_of_head, disorder_of_the_per...</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>55.2</th>\n",
-       "      <td>[headache, pain, urinary_incontinence, hyperte...</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>55.3</th>\n",
-       "      <td>[disorder_of_the_peripheral_nervous_system, my...</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"2\" valign=\"top\">1100009</th>\n",
-       "      <th>55.5</th>\n",
-       "      <td>[abdominal_pain, epigastric_pain, pain_in_pelv...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>66.3</th>\n",
-       "      <td>[urinary_tract_infectious_disease, urinary_inc...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>37467 rows × 791 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                      diagnosis  \\\n",
-       "eid     t                                                         \n",
-       "1000018 56.0  [hypertensive_disorder_systemic_arterial, esse...   \n",
-       "        58.3  [hypertensive_disorder_systemic_arterial, esse...   \n",
-       "        58.9  [disorder_of_the_peripheral_nervous_system, vi...   \n",
-       "1000037 53.3  [osteoarthritis, arthritis, degenerative_joint...   \n",
-       "        54.3  [osteoarthritis, arthritis, degenerative_joint...   \n",
-       "...                                                         ...   \n",
-       "1099996 54.3  [cataract, injury_of_head, disorder_of_the_per...   \n",
-       "        55.2  [headache, pain, urinary_incontinence, hyperte...   \n",
-       "        55.3  [disorder_of_the_peripheral_nervous_system, my...   \n",
-       "1100009 55.5  [abdominal_pain, epigastric_pain, pain_in_pelv...   \n",
-       "        66.3  [urinary_tract_infectious_disease, urinary_inc...   \n",
-       "\n",
-       "              hypertensive_disorder_systemic_arterial  hyperlipidemia  \\\n",
-       "eid     t                                                               \n",
-       "1000018 56.0                                     True           False   \n",
-       "        58.3                                     True           False   \n",
-       "        58.9                                     True           False   \n",
-       "1000037 53.3                                    False           False   \n",
-       "        54.3                                    False           False   \n",
-       "...                                               ...             ...   \n",
-       "1099996 54.3                                     True           False   \n",
-       "        55.2                                     True           False   \n",
-       "        55.3                                     True           False   \n",
-       "1100009 55.5                                    False           False   \n",
-       "        66.3                                    False           False   \n",
-       "\n",
-       "              depressive_disorder  gastroesophageal_reflux_disease  \\\n",
-       "eid     t                                                            \n",
-       "1000018 56.0                False                            False   \n",
-       "        58.3                False                            False   \n",
-       "        58.9                False                            False   \n",
-       "1000037 53.3                False                            False   \n",
-       "        54.3                False                            False   \n",
-       "...                           ...                              ...   \n",
-       "1099996 54.3                False                            False   \n",
-       "        55.2                False                            False   \n",
-       "        55.3                False                            False   \n",
-       "1100009 55.5                False                            False   \n",
-       "        66.3                False                            False   \n",
-       "\n",
-       "              diabetes_mellitus_type_2  essential_hypertension  obesity  \\\n",
-       "eid     t                                                                 \n",
-       "1000018 56.0                     False                    True    False   \n",
-       "        58.3                     False                    True    False   \n",
-       "        58.9                     False                    True    False   \n",
-       "1000037 53.3                     False                   False    False   \n",
-       "        54.3                     False                   False    False   \n",
-       "...                                ...                     ...      ...   \n",
-       "1099996 54.3                     False                    True    False   \n",
-       "        55.2                     False                    True    False   \n",
-       "        55.3                     False                    True    False   \n",
-       "1100009 55.5                     False                   False    False   \n",
-       "        66.3                     False                   False    False   \n",
-       "\n",
-       "              diabetes_mellitus  asthma  ...  chronic_tonsillitis  \\\n",
-       "eid     t                                ...                        \n",
-       "1000018 56.0              False   False  ...                False   \n",
-       "        58.3              False   False  ...                False   \n",
-       "        58.9              False   False  ...                False   \n",
-       "1000037 53.3              False   False  ...                False   \n",
-       "        54.3              False   False  ...                False   \n",
-       "...                         ...     ...  ...                  ...   \n",
-       "1099996 54.3              False   False  ...                False   \n",
-       "        55.2              False   False  ...                False   \n",
-       "        55.3              False   False  ...                False   \n",
-       "1100009 55.5              False   False  ...                False   \n",
-       "        66.3              False   False  ...                False   \n",
-       "\n",
-       "              acute_duodenal_ulcer_with_hemorrhage  hammer_toe  \\\n",
-       "eid     t                                                        \n",
-       "1000018 56.0                                 False       False   \n",
-       "        58.3                                 False       False   \n",
-       "        58.9                                 False       False   \n",
-       "1000037 53.3                                 False       False   \n",
-       "        54.3                                 False       False   \n",
-       "...                                            ...         ...   \n",
-       "1099996 54.3                                 False       False   \n",
-       "        55.2                                 False       False   \n",
-       "        55.3                                 False       False   \n",
-       "1100009 55.5                                 False       False   \n",
-       "        66.3                                 False       False   \n",
-       "\n",
-       "              malignant_tumor_of_cervix  prolapsed_lumbar_intervertebral_disc  \\\n",
-       "eid     t                                                                       \n",
-       "1000018 56.0                      False                                 False   \n",
-       "        58.3                      False                                 False   \n",
-       "        58.9                      False                                 False   \n",
-       "1000037 53.3                      False                                 False   \n",
-       "        54.3                      False                                 False   \n",
-       "...                                 ...                                   ...   \n",
-       "1099996 54.3                      False                                 False   \n",
-       "        55.2                      False                                 False   \n",
-       "        55.3                      False                                 False   \n",
-       "1100009 55.5                      False                                 False   \n",
-       "        66.3                      False                                 False   \n",
-       "\n",
-       "              hematemesis  perianal_abscess  nonvenomous_insect_bite  \\\n",
-       "eid     t                                                              \n",
-       "1000018 56.0        False             False                    False   \n",
-       "        58.3        False             False                    False   \n",
-       "        58.9        False             False                    False   \n",
-       "1000037 53.3        False             False                    False   \n",
-       "        54.3        False             False                    False   \n",
-       "...                   ...               ...                      ...   \n",
-       "1099996 54.3        False             False                    False   \n",
-       "        55.2        False             False                    False   \n",
-       "        55.3        False             False                    False   \n",
-       "1100009 55.5        False             False                    False   \n",
-       "        66.3        False             False                    False   \n",
-       "\n",
-       "              spondylolisthesis  malignant_tumor_of_esophagus  \n",
-       "eid     t                                                      \n",
-       "1000018 56.0              False                         False  \n",
-       "        58.3              False                         False  \n",
-       "        58.9              False                         False  \n",
-       "1000037 53.3              False                         False  \n",
-       "        54.3              False                         False  \n",
-       "...                         ...                           ...  \n",
-       "1099996 54.3              False                         False  \n",
-       "        55.2              False                         False  \n",
-       "        55.3              False                         False  \n",
-       "1100009 55.5              False                         False  \n",
-       "        66.3              False                         False  \n",
-       "\n",
-       "[37467 rows x 791 columns]"
-      ]
-     },
-     "execution_count": 311,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "diagnoses"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 169,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from joblib import Parallel, delayed\n",
-    "from functools import reduce\n",
-    "from numba import jit\n",
-    "\n",
-    "def had_diagnosis_before_per_ph(df_before, ph, ph_codes, temp):\n",
-    "   # regex = \"|\".join(ph_codes)\n",
-    "    #df_ph = df_before.set_index(\"meaning\").loc[ph_codes][[\"eid\"]]\\\n",
-    "    #        .drop_duplicates(subset=[\"eid\"])\\\n",
-    "    #        .assign(phenotype=True) \n",
-    "    df_ph = df_before[df_before.meaning.isin(ph_codes)][[\"eid\"]]\\\n",
-    "            .drop_duplicates(subset=[\"eid\"])\\\n",
-    "            .assign(phenotype=True) \n",
-    "    #df_ph = df_before[df_before.meaning.str.contains(regex, case=False)][[\"eid\"]]\\\n",
-    "    #        .drop_duplicates(subset=[\"eid\"])\\\n",
-    "    #        .assign(phenotype=True)   \n",
-    "    return temp.merge(df_ph, how=\"left\", on=\"eid\").fillna(False).phenotype\n",
-    "\n",
-    "def had_diagnosis_before(data, diagnoses_codes, phenotypes, time0=time0_col):\n",
-    "    diagnoses_codes = diagnoses_codes[diagnoses_codes.eid.isin(data.eid.to_list())].reset_index(drop=True)\n",
-    "    diagnoses_codes_time = diagnoses_codes.merge(data[[\"eid\", time0]], how=\"left\", on=\"eid\")\n",
-    "    \n",
-    "    display(diagnoses_codes_time)\n",
-    "    \n",
-    "    temp = data[[\"eid\"]].copy()\n",
-    "    df_before = diagnoses_codes_time[diagnoses_codes_time.date < diagnoses_codes_time[time0]]\n",
-    "                                                                                         \n",
-    "    df_phs = Parallel(n_jobs=20, require=\"sharedmem\")(delayed(had_diagnosis_before_per_ph)(df_before, ph, phenotypes[ph], temp) for ph in tqdm(list(phenotypes)))\n",
-    "    for ph, df_ph_series in zip(tqdm(list(phenotypes)), df_phs): temp[ph] = df_ph_series#temp.merge(df_ph, how=\"left\", on=\"eid\").fillna(False).phenotype\n",
-    "    \n",
-    "    return temp.sort_values(\"eid\")  #reduce(lambda left,right: pd.merge(left,right,on=['eid'], how='left'), df_phs)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 170,
-   "metadata": {},
-   "outputs": [
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>origin</th>\n",
-       "      <th>instance</th>\n",
-       "      <th>n</th>\n",
-       "      <th>code</th>\n",
-       "      <th>meaning</th>\n",
-       "      <th>date</th>\n",
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-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>hes_icd10</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>S0240</td>\n",
-       "      <td>S02</td>\n",
-       "      <td>2005-06-02</td>\n",
-       "      <td>1960-11-12</td>\n",
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-       "      <th>1</th>\n",
-       "      <td>1000018</td>\n",
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-       "      <td>2</td>\n",
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-       "      <td>3</td>\n",
-       "      <td>K37</td>\n",
-       "      <td>K37</td>\n",
-       "      <td>1998-05-11</td>\n",
-       "      <td>1960-11-12</td>\n",
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-       "      <td>1000018</td>\n",
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-       "      <td>4</td>\n",
-       "      <td>K37</td>\n",
-       "      <td>K37</td>\n",
-       "      <td>1998-05-16</td>\n",
-       "      <td>1960-11-12</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>hes_icd10</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>5</td>\n",
-       "      <td>K37</td>\n",
-       "      <td>K37</td>\n",
-       "      <td>1998-06-01</td>\n",
-       "      <td>1960-11-12</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
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-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>252607</th>\n",
-       "      <td>1100009</td>\n",
-       "      <td>hes_icd10</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>7</td>\n",
-       "      <td>N393</td>\n",
-       "      <td>N39</td>\n",
-       "      <td>2015-02-11</td>\n",
-       "      <td>1948-11-05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>252608</th>\n",
-       "      <td>1100009</td>\n",
-       "      <td>hes_icd10</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>8</td>\n",
-       "      <td>G403</td>\n",
-       "      <td>G40</td>\n",
-       "      <td>2015-02-11</td>\n",
-       "      <td>1948-11-05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>252609</th>\n",
-       "      <td>1100009</td>\n",
-       "      <td>hes_icd10</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>9</td>\n",
-       "      <td>R945</td>\n",
-       "      <td>R94</td>\n",
-       "      <td>2015-02-11</td>\n",
-       "      <td>1948-11-05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>252610</th>\n",
-       "      <td>1100009</td>\n",
-       "      <td>hes_icd10</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>10</td>\n",
-       "      <td>K140</td>\n",
-       "      <td>K14</td>\n",
-       "      <td>2015-03-05</td>\n",
-       "      <td>1948-11-05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>252611</th>\n",
-       "      <td>1100009</td>\n",
-       "      <td>hes_icd10</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>11</td>\n",
-       "      <td>K137</td>\n",
-       "      <td>K13</td>\n",
-       "      <td>2015-03-05</td>\n",
-       "      <td>1948-11-05</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>252612 rows × 8 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            eid     origin  instance   n   code meaning        date  \\\n",
-       "0       1000018  hes_icd10       0.0   1  S0240     S02  2005-06-02   \n",
-       "1       1000018  hes_icd10       0.0   2   W188     W18  2005-06-02   \n",
-       "2       1000018  hes_icd10       0.0   3    K37     K37  1998-05-11   \n",
-       "3       1000018  hes_icd10       0.0   4    K37     K37  1998-05-16   \n",
-       "4       1000018  hes_icd10       0.0   5    K37     K37  1998-06-01   \n",
-       "...         ...        ...       ...  ..    ...     ...         ...   \n",
-       "252607  1100009  hes_icd10       0.0   7   N393     N39  2015-02-11   \n",
-       "252608  1100009  hes_icd10       0.0   8   G403     G40  2015-02-11   \n",
-       "252609  1100009  hes_icd10       0.0   9   R945     R94  2015-02-11   \n",
-       "252610  1100009  hes_icd10       0.0  10   K140     K14  2015-03-05   \n",
-       "252611  1100009  hes_icd10       0.0  11   K137     K13  2015-03-05   \n",
-       "\n",
-       "         birthdate  \n",
-       "0       1960-11-12  \n",
-       "1       1960-11-12  \n",
-       "2       1960-11-12  \n",
-       "3       1960-11-12  \n",
-       "4       1960-11-12  \n",
-       "...            ...  \n",
-       "252607  1948-11-05  \n",
-       "252608  1948-11-05  \n",
-       "252609  1948-11-05  \n",
-       "252610  1948-11-05  \n",
-       "252611  1948-11-05  \n",
-       "\n",
-       "[252612 rows x 8 columns]"
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-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "diagnoses = had_diagnosis_before(df_birthdate.reset_index(), diagnoses_codes, l10, time0=time0_col)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 168,
-   "metadata": {},
-   "outputs": [
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-       "      <th>hypertensive_disorder_systemic_arterial</th>\n",
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-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
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-       "      <td>False</td>\n",
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-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
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-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1000037</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
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-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000043</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
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-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
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-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
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-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
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-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
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-       "      <td>...</td>\n",
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-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
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-       "    <tr>\n",
-       "      <th>9995</th>\n",
-       "      <td>1099964</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9996</th>\n",
-       "      <td>1099977</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9997</th>\n",
-       "      <td>1099988</td>\n",
-       "      <td>False</td>\n",
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-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9998</th>\n",
-       "      <td>1099996</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9999</th>\n",
-       "      <td>1100009</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>10000 rows × 91 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          eid  hypertensive_disorder_systemic_arterial  hyperlipidemia  \\\n",
-       "0     1000018                                    False           False   \n",
-       "1     1000020                                    False           False   \n",
-       "2     1000037                                    False           False   \n",
-       "3     1000043                                    False           False   \n",
-       "4     1000051                                    False           False   \n",
-       "...       ...                                      ...             ...   \n",
-       "9995  1099964                                    False           False   \n",
-       "9996  1099977                                    False           False   \n",
-       "9997  1099988                                    False           False   \n",
-       "9998  1099996                                    False           False   \n",
-       "9999  1100009                                    False           False   \n",
-       "\n",
-       "      depressive_disorder  gastroesophageal_reflux_disease  \\\n",
-       "0                   False                            False   \n",
-       "1                   False                            False   \n",
-       "2                   False                            False   \n",
-       "3                   False                            False   \n",
-       "4                   False                            False   \n",
-       "...                   ...                              ...   \n",
-       "9995                False                            False   \n",
-       "9996                False                            False   \n",
-       "9997                False                            False   \n",
-       "9998                False                            False   \n",
-       "9999                False                            False   \n",
-       "\n",
-       "      diabetes_mellitus_type_2  essential_hypertension  obesity  \\\n",
-       "0                        False                   False    False   \n",
-       "1                        False                   False    False   \n",
-       "2                        False                   False    False   \n",
-       "3                        False                   False    False   \n",
-       "4                        False                   False    False   \n",
-       "...                        ...                     ...      ...   \n",
-       "9995                     False                   False    False   \n",
-       "9996                     False                   False    False   \n",
-       "9997                     False                   False    False   \n",
-       "9998                     False                   False    False   \n",
-       "9999                     False                   False    False   \n",
-       "\n",
-       "      diabetes_mellitus  asthma  ...  fever  osteoarthritis_of_knee  \\\n",
-       "0                 False   False  ...  False                   False   \n",
-       "1                 False   False  ...  False                   False   \n",
-       "2                 False   False  ...  False                   False   \n",
-       "3                 False   False  ...  False                   False   \n",
-       "4                 False   False  ...  False                   False   \n",
-       "...                 ...     ...  ...    ...                     ...   \n",
-       "9995              False   False  ...  False                   False   \n",
-       "9996              False   False  ...  False                   False   \n",
-       "9997              False   False  ...  False                   False   \n",
-       "9998              False   False  ...  False                   False   \n",
-       "9999              False   False  ...  False                   False   \n",
-       "\n",
-       "      actinic_keratosis  urinary_incontinence  hemorrhoids  seizure  \\\n",
-       "0                 False                 False        False    False   \n",
-       "1                 False                 False        False    False   \n",
-       "2                 False                 False        False    False   \n",
-       "3                 False                 False        False    False   \n",
-       "4                 False                 False        False    False   \n",
-       "...                 ...                   ...          ...      ...   \n",
-       "9995              False                 False        False    False   \n",
-       "9996              False                 False        False    False   \n",
-       "9997              False                 False        False    False   \n",
-       "9998              False                 False        False    False   \n",
-       "9999              False                 False        False    False   \n",
-       "\n",
-       "      laceration_-_injury  glaucoma  body_mass_index_30+_-_obesity  \\\n",
-       "0                   False     False                          False   \n",
-       "1                   False     False                          False   \n",
-       "2                   False     False                          False   \n",
-       "3                   False     False                          False   \n",
-       "4                   False     False                          False   \n",
-       "...                   ...       ...                            ...   \n",
-       "9995                False     False                          False   \n",
-       "9996                False     False                          False   \n",
-       "9997                False     False                          False   \n",
-       "9998                False     False                          False   \n",
-       "9999                False     False                          False   \n",
-       "\n",
-       "      breast_lump  \n",
-       "0           False  \n",
-       "1           False  \n",
-       "2           False  \n",
-       "3           False  \n",
-       "4           False  \n",
-       "...           ...  \n",
-       "9995        False  \n",
-       "9996        False  \n",
-       "9997        False  \n",
-       "9998        False  \n",
-       "9999        False  \n",
-       "\n",
-       "[10000 rows x 91 columns]"
-      ]
-     },
-     "execution_count": 168,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "diagnoses"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "outputs": [],
-   "source": [
-    "diagnoses_codes.reset_index(drop=True).assign(eid = lambda x: x.eid.astype(int),\n",
-    "                                              origin = lambda x: x.origin.astype(str),\n",
-    "                                              instance = lambda x: x.instance.astype(int),\n",
-    "                                              n = lambda x: x.n.astype(int),\n",
-    "                                              code = lambda x: x.code.astype(str), \n",
-    "                                              meaning = lambda x: x.meaning.astype(str))\\\n",
-    "    .to_feather(os.path.join(path, dataset_path, 'temp_diagnoses_codes.feather'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "outputs": [],
-   "source": [
-    "diagnoses_codes = pd.read_feather(os.path.join(path, dataset_path, 'temp_diagnoses_codes.feather'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:46:26.189927Z",
-     "start_time": "2020-11-04T12:46:25.117069Z"
-    },
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from joblib import Parallel, delayed\n",
-    "from functools import reduce\n",
-    "from numba import jit\n",
-    "\n",
-    "def had_diagnosis_before_per_ph(df_before, ph, ph_codes, temp):\n",
-    "   # regex = \"|\".join(ph_codes)\n",
-    "    #df_ph = df_before.set_index(\"meaning\").loc[ph_codes][[\"eid\"]]\\\n",
-    "    #        .drop_duplicates(subset=[\"eid\"])\\\n",
-    "    #        .assign(phenotype=True) \n",
-    "    df_ph = df_before[df_before.meaning.isin(ph_codes)][[\"eid\"]]\\\n",
-    "            .drop_duplicates(subset=[\"eid\"])\\\n",
-    "            .assign(phenotype=True) \n",
-    "    #df_ph = df_before[df_before.meaning.str.contains(regex, case=False)][[\"eid\"]]\\\n",
-    "    #        .drop_duplicates(subset=[\"eid\"])\\\n",
-    "    #        .assign(phenotype=True)   \n",
-    "    return temp.merge(df_ph, how=\"left\", on=\"eid\").fillna(False).phenotype\n",
-    "\n",
-    "def had_diagnosis_before(data, diagnoses_codes, phenotypes, time0=time0_col):\n",
-    "    diagnoses_codes_time = diagnoses_codes.merge(data[[\"eid\", time0]], how=\"left\", on=\"eid\")\n",
-    "    \n",
-    "    temp = data[[\"eid\"]].copy()\n",
-    "    df_before = diagnoses_codes_time[diagnoses_codes_time.date < diagnoses_codes_time[time0]]\n",
-    "                                                                                         \n",
-    "    df_phs = Parallel(n_jobs=20, require=\"sharedmem\")(delayed(had_diagnosis_before_per_ph)(df_before, ph, phenotypes[ph], temp) for ph in tqdm(list(phenotypes)))\n",
-    "    for ph, df_ph_series in zip(tqdm(list(phenotypes)), df_phs): temp[ph] = df_ph_series#temp.merge(df_ph, how=\"left\", on=\"eid\").fillna(False).phenotype\n",
-    "    \n",
-    "    return temp.sort_values(\"eid\")  #reduce(lambda left,right: pd.merge(left,right,on=['eid'], how='left'), df_phs)."
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "source": [
-    "from joblib import Parallel, delayed\n",
-    "from functools import reduce\n",
-    "from numba import jit\n",
-    "\n",
-    "def had_diagnosis_before_per_ph(df_before, ph, ph_codes, temp):\n",
-    "    regex = \"|\".join(ph_codes)\n",
-    "    df_ph = df_before[df_before.meaning.str.contains(regex, case=False)][[\"eid\"]]\\\n",
-    "            .drop_duplicates(subset=[\"eid\"])\\\n",
-    "            .assign(phenotype=True)   \n",
-    "    return temp.merge(df_ph, how=\"left\", on=\"eid\").fillna(False).phenotype\n",
-    "\n",
-    "def had_diagnosis_before(data, diagnoses_codes, phenotypes, time0=time0_col):\n",
-    "    diagnoses_codes_time = diagnoses_codes.merge(data[[\"eid\", time0]], how=\"left\", on=\"eid\")\n",
-    "    \n",
-    "    temp = data[[\"eid\"]].copy()\n",
-    "    df_before = diagnoses_codes_time[diagnoses_codes_time.date < diagnoses_codes_time[time0]]\n",
-    "                                                                                         \n",
-    "    df_phs = Parallel(n_jobs=30, require=\"sharedmem\")(delayed(had_diagnosis_before_per_ph)(df_before, ph, phenotypes[ph], temp) for ph in tqdm(list(phenotypes)))\n",
-    "    for ph, ph_series in zip(tqdm(list(phenotypes)), df_phs): temp[ph] = ph_series\n",
-    "    \n",
-    "    return temp.sort_values(\"eid\")  #reduce(lambda left,right: pd.merge(left,right,on=['eid'], how='left'), df_phs)."
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "source": [
-    "phenotypes = l10\n",
-    "time0 = time0_col\n",
-    "diagnoses_codes_time = diagnoses_codes.merge(data[[\"eid\", time0]], how=\"left\", on=\"eid\")\n",
-    "\n",
-    "temp = data[[\"eid\"]].copy()\n",
-    "df_before = diagnoses_codes_time[diagnoses_codes_time.date < diagnoses_codes_time[time0]]\n",
-    "\n",
-    "df_phs = Parallel(n_jobs=10, require=\"sharedmem\")(delayed(had_diagnosis_before_per_ph)(df_before, ph, phenotypes[ph], temp) for ph in tqdm(list(phenotypes)[0:100]))"
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "source": [
-    "def had_diagnosis_before(data, diagnoses_codes, phenotypes, time0=time0_col):\n",
-    "    diagnoses_codes_time = diagnoses_codes.merge(data[[\"eid\", time0]], how=\"left\", on=\"eid\")\n",
-    "    \n",
-    "    temp = data[[\"eid\"]].copy()\n",
-    "    df_before = diagnoses_codes_time[diagnoses_codes_time.date < diagnoses_codes_time[time0]]\n",
-    "    for ph, ph_codes in tqdm(phenotypes.items()):\n",
-    "        regex = \"|\".join(ph_codes)\n",
-    "        df_ph = df_before[df_before.meaning.str.contains(regex, case=False)][[\"eid\"]]\\\n",
-    "            .drop_duplicates(subset=[\"eid\"])\\\n",
-    "            .assign(phenotype=True)\n",
-    "        temp[ph] = temp.merge(df_ph, how=\"left\", on=\"eid\").fillna(False).phenotype\n",
-    "       \n",
-    "    return temp.sort_values(\"eid\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T13:17:52.922023Z",
-     "start_time": "2020-11-04T12:46:26.191314Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
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-       "HBox(children=(FloatProgress(value=0.0, max=3521.0), HTML(value='')))"
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-     "output_type": "display_data"
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-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n"
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-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "502504\n"
-     ]
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-    {
-     "data": {
-      "text/html": [
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-       "\n",
-       "    .dataframe thead th {\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>coronary_heart_disease</th>\n",
-       "      <th>myocardial_infarction</th>\n",
-       "      <th>stroke</th>\n",
-       "      <th>diabetes1</th>\n",
-       "      <th>diabetes2</th>\n",
-       "      <th>chronic_kidney_disease</th>\n",
-       "      <th>atrial_fibrillation</th>\n",
-       "      <th>migraine</th>\n",
-       "      <th>rheumatoid_arthritis</th>\n",
-       "      <th>...</th>\n",
-       "      <th>sleep_terror_disorder</th>\n",
-       "      <th>acute_frontal_sinusitis</th>\n",
-       "      <th>benign_neoplasm_of_pancreas</th>\n",
-       "      <th>primary_malignant_neoplasm_of_soft_tissues_of_lower_limb</th>\n",
-       "      <th>neoplasm_of_uncertain_behavior_of_neck</th>\n",
-       "      <th>injury_of_peroneal_nerve</th>\n",
-       "      <th>dupuytren's_disease</th>\n",
-       "      <th>stem_cell_donor</th>\n",
-       "      <th>endemic_goiter</th>\n",
-       "      <th>diplegic_cerebral_palsy</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1000020</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1000037</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000043</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1000051</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 3522 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       eid  coronary_heart_disease  myocardial_infarction  stroke  diabetes1  \\\n",
-       "0  1000018                   False                  False   False      False   \n",
-       "1  1000020                   False                  False   False      False   \n",
-       "2  1000037                   False                  False   False      False   \n",
-       "3  1000043                   False                  False   False      False   \n",
-       "4  1000051                   False                  False   False      False   \n",
-       "\n",
-       "   diabetes2  chronic_kidney_disease  atrial_fibrillation  migraine  \\\n",
-       "0      False                   False                False     False   \n",
-       "1      False                   False                False     False   \n",
-       "2      False                   False                False     False   \n",
-       "3      False                   False                False     False   \n",
-       "4      False                   False                False     False   \n",
-       "\n",
-       "   rheumatoid_arthritis  ...  sleep_terror_disorder  acute_frontal_sinusitis  \\\n",
-       "0                 False  ...                  False                    False   \n",
-       "1                 False  ...                  False                    False   \n",
-       "2                 False  ...                  False                    False   \n",
-       "3                 False  ...                  False                    False   \n",
-       "4                 False  ...                  False                    False   \n",
-       "\n",
-       "   benign_neoplasm_of_pancreas  \\\n",
-       "0                        False   \n",
-       "1                        False   \n",
-       "2                        False   \n",
-       "3                        False   \n",
-       "4                        False   \n",
-       "\n",
-       "   primary_malignant_neoplasm_of_soft_tissues_of_lower_limb  \\\n",
-       "0                                              False          \n",
-       "1                                              False          \n",
-       "2                                              False          \n",
-       "3                                              False          \n",
-       "4                                              False          \n",
-       "\n",
-       "   neoplasm_of_uncertain_behavior_of_neck  injury_of_peroneal_nerve  \\\n",
-       "0                                   False                     False   \n",
-       "1                                   False                     False   \n",
-       "2                                   False                     False   \n",
-       "3                                   False                     False   \n",
-       "4                                   False                     False   \n",
-       "\n",
-       "   dupuytren's_disease  stem_cell_donor  endemic_goiter  \\\n",
-       "0                False            False           False   \n",
-       "1                False            False           False   \n",
-       "2                False            False           False   \n",
-       "3                False            False           False   \n",
-       "4                False            False           False   \n",
-       "\n",
-       "   diplegic_cerebral_palsy  \n",
-       "0                    False  \n",
-       "1                    False  \n",
-       "2                    False  \n",
-       "3                    False  \n",
-       "4                    False  \n",
-       "\n",
-       "[5 rows x 3522 columns]"
-      ]
-     },
-     "execution_count": 61,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "diagnoses = had_diagnosis_before(basics, diagnoses_codes, l10, time0=time0_col)\n",
-    "print(len(diagnoses))\n",
-    "\n",
-    "diagnoses.to_feather(os.path.join(path, dataset_path, 'temp_diagnoses.feather'))\n",
-    "\n",
-    "diagnoses.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "diagnoses = pd.read_feather(os.path.join(path, dataset_path, 'temp_diagnoses.feather'))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "source": [
-    "# Add embeddings for Snomed Diagnoses"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "source": [
-    "#### Get SNOMED - node2vec dict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "outputs": [],
-   "source": [
-    "snomed_embeddings = pd.read_csv(\"/data/analysis/ag-reils/steinfej/data/snomed_embeddings/snomed.emb.p1.q1.w20.l40.e200.graph_format.txt\", sep=\" \", header=None, skiprows=1)\n",
-    "snomed_embeddings.columns = [\"snomed_id\"]+list(snomed_embeddings.columns)[:-1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "metadata": {
-    "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true,
-     "source_hidden": true
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>snomed_id</th>\n",
-       "      <th>0</th>\n",
-       "      <th>1</th>\n",
-       "      <th>2</th>\n",
-       "      <th>3</th>\n",
-       "      <th>4</th>\n",
-       "      <th>5</th>\n",
-       "      <th>6</th>\n",
-       "      <th>7</th>\n",
-       "      <th>8</th>\n",
-       "      <th>...</th>\n",
-       "      <th>190</th>\n",
-       "      <th>191</th>\n",
-       "      <th>192</th>\n",
-       "      <th>193</th>\n",
-       "      <th>194</th>\n",
-       "      <th>195</th>\n",
-       "      <th>196</th>\n",
-       "      <th>197</th>\n",
-       "      <th>198</th>\n",
-       "      <th>199</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>129265001</td>\n",
-       "      <td>0.027456</td>\n",
-       "      <td>0.171026</td>\n",
-       "      <td>-0.178822</td>\n",
-       "      <td>-0.038667</td>\n",
-       "      <td>0.299777</td>\n",
-       "      <td>0.082540</td>\n",
-       "      <td>-0.201284</td>\n",
-       "      <td>0.215164</td>\n",
-       "      <td>0.093241</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.211030</td>\n",
-       "      <td>0.488475</td>\n",
-       "      <td>0.082177</td>\n",
-       "      <td>-0.047102</td>\n",
-       "      <td>-0.086411</td>\n",
-       "      <td>-0.012141</td>\n",
-       "      <td>-0.258416</td>\n",
-       "      <td>-0.113295</td>\n",
-       "      <td>0.040249</td>\n",
-       "      <td>-0.116884</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>360224006</td>\n",
-       "      <td>-0.090187</td>\n",
-       "      <td>0.086986</td>\n",
-       "      <td>-0.184378</td>\n",
-       "      <td>-0.028722</td>\n",
-       "      <td>-0.010235</td>\n",
-       "      <td>0.242439</td>\n",
-       "      <td>-0.094009</td>\n",
-       "      <td>0.203015</td>\n",
-       "      <td>-0.097894</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.112750</td>\n",
-       "      <td>0.333906</td>\n",
-       "      <td>-0.018193</td>\n",
-       "      <td>0.020459</td>\n",
-       "      <td>-0.170226</td>\n",
-       "      <td>0.277866</td>\n",
-       "      <td>-0.007079</td>\n",
-       "      <td>-0.080419</td>\n",
-       "      <td>0.162456</td>\n",
-       "      <td>-0.101768</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>102272007</td>\n",
-       "      <td>0.553420</td>\n",
-       "      <td>0.460947</td>\n",
-       "      <td>0.143925</td>\n",
-       "      <td>-0.053785</td>\n",
-       "      <td>-0.849588</td>\n",
-       "      <td>0.467587</td>\n",
-       "      <td>-0.654922</td>\n",
-       "      <td>-0.010799</td>\n",
-       "      <td>0.510141</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.187371</td>\n",
-       "      <td>0.248607</td>\n",
-       "      <td>0.079687</td>\n",
-       "      <td>-0.354121</td>\n",
-       "      <td>0.412602</td>\n",
-       "      <td>0.582461</td>\n",
-       "      <td>-0.780365</td>\n",
-       "      <td>0.045450</td>\n",
-       "      <td>-0.196551</td>\n",
-       "      <td>0.045745</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>39937001</td>\n",
-       "      <td>0.123158</td>\n",
-       "      <td>0.019882</td>\n",
-       "      <td>0.050896</td>\n",
-       "      <td>0.037364</td>\n",
-       "      <td>0.200435</td>\n",
-       "      <td>0.312911</td>\n",
-       "      <td>-0.338977</td>\n",
-       "      <td>-0.092584</td>\n",
-       "      <td>-0.167741</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.057770</td>\n",
-       "      <td>-0.012834</td>\n",
-       "      <td>-0.139794</td>\n",
-       "      <td>0.180572</td>\n",
-       "      <td>0.190781</td>\n",
-       "      <td>0.104039</td>\n",
-       "      <td>-0.358235</td>\n",
-       "      <td>-0.137954</td>\n",
-       "      <td>-0.236551</td>\n",
-       "      <td>-0.206458</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>23583003</td>\n",
-       "      <td>0.423193</td>\n",
-       "      <td>0.384338</td>\n",
-       "      <td>-0.041503</td>\n",
-       "      <td>0.116848</td>\n",
-       "      <td>-0.055029</td>\n",
-       "      <td>0.149064</td>\n",
-       "      <td>-0.092810</td>\n",
-       "      <td>-0.050148</td>\n",
-       "      <td>0.113122</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.162375</td>\n",
-       "      <td>-0.076505</td>\n",
-       "      <td>-0.274352</td>\n",
-       "      <td>-0.099204</td>\n",
-       "      <td>-0.281887</td>\n",
-       "      <td>-0.266345</td>\n",
-       "      <td>-0.020257</td>\n",
-       "      <td>-0.003843</td>\n",
-       "      <td>-0.008804</td>\n",
-       "      <td>-0.286832</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 201 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   snomed_id         0         1         2         3         4         5  \\\n",
-       "0  129265001  0.027456  0.171026 -0.178822 -0.038667  0.299777  0.082540   \n",
-       "1  360224006 -0.090187  0.086986 -0.184378 -0.028722 -0.010235  0.242439   \n",
-       "2  102272007  0.553420  0.460947  0.143925 -0.053785 -0.849588  0.467587   \n",
-       "3   39937001  0.123158  0.019882  0.050896  0.037364  0.200435  0.312911   \n",
-       "4   23583003  0.423193  0.384338 -0.041503  0.116848 -0.055029  0.149064   \n",
-       "\n",
-       "          6         7         8  ...       190       191       192       193  \\\n",
-       "0 -0.201284  0.215164  0.093241  ...  0.211030  0.488475  0.082177 -0.047102   \n",
-       "1 -0.094009  0.203015 -0.097894  ...  0.112750  0.333906 -0.018193  0.020459   \n",
-       "2 -0.654922 -0.010799  0.510141  ...  0.187371  0.248607  0.079687 -0.354121   \n",
-       "3 -0.338977 -0.092584 -0.167741  ...  0.057770 -0.012834 -0.139794  0.180572   \n",
-       "4 -0.092810 -0.050148  0.113122  ...  0.162375 -0.076505 -0.274352 -0.099204   \n",
-       "\n",
-       "        194       195       196       197       198       199  \n",
-       "0 -0.086411 -0.012141 -0.258416 -0.113295  0.040249 -0.116884  \n",
-       "1 -0.170226  0.277866 -0.007079 -0.080419  0.162456 -0.101768  \n",
-       "2  0.412602  0.582461 -0.780365  0.045450 -0.196551  0.045745  \n",
-       "3  0.190781  0.104039 -0.358235 -0.137954 -0.236551 -0.206458  \n",
-       "4 -0.281887 -0.266345 -0.020257 -0.003843 -0.008804 -0.286832  \n",
-       "\n",
-       "[5 rows x 201 columns]"
-      ]
-     },
-     "execution_count": 64,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "snomed_embeddings.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "outputs": [],
-   "source": [
-    "diagnoses_snomed = diagnoses.columns[13:].to_list()\n",
-    "diagnoses_snomed_dict = {}\n",
-    "for d in diagnoses_snomed: diagnoses_snomed_dict[d] = phenotype_list_snomed[d]\n",
-    "    \n",
-    "snomed_codes_used = list(phenotype_list_snomed.values())\n",
-    "snomed_codes_emb = snomed_embeddings.snomed_id.to_list()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "outputs": [],
-   "source": [
-    "snomed_id_array = snomed_embeddings[[\"snomed_id\"]].values\n",
-    "node2vec_array = snomed_embeddings.iloc[:, 1:].values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "metadata": {
-    "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true,
-     "source_hidden": true
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "456f56d7481648e48b74e9e76b675f01",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(FloatProgress(value=0.0, max=373286.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "snomed_arrays = {}\n",
-    "for sid, row in zip(tqdm(snomed_id_array), node2vec_array):\n",
-    "    snomed_arrays[sid[0]] = row"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "source": [
-    "#### Get Patient -> Snomed dict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 68,
-   "metadata": {
-    "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true,
-     "source_hidden": true
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "05ac46541972426da8a5b5f7fe461c6e",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(FloatProgress(value=0.0, max=502504.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "diagnoses_array = diagnoses[diagnoses_snomed].values\n",
-    "eid_array = diagnoses[[\"eid\"]].values\n",
-    "\n",
-    "from numba import jit\n",
-    "import numpy as np\n",
-    "\n",
-    "patient_diagnoses = {}\n",
-    "for eid, row in zip(tqdm(eid_array), diagnoses_array):\n",
-    "    patient_diagnoses[eid[0]] = list(np.argwhere(row==True).flatten())\n",
-    "\n",
-    "#diagnoses.query(\"eid==1000092\")[diagnoses_snomed]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 69,
-   "metadata": {
-    "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true,
-     "source_hidden": true
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f4c90d91d08e48119abcf8426cddd37e",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(FloatProgress(value=0.0, max=502504.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "patient_diagnoses_sid = {}\n",
-    "snomed_codes_emb_set = set(snomed_codes_emb)\n",
-    "for eid, p_d_col in tqdm(patient_diagnoses.items()):\n",
-    "    diagnoses_list = [diagnoses_snomed[i] for i in p_d_col]\n",
-    "    sid_list = [phenotype_list_snomed[i] for i in diagnoses_list]\n",
-    "    sid_list = [sid for sid in sid_list if sid in snomed_codes_emb_set]\n",
-    "    patient_diagnoses_sid[eid] = sid_list"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "source": [
-    "#### Get Patient -> node2vec average"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 70,
-   "metadata": {
-    "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true,
-     "source_hidden": true
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "47e934dc64334c82893d23b458108fbf",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(FloatProgress(value=0.0, max=502504.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/steinfej/miniconda3/envs/python/lib/python3.7/site-packages/numpy/core/fromnumeric.py:3335: RuntimeWarning: Mean of empty slice.\n",
-      "  out=out, **kwargs)\n",
-      "/home/steinfej/miniconda3/envs/python/lib/python3.7/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars\n",
-      "  ret = ret.dtype.type(ret / rcount)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "patient_node2vec_dict = {}\n",
-    "for eid, sids in tqdm(patient_diagnoses_sid.items()):\n",
-    "    array_list =  [snomed_arrays[sid] for sid in sids]\n",
-    "    patient_node2vec_dict[eid] = np.mean(array_list, axis=0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "metadata": {
-    "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true,
-     "source_hidden": true
-    }
-   },
-   "outputs": [
-    {
-     "data": {
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-       "        0.16787738, -0.2112512 , -0.10142653, -0.19002809, -0.06400223])"
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-     },
-     "execution_count": 71,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# create imputation vector\n",
-    "arrays = [patient_node2vec_dict[key] for key in list(patient_node2vec_dict)]\n",
-    "arrays_ok = [array for array in tqdm(arrays) if ~np.isnan(array).any()]\n",
-    "imp_vector = np.mean(arrays_ok, axis=0)\n",
-    "imp_vector"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
-   "metadata": {
-    "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true,
-     "source_hidden": true
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-   "outputs": [
-    {
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-     "metadata": {},
-     "output_type": "display_data"
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-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "for eid, array in tqdm(patient_node2vec_dict.items()):\n",
-    "    if np.isnan(array).any(): \n",
-    "        patient_node2vec_dict[eid] = imp_vector"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 73,
-   "metadata": {
-    "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true,
-     "source_hidden": true
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-   },
-   "outputs": [
-    {
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-     "output_type": "display_data"
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-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "array_eids = [key for key in tqdm(list(patient_node2vec_dict))]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 74,
-   "metadata": {
-    "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true,
-     "source_hidden": true
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-   "outputs": [
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-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "ind_ok = [np.array([1]) if ~np.isnan(array).any() else np.array([0])  for array in tqdm(arrays)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 75,
-   "metadata": {
-    "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true,
-     "source_hidden": true
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a85aec2f3e7644dc99c40781f999eb3c",
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-      "text/plain": [
-       "HBox(children=(FloatProgress(value=0.0, max=502504.0), HTML(value='')))"
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-     "metadata": {},
-     "output_type": "display_data"
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-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "arrays_emb = [patient_node2vec_dict[key] for key in tqdm(list(patient_node2vec_dict))]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 76,
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "outputs": [],
-   "source": [
-    "arrays_eids = np.reshape(np.stack(array_eids, axis=0),(-1,1)) \n",
-    "arrays_ind = np.stack(ind_ok, axis=0)\n",
-    "arrays_c = np.stack(arrays_emb, axis=0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 77,
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "outputs": [],
-   "source": [
-    "arrays_complete = np.concatenate([arrays_eids, arrays_ind, arrays_c], axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 78,
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "outputs": [],
-   "source": [
-    "diagnoses_emb = pd.DataFrame(data=arrays_complete, columns=[\"eid\"]+[\"node2vec_available\"]+[f\"node2vec_{e}\" for e in list(range(0, 200))])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 79,
-   "metadata": {
-    "collapsed": true,
-    "jupyter": {
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-       "      <td>-0.077222</td>\n",
-       "      <td>0.167877</td>\n",
-       "      <td>-0.211251</td>\n",
-       "      <td>-0.101427</td>\n",
-       "      <td>-0.190028</td>\n",
-       "      <td>-0.064002</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>502501</th>\n",
-       "      <td>6025173.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.370339</td>\n",
-       "      <td>0.133211</td>\n",
-       "      <td>-0.100303</td>\n",
-       "      <td>0.056253</td>\n",
-       "      <td>-0.084373</td>\n",
-       "      <td>0.115048</td>\n",
-       "      <td>-0.248332</td>\n",
-       "      <td>-0.119365</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.073037</td>\n",
-       "      <td>0.15891</td>\n",
-       "      <td>-0.047578</td>\n",
-       "      <td>-0.057776</td>\n",
-       "      <td>-0.077222</td>\n",
-       "      <td>0.167877</td>\n",
-       "      <td>-0.211251</td>\n",
-       "      <td>-0.101427</td>\n",
-       "      <td>-0.190028</td>\n",
-       "      <td>-0.064002</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>502502</th>\n",
-       "      <td>6025182.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.370339</td>\n",
-       "      <td>0.133211</td>\n",
-       "      <td>-0.100303</td>\n",
-       "      <td>0.056253</td>\n",
-       "      <td>-0.084373</td>\n",
-       "      <td>0.115048</td>\n",
-       "      <td>-0.248332</td>\n",
-       "      <td>-0.119365</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.073037</td>\n",
-       "      <td>0.15891</td>\n",
-       "      <td>-0.047578</td>\n",
-       "      <td>-0.057776</td>\n",
-       "      <td>-0.077222</td>\n",
-       "      <td>0.167877</td>\n",
-       "      <td>-0.211251</td>\n",
-       "      <td>-0.101427</td>\n",
-       "      <td>-0.190028</td>\n",
-       "      <td>-0.064002</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>502503</th>\n",
-       "      <td>6025198.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.370339</td>\n",
-       "      <td>0.133211</td>\n",
-       "      <td>-0.100303</td>\n",
-       "      <td>0.056253</td>\n",
-       "      <td>-0.084373</td>\n",
-       "      <td>0.115048</td>\n",
-       "      <td>-0.248332</td>\n",
-       "      <td>-0.119365</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.073037</td>\n",
-       "      <td>0.15891</td>\n",
-       "      <td>-0.047578</td>\n",
-       "      <td>-0.057776</td>\n",
-       "      <td>-0.077222</td>\n",
-       "      <td>0.167877</td>\n",
-       "      <td>-0.211251</td>\n",
-       "      <td>-0.101427</td>\n",
-       "      <td>-0.190028</td>\n",
-       "      <td>-0.064002</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>502504 rows × 202 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "              eid  node2vec_available  node2vec_0  node2vec_1  node2vec_2  \\\n",
-       "0       1000018.0                 0.0    0.370339    0.133211   -0.100303   \n",
-       "1       1000020.0                 0.0    0.370339    0.133211   -0.100303   \n",
-       "2       1000037.0                 0.0    0.370339    0.133211   -0.100303   \n",
-       "3       1000043.0                 0.0    0.370339    0.133211   -0.100303   \n",
-       "4       1000051.0                 0.0    0.370339    0.133211   -0.100303   \n",
-       "...           ...                 ...         ...         ...         ...   \n",
-       "502499  6025150.0                 0.0    0.370339    0.133211   -0.100303   \n",
-       "502500  6025165.0                 0.0    0.370339    0.133211   -0.100303   \n",
-       "502501  6025173.0                 0.0    0.370339    0.133211   -0.100303   \n",
-       "502502  6025182.0                 0.0    0.370339    0.133211   -0.100303   \n",
-       "502503  6025198.0                 0.0    0.370339    0.133211   -0.100303   \n",
-       "\n",
-       "        node2vec_3  node2vec_4  node2vec_5  node2vec_6  node2vec_7  ...  \\\n",
-       "0         0.056253   -0.084373    0.115048   -0.248332   -0.119365  ...   \n",
-       "1         0.056253   -0.084373    0.115048   -0.248332   -0.119365  ...   \n",
-       "2         0.056253   -0.084373    0.115048   -0.248332   -0.119365  ...   \n",
-       "3         0.056253   -0.084373    0.115048   -0.248332   -0.119365  ...   \n",
-       "4         0.056253   -0.084373    0.115048   -0.248332   -0.119365  ...   \n",
-       "...            ...         ...         ...         ...         ...  ...   \n",
-       "502499    0.056253   -0.084373    0.115048   -0.248332   -0.119365  ...   \n",
-       "502500    0.056253   -0.084373    0.115048   -0.248332   -0.119365  ...   \n",
-       "502501    0.056253   -0.084373    0.115048   -0.248332   -0.119365  ...   \n",
-       "502502    0.056253   -0.084373    0.115048   -0.248332   -0.119365  ...   \n",
-       "502503    0.056253   -0.084373    0.115048   -0.248332   -0.119365  ...   \n",
-       "\n",
-       "        node2vec_190  node2vec_191  node2vec_192  node2vec_193  node2vec_194  \\\n",
-       "0           0.073037       0.15891     -0.047578     -0.057776     -0.077222   \n",
-       "1           0.073037       0.15891     -0.047578     -0.057776     -0.077222   \n",
-       "2           0.073037       0.15891     -0.047578     -0.057776     -0.077222   \n",
-       "3           0.073037       0.15891     -0.047578     -0.057776     -0.077222   \n",
-       "4           0.073037       0.15891     -0.047578     -0.057776     -0.077222   \n",
-       "...              ...           ...           ...           ...           ...   \n",
-       "502499      0.073037       0.15891     -0.047578     -0.057776     -0.077222   \n",
-       "502500      0.073037       0.15891     -0.047578     -0.057776     -0.077222   \n",
-       "502501      0.073037       0.15891     -0.047578     -0.057776     -0.077222   \n",
-       "502502      0.073037       0.15891     -0.047578     -0.057776     -0.077222   \n",
-       "502503      0.073037       0.15891     -0.047578     -0.057776     -0.077222   \n",
-       "\n",
-       "        node2vec_195  node2vec_196  node2vec_197  node2vec_198  node2vec_199  \n",
-       "0           0.167877     -0.211251     -0.101427     -0.190028     -0.064002  \n",
-       "1           0.167877     -0.211251     -0.101427     -0.190028     -0.064002  \n",
-       "2           0.167877     -0.211251     -0.101427     -0.190028     -0.064002  \n",
-       "3           0.167877     -0.211251     -0.101427     -0.190028     -0.064002  \n",
-       "4           0.167877     -0.211251     -0.101427     -0.190028     -0.064002  \n",
-       "...              ...           ...           ...           ...           ...  \n",
-       "502499      0.167877     -0.211251     -0.101427     -0.190028     -0.064002  \n",
-       "502500      0.167877     -0.211251     -0.101427     -0.190028     -0.064002  \n",
-       "502501      0.167877     -0.211251     -0.101427     -0.190028     -0.064002  \n",
-       "502502      0.167877     -0.211251     -0.101427     -0.190028     -0.064002  \n",
-       "502503      0.167877     -0.211251     -0.101427     -0.190028     -0.064002  \n",
-       "\n",
-       "[502504 rows x 202 columns]"
-      ]
-     },
-     "execution_count": 79,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "diagnoses_emb"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "outputs": [
-    {
-     "ename": "FileNotFoundError",
-     "evalue": "[Errno 2] Failed to open local file '/data/analysis/ag-reils/ag-reils-shared/cardioRS/data/2_datasets_pre/cvd_massive_from_birth/temp_diagnoses_emb.feather'. Detail: [errno 2] No such file or directory",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-14-45107a9d4a69>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m#diagnoses_emb.to_feather(os.path.join(path, dataset_path, 'temp_diagnoses_emb.feather'))\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdiagnoses_emb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_feather\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdataset_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'temp_diagnoses_emb.feather'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m~/miniconda3/envs/python/lib/python3.7/site-packages/pandas/io/feather_format.py\u001b[0m in \u001b[0;36mread_feather\u001b[0;34m(path, columns, use_threads)\u001b[0m\n\u001b[1;32m    101\u001b[0m     \u001b[0mpath\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstringify_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    102\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 103\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mfeather\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_feather\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0muse_threads\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbool\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0muse_threads\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m~/miniconda3/envs/python/lib/python3.7/site-packages/pyarrow/feather.py\u001b[0m in \u001b[0;36mread_feather\u001b[0;34m(source, columns, use_threads, memory_map)\u001b[0m\n\u001b[1;32m    212\u001b[0m     \"\"\"\n\u001b[1;32m    213\u001b[0m     \u001b[0m_check_pandas_version\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 214\u001b[0;31m     return (read_table(source, columns=columns, memory_map=memory_map)\n\u001b[0m\u001b[1;32m    215\u001b[0m             .to_pandas(use_threads=use_threads))\n\u001b[1;32m    216\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/python/lib/python3.7/site-packages/pyarrow/feather.py\u001b[0m in \u001b[0;36mread_table\u001b[0;34m(source, columns, memory_map)\u001b[0m\n\u001b[1;32m    234\u001b[0m     \"\"\"\n\u001b[1;32m    235\u001b[0m     \u001b[0mreader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mFeatherReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 236\u001b[0;31m     \u001b[0mreader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msource\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0muse_memory_map\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmemory_map\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    237\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    238\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcolumns\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/python/lib/python3.7/site-packages/pyarrow/feather.pxi\u001b[0m in \u001b[0;36mpyarrow.lib.FeatherReader.open\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/python/lib/python3.7/site-packages/pyarrow/io.pxi\u001b[0m in \u001b[0;36mpyarrow.lib.get_reader\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/python/lib/python3.7/site-packages/pyarrow/io.pxi\u001b[0m in \u001b[0;36mpyarrow.lib._get_native_file\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/python/lib/python3.7/site-packages/pyarrow/io.pxi\u001b[0m in \u001b[0;36mpyarrow.lib.memory_map\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/python/lib/python3.7/site-packages/pyarrow/io.pxi\u001b[0m in \u001b[0;36mpyarrow.lib.MemoryMappedFile._open\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/python/lib/python3.7/site-packages/pyarrow/error.pxi\u001b[0m in \u001b[0;36mpyarrow.lib.pyarrow_internal_check_status\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/python/lib/python3.7/site-packages/pyarrow/error.pxi\u001b[0m in \u001b[0;36mpyarrow.lib.check_status\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] Failed to open local file '/data/analysis/ag-reils/ag-reils-shared/cardioRS/data/2_datasets_pre/cvd_massive_from_birth/temp_diagnoses_emb.feather'. Detail: [errno 2] No such file or directory"
-     ]
-    }
-   ],
-   "source": [
-    "diagnoses_emb.to_feather(os.path.join(path, dataset_path, 'temp_diagnoses_emb.feather'))\n",
-    "diagnoses_emb = pd.read_feather(os.path.join(path, dataset_path, 'temp_diagnoses_emb.feather'))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Endpoints"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "1+1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 89,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:39:55.628580Z",
-     "start_time": "2020-11-04T12:33:33.036Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "### define in snomed and get icd codes from there"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 1. Hospital admissions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:39:55.629459Z",
-     "start_time": "2020-11-04T12:33:34.764Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'myocardial_infarction': ['I21', 'I22', 'I23', 'I24', 'I25', 'I51'],\n",
-       " 'stroke': ['G45', 'G46', 'I60', 'I67', 'I68', 'I69'],\n",
-       " 'cancer_breast': ['C50'],\n",
-       " 'diabetes': ['E10', 'E11', 'E12', 'E13', 'E14'],\n",
-       " 'atrial_fibrillation': ['I47', 'I48'],\n",
-       " 'copd': ['J44'],\n",
-       " 'dementia': ['F00', 'F01', 'F02', 'F03', 'F09', 'G31', 'R54']}"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "endpoint_list = {\n",
-    "    \"myocardial_infarction\": [\"Myocardial infarction\"],\n",
-    "    \"stroke\": [\"Cerebrovascular disease\"],\n",
-    "    \"cancer_breast\" : [\"Breast Cancer\"],\n",
-    "    \"diabetes\" : [\"Diabetes\"],\n",
-    "    \"atrial_fibrillation\": [\"Atrial fibrillation\", \"Atrial flutter\", \"paroxysmal tachycardia\"],\n",
-    "    \"copd\": [\"COPD\"],\n",
-    "    \"dementia\":[\"dementia\"]\n",
-    "}\n",
-    "\n",
-    "endpoint_list = phenotype_children(phecodes, endpoint_list)\n",
-    "endpoint_list[\"cancer_breast\"] = [\"C50\"]\n",
-    "endpoint_list[\"copd\"] = [\"J44\"]\n",
-    "endpoint_list[\"diabetes\"] = [\"E10\", \"E11\", \"E12\", \"E13\", \"E14\"]\n",
-    "endpoint_list[\"atrial_fibrillation\"] = [\"I47\", \"I48\"]\n",
-    "\n",
-    "\n",
-    "with open(os.path.join(path, dataset_path, 'endpoint_list.yaml'), 'w') as file: yaml.dump(endpoint_list, file, default_flow_style=False)\n",
-    "endpoint_list"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from dateutil.relativedelta import relativedelta\n",
-    "\n",
-    "def extract_endpoints_tte(data, diagnoses_codes, endpoint_list, time0_col, level=None):\n",
-    "    if level is not None: diagnoses_codes = diagnoses_codes.query(\"level==@level\")\n",
-    "    diagnoses_codes_time0 = diagnoses_codes.merge(data[[\"eid\", time0_col]], how=\"left\", on=\"eid\")\n",
-    "    \n",
-    "    cens_time_right = min(diagnoses_codes.sort_values('date').groupby('origin').tail(1).date.to_list())\n",
-    "    print(f\"t_0: {time0_col}\")\n",
-    "    print(f\"t_cens: {cens_time_right}\")\n",
-    "    \n",
-    "    df_interval = diagnoses_codes_time0[(diagnoses_codes_time0.date > diagnoses_codes_time0[time0_col]) & \n",
-    "                                        (diagnoses_codes_time0.date < cens_time_right)]\n",
-    "    \n",
-    "    temp = data[[\"eid\", time0_col]].copy()\n",
-    "    for ph, ph_codes in tqdm(endpoint_list.items()):\n",
-    "        regex = \"|\".join(ph_codes)\n",
-    "        ph_df = df_interval[df_interval.meaning.str.contains(regex, case=False)] \\\n",
-    "            .sort_values('date').groupby('eid').head(1).assign(phenotype=1, date=lambda x: x.date)\n",
-    "        temp_ph = temp.merge(ph_df, how=\"left\", on=\"eid\").fillna(0)\n",
-    "        temp[ph+\"_event\"], temp[ph+\"_event_date\"] = temp_ph.phenotype, temp_ph.date\n",
-    "        \n",
-    "        fill_date = {ph+\"_event_date\" : lambda x: [cens_time_right if event==0 else event_date for event, event_date in zip(x[ph+\"_event\"], x[ph+\"_event_date\"])]}\n",
-    "        calc_tte = {ph+\"_event_time\" : lambda x: [(event_date-time0).days/365.25  for time0, event_date in zip(x[time0_col], x[ph+\"_event_date\"])]}\n",
-    "        \n",
-    "        temp = temp.assign(**fill_date).assign(**calc_tte).drop([ph+\"_event_date\"], axis=1)\n",
-    "        \n",
-    "    temp = temp.drop([time0_col], axis=1)     \n",
-    "    \n",
-    "    return temp.drop_duplicates()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "t_0: birth_date\n",
-      "t_cens: 2020-03-14\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6a02ec6d409c4e998c0f1deb9032b535",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(FloatProgress(value=0.0, max=7.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "502504\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>myocardial_infarction_event</th>\n",
-       "      <th>myocardial_infarction_event_time</th>\n",
-       "      <th>stroke_event</th>\n",
-       "      <th>stroke_event_time</th>\n",
-       "      <th>cancer_breast_event</th>\n",
-       "      <th>cancer_breast_event_time</th>\n",
-       "      <th>diabetes_event</th>\n",
-       "      <th>diabetes_event_time</th>\n",
-       "      <th>atrial_fibrillation_event</th>\n",
-       "      <th>atrial_fibrillation_event_time</th>\n",
-       "      <th>copd_event</th>\n",
-       "      <th>copd_event_time</th>\n",
-       "      <th>dementia_event</th>\n",
-       "      <th>dementia_event_time</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>59.334702</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>59.334702</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>59.334702</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>59.334702</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>59.334702</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>59.334702</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>59.334702</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1000020</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>71.063655</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>71.063655</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>71.063655</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>71.063655</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>71.063655</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>71.063655</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>71.063655</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1000037</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>70.338125</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>70.338125</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>70.338125</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>70.338125</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>70.338125</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>70.338125</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>70.338125</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000043</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>68.123203</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>73.779603</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>73.779603</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>73.779603</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>73.779603</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>63.293634</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>73.779603</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1000051</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>64.761123</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>64.761123</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>64.761123</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>45.062286</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>64.761123</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>21.062286</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>64.761123</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       eid  myocardial_infarction_event  myocardial_infarction_event_time  \\\n",
-       "0  1000018                          0.0                         59.334702   \n",
-       "1  1000020                          0.0                         71.063655   \n",
-       "2  1000037                          0.0                         70.338125   \n",
-       "3  1000043                          1.0                         68.123203   \n",
-       "4  1000051                          0.0                         64.761123   \n",
-       "\n",
-       "   stroke_event  stroke_event_time  cancer_breast_event  \\\n",
-       "0           0.0          59.334702                  0.0   \n",
-       "1           0.0          71.063655                  0.0   \n",
-       "2           0.0          70.338125                  0.0   \n",
-       "3           0.0          73.779603                  0.0   \n",
-       "4           0.0          64.761123                  0.0   \n",
-       "\n",
-       "   cancer_breast_event_time  diabetes_event  diabetes_event_time  \\\n",
-       "0                 59.334702             0.0            59.334702   \n",
-       "1                 71.063655             0.0            71.063655   \n",
-       "2                 70.338125             0.0            70.338125   \n",
-       "3                 73.779603             0.0            73.779603   \n",
-       "4                 64.761123             1.0            45.062286   \n",
-       "\n",
-       "   atrial_fibrillation_event  atrial_fibrillation_event_time  copd_event  \\\n",
-       "0                        0.0                       59.334702         0.0   \n",
-       "1                        0.0                       71.063655         0.0   \n",
-       "2                        0.0                       70.338125         0.0   \n",
-       "3                        0.0                       73.779603         1.0   \n",
-       "4                        0.0                       64.761123         1.0   \n",
-       "\n",
-       "   copd_event_time  dementia_event  dementia_event_time  \n",
-       "0        59.334702             0.0            59.334702  \n",
-       "1        71.063655             0.0            71.063655  \n",
-       "2        70.338125             0.0            70.338125  \n",
-       "3        63.293634             0.0            73.779603  \n",
-       "4        21.062286             0.0            64.761123  "
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "endpoints_hospital = extract_endpoints_tte(basics, diagnoses_codes, endpoint_list, time0_col)\n",
-    "print(len(endpoints_hospital))\n",
-    "endpoints_hospital.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 2. Death registry"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "death_list = {\n",
-    "    \"death_allcause\":[],\n",
-    "    \"death_cvd\":['I{:02}'.format(ID+1) for ID in range(0, 98)],\n",
-    "}\n",
-    "\n",
-    "death_codes = pd.read_feather(f\"{data_path}/1_decoded/codes_death_records.feather\")#.drop(\"level\", axis=1)\n",
-    "\n",
-    "with open(os.path.join(path, dataset_path, 'death_list.yaml'), 'w') as file: yaml.dump(death_list, file, default_flow_style=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "t_0: birth_date\n",
-      "t_cens: 2020-06-28\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9ca18723f9984f399b46bce781a3f2dd",
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-       "version_minor": 0
-      },
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-       "HBox(children=(FloatProgress(value=0.0, max=2.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "endpoints_death = extract_endpoints_tte(basics, death_codes, death_list, time0_col, level=\"1\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## SCORES"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "scores_list = {\n",
-    "    \"SCORE\":['I{:02}'.format(ID) for ID in [10, 11, 12, 13, 14, 15, 20, 21, 22, 23, 24, 25, 44, 45, 46, 47, 48, 49, 50, 51, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73]],\n",
-    "    \"ASCVD\":['I{:02}'.format(ID) for ID in [20, 21, 22, 23, 24, 25, 63]],\n",
-    "    \"QRISK3\":[\"G45\", \"I20\", \"I21\", \"I22\", \"I23\", \"I24\", \"I25\", \"I63\", \"I64\"],\n",
-    "    \"MACE\":[\"G45\", \"I21\", \"I22\", \"I23\", \"I24\", \"I25\", \"I63\", \"I64\"],    \n",
-    "}\n",
-    "with open(os.path.join(path, dataset_path, 'scores_list.yaml'), 'w') as file: yaml.dump(scores_list, file, default_flow_style=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "scores_list_hospital = {}\n",
-    "scores_list_death = {}\n",
-    "for score, score_codes in scores_list.items():\n",
-    "    scores_list_hospital[\"hospital_\"+score] = score_codes\n",
-    "    scores_list_death[\"death_\"+score] = score_codes"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "t_0: birth_date\n",
-      "t_cens: 2020-03-14\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "17ed4f235a6e4cc08cdc2789bfc62d73",
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-       "version_minor": 0
-      },
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-       "HBox(children=(FloatProgress(value=0.0, max=4.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "t_0: birth_date\n",
-      "t_cens: 2020-06-28\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2bd6de2f761a464da6f8c6c8ba22a1d8",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(FloatProgress(value=0.0, max=4.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "endpoints_scores = {\n",
-    "    \"hospital\": extract_endpoints_tte(basics, diagnoses_codes, scores_list_hospital, time0_col=time0_col),\n",
-    "    \"death\": extract_endpoints_tte(basics, death_codes, scores_list_death, time0_col=time0_col, level=1)}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "endpoints_scores_all = endpoints_scores[\"hospital\"].merge(endpoints_scores[\"death\"], on=\"eid\", how=\"left\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### ESC SCORE (Conroy 2003)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "5323\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>SCORE_event</th>\n",
-       "      <th>SCORE_event_time</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>45</th>\n",
-       "      <td>1000463</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>74.165640</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>83</th>\n",
-       "      <td>1000841</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>76.005476</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>102</th>\n",
-       "      <td>1001031</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>75.537303</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>122</th>\n",
-       "      <td>1001237</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>50.132786</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>176</th>\n",
-       "      <td>1001777</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>72.238193</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         eid  SCORE_event  SCORE_event_time\n",
-       "45   1000463          1.0         74.165640\n",
-       "83   1000841          1.0         76.005476\n",
-       "102  1001031          1.0         75.537303\n",
-       "122  1001237          1.0         50.132786\n",
-       "176  1001777          1.0         72.238193"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "score = \"SCORE\"\n",
-    "\n",
-    "temp = endpoints_scores_all.filter(regex=\"eid|\"+score).rename(columns={\"death_SCORE_event\":\"SCORE_event\", \"death_SCORE_event_time\":\"SCORE_event_time\"})\n",
-    "score_SCORE = temp = temp[[\"eid\", \"SCORE_event\", \"SCORE_event_time\"]]\n",
-    "print(len(temp.query(score+\"_event==1\")))\n",
-    "temp.query(score+\"_event==1\").head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### ASCVD (Goff 2014)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "61785\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>ASCVD_event</th>\n",
-       "      <th>ASCVD_event_time</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1000037</td>\n",
-       "      <td>1</td>\n",
-       "      <td>66.970568</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000043</td>\n",
-       "      <td>1</td>\n",
-       "      <td>68.123203</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>1000066</td>\n",
-       "      <td>1</td>\n",
-       "      <td>60.944559</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>1000079</td>\n",
-       "      <td>1</td>\n",
-       "      <td>61.054073</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>1000233</td>\n",
-       "      <td>1</td>\n",
-       "      <td>68.673511</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        eid  ASCVD_event  ASCVD_event_time\n",
-       "2   1000037            1         66.970568\n",
-       "3   1000043            1         68.123203\n",
-       "5   1000066            1         60.944559\n",
-       "6   1000079            1         61.054073\n",
-       "22  1000233            1         68.673511"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "score = \"ASCVD\"\n",
-    "temp = endpoints_scores_all.filter(regex=\"eid|\"+score)\n",
-    "\n",
-    "aggr_event = {score +\"_event\" : lambda x: [1 if (hospital_event==1) | (death_event == 1) else 0 \n",
-    "                                                    for hospital_event, death_event in zip(x[\"hospital_\"+score+\"_event\"], x[\"death_\"+score+\"_event\"])]}\n",
-    "aggr_date = {score +\"_event_time\" : lambda x: [min(hospital_event_time, death_event_time)\n",
-    "                                                        for hospital_event_time, death_event_time in zip(x[\"hospital_\"+score+\"_event_time\"], x[\"death_\"+score+\"_event_time\"])]}\n",
-    "\n",
-    "score_ASCVD = temp = temp.assign(**aggr_event).assign(**aggr_date)[[\"eid\", score +\"_event\", score +\"_event_time\"]]\n",
-    "print(len(temp.query(score+\"_event==1\")))\n",
-    "temp.query(score+\"_event==1\").head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### UK QRISK3 (2017)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "69344\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>QRISK3_event</th>\n",
-       "      <th>QRISK3_event_time</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1000037</td>\n",
-       "      <td>1</td>\n",
-       "      <td>66.970568</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000043</td>\n",
-       "      <td>1</td>\n",
-       "      <td>68.123203</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>1000066</td>\n",
-       "      <td>1</td>\n",
-       "      <td>60.944559</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>1000079</td>\n",
-       "      <td>1</td>\n",
-       "      <td>61.054073</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>1000233</td>\n",
-       "      <td>1</td>\n",
-       "      <td>68.673511</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        eid  QRISK3_event  QRISK3_event_time\n",
-       "2   1000037             1          66.970568\n",
-       "3   1000043             1          68.123203\n",
-       "5   1000066             1          60.944559\n",
-       "6   1000079             1          61.054073\n",
-       "22  1000233             1          68.673511"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "score = \"QRISK3\"\n",
-    "temp = endpoints_scores_all.filter(regex=\"eid|\"+score)\n",
-    "\n",
-    "aggr_event = {score +\"_event\" : lambda x: [1 if (hospital_event==1) | (death_event == 1) else 0 \n",
-    "                                                    for hospital_event, death_event in zip(x[\"hospital_\"+score+\"_event\"], x[\"death_\"+score+\"_event\"])]}\n",
-    "aggr_date = {score +\"_event_time\" : lambda x: [min(hospital_event_time, death_event_time)\n",
-    "                                                        for hospital_event_time, death_event_time in zip(x[\"hospital_\"+score+\"_event_time\"], x[\"death_\"+score+\"_event_time\"])]}\n",
-    "\n",
-    "score_QRISK3 = temp = temp.assign(**aggr_event).assign(**aggr_date)[[\"eid\", score +\"_event\", score +\"_event_time\"]]\n",
-    "print(len(temp.query(score+\"_event==1\")))\n",
-    "temp.query(score+\"_event==1\").head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### MACE (2020)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "62097\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>MACE_event</th>\n",
-       "      <th>MACE_event_time</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000043</td>\n",
-       "      <td>1</td>\n",
-       "      <td>68.123203</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>1000233</td>\n",
-       "      <td>1</td>\n",
-       "      <td>68.673511</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30</th>\n",
-       "      <td>1000319</td>\n",
-       "      <td>1</td>\n",
-       "      <td>56.922656</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>45</th>\n",
-       "      <td>1000463</td>\n",
-       "      <td>1</td>\n",
-       "      <td>74.069815</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>53</th>\n",
-       "      <td>1000548</td>\n",
-       "      <td>1</td>\n",
-       "      <td>50.255989</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        eid  MACE_event  MACE_event_time\n",
-       "3   1000043           1        68.123203\n",
-       "22  1000233           1        68.673511\n",
-       "30  1000319           1        56.922656\n",
-       "45  1000463           1        74.069815\n",
-       "53  1000548           1        50.255989"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "score = \"MACE\"\n",
-    "temp = endpoints_scores_all.filter(regex=\"eid|\"+score)\n",
-    "\n",
-    "aggr_event = {score +\"_event\" : lambda x: [1 if (hospital_event==1) | (death_event == 1) else 0 \n",
-    "                                                    for hospital_event, death_event in zip(x[\"hospital_\"+score+\"_event\"], x[\"death_\"+score+\"_event\"])]}\n",
-    "aggr_date = {score +\"_event_time\" : lambda x: [min(hospital_event_time, death_event_time)\n",
-    "                                                        for hospital_event_time, death_event_time in zip(x[\"hospital_\"+score+\"_event_time\"], x[\"death_\"+score+\"_event_time\"])]}\n",
-    "\n",
-    "score_MACE = temp = temp.assign(**aggr_event).assign(**aggr_date)[[\"eid\", score +\"_event\", score +\"_event_time\"]]\n",
-    "print(len(temp.query(score+\"_event==1\")))\n",
-    "temp.query(score+\"_event==1\").head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Merge Everything"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_dfs_dict = {\"basics\": pd.read_feather(os.path.join(path, dataset_path, 'temp_basics.feather')), \n",
-    "                 \"questionnaire\": pd.read_feather(os.path.join(path, dataset_path, 'temp_questionnaire.feather')), \n",
-    "                 \"measurements\": pd.read_feather(os.path.join(path, dataset_path, 'temp_measurements.feather')), \n",
-    "                 \"labs\": pd.read_feather(os.path.join(path, dataset_path, 'temp_labs.feather')), \n",
-    "                 \"family_history\": pd.read_feather(os.path.join(path, dataset_path, 'temp_family_history.feather')), \n",
-    "                 \"diagnoses\": pd.read_feather(os.path.join(path, dataset_path, 'temp_diagnoses.feather')),\n",
-    "                # \"diagnoses_emb\": pd.read_feather(os.path.join(path, dataset_path, 'temp_diagnoses_emb.feather')), \n",
-    "                 \"medications\": pd.read_feather(os.path.join(path, dataset_path, 'temp_medications.feather')), \n",
-    "                 \"endpoints_hospital\":endpoints_hospital, \n",
-    "                 \"endpoints_death\":endpoints_death, \n",
-    "                 \"score_SCORE\":score_SCORE, \n",
-    "                 \"score_ASCVD\":score_ASCVD, \n",
-    "                 \"score_QRISK3\":score_QRISK3,\n",
-    "                 \"score_MACE\":score_MACE}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_cols_clean(df):\n",
-    "    df.columns = df.columns.str.replace(r'_0_0$', '').str.replace(r'_f[0-9]+$', '').str.replace(\"_automated_reading\", '')\n",
-    "    return df.columns\n",
-    "\n",
-    "def clean_df(df):\n",
-    "    df.columns = get_cols_clean(df)\n",
-    "    return df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "from functools import reduce\n",
-    "\n",
-    "data_baseline = reduce(lambda x, y: pd.merge(x, y, on = 'eid'), list(data_dfs_dict.values()))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_baseline = clean_df(data_baseline)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for col in [col for col in list(data_baseline.columns) if (\"_event\" in col) & (\"_time\" not in col)]:\n",
-    "    data_baseline[col] = data_baseline[col].astype(int)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "covariates = [col for col in list(data_baseline.columns) if not \"_event\" in col]\n",
-    "targets = [col for col in list(data_baseline.columns) if \"_event\" in col]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Exporting"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>age_at_recruitment</th>\n",
-       "      <th>sex</th>\n",
-       "      <th>ethnic_background</th>\n",
-       "      <th>townsend_deprivation_index_at_recruitment</th>\n",
-       "      <th>date_of_attending_assessment_centre</th>\n",
-       "      <th>birth_date</th>\n",
-       "      <th>overall_health_rating</th>\n",
-       "      <th>smoking_status</th>\n",
-       "      <th>alcohol_intake_frequency</th>\n",
-       "      <th>...</th>\n",
-       "      <th>death_cvd_event</th>\n",
-       "      <th>death_cvd_event_time</th>\n",
-       "      <th>SCORE_event</th>\n",
-       "      <th>SCORE_event_time</th>\n",
-       "      <th>ASCVD_event</th>\n",
-       "      <th>ASCVD_event_time</th>\n",
-       "      <th>QRISK3_event</th>\n",
-       "      <th>QRISK3_event_time</th>\n",
-       "      <th>MACE_event</th>\n",
-       "      <th>MACE_event_time</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>49.0</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>White</td>\n",
-       "      <td>-1.852930</td>\n",
-       "      <td>2009-11-12</td>\n",
-       "      <td>1960-11-12</td>\n",
-       "      <td>Fair</td>\n",
-       "      <td>Current</td>\n",
-       "      <td>Once or twice a week</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>59.624914</td>\n",
-       "      <td>0</td>\n",
-       "      <td>59.624914</td>\n",
-       "      <td>0</td>\n",
-       "      <td>59.334702</td>\n",
-       "      <td>0</td>\n",
-       "      <td>59.334702</td>\n",
-       "      <td>0</td>\n",
-       "      <td>59.334702</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1000020</td>\n",
-       "      <td>59.0</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>White</td>\n",
-       "      <td>0.204248</td>\n",
-       "      <td>2008-02-19</td>\n",
-       "      <td>1949-02-19</td>\n",
-       "      <td>Good</td>\n",
-       "      <td>Current</td>\n",
-       "      <td>Once or twice a week</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>71.353867</td>\n",
-       "      <td>0</td>\n",
-       "      <td>71.353867</td>\n",
-       "      <td>0</td>\n",
-       "      <td>71.063655</td>\n",
-       "      <td>0</td>\n",
-       "      <td>71.063655</td>\n",
-       "      <td>0</td>\n",
-       "      <td>71.063655</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1000037</td>\n",
-       "      <td>59.0</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>White</td>\n",
-       "      <td>-3.498860</td>\n",
-       "      <td>2008-11-11</td>\n",
-       "      <td>1949-11-11</td>\n",
-       "      <td>Good</td>\n",
-       "      <td>Previous</td>\n",
-       "      <td>Once or twice a week</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>70.628337</td>\n",
-       "      <td>0</td>\n",
-       "      <td>70.628337</td>\n",
-       "      <td>1</td>\n",
-       "      <td>66.970568</td>\n",
-       "      <td>1</td>\n",
-       "      <td>66.970568</td>\n",
-       "      <td>0</td>\n",
-       "      <td>70.338125</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000043</td>\n",
-       "      <td>63.0</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>White</td>\n",
-       "      <td>-5.351150</td>\n",
-       "      <td>2009-06-03</td>\n",
-       "      <td>1946-06-03</td>\n",
-       "      <td>Fair</td>\n",
-       "      <td>Previous</td>\n",
-       "      <td>Three or four times a week</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>74.069815</td>\n",
-       "      <td>0</td>\n",
-       "      <td>74.069815</td>\n",
-       "      <td>1</td>\n",
-       "      <td>68.123203</td>\n",
-       "      <td>1</td>\n",
-       "      <td>68.123203</td>\n",
-       "      <td>1</td>\n",
-       "      <td>68.123203</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1000051</td>\n",
-       "      <td>51.0</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>White</td>\n",
-       "      <td>-1.799080</td>\n",
-       "      <td>2006-06-10</td>\n",
-       "      <td>1955-06-10</td>\n",
-       "      <td>Poor</td>\n",
-       "      <td>Never</td>\n",
-       "      <td>One to three times a month</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>65.051335</td>\n",
-       "      <td>0</td>\n",
-       "      <td>65.051335</td>\n",
-       "      <td>0</td>\n",
-       "      <td>64.761123</td>\n",
-       "      <td>0</td>\n",
-       "      <td>64.761123</td>\n",
-       "      <td>0</td>\n",
-       "      <td>64.761123</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 3746 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       eid  age_at_recruitment     sex ethnic_background  \\\n",
-       "0  1000018                49.0  Female             White   \n",
-       "1  1000020                59.0    Male             White   \n",
-       "2  1000037                59.0  Female             White   \n",
-       "3  1000043                63.0    Male             White   \n",
-       "4  1000051                51.0  Female             White   \n",
-       "\n",
-       "   townsend_deprivation_index_at_recruitment  \\\n",
-       "0                                  -1.852930   \n",
-       "1                                   0.204248   \n",
-       "2                                  -3.498860   \n",
-       "3                                  -5.351150   \n",
-       "4                                  -1.799080   \n",
-       "\n",
-       "  date_of_attending_assessment_centre  birth_date overall_health_rating  \\\n",
-       "0                          2009-11-12  1960-11-12                  Fair   \n",
-       "1                          2008-02-19  1949-02-19                  Good   \n",
-       "2                          2008-11-11  1949-11-11                  Good   \n",
-       "3                          2009-06-03  1946-06-03                  Fair   \n",
-       "4                          2006-06-10  1955-06-10                  Poor   \n",
-       "\n",
-       "  smoking_status    alcohol_intake_frequency  ...  death_cvd_event  \\\n",
-       "0        Current        Once or twice a week  ...                0   \n",
-       "1        Current        Once or twice a week  ...                0   \n",
-       "2       Previous        Once or twice a week  ...                0   \n",
-       "3       Previous  Three or four times a week  ...                0   \n",
-       "4          Never  One to three times a month  ...                0   \n",
-       "\n",
-       "   death_cvd_event_time  SCORE_event  SCORE_event_time  ASCVD_event  \\\n",
-       "0             59.624914            0         59.624914            0   \n",
-       "1             71.353867            0         71.353867            0   \n",
-       "2             70.628337            0         70.628337            1   \n",
-       "3             74.069815            0         74.069815            1   \n",
-       "4             65.051335            0         65.051335            0   \n",
-       "\n",
-       "   ASCVD_event_time  QRISK3_event  QRISK3_event_time  MACE_event  \\\n",
-       "0         59.334702             0          59.334702           0   \n",
-       "1         71.063655             0          71.063655           0   \n",
-       "2         66.970568             1          66.970568           0   \n",
-       "3         68.123203             1          68.123203           1   \n",
-       "4         64.761123             0          64.761123           0   \n",
-       "\n",
-       "   MACE_event_time  \n",
-       "0        59.334702  \n",
-       "1        71.063655  \n",
-       "2        70.338125  \n",
-       "3        68.123203  \n",
-       "4        64.761123  \n",
-       "\n",
-       "[5 rows x 3746 columns]"
-      ]
-     },
-     "execution_count": 38,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data_baseline.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_cols = {}\n",
-    "for topic, df in data_dfs_dict.items(): \n",
-    "    data_cols[\"eid\"] = [\"admin\"]\n",
-    "    data_cols[topic]=list(get_cols_clean(df))[1:]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_cols_single = {}\n",
-    "for topic, columns in data_cols.items():\n",
-    "    for col in columns:\n",
-    "        data_cols_single[col] = topic"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>covariate</th>\n",
-       "      <th>dtype</th>\n",
-       "      <th>isTarget</th>\n",
-       "      <th>based_on</th>\n",
-       "      <th>aggr_fn</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>eid</td>\n",
-       "      <td>integer</td>\n",
-       "      <td>False</td>\n",
-       "      <td>eid</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2</td>\n",
-       "      <td>age_at_recruitment</td>\n",
-       "      <td>numeric</td>\n",
-       "      <td>False</td>\n",
-       "      <td>basics</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>sex</td>\n",
-       "      <td>category</td>\n",
-       "      <td>False</td>\n",
-       "      <td>basics</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4</td>\n",
-       "      <td>ethnic_background</td>\n",
-       "      <td>category</td>\n",
-       "      <td>False</td>\n",
-       "      <td>basics</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5</td>\n",
-       "      <td>townsend_deprivation_index_at_recruitment</td>\n",
-       "      <td>numeric</td>\n",
-       "      <td>False</td>\n",
-       "      <td>basics</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3741</th>\n",
-       "      <td>3742</td>\n",
-       "      <td>ASCVD_event_time</td>\n",
-       "      <td>numeric</td>\n",
-       "      <td>True</td>\n",
-       "      <td>score_ASCVD</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3742</th>\n",
-       "      <td>3743</td>\n",
-       "      <td>QRISK3_event</td>\n",
-       "      <td>integer</td>\n",
-       "      <td>True</td>\n",
-       "      <td>score_QRISK3</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3743</th>\n",
-       "      <td>3744</td>\n",
-       "      <td>QRISK3_event_time</td>\n",
-       "      <td>numeric</td>\n",
-       "      <td>True</td>\n",
-       "      <td>score_QRISK3</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3744</th>\n",
-       "      <td>3745</td>\n",
-       "      <td>MACE_event</td>\n",
-       "      <td>integer</td>\n",
-       "      <td>True</td>\n",
-       "      <td>score_MACE</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3745</th>\n",
-       "      <td>3746</td>\n",
-       "      <td>MACE_event_time</td>\n",
-       "      <td>numeric</td>\n",
-       "      <td>True</td>\n",
-       "      <td>score_MACE</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>3746 rows × 6 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        id                                  covariate     dtype  isTarget  \\\n",
-       "0        1                                        eid   integer     False   \n",
-       "1        2                         age_at_recruitment   numeric     False   \n",
-       "2        3                                        sex  category     False   \n",
-       "3        4                          ethnic_background  category     False   \n",
-       "4        5  townsend_deprivation_index_at_recruitment   numeric     False   \n",
-       "...    ...                                        ...       ...       ...   \n",
-       "3741  3742                           ASCVD_event_time   numeric      True   \n",
-       "3742  3743                               QRISK3_event   integer      True   \n",
-       "3743  3744                          QRISK3_event_time   numeric      True   \n",
-       "3744  3745                                 MACE_event   integer      True   \n",
-       "3745  3746                            MACE_event_time   numeric      True   \n",
-       "\n",
-       "          based_on  aggr_fn  \n",
-       "0              eid      NaN  \n",
-       "1           basics      NaN  \n",
-       "2           basics      NaN  \n",
-       "3           basics      NaN  \n",
-       "4           basics      NaN  \n",
-       "...            ...      ...  \n",
-       "3741   score_ASCVD      NaN  \n",
-       "3742  score_QRISK3      NaN  \n",
-       "3743  score_QRISK3      NaN  \n",
-       "3744    score_MACE      NaN  \n",
-       "3745    score_MACE      NaN  \n",
-       "\n",
-       "[3746 rows x 6 columns]"
-      ]
-     },
-     "execution_count": 41,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dtypes = {\"int32\":\"integer\", \"int64\":\"integer\", \"float64\":\"numeric\", \"category\":\"category\", \"object\":\"category\", \"bool\":\"logical\"}\n",
-    "desc_dict = {\"id\": [*range(1, len(data_baseline.columns.to_list())+1)] , \n",
-    "             \"covariate\": data_baseline.columns.to_list(), \n",
-    "             \"dtype\":[dtypes[str(col)] for col in data_baseline.dtypes.to_list()], \n",
-    "             \"isTarget\":[True if col in targets else False for col in data_baseline.columns.to_list()],\n",
-    "            \"based_on\":[topic for col, topic in data_cols_single.items()],\n",
-    "            \"aggr_fn\": [np.nan for col in data_baseline.columns.to_list()]}\n",
-    "data_baseline_description = pd.DataFrame.from_dict(desc_dict)\n",
-    "data_baseline_description"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Exclusion Criteria"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_baseline_excl = data_baseline.query(\"myocardial_infarction == False & coronary_heart_disease == False & statins == False\").reset_index(drop=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "\n",
-       "    .dataframe thead th {\n",
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-       "</style>\n",
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
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-       "      <th>age_at_recruitment</th>\n",
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-       "      <th>ethnic_background</th>\n",
-       "      <th>townsend_deprivation_index_at_recruitment</th>\n",
-       "      <th>date_of_attending_assessment_centre</th>\n",
-       "      <th>birth_date</th>\n",
-       "      <th>overall_health_rating</th>\n",
-       "      <th>smoking_status</th>\n",
-       "      <th>alcohol_intake_frequency</th>\n",
-       "      <th>...</th>\n",
-       "      <th>death_cvd_event</th>\n",
-       "      <th>death_cvd_event_time</th>\n",
-       "      <th>SCORE_event</th>\n",
-       "      <th>SCORE_event_time</th>\n",
-       "      <th>ASCVD_event</th>\n",
-       "      <th>ASCVD_event_time</th>\n",
-       "      <th>QRISK3_event</th>\n",
-       "      <th>QRISK3_event_time</th>\n",
-       "      <th>MACE_event</th>\n",
-       "      <th>MACE_event_time</th>\n",
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-       "      <td>Female</td>\n",
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-       "      <td>-1.852930</td>\n",
-       "      <td>2009-11-12</td>\n",
-       "      <td>1960-11-12</td>\n",
-       "      <td>Fair</td>\n",
-       "      <td>Current</td>\n",
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-       "      <td>59.624914</td>\n",
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-       "      <td>0.204248</td>\n",
-       "      <td>2008-02-19</td>\n",
-       "      <td>1949-02-19</td>\n",
-       "      <td>Good</td>\n",
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-       "      <td>Once or twice a week</td>\n",
-       "      <td>...</td>\n",
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-       "      <td>71.353867</td>\n",
-       "      <td>0</td>\n",
-       "      <td>71.353867</td>\n",
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-       "      <td>71.063655</td>\n",
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-       "      <th>2</th>\n",
-       "      <td>1000037</td>\n",
-       "      <td>59.0</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>White</td>\n",
-       "      <td>-3.498860</td>\n",
-       "      <td>2008-11-11</td>\n",
-       "      <td>1949-11-11</td>\n",
-       "      <td>Good</td>\n",
-       "      <td>Previous</td>\n",
-       "      <td>Once or twice a week</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>70.628337</td>\n",
-       "      <td>0</td>\n",
-       "      <td>70.628337</td>\n",
-       "      <td>1</td>\n",
-       "      <td>66.970568</td>\n",
-       "      <td>1</td>\n",
-       "      <td>66.970568</td>\n",
-       "      <td>0</td>\n",
-       "      <td>70.338125</td>\n",
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-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000043</td>\n",
-       "      <td>63.0</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>White</td>\n",
-       "      <td>-5.351150</td>\n",
-       "      <td>2009-06-03</td>\n",
-       "      <td>1946-06-03</td>\n",
-       "      <td>Fair</td>\n",
-       "      <td>Previous</td>\n",
-       "      <td>Three or four times a week</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>74.069815</td>\n",
-       "      <td>0</td>\n",
-       "      <td>74.069815</td>\n",
-       "      <td>1</td>\n",
-       "      <td>68.123203</td>\n",
-       "      <td>1</td>\n",
-       "      <td>68.123203</td>\n",
-       "      <td>1</td>\n",
-       "      <td>68.123203</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1000079</td>\n",
-       "      <td>60.0</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>White</td>\n",
-       "      <td>-2.708040</td>\n",
-       "      <td>2008-03-18</td>\n",
-       "      <td>1948-03-18</td>\n",
-       "      <td>Fair</td>\n",
-       "      <td>Never</td>\n",
-       "      <td>Once or twice a week</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>72.279261</td>\n",
-       "      <td>0</td>\n",
-       "      <td>72.279261</td>\n",
-       "      <td>1</td>\n",
-       "      <td>61.054073</td>\n",
-       "      <td>1</td>\n",
-       "      <td>61.054073</td>\n",
-       "      <td>0</td>\n",
-       "      <td>71.989049</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
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-       "    <tr>\n",
-       "      <th>402296</th>\n",
-       "      <td>6025150</td>\n",
-       "      <td>43.0</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>White</td>\n",
-       "      <td>0.046781</td>\n",
-       "      <td>2007-06-30</td>\n",
-       "      <td>1964-06-30</td>\n",
-       "      <td>Excellent</td>\n",
-       "      <td>Never</td>\n",
-       "      <td>Three or four times a week</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>55.994524</td>\n",
-       "      <td>0</td>\n",
-       "      <td>55.994524</td>\n",
-       "      <td>0</td>\n",
-       "      <td>55.704312</td>\n",
-       "      <td>0</td>\n",
-       "      <td>55.704312</td>\n",
-       "      <td>0</td>\n",
-       "      <td>55.704312</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>402297</th>\n",
-       "      <td>6025165</td>\n",
-       "      <td>45.0</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>White</td>\n",
-       "      <td>-2.107040</td>\n",
-       "      <td>2008-09-02</td>\n",
-       "      <td>1963-09-02</td>\n",
-       "      <td>Good</td>\n",
-       "      <td>Never</td>\n",
-       "      <td>Three or four times a week</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>56.821355</td>\n",
-       "      <td>0</td>\n",
-       "      <td>56.821355</td>\n",
-       "      <td>0</td>\n",
-       "      <td>56.531143</td>\n",
-       "      <td>0</td>\n",
-       "      <td>56.531143</td>\n",
-       "      <td>0</td>\n",
-       "      <td>56.531143</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>402298</th>\n",
-       "      <td>6025173</td>\n",
-       "      <td>57.0</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>White</td>\n",
-       "      <td>-1.827220</td>\n",
-       "      <td>2008-09-17</td>\n",
-       "      <td>1951-09-17</td>\n",
-       "      <td>Good</td>\n",
-       "      <td>Never</td>\n",
-       "      <td>Never</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>68.780287</td>\n",
-       "      <td>0</td>\n",
-       "      <td>68.780287</td>\n",
-       "      <td>0</td>\n",
-       "      <td>68.490075</td>\n",
-       "      <td>0</td>\n",
-       "      <td>68.490075</td>\n",
-       "      <td>0</td>\n",
-       "      <td>68.490075</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>402299</th>\n",
-       "      <td>6025182</td>\n",
-       "      <td>56.0</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>White</td>\n",
-       "      <td>-0.010764</td>\n",
-       "      <td>2010-07-01</td>\n",
-       "      <td>1954-07-01</td>\n",
-       "      <td>Excellent</td>\n",
-       "      <td>Previous</td>\n",
-       "      <td>Daily or almost daily</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>65.993155</td>\n",
-       "      <td>0</td>\n",
-       "      <td>65.993155</td>\n",
-       "      <td>0</td>\n",
-       "      <td>65.702943</td>\n",
-       "      <td>0</td>\n",
-       "      <td>65.702943</td>\n",
-       "      <td>0</td>\n",
-       "      <td>65.702943</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>402300</th>\n",
-       "      <td>6025198</td>\n",
-       "      <td>67.0</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>White</td>\n",
-       "      <td>-1.930650</td>\n",
-       "      <td>2010-01-26</td>\n",
-       "      <td>1943-01-26</td>\n",
-       "      <td>Good</td>\n",
-       "      <td>Current</td>\n",
-       "      <td>Daily or almost daily</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>77.420945</td>\n",
-       "      <td>0</td>\n",
-       "      <td>77.420945</td>\n",
-       "      <td>0</td>\n",
-       "      <td>77.130732</td>\n",
-       "      <td>0</td>\n",
-       "      <td>77.130732</td>\n",
-       "      <td>0</td>\n",
-       "      <td>77.130732</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>402301 rows × 3746 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            eid  age_at_recruitment     sex ethnic_background  \\\n",
-       "0       1000018                49.0  Female             White   \n",
-       "1       1000020                59.0    Male             White   \n",
-       "2       1000037                59.0  Female             White   \n",
-       "3       1000043                63.0    Male             White   \n",
-       "4       1000079                60.0  Female             White   \n",
-       "...         ...                 ...     ...               ...   \n",
-       "402296  6025150                43.0  Female             White   \n",
-       "402297  6025165                45.0  Female             White   \n",
-       "402298  6025173                57.0    Male             White   \n",
-       "402299  6025182                56.0    Male             White   \n",
-       "402300  6025198                67.0    Male             White   \n",
-       "\n",
-       "        townsend_deprivation_index_at_recruitment  \\\n",
-       "0                                       -1.852930   \n",
-       "1                                        0.204248   \n",
-       "2                                       -3.498860   \n",
-       "3                                       -5.351150   \n",
-       "4                                       -2.708040   \n",
-       "...                                           ...   \n",
-       "402296                                   0.046781   \n",
-       "402297                                  -2.107040   \n",
-       "402298                                  -1.827220   \n",
-       "402299                                  -0.010764   \n",
-       "402300                                  -1.930650   \n",
-       "\n",
-       "       date_of_attending_assessment_centre  birth_date overall_health_rating  \\\n",
-       "0                               2009-11-12  1960-11-12                  Fair   \n",
-       "1                               2008-02-19  1949-02-19                  Good   \n",
-       "2                               2008-11-11  1949-11-11                  Good   \n",
-       "3                               2009-06-03  1946-06-03                  Fair   \n",
-       "4                               2008-03-18  1948-03-18                  Fair   \n",
-       "...                                    ...         ...                   ...   \n",
-       "402296                          2007-06-30  1964-06-30             Excellent   \n",
-       "402297                          2008-09-02  1963-09-02                  Good   \n",
-       "402298                          2008-09-17  1951-09-17                  Good   \n",
-       "402299                          2010-07-01  1954-07-01             Excellent   \n",
-       "402300                          2010-01-26  1943-01-26                  Good   \n",
-       "\n",
-       "       smoking_status    alcohol_intake_frequency  ...  death_cvd_event  \\\n",
-       "0             Current        Once or twice a week  ...                0   \n",
-       "1             Current        Once or twice a week  ...                0   \n",
-       "2            Previous        Once or twice a week  ...                0   \n",
-       "3            Previous  Three or four times a week  ...                0   \n",
-       "4               Never        Once or twice a week  ...                0   \n",
-       "...               ...                         ...  ...              ...   \n",
-       "402296          Never  Three or four times a week  ...                0   \n",
-       "402297          Never  Three or four times a week  ...                0   \n",
-       "402298          Never                       Never  ...                0   \n",
-       "402299       Previous       Daily or almost daily  ...                0   \n",
-       "402300        Current       Daily or almost daily  ...                0   \n",
-       "\n",
-       "        death_cvd_event_time  SCORE_event  SCORE_event_time  ASCVD_event  \\\n",
-       "0                  59.624914            0         59.624914            0   \n",
-       "1                  71.353867            0         71.353867            0   \n",
-       "2                  70.628337            0         70.628337            1   \n",
-       "3                  74.069815            0         74.069815            1   \n",
-       "4                  72.279261            0         72.279261            1   \n",
-       "...                      ...          ...               ...          ...   \n",
-       "402296             55.994524            0         55.994524            0   \n",
-       "402297             56.821355            0         56.821355            0   \n",
-       "402298             68.780287            0         68.780287            0   \n",
-       "402299             65.993155            0         65.993155            0   \n",
-       "402300             77.420945            0         77.420945            0   \n",
-       "\n",
-       "        ASCVD_event_time  QRISK3_event  QRISK3_event_time  MACE_event  \\\n",
-       "0              59.334702             0          59.334702           0   \n",
-       "1              71.063655             0          71.063655           0   \n",
-       "2              66.970568             1          66.970568           0   \n",
-       "3              68.123203             1          68.123203           1   \n",
-       "4              61.054073             1          61.054073           0   \n",
-       "...                  ...           ...                ...         ...   \n",
-       "402296         55.704312             0          55.704312           0   \n",
-       "402297         56.531143             0          56.531143           0   \n",
-       "402298         68.490075             0          68.490075           0   \n",
-       "402299         65.702943             0          65.702943           0   \n",
-       "402300         77.130732             0          77.130732           0   \n",
-       "\n",
-       "        MACE_event_time  \n",
-       "0             59.334702  \n",
-       "1             71.063655  \n",
-       "2             70.338125  \n",
-       "3             68.123203  \n",
-       "4             71.989049  \n",
-       "...                 ...  \n",
-       "402296        55.704312  \n",
-       "402297        56.531143  \n",
-       "402298        68.490075  \n",
-       "402299        65.702943  \n",
-       "402300        77.130732  \n",
-       "\n",
-       "[402301 rows x 3746 columns]"
-      ]
-     },
-     "execution_count": 43,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data_baseline_excl"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "feature_dict = {}\n",
-    "for group in data_baseline_description.based_on.unique(): feature_dict[group] = data_baseline_description.query(\"based_on==@group\").covariate.to_list()\n",
-    "with open(os.path.join(path, dataset_path, 'feature_list.yaml'), 'w') as file: yaml.dump(feature_dict, file, default_flow_style=False, allow_unicode=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#feature_dict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "### WRITE FEATURES IN YAML!!!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_baseline.to_feather(os.path.join(path, dataset_path, 'baseline_clinical.feather'))\n",
-    "data_baseline_excl.to_feather(os.path.join(path, dataset_path, 'baseline_clinical_excl.feather'))\n",
-    "data_baseline_description.to_feather(os.path.join(path, dataset_path, 'baseline_clinical_description.feather'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#data_baseline.to_csv(os.path.join(path, dataset_path, 'baseline_clinical.csv'), index=False)\n",
-    "#data_baseline_description.to_csv(os.path.join(path, dataset_path, 'baseline_clinical_description.csv'), index=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# !!! REMEMBER IMPUTATION !!!"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python [conda env:miniconda3-pl1.x]",
-   "language": "python",
-   "name": "conda-env-miniconda3-pl1.x-py"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.8"
-  },
-  "toc-autonumbering": true,
-  "toc-showcode": false,
-  "toc-showmarkdowntxt": false
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/neuralcvd/preprocessing/ukbb_tabular/2_preprocessing_clinical_from_birth_PxMxT_cudf.ipynb b/neuralcvd/preprocessing/ukbb_tabular/2_preprocessing_clinical_from_birth_PxMxT_cudf.ipynb
deleted file mode 100644
index efd4d8e..0000000
--- a/neuralcvd/preprocessing/ukbb_tabular/2_preprocessing_clinical_from_birth_PxMxT_cudf.ipynb
+++ /dev/null
@@ -1,15079 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Preprocessing"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:31:49.436340Z",
-     "start_time": "2020-11-04T12:31:48.732042Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import os\n",
-    "import yaml\n",
-    "from tqdm.notebook import trange, tqdm\n",
-    "dataset_name = \"cvd_lifetime_time_series\"\n",
-    "path = \"/data/analysis/ag-reils/steinfej/code/umbrella/pre/ukbb\"\n",
-    "data_path = \"/data/analysis/ag-reils/ag-reils-shared/cardioRS/data\"\n",
-    "dataset_path = f\"{data_path}/2_datasets_pre/{dataset_name}\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:31:49.895222Z",
-     "start_time": "2020-11-04T12:31:49.891332Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from pathlib import Path\n",
-    "Path(dataset_path).mkdir(parents=True, exist_ok=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:33:14.171198Z",
-     "start_time": "2020-11-04T12:31:50.204540Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "%%time\n",
-    "\n",
-    "data = pd.read_feather(f\"{data_path}/1_decoded/ukb_data.feather\")\n",
-    "data_field = pd.read_feather(f\"{data_path}/1_decoded/ukb_data_field.feather\")\n",
-    "data_columns = data.columns.to_list()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>weight_method_f21_0_0</th>\n",
-       "      <th>weight_method_f21_1_0</th>\n",
-       "      <th>weight_method_f21_2_0</th>\n",
-       "      <th>weight_method_f21_3_0</th>\n",
-       "      <th>spirometry_method_f23_0_0</th>\n",
-       "      <th>spirometry_method_f23_1_0</th>\n",
-       "      <th>spirometry_method_f23_2_0</th>\n",
-       "      <th>spirometry_method_f23_3_0</th>\n",
-       "      <th>sex_f31_0_0</th>\n",
-       "      <th>...</th>\n",
-       "      <th>source_of_report_of_i85_oesophageal_varices_f131407_0_0</th>\n",
-       "      <th>source_of_report_of_i89_other_noninfective_disorders_of_lymphatic_vessels_and_lymph_nodes_f131415_0_0</th>\n",
-       "      <th>date_i95_first_reported_hypotension_f131416_0_0</th>\n",
-       "      <th>source_of_report_of_i95_hypotension_f131417_0_0</th>\n",
-       "      <th>date_i97_first_reported_postprocedural_disorders_of_circulatory_system_not_elsewhere_classified_f131418_0_0</th>\n",
-       "      <th>source_of_report_of_i97_postprocedural_disorders_of_circulatory_system_not_elsewhere_classified_f131419_0_0</th>\n",
-       "      <th>date_i98_first_reported_other_disorders_of_circulatory_system_in_diseases_classified_elsewhere_f131420_0_0</th>\n",
-       "      <th>source_of_report_of_i98_other_disorders_of_circulatory_system_in_diseases_classified_elsewhere_f131421_0_0</th>\n",
-       "      <th>date_i99_first_reported_other_and_unspecified_disorders_of_circulatory_system_f131422_0_0</th>\n",
-       "      <th>source_of_report_of_i99_other_and_unspecified_disorders_of_circulatory_system_f131423_0_0</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>Direct entry</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Direct entry</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1000020</td>\n",
-       "      <td>Direct entry</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Direct entry</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1000037</td>\n",
-       "      <td>Direct entry</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Direct entry</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000043</td>\n",
-       "      <td>Direct entry</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Direct entry</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Direct entry</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Direct entry</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1000051</td>\n",
-       "      <td>Direct entry</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
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-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>502500</th>\n",
-       "      <td>6025165</td>\n",
-       "      <td>Direct entry</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Direct entry</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>502501</th>\n",
-       "      <td>6025173</td>\n",
-       "      <td>Direct entry</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Direct entry</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>502502</th>\n",
-       "      <td>6025182</td>\n",
-       "      <td>Direct entry</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Direct entry</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2012-08-16</td>\n",
-       "      <td>Primary care only</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>502503</th>\n",
-       "      <td>6025198</td>\n",
-       "      <td>Direct entry</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Direct entry</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>502504</th>\n",
-       "      <td>6025200</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>502505 rows × 7162 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            eid weight_method_f21_0_0 weight_method_f21_1_0  \\\n",
-       "0       1000018          Direct entry                   NaN   \n",
-       "1       1000020          Direct entry                   NaN   \n",
-       "2       1000037          Direct entry                   NaN   \n",
-       "3       1000043          Direct entry                   NaN   \n",
-       "4       1000051          Direct entry                   NaN   \n",
-       "...         ...                   ...                   ...   \n",
-       "502500  6025165          Direct entry                   NaN   \n",
-       "502501  6025173          Direct entry                   NaN   \n",
-       "502502  6025182          Direct entry                   NaN   \n",
-       "502503  6025198          Direct entry                   NaN   \n",
-       "502504  6025200                   NaN                   NaN   \n",
-       "\n",
-       "       weight_method_f21_2_0 weight_method_f21_3_0 spirometry_method_f23_0_0  \\\n",
-       "0                        NaN                   NaN              Direct entry   \n",
-       "1                        NaN                   NaN              Direct entry   \n",
-       "2                        NaN                   NaN              Direct entry   \n",
-       "3               Direct entry                   NaN              Direct entry   \n",
-       "4                        NaN                   NaN                       NaN   \n",
-       "...                      ...                   ...                       ...   \n",
-       "502500                   NaN                   NaN              Direct entry   \n",
-       "502501                   NaN                   NaN              Direct entry   \n",
-       "502502                   NaN                   NaN              Direct entry   \n",
-       "502503                   NaN                   NaN              Direct entry   \n",
-       "502504                   NaN                   NaN                       NaN   \n",
-       "\n",
-       "       spirometry_method_f23_1_0 spirometry_method_f23_2_0  \\\n",
-       "0                            NaN                       NaN   \n",
-       "1                            NaN                       NaN   \n",
-       "2                            NaN                       NaN   \n",
-       "3                            NaN              Direct entry   \n",
-       "4                            NaN                       NaN   \n",
-       "...                          ...                       ...   \n",
-       "502500                       NaN                       NaN   \n",
-       "502501                       NaN                       NaN   \n",
-       "502502                       NaN                       NaN   \n",
-       "502503                       NaN                       NaN   \n",
-       "502504                       NaN                       NaN   \n",
-       "\n",
-       "       spirometry_method_f23_3_0 sex_f31_0_0  ...  \\\n",
-       "0                            NaN      Female  ...   \n",
-       "1                            NaN        Male  ...   \n",
-       "2                            NaN      Female  ...   \n",
-       "3                            NaN        Male  ...   \n",
-       "4                            NaN      Female  ...   \n",
-       "...                          ...         ...  ...   \n",
-       "502500                       NaN      Female  ...   \n",
-       "502501                       NaN        Male  ...   \n",
-       "502502                       NaN        Male  ...   \n",
-       "502503                       NaN        Male  ...   \n",
-       "502504                       NaN         NaN  ...   \n",
-       "\n",
-       "        source_of_report_of_i85_oesophageal_varices_f131407_0_0  \\\n",
-       "0                                                     NaN         \n",
-       "1                                                     NaN         \n",
-       "2                                                     NaN         \n",
-       "3                                                     NaN         \n",
-       "4                                                     NaN         \n",
-       "...                                                   ...         \n",
-       "502500                                                NaN         \n",
-       "502501                                                NaN         \n",
-       "502502                                                NaN         \n",
-       "502503                                                NaN         \n",
-       "502504                                                NaN         \n",
-       "\n",
-       "       source_of_report_of_i89_other_noninfective_disorders_of_lymphatic_vessels_and_lymph_nodes_f131415_0_0  \\\n",
-       "0                                                     NaN                                                      \n",
-       "1                                                     NaN                                                      \n",
-       "2                                                     NaN                                                      \n",
-       "3                                                     NaN                                                      \n",
-       "4                                                     NaN                                                      \n",
-       "...                                                   ...                                                      \n",
-       "502500                                                NaN                                                      \n",
-       "502501                                                NaN                                                      \n",
-       "502502                                                NaN                                                      \n",
-       "502503                                                NaN                                                      \n",
-       "502504                                                NaN                                                      \n",
-       "\n",
-       "       date_i95_first_reported_hypotension_f131416_0_0  \\\n",
-       "0                                                 None   \n",
-       "1                                                 None   \n",
-       "2                                                 None   \n",
-       "3                                                 None   \n",
-       "4                                                 None   \n",
-       "...                                                ...   \n",
-       "502500                                            None   \n",
-       "502501                                            None   \n",
-       "502502                                            None   \n",
-       "502503                                            None   \n",
-       "502504                                            None   \n",
-       "\n",
-       "       source_of_report_of_i95_hypotension_f131417_0_0  \\\n",
-       "0                                                  NaN   \n",
-       "1                                                  NaN   \n",
-       "2                                                  NaN   \n",
-       "3                                                  NaN   \n",
-       "4                                                  NaN   \n",
-       "...                                                ...   \n",
-       "502500                                             NaN   \n",
-       "502501                                             NaN   \n",
-       "502502                                             NaN   \n",
-       "502503                                             NaN   \n",
-       "502504                                             NaN   \n",
-       "\n",
-       "       date_i97_first_reported_postprocedural_disorders_of_circulatory_system_not_elsewhere_classified_f131418_0_0  \\\n",
-       "0                                                    None                                                            \n",
-       "1                                                    None                                                            \n",
-       "2                                                    None                                                            \n",
-       "3                                                    None                                                            \n",
-       "4                                                    None                                                            \n",
-       "...                                                   ...                                                            \n",
-       "502500                                               None                                                            \n",
-       "502501                                               None                                                            \n",
-       "502502                                               None                                                            \n",
-       "502503                                               None                                                            \n",
-       "502504                                               None                                                            \n",
-       "\n",
-       "       source_of_report_of_i97_postprocedural_disorders_of_circulatory_system_not_elsewhere_classified_f131419_0_0  \\\n",
-       "0                                                     NaN                                                            \n",
-       "1                                                     NaN                                                            \n",
-       "2                                                     NaN                                                            \n",
-       "3                                                     NaN                                                            \n",
-       "4                                                     NaN                                                            \n",
-       "...                                                   ...                                                            \n",
-       "502500                                                NaN                                                            \n",
-       "502501                                                NaN                                                            \n",
-       "502502                                                NaN                                                            \n",
-       "502503                                                NaN                                                            \n",
-       "502504                                                NaN                                                            \n",
-       "\n",
-       "       date_i98_first_reported_other_disorders_of_circulatory_system_in_diseases_classified_elsewhere_f131420_0_0  \\\n",
-       "0                                                    None                                                           \n",
-       "1                                                    None                                                           \n",
-       "2                                                    None                                                           \n",
-       "3                                                    None                                                           \n",
-       "4                                                    None                                                           \n",
-       "...                                                   ...                                                           \n",
-       "502500                                               None                                                           \n",
-       "502501                                               None                                                           \n",
-       "502502                                               None                                                           \n",
-       "502503                                               None                                                           \n",
-       "502504                                               None                                                           \n",
-       "\n",
-       "       source_of_report_of_i98_other_disorders_of_circulatory_system_in_diseases_classified_elsewhere_f131421_0_0  \\\n",
-       "0                                                     NaN                                                           \n",
-       "1                                                     NaN                                                           \n",
-       "2                                                     NaN                                                           \n",
-       "3                                                     NaN                                                           \n",
-       "4                                                     NaN                                                           \n",
-       "...                                                   ...                                                           \n",
-       "502500                                                NaN                                                           \n",
-       "502501                                                NaN                                                           \n",
-       "502502                                                NaN                                                           \n",
-       "502503                                                NaN                                                           \n",
-       "502504                                                NaN                                                           \n",
-       "\n",
-       "       date_i99_first_reported_other_and_unspecified_disorders_of_circulatory_system_f131422_0_0  \\\n",
-       "0                                                    None                                          \n",
-       "1                                                    None                                          \n",
-       "2                                                    None                                          \n",
-       "3                                                    None                                          \n",
-       "4                                                    None                                          \n",
-       "...                                                   ...                                          \n",
-       "502500                                               None                                          \n",
-       "502501                                               None                                          \n",
-       "502502                                         2012-08-16                                          \n",
-       "502503                                               None                                          \n",
-       "502504                                               None                                          \n",
-       "\n",
-       "       source_of_report_of_i99_other_and_unspecified_disorders_of_circulatory_system_f131423_0_0  \n",
-       "0                                                     NaN                                         \n",
-       "1                                                     NaN                                         \n",
-       "2                                                     NaN                                         \n",
-       "3                                                     NaN                                         \n",
-       "4                                                     NaN                                         \n",
-       "...                                                   ...                                         \n",
-       "502500                                                NaN                                         \n",
-       "502501                                                NaN                                         \n",
-       "502502                                  Primary care only                                         \n",
-       "502503                                                NaN                                         \n",
-       "502504                                                NaN                                         \n",
-       "\n",
-       "[502505 rows x 7162 columns]"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Mappings + Vocabulary"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:34:05.867152Z",
-     "start_time": "2020-11-04T12:33:16.878773Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "502505\n",
-      "502504\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Drop obviouse missing data\n",
-    "print(len(data))\n",
-    "data = data.dropna(subset=[\"sex_f31_0_0\"], axis=0)\n",
-    "print(len(data))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Starting information"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:34:05.872216Z",
-     "start_time": "2020-11-04T12:34:05.869505Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "time0_col=\"birthdate\"\n",
-    "# time0_col=\"date_of_attending_assessment_centre_f53_0_0\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Basic Covariates"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:34:05.889725Z",
-     "start_time": "2020-11-04T12:34:05.874587Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "def get_fields(fields, data, data_field):\n",
-    "    f = data_field[data_field[\"field.showcase\"].isin(fields) & data_field[\"field.tab\"].str.contains(\"f\\\\.\\\\d+\\\\.0\\\\.\\\\d\")].copy()\n",
-    "    f[\"field\"] = pd.Categorical(f[\"field.showcase\"], categories=fields, ordered=True)\n",
-    "    f = f.sort_values(\"field\").reset_index().drop(\"field\", axis=1)\n",
-    "    return f\n",
-    "\n",
-    "def get_fields_all(fields, data, data_field):\n",
-    "    f = data_field[data_field[\"field.showcase\"].isin(fields)].copy()\n",
-    "    f[\"field\"] = pd.Categorical(f[\"field.showcase\"], categories=fields, ordered=True)\n",
-    "    f = f.sort_values(\"field\").reset_index().drop(\"field\", axis=1)\n",
-    "    return f\n",
-    "\n",
-    "def get_data_fields(fields, data, data_field):\n",
-    "    f = get_fields(fields, data, data_field)\n",
-    "    return data[[\"eid\"]+f[\"col.name\"].to_list()].copy()\n",
-    "\n",
-    "def get_data_fields_all(fields, data, data_field):\n",
-    "    f = get_fields_all(fields, data, data_field)\n",
-    "    return data[[\"eid\"]+f[\"col.name\"].to_list()].copy()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Basics"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:34:06.176411Z",
-     "start_time": "2020-11-04T12:34:05.891730Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "502504\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>age_at_recruitment_f21022_0_0</th>\n",
-       "      <th>sex_f31_0_0</th>\n",
-       "      <th>ethnic_background_f21000_0_0</th>\n",
-       "      <th>ethnic_background_f21000_1_0</th>\n",
-       "      <th>ethnic_background_f21000_2_0</th>\n",
-       "      <th>townsend_deprivation_index_at_recruitment_f189_0_0</th>\n",
-       "      <th>date_of_attending_assessment_centre_f53_0_0</th>\n",
-       "      <th>date_of_attending_assessment_centre_f53_1_0</th>\n",
-       "      <th>date_of_attending_assessment_centre_f53_2_0</th>\n",
-       "      <th>date_of_attending_assessment_centre_f53_3_0</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>49.0</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>British</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-1.852930</td>\n",
-       "      <td>2009-11-12</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1000020</td>\n",
-       "      <td>59.0</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>British</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.204248</td>\n",
-       "      <td>2008-02-19</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1000037</td>\n",
-       "      <td>59.0</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>British</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-3.498860</td>\n",
-       "      <td>2008-11-11</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000043</td>\n",
-       "      <td>63.0</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>British</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-5.351150</td>\n",
-       "      <td>2009-06-03</td>\n",
-       "      <td>None</td>\n",
-       "      <td>2018-06-08</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1000051</td>\n",
-       "      <td>51.0</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>British</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-1.799080</td>\n",
-       "      <td>2006-06-10</td>\n",
-       "      <td>None</td>\n",
-       "      <td>2019-09-15</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       eid  age_at_recruitment_f21022_0_0 sex_f31_0_0  \\\n",
-       "0  1000018                           49.0      Female   \n",
-       "1  1000020                           59.0        Male   \n",
-       "2  1000037                           59.0      Female   \n",
-       "3  1000043                           63.0        Male   \n",
-       "4  1000051                           51.0      Female   \n",
-       "\n",
-       "  ethnic_background_f21000_0_0 ethnic_background_f21000_1_0  \\\n",
-       "0                      British                          NaN   \n",
-       "1                      British                          NaN   \n",
-       "2                      British                          NaN   \n",
-       "3                      British                          NaN   \n",
-       "4                      British                          NaN   \n",
-       "\n",
-       "  ethnic_background_f21000_2_0  \\\n",
-       "0                          NaN   \n",
-       "1                          NaN   \n",
-       "2                          NaN   \n",
-       "3                          NaN   \n",
-       "4                          NaN   \n",
-       "\n",
-       "   townsend_deprivation_index_at_recruitment_f189_0_0  \\\n",
-       "0                                          -1.852930    \n",
-       "1                                           0.204248    \n",
-       "2                                          -3.498860    \n",
-       "3                                          -5.351150    \n",
-       "4                                          -1.799080    \n",
-       "\n",
-       "  date_of_attending_assessment_centre_f53_0_0  \\\n",
-       "0                                  2009-11-12   \n",
-       "1                                  2008-02-19   \n",
-       "2                                  2008-11-11   \n",
-       "3                                  2009-06-03   \n",
-       "4                                  2006-06-10   \n",
-       "\n",
-       "  date_of_attending_assessment_centre_f53_1_0  \\\n",
-       "0                                        None   \n",
-       "1                                        None   \n",
-       "2                                        None   \n",
-       "3                                        None   \n",
-       "4                                        None   \n",
-       "\n",
-       "  date_of_attending_assessment_centre_f53_2_0  \\\n",
-       "0                                        None   \n",
-       "1                                        None   \n",
-       "2                                        None   \n",
-       "3                                  2018-06-08   \n",
-       "4                                  2019-09-15   \n",
-       "\n",
-       "  date_of_attending_assessment_centre_f53_3_0  \n",
-       "0                                        None  \n",
-       "1                                        None  \n",
-       "2                                        None  \n",
-       "3                                        None  \n",
-       "4                                        None  "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fields_basics = [\n",
-    "    \"21022\", # age at recruitment\n",
-    "    \"31\", # sex\n",
-    "    \"21000\", # ethnicity\n",
-    "    \"189\", # Townsend index\n",
-    "    \"53\", # date of baseline assessment\n",
-    "]\n",
-    "\n",
-    "temp = get_data_fields_all(fields_basics, data, data_field)\n",
-    "\n",
-    "temp[\"sex_f31_0_0\"] = temp[\"sex_f31_0_0\"].cat.set_categories([\"Female\", 'Male'], ordered=False)\n",
-    "temp[\"ethnic_background_f21000_0_0\"] = temp[\"ethnic_background_f21000_0_0\"].replace({\"Do not know\": np.nan, \"Prefer not to answer\": np.nan}).astype(\"category\")\n",
-    "\n",
-    "basics = temp\n",
-    "print(len(temp))\n",
-    "display(temp.head())\n",
-    "\n",
-    "from dateutil.relativedelta import relativedelta\n",
-    "calc_birth_date = [date_of_attending_assessment_centre - relativedelta(years=age_at_recruitment) \n",
-    "                                                             for date_of_attending_assessment_centre, age_at_recruitment \n",
-    "                                                             in zip(basics[\"date_of_attending_assessment_centre_f53_0_0\"], basics[\"age_at_recruitment_f21022_0_0\"])]\n",
-    "#basics = basics.assign(birth_date = calc_birth_date)\n",
-    "\n",
-    "\n",
-    "basics.to_feather(os.path.join(path, dataset_path, 'temp_basics.feather'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#basics[\"t\"] = (basics.date_of_attending_assessment_centre_f53_0_0-basics.birth_date).dt.days/365.2425"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:34:06.186613Z",
-     "start_time": "2020-11-04T12:34:06.178111Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['British', 'Caribbean', 'Other ethnic group', 'Irish', 'Indian', ..., 'White and Black African', 'Any other Black background', 'Asian or Asian British', 'Mixed', 'Black or Black British']\n",
-      "Length: 21\n",
-      "Categories (20, object): ['British', 'Caribbean', 'Other ethnic group', 'Irish', ..., 'Any other Black background', 'Asian or Asian British', 'Mixed', 'Black or Black British']\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(temp[\"ethnic_background_f21000_0_0\"].unique())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Questionnaire"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:34:06.389467Z",
-     "start_time": "2020-11-04T12:34:06.188206Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "502504\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>overall_health_rating_f2178_0_0</th>\n",
-       "      <th>overall_health_rating_f2178_1_0</th>\n",
-       "      <th>overall_health_rating_f2178_2_0</th>\n",
-       "      <th>overall_health_rating_f2178_3_0</th>\n",
-       "      <th>smoking_status_f20116_0_0</th>\n",
-       "      <th>smoking_status_f20116_1_0</th>\n",
-       "      <th>smoking_status_f20116_2_0</th>\n",
-       "      <th>smoking_status_f20116_3_0</th>\n",
-       "      <th>alcohol_intake_frequency_f1558_0_0</th>\n",
-       "      <th>alcohol_intake_frequency_f1558_1_0</th>\n",
-       "      <th>alcohol_intake_frequency_f1558_2_0</th>\n",
-       "      <th>alcohol_intake_frequency_f1558_3_0</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>Fair</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Current</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Once or twice a week</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1000020</td>\n",
-       "      <td>Good</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Current</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Once or twice a week</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1000037</td>\n",
-       "      <td>Good</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Previous</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Once or twice a week</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000043</td>\n",
-       "      <td>Fair</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Fair</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Previous</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Previous</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Three or four times a week</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Three or four times a week</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1000051</td>\n",
-       "      <td>Poor</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Fair</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Never</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Never</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>One to three times a month</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>One to three times a month</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       eid overall_health_rating_f2178_0_0 overall_health_rating_f2178_1_0  \\\n",
-       "0  1000018                            Fair                             NaN   \n",
-       "1  1000020                            Good                             NaN   \n",
-       "2  1000037                            Good                             NaN   \n",
-       "3  1000043                            Fair                             NaN   \n",
-       "4  1000051                            Poor                             NaN   \n",
-       "\n",
-       "  overall_health_rating_f2178_2_0 overall_health_rating_f2178_3_0  \\\n",
-       "0                             NaN                             NaN   \n",
-       "1                             NaN                             NaN   \n",
-       "2                             NaN                             NaN   \n",
-       "3                            Fair                             NaN   \n",
-       "4                            Fair                             NaN   \n",
-       "\n",
-       "  smoking_status_f20116_0_0 smoking_status_f20116_1_0  \\\n",
-       "0                   Current                       NaN   \n",
-       "1                   Current                       NaN   \n",
-       "2                  Previous                       NaN   \n",
-       "3                  Previous                       NaN   \n",
-       "4                     Never                       NaN   \n",
-       "\n",
-       "  smoking_status_f20116_2_0 smoking_status_f20116_3_0  \\\n",
-       "0                       NaN                       NaN   \n",
-       "1                       NaN                       NaN   \n",
-       "2                       NaN                       NaN   \n",
-       "3                  Previous                       NaN   \n",
-       "4                     Never                       NaN   \n",
-       "\n",
-       "  alcohol_intake_frequency_f1558_0_0 alcohol_intake_frequency_f1558_1_0  \\\n",
-       "0               Once or twice a week                                NaN   \n",
-       "1               Once or twice a week                                NaN   \n",
-       "2               Once or twice a week                                NaN   \n",
-       "3         Three or four times a week                                NaN   \n",
-       "4         One to three times a month                                NaN   \n",
-       "\n",
-       "  alcohol_intake_frequency_f1558_2_0 alcohol_intake_frequency_f1558_3_0  \n",
-       "0                                NaN                                NaN  \n",
-       "1                                NaN                                NaN  \n",
-       "2                                NaN                                NaN  \n",
-       "3         Three or four times a week                                NaN  \n",
-       "4         One to three times a month                                NaN  "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fields_questionnaire = [\n",
-    "    \"2178\", # Overall health\n",
-    "    \"20116\", # Smoking status\n",
-    "    \"1558\",\n",
-    "]\n",
-    "\n",
-    "temp = get_data_fields_all(fields_questionnaire, data, data_field)\n",
-    "\n",
-    "temp[\"overall_health_rating_f2178_0_0\"] = temp[\"overall_health_rating_f2178_0_0\"]\\\n",
-    "    .replace({\"Do not know\": np.nan, \"Prefer not to answer\": np.nan})\\\n",
-    "    .astype(\"category\").cat.set_categories(['Poor', 'Fair', 'Good', 'Excellent'], ordered=True)\n",
-    "\n",
-    "\n",
-    "temp[\"smoking_status_f20116_0_0\"] = temp[\"smoking_status_f20116_0_0\"]\\\n",
-    "    .replace({\"Prefer not to answer\": np.nan}, inplace=False)\\\n",
-    "    .astype(\"category\").cat.set_categories(['Current', 'Previous', 'Never'], ordered=True)\n",
-    "\n",
-    "temp[\"alcohol_intake_frequency_f1558_0_0\"] = temp[\"alcohol_intake_frequency_f1558_0_0\"]\\\n",
-    "    .replace({\"Prefer not to answer\": np.nan}, inplace=False)\\\n",
-    "    .astype(\"category\").cat.set_categories([\n",
-    "        'Daily or almost daily', \n",
-    "        'Three or four times a week', \n",
-    "        'Once or twice a week',\n",
-    "        'One to three times a month',\n",
-    "        'Special occasions only', \n",
-    "        'Never'], ordered=True)\n",
-    "\n",
-    "questionnaire = temp\n",
-    "print(len(temp))\n",
-    "display(temp.head())\n",
-    "\n",
-    "questionnaire.to_feather(os.path.join(path, dataset_path, 'temp_questionnaire.feather'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:34:06.398168Z",
-     "start_time": "2020-11-04T12:34:06.391082Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['Once or twice a week', 'Three or four times a week', 'One to three times a month', 'Daily or almost daily', 'Special occasions only', 'Never', NaN]\n",
-      "Categories (6, object): ['Daily or almost daily' < 'Three or four times a week' < 'Once or twice a week' < 'One to three times a month' < 'Special occasions only' < 'Never']\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(temp[\"alcohol_intake_frequency_f1558_0_0\"].unique())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Physical measurements"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:34:07.052989Z",
-     "start_time": "2020-11-04T12:34:06.400858Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "502504\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>body_mass_index_bmi_f21001_2_0</th>\n",
-       "      <th>body_mass_index_bmi_f21001_3_0</th>\n",
-       "      <th>body_mass_index_bmi_f21001_0_0</th>\n",
-       "      <th>body_mass_index_bmi_f21001_1_0</th>\n",
-       "      <th>weight_f21002_3_0</th>\n",
-       "      <th>weight_f21002_2_0</th>\n",
-       "      <th>weight_f21002_1_0</th>\n",
-       "      <th>weight_f21002_0_0</th>\n",
-       "      <th>systolic_blood_pressure_automated_reading_f4080_3_0</th>\n",
-       "      <th>...</th>\n",
-       "      <th>peak_expiratory_flow_pef_f3064_2_0</th>\n",
-       "      <th>peak_expiratory_flow_pef_f3064_1_2</th>\n",
-       "      <th>peak_expiratory_flow_pef_f3064_1_1</th>\n",
-       "      <th>peak_expiratory_flow_pef_f3064_1_0</th>\n",
-       "      <th>peak_expiratory_flow_pef_f3064_0_2</th>\n",
-       "      <th>peak_expiratory_flow_pef_f3064_0_1</th>\n",
-       "      <th>peak_expiratory_flow_pef_f3064_3_0</th>\n",
-       "      <th>peak_expiratory_flow_pef_f3064_3_1</th>\n",
-       "      <th>peak_expiratory_flow_pef_f3064_2_2</th>\n",
-       "      <th>peak_expiratory_flow_pef_f3064_3_2</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
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-       "      <th>0</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>26.5557</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>63.8</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>317.0</td>\n",
-       "      <td>312.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1000020</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>22.7465</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>70.7</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>301.0</td>\n",
-       "      <td>496.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1000037</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>32.4211</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>78.9</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>185.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000043</td>\n",
-       "      <td>28.4349</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>29.5679</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>90.6</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>95.8</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>476.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>557.0</td>\n",
-       "      <td>513.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>390.0</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1000051</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>41.0222</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>92.3</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 80 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       eid  body_mass_index_bmi_f21001_2_0  body_mass_index_bmi_f21001_3_0  \\\n",
-       "0  1000018                             NaN                             NaN   \n",
-       "1  1000020                             NaN                             NaN   \n",
-       "2  1000037                             NaN                             NaN   \n",
-       "3  1000043                         28.4349                             NaN   \n",
-       "4  1000051                             NaN                             NaN   \n",
-       "\n",
-       "   body_mass_index_bmi_f21001_0_0  body_mass_index_bmi_f21001_1_0  \\\n",
-       "0                         26.5557                             NaN   \n",
-       "1                         22.7465                             NaN   \n",
-       "2                         32.4211                             NaN   \n",
-       "3                         29.5679                             NaN   \n",
-       "4                         41.0222                             NaN   \n",
-       "\n",
-       "   weight_f21002_3_0  weight_f21002_2_0  weight_f21002_1_0  weight_f21002_0_0  \\\n",
-       "0                NaN                NaN                NaN               63.8   \n",
-       "1                NaN                NaN                NaN               70.7   \n",
-       "2                NaN                NaN                NaN               78.9   \n",
-       "3                NaN               90.6                NaN               95.8   \n",
-       "4                NaN                NaN                NaN               92.3   \n",
-       "\n",
-       "   systolic_blood_pressure_automated_reading_f4080_3_0  ...  \\\n",
-       "0                                                NaN    ...   \n",
-       "1                                                NaN    ...   \n",
-       "2                                                NaN    ...   \n",
-       "3                                                NaN    ...   \n",
-       "4                                                NaN    ...   \n",
-       "\n",
-       "   peak_expiratory_flow_pef_f3064_2_0  peak_expiratory_flow_pef_f3064_1_2  \\\n",
-       "0                                 NaN                                 NaN   \n",
-       "1                                 NaN                                 NaN   \n",
-       "2                                 NaN                                 NaN   \n",
-       "3                               476.0                                 NaN   \n",
-       "4                                 NaN                                 NaN   \n",
-       "\n",
-       "   peak_expiratory_flow_pef_f3064_1_1  peak_expiratory_flow_pef_f3064_1_0  \\\n",
-       "0                                 NaN                                 NaN   \n",
-       "1                                 NaN                                 NaN   \n",
-       "2                                 NaN                                 NaN   \n",
-       "3                                 NaN                                 NaN   \n",
-       "4                                 NaN                                 NaN   \n",
-       "\n",
-       "   peak_expiratory_flow_pef_f3064_0_2  peak_expiratory_flow_pef_f3064_0_1  \\\n",
-       "0                               317.0                               312.0   \n",
-       "1                               301.0                               496.0   \n",
-       "2                                 NaN                               185.0   \n",
-       "3                               557.0                               513.0   \n",
-       "4                                 NaN                                 NaN   \n",
-       "\n",
-       "   peak_expiratory_flow_pef_f3064_3_0  peak_expiratory_flow_pef_f3064_3_1  \\\n",
-       "0                                 NaN                                 NaN   \n",
-       "1                                 NaN                                 NaN   \n",
-       "2                                 NaN                                 NaN   \n",
-       "3                                 NaN                                 NaN   \n",
-       "4                                 NaN                                 NaN   \n",
-       "\n",
-       "   peak_expiratory_flow_pef_f3064_2_2  peak_expiratory_flow_pef_f3064_3_2  \n",
-       "0                                 NaN                                 NaN  \n",
-       "1                                 NaN                                 NaN  \n",
-       "2                                 NaN                                 NaN  \n",
-       "3                               390.0                                 NaN  \n",
-       "4                                 NaN                                 NaN  \n",
-       "\n",
-       "[5 rows x 80 columns]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from statistics import mean\n",
-    "\n",
-    "fields_measurements = [\n",
-    "#    \"100313\", # Walking speed !!! MISSING !!!\n",
-    "    \"21001\", # BMI\n",
-    "    \"21002\", # weight\n",
-    "    \"4080\", # Syst. BP\n",
-    "    \"4079\", # Diast. BP\n",
-    "    \"102\",\n",
-    "    \"21021\",\n",
-    "    \"4195\",\n",
-    "    \"48\",\n",
-    "    \"49\",\n",
-    "    \"50\",\n",
-    "    \"23127\",\n",
-    "    \"23099\",\n",
-    "    \"23105\",\n",
-    "    \"20151\",\n",
-    "    \"20150\",\n",
-    "    \"20258\",\n",
-    "    \"3064\",\n",
-    "    \n",
-    "]\n",
-    "temp = get_data_fields_all(fields_measurements, data, data_field)\n",
-    "\n",
-    "#sbp_cols = [\"systolic_blood_pressure_automated_reading_f4080_0_0\", \"systolic_blood_pressure_automated_reading_f4080_0_1\"]\n",
-    "#dbp_cols = [\"diastolic_blood_pressure_automated_reading_f4079_0_0\", \"diastolic_blood_pressure_automated_reading_f4079_0_1\"]\n",
-    "#pr_cols = [\"pulse_rate_automated_reading_f102_0_0\", \"pulse_rate_automated_reading_f102_0_1\"]\n",
-    "\n",
-    "#temp = temp.assign(systolic_blood_pressure_automated_reading_f4080 = temp[sbp_cols].mean(axis=1),\n",
-    "#                   diastolic_blood_pressure_automated_reading_f4079 = temp[dbp_cols].mean(axis=1),\n",
-    "#                   pulse_rate_automated_reading_f102 = temp[pr_cols].mean(axis=1))\\\n",
-    "#    .drop(sbp_cols + dbp_cols + pr_cols, axis=1)\n",
-    "\n",
-    "measurements = temp\n",
-    "print(len(temp))\n",
-    "display(temp.head())\n",
-    "\n",
-    "measurements.to_feather(os.path.join(path, dataset_path, 'temp_measurements.feather'))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Lab measurements"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:34:07.647955Z",
-     "start_time": "2020-11-04T12:34:07.055242Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "502504\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
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-       "<style scoped>\n",
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-       "\n",
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-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>basophill_count_f30160_2_0</th>\n",
-       "      <th>basophill_count_f30160_1_0</th>\n",
-       "      <th>basophill_count_f30160_0_0</th>\n",
-       "      <th>basophill_percentage_f30220_2_0</th>\n",
-       "      <th>basophill_percentage_f30220_0_0</th>\n",
-       "      <th>basophill_percentage_f30220_1_0</th>\n",
-       "      <th>eosinophill_count_f30150_0_0</th>\n",
-       "      <th>eosinophill_count_f30150_1_0</th>\n",
-       "      <th>eosinophill_count_f30150_2_0</th>\n",
-       "      <th>...</th>\n",
-       "      <th>total_protein_f30860_0_0</th>\n",
-       "      <th>total_protein_f30860_1_0</th>\n",
-       "      <th>triglycerides_f30870_0_0</th>\n",
-       "      <th>triglycerides_f30870_1_0</th>\n",
-       "      <th>urate_f30880_0_0</th>\n",
-       "      <th>urate_f30880_1_0</th>\n",
-       "      <th>urea_f30670_1_0</th>\n",
-       "      <th>urea_f30670_0_0</th>\n",
-       "      <th>vitamin_d_f30890_0_0</th>\n",
-       "      <th>vitamin_d_f30890_1_0</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.04</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.26</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.25</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>71.97</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.247</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>221.3</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>5.48</td>\n",
-       "      <td>70.7</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1000020</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.30</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.30</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>78.45</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.906</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>374.7</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>5.28</td>\n",
-       "      <td>35.9</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1000037</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.04</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.57</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.10</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000043</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.02</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.32</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.11</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>69.70</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>5.184</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>322.8</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>6.67</td>\n",
-       "      <td>63.6</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1000051</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 154 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       eid  basophill_count_f30160_2_0  basophill_count_f30160_1_0  \\\n",
-       "0  1000018                         NaN                         NaN   \n",
-       "1  1000020                         NaN                         NaN   \n",
-       "2  1000037                         NaN                         NaN   \n",
-       "3  1000043                         NaN                         NaN   \n",
-       "4  1000051                         NaN                         NaN   \n",
-       "\n",
-       "   basophill_count_f30160_0_0  basophill_percentage_f30220_2_0  \\\n",
-       "0                        0.04                              NaN   \n",
-       "1                        0.00                              NaN   \n",
-       "2                        0.04                              NaN   \n",
-       "3                        0.02                              NaN   \n",
-       "4                         NaN                              NaN   \n",
-       "\n",
-       "   basophill_percentage_f30220_0_0  basophill_percentage_f30220_1_0  \\\n",
-       "0                             0.26                              NaN   \n",
-       "1                             0.30                              NaN   \n",
-       "2                             0.57                              NaN   \n",
-       "3                             0.32                              NaN   \n",
-       "4                              NaN                              NaN   \n",
-       "\n",
-       "   eosinophill_count_f30150_0_0  eosinophill_count_f30150_1_0  \\\n",
-       "0                          0.25                           NaN   \n",
-       "1                          0.30                           NaN   \n",
-       "2                          0.10                           NaN   \n",
-       "3                          0.11                           NaN   \n",
-       "4                           NaN                           NaN   \n",
-       "\n",
-       "   eosinophill_count_f30150_2_0  ...  total_protein_f30860_0_0  \\\n",
-       "0                           NaN  ...                     71.97   \n",
-       "1                           NaN  ...                     78.45   \n",
-       "2                           NaN  ...                       NaN   \n",
-       "3                           NaN  ...                     69.70   \n",
-       "4                           NaN  ...                       NaN   \n",
-       "\n",
-       "   total_protein_f30860_1_0  triglycerides_f30870_0_0  \\\n",
-       "0                       NaN                     1.247   \n",
-       "1                       NaN                     1.906   \n",
-       "2                       NaN                       NaN   \n",
-       "3                       NaN                     5.184   \n",
-       "4                       NaN                       NaN   \n",
-       "\n",
-       "   triglycerides_f30870_1_0  urate_f30880_0_0  urate_f30880_1_0  \\\n",
-       "0                       NaN             221.3               NaN   \n",
-       "1                       NaN             374.7               NaN   \n",
-       "2                       NaN               NaN               NaN   \n",
-       "3                       NaN             322.8               NaN   \n",
-       "4                       NaN               NaN               NaN   \n",
-       "\n",
-       "   urea_f30670_1_0  urea_f30670_0_0  vitamin_d_f30890_0_0  \\\n",
-       "0              NaN             5.48                  70.7   \n",
-       "1              NaN             5.28                  35.9   \n",
-       "2              NaN              NaN                   NaN   \n",
-       "3              NaN             6.67                  63.6   \n",
-       "4              NaN              NaN                   NaN   \n",
-       "\n",
-       "   vitamin_d_f30890_1_0  \n",
-       "0                   NaN  \n",
-       "1                   NaN  \n",
-       "2                   NaN  \n",
-       "3                   NaN  \n",
-       "4                   NaN  \n",
-       "\n",
-       "[5 rows x 154 columns]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fields_blood_count = [\n",
-    "    \"30160\", #\tBasophill count\n",
-    "    \"30220\", #\tBasophill percentage\n",
-    "    \"30150\", #\tEosinophill count\n",
-    "    \"30210\", #\tEosinophill percentage\n",
-    "    \"30030\", #\tHaematocrit percentage\n",
-    "    \"30020\", #\tHaemoglobin concentration\n",
-    "    \"30300\", #\tHigh light scatter reticulocyte count\n",
-    "    \"30290\", #\tHigh light scatter reticulocyte percentage\n",
-    "    \"30280\", #\tImmature reticulocyte fraction\n",
-    "    \"30120\", #\tLymphocyte count\n",
-    "    \"30180\", #\tLymphocyte percentage\n",
-    "    \"30050\", #\tMean corpuscular haemoglobin\n",
-    "    \"30060\", #\tMean corpuscular haemoglobin concentration\n",
-    "    \"30040\", #\tMean corpuscular volume\n",
-    "    \"30100\", #\tMean platelet (thrombocyte) volume\n",
-    "    \"30260\", #\tMean reticulocyte volume\n",
-    "    \"30270\", #\tMean sphered cell volume\n",
-    "    \"30130\", #\tMonocyte count\n",
-    "    \"30190\", #\tMonocyte percentage\n",
-    "    \"30140\", #\tNeutrophill count\n",
-    "    \"30200\", #\tNeutrophill percentage\n",
-    "    \"30170\", #\tNucleated red blood cell count\n",
-    "    \"30230\", #\tNucleated red blood cell percentage\n",
-    "    \"30080\", #\tPlatelet count\n",
-    "    \"30090\", #\tPlatelet crit\n",
-    "    \"30110\", #\tPlatelet distribution width\n",
-    "    \"30010\", #\tRed blood cell (erythrocyte) count\n",
-    "    \"30070\", #\tRed blood cell (erythrocyte) distribution width\n",
-    "    \"30250\", #\tReticulocyte count\n",
-    "    \"30240\", #\tReticulocyte percentage\n",
-    "    \"30000\", #\tWhite blood cell (leukocyte) count\n",
-    "]\n",
-    "\n",
-    "fields_blood_biochemistry = [\n",
-    "    \"30620\",#\tAlanine aminotransferase\n",
-    "    \"30600\",#\tAlbumin\n",
-    "    \"30610\",#\tAlkaline phosphatase\n",
-    "    \"30630\",#\tApolipoprotein A\n",
-    "    \"30640\",#\tApolipoprotein B\n",
-    "    \"30650\",#\tAspartate aminotransferase\n",
-    "    \"30710\",#\tC-reactive protein\n",
-    "    \"30680\",#\tCalcium\n",
-    "    \"30690\",#\tCholesterol\n",
-    "    \"30700\",#\tCreatinine\n",
-    "    \"30720\",#\tCystatin C\n",
-    "    \"30660\",#\tDirect bilirubin\n",
-    "    \"30730\",#\tGamma glutamyltransferase\n",
-    "    \"30740\",#\tGlucose\n",
-    "    \"30750\",#\tGlycated haemoglobin (HbA1c)\n",
-    "    \"30760\",#\tHDL cholesterol\n",
-    "    \"30770\",#\tIGF-1\n",
-    "    \"30780\",#\tLDL direct\n",
-    "    \"30790\",#\tLipoprotein A\n",
-    "    \"30800\",#\tOestradiol\n",
-    "    \"30810\",#\tPhosphate\n",
-    "    \"30820\",#\tRheumatoid factor\n",
-    "    \"30830\",#\tSHBG\n",
-    "    \"30850\",#\tTestosterone\n",
-    "    \"30840\",#\tTotal bilirubin\n",
-    "    \"30860\",#\tTotal protein\n",
-    "    \"30870\",#\tTriglycerides\n",
-    "    \"30880\",#\tUrate\n",
-    "    \"30670\",#\tUrea\n",
-    "    \"30890\",#\tVitamin D\n",
-    "]\n",
-    "\n",
-    "fields_blood_infectious = [\n",
-    "    \"23000\", #\t1gG antigen for Herpes Simplex virus-1\n",
-    "    \"23001\", #\t2mgG unique antigen for Herpes Simplex virus-2\n",
-    "    \"23049\", #\tAntigen assay QC indicator\n",
-    "    \"23048\", #\tAntigen assay date\n",
-    "    \"23026\", #\tBK VP1 antigen for Human Polyomavirus BKV\n",
-    "    \"23039\", #\tCagA antigen for Helicobacter pylori\n",
-    "    \"23043\", #\tCatalase antigen for Helicobacter pylori\n",
-    "    \"23018\", #\tCore antigen for Hepatitis C Virus\n",
-    "    \"23030\", #\tE6 antigen for Human Papillomavirus type-16\n",
-    "    \"23031\", #\tE7 antigen for Human Papillomavirus type-16\n",
-    "    \"23006\", #\tEA-D antigen for Epstein-Barr Virus\n",
-    "    \"23004\", #\tEBNA-1 antigen for Epstein-Barr Virus\n",
-    "    \"23042\", #\tGroEL antigen for Helicobacter pylori\n",
-    "    \"23016\", #\tHBc antigen for Hepatitis B Virus\n",
-    "    \"23017\", #\tHBe antigen for Hepatitis B Virus\n",
-    "    \"23025\", #\tHIV-1 env antigen for Human Immunodeficiency Virus\n",
-    "    \"23024\", #\tHIV-1 gag antigen for Human Immunodeficiency Virus\n",
-    "    \"23023\", #\tHTLV-1 env antigen for Human T-Lymphotropic Virus 1\n",
-    "    \"23022\", #\tHTLV-1 gag antigen for Human T-Lymphotropic Virus 1\n",
-    "    \"23010\", #\tIE1A antigen for Human Herpesvirus-6\n",
-    "    \"23011\", #\tIE1B antigen for Human Herpesvirus-6\n",
-    "    \"23027\", #\tJC VP1 antigen for Human Polyomavirus JCV\n",
-    "    \"23015\", #\tK8.1 antigen for Kaposi's Sarcoma-Associated Herpesvirus\n",
-    "    \"23029\", #\tL1 antigen for Human Papillomavirus type-16\n",
-    "    \"23032\", #\tL1 antigen for Human Papillomavirus type-18\n",
-    "    \"23014\", #\tLANA antigen for Kaposi's Sarcoma-Associated Herpesvirus\n",
-    "    \"23028\", #\tMC VP1 antigen for Merkel Cell Polyomavirus\n",
-    "    \"23019\", #\tNS3 antigen for Hepatitis C Virus\n",
-    "    \"23041\", #\tOMP antigen for Helicobacter pylori\n",
-    "    \"23037\", #\tPorB antigen for Chlamydia trachomatis\n",
-    "    \"23013\", #\tU14 antigen for Human Herpesvirus-7\n",
-    "    \"23044\", #\tUreA antigen for Helicobacter pylori\n",
-    "    \"23003\", #\tVCA p18 antigen for Epstein-Barr Virus\n",
-    "    \"23040\", #\tVacA antigen for Helicobacter pylori\n",
-    "    \"23005\", #\tZEBRA antigen for Epstein-Barr Virus\n",
-    "    \"23002\", #\tgE / gI antigen for Varicella Zoster Virus\n",
-    "    \"23034\", #\tmomp A antigen for Chlamydia trachomatis\n",
-    "    \"23033\", #\tmomp D antigen for Chlamydia trachomatis\n",
-    "    \"23012\", #\tp101 k antigen for Human Herpesvirus-6\n",
-    "    \"23020\", #\tp22 antigen for Toxoplasma gondii\n",
-    "    \"23038\", #\tpGP3 antigen for Chlamydia trachomatis\n",
-    "    \"23009\", #\tpp 28 antigen for Human Cytomegalovirus\n",
-    "    \"23008\", #\tpp 52 antigen for Human Cytomegalovirus\n",
-    "    \"23007\", #\tpp150 Nter antigen for Human Cytomegalovirus\n",
-    "    \"23021\", #\tsag1 antigen for Toxoplasma gondii\n",
-    "    \"23035\", #\ttarp-D F1 antigen for Chlamydia trachomatis\n",
-    "    \"23036\", #\ttarp-D F2 antigen for Chlamydia trachomatis\n",
-    "]\n",
-    "\n",
-    "labs = temp = get_data_fields_all(fields_blood_count+fields_blood_biochemistry+fields_blood_infectious, data, data_field)\n",
-    "print(len(temp))\n",
-    "display(temp.head())\n",
-    "\n",
-    "labs.to_feather(os.path.join(path, dataset_path, 'temp_labs.feather'))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Get Demographic Data with times"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "standard = pd.concat([basics.set_index(\"eid\"), questionnaire.set_index(\"eid\"), measurements.set_index(\"eid\"), labs.set_index(\"eid\")], axis=1).reset_index()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cols_raw = [c[:-4] for c in standard.drop(\"eid\", axis=1).columns.to_list()]\n",
-    "cols = list(dict.fromkeys(cols_raw))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>age_at_recruitment_f21022_0_0</th>\n",
-       "      <th>sex_f31_0_0</th>\n",
-       "      <th>ethnic_background_f21000_0_0</th>\n",
-       "      <th>ethnic_background_f21000_1_0</th>\n",
-       "      <th>ethnic_background_f21000_2_0</th>\n",
-       "      <th>townsend_deprivation_index_at_recruitment_f189_0_0</th>\n",
-       "      <th>date_of_attending_assessment_centre_f53_0_0</th>\n",
-       "      <th>date_of_attending_assessment_centre_f53_1_0</th>\n",
-       "      <th>date_of_attending_assessment_centre_f53_2_0</th>\n",
-       "      <th>...</th>\n",
-       "      <th>total_protein_f30860_0_0</th>\n",
-       "      <th>total_protein_f30860_1_0</th>\n",
-       "      <th>triglycerides_f30870_0_0</th>\n",
-       "      <th>triglycerides_f30870_1_0</th>\n",
-       "      <th>urate_f30880_0_0</th>\n",
-       "      <th>urate_f30880_1_0</th>\n",
-       "      <th>urea_f30670_1_0</th>\n",
-       "      <th>urea_f30670_0_0</th>\n",
-       "      <th>vitamin_d_f30890_0_0</th>\n",
-       "      <th>vitamin_d_f30890_1_0</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>49.0</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>British</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-1.852930</td>\n",
-       "      <td>2009-11-12</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>...</td>\n",
-       "      <td>71.97</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.247</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>221.3</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>5.48</td>\n",
-       "      <td>70.7</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1000020</td>\n",
-       "      <td>59.0</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>British</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.204248</td>\n",
-       "      <td>2008-02-19</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>...</td>\n",
-       "      <td>78.45</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.906</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>374.7</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>5.28</td>\n",
-       "      <td>35.9</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1000037</td>\n",
-       "      <td>59.0</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>British</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-3.498860</td>\n",
-       "      <td>2008-11-11</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000043</td>\n",
-       "      <td>63.0</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>British</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-5.351150</td>\n",
-       "      <td>2009-06-03</td>\n",
-       "      <td>None</td>\n",
-       "      <td>2018-06-08</td>\n",
-       "      <td>...</td>\n",
-       "      <td>69.70</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>5.184</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>322.8</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>6.67</td>\n",
-       "      <td>63.6</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1000051</td>\n",
-       "      <td>51.0</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>British</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-1.799080</td>\n",
-       "      <td>2006-06-10</td>\n",
-       "      <td>None</td>\n",
-       "      <td>2019-09-15</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>502499</th>\n",
-       "      <td>6025150</td>\n",
-       "      <td>43.0</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>British</td>\n",
-       "      <td>British</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.046781</td>\n",
-       "      <td>2007-06-30</td>\n",
-       "      <td>2012-11-17</td>\n",
-       "      <td>2017-08-12</td>\n",
-       "      <td>...</td>\n",
-       "      <td>72.10</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.730</td>\n",
-       "      <td>2.285</td>\n",
-       "      <td>298.8</td>\n",
-       "      <td>356.1</td>\n",
-       "      <td>5.65</td>\n",
-       "      <td>4.21</td>\n",
-       "      <td>41.6</td>\n",
-       "      <td>17.9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>502500</th>\n",
-       "      <td>6025165</td>\n",
-       "      <td>45.0</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>British</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-2.107040</td>\n",
-       "      <td>2008-09-02</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>...</td>\n",
-       "      <td>74.20</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.442</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>220.2</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4.01</td>\n",
-       "      <td>72.7</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>502501</th>\n",
-       "      <td>6025173</td>\n",
-       "      <td>57.0</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>British</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-1.827220</td>\n",
-       "      <td>2008-09-17</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>...</td>\n",
-       "      <td>72.03</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.136</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>255.5</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>5.25</td>\n",
-       "      <td>41.6</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>502502</th>\n",
-       "      <td>6025182</td>\n",
-       "      <td>56.0</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>British</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-0.010764</td>\n",
-       "      <td>2010-07-01</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>...</td>\n",
-       "      <td>70.65</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>5.756</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>353.6</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4.42</td>\n",
-       "      <td>45.9</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>502503</th>\n",
-       "      <td>6025198</td>\n",
-       "      <td>67.0</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>British</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-1.930650</td>\n",
-       "      <td>2010-01-26</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>...</td>\n",
-       "      <td>70.62</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2.327</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>454.8</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>5.14</td>\n",
-       "      <td>20.2</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>502504 rows × 255 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            eid  age_at_recruitment_f21022_0_0 sex_f31_0_0  \\\n",
-       "0       1000018                           49.0      Female   \n",
-       "1       1000020                           59.0        Male   \n",
-       "2       1000037                           59.0      Female   \n",
-       "3       1000043                           63.0        Male   \n",
-       "4       1000051                           51.0      Female   \n",
-       "...         ...                            ...         ...   \n",
-       "502499  6025150                           43.0      Female   \n",
-       "502500  6025165                           45.0      Female   \n",
-       "502501  6025173                           57.0        Male   \n",
-       "502502  6025182                           56.0        Male   \n",
-       "502503  6025198                           67.0        Male   \n",
-       "\n",
-       "       ethnic_background_f21000_0_0 ethnic_background_f21000_1_0  \\\n",
-       "0                           British                          NaN   \n",
-       "1                           British                          NaN   \n",
-       "2                           British                          NaN   \n",
-       "3                           British                          NaN   \n",
-       "4                           British                          NaN   \n",
-       "...                             ...                          ...   \n",
-       "502499                      British                      British   \n",
-       "502500                      British                          NaN   \n",
-       "502501                      British                          NaN   \n",
-       "502502                      British                          NaN   \n",
-       "502503                      British                          NaN   \n",
-       "\n",
-       "       ethnic_background_f21000_2_0  \\\n",
-       "0                               NaN   \n",
-       "1                               NaN   \n",
-       "2                               NaN   \n",
-       "3                               NaN   \n",
-       "4                               NaN   \n",
-       "...                             ...   \n",
-       "502499                          NaN   \n",
-       "502500                          NaN   \n",
-       "502501                          NaN   \n",
-       "502502                          NaN   \n",
-       "502503                          NaN   \n",
-       "\n",
-       "        townsend_deprivation_index_at_recruitment_f189_0_0  \\\n",
-       "0                                               -1.852930    \n",
-       "1                                                0.204248    \n",
-       "2                                               -3.498860    \n",
-       "3                                               -5.351150    \n",
-       "4                                               -1.799080    \n",
-       "...                                                   ...    \n",
-       "502499                                           0.046781    \n",
-       "502500                                          -2.107040    \n",
-       "502501                                          -1.827220    \n",
-       "502502                                          -0.010764    \n",
-       "502503                                          -1.930650    \n",
-       "\n",
-       "       date_of_attending_assessment_centre_f53_0_0  \\\n",
-       "0                                       2009-11-12   \n",
-       "1                                       2008-02-19   \n",
-       "2                                       2008-11-11   \n",
-       "3                                       2009-06-03   \n",
-       "4                                       2006-06-10   \n",
-       "...                                            ...   \n",
-       "502499                                  2007-06-30   \n",
-       "502500                                  2008-09-02   \n",
-       "502501                                  2008-09-17   \n",
-       "502502                                  2010-07-01   \n",
-       "502503                                  2010-01-26   \n",
-       "\n",
-       "       date_of_attending_assessment_centre_f53_1_0  \\\n",
-       "0                                             None   \n",
-       "1                                             None   \n",
-       "2                                             None   \n",
-       "3                                             None   \n",
-       "4                                             None   \n",
-       "...                                            ...   \n",
-       "502499                                  2012-11-17   \n",
-       "502500                                        None   \n",
-       "502501                                        None   \n",
-       "502502                                        None   \n",
-       "502503                                        None   \n",
-       "\n",
-       "       date_of_attending_assessment_centre_f53_2_0  ...  \\\n",
-       "0                                             None  ...   \n",
-       "1                                             None  ...   \n",
-       "2                                             None  ...   \n",
-       "3                                       2018-06-08  ...   \n",
-       "4                                       2019-09-15  ...   \n",
-       "...                                            ...  ...   \n",
-       "502499                                  2017-08-12  ...   \n",
-       "502500                                        None  ...   \n",
-       "502501                                        None  ...   \n",
-       "502502                                        None  ...   \n",
-       "502503                                        None  ...   \n",
-       "\n",
-       "       total_protein_f30860_0_0 total_protein_f30860_1_0  \\\n",
-       "0                         71.97                      NaN   \n",
-       "1                         78.45                      NaN   \n",
-       "2                           NaN                      NaN   \n",
-       "3                         69.70                      NaN   \n",
-       "4                           NaN                      NaN   \n",
-       "...                         ...                      ...   \n",
-       "502499                    72.10                      NaN   \n",
-       "502500                    74.20                      NaN   \n",
-       "502501                    72.03                      NaN   \n",
-       "502502                    70.65                      NaN   \n",
-       "502503                    70.62                      NaN   \n",
-       "\n",
-       "       triglycerides_f30870_0_0 triglycerides_f30870_1_0 urate_f30880_0_0  \\\n",
-       "0                         1.247                      NaN            221.3   \n",
-       "1                         1.906                      NaN            374.7   \n",
-       "2                           NaN                      NaN              NaN   \n",
-       "3                         5.184                      NaN            322.8   \n",
-       "4                           NaN                      NaN              NaN   \n",
-       "...                         ...                      ...              ...   \n",
-       "502499                    0.730                    2.285            298.8   \n",
-       "502500                    1.442                      NaN            220.2   \n",
-       "502501                    1.136                      NaN            255.5   \n",
-       "502502                    5.756                      NaN            353.6   \n",
-       "502503                    2.327                      NaN            454.8   \n",
-       "\n",
-       "       urate_f30880_1_0 urea_f30670_1_0 urea_f30670_0_0 vitamin_d_f30890_0_0  \\\n",
-       "0                   NaN             NaN            5.48                 70.7   \n",
-       "1                   NaN             NaN            5.28                 35.9   \n",
-       "2                   NaN             NaN             NaN                  NaN   \n",
-       "3                   NaN             NaN            6.67                 63.6   \n",
-       "4                   NaN             NaN             NaN                  NaN   \n",
-       "...                 ...             ...             ...                  ...   \n",
-       "502499            356.1            5.65            4.21                 41.6   \n",
-       "502500              NaN             NaN            4.01                 72.7   \n",
-       "502501              NaN             NaN            5.25                 41.6   \n",
-       "502502              NaN             NaN            4.42                 45.9   \n",
-       "502503              NaN             NaN            5.14                 20.2   \n",
-       "\n",
-       "       vitamin_d_f30890_1_0  \n",
-       "0                       NaN  \n",
-       "1                       NaN  \n",
-       "2                       NaN  \n",
-       "3                       NaN  \n",
-       "4                       NaN  \n",
-       "...                     ...  \n",
-       "502499                 17.9  \n",
-       "502500                  NaN  \n",
-       "502501                  NaN  \n",
-       "502502                  NaN  \n",
-       "502503                  NaN  \n",
-       "\n",
-       "[502504 rows x 255 columns]"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "standard"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df = standard.copy()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_long = df.set_index([\"eid\"]).stack().reset_index()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_long.columns = [\"eid\", \"column\", \"value\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def split_ukb_column(df, idx_col=\"column\"):\n",
-    "    column = df[\"column\"].to_list()\n",
-    "    df[\"column\"] = [e[:-4] for e in column]\n",
-    "    df[\"t\"]= [e[-3:] for e in column]\n",
-    "    return df\n",
-    "\n",
-    "def split_ukb_index(df, idx_col=\"t\"):\n",
-    "    new = df[idx_col].str.split(\"_\", n = 1, expand = True) \n",
-    "    df[\"visit\"] = new[0]\n",
-    "    df[\"measurement\"]= new[1]\n",
-    "    return df.drop(columns =[idx_col]) "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%%time\n",
-    "df_long_split = split_ukb_column(df_long, idx_col=\"column\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 70,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def df_sort_cols(df, cols): return df[start_cols+[c for c in df.columns.to_list() if c not in start_cols]]\n",
-    "\n",
-    "start_cols = [\"eid\", \"t\", \"column\", \"value\"]\n",
-    "df_long_split = df_sort_cols(df_long_split, start_cols)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_test = df_long_split.set_index([\"eid\", \"t\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 77,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_test2 = df_test.set_index([\"column\"], append=True).unstack(level=-1).reset_index()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 88,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mi = df_test2.columns.to_flat_index().to_list()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 91,
-   "metadata": {
-    "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['eid', 't', 'age_at_recruitment_f21022',\n",
-       "       'alanine_aminotransferase_f30620', 'albumin_f30600',\n",
-       "       'alcohol_intake_frequency_f1558', 'alkaline_phosphatase_f30610',\n",
-       "       'apolipoprotein_a_f30630', 'apolipoprotein_b_f30640',\n",
-       "       'aspartate_aminotransferase_f30650',\n",
-       "       ...\n",
-       "       'total_protein_f30860',\n",
-       "       'townsend_deprivation_index_at_recruitment_f189',\n",
-       "       'triglycerides_f30870', 'trunk_fat_percentage_f23127', 'urate_f30880',\n",
-       "       'urea_f30670', 'vitamin_d_f30890', 'waist_circumference_f48',\n",
-       "       'weight_f21002', 'white_blood_cell_leukocyte_count_f30000'],\n",
-       "      dtype='object', length=260)"
-      ]
-     },
-     "execution_count": 91,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ind = pd.Index([e[1] if e[1] != \"\" else e[0] for e in mi])\n",
-    "df_test2.columns = ind"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 95,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_test2.columns = ind"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 99,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "start_cols = [\"eid\", \"t\"]+cols\n",
-    "df_test2  = df_sort_cols(df_test2, start_cols)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 100,
-   "metadata": {
-    "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>t</th>\n",
-       "      <th>age_at_recruitment_f21022</th>\n",
-       "      <th>age_at_recruitment_f21022</th>\n",
-       "      <th>age_at_recruitment_f21022</th>\n",
-       "      <th>sex_f31</th>\n",
-       "      <th>sex_f31</th>\n",
-       "      <th>sex_f31</th>\n",
-       "      <th>ethnic_background_f21000</th>\n",
-       "      <th>ethnic_background_f21000</th>\n",
-       "      <th>...</th>\n",
-       "      <th>triglycerides_f30870</th>\n",
-       "      <th>urate_f30880</th>\n",
-       "      <th>urate_f30880</th>\n",
-       "      <th>urate_f30880</th>\n",
-       "      <th>urea_f30670</th>\n",
-       "      <th>urea_f30670</th>\n",
-       "      <th>urea_f30670</th>\n",
-       "      <th>vitamin_d_f30890</th>\n",
-       "      <th>vitamin_d_f30890</th>\n",
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-       "      <td>0</td>\n",
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-       "      <td>221.3</td>\n",
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-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
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-       "      <td>NaN</td>\n",
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-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000020</td>\n",
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-       "      <td>59</td>\n",
-       "      <td>0</td>\n",
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-       "      <td>NaN</td>\n",
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-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
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-       "    <tr>\n",
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-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
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-       "      <td>0</td>\n",
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-       "      <td>NaN</td>\n",
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-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1521933</th>\n",
-       "      <td>6025198</td>\n",
-       "      <td>0_2</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1521934 rows × 260 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "             eid    t age_at_recruitment_f21022 age_at_recruitment_f21022  \\\n",
-       "0        1000018  0_0                        49                         0   \n",
-       "1        1000018  0_1                       NaN                       NaN   \n",
-       "2        1000018  0_2                       NaN                       NaN   \n",
-       "3        1000020  0_0                        59                         0   \n",
-       "4        1000020  0_1                       NaN                       NaN   \n",
-       "...          ...  ...                       ...                       ...   \n",
-       "1521929  6025182  0_1                       NaN                       NaN   \n",
-       "1521930  6025182  0_2                       NaN                       NaN   \n",
-       "1521931  6025198  0_0                        67                         0   \n",
-       "1521932  6025198  0_1                       NaN                       NaN   \n",
-       "1521933  6025198  0_2                       NaN                       NaN   \n",
-       "\n",
-       "        age_at_recruitment_f21022 sex_f31 sex_f31 sex_f31  \\\n",
-       "0                               0  Female       0       0   \n",
-       "1                             NaN     NaN     NaN     NaN   \n",
-       "2                             NaN     NaN     NaN     NaN   \n",
-       "3                               0    Male       0       0   \n",
-       "4                             NaN     NaN     NaN     NaN   \n",
-       "...                           ...     ...     ...     ...   \n",
-       "1521929                       NaN     NaN     NaN     NaN   \n",
-       "1521930                       NaN     NaN     NaN     NaN   \n",
-       "1521931                         0    Male       0       0   \n",
-       "1521932                       NaN     NaN     NaN     NaN   \n",
-       "1521933                       NaN     NaN     NaN     NaN   \n",
-       "\n",
-       "        ethnic_background_f21000 ethnic_background_f21000  ...  \\\n",
-       "0                        British                        0  ...   \n",
-       "1                            NaN                      NaN  ...   \n",
-       "2                            NaN                      NaN  ...   \n",
-       "3                        British                        0  ...   \n",
-       "4                            NaN                      NaN  ...   \n",
-       "...                          ...                      ...  ...   \n",
-       "1521929                      NaN                      NaN  ...   \n",
-       "1521930                      NaN                      NaN  ...   \n",
-       "1521931                  British                        0  ...   \n",
-       "1521932                      NaN                      NaN  ...   \n",
-       "1521933                      NaN                      NaN  ...   \n",
-       "\n",
-       "        triglycerides_f30870 urate_f30880 urate_f30880 urate_f30880  \\\n",
-       "0                          0        221.3            0            0   \n",
-       "1                        NaN          NaN          NaN          NaN   \n",
-       "2                        NaN          NaN          NaN          NaN   \n",
-       "3                          0        374.7            0            0   \n",
-       "4                        NaN          NaN          NaN          NaN   \n",
-       "...                      ...          ...          ...          ...   \n",
-       "1521929                  NaN          NaN          NaN          NaN   \n",
-       "1521930                  NaN          NaN          NaN          NaN   \n",
-       "1521931                    0        454.8            0            0   \n",
-       "1521932                  NaN          NaN          NaN          NaN   \n",
-       "1521933                  NaN          NaN          NaN          NaN   \n",
-       "\n",
-       "        urea_f30670 urea_f30670 urea_f30670 vitamin_d_f30890 vitamin_d_f30890  \\\n",
-       "0              5.48           0           0             70.7                0   \n",
-       "1               NaN         NaN         NaN              NaN              NaN   \n",
-       "2               NaN         NaN         NaN              NaN              NaN   \n",
-       "3              5.28           0           0             35.9                0   \n",
-       "4               NaN         NaN         NaN              NaN              NaN   \n",
-       "...             ...         ...         ...              ...              ...   \n",
-       "1521929         NaN         NaN         NaN              NaN              NaN   \n",
-       "1521930         NaN         NaN         NaN              NaN              NaN   \n",
-       "1521931        5.14           0           0             20.2                0   \n",
-       "1521932         NaN         NaN         NaN              NaN              NaN   \n",
-       "1521933         NaN         NaN         NaN              NaN              NaN   \n",
-       "\n",
-       "        vitamin_d_f30890  \n",
-       "0                      0  \n",
-       "1                    NaN  \n",
-       "2                    NaN  \n",
-       "3                      0  \n",
-       "4                    NaN  \n",
-       "...                  ...  \n",
-       "1521929              NaN  \n",
-       "1521930              NaN  \n",
-       "1521931                0  \n",
-       "1521932              NaN  \n",
-       "1521933              NaN  \n",
-       "\n",
-       "[1521934 rows x 260 columns]"
-      ]
-     },
-     "execution_count": 100,
-     "metadata": {},
-     "output_type": "execute_result"
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-   ],
-   "source": [
-    "df_test2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 98,
-   "metadata": {
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-       "      <td>NaN</td>\n",
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-       "<p>1521934 rows × 88 columns</p>\n",
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-      "text/plain": [
-       "             eid    t  age_at_recruitment_f21022 sex_f31  \\\n",
-       "0        1000018  0_0                       49.0  Female   \n",
-       "1        1000018  0_1                        NaN     NaN   \n",
-       "2        1000018  0_2                        NaN     NaN   \n",
-       "3        1000020  0_0                       59.0    Male   \n",
-       "4        1000020  0_1                        NaN     NaN   \n",
-       "...          ...  ...                        ...     ...   \n",
-       "1521929  6025182  0_1                        NaN     NaN   \n",
-       "1521930  6025182  0_2                        NaN     NaN   \n",
-       "1521931  6025198  0_0                       67.0    Male   \n",
-       "1521932  6025198  0_1                        NaN     NaN   \n",
-       "1521933  6025198  0_2                        NaN     NaN   \n",
-       "\n",
-       "        ethnic_background_f21000  \\\n",
-       "0                        British   \n",
-       "1                            NaN   \n",
-       "2                            NaN   \n",
-       "3                        British   \n",
-       "4                            NaN   \n",
-       "...                          ...   \n",
-       "1521929                      NaN   \n",
-       "1521930                      NaN   \n",
-       "1521931                  British   \n",
-       "1521932                      NaN   \n",
-       "1521933                      NaN   \n",
-       "\n",
-       "         townsend_deprivation_index_at_recruitment_f189  \\\n",
-       "0                                             -1.852930   \n",
-       "1                                                   NaN   \n",
-       "2                                                   NaN   \n",
-       "3                                              0.204248   \n",
-       "4                                                   NaN   \n",
-       "...                                                 ...   \n",
-       "1521929                                             NaN   \n",
-       "1521930                                             NaN   \n",
-       "1521931                                       -1.930650   \n",
-       "1521932                                             NaN   \n",
-       "1521933                                             NaN   \n",
-       "\n",
-       "        date_of_attending_assessment_centre_f53 overall_health_rating_f2178  \\\n",
-       "0                                    2009-11-12                        Fair   \n",
-       "1                                           NaN                         NaN   \n",
-       "2                                           NaN                         NaN   \n",
-       "3                                    2008-02-19                        Good   \n",
-       "4                                           NaN                         NaN   \n",
-       "...                                         ...                         ...   \n",
-       "1521929                                     NaN                         NaN   \n",
-       "1521930                                     NaN                         NaN   \n",
-       "1521931                              2010-01-26                        Good   \n",
-       "1521932                                     NaN                         NaN   \n",
-       "1521933                                     NaN                         NaN   \n",
-       "\n",
-       "        smoking_status_f20116 alcohol_intake_frequency_f1558  ...  \\\n",
-       "0                     Current           Once or twice a week  ...   \n",
-       "1                         NaN                            NaN  ...   \n",
-       "2                         NaN                            NaN  ...   \n",
-       "3                     Current           Once or twice a week  ...   \n",
-       "4                         NaN                            NaN  ...   \n",
-       "...                       ...                            ...  ...   \n",
-       "1521929                   NaN                            NaN  ...   \n",
-       "1521930                   NaN                            NaN  ...   \n",
-       "1521931               Current          Daily or almost daily  ...   \n",
-       "1521932                   NaN                            NaN  ...   \n",
-       "1521933                   NaN                            NaN  ...   \n",
-       "\n",
-       "         phosphate_f30810  rheumatoid_factor_f30820  shbg_f30830  \\\n",
-       "0                   1.422                       NaN        70.11   \n",
-       "1                     NaN                       NaN          NaN   \n",
-       "2                     NaN                       NaN          NaN   \n",
-       "3                   1.264                       NaN        55.31   \n",
-       "4                     NaN                       NaN          NaN   \n",
-       "...                   ...                       ...          ...   \n",
-       "1521929               NaN                       NaN          NaN   \n",
-       "1521930               NaN                       NaN          NaN   \n",
-       "1521931             1.163                       NaN        45.09   \n",
-       "1521932               NaN                       NaN          NaN   \n",
-       "1521933               NaN                       NaN          NaN   \n",
-       "\n",
-       "         testosterone_f30850  total_bilirubin_f30840  total_protein_f30860  \\\n",
-       "0                      1.560                    7.41                 71.97   \n",
-       "1                        NaN                     NaN                   NaN   \n",
-       "2                        NaN                     NaN                   NaN   \n",
-       "3                     12.237                    8.07                 78.45   \n",
-       "4                        NaN                     NaN                   NaN   \n",
-       "...                      ...                     ...                   ...   \n",
-       "1521929                  NaN                     NaN                   NaN   \n",
-       "1521930                  NaN                     NaN                   NaN   \n",
-       "1521931               15.030                   11.85                 70.62   \n",
-       "1521932                  NaN                     NaN                   NaN   \n",
-       "1521933                  NaN                     NaN                   NaN   \n",
-       "\n",
-       "         triglycerides_f30870  urate_f30880  urea_f30670  vitamin_d_f30890  \n",
-       "0                       1.247         221.3         5.48              70.7  \n",
-       "1                         NaN           NaN          NaN               NaN  \n",
-       "2                         NaN           NaN          NaN               NaN  \n",
-       "3                       1.906         374.7         5.28              35.9  \n",
-       "4                         NaN           NaN          NaN               NaN  \n",
-       "...                       ...           ...          ...               ...  \n",
-       "1521929                   NaN           NaN          NaN               NaN  \n",
-       "1521930                   NaN           NaN          NaN               NaN  \n",
-       "1521931                 2.327         454.8         5.14              20.2  \n",
-       "1521932                   NaN           NaN          NaN               NaN  \n",
-       "1521933                   NaN           NaN          NaN               NaN  \n",
-       "\n",
-       "[1521934 rows x 88 columns]"
-      ]
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-       "             eid    t  age_at_recruitment_f21022 sex_f31  \\\n",
-       "0        1000018  0_0                       49.0  Female   \n",
-       "1        1000018  0_1                        NaN     NaN   \n",
-       "2        1000018  0_2                        NaN     NaN   \n",
-       "3        1000020  0_0                       59.0    Male   \n",
-       "4        1000020  0_1                        NaN     NaN   \n",
-       "...          ...  ...                        ...     ...   \n",
-       "1521929  6025182  0_1                        NaN     NaN   \n",
-       "1521930  6025182  0_2                        NaN     NaN   \n",
-       "1521931  6025198  0_0                       67.0    Male   \n",
-       "1521932  6025198  0_1                        NaN     NaN   \n",
-       "1521933  6025198  0_2                        NaN     NaN   \n",
-       "\n",
-       "        ethnic_background_f21000  \\\n",
-       "0                        British   \n",
-       "1                            NaN   \n",
-       "2                            NaN   \n",
-       "3                        British   \n",
-       "4                            NaN   \n",
-       "...                          ...   \n",
-       "1521929                      NaN   \n",
-       "1521930                      NaN   \n",
-       "1521931                  British   \n",
-       "1521932                      NaN   \n",
-       "1521933                      NaN   \n",
-       "\n",
-       "         townsend_deprivation_index_at_recruitment_f189  \\\n",
-       "0                                             -1.852930   \n",
-       "1                                                   NaN   \n",
-       "2                                                   NaN   \n",
-       "3                                              0.204248   \n",
-       "4                                                   NaN   \n",
-       "...                                                 ...   \n",
-       "1521929                                             NaN   \n",
-       "1521930                                             NaN   \n",
-       "1521931                                       -1.930650   \n",
-       "1521932                                             NaN   \n",
-       "1521933                                             NaN   \n",
-       "\n",
-       "        date_of_attending_assessment_centre_f53 overall_health_rating_f2178  \\\n",
-       "0                                    2009-11-12                        Fair   \n",
-       "1                                           NaN                         NaN   \n",
-       "2                                           NaN                         NaN   \n",
-       "3                                    2008-02-19                        Good   \n",
-       "4                                           NaN                         NaN   \n",
-       "...                                         ...                         ...   \n",
-       "1521929                                     NaN                         NaN   \n",
-       "1521930                                     NaN                         NaN   \n",
-       "1521931                              2010-01-26                        Good   \n",
-       "1521932                                     NaN                         NaN   \n",
-       "1521933                                     NaN                         NaN   \n",
-       "\n",
-       "        smoking_status_f20116 alcohol_intake_frequency_f1558  ...  \\\n",
-       "0                     Current           Once or twice a week  ...   \n",
-       "1                         NaN                            NaN  ...   \n",
-       "2                         NaN                            NaN  ...   \n",
-       "3                     Current           Once or twice a week  ...   \n",
-       "4                         NaN                            NaN  ...   \n",
-       "...                       ...                            ...  ...   \n",
-       "1521929                   NaN                            NaN  ...   \n",
-       "1521930                   NaN                            NaN  ...   \n",
-       "1521931               Current          Daily or almost daily  ...   \n",
-       "1521932                   NaN                            NaN  ...   \n",
-       "1521933                   NaN                            NaN  ...   \n",
-       "\n",
-       "         phosphate_f30810  rheumatoid_factor_f30820  shbg_f30830  \\\n",
-       "0                   1.422                       NaN        70.11   \n",
-       "1                     NaN                       NaN          NaN   \n",
-       "2                     NaN                       NaN          NaN   \n",
-       "3                   1.264                       NaN        55.31   \n",
-       "4                     NaN                       NaN          NaN   \n",
-       "...                   ...                       ...          ...   \n",
-       "1521929               NaN                       NaN          NaN   \n",
-       "1521930               NaN                       NaN          NaN   \n",
-       "1521931             1.163                       NaN        45.09   \n",
-       "1521932               NaN                       NaN          NaN   \n",
-       "1521933               NaN                       NaN          NaN   \n",
-       "\n",
-       "         testosterone_f30850  total_bilirubin_f30840  total_protein_f30860  \\\n",
-       "0                      1.560                    7.41                 71.97   \n",
-       "1                        NaN                     NaN                   NaN   \n",
-       "2                        NaN                     NaN                   NaN   \n",
-       "3                     12.237                    8.07                 78.45   \n",
-       "4                        NaN                     NaN                   NaN   \n",
-       "...                      ...                     ...                   ...   \n",
-       "1521929                  NaN                     NaN                   NaN   \n",
-       "1521930                  NaN                     NaN                   NaN   \n",
-       "1521931               15.030                   11.85                 70.62   \n",
-       "1521932                  NaN                     NaN                   NaN   \n",
-       "1521933                  NaN                     NaN                   NaN   \n",
-       "\n",
-       "         triglycerides_f30870  urate_f30880  urea_f30670  vitamin_d_f30890  \n",
-       "0                       1.247         221.3         5.48              70.7  \n",
-       "1                         NaN           NaN          NaN               NaN  \n",
-       "2                         NaN           NaN          NaN               NaN  \n",
-       "3                       1.906         374.7         5.28              35.9  \n",
-       "4                         NaN           NaN          NaN               NaN  \n",
-       "...                       ...           ...          ...               ...  \n",
-       "1521929                   NaN           NaN          NaN               NaN  \n",
-       "1521930                   NaN           NaN          NaN               NaN  \n",
-       "1521931                 2.327         454.8         5.14              20.2  \n",
-       "1521932                   NaN           NaN          NaN               NaN  \n",
-       "1521933                   NaN           NaN          NaN               NaN  \n",
-       "\n",
-       "[1521934 rows x 88 columns]"
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-     "execution_count": 79,
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-   "execution_count": 61,
-   "metadata": {},
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-   "source": [
-    "%%time\n",
-    "df_raw = split_ukb_index(df_long_split, idx_col=\"t\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def df_sort_cols(df, cols): return df[start_cols+[c for c in df.columns.to_list() if c not in start_cols]]\n",
-    "\n",
-    "start_cols = [\"eid\", \"visit\", \"measurement\", \"column\", \"value\"]\n",
-    "df_raw = df_sort_cols(df_raw, start_cols)"
-   ]
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-   "cell_type": "code",
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-       "      <th>...</th>\n",
-       "      <th>...</th>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"5\" valign=\"top\">6025198</th>\n",
-       "      <th rowspan=\"5\" valign=\"top\">0</th>\n",
-       "      <th>0</th>\n",
-       "      <td>total_protein_f30860</td>\n",
-       "      <td>70.62</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>triglycerides_f30870</td>\n",
-       "      <td>2.327</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>urate_f30880</td>\n",
-       "      <td>454.8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>urea_f30670</td>\n",
-       "      <td>5.14</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>vitamin_d_f30890</td>\n",
-       "      <td>20.2</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>43659996 rows × 2 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                                   column  \\\n",
-       "eid     visit measurement                                                   \n",
-       "1000018 0     0                                 age_at_recruitment_f21022   \n",
-       "              0                                                   sex_f31   \n",
-       "              0                                  ethnic_background_f21000   \n",
-       "              0            townsend_deprivation_index_at_recruitment_f189   \n",
-       "              0                   date_of_attending_assessment_centre_f53   \n",
-       "...                                                                   ...   \n",
-       "6025198 0     0                                      total_protein_f30860   \n",
-       "              0                                      triglycerides_f30870   \n",
-       "              0                                              urate_f30880   \n",
-       "              0                                               urea_f30670   \n",
-       "              0                                          vitamin_d_f30890   \n",
-       "\n",
-       "                                value  \n",
-       "eid     visit measurement              \n",
-       "1000018 0     0                    49  \n",
-       "              0                Female  \n",
-       "              0               British  \n",
-       "              0              -1.85293  \n",
-       "              0            2009-11-12  \n",
-       "...                               ...  \n",
-       "6025198 0     0                 70.62  \n",
-       "              0                 2.327  \n",
-       "              0                 454.8  \n",
-       "              0                  5.14  \n",
-       "              0                  20.2  \n",
-       "\n",
-       "[43659996 rows x 2 columns]"
-      ]
-     },
-     "execution_count": 68,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_raw.set_index([\"eid\", \"visit\", \"measurement\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 102,
-   "metadata": {
-    "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true
-    }
-   },
-   "outputs": [
-    {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/IPython/core/magics/execution.py\u001b[0m in \u001b[0;36mtime\u001b[0;34m(self, line, cell, local_ns)\u001b[0m\n\u001b[1;32m   1320\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1321\u001b[0;31m                 \u001b[0mexec\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mglob\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlocal_ns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1322\u001b[0m                 \u001b[0mout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m<timed exec>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n",
-      "\u001b[0;32m<timed exec>\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2884\u001b[0m             \u001b[0;31m#  to a slice for partial-string date indexing\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2885\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2886\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_slice\u001b[0;34m(self, slobj, axis)\u001b[0m\n\u001b[1;32m   3558\u001b[0m         \u001b[0maxis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_block_manager_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3559\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_constructor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_slice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mslobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3560\u001b[0m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__finalize__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36mget_slice\u001b[0;34m(self, slobj, axis)\u001b[0m\n\u001b[1;32m    762\u001b[0m             \u001b[0mslicer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mslice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mslobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 763\u001b[0;31m             \u001b[0mnew_blocks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mblk\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetitem_block\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mslicer\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mblk\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mblocks\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    764\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    762\u001b[0m             \u001b[0mslicer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mslice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mslobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 763\u001b[0;31m             \u001b[0mnew_blocks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mblk\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetitem_block\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mslicer\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mblk\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mblocks\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    764\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/pandas/core/internals/blocks.py\u001b[0m in \u001b[0;36mgetitem_block\u001b[0;34m(self, slicer, new_mgr_locs)\u001b[0m\n\u001b[1;32m    303\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 304\u001b[0;31m         \u001b[0mnew_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mslicer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    305\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/pandas/core/internals/blocks.py\u001b[0m in \u001b[0;36m_slice\u001b[0;34m(self, slicer)\u001b[0m\n\u001b[1;32m   1774\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1775\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mslicer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1776\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/pandas/core/arrays/categorical.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1973\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1974\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_constructor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfastpath\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1975\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/pandas/core/arrays/categorical.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, values, categories, ordered, dtype, fastpath)\u001b[0m\n\u001b[1;32m    314\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mfastpath\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 315\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_codes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcoerce_indexer_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcategories\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    316\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dtype\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m: ",
-      "\nDuring handling of the above exception, another exception occurred:\n",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-102-1fc1108d9e49>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_cell_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'time'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m''\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'\\nfrom joblib import Parallel, delayed\\n\\ndef df_wide_to_long(df): return pd.wide_to_long(standard, cols, i=\"eid\", j=\"t\", sep=\"_\", suffix=\\'\\\\w+\\').reset_index()\\n\\ndf_input = standard\\nn = 10 \\nlist_df = [df_input[i:i+n] for i in range(0,df_input.shape[0],n)]\\n\\ndf_list = Parallel(n_jobs=10, require=\"sharedmem\")(delayed(df_wide_to_long)(df) for df in tqdm(list_df))\\ndf_concat = pd.concat(df_list, axis=0).reset_index(drop=True)\\n'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mrun_cell_magic\u001b[0;34m(self, magic_name, line, cell)\u001b[0m\n\u001b[1;32m   2379\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuiltin_trap\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2380\u001b[0m                 \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mmagic_arg_s\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcell\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2381\u001b[0;31m                 \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2382\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2383\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m<decorator-gen-54>\u001b[0m in \u001b[0;36mtime\u001b[0;34m(self, line, cell, local_ns)\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/IPython/core/magic.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(f, *a, **k)\u001b[0m\n\u001b[1;32m    185\u001b[0m     \u001b[0;31m# but it's overkill for just that one bit of state.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    186\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mmagic_deco\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 187\u001b[0;31m         \u001b[0mcall\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    188\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    189\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/IPython/core/magics/execution.py\u001b[0m in \u001b[0;36mtime\u001b[0;34m(self, line, cell, local_ns)\u001b[0m\n\u001b[1;32m   1319\u001b[0m             \u001b[0mst\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclock2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1320\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1321\u001b[0;31m                 \u001b[0mexec\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mglob\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlocal_ns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1322\u001b[0m                 \u001b[0mout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1323\u001b[0m                 \u001b[0;31m# multi-line %%time case\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
-     ]
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "\n",
-    "from joblib import Parallel, delayed\n",
-    "\n",
-    "def df_wide_to_long(df): return pd.wide_to_long(standard, cols, i=\"eid\", j=\"t\", sep=\"_\", suffix='\\w+').reset_index()\n",
-    "\n",
-    "df_input = standard\n",
-    "n = 100\n",
-    "list_df = [df_input[i:i+n] for i in range(0,df_input.shape[0],n)]\n",
-    "\n",
-    "df_list = Parallel(n_jobs=10, require=\"sharedmem\")(delayed(df_wide_to_long)(df) for df in tqdm(list_df))\n",
-    "df_concat = pd.concat(df_list, axis=0).reset_index(drop=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {
-    "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ee25fa009d6e4e858f160756345d9518",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(HTML(value=''), FloatProgress(value=0.0, max=503.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36mretrieve\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    939\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'supports_timeout'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 940\u001b[0;31m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_output\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    941\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36mwrap_future_result\u001b[0;34m(future, timeout)\u001b[0m\n\u001b[1;32m    541\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 542\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfuture\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    543\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mCfTimeoutError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/concurrent/futures/_base.py\u001b[0m in \u001b[0;36mresult\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    429\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 430\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_condition\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    431\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/threading.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    295\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 296\u001b[0;31m                 \u001b[0mwaiter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    297\u001b[0m                 \u001b[0mgotit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m: ",
-      "\nDuring handling of the above exception, another exception occurred:\n",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-31-5b9916c88d67>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mjoblib\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mParallel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdelayed\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdf_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mParallel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdelayed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_wide_to_long\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mdf\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtqdm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlist_df\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m   1059\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1060\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretrieval_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1061\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretrieve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1062\u001b[0m             \u001b[0;31m# Make sure that we get a last message telling us we are done\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1063\u001b[0m             \u001b[0melapsed_time\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_start_time\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36mretrieve\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    960\u001b[0m                     \u001b[0;31m# scheduling.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    961\u001b[0m                     \u001b[0mensure_ready\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_managed_backend\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 962\u001b[0;31m                     \u001b[0mbackend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mabort_everything\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mensure_ready\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mensure_ready\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    963\u001b[0m                 \u001b[0;32mraise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    964\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36mabort_everything\u001b[0;34m(self, ensure_ready)\u001b[0m\n\u001b[1;32m    559\u001b[0m         \"\"\"Shutdown the workers and restart a new one with the same parameters\n\u001b[1;32m    560\u001b[0m         \"\"\"\n\u001b[0;32m--> 561\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_workers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mterminate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkill_workers\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    562\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_workers\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    563\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/joblib/executor.py\u001b[0m in \u001b[0;36mterminate\u001b[0;34m(self, kill_workers)\u001b[0m\n\u001b[1;32m     72\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     73\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mterminate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkill_workers\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 74\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshutdown\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkill_workers\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkill_workers\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     75\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mkill_workers\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     76\u001b[0m             \u001b[0;31m# When workers are killed in such a brutal manner, they cannot\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/joblib/externals/loky/process_executor.py\u001b[0m in \u001b[0;36mshutdown\u001b[0;34m(self, wait, kill_workers)\u001b[0m\n\u001b[1;32m   1169\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1170\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mexecutor_manager_thread\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mwait\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1171\u001b[0;31m             \u001b[0mexecutor_manager_thread\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1172\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1173\u001b[0m         \u001b[0;31m# To reduce the risk of opening too many files, remove references to\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/threading.py\u001b[0m in \u001b[0;36mjoin\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m   1042\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1043\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1044\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_wait_for_tstate_lock\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1045\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1046\u001b[0m             \u001b[0;31m# the behavior of a negative timeout isn't documented, but\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/threading.py\u001b[0m in \u001b[0;36m_wait_for_tstate_lock\u001b[0;34m(self, block, timeout)\u001b[0m\n\u001b[1;32m   1058\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlock\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# already determined that the C code is done\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1059\u001b[0m             \u001b[0;32massert\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_is_stopped\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1060\u001b[0;31m         \u001b[0;32melif\u001b[0m \u001b[0mlock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mblock\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1061\u001b[0m             \u001b[0mlock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrelease\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1062\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
-     ]
-    }
-   ],
-   "source": [
-    "from joblib import Parallel, delayed\n",
-    "df_list = Parallel(n_jobs=50)(delayed(df_wide_to_long)(df) for df in tqdm(list_df))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 42min 7s, sys: 6min 58s, total: 49min 6s\n",
-      "Wall time: 50min 49s\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "\n",
-    "standard_long = pd.wide_to_long(standard, cols, i=\"eid\", j=\"t\", sep=\"_\", suffix='\\w+').reset_index()#.set_index(\"eid\")\n",
-    "standard_long = standard_long.dropna(how=\"all\", subset=cols, axis=0).reset_index(drop=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def split_ukb_index(df, idx_col=\"t\"):\n",
-    "    new = df[idx_col].str.split(\"_\", n = 1, expand = True) \n",
-    "    df[\"visit\"] = new[0]\n",
-    "    df[\"measurement\"]= new[1]\n",
-    "    return df.drop(columns =[idx_col]) \n",
-    "df_raw = split_ukb_index(standard_long, idx_col=\"t\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def process_multiple_measurements(df):\n",
-    "    df_nonfloat = df.set_index([\"eid\", \"visit\"]).select_dtypes(exclude=np.number)\n",
-    "    nonfloat_columns = [c for c in df_nonfloat.columns if c not in [\"measurement\"]]\n",
-    "    df_nonfloat = df_nonfloat.dropna(subset=nonfloat_columns, how=\"all\")[nonfloat_columns]\n",
-    "    df_float = df.set_index([\"eid\", \"visit\"]).select_dtypes(include=np.number).groupby([\"eid\", \"visit\"]).mean(numeric_only=True)\n",
-    "    df_complete = pd.concat([df_nonfloat, df_float], axis=1).reset_index()\n",
-    "    return df_complete\n",
-    "\n",
-    "def df_sort_cols(df, cols): return df[start_cols+[c for c in df.columns.to_list() if c not in start_cols]]\n",
-    "\n",
-    "start_cols = [\"eid\", \"visit\", \"date_of_attending_assessment_centre_f53\", \"age_at_recruitment_f21022\", \"sex_f31\", \"ethnic_background_f21000\"]\n",
-    "df_agg_measurement = df_sort_cols(process_multiple_measurements(df_raw), start_cols)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_birthdate(df_complete):\n",
-    "    from dateutil.relativedelta import relativedelta\n",
-    "    df = df_complete[df_complete.visit==\"0\"].copy()#.reset_index()\n",
-    "    df[\"birthdate\"] =  [date - relativedelta(years=age) for date, age in zip(df.date_of_attending_assessment_centre_f53, df.age_at_recruitment_f21022)]\n",
-    "    df_birthdate = df.set_index(\"eid\")[[\"birthdate\"]]\n",
-    "    return df_birthdate\n",
-    "\n",
-    "def convert_dates_to_timedelta(df_birthdate, df_complete):\n",
-    "    df_complete_bd = pd.concat([df_birthdate, df_complete.set_index([\"eid\"])], axis=1).reset_index()\n",
-    "\n",
-    "    start_cols = [\"eid\", \"birthdate\",  \"sex_f31\", \"ethnic_background_f21000\", \"visit\", \"date_of_attending_assessment_centre_f53\"]\n",
-    "    df_complete_bd = df_complete_bd[start_cols+[c for c in df_complete_bd.columns.to_list() if c not in start_cols]]\n",
-    "\n",
-    "    df_complete_bd = df_complete_bd.rename(columns={\"visit\":\"t\"}).assign(t= lambda x: (x.date_of_attending_assessment_centre_f53-x.birthdate).dt.days/365.2425)\n",
-    "    df_complete_bd = df_complete_bd.set_index([\"eid\", \"t\"]).drop([\"date_of_attending_assessment_centre_f53\", \"age_at_recruitment_f21022\"], axis=1)\n",
-    "    return df_complete_bd.reset_index()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_birthdate = get_birthdate(df_agg_measurement)\n",
-    "df_baseline_time = convert_dates_to_timedelta(df_birthdate, df_agg_measurement)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Medications"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:34:25.842601Z",
-     "start_time": "2020-11-04T12:34:25.840607Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# https://list.essentialmeds.org/?showRemoved=0\n",
-    "# essential medicines WHO?!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:34:32.471831Z",
-     "start_time": "2020-11-04T12:34:25.844339Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "atc_mapping = pd.read_csv(f\"{path}/mapping/atc/atc_matched_list.csv\")\n",
-    "athena_concepts = pd.read_csv(f\"{data_path}/athena_vocabulary/CONCEPT.csv\", sep=\"\\t\").assign(vocabulary_id = lambda x: x.vocabulary_id.astype(\"string\"), concept_class_id = lambda x: x.concept_class_id.astype(\"string\"))\n",
-    "atc_concepts = athena_concepts[athena_concepts.vocabulary_id==\"ATC\"]\n",
-    "atc2_concepts = atc_concepts[atc_concepts.concept_class_id==\"ATC 2nd\"].sort_values(\"concept_code\")\n",
-    "medication_list = dict(zip([x.lower().replace(\" \", \"_\") for x in atc2_concepts.concept_name.to_list()], [[x] for x in atc2_concepts.concept_code.to_list()]))\n",
-    "medication_list_extra = {\n",
-    "    \"antihypertensives\": [\"C02\"],\n",
-    "    \"statins\": [\"C10A\", \"C10B\"],\n",
-    "    \"ass\": [\"B01\"],\n",
-    "    \"atypical_antipsychotics\" : [\"N05\"],\n",
-    "    \"glucocorticoids\" : [\"H02\"]                        \n",
-    "}\n",
-    "medication_list.update(medication_list_extra)\n",
-    "\n",
-    "with open(os.path.join(path, dataset_path, 'medication_list.yaml'), 'w') as file: yaml.dump(medication_list, file, default_flow_style=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def had_medication_before(data, data_field, medications, atc_mapping):\n",
-    "    fields = [\"20003\"]\n",
-    "    raw = get_data_fields(fields, data, data_field)\n",
-    "    temp = pd.melt(raw, id_vars=[\"eid\"], value_vars=raw.drop(\"eid\", axis=1).columns.to_list(), var_name = \"field\", value_name=\"UKBB_code\").drop(\"field\", axis=1).drop_duplicates()\n",
-    "\n",
-    "    temp.UKBB_code = temp.UKBB_code.astype(str)\n",
-    "    temp = temp[temp.UKBB_code!=\"None\"].copy()\n",
-    "    temp.UKBB_code = temp.UKBB_code.astype(int)\n",
-    "\n",
-    "    temp_atc = temp.merge(atc_mapping, how=\"left\", on=\"UKBB_code\").sort_values(\"eid\").reset_index(drop=True).dropna(subset=[\"ATC_code\"], axis=0)\n",
-    "    temp_atc.ATC_code = temp_atc.ATC_code.astype(\"string\")\n",
-    "    temp = data[[\"eid\"]].copy()\n",
-    "    for med, med_codes in tqdm(medication_list.items()):\n",
-    "        regex_str = \"^\"+\"|^\".join(med_codes)\n",
-    "        df = temp_atc[temp_atc.ATC_code.str.contains(regex_str, case=False)][[\"eid\"]]\\\n",
-    "            .drop_duplicates(subset=[\"eid\"])\\\n",
-    "            .assign(medication=True)\n",
-    "        temp[med] = temp.merge(df, how=\"left\", on=\"eid\").fillna(False).medication\n",
-    "        \n",
-    "    return temp.sort_values(\"eid\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:34:32.481137Z",
-     "start_time": "2020-11-04T12:34:32.473698Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "def had_diagnosis_before_per_ph(df_before, ph, ph_codes, temp):\n",
-    "   # regex = \"|\".join(ph_codes)\n",
-    "    #df_ph = df_before.set_index(\"meaning\").loc[ph_codes][[\"eid\"]]\\\n",
-    "    #        .drop_duplicates(subset=[\"eid\"])\\\n",
-    "    #        .assign(phenotype=True) \n",
-    "    df_ph = df_before[df_before.meaning.isin(ph_codes)][[\"eid\"]]\\\n",
-    "            .drop_duplicates(subset=[\"eid\"])\\\n",
-    "            .assign(phenotype=True) \n",
-    "    #df_ph = df_before[df_before.meaning.str.contains(regex, case=False)][[\"eid\"]]\\\n",
-    "    #        .drop_duplicates(subset=[\"eid\"])\\\n",
-    "    #        .assign(phenotype=True)   \n",
-    "    return temp.merge(df_ph, how=\"left\", on=\"eid\").fillna(False).phenotype\n",
-    "\n",
-    "def had_diagnosis_before(data, diagnoses_codes, phenotypes, time0=time0_col):\n",
-    "    diagnoses_codes_time = diagnoses_codes.merge(data[[\"eid\", time0]], how=\"left\", on=\"eid\")\n",
-    "    \n",
-    "    temp = data[[\"eid\"]].copy()\n",
-    "    df_before = diagnoses_codes_time[diagnoses_codes_time.date < diagnoses_codes_time[time0]]\n",
-    "                                                                                         \n",
-    "    df_phs = Parallel(n_jobs=20, require=\"sharedmem\")(delayed(had_diagnosis_before_per_ph)(df_before, ph, phenotypes[ph], temp) for ph in tqdm(list(phenotypes)))\n",
-    "    for ph, df_ph_series in zip(tqdm(list(phenotypes)), df_phs): temp[ph] = df_ph_series#temp.merge(df_ph, how=\"left\", on=\"eid\").fillna(False).phenotype\n",
-    "    \n",
-    "    return temp.sort_values(\"eid\")  #reduce(lambda left,right: pd.merge(left,right,on=['eid'], how='left'), df_phs)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:36:14.425612Z",
-     "start_time": "2020-11-04T12:34:32.482863Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "13ef8609a6b64b219928981b7752c608",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(FloatProgress(value=0.0, max=98.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "502504\n"
-     ]
-    }
-   ],
-   "source": [
-    "medications = had_medication_before(data, data_field, medication_list, atc_mapping)\n",
-    "print(len(medications))\n",
-    "medications.head(100)\n",
-    "\n",
-    "medications.to_feather(os.path.join(path, dataset_path, 'temp_medications.feather'))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Diagnoses and events"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:37:14.667281Z",
-     "start_time": "2020-11-04T12:36:14.427693Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3146: DtypeWarning: Columns (5,6,9) have mixed types.Specify dtype option on import or set low_memory=False.\n",
-      "  interactivity=interactivity, compiler=compiler, result=result)\n",
-      "/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3146: DtypeWarning: Columns (11) have mixed types.Specify dtype option on import or set low_memory=False.\n",
-      "  interactivity=interactivity, compiler=compiler, result=result)\n"
-     ]
-    }
-   ],
-   "source": [
-    "vocab_dir = f\"{data_path}/athena_vocabulary_covid\"\n",
-    "vocab = {\n",
-    "    \"concept\": pd.read_csv(f\"{vocab_dir}/CONCEPT.csv\", sep='\\t'),\n",
-    "    \"domain\": pd.read_csv(f\"{vocab_dir}/DOMAIN.csv\", sep='\\t'),\n",
-    "    \"class\": pd.read_csv(f\"{vocab_dir}/CONCEPT_CLASS.csv\", sep='\\t'),\n",
-    "    \"relationship\": pd.read_csv(f\"{vocab_dir}/RELATIONSHIP.csv\", sep='\\t'),\n",
-    "    \"drug_strength\": pd.read_csv(f\"{vocab_dir}/DRUG_STRENGTH.csv\", sep='\\t'),\n",
-    "    \"vocabulary\": pd.read_csv(f\"{vocab_dir}/VOCABULARY.csv\", sep='\\t'),\n",
-    "    \"concept_synonym\": pd.read_csv(f\"{vocab_dir}/CONCEPT_SYNONYM.csv\", sep='\\t'),\n",
-    "    \"concept_ancestor\": pd.read_csv(f\"{vocab_dir}/CONCEPT_ANCESTOR.csv\", sep='\\t'),\n",
-    "    \"concept_relationship\": pd.read_csv(f\"{vocab_dir}/CONCEPT_RELATIONSHIP.csv\", sep='\\t')                       \n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Definitions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:37:14.772869Z",
-     "start_time": "2020-11-04T12:37:14.669541Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "coding1836 = pd.read_csv(f\"{path}/mapping/codings/coding1836.tsv\", sep=\"\\t\").rename(columns={\"coding\":\"code\"})\n",
-    "phecodes = pd.read_csv(f\"{path}/mapping/phecodes/phecode_icd10.csv\")\n",
-    "def phenotype_children(phecodes, phenotype_list):\n",
-    "    l={}\n",
-    "    phecodes = phecodes.dropna(subset=[\"Phenotype\"], axis=0)\n",
-    "    for ph, ph_names in phenotype_list.items():\n",
-    "        regex = \"|\".join(ph_names)\n",
-    "        l[ph] = list(phecodes[phecodes.Phenotype.str.contains(regex, case=False)].ICD10.str.replace(\"\\\\.\", \"\").str.slice(0, 3).unique())\n",
-    "    return l"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:37:14.851438Z",
-     "start_time": "2020-11-04T12:37:14.774599Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "snomed_core = pd.read_csv(f\"{path}/mapping/snomed_core_list.txt\", sep=\"|\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:37:14.909023Z",
-     "start_time": "2020-11-04T12:37:14.853286Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "snomed_core = snomed_core.query(\"SNOMED_CONCEPT_STATUS == 'Current'\").copy()\n",
-    "new = snomed_core.SNOMED_FSN.str.split(\"(\", n=1, expand=True)\n",
-    "snomed_core[\"snomed_name\"] = new[0].str.rstrip(' ')\n",
-    "snomed_core[\"snomed_type\"] = new[1].str.rstrip(')')\n",
-    "snomed_core_data = snomed_core.query(\"(snomed_type=='disorder' | snomed_type=='finding') & USAGE>0.01\").reset_index(drop=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>SNOMED_CID</th>\n",
-       "      <th>SNOMED_FSN</th>\n",
-       "      <th>SNOMED_CONCEPT_STATUS</th>\n",
-       "      <th>UMLS_CUI</th>\n",
-       "      <th>OCCURRENCE</th>\n",
-       "      <th>USAGE</th>\n",
-       "      <th>FIRST_IN_SUBSET</th>\n",
-       "      <th>IS_RETIRED_FROM_SUBSET</th>\n",
-       "      <th>LAST_IN_SUBSET</th>\n",
-       "      <th>REPLACED_BY_SNOMED_CID</th>\n",
-       "      <th>snomed_name</th>\n",
-       "      <th>snomed_type</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>38341003</td>\n",
-       "      <td>Hypertensive disorder, systemic arterial (diso...</td>\n",
-       "      <td>Current</td>\n",
-       "      <td>C0020538</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>3.2242</td>\n",
-       "      <td>200907</td>\n",
-       "      <td>False</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Hypertensive disorder, systemic arterial</td>\n",
-       "      <td>disorder</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>55822004</td>\n",
-       "      <td>Hyperlipidemia (disorder)</td>\n",
-       "      <td>Current</td>\n",
-       "      <td>C0020473</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>2.1369</td>\n",
-       "      <td>200907</td>\n",
-       "      <td>False</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Hyperlipidemia</td>\n",
-       "      <td>disorder</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>35489007</td>\n",
-       "      <td>Depressive disorder (disorder)</td>\n",
-       "      <td>Current</td>\n",
-       "      <td>C0011581</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>1.5077</td>\n",
-       "      <td>200907</td>\n",
-       "      <td>False</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Depressive disorder</td>\n",
-       "      <td>disorder</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>235595009</td>\n",
-       "      <td>Gastroesophageal reflux disease (disorder)</td>\n",
-       "      <td>Current</td>\n",
-       "      <td>C0017168</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>1.3691</td>\n",
-       "      <td>200907</td>\n",
-       "      <td>False</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Gastroesophageal reflux disease</td>\n",
-       "      <td>disorder</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>44054006</td>\n",
-       "      <td>Diabetes mellitus type 2 (disorder)</td>\n",
-       "      <td>Current</td>\n",
-       "      <td>C0011860</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>1.0432</td>\n",
-       "      <td>200907</td>\n",
-       "      <td>False</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Diabetes mellitus type 2</td>\n",
-       "      <td>disorder</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1013</th>\n",
-       "      <td>125601008</td>\n",
-       "      <td>Injury of knee (disorder)</td>\n",
-       "      <td>Current</td>\n",
-       "      <td>C0022744</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>0.0101</td>\n",
-       "      <td>200907</td>\n",
-       "      <td>False</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Injury of knee</td>\n",
-       "      <td>disorder</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1014</th>\n",
-       "      <td>127295002</td>\n",
-       "      <td>Traumatic brain injury (disorder)</td>\n",
-       "      <td>Current</td>\n",
-       "      <td>C0876926</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0101</td>\n",
-       "      <td>200907</td>\n",
-       "      <td>False</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Traumatic brain injury</td>\n",
-       "      <td>disorder</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1015</th>\n",
-       "      <td>373623009</td>\n",
-       "      <td>Osteoarthritis of glenohumeral joint (disorder)</td>\n",
-       "      <td>Current</td>\n",
-       "      <td>C0409939</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.0101</td>\n",
-       "      <td>200907</td>\n",
-       "      <td>False</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Osteoarthritis of glenohumeral joint</td>\n",
-       "      <td>disorder</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1016</th>\n",
-       "      <td>206002004</td>\n",
-       "      <td>Fetal or neonatal effect of maternal medical p...</td>\n",
-       "      <td>Current</td>\n",
-       "      <td>C0411175</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0101</td>\n",
-       "      <td>200907</td>\n",
-       "      <td>False</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Fetal or neonatal effect of maternal medical p...</td>\n",
-       "      <td>disorder</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1017</th>\n",
-       "      <td>271857006</td>\n",
-       "      <td>Loin pain (finding)</td>\n",
-       "      <td>Current</td>\n",
-       "      <td>C0235632</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0101</td>\n",
-       "      <td>200907</td>\n",
-       "      <td>False</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Loin pain</td>\n",
-       "      <td>finding</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1018 rows × 12 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      SNOMED_CID                                         SNOMED_FSN  \\\n",
-       "0       38341003  Hypertensive disorder, systemic arterial (diso...   \n",
-       "1       55822004                          Hyperlipidemia (disorder)   \n",
-       "2       35489007                     Depressive disorder (disorder)   \n",
-       "3      235595009         Gastroesophageal reflux disease (disorder)   \n",
-       "4       44054006                Diabetes mellitus type 2 (disorder)   \n",
-       "...          ...                                                ...   \n",
-       "1013   125601008                          Injury of knee (disorder)   \n",
-       "1014   127295002                  Traumatic brain injury (disorder)   \n",
-       "1015   373623009    Osteoarthritis of glenohumeral joint (disorder)   \n",
-       "1016   206002004  Fetal or neonatal effect of maternal medical p...   \n",
-       "1017   271857006                                Loin pain (finding)   \n",
-       "\n",
-       "     SNOMED_CONCEPT_STATUS  UMLS_CUI  OCCURRENCE   USAGE  FIRST_IN_SUBSET  \\\n",
-       "0                  Current  C0020538         8.0  3.2242           200907   \n",
-       "1                  Current  C0020473         8.0  2.1369           200907   \n",
-       "2                  Current  C0011581         8.0  1.5077           200907   \n",
-       "3                  Current  C0017168         8.0  1.3691           200907   \n",
-       "4                  Current  C0011860         8.0  1.0432           200907   \n",
-       "...                    ...       ...         ...     ...              ...   \n",
-       "1013               Current  C0022744         4.0  0.0101           200907   \n",
-       "1014               Current  C0876926         3.0  0.0101           200907   \n",
-       "1015               Current  C0409939         2.0  0.0101           200907   \n",
-       "1016               Current  C0411175         1.0  0.0101           200907   \n",
-       "1017               Current  C0235632         1.0  0.0101           200907   \n",
-       "\n",
-       "      IS_RETIRED_FROM_SUBSET  LAST_IN_SUBSET REPLACED_BY_SNOMED_CID  \\\n",
-       "0                      False             NaN                    NaN   \n",
-       "1                      False             NaN                    NaN   \n",
-       "2                      False             NaN                    NaN   \n",
-       "3                      False             NaN                    NaN   \n",
-       "4                      False             NaN                    NaN   \n",
-       "...                      ...             ...                    ...   \n",
-       "1013                   False             NaN                    NaN   \n",
-       "1014                   False             NaN                    NaN   \n",
-       "1015                   False             NaN                    NaN   \n",
-       "1016                   False             NaN                    NaN   \n",
-       "1017                   False             NaN                    NaN   \n",
-       "\n",
-       "                                            snomed_name snomed_type  \n",
-       "0              Hypertensive disorder, systemic arterial    disorder  \n",
-       "1                                        Hyperlipidemia    disorder  \n",
-       "2                                   Depressive disorder    disorder  \n",
-       "3                       Gastroesophageal reflux disease    disorder  \n",
-       "4                              Diabetes mellitus type 2    disorder  \n",
-       "...                                                 ...         ...  \n",
-       "1013                                     Injury of knee    disorder  \n",
-       "1014                             Traumatic brain injury    disorder  \n",
-       "1015               Osteoarthritis of glenohumeral joint    disorder  \n",
-       "1016  Fetal or neonatal effect of maternal medical p...    disorder  \n",
-       "1017                                          Loin pain     finding  \n",
-       "\n",
-       "[1018 rows x 12 columns]"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "snomed_core_data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:37:14.914973Z",
-     "start_time": "2020-11-04T12:37:14.910857Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "snomed_names = snomed_core_data.snomed_name.to_list()\n",
-    "snomed_names = [str(item).lower().strip().replace(\" \", \"_\").replace(\";\", \"\").replace(\",\", \"\") for item in snomed_names]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:39:55.342937Z",
-     "start_time": "2020-11-04T12:37:14.916906Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "479499b40d1d4a1ebd67751ca0dfc88a",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(HTML(value=''), FloatProgress(value=0.0, max=1018.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "phenotype_list_snomed = dict(zip(snomed_names, snomed_core_data.SNOMED_CID.to_list()))\n",
-    "snomed_ids = vocab[\"concept\"].query(\"vocabulary_id == 'SNOMED'\").concept_id.to_list()\n",
-    "icd10_ids = vocab[\"concept\"].query(\"vocabulary_id == 'ICD10CM'\").concept_id.to_list()\n",
-    "\n",
-    "ph_to_icd10_mapping = {}\n",
-    "\n",
-    "def map_snomed_to_icd10(ph, snomed_code, concept, concept_ancestor, concept_relationship):\n",
-    "    concept_ids = concept.query(\"vocabulary_id == 'SNOMED' & concept_code == @snomed_code\").concept_id.to_list()\n",
-    "    snomed_desc_ids = concept_ancestor.query(\"ancestor_concept_id== @concept_ids\").descendant_concept_id.to_list()\n",
-    "    ph_desc = concept.query(\"concept_id == @snomed_desc_ids\").query(\"vocabulary_id == 'SNOMED'\")\n",
-    "    l_ph_desc_ids = ph_desc.concept_id.to_list()\n",
-    "    ph_icd10_ids = list(concept_relationship.query(\"concept_id_1==@l_ph_desc_ids\").query(\"concept_id_2 == @icd10_ids\").concept_id_2.unique())\n",
-    "    #ph_icd10_ids = list(concept_relationship.set_index(\"concept_id_1\").query(\"index==@l_ph_desc_ids\").query(\"concept_id_2 == @icd10_ids\").query(\"relationship_id == 'Mapped from'\").concept_id_2.unique()\n",
-    "    \n",
-    "    #ph_icd10_ids = list(temp.concept_id_2.unique())\n",
-    "    df = concept.query(\"concept_id == @ph_icd10_ids & vocabulary_id == 'ICD10CM'\")\n",
-    "    icd10_list = list(df[~df.concept_code.str.contains(\"OMOP\", na=False)].concept_code.unique())\n",
-    "    icd10_list = sorted(list(set([e[:3] for e in icd10_list])))\n",
-    "    #print(f\"{ph}: {icd10_list}\")\n",
-    "    return {ph: sorted(list(dict.fromkeys([str(e) for e in icd10_list])))}\n",
-    "\n",
-    "from joblib import Parallel, delayed\n",
-    "concept_ids = vocab[\"concept\"].query(\"(vocabulary_id == 'SNOMED') | (vocabulary_id == 'ICD10CM')\")\n",
-    "vocab_concept_ids = concept_ids.concept_id.to_list()\n",
-    "concept_ancestor = vocab[\"concept_ancestor\"][[\"ancestor_concept_id\", \"descendant_concept_id\"]].query(\"ancestor_concept_id == @vocab_concept_ids\")\n",
-    "concept_rel = vocab[\"concept_relationship\"][[\"concept_id_1\", \"concept_id_2\", \"relationship_id\"]].query(\"(concept_id_1 == @vocab_concept_ids) & (concept_id_2 == @vocab_concept_ids) & (relationship_id == 'Mapped from')\")\n",
-    "icd10_codes = Parallel(n_jobs=10, require=\"sharedmem\")(delayed(map_snomed_to_icd10)(ph, snomed_code, \n",
-    "                                                              concept_ids, concept_ancestor, concept_rel) for ph, snomed_code in tqdm(phenotype_list_snomed.items()))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:39:55.348009Z",
-     "start_time": "2020-11-04T12:39:55.344704Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "l10_snomed = {}\n",
-    "for ph in icd10_codes: l10_snomed.update(ph)"
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:39:55.468545Z",
-     "start_time": "2020-11-04T12:39:55.349545Z"
-    }
-   },
-   "source": [
-    "phenotype_list_basic = {\n",
-    "    \"coronary_heart_disease\": [\"Ischemic heart disease\"],\n",
-    "    \"myocardial_infarction\": [\"Myocardial infarction\"],\n",
-    "    \"stroke\": [\"Cerebrovascular disease\"],\n",
-    "    \"diabetes1\" : [\"Type 1 diabetes\"],\n",
-    "    \"diabetes2\" : [\"Diabetes mellitus\", \"Type 2 diabetes\"],\n",
-    "    \"chronic_kidney_disease\": [\"Chronic kidney disease\", \"chronic renal failure\"],\n",
-    "    \"atrial_fibrillation\": [\"Atrial fibrillation\", \"Atrial flutter\", \"paroxysmal tachycardia\"],\n",
-    "    \"migraine\": [\"Migraine\"],\n",
-    "    \"rheumatoid_arthritis\": [\"Rheumatoid arthritis\"],\n",
-    "    \"systemic_lupus_erythematosus\": [\"Systemic lupus erythematosus\"],\n",
-    "    \"severe_mental_illness\": [\"Schizophrenia\", \"bipolar\", \"Major depressive disorder\"],\n",
-    "    \"erectile_dysfunction\" : [\"Erectile dysfunction\"],  \n",
-    "}\n",
-    "\n",
-    "l10_basic = phenotype_children(phecodes, phenotype_list_basic)\n",
-    "l10_all = l10_basic\n",
-    "for key, value in l10_snomed.items(): \n",
-    "    if key not in l10_basic: l10_all[key] = value"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:39:55.620916Z",
-     "start_time": "2020-11-04T12:39:55.475137Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "l10 = {k: v for k, v in l10_snomed.items() if len(v)!=0}\n",
-    "\n",
-    "#phenotype_list = {k: v for k, v in phenotype_list.items() if k in list(l10.keys())}\n",
-    "\n",
-    "with open(os.path.join(path, dataset_path, 'phenotype_list.yaml'), 'w') as file: yaml.dump(l10, file, default_flow_style=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 1. Self Reported"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:43:13.288198Z",
-     "start_time": "2020-11-04T12:43:13.264683Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "coding609 = pd.read_csv(f\"{path}/mapping/codings/coding609.tsv\", sep=\"\\t\").rename(columns={\"coding\":\"code\"})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:43:13.304423Z",
-     "start_time": "2020-11-04T12:43:13.289982Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from datetime import datetime, timedelta\n",
-    "\n",
-    "def datetime_from_dec_year(dec_year):\n",
-    "    start = dec_year\n",
-    "    year = int(start)\n",
-    "    rem = start - year\n",
-    "\n",
-    "    base = datetime(year, 1, 1)\n",
-    "    result = base + timedelta(seconds=(base.replace(year=base.year + 1) - base).total_seconds() * rem)\n",
-    "    #result.strftime(\"%Y-%m-%d\")\n",
-    "    return result.date()\n",
-    "\n",
-    "def extract_map_self_reported(data, data_field, code_map):\n",
-    "    pbar = tqdm(total=16)\n",
-    "    ### codes\n",
-    "    fields = [\"20002\"]; pbar.update(1)\n",
-    "    raw = get_data_fields_all(fields, data, data_field); pbar.update(1)\n",
-    "    col = \"noncancer_illness_code_selfreported_f20002\"; pbar.update(1)\n",
-    "    temp = pd.wide_to_long(raw, stubnames=[col], i=\"eid\", j=\"instance_index\", sep=\"_\", suffix=\"\\w+\").reset_index(); pbar.update(1)\n",
-    "    codes = temp.rename(columns={col:\"code\"})\\\n",
-    "        .assign(code=lambda x: x.code.astype(str))\\\n",
-    "        .replace(\"None\", np.nan) \\\n",
-    "        .dropna(subset=[\"code\"], axis=0)\\\n",
-    "        .assign(code=lambda x: x.code.astype(int)) \\\n",
-    "        .merge(code_map, how=\"left\",on=\"code\") \\\n",
-    "        .dropna(subset=[\"meaning\"], axis=0)\\\n",
-    "        .sort_values([\"eid\", \"instance_index\"]) \\\n",
-    "        .reset_index(drop=True); pbar.update(1)\n",
-    "    \n",
-    "    ### dates\n",
-    "    fields = [\"20008\"]; pbar.update(1)\n",
-    "    raw = get_data_fields_all(fields, data, data_field); pbar.update(1)\n",
-    "    col=\"interpolated_year_when_noncancer_illness_first_diagnosed_f20008\"; pbar.update(1)\n",
-    "    temp = pd.wide_to_long(raw, stubnames=[col], i=\"eid\", j=\"instance_index\", sep=\"_\", suffix=\"\\w+\").reset_index(); pbar.update(1)\n",
-    "    dates = temp.rename(columns={col:\"date\"})\\\n",
-    "        .dropna(subset=[\"date\"], axis=0)\\\n",
-    "        .sort_values([\"eid\", \"instance_index\"]) \\\n",
-    "        .reset_index(drop=True); pbar.update(1)\n",
-    "\n",
-    "    dates = dates[dates.date!=-1]; pbar.update(1)\n",
-    "    dates = dates[dates.date!=-3]; pbar.update(1)\n",
-    "    dates.date = dates.date.apply(datetime_from_dec_year); pbar.update(1)\n",
-    "    \n",
-    "    test = codes.merge(dates, how=\"left\", on=[\"eid\", \"instance_index\"]).assign(origin=\"self_reported\").copy(); pbar.update(1)\n",
-    "    \n",
-    "    test[\"instance_index\"] = test[\"instance_index\"].astype(\"string\"); pbar.update(1)\n",
-    "    test[['instance','n']] = test.instance_index.str.split(\"_\",expand=True); pbar.update(1)\n",
-    "    pbar.close()\n",
-    "    \n",
-    "    return test[[\"eid\", \"origin\", 'instance','n', \"code\", \"meaning\", \"date\"]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 126,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:46:00.098893Z",
-     "start_time": "2020-11-04T12:43:13.432479Z"
-    },
-    "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "aaecac4f22c148f09d00f7036aef37ff",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(HTML(value=''), FloatProgress(value=0.0, max=16.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "ename": "ValueError",
-     "evalue": "invalid literal for int() with base 10: 'nan'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-126-8b0ceffca143>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcodes_self_reported\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mextract_map_self_reported\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata_field\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcoding609\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m<ipython-input-125-cc3ce51f6345>\u001b[0m in \u001b[0;36mextract_map_self_reported\u001b[0;34m(data, data_field, code_map)\u001b[0m\n\u001b[1;32m     22\u001b[0m         \u001b[0;34m.\u001b[0m\u001b[0mreplace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"None\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnan\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m         \u001b[0;34m.\u001b[0m\u001b[0mdropna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msubset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"code\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 24\u001b[0;31m         \u001b[0;34m.\u001b[0m\u001b[0massign\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     25\u001b[0m         \u001b[0;34m.\u001b[0m\u001b[0mmerge\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcode_map\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"left\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mon\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"code\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     26\u001b[0m         \u001b[0;34m.\u001b[0m\u001b[0mdropna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msubset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"meaning\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36massign\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m   3693\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3694\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3695\u001b[0;31m             \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_if_callable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3696\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3697\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
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-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m    593\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"raise\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    594\u001b[0m     ) -> \"BlockManager\":\n\u001b[0;32m--> 595\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"astype\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    596\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    597\u001b[0m     def convert(\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, f, align_keys, **kwargs)\u001b[0m\n\u001b[1;32m    404\u001b[0m                 \u001b[0mapplied\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    405\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 406\u001b[0;31m                 \u001b[0mapplied\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    407\u001b[0m             \u001b[0mresult_blocks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_extend_blocks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mapplied\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult_blocks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    408\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/pandas/core/internals/blocks.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m    593\u001b[0m             \u001b[0mvals1d\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    594\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 595\u001b[0;31m                 \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mastype_nansafe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvals1d\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    596\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mValueError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    597\u001b[0m                 \u001b[0;31m# e.g. astype_nansafe can fail on object-dtype of strings\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/pandas/core/dtypes/cast.py\u001b[0m in \u001b[0;36mastype_nansafe\u001b[0;34m(arr, dtype, copy, skipna)\u001b[0m\n\u001b[1;32m    970\u001b[0m         \u001b[0;31m# work around NumPy brokenness, #1987\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    971\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0missubdtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minteger\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 972\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype_intsafe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    973\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    974\u001b[0m         \u001b[0;31m# if we have a datetime/timedelta array of objects\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32mpandas/_libs/lib.pyx\u001b[0m in \u001b[0;36mpandas._libs.lib.astype_intsafe\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;31mValueError\u001b[0m: invalid literal for int() with base 10: 'nan'"
-     ]
-    }
-   ],
-   "source": [
-    "codes_self_reported = extract_map_self_reported(data, data_field, coding609)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 2. Primary Care"
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {},
-   "source": [
-    "# 2. Primary Care\n",
-    "fields = c(\n",
-    "    42040 # GP clinical event records\n",
-    ")\n",
-    "# extract covariates at baseline (Instance 0)\n",
-    "def = data_field %>% filter((field.showcase %in% fields),ignore.case = TRUE)\n",
-    "diagnoses_primary_care = data[, append(\"eid\", def$col.name)]\n",
-    "head(diagnoses_primary_care)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 3. Hospital episode statistics"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:46:08.959617Z",
-     "start_time": "2020-11-04T12:46:00.100618Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>origin</th>\n",
-       "      <th>instance</th>\n",
-       "      <th>n</th>\n",
-       "      <th>code</th>\n",
-       "      <th>meaning</th>\n",
-       "      <th>date</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>hes_icd10</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>S0240</td>\n",
-       "      <td>S02</td>\n",
-       "      <td>2005-06-02</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>hes_icd10</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>W188</td>\n",
-       "      <td>W18</td>\n",
-       "      <td>2005-06-02</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>hes_icd10</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>K37</td>\n",
-       "      <td>K37</td>\n",
-       "      <td>1998-05-11</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>hes_icd10</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>K37</td>\n",
-       "      <td>K37</td>\n",
-       "      <td>1998-05-16</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>hes_icd10</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>5</td>\n",
-       "      <td>K37</td>\n",
-       "      <td>K37</td>\n",
-       "      <td>1998-06-01</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       eid     origin  instance  n   code meaning        date\n",
-       "0  1000018  hes_icd10       0.0  1  S0240     S02  2005-06-02\n",
-       "1  1000018  hes_icd10       0.0  2   W188     W18  2005-06-02\n",
-       "2  1000018  hes_icd10       0.0  3    K37     K37  1998-05-11\n",
-       "3  1000018  hes_icd10       0.0  4    K37     K37  1998-05-16\n",
-       "4  1000018  hes_icd10       0.0  5    K37     K37  1998-06-01"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "codes_hospital_records = pd.read_feather(f\"{data_path}/1_decoded/codes_hospital_records.feather\").drop(\"level\", axis=1)\n",
-    "# self reported bypass\n",
-    "diagnoses_codes = codes_hospital_records \n",
-    "diagnoses_codes.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Combine Basics and Diagnosis"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:46:25.115010Z",
-     "start_time": "2020-11-04T12:46:08.961388Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "#diagnoses_codes = codes_self_reported.append(codes_hospital_records).sort_values([\"eid\", \"instance\", \"n\"]).dropna(subset=[\"date\"], axis=0)\n",
-    "#diagnoses_codes.head()\n",
-    "#diagnoses_codes.reset_index(drop=True).info()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d42e575a8af7490da42d1af0164795c9",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(HTML(value=''), FloatProgress(value=0.0, max=1168.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "l_test = l10\n",
-    "icd_list = [item for sublist in l_test.values() for item in sublist]\n",
-    "icd_list = sorted(list(dict.fromkeys(icd_list)))\n",
-    "\n",
-    "icd_dict = {}\n",
-    "for code in tqdm(icd_list):\n",
-    "    diag_list = []\n",
-    "    for key in l_test:\n",
-    "        if code in l_test[key]: diag_list.append(key)\n",
-    "    icd_dict[code] = diag_list"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "time0=time0_col\n",
-    "diagnoses_codes_eid = diagnoses_codes[diagnoses_codes.eid.isin(df_birthdate.reset_index().eid.to_list())].reset_index(drop=True)\n",
-    "diagnoses_codes_eid_icd = diagnoses_codes_eid[diagnoses_codes_eid.meaning.isin(icd_dict)]\n",
-    "diagnoses_codes_time = diagnoses_codes_eid_icd.merge(df_birthdate.reset_index()[[\"eid\", time0]], how=\"left\", on=\"eid\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dct_simple = diagnoses_codes_time.assign(t= lambda x: (x.date-x.birthdate).dt.days/365.2425)[[\"eid\", \"t\", \"meaning\"]]\n",
-    "dct_simple.t= dct_simple.t.round(1)\n",
-    "dct_simple[\"diagnosis\"] = [icd_dict[code] for code in dct_simple.meaning]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 331,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#for col in list(l10.keys()): dct_simple[col]=False"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dct_simple_eids = df_birthdate.reset_index()[[\"eid\"]].merge(dct_simple, how=\"left\", on=\"eid\").drop([\"meaning\"], axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>t</th>\n",
-       "      <th>diagnosis</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>44.6</td>\n",
-       "      <td>[injury_of_head, fracture_of_bone]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>37.5</td>\n",
-       "      <td>[appendicitis]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>37.5</td>\n",
-       "      <td>[appendicitis]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>37.6</td>\n",
-       "      <td>[appendicitis]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>56.0</td>\n",
-       "      <td>[vaginitis, postmenopausal_bleeding, bleeding_...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9517540</th>\n",
-       "      <td>6025198</td>\n",
-       "      <td>75.9</td>\n",
-       "      <td>[anemia]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9517541</th>\n",
-       "      <td>6025198</td>\n",
-       "      <td>75.9</td>\n",
-       "      <td>[liver_function_tests_abnormal]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9517542</th>\n",
-       "      <td>6025198</td>\n",
-       "      <td>75.9</td>\n",
-       "      <td>[tobacco_dependence_syndrome, tobacco_user, sm...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9517543</th>\n",
-       "      <td>6025198</td>\n",
-       "      <td>75.9</td>\n",
-       "      <td>[hypertensive_disorder_systemic_arterial, esse...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9517544</th>\n",
-       "      <td>6025198</td>\n",
-       "      <td>75.9</td>\n",
-       "      <td>[hyperlipidemia, hypercholesterolemia, arthrit...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>9517545 rows × 3 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "             eid     t                                          diagnosis\n",
-       "0        1000018  44.6                 [injury_of_head, fracture_of_bone]\n",
-       "1        1000018  37.5                                     [appendicitis]\n",
-       "2        1000018  37.5                                     [appendicitis]\n",
-       "3        1000018  37.6                                     [appendicitis]\n",
-       "4        1000018  56.0  [vaginitis, postmenopausal_bleeding, bleeding_...\n",
-       "...          ...   ...                                                ...\n",
-       "9517540  6025198  75.9                                           [anemia]\n",
-       "9517541  6025198  75.9                    [liver_function_tests_abnormal]\n",
-       "9517542  6025198  75.9  [tobacco_dependence_syndrome, tobacco_user, sm...\n",
-       "9517543  6025198  75.9  [hypertensive_disorder_systemic_arterial, esse...\n",
-       "9517544  6025198  75.9  [hyperlipidemia, hypercholesterolemia, arthrit...\n",
-       "\n",
-       "[9517545 rows x 3 columns]"
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dct_simple_eids"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dct = dct_simple_eids.groupby([\"eid\", \"t\"]).agg({'diagnosis': \"sum\"})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th>diagnosis</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>eid</th>\n",
-       "      <th>t</th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"5\" valign=\"top\">1000018</th>\n",
-       "      <th>37.5</th>\n",
-       "      <td>[appendicitis, appendicitis]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>37.6</th>\n",
-       "      <td>[appendicitis]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>44.6</th>\n",
-       "      <td>[injury_of_head, fracture_of_bone]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>56.0</th>\n",
-       "      <td>[vaginitis, postmenopausal_bleeding, bleeding_...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>58.3</th>\n",
-       "      <td>[melanocytic_nevus, hypertensive_disorder_syst...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6025173</th>\n",
-       "      <th>66.5</th>\n",
-       "      <td>[neutropenic_disorder, leukopenia]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"2\" valign=\"top\">6025182</th>\n",
-       "      <th>44.2</th>\n",
-       "      <td>[urinary_tract_infectious_disease, urinary_inc...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50.3</th>\n",
-       "      <td>[headache, pain]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"2\" valign=\"top\">6025198</th>\n",
-       "      <th>75.8</th>\n",
-       "      <td>[sepsis, methicillin_resistant_staphylococcus_...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75.9</th>\n",
-       "      <td>[cardiac_arrest, chronic_obstructive_lung_dise...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>2413496 rows × 1 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                      diagnosis\n",
-       "eid     t                                                      \n",
-       "1000018 37.5                       [appendicitis, appendicitis]\n",
-       "        37.6                                     [appendicitis]\n",
-       "        44.6                 [injury_of_head, fracture_of_bone]\n",
-       "        56.0  [vaginitis, postmenopausal_bleeding, bleeding_...\n",
-       "        58.3  [melanocytic_nevus, hypertensive_disorder_syst...\n",
-       "...                                                         ...\n",
-       "6025173 66.5                 [neutropenic_disorder, leukopenia]\n",
-       "6025182 44.2  [urinary_tract_infectious_disease, urinary_inc...\n",
-       "        50.3                                   [headache, pain]\n",
-       "6025198 75.8  [sepsis, methicillin_resistant_staphylococcus_...\n",
-       "        75.9  [cardiac_arrest, chronic_obstructive_lung_dise...\n",
-       "\n",
-       "[2413496 rows x 1 columns]"
-      ]
-     },
-     "execution_count": 44,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dct"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 336,
-   "metadata": {
-    "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "aa7a9a7015a64772b9997990b5232e50",
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-       "HBox(children=(HTML(value=''), FloatProgress(value=0.0, max=2413496.0), HTML(value='')))"
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-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    },
-    {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-336-c1ce43766891>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mdss_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0md_list\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtqdm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdct\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"diagnosis\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0mdss_list\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;32mTrue\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0me\u001b[0m \u001b[0;32min\u001b[0m \u001b[0md_list\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;32mFalse\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0me\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ml10\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m<ipython-input-336-c1ce43766891>\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mdss_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0md_list\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtqdm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdct\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"diagnosis\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0mdss_list\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;32mTrue\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0me\u001b[0m \u001b[0;32min\u001b[0m \u001b[0md_list\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;32mFalse\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0me\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ml10\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
-     ]
-    }
-   ],
-   "source": [
-    "dss_list = []\n",
-    "keys = list(l10.keys())\n",
-    "for d_list in tqdm(dct[\"diagnosis\"].values):\n",
-    "    dss_list.append([True if e in d_list else False for e in list(l10.keys())])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 348,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%load_ext Cython"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_list(d_list, keys): \n",
-    "    return [True if e in d_list else False for e in list(keys)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_diagnoses_wide(diagnoses_array, keys): \n",
-    "    dss_list = []\n",
-    "    for d_list in diagnoses_array:\n",
-    "        dss_list.append([True if e in d_list else False for e in keys])  \n",
-    "    return dss_list"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "diagnoses_array = dct[\"diagnosis\"].values\n",
-    "keys = list(l10.keys())\n",
-    "dss_list = get_diagnoses_wide(diagnoses_array, keys)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "diagnoses_array"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "4"
-      ]
-     },
-     "execution_count": 62,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "2+2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "50da22a83855421e860f0e19dee0b8da",
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-      },
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-       "HBox(children=(HTML(value=''), FloatProgress(value=0.0, max=2413496.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "def get_list(d_list, keys): return [True if e in d_list else False for e in keys]\n",
-    "\n",
-    "keys = list(l10.keys())\n",
-    "dss_list = [get_list(d_list, keys) for d_list in tqdm(dct[\"diagnosis\"].values)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "diagnoses_df = pd.DataFrame(data=np.array(dss_list), columns=list(l10.keys()))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "diagnoses = pd.concat([dct.reset_index(), diagnoses_df], axis=1).set_index([\"eid\", \"t\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th>diagnosis</th>\n",
-       "      <th>hypertensive_disorder_systemic_arterial</th>\n",
-       "      <th>hyperlipidemia</th>\n",
-       "      <th>depressive_disorder</th>\n",
-       "      <th>gastroesophageal_reflux_disease</th>\n",
-       "      <th>diabetes_mellitus_type_2</th>\n",
-       "      <th>essential_hypertension</th>\n",
-       "      <th>obesity</th>\n",
-       "      <th>diabetes_mellitus</th>\n",
-       "      <th>asthma</th>\n",
-       "      <th>...</th>\n",
-       "      <th>nonvenomous_insect_bite</th>\n",
-       "      <th>spondylolisthesis</th>\n",
-       "      <th>malignant_tumor_of_esophagus</th>\n",
-       "      <th>aphthous_ulcer_of_mouth</th>\n",
-       "      <th>ventricular_septal_defect</th>\n",
-       "      <th>oropharyngeal_dysphagia</th>\n",
-       "      <th>injury_of_knee</th>\n",
-       "      <th>traumatic_brain_injury</th>\n",
-       "      <th>osteoarthritis_of_glenohumeral_joint</th>\n",
-       "      <th>fetal_or_neonatal_effect_of_maternal_medical_problem</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>eid</th>\n",
-       "      <th>t</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
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-       "      <th></th>\n",
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-       "      <th></th>\n",
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-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"5\" valign=\"top\">1000018</th>\n",
-       "      <th>37.5</th>\n",
-       "      <td>[appendicitis, appendicitis]</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>37.6</th>\n",
-       "      <td>[appendicitis]</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>44.6</th>\n",
-       "      <td>[injury_of_head, fracture_of_bone]</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>56.0</th>\n",
-       "      <td>[vaginitis, postmenopausal_bleeding, bleeding_...</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>58.3</th>\n",
-       "      <td>[melanocytic_nevus, hypertensive_disorder_syst...</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6025173</th>\n",
-       "      <th>66.5</th>\n",
-       "      <td>[neutropenic_disorder, leukopenia]</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"2\" valign=\"top\">6025182</th>\n",
-       "      <th>44.2</th>\n",
-       "      <td>[urinary_tract_infectious_disease, urinary_inc...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50.3</th>\n",
-       "      <td>[headache, pain]</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"2\" valign=\"top\">6025198</th>\n",
-       "      <th>75.8</th>\n",
-       "      <td>[sepsis, methicillin_resistant_staphylococcus_...</td>\n",
-       "      <td>True</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75.9</th>\n",
-       "      <td>[cardiac_arrest, chronic_obstructive_lung_dise...</td>\n",
-       "      <td>True</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>2413496 rows × 798 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                      diagnosis  \\\n",
-       "eid     t                                                         \n",
-       "1000018 37.5                       [appendicitis, appendicitis]   \n",
-       "        37.6                                     [appendicitis]   \n",
-       "        44.6                 [injury_of_head, fracture_of_bone]   \n",
-       "        56.0  [vaginitis, postmenopausal_bleeding, bleeding_...   \n",
-       "        58.3  [melanocytic_nevus, hypertensive_disorder_syst...   \n",
-       "...                                                         ...   \n",
-       "6025173 66.5                 [neutropenic_disorder, leukopenia]   \n",
-       "6025182 44.2  [urinary_tract_infectious_disease, urinary_inc...   \n",
-       "        50.3                                   [headache, pain]   \n",
-       "6025198 75.8  [sepsis, methicillin_resistant_staphylococcus_...   \n",
-       "        75.9  [cardiac_arrest, chronic_obstructive_lung_dise...   \n",
-       "\n",
-       "              hypertensive_disorder_systemic_arterial  hyperlipidemia  \\\n",
-       "eid     t                                                               \n",
-       "1000018 37.5                                    False           False   \n",
-       "        37.6                                    False           False   \n",
-       "        44.6                                    False           False   \n",
-       "        56.0                                     True           False   \n",
-       "        58.3                                     True           False   \n",
-       "...                                               ...             ...   \n",
-       "6025173 66.5                                    False           False   \n",
-       "6025182 44.2                                    False           False   \n",
-       "        50.3                                    False           False   \n",
-       "6025198 75.8                                     True            True   \n",
-       "        75.9                                     True            True   \n",
-       "\n",
-       "              depressive_disorder  gastroesophageal_reflux_disease  \\\n",
-       "eid     t                                                            \n",
-       "1000018 37.5                False                            False   \n",
-       "        37.6                False                            False   \n",
-       "        44.6                False                            False   \n",
-       "        56.0                False                            False   \n",
-       "        58.3                False                            False   \n",
-       "...                           ...                              ...   \n",
-       "6025173 66.5                False                            False   \n",
-       "6025182 44.2                False                            False   \n",
-       "        50.3                False                            False   \n",
-       "6025198 75.8                False                            False   \n",
-       "        75.9                False                            False   \n",
-       "\n",
-       "              diabetes_mellitus_type_2  essential_hypertension  obesity  \\\n",
-       "eid     t                                                                 \n",
-       "1000018 37.5                     False                   False    False   \n",
-       "        37.6                     False                   False    False   \n",
-       "        44.6                     False                   False    False   \n",
-       "        56.0                     False                    True    False   \n",
-       "        58.3                     False                    True    False   \n",
-       "...                                ...                     ...      ...   \n",
-       "6025173 66.5                     False                   False    False   \n",
-       "6025182 44.2                     False                   False    False   \n",
-       "        50.3                     False                   False    False   \n",
-       "6025198 75.8                     False                    True    False   \n",
-       "        75.9                     False                    True    False   \n",
-       "\n",
-       "              diabetes_mellitus  asthma  ...  nonvenomous_insect_bite  \\\n",
-       "eid     t                                ...                            \n",
-       "1000018 37.5              False   False  ...                    False   \n",
-       "        37.6              False   False  ...                    False   \n",
-       "        44.6              False   False  ...                    False   \n",
-       "        56.0              False   False  ...                    False   \n",
-       "        58.3              False   False  ...                    False   \n",
-       "...                         ...     ...  ...                      ...   \n",
-       "6025173 66.5              False   False  ...                    False   \n",
-       "6025182 44.2              False   False  ...                    False   \n",
-       "        50.3              False   False  ...                    False   \n",
-       "6025198 75.8              False   False  ...                    False   \n",
-       "        75.9              False   False  ...                    False   \n",
-       "\n",
-       "              spondylolisthesis  malignant_tumor_of_esophagus  \\\n",
-       "eid     t                                                       \n",
-       "1000018 37.5              False                         False   \n",
-       "        37.6              False                         False   \n",
-       "        44.6              False                         False   \n",
-       "        56.0              False                         False   \n",
-       "        58.3              False                         False   \n",
-       "...                         ...                           ...   \n",
-       "6025173 66.5              False                         False   \n",
-       "6025182 44.2              False                         False   \n",
-       "        50.3              False                         False   \n",
-       "6025198 75.8              False                         False   \n",
-       "        75.9              False                         False   \n",
-       "\n",
-       "              aphthous_ulcer_of_mouth  ventricular_septal_defect  \\\n",
-       "eid     t                                                          \n",
-       "1000018 37.5                    False                      False   \n",
-       "        37.6                    False                      False   \n",
-       "        44.6                    False                      False   \n",
-       "        56.0                    False                      False   \n",
-       "        58.3                    False                      False   \n",
-       "...                               ...                        ...   \n",
-       "6025173 66.5                    False                      False   \n",
-       "6025182 44.2                    False                      False   \n",
-       "        50.3                    False                      False   \n",
-       "6025198 75.8                    False                      False   \n",
-       "        75.9                    False                      False   \n",
-       "\n",
-       "              oropharyngeal_dysphagia  injury_of_knee  traumatic_brain_injury  \\\n",
-       "eid     t                                                                       \n",
-       "1000018 37.5                    False           False                   False   \n",
-       "        37.6                    False           False                   False   \n",
-       "        44.6                    False           False                   False   \n",
-       "        56.0                    False           False                   False   \n",
-       "        58.3                    False           False                   False   \n",
-       "...                               ...             ...                     ...   \n",
-       "6025173 66.5                    False           False                   False   \n",
-       "6025182 44.2                    False           False                   False   \n",
-       "        50.3                    False           False                   False   \n",
-       "6025198 75.8                    False           False                   False   \n",
-       "        75.9                    False           False                   False   \n",
-       "\n",
-       "              osteoarthritis_of_glenohumeral_joint  \\\n",
-       "eid     t                                            \n",
-       "1000018 37.5                                 False   \n",
-       "        37.6                                 False   \n",
-       "        44.6                                 False   \n",
-       "        56.0                                 False   \n",
-       "        58.3                                 False   \n",
-       "...                                            ...   \n",
-       "6025173 66.5                                 False   \n",
-       "6025182 44.2                                 False   \n",
-       "        50.3                                 False   \n",
-       "6025198 75.8                                 False   \n",
-       "        75.9                                 False   \n",
-       "\n",
-       "              fetal_or_neonatal_effect_of_maternal_medical_problem  \n",
-       "eid     t                                                           \n",
-       "1000018 37.5                                              False     \n",
-       "        37.6                                              False     \n",
-       "        44.6                                              False     \n",
-       "        56.0                                              False     \n",
-       "        58.3                                              False     \n",
-       "...                                                         ...     \n",
-       "6025173 66.5                                              False     \n",
-       "6025182 44.2                                              False     \n",
-       "        50.3                                              False     \n",
-       "6025198 75.8                                              False     \n",
-       "        75.9                                              False     \n",
-       "\n",
-       "[2413496 rows x 798 columns]"
-      ]
-     },
-     "execution_count": 48,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "diagnoses"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_baseline_time.t= df_baseline_time.t.round(1)\n",
-    "df_baseline_time = df_baseline_time.set_index([\"eid\", \"t\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 102,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "na_cols = df_baseline_time.columns.to_list()[1:]\n",
-    "df_baseline_time = df_baseline_time.dropna(how=\"all\", subset=na_cols, axis=0)#.reset_index(drop=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 103,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th>birthdate</th>\n",
-       "      <th>sex_f31</th>\n",
-       "      <th>ethnic_background_f21000</th>\n",
-       "      <th>overall_health_rating_f2178</th>\n",
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-       "      <th>alcohol_intake_frequency_f1558</th>\n",
-       "      <th>townsend_deprivation_index_at_recruitment_f189</th>\n",
-       "      <th>body_mass_index_bmi_f21001</th>\n",
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-       "      <th>systolic_blood_pressure_automated_reading_f4080</th>\n",
-       "      <th>...</th>\n",
-       "      <th>phosphate_f30810</th>\n",
-       "      <th>rheumatoid_factor_f30820</th>\n",
-       "      <th>shbg_f30830</th>\n",
-       "      <th>testosterone_f30850</th>\n",
-       "      <th>total_bilirubin_f30840</th>\n",
-       "      <th>total_protein_f30860</th>\n",
-       "      <th>triglycerides_f30870</th>\n",
-       "      <th>urate_f30880</th>\n",
-       "      <th>urea_f30670</th>\n",
-       "      <th>vitamin_d_f30890</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>eid</th>\n",
-       "      <th>t</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
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-       "      <th></th>\n",
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-       "      <th></th>\n",
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-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1000018</th>\n",
-       "      <th>49.0</th>\n",
-       "      <td>1960-11-12</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>British</td>\n",
-       "      <td>Fair</td>\n",
-       "      <td>Current</td>\n",
-       "      <td>Once or twice a week</td>\n",
-       "      <td>-1.852930</td>\n",
-       "      <td>26.5557</td>\n",
-       "      <td>63.8</td>\n",
-       "      <td>159.5</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.422</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>70.11</td>\n",
-       "      <td>1.560</td>\n",
-       "      <td>7.41</td>\n",
-       "      <td>71.97</td>\n",
-       "      <td>1.247</td>\n",
-       "      <td>221.3</td>\n",
-       "      <td>5.48</td>\n",
-       "      <td>70.7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1000020</th>\n",
-       "      <th>59.0</th>\n",
-       "      <td>1949-02-19</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>British</td>\n",
-       "      <td>Good</td>\n",
-       "      <td>Current</td>\n",
-       "      <td>Once or twice a week</td>\n",
-       "      <td>0.204248</td>\n",
-       "      <td>22.7465</td>\n",
-       "      <td>70.7</td>\n",
-       "      <td>133.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.264</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>55.31</td>\n",
-       "      <td>12.237</td>\n",
-       "      <td>8.07</td>\n",
-       "      <td>78.45</td>\n",
-       "      <td>1.906</td>\n",
-       "      <td>374.7</td>\n",
-       "      <td>5.28</td>\n",
-       "      <td>35.9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1000037</th>\n",
-       "      <th>59.0</th>\n",
-       "      <td>1949-11-11</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>British</td>\n",
-       "      <td>Good</td>\n",
-       "      <td>Previous</td>\n",
-       "      <td>Once or twice a week</td>\n",
-       "      <td>-3.498860</td>\n",
-       "      <td>32.4211</td>\n",
-       "      <td>78.9</td>\n",
-       "      <td>118.5</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"2\" valign=\"top\">1000043</th>\n",
-       "      <th>63.0</th>\n",
-       "      <td>1946-06-03</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>British</td>\n",
-       "      <td>Fair</td>\n",
-       "      <td>Previous</td>\n",
-       "      <td>Three or four times a week</td>\n",
-       "      <td>-5.351150</td>\n",
-       "      <td>29.5679</td>\n",
-       "      <td>95.8</td>\n",
-       "      <td>141.5</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.928</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>31.63</td>\n",
-       "      <td>11.398</td>\n",
-       "      <td>8.65</td>\n",
-       "      <td>69.70</td>\n",
-       "      <td>5.184</td>\n",
-       "      <td>322.8</td>\n",
-       "      <td>6.67</td>\n",
-       "      <td>63.6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>72.0</th>\n",
-       "      <td>1946-06-03</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Fair</td>\n",
-       "      <td>Previous</td>\n",
-       "      <td>Three or four times a week</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>28.4349</td>\n",
-       "      <td>90.6</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6025150</th>\n",
-       "      <th>55.3</th>\n",
-       "      <td>1964-06-30</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Good</td>\n",
-       "      <td>Never</td>\n",
-       "      <td>Three or four times a week</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>33.5072</td>\n",
-       "      <td>95.7</td>\n",
-       "      <td>131.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6025165</th>\n",
-       "      <th>45.0</th>\n",
-       "      <td>1963-09-02</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>British</td>\n",
-       "      <td>Good</td>\n",
-       "      <td>Never</td>\n",
-       "      <td>Three or four times a week</td>\n",
-       "      <td>-2.107040</td>\n",
-       "      <td>24.2275</td>\n",
-       "      <td>62.8</td>\n",
-       "      <td>152.5</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.996</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>73.38</td>\n",
-       "      <td>0.652</td>\n",
-       "      <td>11.19</td>\n",
-       "      <td>74.20</td>\n",
-       "      <td>1.442</td>\n",
-       "      <td>220.2</td>\n",
-       "      <td>4.01</td>\n",
-       "      <td>72.7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6025173</th>\n",
-       "      <th>57.0</th>\n",
-       "      <td>1951-09-17</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>British</td>\n",
-       "      <td>Good</td>\n",
-       "      <td>Never</td>\n",
-       "      <td>Never</td>\n",
-       "      <td>-1.827220</td>\n",
-       "      <td>25.9504</td>\n",
-       "      <td>81.3</td>\n",
-       "      <td>131.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.119</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>50.13</td>\n",
-       "      <td>13.517</td>\n",
-       "      <td>6.31</td>\n",
-       "      <td>72.03</td>\n",
-       "      <td>1.136</td>\n",
-       "      <td>255.5</td>\n",
-       "      <td>5.25</td>\n",
-       "      <td>41.6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6025182</th>\n",
-       "      <th>56.0</th>\n",
-       "      <td>1954-07-01</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>British</td>\n",
-       "      <td>Excellent</td>\n",
-       "      <td>Previous</td>\n",
-       "      <td>Daily or almost daily</td>\n",
-       "      <td>-0.010764</td>\n",
-       "      <td>29.1425</td>\n",
-       "      <td>104.1</td>\n",
-       "      <td>127.5</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.986</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>24.48</td>\n",
-       "      <td>10.951</td>\n",
-       "      <td>9.95</td>\n",
-       "      <td>70.65</td>\n",
-       "      <td>5.756</td>\n",
-       "      <td>353.6</td>\n",
-       "      <td>4.42</td>\n",
-       "      <td>45.9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6025198</th>\n",
-       "      <th>67.0</th>\n",
-       "      <td>1943-01-26</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>British</td>\n",
-       "      <td>Good</td>\n",
-       "      <td>Current</td>\n",
-       "      <td>Daily or almost daily</td>\n",
-       "      <td>-1.930650</td>\n",
-       "      <td>29.5988</td>\n",
-       "      <td>102.4</td>\n",
-       "      <td>156.5</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.163</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>45.09</td>\n",
-       "      <td>15.030</td>\n",
-       "      <td>11.85</td>\n",
-       "      <td>70.62</td>\n",
-       "      <td>2.327</td>\n",
-       "      <td>454.8</td>\n",
-       "      <td>5.14</td>\n",
-       "      <td>20.2</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>574976 rows × 85 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "               birthdate sex_f31 ethnic_background_f21000  \\\n",
-       "eid     t                                                   \n",
-       "1000018 49.0  1960-11-12  Female                  British   \n",
-       "1000020 59.0  1949-02-19    Male                  British   \n",
-       "1000037 59.0  1949-11-11  Female                  British   \n",
-       "1000043 63.0  1946-06-03    Male                  British   \n",
-       "        72.0  1946-06-03     NaN                      NaN   \n",
-       "...                  ...     ...                      ...   \n",
-       "6025150 55.3  1964-06-30     NaN                      NaN   \n",
-       "6025165 45.0  1963-09-02  Female                  British   \n",
-       "6025173 57.0  1951-09-17    Male                  British   \n",
-       "6025182 56.0  1954-07-01    Male                  British   \n",
-       "6025198 67.0  1943-01-26    Male                  British   \n",
-       "\n",
-       "             overall_health_rating_f2178 smoking_status_f20116  \\\n",
-       "eid     t                                                        \n",
-       "1000018 49.0                        Fair               Current   \n",
-       "1000020 59.0                        Good               Current   \n",
-       "1000037 59.0                        Good              Previous   \n",
-       "1000043 63.0                        Fair              Previous   \n",
-       "        72.0                        Fair              Previous   \n",
-       "...                                  ...                   ...   \n",
-       "6025150 55.3                        Good                 Never   \n",
-       "6025165 45.0                        Good                 Never   \n",
-       "6025173 57.0                        Good                 Never   \n",
-       "6025182 56.0                   Excellent              Previous   \n",
-       "6025198 67.0                        Good               Current   \n",
-       "\n",
-       "             alcohol_intake_frequency_f1558  \\\n",
-       "eid     t                                     \n",
-       "1000018 49.0           Once or twice a week   \n",
-       "1000020 59.0           Once or twice a week   \n",
-       "1000037 59.0           Once or twice a week   \n",
-       "1000043 63.0     Three or four times a week   \n",
-       "        72.0     Three or four times a week   \n",
-       "...                                     ...   \n",
-       "6025150 55.3     Three or four times a week   \n",
-       "6025165 45.0     Three or four times a week   \n",
-       "6025173 57.0                          Never   \n",
-       "6025182 56.0          Daily or almost daily   \n",
-       "6025198 67.0          Daily or almost daily   \n",
-       "\n",
-       "              townsend_deprivation_index_at_recruitment_f189  \\\n",
-       "eid     t                                                      \n",
-       "1000018 49.0                                       -1.852930   \n",
-       "1000020 59.0                                        0.204248   \n",
-       "1000037 59.0                                       -3.498860   \n",
-       "1000043 63.0                                       -5.351150   \n",
-       "        72.0                                             NaN   \n",
-       "...                                                      ...   \n",
-       "6025150 55.3                                             NaN   \n",
-       "6025165 45.0                                       -2.107040   \n",
-       "6025173 57.0                                       -1.827220   \n",
-       "6025182 56.0                                       -0.010764   \n",
-       "6025198 67.0                                       -1.930650   \n",
-       "\n",
-       "              body_mass_index_bmi_f21001  weight_f21002  \\\n",
-       "eid     t                                                 \n",
-       "1000018 49.0                     26.5557           63.8   \n",
-       "1000020 59.0                     22.7465           70.7   \n",
-       "1000037 59.0                     32.4211           78.9   \n",
-       "1000043 63.0                     29.5679           95.8   \n",
-       "        72.0                     28.4349           90.6   \n",
-       "...                                  ...            ...   \n",
-       "6025150 55.3                     33.5072           95.7   \n",
-       "6025165 45.0                     24.2275           62.8   \n",
-       "6025173 57.0                     25.9504           81.3   \n",
-       "6025182 56.0                     29.1425          104.1   \n",
-       "6025198 67.0                     29.5988          102.4   \n",
-       "\n",
-       "              systolic_blood_pressure_automated_reading_f4080  ...  \\\n",
-       "eid     t                                                      ...   \n",
-       "1000018 49.0                                            159.5  ...   \n",
-       "1000020 59.0                                            133.0  ...   \n",
-       "1000037 59.0                                            118.5  ...   \n",
-       "1000043 63.0                                            141.5  ...   \n",
-       "        72.0                                              NaN  ...   \n",
-       "...                                                       ...  ...   \n",
-       "6025150 55.3                                            131.0  ...   \n",
-       "6025165 45.0                                            152.5  ...   \n",
-       "6025173 57.0                                            131.0  ...   \n",
-       "6025182 56.0                                            127.5  ...   \n",
-       "6025198 67.0                                            156.5  ...   \n",
-       "\n",
-       "              phosphate_f30810  rheumatoid_factor_f30820  shbg_f30830  \\\n",
-       "eid     t                                                               \n",
-       "1000018 49.0             1.422                       NaN        70.11   \n",
-       "1000020 59.0             1.264                       NaN        55.31   \n",
-       "1000037 59.0               NaN                       NaN          NaN   \n",
-       "1000043 63.0             0.928                       NaN        31.63   \n",
-       "        72.0               NaN                       NaN          NaN   \n",
-       "...                        ...                       ...          ...   \n",
-       "6025150 55.3               NaN                       NaN          NaN   \n",
-       "6025165 45.0             0.996                       NaN        73.38   \n",
-       "6025173 57.0             1.119                       NaN        50.13   \n",
-       "6025182 56.0             0.986                       NaN        24.48   \n",
-       "6025198 67.0             1.163                       NaN        45.09   \n",
-       "\n",
-       "              testosterone_f30850  total_bilirubin_f30840  \\\n",
-       "eid     t                                                   \n",
-       "1000018 49.0                1.560                    7.41   \n",
-       "1000020 59.0               12.237                    8.07   \n",
-       "1000037 59.0                  NaN                     NaN   \n",
-       "1000043 63.0               11.398                    8.65   \n",
-       "        72.0                  NaN                     NaN   \n",
-       "...                           ...                     ...   \n",
-       "6025150 55.3                  NaN                     NaN   \n",
-       "6025165 45.0                0.652                   11.19   \n",
-       "6025173 57.0               13.517                    6.31   \n",
-       "6025182 56.0               10.951                    9.95   \n",
-       "6025198 67.0               15.030                   11.85   \n",
-       "\n",
-       "              total_protein_f30860  triglycerides_f30870  urate_f30880  \\\n",
-       "eid     t                                                                \n",
-       "1000018 49.0                 71.97                 1.247         221.3   \n",
-       "1000020 59.0                 78.45                 1.906         374.7   \n",
-       "1000037 59.0                   NaN                   NaN           NaN   \n",
-       "1000043 63.0                 69.70                 5.184         322.8   \n",
-       "        72.0                   NaN                   NaN           NaN   \n",
-       "...                            ...                   ...           ...   \n",
-       "6025150 55.3                   NaN                   NaN           NaN   \n",
-       "6025165 45.0                 74.20                 1.442         220.2   \n",
-       "6025173 57.0                 72.03                 1.136         255.5   \n",
-       "6025182 56.0                 70.65                 5.756         353.6   \n",
-       "6025198 67.0                 70.62                 2.327         454.8   \n",
-       "\n",
-       "              urea_f30670  vitamin_d_f30890  \n",
-       "eid     t                                    \n",
-       "1000018 49.0         5.48              70.7  \n",
-       "1000020 59.0         5.28              35.9  \n",
-       "1000037 59.0          NaN               NaN  \n",
-       "1000043 63.0         6.67              63.6  \n",
-       "        72.0          NaN               NaN  \n",
-       "...                   ...               ...  \n",
-       "6025150 55.3          NaN               NaN  \n",
-       "6025165 45.0         4.01              72.7  \n",
-       "6025173 57.0         5.25              41.6  \n",
-       "6025182 56.0         4.42              45.9  \n",
-       "6025198 67.0         5.14              20.2  \n",
-       "\n",
-       "[574976 rows x 85 columns]"
-      ]
-     },
-     "execution_count": 103,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_baseline_time"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 104,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 3min 26s, sys: 1min 11s, total: 4min 37s\n",
-      "Wall time: 4min 36s\n"
-     ]
-    },
-    {
-     "data": {
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-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th>birthdate</th>\n",
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-       "    <tr>\n",
-       "      <th rowspan=\"5\" valign=\"top\">1000018</th>\n",
-       "      <th>37.5</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>37.6</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>44.6</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>49.0</th>\n",
-       "      <td>1960-11-12</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>British</td>\n",
-       "      <td>Fair</td>\n",
-       "      <td>Current</td>\n",
-       "      <td>Once or twice a week</td>\n",
-       "      <td>-1.852930</td>\n",
-       "      <td>26.5557</td>\n",
-       "      <td>63.8</td>\n",
-       "      <td>159.5</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>56.0</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"2\" valign=\"top\">6025182</th>\n",
-       "      <th>50.3</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>56.0</th>\n",
-       "      <td>1954-07-01</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>British</td>\n",
-       "      <td>Excellent</td>\n",
-       "      <td>Previous</td>\n",
-       "      <td>Daily or almost daily</td>\n",
-       "      <td>-0.010764</td>\n",
-       "      <td>29.1425</td>\n",
-       "      <td>104.1</td>\n",
-       "      <td>127.5</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"3\" valign=\"top\">6025198</th>\n",
-       "      <th>67.0</th>\n",
-       "      <td>1943-01-26</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>British</td>\n",
-       "      <td>Good</td>\n",
-       "      <td>Current</td>\n",
-       "      <td>Daily or almost daily</td>\n",
-       "      <td>-1.930650</td>\n",
-       "      <td>29.5988</td>\n",
-       "      <td>102.4</td>\n",
-       "      <td>156.5</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75.8</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75.9</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>2979355 rows × 883 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "               birthdate sex_f31 ethnic_background_f21000  \\\n",
-       "eid     t                                                   \n",
-       "1000018 37.5         NaN     NaN                      NaN   \n",
-       "        37.6         NaN     NaN                      NaN   \n",
-       "        44.6         NaN     NaN                      NaN   \n",
-       "        49.0  1960-11-12  Female                  British   \n",
-       "        56.0         NaN     NaN                      NaN   \n",
-       "...                  ...     ...                      ...   \n",
-       "6025182 50.3         NaN     NaN                      NaN   \n",
-       "        56.0  1954-07-01    Male                  British   \n",
-       "6025198 67.0  1943-01-26    Male                  British   \n",
-       "        75.8         NaN     NaN                      NaN   \n",
-       "        75.9         NaN     NaN                      NaN   \n",
-       "\n",
-       "             overall_health_rating_f2178 smoking_status_f20116  \\\n",
-       "eid     t                                                        \n",
-       "1000018 37.5                         NaN                   NaN   \n",
-       "        37.6                         NaN                   NaN   \n",
-       "        44.6                         NaN                   NaN   \n",
-       "        49.0                        Fair               Current   \n",
-       "        56.0                         NaN                   NaN   \n",
-       "...                                  ...                   ...   \n",
-       "6025182 50.3                         NaN                   NaN   \n",
-       "        56.0                   Excellent              Previous   \n",
-       "6025198 67.0                        Good               Current   \n",
-       "        75.8                         NaN                   NaN   \n",
-       "        75.9                         NaN                   NaN   \n",
-       "\n",
-       "             alcohol_intake_frequency_f1558  \\\n",
-       "eid     t                                     \n",
-       "1000018 37.5                            NaN   \n",
-       "        37.6                            NaN   \n",
-       "        44.6                            NaN   \n",
-       "        49.0           Once or twice a week   \n",
-       "        56.0                            NaN   \n",
-       "...                                     ...   \n",
-       "6025182 50.3                            NaN   \n",
-       "        56.0          Daily or almost daily   \n",
-       "6025198 67.0          Daily or almost daily   \n",
-       "        75.8                            NaN   \n",
-       "        75.9                            NaN   \n",
-       "\n",
-       "              townsend_deprivation_index_at_recruitment_f189  \\\n",
-       "eid     t                                                      \n",
-       "1000018 37.5                                             NaN   \n",
-       "        37.6                                             NaN   \n",
-       "        44.6                                             NaN   \n",
-       "        49.0                                       -1.852930   \n",
-       "        56.0                                             NaN   \n",
-       "...                                                      ...   \n",
-       "6025182 50.3                                             NaN   \n",
-       "        56.0                                       -0.010764   \n",
-       "6025198 67.0                                       -1.930650   \n",
-       "        75.8                                             NaN   \n",
-       "        75.9                                             NaN   \n",
-       "\n",
-       "              body_mass_index_bmi_f21001  weight_f21002  \\\n",
-       "eid     t                                                 \n",
-       "1000018 37.5                         NaN            NaN   \n",
-       "        37.6                         NaN            NaN   \n",
-       "        44.6                         NaN            NaN   \n",
-       "        49.0                     26.5557           63.8   \n",
-       "        56.0                         NaN            NaN   \n",
-       "...                                  ...            ...   \n",
-       "6025182 50.3                         NaN            NaN   \n",
-       "        56.0                     29.1425          104.1   \n",
-       "6025198 67.0                     29.5988          102.4   \n",
-       "        75.8                         NaN            NaN   \n",
-       "        75.9                         NaN            NaN   \n",
-       "\n",
-       "              systolic_blood_pressure_automated_reading_f4080  ...  \\\n",
-       "eid     t                                                      ...   \n",
-       "1000018 37.5                                              NaN  ...   \n",
-       "        37.6                                              NaN  ...   \n",
-       "        44.6                                              NaN  ...   \n",
-       "        49.0                                            159.5  ...   \n",
-       "        56.0                                              NaN  ...   \n",
-       "...                                                       ...  ...   \n",
-       "6025182 50.3                                              NaN  ...   \n",
-       "        56.0                                            127.5  ...   \n",
-       "6025198 67.0                                            156.5  ...   \n",
-       "        75.8                                              NaN  ...   \n",
-       "        75.9                                              NaN  ...   \n",
-       "\n",
-       "              nonvenomous_insect_bite  spondylolisthesis  \\\n",
-       "eid     t                                                  \n",
-       "1000018 37.5                    False              False   \n",
-       "        37.6                    False              False   \n",
-       "        44.6                    False              False   \n",
-       "        49.0                      NaN                NaN   \n",
-       "        56.0                    False              False   \n",
-       "...                               ...                ...   \n",
-       "6025182 50.3                    False              False   \n",
-       "        56.0                      NaN                NaN   \n",
-       "6025198 67.0                      NaN                NaN   \n",
-       "        75.8                    False              False   \n",
-       "        75.9                    False              False   \n",
-       "\n",
-       "              malignant_tumor_of_esophagus  aphthous_ulcer_of_mouth  \\\n",
-       "eid     t                                                             \n",
-       "1000018 37.5                         False                    False   \n",
-       "        37.6                         False                    False   \n",
-       "        44.6                         False                    False   \n",
-       "        49.0                           NaN                      NaN   \n",
-       "        56.0                         False                    False   \n",
-       "...                                    ...                      ...   \n",
-       "6025182 50.3                         False                    False   \n",
-       "        56.0                           NaN                      NaN   \n",
-       "6025198 67.0                           NaN                      NaN   \n",
-       "        75.8                         False                    False   \n",
-       "        75.9                         False                    False   \n",
-       "\n",
-       "              ventricular_septal_defect  oropharyngeal_dysphagia  \\\n",
-       "eid     t                                                          \n",
-       "1000018 37.5                      False                    False   \n",
-       "        37.6                      False                    False   \n",
-       "        44.6                      False                    False   \n",
-       "        49.0                        NaN                      NaN   \n",
-       "        56.0                      False                    False   \n",
-       "...                                 ...                      ...   \n",
-       "6025182 50.3                      False                    False   \n",
-       "        56.0                        NaN                      NaN   \n",
-       "6025198 67.0                        NaN                      NaN   \n",
-       "        75.8                      False                    False   \n",
-       "        75.9                      False                    False   \n",
-       "\n",
-       "              injury_of_knee  traumatic_brain_injury  \\\n",
-       "eid     t                                              \n",
-       "1000018 37.5           False                   False   \n",
-       "        37.6           False                   False   \n",
-       "        44.6           False                   False   \n",
-       "        49.0             NaN                     NaN   \n",
-       "        56.0           False                   False   \n",
-       "...                      ...                     ...   \n",
-       "6025182 50.3           False                   False   \n",
-       "        56.0             NaN                     NaN   \n",
-       "6025198 67.0             NaN                     NaN   \n",
-       "        75.8           False                   False   \n",
-       "        75.9           False                   False   \n",
-       "\n",
-       "              osteoarthritis_of_glenohumeral_joint  \\\n",
-       "eid     t                                            \n",
-       "1000018 37.5                                 False   \n",
-       "        37.6                                 False   \n",
-       "        44.6                                 False   \n",
-       "        49.0                                   NaN   \n",
-       "        56.0                                 False   \n",
-       "...                                            ...   \n",
-       "6025182 50.3                                 False   \n",
-       "        56.0                                   NaN   \n",
-       "6025198 67.0                                   NaN   \n",
-       "        75.8                                 False   \n",
-       "        75.9                                 False   \n",
-       "\n",
-       "              fetal_or_neonatal_effect_of_maternal_medical_problem  \n",
-       "eid     t                                                           \n",
-       "1000018 37.5                                              False     \n",
-       "        37.6                                              False     \n",
-       "        44.6                                              False     \n",
-       "        49.0                                                NaN     \n",
-       "        56.0                                              False     \n",
-       "...                                                         ...     \n",
-       "6025182 50.3                                              False     \n",
-       "        56.0                                                NaN     \n",
-       "6025198 67.0                                                NaN     \n",
-       "        75.8                                              False     \n",
-       "        75.9                                              False     \n",
-       "\n",
-       "[2979355 rows x 883 columns]"
-      ]
-     },
-     "execution_count": 104,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "\n",
-    "df_baseline_diagnoses= pd.concat([df_baseline_time, diagnoses], axis=1)\n",
-    "df_baseline_diagnoses"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 106,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_baseline_diagnoses.reset_index().to_feather(os.path.join(path, dataset_path, 'baseline_diagnoses_times.feather'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 107,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "MultiIndex: 2979355 entries, (1000018, 37.5) to (6025198, 75.9)\n",
-      "Columns: 883 entries, birthdate to fetal_or_neonatal_effect_of_maternal_medical_problem\n",
-      "dtypes: float64(79), object(804)\n",
-      "memory usage: 19.6+ GB\n"
-     ]
-    }
-   ],
-   "source": [
-    "df_baseline_diagnoses.info()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_baseline_diagnoses = pd.read_feather(os.path.join(path, dataset_path, 'baseline_diagnoses_times.feather'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>t</th>\n",
-       "      <th>birthdate</th>\n",
-       "      <th>sex_f31</th>\n",
-       "      <th>ethnic_background_f21000</th>\n",
-       "      <th>overall_health_rating_f2178</th>\n",
-       "      <th>smoking_status_f20116</th>\n",
-       "      <th>alcohol_intake_frequency_f1558</th>\n",
-       "      <th>townsend_deprivation_index_at_recruitment_f189</th>\n",
-       "      <th>body_mass_index_bmi_f21001</th>\n",
-       "      <th>...</th>\n",
-       "      <th>nonvenomous_insect_bite</th>\n",
-       "      <th>spondylolisthesis</th>\n",
-       "      <th>malignant_tumor_of_esophagus</th>\n",
-       "      <th>aphthous_ulcer_of_mouth</th>\n",
-       "      <th>ventricular_septal_defect</th>\n",
-       "      <th>oropharyngeal_dysphagia</th>\n",
-       "      <th>injury_of_knee</th>\n",
-       "      <th>traumatic_brain_injury</th>\n",
-       "      <th>osteoarthritis_of_glenohumeral_joint</th>\n",
-       "      <th>fetal_or_neonatal_effect_of_maternal_medical_problem</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>37.5</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>37.6</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>44.6</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>49.0</td>\n",
-       "      <td>1960-11-12</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>British</td>\n",
-       "      <td>Fair</td>\n",
-       "      <td>Current</td>\n",
-       "      <td>Once or twice a week</td>\n",
-       "      <td>-1.852930</td>\n",
-       "      <td>26.5557</td>\n",
-       "      <td>...</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>56.0</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2979350</th>\n",
-       "      <td>6025182</td>\n",
-       "      <td>50.3</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2979351</th>\n",
-       "      <td>6025182</td>\n",
-       "      <td>56.0</td>\n",
-       "      <td>1954-07-01</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>British</td>\n",
-       "      <td>Excellent</td>\n",
-       "      <td>Previous</td>\n",
-       "      <td>Daily or almost daily</td>\n",
-       "      <td>-0.010764</td>\n",
-       "      <td>29.1425</td>\n",
-       "      <td>...</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2979352</th>\n",
-       "      <td>6025198</td>\n",
-       "      <td>67.0</td>\n",
-       "      <td>1943-01-26</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>British</td>\n",
-       "      <td>Good</td>\n",
-       "      <td>Current</td>\n",
-       "      <td>Daily or almost daily</td>\n",
-       "      <td>-1.930650</td>\n",
-       "      <td>29.5988</td>\n",
-       "      <td>...</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2979353</th>\n",
-       "      <td>6025198</td>\n",
-       "      <td>75.8</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2979354</th>\n",
-       "      <td>6025198</td>\n",
-       "      <td>75.9</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>2979355 rows × 885 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "             eid     t   birthdate sex_f31 ethnic_background_f21000  \\\n",
-       "0        1000018  37.5        None    None                     None   \n",
-       "1        1000018  37.6        None    None                     None   \n",
-       "2        1000018  44.6        None    None                     None   \n",
-       "3        1000018  49.0  1960-11-12  Female                  British   \n",
-       "4        1000018  56.0        None    None                     None   \n",
-       "...          ...   ...         ...     ...                      ...   \n",
-       "2979350  6025182  50.3        None    None                     None   \n",
-       "2979351  6025182  56.0  1954-07-01    Male                  British   \n",
-       "2979352  6025198  67.0  1943-01-26    Male                  British   \n",
-       "2979353  6025198  75.8        None    None                     None   \n",
-       "2979354  6025198  75.9        None    None                     None   \n",
-       "\n",
-       "        overall_health_rating_f2178 smoking_status_f20116  \\\n",
-       "0                              None                  None   \n",
-       "1                              None                  None   \n",
-       "2                              None                  None   \n",
-       "3                              Fair               Current   \n",
-       "4                              None                  None   \n",
-       "...                             ...                   ...   \n",
-       "2979350                        None                  None   \n",
-       "2979351                   Excellent              Previous   \n",
-       "2979352                        Good               Current   \n",
-       "2979353                        None                  None   \n",
-       "2979354                        None                  None   \n",
-       "\n",
-       "        alcohol_intake_frequency_f1558  \\\n",
-       "0                                 None   \n",
-       "1                                 None   \n",
-       "2                                 None   \n",
-       "3                 Once or twice a week   \n",
-       "4                                 None   \n",
-       "...                                ...   \n",
-       "2979350                           None   \n",
-       "2979351          Daily or almost daily   \n",
-       "2979352          Daily or almost daily   \n",
-       "2979353                           None   \n",
-       "2979354                           None   \n",
-       "\n",
-       "         townsend_deprivation_index_at_recruitment_f189  \\\n",
-       "0                                                   NaN   \n",
-       "1                                                   NaN   \n",
-       "2                                                   NaN   \n",
-       "3                                             -1.852930   \n",
-       "4                                                   NaN   \n",
-       "...                                                 ...   \n",
-       "2979350                                             NaN   \n",
-       "2979351                                       -0.010764   \n",
-       "2979352                                       -1.930650   \n",
-       "2979353                                             NaN   \n",
-       "2979354                                             NaN   \n",
-       "\n",
-       "         body_mass_index_bmi_f21001  ...  nonvenomous_insect_bite  \\\n",
-       "0                               NaN  ...                    False   \n",
-       "1                               NaN  ...                    False   \n",
-       "2                               NaN  ...                    False   \n",
-       "3                           26.5557  ...                     None   \n",
-       "4                               NaN  ...                    False   \n",
-       "...                             ...  ...                      ...   \n",
-       "2979350                         NaN  ...                    False   \n",
-       "2979351                     29.1425  ...                     None   \n",
-       "2979352                     29.5988  ...                     None   \n",
-       "2979353                         NaN  ...                    False   \n",
-       "2979354                         NaN  ...                    False   \n",
-       "\n",
-       "         spondylolisthesis  malignant_tumor_of_esophagus  \\\n",
-       "0                    False                         False   \n",
-       "1                    False                         False   \n",
-       "2                    False                         False   \n",
-       "3                     None                          None   \n",
-       "4                    False                         False   \n",
-       "...                    ...                           ...   \n",
-       "2979350              False                         False   \n",
-       "2979351               None                          None   \n",
-       "2979352               None                          None   \n",
-       "2979353              False                         False   \n",
-       "2979354              False                         False   \n",
-       "\n",
-       "         aphthous_ulcer_of_mouth  ventricular_septal_defect  \\\n",
-       "0                          False                      False   \n",
-       "1                          False                      False   \n",
-       "2                          False                      False   \n",
-       "3                           None                       None   \n",
-       "4                          False                      False   \n",
-       "...                          ...                        ...   \n",
-       "2979350                    False                      False   \n",
-       "2979351                     None                       None   \n",
-       "2979352                     None                       None   \n",
-       "2979353                    False                      False   \n",
-       "2979354                    False                      False   \n",
-       "\n",
-       "         oropharyngeal_dysphagia  injury_of_knee  traumatic_brain_injury  \\\n",
-       "0                          False           False                   False   \n",
-       "1                          False           False                   False   \n",
-       "2                          False           False                   False   \n",
-       "3                           None            None                    None   \n",
-       "4                          False           False                   False   \n",
-       "...                          ...             ...                     ...   \n",
-       "2979350                    False           False                   False   \n",
-       "2979351                     None            None                    None   \n",
-       "2979352                     None            None                    None   \n",
-       "2979353                    False           False                   False   \n",
-       "2979354                    False           False                   False   \n",
-       "\n",
-       "         osteoarthritis_of_glenohumeral_joint  \\\n",
-       "0                                       False   \n",
-       "1                                       False   \n",
-       "2                                       False   \n",
-       "3                                        None   \n",
-       "4                                       False   \n",
-       "...                                       ...   \n",
-       "2979350                                 False   \n",
-       "2979351                                  None   \n",
-       "2979352                                  None   \n",
-       "2979353                                 False   \n",
-       "2979354                                 False   \n",
-       "\n",
-       "         fetal_or_neonatal_effect_of_maternal_medical_problem  \n",
-       "0                                                    False     \n",
-       "1                                                    False     \n",
-       "2                                                    False     \n",
-       "3                                                     None     \n",
-       "4                                                    False     \n",
-       "...                                                    ...     \n",
-       "2979350                                              False     \n",
-       "2979351                                               None     \n",
-       "2979352                                               None     \n",
-       "2979353                                              False     \n",
-       "2979354                                              False     \n",
-       "\n",
-       "[2979355 rows x 885 columns]"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_baseline_diagnoses"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "basic_data = pd.concat([basics_birthdate.set_index(\"eid\")[[\"birthdate\"]], df_baseline_diagnoses.set_index([\"eid\"]).drop(columns=\"birthdate\")], axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "basic_data.reset_index().to_feather(os.path.join(path, dataset_path, 'basic_data.feather'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "basic_data = pd.read_feather(os.path.join(path, dataset_path, 'basic_data.feather')).set_index([\"eid\", \"t\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th>birthdate</th>\n",
-       "      <th>sex_f31</th>\n",
-       "      <th>ethnic_background_f21000</th>\n",
-       "      <th>overall_health_rating_f2178</th>\n",
-       "      <th>smoking_status_f20116</th>\n",
-       "      <th>alcohol_intake_frequency_f1558</th>\n",
-       "      <th>townsend_deprivation_index_at_recruitment_f189</th>\n",
-       "      <th>body_mass_index_bmi_f21001</th>\n",
-       "      <th>weight_f21002</th>\n",
-       "      <th>systolic_blood_pressure_automated_reading_f4080</th>\n",
-       "      <th>...</th>\n",
-       "      <th>nonvenomous_insect_bite</th>\n",
-       "      <th>spondylolisthesis</th>\n",
-       "      <th>malignant_tumor_of_esophagus</th>\n",
-       "      <th>aphthous_ulcer_of_mouth</th>\n",
-       "      <th>ventricular_septal_defect</th>\n",
-       "      <th>oropharyngeal_dysphagia</th>\n",
-       "      <th>injury_of_knee</th>\n",
-       "      <th>traumatic_brain_injury</th>\n",
-       "      <th>osteoarthritis_of_glenohumeral_joint</th>\n",
-       "      <th>fetal_or_neonatal_effect_of_maternal_medical_problem</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>eid</th>\n",
-       "      <th>t</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"5\" valign=\"top\">1000018</th>\n",
-       "      <th>37.5</th>\n",
-       "      <td>1960-11-12</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>37.6</th>\n",
-       "      <td>1960-11-12</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>44.6</th>\n",
-       "      <td>1960-11-12</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>49.0</th>\n",
-       "      <td>1960-11-12</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>British</td>\n",
-       "      <td>Fair</td>\n",
-       "      <td>Current</td>\n",
-       "      <td>Once or twice a week</td>\n",
-       "      <td>-1.852930</td>\n",
-       "      <td>26.5557</td>\n",
-       "      <td>63.8</td>\n",
-       "      <td>159.5</td>\n",
-       "      <td>...</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>56.0</th>\n",
-       "      <td>1960-11-12</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"2\" valign=\"top\">6025182</th>\n",
-       "      <th>50.3</th>\n",
-       "      <td>1954-07-01</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>56.0</th>\n",
-       "      <td>1954-07-01</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>British</td>\n",
-       "      <td>Excellent</td>\n",
-       "      <td>Previous</td>\n",
-       "      <td>Daily or almost daily</td>\n",
-       "      <td>-0.010764</td>\n",
-       "      <td>29.1425</td>\n",
-       "      <td>104.1</td>\n",
-       "      <td>127.5</td>\n",
-       "      <td>...</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"3\" valign=\"top\">6025198</th>\n",
-       "      <th>67.0</th>\n",
-       "      <td>1943-01-26</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>British</td>\n",
-       "      <td>Good</td>\n",
-       "      <td>Current</td>\n",
-       "      <td>Daily or almost daily</td>\n",
-       "      <td>-1.930650</td>\n",
-       "      <td>29.5988</td>\n",
-       "      <td>102.4</td>\n",
-       "      <td>156.5</td>\n",
-       "      <td>...</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75.8</th>\n",
-       "      <td>1943-01-26</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75.9</th>\n",
-       "      <td>1943-01-26</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>2979355 rows × 883 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "               birthdate sex_f31 ethnic_background_f21000  \\\n",
-       "eid     t                                                   \n",
-       "1000018 37.5  1960-11-12    None                     None   \n",
-       "        37.6  1960-11-12    None                     None   \n",
-       "        44.6  1960-11-12    None                     None   \n",
-       "        49.0  1960-11-12  Female                  British   \n",
-       "        56.0  1960-11-12    None                     None   \n",
-       "...                  ...     ...                      ...   \n",
-       "6025182 50.3  1954-07-01    None                     None   \n",
-       "        56.0  1954-07-01    Male                  British   \n",
-       "6025198 67.0  1943-01-26    Male                  British   \n",
-       "        75.8  1943-01-26    None                     None   \n",
-       "        75.9  1943-01-26    None                     None   \n",
-       "\n",
-       "             overall_health_rating_f2178 smoking_status_f20116  \\\n",
-       "eid     t                                                        \n",
-       "1000018 37.5                        None                  None   \n",
-       "        37.6                        None                  None   \n",
-       "        44.6                        None                  None   \n",
-       "        49.0                        Fair               Current   \n",
-       "        56.0                        None                  None   \n",
-       "...                                  ...                   ...   \n",
-       "6025182 50.3                        None                  None   \n",
-       "        56.0                   Excellent              Previous   \n",
-       "6025198 67.0                        Good               Current   \n",
-       "        75.8                        None                  None   \n",
-       "        75.9                        None                  None   \n",
-       "\n",
-       "             alcohol_intake_frequency_f1558  \\\n",
-       "eid     t                                     \n",
-       "1000018 37.5                           None   \n",
-       "        37.6                           None   \n",
-       "        44.6                           None   \n",
-       "        49.0           Once or twice a week   \n",
-       "        56.0                           None   \n",
-       "...                                     ...   \n",
-       "6025182 50.3                           None   \n",
-       "        56.0          Daily or almost daily   \n",
-       "6025198 67.0          Daily or almost daily   \n",
-       "        75.8                           None   \n",
-       "        75.9                           None   \n",
-       "\n",
-       "              townsend_deprivation_index_at_recruitment_f189  \\\n",
-       "eid     t                                                      \n",
-       "1000018 37.5                                             NaN   \n",
-       "        37.6                                             NaN   \n",
-       "        44.6                                             NaN   \n",
-       "        49.0                                       -1.852930   \n",
-       "        56.0                                             NaN   \n",
-       "...                                                      ...   \n",
-       "6025182 50.3                                             NaN   \n",
-       "        56.0                                       -0.010764   \n",
-       "6025198 67.0                                       -1.930650   \n",
-       "        75.8                                             NaN   \n",
-       "        75.9                                             NaN   \n",
-       "\n",
-       "              body_mass_index_bmi_f21001  weight_f21002  \\\n",
-       "eid     t                                                 \n",
-       "1000018 37.5                         NaN            NaN   \n",
-       "        37.6                         NaN            NaN   \n",
-       "        44.6                         NaN            NaN   \n",
-       "        49.0                     26.5557           63.8   \n",
-       "        56.0                         NaN            NaN   \n",
-       "...                                  ...            ...   \n",
-       "6025182 50.3                         NaN            NaN   \n",
-       "        56.0                     29.1425          104.1   \n",
-       "6025198 67.0                     29.5988          102.4   \n",
-       "        75.8                         NaN            NaN   \n",
-       "        75.9                         NaN            NaN   \n",
-       "\n",
-       "              systolic_blood_pressure_automated_reading_f4080  ...  \\\n",
-       "eid     t                                                      ...   \n",
-       "1000018 37.5                                              NaN  ...   \n",
-       "        37.6                                              NaN  ...   \n",
-       "        44.6                                              NaN  ...   \n",
-       "        49.0                                            159.5  ...   \n",
-       "        56.0                                              NaN  ...   \n",
-       "...                                                       ...  ...   \n",
-       "6025182 50.3                                              NaN  ...   \n",
-       "        56.0                                            127.5  ...   \n",
-       "6025198 67.0                                            156.5  ...   \n",
-       "        75.8                                              NaN  ...   \n",
-       "        75.9                                              NaN  ...   \n",
-       "\n",
-       "              nonvenomous_insect_bite  spondylolisthesis  \\\n",
-       "eid     t                                                  \n",
-       "1000018 37.5                    False              False   \n",
-       "        37.6                    False              False   \n",
-       "        44.6                    False              False   \n",
-       "        49.0                     None               None   \n",
-       "        56.0                    False              False   \n",
-       "...                               ...                ...   \n",
-       "6025182 50.3                    False              False   \n",
-       "        56.0                     None               None   \n",
-       "6025198 67.0                     None               None   \n",
-       "        75.8                    False              False   \n",
-       "        75.9                    False              False   \n",
-       "\n",
-       "              malignant_tumor_of_esophagus  aphthous_ulcer_of_mouth  \\\n",
-       "eid     t                                                             \n",
-       "1000018 37.5                         False                    False   \n",
-       "        37.6                         False                    False   \n",
-       "        44.6                         False                    False   \n",
-       "        49.0                          None                     None   \n",
-       "        56.0                         False                    False   \n",
-       "...                                    ...                      ...   \n",
-       "6025182 50.3                         False                    False   \n",
-       "        56.0                          None                     None   \n",
-       "6025198 67.0                          None                     None   \n",
-       "        75.8                         False                    False   \n",
-       "        75.9                         False                    False   \n",
-       "\n",
-       "              ventricular_septal_defect  oropharyngeal_dysphagia  \\\n",
-       "eid     t                                                          \n",
-       "1000018 37.5                      False                    False   \n",
-       "        37.6                      False                    False   \n",
-       "        44.6                      False                    False   \n",
-       "        49.0                       None                     None   \n",
-       "        56.0                      False                    False   \n",
-       "...                                 ...                      ...   \n",
-       "6025182 50.3                      False                    False   \n",
-       "        56.0                       None                     None   \n",
-       "6025198 67.0                       None                     None   \n",
-       "        75.8                      False                    False   \n",
-       "        75.9                      False                    False   \n",
-       "\n",
-       "              injury_of_knee  traumatic_brain_injury  \\\n",
-       "eid     t                                              \n",
-       "1000018 37.5           False                   False   \n",
-       "        37.6           False                   False   \n",
-       "        44.6           False                   False   \n",
-       "        49.0            None                    None   \n",
-       "        56.0           False                   False   \n",
-       "...                      ...                     ...   \n",
-       "6025182 50.3           False                   False   \n",
-       "        56.0            None                    None   \n",
-       "6025198 67.0            None                    None   \n",
-       "        75.8           False                   False   \n",
-       "        75.9           False                   False   \n",
-       "\n",
-       "              osteoarthritis_of_glenohumeral_joint  \\\n",
-       "eid     t                                            \n",
-       "1000018 37.5                                 False   \n",
-       "        37.6                                 False   \n",
-       "        44.6                                 False   \n",
-       "        49.0                                  None   \n",
-       "        56.0                                 False   \n",
-       "...                                            ...   \n",
-       "6025182 50.3                                 False   \n",
-       "        56.0                                  None   \n",
-       "6025198 67.0                                  None   \n",
-       "        75.8                                 False   \n",
-       "        75.9                                 False   \n",
-       "\n",
-       "              fetal_or_neonatal_effect_of_maternal_medical_problem  \n",
-       "eid     t                                                           \n",
-       "1000018 37.5                                              False     \n",
-       "        37.6                                              False     \n",
-       "        44.6                                              False     \n",
-       "        49.0                                               None     \n",
-       "        56.0                                              False     \n",
-       "...                                                         ...     \n",
-       "6025182 50.3                                              False     \n",
-       "        56.0                                               None     \n",
-       "6025198 67.0                                               None     \n",
-       "        75.8                                              False     \n",
-       "        75.9                                              False     \n",
-       "\n",
-       "[2979355 rows x 883 columns]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "basic_data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import xarray as xa\n",
-    "x_data = basic_data.to_xarray()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
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-       ":root {\n",
-       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
-       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
-       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
-       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
-       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
-       "  --xr-background-color: var(--jp-layout-color0, white);\n",
-       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
-       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
-       "}\n",
-       "\n",
-       "html[theme=dark],\n",
-       "body.vscode-dark {\n",
-       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
-       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
-       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
-       "  --xr-border-color: #1F1F1F;\n",
-       "  --xr-disabled-color: #515151;\n",
-       "  --xr-background-color: #111111;\n",
-       "  --xr-background-color-row-even: #111111;\n",
-       "  --xr-background-color-row-odd: #313131;\n",
-       "}\n",
-       "\n",
-       ".xr-wrap {\n",
-       "  display: block;\n",
-       "  min-width: 300px;\n",
-       "  max-width: 700px;\n",
-       "}\n",
-       "\n",
-       ".xr-text-repr-fallback {\n",
-       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-header {\n",
-       "  padding-top: 6px;\n",
-       "  padding-bottom: 6px;\n",
-       "  margin-bottom: 4px;\n",
-       "  border-bottom: solid 1px var(--xr-border-color);\n",
-       "}\n",
-       "\n",
-       ".xr-header > div,\n",
-       ".xr-header > ul {\n",
-       "  display: inline;\n",
-       "  margin-top: 0;\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-obj-type,\n",
-       ".xr-array-name {\n",
-       "  margin-left: 2px;\n",
-       "  margin-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-obj-type {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-sections {\n",
-       "  padding-left: 0 !important;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input + label {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label {\n",
-       "  cursor: pointer;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label:hover {\n",
-       "  color: var(--xr-font-color0);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary {\n",
-       "  grid-column: 1;\n",
-       "  color: var(--xr-font-color2);\n",
-       "  font-weight: 500;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary > span {\n",
-       "  display: inline-block;\n",
-       "  padding-left: 0.5em;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in + label:before {\n",
-       "  display: inline-block;\n",
-       "  content: '►';\n",
-       "  font-size: 11px;\n",
-       "  width: 15px;\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label:before {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label:before {\n",
-       "  content: '▼';\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label > span {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary,\n",
-       ".xr-section-inline-details {\n",
-       "  padding-top: 4px;\n",
-       "  padding-bottom: 4px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-inline-details {\n",
-       "  grid-column: 2 / -1;\n",
-       "}\n",
-       "\n",
-       ".xr-section-details {\n",
-       "  display: none;\n",
-       "  grid-column: 1 / -1;\n",
-       "  margin-bottom: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap {\n",
-       "  grid-column: 1 / -1;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 20px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap > label {\n",
-       "  grid-column: 1;\n",
-       "  vertical-align: top;\n",
-       "}\n",
-       "\n",
-       ".xr-preview {\n",
-       "  color: var(--xr-font-color3);\n",
-       "}\n",
-       "\n",
-       ".xr-array-preview,\n",
-       ".xr-array-data {\n",
-       "  padding: 0 5px !important;\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-array-data,\n",
-       ".xr-array-in:checked ~ .xr-array-preview {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-array-in:checked ~ .xr-array-data,\n",
-       ".xr-array-preview {\n",
-       "  display: inline-block;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list {\n",
-       "  display: inline-block !important;\n",
-       "  list-style: none;\n",
-       "  padding: 0 !important;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li {\n",
-       "  display: inline-block;\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:before {\n",
-       "  content: '(';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:after {\n",
-       "  content: ')';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: ',';\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-has-index {\n",
-       "  font-weight: bold;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list,\n",
-       ".xr-var-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > div,\n",
-       ".xr-var-item label,\n",
-       ".xr-var-item > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-even);\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > .xr-var-name:hover span {\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list > li:nth-child(odd) > div,\n",
-       ".xr-var-list > li:nth-child(odd) > label,\n",
-       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-odd);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name {\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dims {\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dtype {\n",
-       "  grid-column: 3;\n",
-       "  text-align: right;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-preview {\n",
-       "  grid-column: 4;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name,\n",
-       ".xr-var-dims,\n",
-       ".xr-var-dtype,\n",
-       ".xr-preview,\n",
-       ".xr-attrs dt {\n",
-       "  white-space: nowrap;\n",
-       "  overflow: hidden;\n",
-       "  text-overflow: ellipsis;\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name:hover,\n",
-       ".xr-var-dims:hover,\n",
-       ".xr-var-dtype:hover,\n",
-       ".xr-attrs dt:hover {\n",
-       "  overflow: visible;\n",
-       "  width: auto;\n",
-       "  z-index: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data {\n",
-       "  display: none;\n",
-       "  background-color: var(--xr-background-color) !important;\n",
-       "  padding-bottom: 5px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt,\n",
-       ".xr-attrs dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2 {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
-       "Dimensions:                                                                  (index: 2979355)\n",
-       "Coordinates:\n",
-       "  * index                                                                    (index) int64 ...\n",
-       "Data variables:\n",
-       "    eid                                                                      (index) int64 ...\n",
-       "    birthdate                                                                (index) object ...\n",
-       "    t                                                                        (index) float64 ...\n",
-       "    sex_f31                                                                  (index) object ...\n",
-       "    ethnic_background_f21000                                                 (index) object ...\n",
-       "    overall_health_rating_f2178                                              (index) object ...\n",
-       "    smoking_status_f20116                                                    (index) object ...\n",
-       "    alcohol_intake_frequency_f1558                                           (index) object ...\n",
-       "    townsend_deprivation_index_at_recruitment_f189                           (index) float64 ...\n",
-       "    body_mass_index_bmi_f21001                                               (index) float64 ...\n",
-       "    weight_f21002                                                            (index) float64 ...\n",
-       "    systolic_blood_pressure_automated_reading_f4080                          (index) float64 ...\n",
-       "    diastolic_blood_pressure_automated_reading_f4079                         (index) float64 ...\n",
-       "    pulse_rate_automated_reading_f102                                        (index) float64 ...\n",
-       "    pulse_wave_arterial_stiffness_index_f21021                               (index) float64 ...\n",
-       "    pulse_wave_reflection_index_f4195                                        (index) float64 ...\n",
-       "    waist_circumference_f48                                                  (index) float64 ...\n",
-       "    hip_circumference_f49                                                    (index) float64 ...\n",
-       "    standing_height_f50                                                      (index) float64 ...\n",
-       "    trunk_fat_percentage_f23127                                              (index) float64 ...\n",
-       "    body_fat_percentage_f23099                                               (index) float64 ...\n",
-       "    basal_metabolic_rate_f23105                                              (index) float64 ...\n",
-       "    forced_vital_capacity_fvc_best_measure_f20151                            (index) float64 ...\n",
-       "    forced_expiratory_volume_in_1second_fev1_best_measure_f20150             (index) float64 ...\n",
-       "    fev1_fvc_ratio_zscore_f20258                                             (index) float64 ...\n",
-       "    peak_expiratory_flow_pef_f3064                                           (index) float64 ...\n",
-       "    basophill_count_f30160                                                   (index) float64 ...\n",
-       "    basophill_percentage_f30220                                              (index) float64 ...\n",
-       "    eosinophill_count_f30150                                                 (index) float64 ...\n",
-       "    eosinophill_percentage_f30210                                            (index) float64 ...\n",
-       "    haematocrit_percentage_f30030                                            (index) float64 ...\n",
-       "    haemoglobin_concentration_f30020                                         (index) float64 ...\n",
-       "    high_light_scatter_reticulocyte_count_f30300                             (index) float64 ...\n",
-       "    high_light_scatter_reticulocyte_percentage_f30290                        (index) float64 ...\n",
-       "    immature_reticulocyte_fraction_f30280                                    (index) float64 ...\n",
-       "    lymphocyte_count_f30120                                                  (index) float64 ...\n",
-       "    lymphocyte_percentage_f30180                                             (index) float64 ...\n",
-       "    mean_corpuscular_haemoglobin_f30050                                      (index) float64 ...\n",
-       "    mean_corpuscular_haemoglobin_concentration_f30060                        (index) float64 ...\n",
-       "    mean_corpuscular_volume_f30040                                           (index) float64 ...\n",
-       "    mean_platelet_thrombocyte_volume_f30100                                  (index) float64 ...\n",
-       "    mean_reticulocyte_volume_f30260                                          (index) float64 ...\n",
-       "    mean_sphered_cell_volume_f30270                                          (index) float64 ...\n",
-       "    monocyte_count_f30130                                                    (index) float64 ...\n",
-       "    monocyte_percentage_f30190                                               (index) float64 ...\n",
-       "    neutrophill_count_f30140                                                 (index) float64 ...\n",
-       "    neutrophill_percentage_f30200                                            (index) float64 ...\n",
-       "    nucleated_red_blood_cell_count_f30170                                    (index) float64 ...\n",
-       "    nucleated_red_blood_cell_percentage_f30230                               (index) float64 ...\n",
-       "    platelet_count_f30080                                                    (index) float64 ...\n",
-       "    platelet_crit_f30090                                                     (index) float64 ...\n",
-       "    platelet_distribution_width_f30110                                       (index) float64 ...\n",
-       "    red_blood_cell_erythrocyte_count_f30010                                  (index) float64 ...\n",
-       "    red_blood_cell_erythrocyte_distribution_width_f30070                     (index) float64 ...\n",
-       "    reticulocyte_count_f30250                                                (index) float64 ...\n",
-       "    reticulocyte_percentage_f30240                                           (index) float64 ...\n",
-       "    white_blood_cell_leukocyte_count_f30000                                  (index) float64 ...\n",
-       "    alanine_aminotransferase_f30620                                          (index) float64 ...\n",
-       "    albumin_f30600                                                           (index) float64 ...\n",
-       "    alkaline_phosphatase_f30610                                              (index) float64 ...\n",
-       "    apolipoprotein_a_f30630                                                  (index) float64 ...\n",
-       "    apolipoprotein_b_f30640                                                  (index) float64 ...\n",
-       "    aspartate_aminotransferase_f30650                                        (index) float64 ...\n",
-       "    creactive_protein_f30710                                                 (index) float64 ...\n",
-       "    calcium_f30680                                                           (index) float64 ...\n",
-       "    cholesterol_f30690                                                       (index) float64 ...\n",
-       "    creatinine_f30700                                                        (index) float64 ...\n",
-       "    cystatin_c_f30720                                                        (index) float64 ...\n",
-       "    direct_bilirubin_f30660                                                  (index) float64 ...\n",
-       "    gamma_glutamyltransferase_f30730                                         (index) float64 ...\n",
-       "    glucose_f30740                                                           (index) float64 ...\n",
-       "    glycated_haemoglobin_hba1c_f30750                                        (index) float64 ...\n",
-       "    hdl_cholesterol_f30760                                                   (index) float64 ...\n",
-       "    igf1_f30770                                                              (index) float64 ...\n",
-       "    ldl_direct_f30780                                                        (index) float64 ...\n",
-       "    lipoprotein_a_f30790                                                     (index) float64 ...\n",
-       "    oestradiol_f30800                                                        (index) float64 ...\n",
-       "    phosphate_f30810                                                         (index) float64 ...\n",
-       "    rheumatoid_factor_f30820                                                 (index) float64 ...\n",
-       "    shbg_f30830                                                              (index) float64 ...\n",
-       "    testosterone_f30850                                                      (index) float64 ...\n",
-       "    total_bilirubin_f30840                                                   (index) float64 ...\n",
-       "    total_protein_f30860                                                     (index) float64 ...\n",
-       "    triglycerides_f30870                                                     (index) float64 ...\n",
-       "    urate_f30880                                                             (index) float64 ...\n",
-       "    urea_f30670                                                              (index) float64 ...\n",
-       "    vitamin_d_f30890                                                         (index) float64 ...\n",
-       "    diagnosis                                                                (index) object ...\n",
-       "    hypertensive_disorder_systemic_arterial                                  (index) object ...\n",
-       "    hyperlipidemia                                                           (index) object ...\n",
-       "    depressive_disorder                                                      (index) object ...\n",
-       "    gastroesophageal_reflux_disease                                          (index) object ...\n",
-       "    diabetes_mellitus_type_2                                                 (index) object ...\n",
-       "    essential_hypertension                                                   (index) object ...\n",
-       "    obesity                                                                  (index) object ...\n",
-       "    diabetes_mellitus                                                        (index) object ...\n",
-       "    asthma                                                                   (index) object ...\n",
-       "    coronary_arteriosclerosis                                                (index) object ...\n",
-       "    allergic_rhinitis                                                        (index) object ...\n",
-       "    hypothyroidism                                                           (index) object ...\n",
-       "    upper_respiratory_infection                                              (index) object ...\n",
-       "    hypercholesterolemia                                                     (index) object ...\n",
-       "    backache                                                                 (index) object ...\n",
-       "    abdominal_pain                                                           (index) object ...\n",
-       "    osteoarthritis                                                           (index) object ...\n",
-       "    low_back_pain                                                            (index) object ...\n",
-       "    anemia                                                                   (index) object ...\n",
-       "    anxiety                                                                  (index) object ...\n",
-       "    urinary_tract_infectious_disease                                         (index) object ...\n",
-       "    chronic_obstructive_lung_disease                                         (index) object ...\n",
-       "    atrial_fibrillation                                                      (index) object ...\n",
-       "    pneumonia                                                                (index) object ...\n",
-       "    chest_pain                                                               (index) object ...\n",
-       "    congestive_heart_failure                                                 (index) object ...\n",
-       "    headache                                                                 (index) object ...\n",
-       "    migraine                                                                 (index) object ...\n",
-       "    pregnant                                                                 (index) object ...\n",
-       "    knee_pain                                                                (index) object ...\n",
-       "    osteoporosis                                                             (index) object ...\n",
-       "    polyp_of_colon                                                           (index) object ...\n",
-       "    otitis_media                                                             (index) object ...\n",
-       "    sinusitis                                                                (index) object ...\n",
-       "    cough                                                                    (index) object ...\n",
-       "    sleep_apnea                                                              (index) object ...\n",
-       "    insomnia                                                                 (index) object ...\n",
-       "    inflammatory_disorder_due_to_increased_blood_urate_level                 (index) object ...\n",
-       "    tobacco_dependence_syndrome                                              (index) object ...\n",
-       "    malignant_tumor_of_prostate                                              (index) object ...\n",
-       "    constipation                                                             (index) object ...\n",
-       "    hearing_loss                                                             (index) object ...\n",
-       "    fatigue                                                                  (index) object ...\n",
-       "    obstructive_sleep_apnea_syndrome                                         (index) object ...\n",
-       "    malignant_neoplasm_of_breast                                             (index) object ...\n",
-       "    delivery_normal                                                          (index) object ...\n",
-       "    irritable_bowel_syndrome                                                 (index) object ...\n",
-       "    tobacco_user                                                             (index) object ...\n",
-       "    neck_pain                                                                (index) object ...\n",
-       "    cerebrovascular_accident                                                 (index) object ...\n",
-       "    asthenia                                                                 (index) object ...\n",
-       "    shoulder_pain                                                            (index) object ...\n",
-       "    acne_vulgaris                                                            (index) object ...\n",
-       "    benign_prostatic_hyperplasia                                             (index) object ...\n",
-       "    dyspnea                                                                  (index) object ...\n",
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-       "    hydronephrosis                                                           (index) object ...\n",
-       "    borderline_personality_disorder                                          (index) object ...\n",
-       "    esophageal_varices                                                       (index) object ...\n",
-       "    hypersomnia                                                              (index) object ...\n",
-       "    sensorineural_hearing_loss_bilateral                                     (index) object ...\n",
-       "    varicocele                                                               (index) object ...\n",
-       "    subarachnoid_intracranial_hemorrhage                                     (index) object ...\n",
-       "    incisional_hernia                                                        (index) object ...\n",
-       "    varicella                                                                (index) object ...\n",
-       "    pain_in_testicle                                                         (index) object ...\n",
-       "    transplant_follow-up                                                     (index) object ...\n",
-       "    tinea_cruris                                                             (index) object ...\n",
-       "    laryngitis                                                               (index) object ...\n",
-       "    hypertrophy_of_nail                                                      (index) object ...\n",
-       "    amblyopia                                                                (index) object ...\n",
-       "    polyp_of_cervix                                                          (index) object ...\n",
-       "    cyst_of_kidney                                                           (index) object ...\n",
-       "    hepatic_encephalopathy                                                   (index) object ...\n",
-       "    blood_glucose_abnormal                                                   (index) object ...\n",
-       "    postherpetic_neuralgia                                                   (index) object ...\n",
-       "    frank_hematuria                                                          (index) object ...\n",
-       "    cramp                                                                    (index) object ...\n",
-       "    interstitial_lung_disease                                                (index) object ...\n",
-       "    complete_atrioventricular_block                                          (index) object ...\n",
-       "    malignant_tumor_of_kidney                                                (index) object ...\n",
-       "    otitis                                                                   (index) object ...\n",
-       "    septic_shock                                                             (index) object ...\n",
-       "    disorder_of_thyroid_gland                                                (index) object ...\n",
-       "    hypertrophic_cardiomyopathy                                              (index) object ...\n",
-       "    respiratory_distress_syndrome_in_the_newborn                             (index) object ...\n",
-       "    infectious_gastroenteritis                                               (index) object ...\n",
-       "    subdural_intracranial_hemorrhage                                         (index) object ...\n",
-       "    hepatitis_b_carrier                                                      (index) object ...\n",
-       "    manic_bipolar_i_disorder                                                 (index) object ...\n",
-       "    secondary_pulmonary_hypertension                                         (index) object ...\n",
-       "    gonorrhea                                                                (index) object ...\n",
-       "    derangement_of_knee                                                      (index) object ...\n",
-       "    appendicitis                                                             (index) object ...\n",
-       "    polyneuropathy_due_to_diabetes_mellitus                                  (index) object ...\n",
-       "    neonatal_hypoglycemia                                                    (index) object ...\n",
-       "    prolonged_rupture_of_membranes                                           (index) object ...\n",
-       "    vasomotor_rhinitis                                                       (index) object ...\n",
-       "    renal_disorder_due_to_type_1_diabetes_mellitus                           (index) object ...\n",
-       "    tuberculosis                                                             (index) object ...\n",
-       "    feeding_problem                                                          (index) object ...\n",
-       "    chronic_tonsillitis                                                      (index) object ...\n",
-       "    acute_duodenal_ulcer_with_hemorrhage                                     (index) object ...\n",
-       "    hammer_toe                                                               (index) object ...\n",
-       "    malignant_tumor_of_cervix                                                (index) object ...\n",
-       "    prolapsed_lumbar_intervertebral_disc                                     (index) object ...\n",
-       "    hematemesis                                                              (index) object ...\n",
-       "    perianal_abscess                                                         (index) object ...\n",
-       "    nonvenomous_insect_bite                                                  (index) object ...\n",
-       "    spondylolisthesis                                                        (index) object ...\n",
-       "    malignant_tumor_of_esophagus                                             (index) object ...\n",
-       "    aphthous_ulcer_of_mouth                                                  (index) object ...\n",
-       "    ventricular_septal_defect                                                (index) object ...\n",
-       "    oropharyngeal_dysphagia                                                  (index) object ...\n",
-       "    injury_of_knee                                                           (index) object ...\n",
-       "    traumatic_brain_injury                                                   (index) object ...\n",
-       "    osteoarthritis_of_glenohumeral_joint                                     (index) object ...\n",
-       "    fetal_or_neonatal_effect_of_maternal_medical_problem                     (index) object ...</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-0bfca0c7-08df-43d2-a37c-a1479ab9f7c4' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0bfca0c7-08df-43d2-a37c-a1479ab9f7c4' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>index</span>: 2979355</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-51d796ce-c6b2-4493-b267-7ceb5c81a44a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-51d796ce-c6b2-4493-b267-7ceb5c81a44a' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>index</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 ... 2979352 2979353 2979354</div><input id='attrs-68740de7-cf77-4bd6-9932-dafc1fe58854' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-68740de7-cf77-4bd6-9932-dafc1fe58854' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8968c82d-7e2b-4f07-a9b6-3b3a3a57b25b' class='xr-var-data-in' type='checkbox'><label for='data-8968c82d-7e2b-4f07-a9b6-3b3a3a57b25b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([      0,       1,       2, ..., 2979352, 2979353, 2979354])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-84f35b7c-7869-48df-8f07-8ddf00471af0' class='xr-section-summary-in' type='checkbox'  ><label for='section-84f35b7c-7869-48df-8f07-8ddf00471af0' class='xr-section-summary' >Data variables: <span>(885)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>eid</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1000018 1000018 ... 6025198 6025198</div><input id='attrs-9f23c7cc-3f68-42da-b5b4-9f65e854096f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9f23c7cc-3f68-42da-b5b4-9f65e854096f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b78957a4-3fee-413c-9f1e-3343832536b1' class='xr-var-data-in' type='checkbox'><label for='data-b78957a4-3fee-413c-9f1e-3343832536b1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1000018, 1000018, 1000018, ..., 6025198, 6025198, 6025198])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>birthdate</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1960-11-12 ... 1943-01-26</div><input id='attrs-a2b5466c-2668-43b3-9c04-41e56bcc3f88' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a2b5466c-2668-43b3-9c04-41e56bcc3f88' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-39d97dff-33aa-4fdc-b279-db641ca4b083' class='xr-var-data-in' type='checkbox'><label for='data-39d97dff-33aa-4fdc-b279-db641ca4b083' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([datetime.date(1960, 11, 12), datetime.date(1960, 11, 12),\n",
-       "       datetime.date(1960, 11, 12), ..., datetime.date(1943, 1, 26),\n",
-       "       datetime.date(1943, 1, 26), datetime.date(1943, 1, 26)],\n",
-       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>37.5 37.6 44.6 ... 67.0 75.8 75.9</div><input id='attrs-fcba2232-8563-401c-b79b-0e59d814b02f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fcba2232-8563-401c-b79b-0e59d814b02f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-12116bb4-d100-42bb-8b5b-0974ab341d61' class='xr-var-data-in' type='checkbox'><label for='data-12116bb4-d100-42bb-8b5b-0974ab341d61' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([37.5, 37.6, 44.6, ..., 67. , 75.8, 75.9])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sex_f31</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>None None None ... &#x27;Male&#x27; None None</div><input id='attrs-d86f8eae-d082-4d2e-9b40-32b5125c2a7a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d86f8eae-d082-4d2e-9b40-32b5125c2a7a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-67d7443c-93c9-405b-aca1-697d20cc3321' class='xr-var-data-in' type='checkbox'><label for='data-67d7443c-93c9-405b-aca1-697d20cc3321' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([None, None, None, ..., &#x27;Male&#x27;, None, None], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ethnic_background_f21000</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>None None None ... None None</div><input id='attrs-766efc31-1b40-4404-ae1a-eddfdc48b660' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-766efc31-1b40-4404-ae1a-eddfdc48b660' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d06eb6b7-7da5-4034-9be6-0940f040e8d6' class='xr-var-data-in' type='checkbox'><label for='data-d06eb6b7-7da5-4034-9be6-0940f040e8d6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([None, None, None, ..., &#x27;British&#x27;, None, None], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>overall_health_rating_f2178</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>None None None ... &#x27;Good&#x27; None None</div><input id='attrs-ce515053-ed52-4dd5-b1f5-6ac198a71b6b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ce515053-ed52-4dd5-b1f5-6ac198a71b6b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ca6691b0-8c48-4fcd-bbea-8f99df037ea0' class='xr-var-data-in' type='checkbox'><label for='data-ca6691b0-8c48-4fcd-bbea-8f99df037ea0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([None, None, None, ..., &#x27;Good&#x27;, None, None], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>smoking_status_f20116</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>None None None ... None None</div><input id='attrs-b9378ace-799b-4b41-af58-a8abafda798e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b9378ace-799b-4b41-af58-a8abafda798e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ce27a81b-1d45-45c9-a366-48eac99766a6' class='xr-var-data-in' type='checkbox'><label for='data-ce27a81b-1d45-45c9-a366-48eac99766a6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([None, None, None, ..., &#x27;Current&#x27;, None, None], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>alcohol_intake_frequency_f1558</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>None None None ... None None</div><input id='attrs-fcaf3f0e-08a8-45d9-9007-1e3217829839' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fcaf3f0e-08a8-45d9-9007-1e3217829839' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-39fc9439-2d5a-45d3-a2df-cb6f0fa932f0' class='xr-var-data-in' type='checkbox'><label for='data-39fc9439-2d5a-45d3-a2df-cb6f0fa932f0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([None, None, None, ..., &#x27;Daily or almost daily&#x27;, None, None],\n",
-       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>townsend_deprivation_index_at_recruitment_f189</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan ... -1.931 nan nan</div><input id='attrs-bceac230-e815-4d16-8305-f8dea3a6cc5a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bceac230-e815-4d16-8305-f8dea3a6cc5a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a425dfc2-ac87-49a7-b7be-ba37c287dbab' class='xr-var-data-in' type='checkbox'><label for='data-a425dfc2-ac87-49a7-b7be-ba37c287dbab' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([     nan,      nan,      nan, ..., -1.93065,      nan,      nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>body_mass_index_bmi_f21001</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan 26.56 ... 29.6 nan nan</div><input id='attrs-ebc5573d-f168-4bd6-b2ce-a4348a07b166' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ebc5573d-f168-4bd6-b2ce-a4348a07b166' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-16bb1bf0-dc21-45f6-9a59-bc23a46a0def' class='xr-var-data-in' type='checkbox'><label for='data-16bb1bf0-dc21-45f6-9a59-bc23a46a0def' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([    nan,     nan,     nan, ..., 29.5988,     nan,     nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>weight_f21002</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan 63.8 ... 102.4 nan nan</div><input id='attrs-858ae83a-751e-49ad-83ef-63074a9fd774' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-858ae83a-751e-49ad-83ef-63074a9fd774' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b7689224-cd92-48e2-913f-a336f5d89aaf' class='xr-var-data-in' type='checkbox'><label for='data-b7689224-cd92-48e2-913f-a336f5d89aaf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  nan,   nan,   nan, ..., 102.4,   nan,   nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>systolic_blood_pressure_automated_reading_f4080</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan 159.5 ... 156.5 nan nan</div><input id='attrs-13900557-9284-493a-8ab6-37f6af0c663c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-13900557-9284-493a-8ab6-37f6af0c663c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2f5de50d-d85a-4d66-aea1-ec28d2d24320' class='xr-var-data-in' type='checkbox'><label for='data-2f5de50d-d85a-4d66-aea1-ec28d2d24320' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  nan,   nan,   nan, ..., 156.5,   nan,   nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diastolic_blood_pressure_automated_reading_f4079</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan 88.0 ... 103.0 nan nan</div><input id='attrs-b6dd0303-20f3-4a93-bb5b-7502696c042f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b6dd0303-20f3-4a93-bb5b-7502696c042f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-502430bd-865d-4ae7-9367-d54959c815a1' class='xr-var-data-in' type='checkbox'><label for='data-502430bd-865d-4ae7-9367-d54959c815a1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ nan,  nan,  nan, ..., 103.,  nan,  nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pulse_rate_automated_reading_f102</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan 50.0 ... 82.0 nan nan</div><input id='attrs-2f418b69-ccc3-4655-9520-b7d31e651df4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2f418b69-ccc3-4655-9520-b7d31e651df4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-928a6261-006c-4f5d-9606-9fb2f632887e' class='xr-var-data-in' type='checkbox'><label for='data-928a6261-006c-4f5d-9606-9fb2f632887e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([nan, nan, nan, ..., 82., nan, nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pulse_wave_arterial_stiffness_index_f21021</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan 7.277 ... 16.03 nan nan</div><input id='attrs-da44da63-acc1-4b10-b244-d24d8fa9ed01' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-da44da63-acc1-4b10-b244-d24d8fa9ed01' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-10d79d5a-1014-4e37-8fb9-814ede06cc83' class='xr-var-data-in' type='checkbox'><label for='data-10d79d5a-1014-4e37-8fb9-814ede06cc83' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([    nan,     nan,     nan, ..., 16.0345,     nan,     nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pulse_wave_reflection_index_f4195</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan 80.0 ... 74.0 nan nan</div><input id='attrs-7a124243-fd5d-4465-8927-9e20785511bd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7a124243-fd5d-4465-8927-9e20785511bd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-971bc27c-2c35-4d62-bd31-c1505583b07d' class='xr-var-data-in' type='checkbox'><label for='data-971bc27c-2c35-4d62-bd31-c1505583b07d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([nan, nan, nan, ..., 74., nan, nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>waist_circumference_f48</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan 85.0 ... 110.0 nan nan</div><input id='attrs-1542ff54-51f2-48f0-862e-af6a68cd4a8e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1542ff54-51f2-48f0-862e-af6a68cd4a8e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-29ed6ec5-57e9-4a91-a280-12a361e22ae1' class='xr-var-data-in' type='checkbox'><label for='data-29ed6ec5-57e9-4a91-a280-12a361e22ae1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ nan,  nan,  nan, ..., 110.,  nan,  nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hip_circumference_f49</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan 107.0 ... 108.0 nan nan</div><input id='attrs-1cc8e74f-86fa-404d-bdaa-eb02cf653c2e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1cc8e74f-86fa-404d-bdaa-eb02cf653c2e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-56b3b38b-4b9a-42a1-96e6-b2bfd4d05b29' class='xr-var-data-in' type='checkbox'><label for='data-56b3b38b-4b9a-42a1-96e6-b2bfd4d05b29' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ nan,  nan,  nan, ..., 108.,  nan,  nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>standing_height_f50</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan 155.0 ... 186.0 nan nan</div><input id='attrs-3f91e8a3-44e4-463f-9508-8dccd0897943' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3f91e8a3-44e4-463f-9508-8dccd0897943' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5b2f225e-22a2-48bb-9ff7-93cddb4a1eb2' class='xr-var-data-in' type='checkbox'><label for='data-5b2f225e-22a2-48bb-9ff7-93cddb4a1eb2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ nan,  nan,  nan, ..., 186.,  nan,  nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>trunk_fat_percentage_f23127</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan 37.5 ... 31.5 nan nan</div><input id='attrs-1e0e18c9-66ff-4376-b860-d38dc7cd1365' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1e0e18c9-66ff-4376-b860-d38dc7cd1365' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e330042b-00d4-4186-942b-ba803ae39c05' class='xr-var-data-in' type='checkbox'><label for='data-e330042b-00d4-4186-942b-ba803ae39c05' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ nan,  nan,  nan, ..., 31.5,  nan,  nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>body_fat_percentage_f23099</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan 39.5 ... 30.0 nan nan</div><input id='attrs-835c81dd-63d8-43d1-82b5-f11ab28b0f58' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-835c81dd-63d8-43d1-82b5-f11ab28b0f58' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b86433d6-2b47-4345-bae3-b50b7b0153ed' class='xr-var-data-in' type='checkbox'><label for='data-b86433d6-2b47-4345-bae3-b50b7b0153ed' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([nan, nan, nan, ..., 30., nan, nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>basal_metabolic_rate_f23105</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan ... 8.849e+03 nan nan</div><input id='attrs-77f28e29-2b64-4910-bd4e-313e3d9fe16c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-77f28e29-2b64-4910-bd4e-313e3d9fe16c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bb4ce95b-5ed5-4990-8d0c-af68364a6ddf' class='xr-var-data-in' type='checkbox'><label for='data-bb4ce95b-5ed5-4990-8d0c-af68364a6ddf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  nan,   nan,   nan, ..., 8849.,   nan,   nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>forced_vital_capacity_fvc_best_measure_f20151</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan 3.21 ... 4.26 nan nan</div><input id='attrs-8bb7de9f-786a-42c8-a148-1e604f556df7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8bb7de9f-786a-42c8-a148-1e604f556df7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-940bcf73-628c-4024-b91e-93f2b009473c' class='xr-var-data-in' type='checkbox'><label for='data-940bcf73-628c-4024-b91e-93f2b009473c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ nan,  nan,  nan, ..., 4.26,  nan,  nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>forced_expiratory_volume_in_1second_fev1_best_measure_f20150</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan 2.16 ... 3.01 nan nan</div><input id='attrs-1a6e3a1f-0c62-4882-ba0c-ef7048b31605' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1a6e3a1f-0c62-4882-ba0c-ef7048b31605' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-221aec42-10dc-4437-8f03-db269d920e96' class='xr-var-data-in' type='checkbox'><label for='data-221aec42-10dc-4437-8f03-db269d920e96' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ nan,  nan,  nan, ..., 3.01,  nan,  nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fev1_fvc_ratio_zscore_f20258</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan 1.978 ... nan nan nan</div><input id='attrs-b6162e6d-7cb1-48f7-bf3c-eeee6e295a33' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b6162e6d-7cb1-48f7-bf3c-eeee6e295a33' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6dd8a037-9958-41f5-bd8e-f4df0bf383ef' class='xr-var-data-in' type='checkbox'><label for='data-6dd8a037-9958-41f5-bd8e-f4df0bf383ef' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([nan, nan, nan, ..., nan, nan, nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>peak_expiratory_flow_pef_f3064</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan 322.7 ... 416.7 nan nan</div><input id='attrs-aa0d2667-01ea-4cc5-9e11-ee9734bb3aac' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-aa0d2667-01ea-4cc5-9e11-ee9734bb3aac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-05be313e-293d-4ac7-96bf-3a8affdee812' class='xr-var-data-in' type='checkbox'><label for='data-05be313e-293d-4ac7-96bf-3a8affdee812' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([         nan,          nan,          nan, ..., 416.66666667,\n",
-       "                nan,          nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>basophill_count_f30160</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan 0.04 ... 0.06 nan nan</div><input id='attrs-25691e3e-c6cd-4aac-8aec-1078e96de74d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-25691e3e-c6cd-4aac-8aec-1078e96de74d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-89512342-1def-4a51-bb52-47593e12938d' class='xr-var-data-in' type='checkbox'><label for='data-89512342-1def-4a51-bb52-47593e12938d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ nan,  nan,  nan, ..., 0.06,  nan,  nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>basophill_percentage_f30220</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan 0.26 ... 0.73 nan nan</div><input id='attrs-a11849fa-4c52-40fa-b7ad-be427160da34' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a11849fa-4c52-40fa-b7ad-be427160da34' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d2957787-4224-4456-a593-59fbe1b2f74b' class='xr-var-data-in' type='checkbox'><label for='data-d2957787-4224-4456-a593-59fbe1b2f74b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ nan,  nan,  nan, ..., 0.73,  nan,  nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>eosinophill_count_f30150</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan 0.25 ... 0.32 nan nan</div><input id='attrs-6d5b1c99-52bc-467a-8a62-4d117428f972' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6d5b1c99-52bc-467a-8a62-4d117428f972' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cca3a2e5-fadd-48e9-a2a0-1b48c973a099' class='xr-var-data-in' type='checkbox'><label for='data-cca3a2e5-fadd-48e9-a2a0-1b48c973a099' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ nan,  nan,  nan, ..., 0.32,  nan,  nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>eosinophill_percentage_f30210</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan 1.75 ... 3.78 nan nan</div><input id='attrs-29f6cd43-37da-4bcd-9aac-e92acf9fb9c6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-29f6cd43-37da-4bcd-9aac-e92acf9fb9c6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d4606694-0be7-4666-a68c-1c1a392c4ffa' class='xr-var-data-in' type='checkbox'><label for='data-d4606694-0be7-4666-a68c-1c1a392c4ffa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ nan,  nan,  nan, ..., 3.78,  nan,  nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>haematocrit_percentage_f30030</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan 39.79 ... 47.21 nan nan</div><input id='attrs-6fd6e055-d84a-4816-8bfd-4b7ea0bd5658' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6fd6e055-d84a-4816-8bfd-4b7ea0bd5658' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-86a9bad4-4070-4f1f-8d47-697bd4c09e1d' class='xr-var-data-in' type='checkbox'><label for='data-86a9bad4-4070-4f1f-8d47-697bd4c09e1d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  nan,   nan,   nan, ..., 47.21,   nan,   nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>haemoglobin_concentration_f30020</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan 13.9 ... 16.57 nan nan</div><input id='attrs-8386d239-7fc2-4ff6-ae43-4e9efeac6007' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8386d239-7fc2-4ff6-ae43-4e9efeac6007' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-daa856af-7aa0-4200-8094-1f4a33207df6' class='xr-var-data-in' type='checkbox'><label for='data-daa856af-7aa0-4200-8094-1f4a33207df6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  nan,   nan,   nan, ..., 16.57,   nan,   nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>high_light_scatter_reticulocyte_count_f30300</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan 0.022 ... nan nan nan</div><input id='attrs-fb73e2e0-8dda-4815-8402-5c7fd6b91c60' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fb73e2e0-8dda-4815-8402-5c7fd6b91c60' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e8bf0266-9c15-45f8-80bb-15e7ca9ef624' class='xr-var-data-in' type='checkbox'><label for='data-e8bf0266-9c15-45f8-80bb-15e7ca9ef624' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([nan, nan, nan, ..., nan, nan, nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>high_light_scatter_reticulocyte_percentage_f30290</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan 0.464 ... nan nan nan</div><input id='attrs-ebbb8cd9-eeb0-4e39-9470-f749052eda26' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ebbb8cd9-eeb0-4e39-9470-f749052eda26' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-61ea9529-b703-4b5a-b2ff-862a7ae120a1' class='xr-var-data-in' type='checkbox'><label for='data-61ea9529-b703-4b5a-b2ff-862a7ae120a1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([nan, nan, nan, ..., nan, nan, nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>immature_reticulocyte_fraction_f30280</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan 0.378 ... nan nan nan</div><input id='attrs-3c23e1c7-1bdd-43f3-ac33-968fb8e11588' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3c23e1c7-1bdd-43f3-ac33-968fb8e11588' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e2b7f863-2a27-4ca8-b3ec-88454212c09b' class='xr-var-data-in' type='checkbox'><label for='data-e2b7f863-2a27-4ca8-b3ec-88454212c09b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([nan, nan, nan, ..., nan, nan, nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lymphocyte_count_f30120</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan 3.01 ... 1.95 nan nan</div><input id='attrs-94ef5930-cc34-4680-97e7-8cbf3dc5679f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-94ef5930-cc34-4680-97e7-8cbf3dc5679f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d959794b-8254-4471-8a54-e94bd2407e1c' class='xr-var-data-in' type='checkbox'><label for='data-d959794b-8254-4471-8a54-e94bd2407e1c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ nan,  nan,  nan, ..., 1.95,  nan,  nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lymphocyte_percentage_f30180</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan 21.21 ... 23.06 nan nan</div><input id='attrs-9c9f86e6-6448-4ba7-8010-8ea426fd7d98' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9c9f86e6-6448-4ba7-8010-8ea426fd7d98' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-242e721f-8e13-413f-a2a0-6da795bb7ca1' class='xr-var-data-in' type='checkbox'><label for='data-242e721f-8e13-413f-a2a0-6da795bb7ca1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  nan,   nan,   nan, ..., 23.06,   nan,   nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mean_corpuscular_haemoglobin_f30050</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan 29.69 ... 34.55 nan nan</div><input id='attrs-30ba006a-44c7-4273-b4ad-788d12d51232' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-30ba006a-44c7-4273-b4ad-788d12d51232' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0e13e342-52b5-4f7e-af82-458044cc3f13' class='xr-var-data-in' type='checkbox'><label for='data-0e13e342-52b5-4f7e-af82-458044cc3f13' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  nan,   nan,   nan, ..., 34.55,   nan,   nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mean_corpuscular_haemoglobin_concentration_f30060</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan 34.92 ... 35.1 nan nan</div><input id='attrs-e1634d7c-d58b-4a28-b354-42c69abdef1a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e1634d7c-d58b-4a28-b354-42c69abdef1a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a23e5fcb-d690-4e7f-9d31-7151ab071de2' class='xr-var-data-in' type='checkbox'><label for='data-a23e5fcb-d690-4e7f-9d31-7151ab071de2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ nan,  nan,  nan, ..., 35.1,  nan,  nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mean_corpuscular_volume_f30040</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan 85.01 ... 98.45 nan nan</div><input id='attrs-053cc52c-6ed9-487f-9414-d9cd70bdb6cc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-053cc52c-6ed9-487f-9414-d9cd70bdb6cc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-df88abd9-225b-4bc2-a35a-2052b3eadc72' class='xr-var-data-in' type='checkbox'><label for='data-df88abd9-225b-4bc2-a35a-2052b3eadc72' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  nan,   nan,   nan, ..., 98.45,   nan,   nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mean_platelet_thrombocyte_volume_f30100</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan 8.82 ... 7.87 nan nan</div><input id='attrs-9b995b2c-f372-423f-a820-221a6fbdff63' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9b995b2c-f372-423f-a820-221a6fbdff63' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-55f81cb3-c1be-4d25-a7ef-ec5dddf60c26' class='xr-var-data-in' type='checkbox'><label for='data-55f81cb3-c1be-4d25-a7ef-ec5dddf60c26' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ nan,  nan,  nan, ..., 7.87,  nan,  nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mean_reticulocyte_volume_f30260</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan 108.2 ... nan nan nan</div><input id='attrs-6e68c5d9-3d26-44fd-962b-a5a958b476b5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6e68c5d9-3d26-44fd-962b-a5a958b476b5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b7cfdce5-163d-473c-90e9-b63a4b75f20f' class='xr-var-data-in' type='checkbox'><label for='data-b7cfdce5-163d-473c-90e9-b63a4b75f20f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([nan, nan, nan, ..., nan, nan, nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mean_sphered_cell_volume_f30270</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan 78.55 ... nan nan nan</div><input id='attrs-2eee8192-e1cc-48de-b473-d5f6f77c4988' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2eee8192-e1cc-48de-b473-d5f6f77c4988' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e12d7992-51fa-47b1-8b7d-9bca47a1839a' class='xr-var-data-in' type='checkbox'><label for='data-e12d7992-51fa-47b1-8b7d-9bca47a1839a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([nan, nan, nan, ..., nan, nan, nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>monocyte_count_f30130</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan 0.6 ... 0.86 nan nan</div><input id='attrs-e1b75fe5-c456-49a5-ab50-287c08c39e59' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e1b75fe5-c456-49a5-ab50-287c08c39e59' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a604d914-6ea7-4b76-804a-d6bd588bf365' class='xr-var-data-in' type='checkbox'><label for='data-a604d914-6ea7-4b76-804a-d6bd588bf365' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ nan,  nan,  nan, ..., 0.86,  nan,  nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>monocyte_percentage_f30190</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan 4.19 ... 10.17 nan nan</div><input id='attrs-9843c1e5-2aee-489f-8c93-069c421587ef' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9843c1e5-2aee-489f-8c93-069c421587ef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3357dcff-440a-406b-bac2-5cff20d40733' class='xr-var-data-in' type='checkbox'><label for='data-3357dcff-440a-406b-bac2-5cff20d40733' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  nan,   nan,   nan, ..., 10.17,   nan,   nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>neutrophill_count_f30140</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan 10.3 ... 5.26 nan nan</div><input id='attrs-317acf53-3438-4019-a124-12baa196e55a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-317acf53-3438-4019-a124-12baa196e55a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7dec366b-a7c6-427b-bf48-20b058792a4d' class='xr-var-data-in' type='checkbox'><label for='data-7dec366b-a7c6-427b-bf48-20b058792a4d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ nan,  nan,  nan, ..., 5.26,  nan,  nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>neutrophill_percentage_f30200</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan 72.5 ... 62.2 nan nan</div><input id='attrs-d930b492-29c8-46cc-a8e4-b3abb428bf1c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d930b492-29c8-46cc-a8e4-b3abb428bf1c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-29232054-c0eb-4547-91c8-4a454f7188a7' class='xr-var-data-in' type='checkbox'><label for='data-29232054-c0eb-4547-91c8-4a454f7188a7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ nan,  nan,  nan, ..., 62.2,  nan,  nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nucleated_red_blood_cell_count_f30170</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan 0.0 ... 0.0 0.0 nan nan</div><input id='attrs-82b1c8bb-e025-4155-b006-3ea1e63dba8d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-82b1c8bb-e025-4155-b006-3ea1e63dba8d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1df6e3f4-5558-4be1-9fa4-27fb109053d7' class='xr-var-data-in' type='checkbox'><label for='data-1df6e3f4-5558-4be1-9fa4-27fb109053d7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([nan, nan, nan, ...,  0., nan, nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nucleated_red_blood_cell_percentage_f30230</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan 0.0 ... 0.0 0.0 nan nan</div><input id='attrs-cb3e5591-3f16-4bdf-930a-6b3a928f8b82' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cb3e5591-3f16-4bdf-930a-6b3a928f8b82' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8ea90482-ed68-41c6-bc92-4a9cbdeac1c3' class='xr-var-data-in' type='checkbox'><label for='data-8ea90482-ed68-41c6-bc92-4a9cbdeac1c3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([nan, nan, nan, ...,  0., nan, nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>platelet_count_f30080</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan 279.5 ... 237.1 nan nan</div><input id='attrs-dfd9f738-3747-4007-894c-e57f4453a085' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dfd9f738-3747-4007-894c-e57f4453a085' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a5d060f6-8e0b-4467-b884-4b07217deb65' class='xr-var-data-in' type='checkbox'><label for='data-a5d060f6-8e0b-4467-b884-4b07217deb65' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  nan,   nan,   nan, ..., 237.1,   nan,   nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>platelet_crit_f30090</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan 0.246 ... 0.186 nan nan</div><input id='attrs-c68970de-543d-44df-9460-3b6f21a8feef' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c68970de-543d-44df-9460-3b6f21a8feef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-144dad91-1f6d-493b-af63-9494c7c585aa' class='xr-var-data-in' type='checkbox'><label for='data-144dad91-1f6d-493b-af63-9494c7c585aa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  nan,   nan,   nan, ..., 0.186,   nan,   nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>platelet_distribution_width_f30110</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan 16.51 ... 15.92 nan nan</div><input id='attrs-3bbf7f94-3091-40dc-9790-53443a1678d9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3bbf7f94-3091-40dc-9790-53443a1678d9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-562b7037-fc4c-4fc7-b4fb-007e15181fbb' class='xr-var-data-in' type='checkbox'><label for='data-562b7037-fc4c-4fc7-b4fb-007e15181fbb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  nan,   nan,   nan, ..., 15.92,   nan,   nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>red_blood_cell_erythrocyte_count_f30010</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan 4.681 ... 4.795 nan nan</div><input id='attrs-6de75f89-5202-4eda-ab5b-dbc75285a670' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6de75f89-5202-4eda-ab5b-dbc75285a670' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-08102d36-b913-4c2e-902a-65ca86c43c2f' class='xr-var-data-in' type='checkbox'><label for='data-08102d36-b913-4c2e-902a-65ca86c43c2f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  nan,   nan,   nan, ..., 4.795,   nan,   nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>red_blood_cell_erythrocyte_distribution_width_f30070</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan 14.87 ... 12.77 nan nan</div><input id='attrs-645c7725-c771-4551-aa91-5441ebfdf81c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-645c7725-c771-4551-aa91-5441ebfdf81c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-867d6184-831a-4d46-93c4-ef85b0962c65' class='xr-var-data-in' type='checkbox'><label for='data-867d6184-831a-4d46-93c4-ef85b0962c65' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  nan,   nan,   nan, ..., 12.77,   nan,   nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>reticulocyte_count_f30250</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan 0.057 ... nan nan nan</div><input id='attrs-34b9969c-cdd0-41d6-9cf0-340d49b9554a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-34b9969c-cdd0-41d6-9cf0-340d49b9554a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-502d2472-0680-432b-9708-b16a9a1c60c1' class='xr-var-data-in' type='checkbox'><label for='data-502d2472-0680-432b-9708-b16a9a1c60c1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([nan, nan, nan, ..., nan, nan, nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>reticulocyte_percentage_f30240</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan 1.228 ... nan nan nan</div><input id='attrs-f16d6974-da73-4e05-b1c3-361b87b3edee' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f16d6974-da73-4e05-b1c3-361b87b3edee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6d0972e3-9e20-44f6-97a0-bda990e85b42' class='xr-var-data-in' type='checkbox'><label for='data-6d0972e3-9e20-44f6-97a0-bda990e85b42' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([nan, nan, nan, ..., nan, nan, nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>white_blood_cell_leukocyte_count_f30000</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan 14.21 ... 8.45 nan nan</div><input id='attrs-1a41cbbd-b4f6-4564-9724-832129a6b45b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1a41cbbd-b4f6-4564-9724-832129a6b45b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2851c919-89da-4bca-99b1-4c555c4147e6' class='xr-var-data-in' type='checkbox'><label for='data-2851c919-89da-4bca-99b1-4c555c4147e6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ nan,  nan,  nan, ..., 8.45,  nan,  nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>alanine_aminotransferase_f30620</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan 12.54 ... 33.67 nan nan</div><input id='attrs-1da4a0a7-47f6-4d71-b5b2-6c26aa4938a3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1da4a0a7-47f6-4d71-b5b2-6c26aa4938a3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cc8b733a-9584-4587-be18-8038bed998d8' class='xr-var-data-in' type='checkbox'><label for='data-cc8b733a-9584-4587-be18-8038bed998d8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  nan,   nan,   nan, ..., 33.67,   nan,   nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>albumin_f30600</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan 42.5 ... 42.02 nan nan</div><input id='attrs-9b92e1ee-df14-4709-aa5f-b49cadf3f500' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9b92e1ee-df14-4709-aa5f-b49cadf3f500' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c372fe16-42f5-42ee-b0e7-28c7444ad35c' class='xr-var-data-in' type='checkbox'><label for='data-c372fe16-42f5-42ee-b0e7-28c7444ad35c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  nan,   nan,   nan, ..., 42.02,   nan,   nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>alkaline_phosphatase_f30610</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan 108.6 ... 87.8 nan nan</div><input id='attrs-31e364e6-b1b9-4be8-8845-dcfc61447724' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-31e364e6-b1b9-4be8-8845-dcfc61447724' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-988a8689-5a08-48ec-b548-fe825462c4d7' class='xr-var-data-in' type='checkbox'><label for='data-988a8689-5a08-48ec-b548-fe825462c4d7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ nan,  nan,  nan, ..., 87.8,  nan,  nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>apolipoprotein_a_f30630</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan 1.545 ... 1.456 nan nan</div><input id='attrs-97962da3-86ae-4e09-bd32-816309a1ea2f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-97962da3-86ae-4e09-bd32-816309a1ea2f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-288d8618-f67e-4815-87a9-a13755d8ea85' class='xr-var-data-in' type='checkbox'><label for='data-288d8618-f67e-4815-87a9-a13755d8ea85' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  nan,   nan,   nan, ..., 1.456,   nan,   nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>apolipoprotein_b_f30640</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan 0.99 ... 1.328 nan nan</div><input id='attrs-8f65ec92-7aac-463c-9331-6f5847052e7a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8f65ec92-7aac-463c-9331-6f5847052e7a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aee23c0d-c026-4524-b3b3-04023bff8368' class='xr-var-data-in' type='checkbox'><label for='data-aee23c0d-c026-4524-b3b3-04023bff8368' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  nan,   nan,   nan, ..., 1.328,   nan,   nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>aspartate_aminotransferase_f30650</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan 20.3 ... 24.0 nan nan</div><input id='attrs-eab5edbc-2af1-4bdd-b544-9d3ad153a1dd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-eab5edbc-2af1-4bdd-b544-9d3ad153a1dd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7c30a644-4690-49f8-b18c-cb929c782f37' class='xr-var-data-in' type='checkbox'><label for='data-7c30a644-4690-49f8-b18c-cb929c782f37' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([nan, nan, nan, ..., 24., nan, nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>creactive_protein_f30710</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan 3.4 ... 4.87 nan nan</div><input id='attrs-7cd57138-2475-4c24-b23c-e1c704ef0dcb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7cd57138-2475-4c24-b23c-e1c704ef0dcb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6d38f941-c5ca-43aa-ad2f-146bb6aeea1e' class='xr-var-data-in' type='checkbox'><label for='data-6d38f941-c5ca-43aa-ad2f-146bb6aeea1e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ nan,  nan,  nan, ..., 4.87,  nan,  nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>calcium_f30680</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan 2.52 ... 2.374 nan nan</div><input id='attrs-a87ef89e-9eba-4ca2-bd8f-52a7a76df2f8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a87ef89e-9eba-4ca2-bd8f-52a7a76df2f8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a5e87f9a-968a-43c3-9d1e-d0fabe069a70' class='xr-var-data-in' type='checkbox'><label for='data-a5e87f9a-968a-43c3-9d1e-d0fabe069a70' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  nan,   nan,   nan, ..., 2.374,   nan,   nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cholesterol_f30690</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan 5.255 ... 7.049 nan nan</div><input id='attrs-7560619f-0782-4492-b71e-3bbc66eaf5a2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7560619f-0782-4492-b71e-3bbc66eaf5a2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4f07bc6a-2dc1-4260-b931-a89218f8e666' class='xr-var-data-in' type='checkbox'><label for='data-4f07bc6a-2dc1-4260-b931-a89218f8e666' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  nan,   nan,   nan, ..., 7.049,   nan,   nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>creatinine_f30700</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan 68.6 ... 91.9 nan nan</div><input id='attrs-efa84b0f-3182-4360-a794-99ed02540e49' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-efa84b0f-3182-4360-a794-99ed02540e49' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eb472596-96e2-47f5-9821-e5c420086924' class='xr-var-data-in' type='checkbox'><label for='data-eb472596-96e2-47f5-9821-e5c420086924' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ nan,  nan,  nan, ..., 91.9,  nan,  nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cystatin_c_f30720</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan 1.095 ... 1.26 nan nan</div><input id='attrs-efa9f6dd-a1af-4238-aa2f-ca6e3df11cd2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-efa9f6dd-a1af-4238-aa2f-ca6e3df11cd2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c070b3f9-462e-45e9-a312-f3df435e1e04' class='xr-var-data-in' type='checkbox'><label for='data-c070b3f9-462e-45e9-a312-f3df435e1e04' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ nan,  nan,  nan, ..., 1.26,  nan,  nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>direct_bilirubin_f30660</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan 1.28 ... 2.01 nan nan</div><input id='attrs-7a27e2b4-1132-4623-b709-1edcac70f279' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7a27e2b4-1132-4623-b709-1edcac70f279' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-414dac32-5926-450c-a66e-f376fa28a80a' class='xr-var-data-in' type='checkbox'><label for='data-414dac32-5926-450c-a66e-f376fa28a80a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ nan,  nan,  nan, ..., 2.01,  nan,  nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>gamma_glutamyltransferase_f30730</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan 14.6 ... 101.7 nan nan</div><input id='attrs-5952a49a-3d7a-40b4-989b-19edb8cab3ec' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5952a49a-3d7a-40b4-989b-19edb8cab3ec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0846f262-fa08-447b-87b8-1b10a582fea5' class='xr-var-data-in' type='checkbox'><label for='data-0846f262-fa08-447b-87b8-1b10a582fea5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  nan,   nan,   nan, ..., 101.7,   nan,   nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>glucose_f30740</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan 5.101 ... 5.322 nan nan</div><input id='attrs-716ab09b-a6de-401b-b481-d010a4cb62be' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-716ab09b-a6de-401b-b481-d010a4cb62be' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2185b9f0-21d1-49ea-9289-f97d4f9b6ef5' class='xr-var-data-in' type='checkbox'><label for='data-2185b9f0-21d1-49ea-9289-f97d4f9b6ef5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  nan,   nan,   nan, ..., 5.322,   nan,   nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>glycated_haemoglobin_hba1c_f30750</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan 33.5 ... 35.2 nan nan</div><input id='attrs-92307694-4e99-4bbb-9d6d-cd9a91d0697d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-92307694-4e99-4bbb-9d6d-cd9a91d0697d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9e24107a-bafc-45c1-a4f1-60ddc46b0fc3' class='xr-var-data-in' type='checkbox'><label for='data-9e24107a-bafc-45c1-a4f1-60ddc46b0fc3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ nan,  nan,  nan, ..., 35.2,  nan,  nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hdl_cholesterol_f30760</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan 1.455 ... 1.247 nan nan</div><input id='attrs-c80bb277-b0ea-4162-a819-9e0375157b4c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c80bb277-b0ea-4162-a819-9e0375157b4c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-944f7d54-898a-4339-af56-b13b5a41f755' class='xr-var-data-in' type='checkbox'><label for='data-944f7d54-898a-4339-af56-b13b5a41f755' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  nan,   nan,   nan, ..., 1.247,   nan,   nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>igf1_f30770</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan 27.9 ... 20.02 nan nan</div><input id='attrs-9f13479e-a1d5-4f4e-b961-77ae497ac005' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9f13479e-a1d5-4f4e-b961-77ae497ac005' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9b5bc2be-0390-41ac-b758-fd4e9569b530' class='xr-var-data-in' type='checkbox'><label for='data-9b5bc2be-0390-41ac-b758-fd4e9569b530' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([   nan,    nan,    nan, ..., 20.022,    nan,    nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ldl_direct_f30780</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan 3.352 ... 4.802 nan nan</div><input id='attrs-8e7cf1cd-9de0-49b5-bef6-c897a5c34ece' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8e7cf1cd-9de0-49b5-bef6-c897a5c34ece' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ca1ab099-4733-40fb-aaea-e03ad3e33de4' class='xr-var-data-in' type='checkbox'><label for='data-ca1ab099-4733-40fb-aaea-e03ad3e33de4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  nan,   nan,   nan, ..., 4.802,   nan,   nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lipoprotein_a_f30790</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan 6.47 ... nan nan nan</div><input id='attrs-7a88c57d-82d9-40bc-b417-7a75839bfca9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7a88c57d-82d9-40bc-b417-7a75839bfca9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a296a63e-0681-47a7-b4f0-371d50300f2a' class='xr-var-data-in' type='checkbox'><label for='data-a296a63e-0681-47a7-b4f0-371d50300f2a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([nan, nan, nan, ..., nan, nan, nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>oestradiol_f30800</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan 665.4 ... nan nan nan</div><input id='attrs-4d943f5f-289a-46ad-8eb0-4b5523e3b89a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4d943f5f-289a-46ad-8eb0-4b5523e3b89a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6b62fa38-7403-4525-a407-09a1c5ad9a19' class='xr-var-data-in' type='checkbox'><label for='data-6b62fa38-7403-4525-a407-09a1c5ad9a19' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([nan, nan, nan, ..., nan, nan, nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>phosphate_f30810</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan 1.422 ... 1.163 nan nan</div><input id='attrs-7968a439-6d3f-4255-9f36-26cd955ea1a2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7968a439-6d3f-4255-9f36-26cd955ea1a2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-59de2ef2-6c35-413a-b39f-8c94513eaf0a' class='xr-var-data-in' type='checkbox'><label for='data-59de2ef2-6c35-413a-b39f-8c94513eaf0a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  nan,   nan,   nan, ..., 1.163,   nan,   nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rheumatoid_factor_f30820</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-4211fea0-920c-450c-a57e-239b301e49cf' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4211fea0-920c-450c-a57e-239b301e49cf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-72ddf3c8-53e5-43c9-a89d-3d08324fb116' class='xr-var-data-in' type='checkbox'><label for='data-72ddf3c8-53e5-43c9-a89d-3d08324fb116' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([nan, nan, nan, ..., nan, nan, nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>shbg_f30830</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan 70.11 ... 45.09 nan nan</div><input id='attrs-825d5d7c-225e-4946-9768-1f9f90d058bb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-825d5d7c-225e-4946-9768-1f9f90d058bb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d8ea0dc6-1b1b-4874-a5cb-5363e6871df7' class='xr-var-data-in' type='checkbox'><label for='data-d8ea0dc6-1b1b-4874-a5cb-5363e6871df7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  nan,   nan,   nan, ..., 45.09,   nan,   nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>testosterone_f30850</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan 1.56 ... 15.03 nan nan</div><input id='attrs-76074364-68a3-4e40-b0af-adc14b94d635' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-76074364-68a3-4e40-b0af-adc14b94d635' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-62462356-22fa-447e-8716-c5583ab5bca9' class='xr-var-data-in' type='checkbox'><label for='data-62462356-22fa-447e-8716-c5583ab5bca9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  nan,   nan,   nan, ..., 15.03,   nan,   nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>total_bilirubin_f30840</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan 7.41 ... 11.85 nan nan</div><input id='attrs-b2c10966-bf07-4b81-9059-be211ca69451' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b2c10966-bf07-4b81-9059-be211ca69451' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bd019c07-310d-4700-bee0-be1244c452c4' class='xr-var-data-in' type='checkbox'><label for='data-bd019c07-310d-4700-bee0-be1244c452c4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  nan,   nan,   nan, ..., 11.85,   nan,   nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>total_protein_f30860</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan 71.97 ... 70.62 nan nan</div><input id='attrs-d6f44bc7-e360-4b42-a2da-03e448f43247' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d6f44bc7-e360-4b42-a2da-03e448f43247' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-75fd8dab-eb64-406a-b13e-b4b0a470862f' class='xr-var-data-in' type='checkbox'><label for='data-75fd8dab-eb64-406a-b13e-b4b0a470862f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  nan,   nan,   nan, ..., 70.62,   nan,   nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>triglycerides_f30870</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan 1.247 ... 2.327 nan nan</div><input id='attrs-7313bb95-d346-4a77-99d5-7825626dd7bd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7313bb95-d346-4a77-99d5-7825626dd7bd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4a18e3e5-a116-4641-930f-1f37755c5c69' class='xr-var-data-in' type='checkbox'><label for='data-4a18e3e5-a116-4641-930f-1f37755c5c69' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  nan,   nan,   nan, ..., 2.327,   nan,   nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>urate_f30880</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan 221.3 ... 454.8 nan nan</div><input id='attrs-5be8af91-049c-4681-8b0f-1f2e37c61cc2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5be8af91-049c-4681-8b0f-1f2e37c61cc2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-102119bb-3ee8-4662-998f-edece4380d04' class='xr-var-data-in' type='checkbox'><label for='data-102119bb-3ee8-4662-998f-edece4380d04' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  nan,   nan,   nan, ..., 454.8,   nan,   nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>urea_f30670</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan 5.48 ... 5.14 nan nan</div><input id='attrs-b5a1b236-7621-44cb-9df0-3963915e128a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b5a1b236-7621-44cb-9df0-3963915e128a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-357e57c1-f5a9-4a75-a034-dcf974968d02' class='xr-var-data-in' type='checkbox'><label for='data-357e57c1-f5a9-4a75-a034-dcf974968d02' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ nan,  nan,  nan, ..., 5.14,  nan,  nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vitamin_d_f30890</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan 70.7 ... 20.2 nan nan</div><input id='attrs-aafc48b2-c0a9-4c6e-a399-3361bc7ff2c6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-aafc48b2-c0a9-4c6e-a399-3361bc7ff2c6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fdde6943-67ba-4b59-b01b-2d8b97567118' class='xr-var-data-in' type='checkbox'><label for='data-fdde6943-67ba-4b59-b01b-2d8b97567118' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ nan,  nan,  nan, ..., 20.2,  nan,  nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diagnosis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>[&#x27;appendicitis&#x27; &#x27;appendicitis&#x27;] ...</div><input id='attrs-a820d51a-aa6b-4119-9691-2f697a66097f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a820d51a-aa6b-4119-9691-2f697a66097f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c4b54ac9-bd53-44ba-8ef1-60a31138c883' class='xr-var-data-in' type='checkbox'><label for='data-c4b54ac9-bd53-44ba-8ef1-60a31138c883' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([array([&#x27;appendicitis&#x27;, &#x27;appendicitis&#x27;], dtype=object),\n",
-       "       array([&#x27;appendicitis&#x27;], dtype=object),\n",
-       "       array([&#x27;injury_of_head&#x27;, &#x27;fracture_of_bone&#x27;], dtype=object), ...,\n",
-       "       None,\n",
-       "       array([&#x27;sepsis&#x27;, &#x27;methicillin_resistant_staphylococcus_aureus_infection&#x27;,\n",
-       "       &#x27;respiratory_failure&#x27;, &#x27;hypoxemia&#x27;, &#x27;renal_failure_syndrome&#x27;,\n",
-       "       &#x27;acute_renal_failure_syndrome&#x27;, &#x27;kidney_disease&#x27;,\n",
-       "       &#x27;renal_impairment&#x27;, &#x27;renal_failure_syndrome&#x27;,\n",
-       "       &#x27;chronic_kidney_disease&#x27;, &#x27;chronic_kidney_disease_stage_3&#x27;,\n",
-       "       &#x27;kidney_disease&#x27;, &#x27;chronic_kidney_disease_stage_4&#x27;,\n",
-       "       &#x27;chronic_kidney_disease_stage_2&#x27;, &#x27;renal_impairment&#x27;, &#x27;anemia&#x27;,\n",
-       "       &#x27;tobacco_dependence_syndrome&#x27;, &#x27;tobacco_user&#x27;, &#x27;smoker&#x27;,\n",
-       "       &#x27;nicotine_dependence&#x27;, &#x27;tobacco_dependence_in_remission&#x27;,\n",
-       "       &#x27;cigarette_smoker&#x27;, &#x27;hypertensive_disorder_systemic_arterial&#x27;,\n",
-       "       &#x27;essential_hypertension&#x27;, &#x27;hyperlipidemia&#x27;, &#x27;hypercholesterolemia&#x27;,\n",
-       "       &#x27;arthritis&#x27;, &#x27;hypertriglyceridemia&#x27;, &#x27;mixed_hyperlipidemia&#x27;,\n",
-       "       &#x27;pure_hypercholesterolemia&#x27;, &#x27;abdominal_aortic_aneurysm&#x27;,\n",
-       "       &#x27;abdominal_aortic_aneurysm_without_rupture&#x27;, &#x27;disorder_of_artery&#x27;,\n",
-       "       &#x27;renal_failure_syndrome&#x27;, &#x27;acute_renal_failure_syndrome&#x27;,\n",
-       "       &#x27;kidney_disease&#x27;, &#x27;renal_impairment&#x27;, &#x27;renal_failure_syndrome&#x27;,\n",
-       "...\n",
-       "       &#x27;disorder_of_lung&#x27;, &#x27;lower_respiratory_tract_infection&#x27;,\n",
-       "       &#x27;renal_failure_syndrome&#x27;, &#x27;acute_renal_failure_syndrome&#x27;,\n",
-       "       &#x27;kidney_disease&#x27;, &#x27;renal_impairment&#x27;, &#x27;renal_failure_syndrome&#x27;,\n",
-       "       &#x27;chronic_kidney_disease&#x27;, &#x27;chronic_kidney_disease_stage_3&#x27;,\n",
-       "       &#x27;kidney_disease&#x27;, &#x27;chronic_kidney_disease_stage_4&#x27;,\n",
-       "       &#x27;chronic_kidney_disease_stage_2&#x27;, &#x27;renal_impairment&#x27;,\n",
-       "       &#x27;abdominal_aortic_aneurysm&#x27;,\n",
-       "       &#x27;abdominal_aortic_aneurysm_without_rupture&#x27;, &#x27;disorder_of_artery&#x27;,\n",
-       "       &#x27;hypokalemia&#x27;, &#x27;hyperkalemia&#x27;, &#x27;hypervolemia&#x27;, &#x27;hypokalemia&#x27;,\n",
-       "       &#x27;hyperkalemia&#x27;, &#x27;hypervolemia&#x27;,\n",
-       "       &#x27;foreign_body_in_respiratory_tract&#x27;, &#x27;foreign_body_in_pharynx&#x27;,\n",
-       "       &#x27;urinary_tract_infectious_disease&#x27;, &#x27;pyelonephritis&#x27;,\n",
-       "       &#x27;kidney_disease&#x27;, &#x27;ureteric_stone&#x27;, &#x27;hydronephrosis&#x27;, &#x27;anemia&#x27;,\n",
-       "       &#x27;liver_function_tests_abnormal&#x27;, &#x27;tobacco_dependence_syndrome&#x27;,\n",
-       "       &#x27;tobacco_user&#x27;, &#x27;smoker&#x27;, &#x27;nicotine_dependence&#x27;,\n",
-       "       &#x27;tobacco_dependence_in_remission&#x27;, &#x27;cigarette_smoker&#x27;,\n",
-       "       &#x27;hypertensive_disorder_systemic_arterial&#x27;,\n",
-       "       &#x27;essential_hypertension&#x27;, &#x27;hyperlipidemia&#x27;, &#x27;hypercholesterolemia&#x27;,\n",
-       "       &#x27;arthritis&#x27;, &#x27;hypertriglyceridemia&#x27;, &#x27;mixed_hyperlipidemia&#x27;,\n",
-       "       &#x27;pure_hypercholesterolemia&#x27;], dtype=object)], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hypertensive_disorder_systemic_arterial</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... True True</div><input id='attrs-3c41edc4-a152-4aa1-b458-9580278ae01a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3c41edc4-a152-4aa1-b458-9580278ae01a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-715a01c0-9f9a-4c85-a47d-5467f79c5f25' class='xr-var-data-in' type='checkbox'><label for='data-715a01c0-9f9a-4c85-a47d-5467f79c5f25' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, True, True], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hyperlipidemia</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... True True</div><input id='attrs-3aa4c43a-3fa8-4e07-9795-79ac05d69091' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3aa4c43a-3fa8-4e07-9795-79ac05d69091' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aea81bea-3b31-4df5-bbe9-cf01d81a91f7' class='xr-var-data-in' type='checkbox'><label for='data-aea81bea-3b31-4df5-bbe9-cf01d81a91f7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, True, True], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>depressive_disorder</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-26133636-0d07-4dc0-850a-be6550d5d206' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-26133636-0d07-4dc0-850a-be6550d5d206' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-49657e75-5192-4f61-84c5-fc4283f3d927' class='xr-var-data-in' type='checkbox'><label for='data-49657e75-5192-4f61-84c5-fc4283f3d927' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>gastroesophageal_reflux_disease</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-246bda69-3a10-4124-914a-e708011bb7a0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-246bda69-3a10-4124-914a-e708011bb7a0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-85263a18-4e19-4eb5-b9b4-8803dde9d9cb' class='xr-var-data-in' type='checkbox'><label for='data-85263a18-4e19-4eb5-b9b4-8803dde9d9cb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diabetes_mellitus_type_2</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-2889b1d4-62e5-40e8-8c90-c4646ce71e0c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2889b1d4-62e5-40e8-8c90-c4646ce71e0c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-09c3055e-cd53-49be-948f-d1a130b860c4' class='xr-var-data-in' type='checkbox'><label for='data-09c3055e-cd53-49be-948f-d1a130b860c4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>essential_hypertension</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... True True</div><input id='attrs-883720b9-70ac-4aa5-bd74-0e875ffabe7e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-883720b9-70ac-4aa5-bd74-0e875ffabe7e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-14ea4312-842f-4cfb-8087-e8259e5a8616' class='xr-var-data-in' type='checkbox'><label for='data-14ea4312-842f-4cfb-8087-e8259e5a8616' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, True, True], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>obesity</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-96f77e09-692f-4494-b425-0327baa0331a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-96f77e09-692f-4494-b425-0327baa0331a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5bcc8e65-ae76-4352-8f6c-9a240135fa98' class='xr-var-data-in' type='checkbox'><label for='data-5bcc8e65-ae76-4352-8f6c-9a240135fa98' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diabetes_mellitus</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-3babb540-e2b2-4ca5-af41-4e13c55df4f2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3babb540-e2b2-4ca5-af41-4e13c55df4f2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-74dc9358-6487-4858-90ec-10d59eebdb92' class='xr-var-data-in' type='checkbox'><label for='data-74dc9358-6487-4858-90ec-10d59eebdb92' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>asthma</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-18a861a8-8fdb-45a1-a898-9b40667610ec' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-18a861a8-8fdb-45a1-a898-9b40667610ec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-72d36afc-d1ce-4bf9-8d05-bc5af7fa86f8' class='xr-var-data-in' type='checkbox'><label for='data-72d36afc-d1ce-4bf9-8d05-bc5af7fa86f8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>coronary_arteriosclerosis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-759ef560-36b3-483f-9b96-a9fd5dd3e2ed' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-759ef560-36b3-483f-9b96-a9fd5dd3e2ed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0fab760d-b6ef-4ce1-9412-30b86051e0a2' class='xr-var-data-in' type='checkbox'><label for='data-0fab760d-b6ef-4ce1-9412-30b86051e0a2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>allergic_rhinitis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-9ae3bd14-8c24-468d-a2a4-3c72d46262cf' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9ae3bd14-8c24-468d-a2a4-3c72d46262cf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-91f47e01-cdf0-4648-ba8e-dfc970bfcdaf' class='xr-var-data-in' type='checkbox'><label for='data-91f47e01-cdf0-4648-ba8e-dfc970bfcdaf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hypothyroidism</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-4ea39784-b135-451b-a4ca-656da91a303f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4ea39784-b135-451b-a4ca-656da91a303f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d9247446-64a9-47d4-ad7e-15073805cfde' class='xr-var-data-in' type='checkbox'><label for='data-d9247446-64a9-47d4-ad7e-15073805cfde' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>upper_respiratory_infection</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-8cb7d505-615c-44ae-a2e8-8f80127ad50f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8cb7d505-615c-44ae-a2e8-8f80127ad50f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ce3c371d-cefa-48b7-8423-63a8da294e2c' class='xr-var-data-in' type='checkbox'><label for='data-ce3c371d-cefa-48b7-8423-63a8da294e2c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hypercholesterolemia</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... True True</div><input id='attrs-671b26eb-192b-4168-9e33-0ce20eb8da33' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-671b26eb-192b-4168-9e33-0ce20eb8da33' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4d2e3dae-7f58-4ebb-ad37-3cf4f0fd2b87' class='xr-var-data-in' type='checkbox'><label for='data-4d2e3dae-7f58-4ebb-ad37-3cf4f0fd2b87' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, True, True], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>backache</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-d2015a0e-c5c0-4590-8fd0-b249a78e87c3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d2015a0e-c5c0-4590-8fd0-b249a78e87c3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3138f4ab-cfed-4bc6-a4ee-36fe84bfec44' class='xr-var-data-in' type='checkbox'><label for='data-3138f4ab-cfed-4bc6-a4ee-36fe84bfec44' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>abdominal_pain</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-6744a638-556e-4724-984c-e094479a5a2d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6744a638-556e-4724-984c-e094479a5a2d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3db1033a-821d-4297-a80e-f9c005dc67e5' class='xr-var-data-in' type='checkbox'><label for='data-3db1033a-821d-4297-a80e-f9c005dc67e5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>osteoarthritis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-b98e9f88-9db4-4069-8fba-0a3a1a1fda96' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b98e9f88-9db4-4069-8fba-0a3a1a1fda96' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ebb207d5-76bd-4b92-ab12-7c4b7363c2cc' class='xr-var-data-in' type='checkbox'><label for='data-ebb207d5-76bd-4b92-ab12-7c4b7363c2cc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>low_back_pain</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-aee22b86-ef81-4bd9-aa93-10cbd9aeeb49' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-aee22b86-ef81-4bd9-aa93-10cbd9aeeb49' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6c881b0b-f1c5-474f-ba4b-5705eecb2d2b' class='xr-var-data-in' type='checkbox'><label for='data-6c881b0b-f1c5-474f-ba4b-5705eecb2d2b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>anemia</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... True True</div><input id='attrs-d2f95c61-55cd-4931-886c-72fc861d0f79' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d2f95c61-55cd-4931-886c-72fc861d0f79' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f02c7db2-2d1a-40bf-974a-a3732bddf63f' class='xr-var-data-in' type='checkbox'><label for='data-f02c7db2-2d1a-40bf-974a-a3732bddf63f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, True, True], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>anxiety</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-64c3bce6-3c0a-498b-955a-12413eade43b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-64c3bce6-3c0a-498b-955a-12413eade43b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b79a496b-2d3f-469e-9f08-cab31f951d5c' class='xr-var-data-in' type='checkbox'><label for='data-b79a496b-2d3f-469e-9f08-cab31f951d5c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>urinary_tract_infectious_disease</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... True True</div><input id='attrs-480c02ec-305b-42ba-b0e8-853862ba7b48' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-480c02ec-305b-42ba-b0e8-853862ba7b48' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-071a6105-05dd-47d6-995f-2ddc90f61b4b' class='xr-var-data-in' type='checkbox'><label for='data-071a6105-05dd-47d6-995f-2ddc90f61b4b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, True, True], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>chronic_obstructive_lung_disease</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... True True</div><input id='attrs-c9ba9cc6-b722-4496-bf74-9e3f5dfd966c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c9ba9cc6-b722-4496-bf74-9e3f5dfd966c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-95c21e89-f058-4277-8687-6d0b948421c8' class='xr-var-data-in' type='checkbox'><label for='data-95c21e89-f058-4277-8687-6d0b948421c8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, True, True], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>atrial_fibrillation</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-b7df31e6-e55c-4e58-a1bd-b571785d4d54' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b7df31e6-e55c-4e58-a1bd-b571785d4d54' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b8822f15-aaef-48b2-a035-6f9195680e94' class='xr-var-data-in' type='checkbox'><label for='data-b8822f15-aaef-48b2-a035-6f9195680e94' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pneumonia</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-9d624267-0319-4eac-bb6a-e588c8c71c6f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9d624267-0319-4eac-bb6a-e588c8c71c6f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1c7da0d1-fd0f-4988-aefa-294dad99f002' class='xr-var-data-in' type='checkbox'><label for='data-1c7da0d1-fd0f-4988-aefa-294dad99f002' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>chest_pain</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-adecd015-0cec-4eab-8277-8cb4299b311c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-adecd015-0cec-4eab-8277-8cb4299b311c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-54eb734a-c813-4461-acb0-e988c4297b6e' class='xr-var-data-in' type='checkbox'><label for='data-54eb734a-c813-4461-acb0-e988c4297b6e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>congestive_heart_failure</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-c82e597c-146e-4a90-b427-ff4810e8a979' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c82e597c-146e-4a90-b427-ff4810e8a979' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fc208b57-954e-4b5d-8d57-1884d1b47e79' class='xr-var-data-in' type='checkbox'><label for='data-fc208b57-954e-4b5d-8d57-1884d1b47e79' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>headache</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-b4ed43d9-3e89-4985-aac7-6c51d89b0202' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b4ed43d9-3e89-4985-aac7-6c51d89b0202' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9199a5f7-4f9c-414b-9e87-82ee5750e068' class='xr-var-data-in' type='checkbox'><label for='data-9199a5f7-4f9c-414b-9e87-82ee5750e068' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>migraine</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-d0f31bc4-93d4-4fd3-93cb-8546dcc715d5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d0f31bc4-93d4-4fd3-93cb-8546dcc715d5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d51c3289-03c2-487d-8c77-1859c94f8011' class='xr-var-data-in' type='checkbox'><label for='data-d51c3289-03c2-487d-8c77-1859c94f8011' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pregnant</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-d298b0b2-5bab-4e01-b108-580c89ed7236' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d298b0b2-5bab-4e01-b108-580c89ed7236' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-846a96ac-e4db-4f7f-9ac3-130d6937ae58' class='xr-var-data-in' type='checkbox'><label for='data-846a96ac-e4db-4f7f-9ac3-130d6937ae58' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>knee_pain</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-4fe0b725-09eb-40ce-a982-b54657c33db8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4fe0b725-09eb-40ce-a982-b54657c33db8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e1a0e661-b79e-4e0f-a656-91da4ebc6574' class='xr-var-data-in' type='checkbox'><label for='data-e1a0e661-b79e-4e0f-a656-91da4ebc6574' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>osteoporosis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-19d9ae83-1233-4989-800e-e122c4371ec4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-19d9ae83-1233-4989-800e-e122c4371ec4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d1ac8af1-108d-4dc6-9473-0cde5115b8da' class='xr-var-data-in' type='checkbox'><label for='data-d1ac8af1-108d-4dc6-9473-0cde5115b8da' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>polyp_of_colon</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-5018f1f2-f021-403c-8404-63ea1a8d6f5d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5018f1f2-f021-403c-8404-63ea1a8d6f5d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-21568999-768d-43a5-8891-7cf754f9ebe9' class='xr-var-data-in' type='checkbox'><label for='data-21568999-768d-43a5-8891-7cf754f9ebe9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>otitis_media</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-9e5a5ce6-c3fe-47cc-9c8f-5c526aa7a3f3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9e5a5ce6-c3fe-47cc-9c8f-5c526aa7a3f3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d6c84b34-f762-4707-a543-2e35168eac4d' class='xr-var-data-in' type='checkbox'><label for='data-d6c84b34-f762-4707-a543-2e35168eac4d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sinusitis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-86f1cdb2-6adf-424b-9064-7560abd1907e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-86f1cdb2-6adf-424b-9064-7560abd1907e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-62962937-9ab0-4e02-a498-1d4634f1462b' class='xr-var-data-in' type='checkbox'><label for='data-62962937-9ab0-4e02-a498-1d4634f1462b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cough</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-9c34fc43-3081-4ecb-b68e-e42b17e5e8f2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9c34fc43-3081-4ecb-b68e-e42b17e5e8f2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e8cb32ec-bcb9-4600-b382-e00e5adc80c0' class='xr-var-data-in' type='checkbox'><label for='data-e8cb32ec-bcb9-4600-b382-e00e5adc80c0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sleep_apnea</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-a7b4c206-0f58-4829-b1b6-d25db0360024' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a7b4c206-0f58-4829-b1b6-d25db0360024' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-991bbae6-908c-42a2-9c85-eae1c79212ed' class='xr-var-data-in' type='checkbox'><label for='data-991bbae6-908c-42a2-9c85-eae1c79212ed' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>insomnia</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-b3b6d9f1-5e5e-43a5-a581-4425c037fe89' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b3b6d9f1-5e5e-43a5-a581-4425c037fe89' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fffd2331-c111-4ea8-8d2d-aea9595a73aa' class='xr-var-data-in' type='checkbox'><label for='data-fffd2331-c111-4ea8-8d2d-aea9595a73aa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>inflammatory_disorder_due_to_increased_blood_urate_level</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-948e9ef8-1300-419f-ac48-d869ff22c81b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-948e9ef8-1300-419f-ac48-d869ff22c81b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d0150fd6-38ee-487a-9661-4123d3089736' class='xr-var-data-in' type='checkbox'><label for='data-d0150fd6-38ee-487a-9661-4123d3089736' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tobacco_dependence_syndrome</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... True True</div><input id='attrs-c6d0ba3e-d32c-4c19-be11-8d7811448579' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c6d0ba3e-d32c-4c19-be11-8d7811448579' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f811a87e-7c87-49aa-ae59-e1624c60c619' class='xr-var-data-in' type='checkbox'><label for='data-f811a87e-7c87-49aa-ae59-e1624c60c619' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, True, True], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>malignant_tumor_of_prostate</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-5e9daa58-d2f9-4367-a361-783a94c74951' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5e9daa58-d2f9-4367-a361-783a94c74951' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5f362da9-90b5-4c23-b14e-2ce50d6ebeae' class='xr-var-data-in' type='checkbox'><label for='data-5f362da9-90b5-4c23-b14e-2ce50d6ebeae' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>constipation</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-360503f6-c09b-47af-bb1c-0f0a15ee6308' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-360503f6-c09b-47af-bb1c-0f0a15ee6308' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0b9ea6ab-db75-4222-92f3-1a9059bff14c' class='xr-var-data-in' type='checkbox'><label for='data-0b9ea6ab-db75-4222-92f3-1a9059bff14c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hearing_loss</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-b35b2974-e08a-4dc4-8695-f2b458410101' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b35b2974-e08a-4dc4-8695-f2b458410101' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8e1e95f2-5c58-4851-aac4-e1cf3a45ec30' class='xr-var-data-in' type='checkbox'><label for='data-8e1e95f2-5c58-4851-aac4-e1cf3a45ec30' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fatigue</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-d9c5eeea-65aa-4de4-aa56-de290aa09e1e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d9c5eeea-65aa-4de4-aa56-de290aa09e1e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fdbc34a2-e1f4-4bd8-9f42-99b4a06ebe4a' class='xr-var-data-in' type='checkbox'><label for='data-fdbc34a2-e1f4-4bd8-9f42-99b4a06ebe4a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>obstructive_sleep_apnea_syndrome</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-deede88b-81aa-42ac-a748-b6fb999d43e6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-deede88b-81aa-42ac-a748-b6fb999d43e6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d1837ef3-4b9b-47d2-b41a-276c028af9aa' class='xr-var-data-in' type='checkbox'><label for='data-d1837ef3-4b9b-47d2-b41a-276c028af9aa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>malignant_neoplasm_of_breast</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-9738fe57-3edb-44d1-87e3-271fa0771ea6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9738fe57-3edb-44d1-87e3-271fa0771ea6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1eaaaf06-f7b8-404e-af2b-f324050655f9' class='xr-var-data-in' type='checkbox'><label for='data-1eaaaf06-f7b8-404e-af2b-f324050655f9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>delivery_normal</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-834c49a2-bc61-4382-8842-34b6e43b92ac' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-834c49a2-bc61-4382-8842-34b6e43b92ac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-790724a7-e4e4-4a37-9ca0-4c5afdf45cff' class='xr-var-data-in' type='checkbox'><label for='data-790724a7-e4e4-4a37-9ca0-4c5afdf45cff' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>irritable_bowel_syndrome</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-40a4df70-bab7-40d4-af74-2de4fb150328' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-40a4df70-bab7-40d4-af74-2de4fb150328' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-384816c2-c6c2-4543-abdf-1328907b839c' class='xr-var-data-in' type='checkbox'><label for='data-384816c2-c6c2-4543-abdf-1328907b839c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tobacco_user</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... True True</div><input id='attrs-0ccafc74-742c-4578-a835-71a233f4af9a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0ccafc74-742c-4578-a835-71a233f4af9a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-85cfda2c-15ca-4958-a042-4838d331e6c2' class='xr-var-data-in' type='checkbox'><label for='data-85cfda2c-15ca-4958-a042-4838d331e6c2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, True, True], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>neck_pain</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-53560011-fae9-4301-9d32-c80df5381147' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-53560011-fae9-4301-9d32-c80df5381147' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b1d191bf-dea0-49c9-943a-83b23ca49a9b' class='xr-var-data-in' type='checkbox'><label for='data-b1d191bf-dea0-49c9-943a-83b23ca49a9b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cerebrovascular_accident</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-e8dedef4-fb36-4e4a-a71b-ffb7a2be4d0a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e8dedef4-fb36-4e4a-a71b-ffb7a2be4d0a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-97d22d87-9f0f-4686-abcc-a316eb0b8e8b' class='xr-var-data-in' type='checkbox'><label for='data-97d22d87-9f0f-4686-abcc-a316eb0b8e8b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>asthenia</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-85b9b37b-243a-47d5-bf46-6a1633a280ea' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-85b9b37b-243a-47d5-bf46-6a1633a280ea' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-59c31d2a-cb68-4eb7-ab65-fb92c201a984' class='xr-var-data-in' type='checkbox'><label for='data-59c31d2a-cb68-4eb7-ab65-fb92c201a984' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>shoulder_pain</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-c4b2ce35-13fa-4dd1-b578-32409e6565c2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c4b2ce35-13fa-4dd1-b578-32409e6565c2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2d8412c0-7f97-4935-84b7-9c789f5d956e' class='xr-var-data-in' type='checkbox'><label for='data-2d8412c0-7f97-4935-84b7-9c789f5d956e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>acne_vulgaris</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-eeba181f-8a0a-4b99-a855-56d8e8ce1504' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-eeba181f-8a0a-4b99-a855-56d8e8ce1504' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bda4dfe8-2a1a-4b02-b51a-37b4d308d486' class='xr-var-data-in' type='checkbox'><label for='data-bda4dfe8-2a1a-4b02-b51a-37b4d308d486' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>benign_prostatic_hyperplasia</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-41993e3f-1ab1-4795-98c2-973f578065f1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-41993e3f-1ab1-4795-98c2-973f578065f1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9db812a8-a5a9-4ef1-ac68-dc4901a221fb' class='xr-var-data-in' type='checkbox'><label for='data-9db812a8-a5a9-4ef1-ac68-dc4901a221fb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dyspnea</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-a4264d14-82ff-4690-aeb4-ebfb4a161077' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a4264d14-82ff-4690-aeb4-ebfb4a161077' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-83ee476f-7b67-4ede-9d8f-a345758571e1' class='xr-var-data-in' type='checkbox'><label for='data-83ee476f-7b67-4ede-9d8f-a345758571e1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>carpal_tunnel_syndrome</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-254e422a-0a6c-4db4-a32e-357d4e1069f5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-254e422a-0a6c-4db4-a32e-357d4e1069f5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-63b2a24a-55a3-4148-83a3-c8985475ef61' class='xr-var-data-in' type='checkbox'><label for='data-63b2a24a-55a3-4148-83a3-c8985475ef61' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>bronchitis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-211af6d6-ac20-4da1-a520-8630fe8971cc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-211af6d6-ac20-4da1-a520-8630fe8971cc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-08fd18aa-adc1-499f-b547-9a961639f0d3' class='xr-var-data-in' type='checkbox'><label for='data-08fd18aa-adc1-499f-b547-9a961639f0d3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pharyngitis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-52c196b2-ad36-4351-afe1-d4110c7325e4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-52c196b2-ad36-4351-afe1-d4110c7325e4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-52a20bb1-ad2a-48c2-8485-f612b06d3314' class='xr-var-data-in' type='checkbox'><label for='data-52a20bb1-ad2a-48c2-8485-f612b06d3314' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>arthritis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... True True</div><input id='attrs-9d3a050e-e09e-4a31-91c5-8d9788d00949' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9d3a050e-e09e-4a31-91c5-8d9788d00949' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4085fb0d-d9d5-49f7-8394-0b9d8fdcb4e3' class='xr-var-data-in' type='checkbox'><label for='data-4085fb0d-d9d5-49f7-8394-0b9d8fdcb4e3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, True, True], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diarrhea</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-7b088e05-26d4-4fbd-9b68-b5599d807e5d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7b088e05-26d4-4fbd-9b68-b5599d807e5d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f51d941a-c838-448e-b709-b4514c1027a5' class='xr-var-data-in' type='checkbox'><label for='data-f51d941a-c838-448e-b709-b4514c1027a5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dizziness</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-7a3ebc40-315b-4bbf-8cb6-85c619271bd2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7a3ebc40-315b-4bbf-8cb6-85c619271bd2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6c837097-f20a-4ec0-aa1f-d4046c86d910' class='xr-var-data-in' type='checkbox'><label for='data-6c837097-f20a-4ec0-aa1f-d4046c86d910' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>alcohol_abuse</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-a21e0609-03b5-4cdf-8f38-97040468e766' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a21e0609-03b5-4cdf-8f38-97040468e766' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c5cbec66-af0a-489f-8aaf-7588a7bf6e16' class='xr-var-data-in' type='checkbox'><label for='data-c5cbec66-af0a-489f-8aaf-7588a7bf6e16' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dementia</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-8362a968-8180-48df-9145-95f7b3794573' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8362a968-8180-48df-9145-95f7b3794573' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3539bf9e-98ff-4003-b4ce-2bbe9f34a78c' class='xr-var-data-in' type='checkbox'><label for='data-3539bf9e-98ff-4003-b4ce-2bbe9f34a78c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>eczema</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-0a59a37a-023e-44b6-a39b-6ff53e8d32e1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0a59a37a-023e-44b6-a39b-6ff53e8d32e1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0fcdf6a9-4426-490b-aeee-90152044547f' class='xr-var-data-in' type='checkbox'><label for='data-0fcdf6a9-4426-490b-aeee-90152044547f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>syncope</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-13457848-0d38-4c59-9202-c724e270bf6b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-13457848-0d38-4c59-9202-c724e270bf6b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2df4f8aa-c85e-4035-a60e-158b59ce1fab' class='xr-var-data-in' type='checkbox'><label for='data-2df4f8aa-c85e-4035-a60e-158b59ce1fab' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>acute_sinusitis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-20cc13cb-0127-46e0-8e1e-7672189fdd16' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-20cc13cb-0127-46e0-8e1e-7672189fdd16' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d583a459-23b8-4191-8ab6-b3d3cf79ef24' class='xr-var-data-in' type='checkbox'><label for='data-d583a459-23b8-4191-8ab6-b3d3cf79ef24' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>iron_deficiency_anemia</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-ebed3ad2-08f2-4d8b-865a-7e622d2d95c8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ebed3ad2-08f2-4d8b-865a-7e622d2d95c8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9a15d1f7-f7ca-4aee-b184-c160c1f17711' class='xr-var-data-in' type='checkbox'><label for='data-9a15d1f7-f7ca-4aee-b184-c160c1f17711' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>allergic_rhinitis_caused_by_pollen</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-62939356-9177-437d-97e0-3eb0cb5023f6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-62939356-9177-437d-97e0-3eb0cb5023f6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-48773863-a5d5-4192-ae71-81c5399b890e' class='xr-var-data-in' type='checkbox'><label for='data-48773863-a5d5-4192-ae71-81c5399b890e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>gastritis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-9583c1d5-564f-4eb3-966a-5e591b841c0a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9583c1d5-564f-4eb3-966a-5e591b841c0a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-20962064-9fac-4b1b-b2f5-ebfe394cec7f' class='xr-var-data-in' type='checkbox'><label for='data-20962064-9fac-4b1b-b2f5-ebfe394cec7f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cataract</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-e393b0df-85ef-447d-b3f6-3d41ead949f9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e393b0df-85ef-447d-b3f6-3d41ead949f9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-108cfd7f-042a-42fb-a449-95f1ffb29bf5' class='xr-var-data-in' type='checkbox'><label for='data-108cfd7f-042a-42fb-a449-95f1ffb29bf5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hematuria_syndrome</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-23605d85-8b16-42c7-8426-197b3c252cad' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-23605d85-8b16-42c7-8426-197b3c252cad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f857f4f6-030f-4b16-9086-bd42b4f6a0c3' class='xr-var-data-in' type='checkbox'><label for='data-f857f4f6-030f-4b16-9086-bd42b4f6a0c3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>disorder_of_the_peripheral_nervous_system</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-44b8c491-c882-4368-8815-52a033db5294' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-44b8c491-c882-4368-8815-52a033db5294' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-af00b6fa-e1d3-49b9-af40-f3d72e099323' class='xr-var-data-in' type='checkbox'><label for='data-af00b6fa-e1d3-49b9-af40-f3d72e099323' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>viral_hepatitis_type_c</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-86d22417-3df8-48f4-82d5-2ac22593f249' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-86d22417-3df8-48f4-82d5-2ac22593f249' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6e990cf4-033f-42d8-baa7-ebbbfed4631c' class='xr-var-data-in' type='checkbox'><label for='data-6e990cf4-033f-42d8-baa7-ebbbfed4631c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>palpitations</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-31819035-ffca-4e6e-9e6f-84645e335ad5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-31819035-ffca-4e6e-9e6f-84645e335ad5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-81684077-ec78-4b47-b68d-540b947f5ce3' class='xr-var-data-in' type='checkbox'><label for='data-81684077-ec78-4b47-b68d-540b947f5ce3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>eruption_of_skin</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-f43ea4b2-5a3a-4dc0-b072-0aca0c53fd4d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f43ea4b2-5a3a-4dc0-b072-0aca0c53fd4d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f05edf88-ccdc-44c2-866a-acdc7039d0f9' class='xr-var-data-in' type='checkbox'><label for='data-f05edf88-ccdc-44c2-866a-acdc7039d0f9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diabetes_mellitus_type_1</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-622400bf-5c66-4ec0-b1d8-b8e4140e2b06' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-622400bf-5c66-4ec0-b1d8-b8e4140e2b06' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-19c4b5a8-7ac0-4b34-8d29-1a14587f6c06' class='xr-var-data-in' type='checkbox'><label for='data-19c4b5a8-7ac0-4b34-8d29-1a14587f6c06' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>renal_failure_syndrome</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... True True</div><input id='attrs-74db247d-b58a-4cbb-aea2-2e1338d14bd5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-74db247d-b58a-4cbb-aea2-2e1338d14bd5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c78c91f9-4650-474a-8b25-faf3f3656c43' class='xr-var-data-in' type='checkbox'><label for='data-c78c91f9-4650-474a-8b25-faf3f3656c43' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, True, True], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>peripheral_vascular_disease</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-920eb621-302b-471a-909e-5af7f1534947' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-920eb621-302b-471a-909e-5af7f1534947' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-266a963b-bda4-4a0f-85bd-ba73344651c0' class='xr-var-data-in' type='checkbox'><label for='data-266a963b-bda4-4a0f-85bd-ba73344651c0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hyperglycemia</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-d16589d7-ac04-4e35-b111-a6433037d619' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d16589d7-ac04-4e35-b111-a6433037d619' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6ec4ba54-514b-41b4-b863-bd1f8472b918' class='xr-var-data-in' type='checkbox'><label for='data-6ec4ba54-514b-41b4-b863-bd1f8472b918' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>seizure_disorder</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-e6480840-7512-44bc-b8e3-752a2af4ae17' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e6480840-7512-44bc-b8e3-752a2af4ae17' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f903bb76-ae5f-401e-90d0-b260a7ac9504' class='xr-var-data-in' type='checkbox'><label for='data-f903bb76-ae5f-401e-90d0-b260a7ac9504' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fever</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-44f801e7-b482-42dc-9017-46de57f681b5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-44f801e7-b482-42dc-9017-46de57f681b5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bbd021a5-ef70-4f63-a391-41c651b5b411' class='xr-var-data-in' type='checkbox'><label for='data-bbd021a5-ef70-4f63-a391-41c651b5b411' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>osteoarthritis_of_knee</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-5527a89b-0ca5-450f-ba7e-55f1751872e1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5527a89b-0ca5-450f-ba7e-55f1751872e1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fa12500e-1e29-4afb-ab82-8d77104e8e42' class='xr-var-data-in' type='checkbox'><label for='data-fa12500e-1e29-4afb-ab82-8d77104e8e42' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>actinic_keratosis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-6cc99cf2-599a-41f5-83f9-34b34a810d33' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6cc99cf2-599a-41f5-83f9-34b34a810d33' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c14dbc8d-06d4-47f4-b5ad-46e096f03f1b' class='xr-var-data-in' type='checkbox'><label for='data-c14dbc8d-06d4-47f4-b5ad-46e096f03f1b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>urinary_incontinence</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-ca840c6c-f4c0-4a55-9743-bf6d846d3c22' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ca840c6c-f4c0-4a55-9743-bf6d846d3c22' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3269c062-d6ea-4859-9e79-dbe1a9a5d12f' class='xr-var-data-in' type='checkbox'><label for='data-3269c062-d6ea-4859-9e79-dbe1a9a5d12f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hemorrhoids</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-d1b140e7-afc0-4be3-9827-b093c346a880' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d1b140e7-afc0-4be3-9827-b093c346a880' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8d783cff-5b93-4187-a64d-def61796b7bf' class='xr-var-data-in' type='checkbox'><label for='data-8d783cff-5b93-4187-a64d-def61796b7bf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>seizure</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-6c88f0af-5dcb-4622-82cd-9718226756c2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6c88f0af-5dcb-4622-82cd-9718226756c2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-94c3ff48-fa63-43a2-9681-ddddc6f33fb9' class='xr-var-data-in' type='checkbox'><label for='data-94c3ff48-fa63-43a2-9681-ddddc6f33fb9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>laceration_-_injury</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-79cc0717-6e6e-47c3-a700-8b846a390b83' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-79cc0717-6e6e-47c3-a700-8b846a390b83' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-50a214cc-7a25-4274-ac3e-db002a405bd7' class='xr-var-data-in' type='checkbox'><label for='data-50a214cc-7a25-4274-ac3e-db002a405bd7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>glaucoma</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-dee2d09d-5b53-4ba1-85e3-273afb6de8fa' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dee2d09d-5b53-4ba1-85e3-273afb6de8fa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-20dcdde4-9a6c-4ba7-b1cc-24a173471b6c' class='xr-var-data-in' type='checkbox'><label for='data-20dcdde4-9a6c-4ba7-b1cc-24a173471b6c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>body_mass_index_30+_-_obesity</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-7fb87ccb-0866-4289-8b14-af893fa3e91f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7fb87ccb-0866-4289-8b14-af893fa3e91f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-caca36d6-ca63-443a-8f43-2768962aa672' class='xr-var-data-in' type='checkbox'><label for='data-caca36d6-ca63-443a-8f43-2768962aa672' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>breast_lump</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-368f1d97-7450-40c5-9f95-536168ff7453' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-368f1d97-7450-40c5-9f95-536168ff7453' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9e07f906-0300-4aa9-9004-ab7c35392f19' class='xr-var-data-in' type='checkbox'><label for='data-9e07f906-0300-4aa9-9004-ab7c35392f19' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>viral_disease</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-b2a74128-3512-4e30-b63b-89dd8a4cc4c9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b2a74128-3512-4e30-b63b-89dd8a4cc4c9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9e3e4d2c-8750-4598-a857-88369fed3d71' class='xr-var-data-in' type='checkbox'><label for='data-9e3e4d2c-8750-4598-a857-88369fed3d71' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>abnormal_cervical_smear</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-5cd5e984-0c57-43f0-b2f9-79116131f312' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5cd5e984-0c57-43f0-b2f9-79116131f312' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ac1ca58c-da01-4ced-a13f-4774d69927e2' class='xr-var-data-in' type='checkbox'><label for='data-ac1ca58c-da01-4ced-a13f-4774d69927e2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cellulitis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-49495f4c-81e5-4a36-a877-b841d3883a64' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-49495f4c-81e5-4a36-a877-b841d3883a64' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2a0f9e3b-ce56-46c8-a887-77cee4a4f8fe' class='xr-var-data-in' type='checkbox'><label for='data-2a0f9e3b-ce56-46c8-a887-77cee4a4f8fe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rheumatoid_arthritis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-9b2b55b4-0088-4d36-8a9e-956e2b86ebcd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9b2b55b4-0088-4d36-8a9e-956e2b86ebcd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8f51bd05-1194-425b-88cc-9fe8675aff5f' class='xr-var-data-in' type='checkbox'><label for='data-8f51bd05-1194-425b-88cc-9fe8675aff5f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>senile_hyperkeratosis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-029ec8c9-8477-46c5-af94-e212fadb29f6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-029ec8c9-8477-46c5-af94-e212fadb29f6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1e61aa04-e92c-4462-8ee8-c090390d6fe2' class='xr-var-data-in' type='checkbox'><label for='data-1e61aa04-e92c-4462-8ee8-c090390d6fe2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>anxiety_disorder</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-f2717040-bc61-49db-8d70-5d09117a422f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f2717040-bc61-49db-8d70-5d09117a422f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ade89675-3b79-43a6-a18c-40b39b7abcc4' class='xr-var-data-in' type='checkbox'><label for='data-ade89675-3b79-43a6-a18c-40b39b7abcc4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vertigo</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-0142bf29-9de6-411c-aae3-e423e3b5422c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0142bf29-9de6-411c-aae3-e423e3b5422c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bb64916d-8b2f-47cc-a5ee-46292244b354' class='xr-var-data-in' type='checkbox'><label for='data-bb64916d-8b2f-47cc-a5ee-46292244b354' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>chronic_kidney_disease</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... True True</div><input id='attrs-196e166a-5ac6-4e1c-9320-9688aa3003d2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-196e166a-5ac6-4e1c-9320-9688aa3003d2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3675b178-cd11-4944-91ae-a59738460da0' class='xr-var-data-in' type='checkbox'><label for='data-3675b178-cd11-4944-91ae-a59738460da0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, True, True], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dysphagia</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-30cb50c0-ea33-46ba-9aac-5be0c46089a9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-30cb50c0-ea33-46ba-9aac-5be0c46089a9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-35b88bb1-d97a-4bf9-88f1-de286b048f11' class='xr-var-data-in' type='checkbox'><label for='data-35b88bb1-d97a-4bf9-88f1-de286b048f11' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>edema</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-361e02b6-3d6d-48ef-b109-fbb2d17b1e52' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-361e02b6-3d6d-48ef-b109-fbb2d17b1e52' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-08e9bb4b-66d3-40f1-a451-a75456ec734b' class='xr-var-data-in' type='checkbox'><label for='data-08e9bb4b-66d3-40f1-a451-a75456ec734b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>malignant_neoplasm_of_colon</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-01965674-3727-407b-8c92-0e03b959cbeb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-01965674-3727-407b-8c92-0e03b959cbeb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-04b042b7-b43c-47b9-a892-eaa20d360be1' class='xr-var-data-in' type='checkbox'><label for='data-04b042b7-b43c-47b9-a892-eaa20d360be1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hip_pain</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-765620e8-e26b-4008-952a-d673cd626835' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-765620e8-e26b-4008-952a-d673cd626835' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-82548720-880f-44bf-9410-54477ddff706' class='xr-var-data-in' type='checkbox'><label for='data-82548720-880f-44bf-9410-54477ddff706' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>posttraumatic_stress_disorder</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-ffbbf3b1-93af-42df-a48d-5046de429ac5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ffbbf3b1-93af-42df-a48d-5046de429ac5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bc243028-1e1e-4587-a659-da828607478a' class='xr-var-data-in' type='checkbox'><label for='data-bc243028-1e1e-4587-a659-da828607478a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>inflammatory_dermatosis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-6deb8cea-1cbe-4399-86f1-c7c37f55d1d3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6deb8cea-1cbe-4399-86f1-c7c37f55d1d3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b418897e-6175-4d28-b278-d8676469b74a' class='xr-var-data-in' type='checkbox'><label for='data-b418897e-6175-4d28-b278-d8676469b74a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>psoriasis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-a9f4665b-0542-422f-b15f-0931b5f75cfb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a9f4665b-0542-422f-b15f-0931b5f75cfb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a8fdb72f-50e4-4cf2-adb3-25e05a02c606' class='xr-var-data-in' type='checkbox'><label for='data-a8fdb72f-50e4-4cf2-adb3-25e05a02c606' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>myopia</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-da3a587a-7c3b-4231-b5fe-4e2dede1a00d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-da3a587a-7c3b-4231-b5fe-4e2dede1a00d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-84a6ccf5-3c1e-492e-93d7-5fb839b06355' class='xr-var-data-in' type='checkbox'><label for='data-84a6ccf5-3c1e-492e-93d7-5fb839b06355' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>senile_cataract</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-c9a0924d-b239-4ab9-a0e3-c364f4fde8d3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c9a0924d-b239-4ab9-a0e3-c364f4fde8d3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0af06193-2a34-4a68-b988-d50e4c6a344b' class='xr-var-data-in' type='checkbox'><label for='data-0af06193-2a34-4a68-b988-d50e4c6a344b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>heart_murmur</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-ec8d28a4-8555-4e2b-9d3e-b15dd48c8980' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ec8d28a4-8555-4e2b-9d3e-b15dd48c8980' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2f5d6b22-a53c-4e2b-b3e6-022dc6fdaa8b' class='xr-var-data-in' type='checkbox'><label for='data-2f5d6b22-a53c-4e2b-b3e6-022dc6fdaa8b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>liver_function_tests_abnormal</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... True True</div><input id='attrs-166c5cba-84cc-4d03-8572-1cc0a5fb3236' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-166c5cba-84cc-4d03-8572-1cc0a5fb3236' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4de88025-4238-4f03-8779-ef715e90e2bf' class='xr-var-data-in' type='checkbox'><label for='data-4de88025-4238-4f03-8779-ef715e90e2bf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, True, True], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>angina</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-7514f0e0-fe07-4cfe-9eb1-a6cc46344274' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7514f0e0-fe07-4cfe-9eb1-a6cc46344274' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9822cb69-7e26-47c2-b939-3772a298b8fc' class='xr-var-data-in' type='checkbox'><label for='data-9822cb69-7e26-47c2-b939-3772a298b8fc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>impaired_fasting_glycemia</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-d5ad4665-c3ee-4e63-908b-28aa8f59f7e8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d5ad4665-c3ee-4e63-908b-28aa8f59f7e8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1262cee5-1cbb-4fd2-b425-a4700555eea3' class='xr-var-data-in' type='checkbox'><label for='data-1262cee5-1cbb-4fd2-b425-a4700555eea3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>chronic_ischemic_heart_disease</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-c6fecf87-cc47-4a1e-9c33-773e7023aba3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c6fecf87-cc47-4a1e-9c33-773e7023aba3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e1ef86f3-ca9d-4f2b-b67c-2fd34ddf11c2' class='xr-var-data-in' type='checkbox'><label for='data-e1ef86f3-ca9d-4f2b-b67c-2fd34ddf11c2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>chronic_sinusitis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-6656ae45-9164-4e58-a078-1709db27161b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6656ae45-9164-4e58-a078-1709db27161b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-87ced8b3-421b-4dba-a134-896ff54e56b1' class='xr-var-data-in' type='checkbox'><label for='data-87ced8b3-421b-4dba-a134-896ff54e56b1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>menopause_present</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-12f82d69-8b28-47e1-a476-b1b13f380a3d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-12f82d69-8b28-47e1-a476-b1b13f380a3d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-34a6d316-8252-41ef-9354-99221d8ee008' class='xr-var-data-in' type='checkbox'><label for='data-34a6d316-8252-41ef-9354-99221d8ee008' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>basal_cell_carcinoma_of_skin</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-d335ce74-ac7b-4bd3-9163-459becdbe2c7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d335ce74-ac7b-4bd3-9163-459becdbe2c7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ce748b15-d1fd-422c-8816-5a5009ad2f37' class='xr-var-data-in' type='checkbox'><label for='data-ce748b15-d1fd-422c-8816-5a5009ad2f37' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>raised_prostate_specific_antigen</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-47dfcc20-06d9-4255-8c74-7ed3314b25d0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-47dfcc20-06d9-4255-8c74-7ed3314b25d0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b8f30e93-14d4-4bbc-82f1-e59843ff1da7' class='xr-var-data-in' type='checkbox'><label for='data-b8f30e93-14d4-4bbc-82f1-e59843ff1da7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>impaired_glucose_tolerance</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-d6d8b9f7-3d7e-4732-a796-7289855926d3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d6d8b9f7-3d7e-4732-a796-7289855926d3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-51f64381-b59b-4cc5-b524-72dd58dc327b' class='xr-var-data-in' type='checkbox'><label for='data-51f64381-b59b-4cc5-b524-72dd58dc327b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>smoker</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... True True</div><input id='attrs-ba99e73e-22da-4415-9286-8b8a0fec33de' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ba99e73e-22da-4415-9286-8b8a0fec33de' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fd99214d-778a-429b-831f-ddf7b91e508c' class='xr-var-data-in' type='checkbox'><label for='data-fd99214d-778a-429b-831f-ddf7b91e508c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, True, True], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hypertriglyceridemia</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... True True</div><input id='attrs-0a4387e6-9c55-4f26-b779-267a2e6789c4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0a4387e6-9c55-4f26-b779-267a2e6789c4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b37d1d96-7851-436d-8656-4a95f128c002' class='xr-var-data-in' type='checkbox'><label for='data-b37d1d96-7851-436d-8656-4a95f128c002' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, True, True], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>irregular_periods</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-bcc7ecdc-6cc2-49dc-9055-d25afe441733' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bcc7ecdc-6cc2-49dc-9055-d25afe441733' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-84f1ad03-6e4e-472a-a7bf-2170de443951' class='xr-var-data-in' type='checkbox'><label for='data-84f1ad03-6e4e-472a-a7bf-2170de443951' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>herpes_zoster</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-21d517e6-4895-411c-ab89-492f93ced980' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-21d517e6-4895-411c-ab89-492f93ced980' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9e090e3f-7110-4e10-955b-0f2d5572de94' class='xr-var-data-in' type='checkbox'><label for='data-9e090e3f-7110-4e10-955b-0f2d5572de94' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sensorineural_hearing_loss</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-2d624304-c8f2-425f-9211-6d8ead69124a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2d624304-c8f2-425f-9211-6d8ead69124a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3a8cc3d4-ed98-49ff-9830-cfca77c87585' class='xr-var-data-in' type='checkbox'><label for='data-3a8cc3d4-ed98-49ff-9830-cfca77c87585' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rectal_hemorrhage</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-2cae116b-ae56-4b33-b418-5deecdf4f9ab' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2cae116b-ae56-4b33-b418-5deecdf4f9ab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ba7480bf-3f5a-4600-b333-8ca9dc5cca5b' class='xr-var-data-in' type='checkbox'><label for='data-ba7480bf-3f5a-4600-b333-8ca9dc5cca5b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>peptic_ulcer</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-07a24e12-9c76-455b-9571-0d8ef4ba2b29' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-07a24e12-9c76-455b-9571-0d8ef4ba2b29' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f3775abb-51c0-429d-9a53-8cb1452daf99' class='xr-var-data-in' type='checkbox'><label for='data-f3775abb-51c0-429d-9a53-8cb1452daf99' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tinnitus</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-5b1b186c-6c80-44f3-9151-85482003bfcc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5b1b186c-6c80-44f3-9151-85482003bfcc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1d621f82-f6e9-4695-bd8d-be43f02a71a5' class='xr-var-data-in' type='checkbox'><label for='data-1d621f82-f6e9-4695-bd8d-be43f02a71a5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>bipolar_disorder</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-e7444429-581a-43c8-9137-90c368181018' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e7444429-581a-43c8-9137-90c368181018' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-22dfc14a-7b39-4efe-a23e-761825fe3ccc' class='xr-var-data-in' type='checkbox'><label for='data-22dfc14a-7b39-4efe-a23e-761825fe3ccc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vitamin_d_deficiency</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-ea61d583-ad9b-4102-bc0f-4364d644abdc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ea61d583-ad9b-4102-bc0f-4364d644abdc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-27488755-c278-497a-b057-9d0b4abe2d33' class='xr-var-data-in' type='checkbox'><label for='data-27488755-c278-497a-b057-9d0b4abe2d33' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>transient_ischemic_attack</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-213a0990-e0cc-41cf-9421-e8e69980ce21' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-213a0990-e0cc-41cf-9421-e8e69980ce21' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-916db78a-f834-41f6-9605-a46ca5347210' class='xr-var-data-in' type='checkbox'><label for='data-916db78a-f834-41f6-9605-a46ca5347210' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>streptococcal_sore_throat</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-aa1422d0-d728-4508-9b1d-6c393dae0056' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-aa1422d0-d728-4508-9b1d-6c393dae0056' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4ba2230d-8240-4af2-bc1e-2a43c67f50f3' class='xr-var-data-in' type='checkbox'><label for='data-4ba2230d-8240-4af2-bc1e-2a43c67f50f3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>onychomycosis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-6d1ba79e-7c49-48f3-9ae5-0e5c7291e579' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6d1ba79e-7c49-48f3-9ae5-0e5c7291e579' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ea1bb428-b946-4fb4-8788-58329969eb3d' class='xr-var-data-in' type='checkbox'><label for='data-ea1bb428-b946-4fb4-8788-58329969eb3d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>deep_venous_thrombosis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-01976a76-7e23-4fc7-b8c8-1d7fe5b08d40' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-01976a76-7e23-4fc7-b8c8-1d7fe5b08d40' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8f7d2a94-0a90-47fb-a8f9-13417b8f4798' class='xr-var-data-in' type='checkbox'><label for='data-8f7d2a94-0a90-47fb-a8f9-13417b8f4798' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>presbyopia</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-1682c986-20a9-4cdf-9956-a15d95e23a67' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1682c986-20a9-4cdf-9956-a15d95e23a67' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b64983c2-ac1a-4039-b88c-f7c7b387c4fd' class='xr-var-data-in' type='checkbox'><label for='data-b64983c2-ac1a-4039-b88c-f7c7b387c4fd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>neonatal_jaundice</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-31658cae-468f-487d-9c44-acea2aa6c9c3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-31658cae-468f-487d-9c44-acea2aa6c9c3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-46ed1388-3ea2-4d34-ac43-ba5409b591b0' class='xr-var-data-in' type='checkbox'><label for='data-46ed1388-3ea2-4d34-ac43-ba5409b591b0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>bacterial_vaginosis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-ca5a7371-d043-4586-92b3-4a9be4e7f414' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ca5a7371-d043-4586-92b3-4a9be4e7f414' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-90e90600-8d6a-4938-aba8-38d1922e6854' class='xr-var-data-in' type='checkbox'><label for='data-90e90600-8d6a-4938-aba8-38d1922e6854' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>impacted_cerumen</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-29b968dd-9a34-40a6-8f4a-e3aef99add2d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-29b968dd-9a34-40a6-8f4a-e3aef99add2d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-db8910a9-3850-4b69-9722-163747c3fa10' class='xr-var-data-in' type='checkbox'><label for='data-db8910a9-3850-4b69-9722-163747c3fa10' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>foot_pain</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-1de798c8-12f0-4b05-bf2a-705e1f0a6078' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1de798c8-12f0-4b05-bf2a-705e1f0a6078' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ea98c792-9a8d-4e7a-bacb-4c5cc2e1e928' class='xr-var-data-in' type='checkbox'><label for='data-ea98c792-9a8d-4e7a-bacb-4c5cc2e1e928' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sciatica</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-be898ba4-dd5d-4174-997a-0749f9e44895' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-be898ba4-dd5d-4174-997a-0749f9e44895' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-14a18f12-8b71-49b7-a6b2-886bdfcce5c4' class='xr-var-data-in' type='checkbox'><label for='data-14a18f12-8b71-49b7-a6b2-886bdfcce5c4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vomiting</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-605c7f5f-433d-4304-be90-2239b35fd050' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-605c7f5f-433d-4304-be90-2239b35fd050' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9930e30c-b878-47ba-94ff-16cc21aad2c5' class='xr-var-data-in' type='checkbox'><label for='data-9930e30c-b878-47ba-94ff-16cc21aad2c5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>benign_prostatic_hypertrophy_with_outflow_obstruction</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-5a6c7b48-2cdc-4958-a0ca-672b7de1e6ef' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5a6c7b48-2cdc-4958-a0ca-672b7de1e6ef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b7fa944b-01d2-4aa5-9481-232e201a12db' class='xr-var-data-in' type='checkbox'><label for='data-b7fa944b-01d2-4aa5-9481-232e201a12db' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>type_ii_diabetes_mellitus_without_complication</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-30886972-796f-4b8a-8515-358fbda80e71' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-30886972-796f-4b8a-8515-358fbda80e71' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1bdb075a-920e-4518-8b5c-104ac1369c2b' class='xr-var-data-in' type='checkbox'><label for='data-1bdb075a-920e-4518-8b5c-104ac1369c2b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>calculus_in_biliary_tract</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-335e4592-c4ab-4d3b-9459-bd52aafb63bb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-335e4592-c4ab-4d3b-9459-bd52aafb63bb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cd6dd215-08e1-474d-8bc6-69a96a47f03d' class='xr-var-data-in' type='checkbox'><label for='data-cd6dd215-08e1-474d-8bc6-69a96a47f03d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>epigastric_pain</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-9624bded-e4c8-4a3b-838e-025237ad04a6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9624bded-e4c8-4a3b-838e-025237ad04a6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d9a02578-cbfc-49c5-a47a-53fe3811dffb' class='xr-var-data-in' type='checkbox'><label for='data-d9a02578-cbfc-49c5-a47a-53fe3811dffb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>late_effects_of_cerebrovascular_disease</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-a2cb2d41-9acf-4ede-b892-0f7f30b529fa' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a2cb2d41-9acf-4ede-b892-0f7f30b529fa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1ce9c15d-6e1f-4c08-9703-d068a32ae985' class='xr-var-data-in' type='checkbox'><label for='data-1ce9c15d-6e1f-4c08-9703-d068a32ae985' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>gastroenteritis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-e4d784b9-c160-4b4d-92d9-0c62b8c928a5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e4d784b9-c160-4b4d-92d9-0c62b8c928a5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-762aab32-eccd-4a52-b54a-4c383dfd3f8e' class='xr-var-data-in' type='checkbox'><label for='data-762aab32-eccd-4a52-b54a-4c383dfd3f8e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pulmonary_embolism</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-6cce7f5c-494d-420c-838a-a4b0f284b224' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6cce7f5c-494d-420c-838a-a4b0f284b224' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ab8d4fca-70ae-40f6-9b49-6c4a202956ad' class='xr-var-data-in' type='checkbox'><label for='data-ab8d4fca-70ae-40f6-9b49-6c4a202956ad' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>inguinal_hernia</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-10c8f9d1-6eb5-418f-af9a-6a7aa72d2519' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-10c8f9d1-6eb5-418f-af9a-6a7aa72d2519' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-92b2f53b-5042-4a68-88b4-243cc15291fa' class='xr-var-data-in' type='checkbox'><label for='data-92b2f53b-5042-4a68-88b4-243cc15291fa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>verruca_vulgaris</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-1d46b13a-1d40-45e3-900b-4bd5d2332620' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1d46b13a-1d40-45e3-900b-4bd5d2332620' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1474f0d3-ab74-45b4-83ac-a4dd107694ea' class='xr-var-data-in' type='checkbox'><label for='data-1474f0d3-ab74-45b4-83ac-a4dd107694ea' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sepsis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... True False</div><input id='attrs-cf74d25c-c1e7-4340-9574-5c4f3f9d8f42' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cf74d25c-c1e7-4340-9574-5c4f3f9d8f42' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e5b2f0e4-b497-4ef3-ab40-c31bd244a2b7' class='xr-var-data-in' type='checkbox'><label for='data-e5b2f0e4-b497-4ef3-ab40-c31bd244a2b7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, True, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>disorder_of_kidney_due_to_diabetes_mellitus</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-0a455fc4-5186-4d0d-b2e0-b6251373f3dd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0a455fc4-5186-4d0d-b2e0-b6251373f3dd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2f594fcc-8299-4095-898e-9772c8085b17' class='xr-var-data-in' type='checkbox'><label for='data-2f594fcc-8299-4095-898e-9772c8085b17' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nausea_and_vomiting</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-609f39e4-c6ab-4f1c-993c-22d703d7aed8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-609f39e4-c6ab-4f1c-993c-22d703d7aed8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0e761abf-bb1b-49ac-9599-34dfc97a374e' class='xr-var-data-in' type='checkbox'><label for='data-0e761abf-bb1b-49ac-9599-34dfc97a374e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hyperthyroidism</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-b19588f0-6d3c-415d-abe0-da2087ddf58f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b19588f0-6d3c-415d-abe0-da2087ddf58f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a8deb8e0-03ba-4a48-8057-7b6b39a51ebd' class='xr-var-data-in' type='checkbox'><label for='data-a8deb8e0-03ba-4a48-8057-7b6b39a51ebd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>abscess</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-43c332a2-30f7-4149-b5b0-06a9a51b70da' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-43c332a2-30f7-4149-b5b0-06a9a51b70da' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dde2c633-82a8-4b39-8412-e8af6441f093' class='xr-var-data-in' type='checkbox'><label for='data-dde2c633-82a8-4b39-8412-e8af6441f093' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dental_caries</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-617bb957-2904-47c8-87e5-11f502935389' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-617bb957-2904-47c8-87e5-11f502935389' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f20be2b0-faa7-40bd-a39b-142b338fd03d' class='xr-var-data-in' type='checkbox'><label for='data-f20be2b0-faa7-40bd-a39b-142b338fd03d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>gastrointestinal_hemorrhage</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-a70c4aed-4af8-470d-bb1f-7d01eaae8276' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a70c4aed-4af8-470d-bb1f-7d01eaae8276' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-15d959ee-cdec-484d-a9be-155e0a3cb6d9' class='xr-var-data-in' type='checkbox'><label for='data-15d959ee-cdec-484d-a9be-155e0a3cb6d9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rosacea</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-493c5655-9d4a-40c0-a657-e980810d2a64' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-493c5655-9d4a-40c0-a657-e980810d2a64' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7d3a8dd7-1dd5-4b56-80ad-8a8290a050dc' class='xr-var-data-in' type='checkbox'><label for='data-7d3a8dd7-1dd5-4b56-80ad-8a8290a050dc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>parkinson&#x27;s_disease</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-302e4d36-454a-4b44-b51e-a250ada8282e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-302e4d36-454a-4b44-b51e-a250ada8282e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d88d1b5a-3b02-484d-8a93-4d570cef1da9' class='xr-var-data-in' type='checkbox'><label for='data-d88d1b5a-3b02-484d-8a93-4d570cef1da9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>menorrhagia</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-f343d74d-dc4c-4da8-819a-b01aeedadd1d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f343d74d-dc4c-4da8-819a-b01aeedadd1d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1cd7a227-65d9-4516-826a-dc381c7d4c0a' class='xr-var-data-in' type='checkbox'><label for='data-1cd7a227-65d9-4516-826a-dc381c7d4c0a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>malignant_tumor_of_lung</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-71b42e80-3d10-4ce5-b5ca-8ed1111e62ca' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-71b42e80-3d10-4ce5-b5ca-8ed1111e62ca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d302edea-ceeb-4cce-bfec-58eac94ab983' class='xr-var-data-in' type='checkbox'><label for='data-d302edea-ceeb-4cce-bfec-58eac94ab983' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>joint_pain</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-af6c6449-b9f8-488f-9629-27a563b4be64' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-af6c6449-b9f8-488f-9629-27a563b4be64' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d7e8fd34-ebe0-4f6e-97ed-853c3a4fa4af' class='xr-var-data-in' type='checkbox'><label for='data-d7e8fd34-ebe0-4f6e-97ed-853c3a4fa4af' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>morbid_obesity</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-517adbae-beda-424d-be8c-5b9ea8b69fcf' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-517adbae-beda-424d-be8c-5b9ea8b69fcf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9c380315-2580-497e-aeb2-a82d2c6dfe0c' class='xr-var-data-in' type='checkbox'><label for='data-9c380315-2580-497e-aeb2-a82d2c6dfe0c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hiatal_hernia</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-dddb0e94-ed28-45a0-a289-57bd16f311c5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dddb0e94-ed28-45a0-a289-57bd16f311c5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-52d8caba-cadf-4f2b-9c08-ab62b7eeaf81' class='xr-var-data-in' type='checkbox'><label for='data-52d8caba-cadf-4f2b-9c08-ab62b7eeaf81' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>arthralgia_of_the_ankle_and/or_foot</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-15329521-5436-4681-918f-1d7f964c4973' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-15329521-5436-4681-918f-1d7f964c4973' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6b80e4e8-3d7b-4948-bbd1-313eadce87b9' class='xr-var-data-in' type='checkbox'><label for='data-6b80e4e8-3d7b-4948-bbd1-313eadce87b9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>restless_legs</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-f86e2463-fa95-4991-84bd-94fefe89925d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f86e2463-fa95-4991-84bd-94fefe89925d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0ea72c22-edab-4d5c-8031-196d83b3d8bc' class='xr-var-data-in' type='checkbox'><label for='data-0ea72c22-edab-4d5c-8031-196d83b3d8bc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>thrombocytopenic_disorder</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-b0f2d1c8-2ce9-4e02-9c00-e1bd6d3ef4dc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b0f2d1c8-2ce9-4e02-9c00-e1bd6d3ef4dc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2682d2ee-4853-4e07-8f33-3f4cebcd0b5e' class='xr-var-data-in' type='checkbox'><label for='data-2682d2ee-4853-4e07-8f33-3f4cebcd0b5e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>old_myocardial_infarction</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-09dc2170-ea3d-4fed-a635-1a7f4fe0cdbd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-09dc2170-ea3d-4fed-a635-1a7f4fe0cdbd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-faaae0a7-8ab1-4c8c-8f4f-f72c149713b2' class='xr-var-data-in' type='checkbox'><label for='data-faaae0a7-8ab1-4c8c-8f4f-f72c149713b2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>neuropathy</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-e62a246e-4b71-4669-811f-275bf9866811' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e62a246e-4b71-4669-811f-275bf9866811' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7be421f1-4294-43d8-98da-4f964c352693' class='xr-var-data-in' type='checkbox'><label for='data-7be421f1-4294-43d8-98da-4f964c352693' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cardiomyopathy</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-f135a558-a176-4f40-bdb6-852b7669f31b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f135a558-a176-4f40-bdb6-852b7669f31b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9b6fc760-cc63-45f6-adfd-2dc916b9f9a3' class='xr-var-data-in' type='checkbox'><label for='data-9b6fc760-cc63-45f6-adfd-2dc916b9f9a3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>atopic_dermatitis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-cd62fcf6-3ff9-4b00-93df-2f461546cb28' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cd62fcf6-3ff9-4b00-93df-2f461546cb28' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e8b1c1e4-b035-4e45-898d-adec4f79858b' class='xr-var-data-in' type='checkbox'><label for='data-e8b1c1e4-b035-4e45-898d-adec4f79858b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pain_in_pelvis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-c3beaf62-f10d-4865-8c56-9a7b6b238d78' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c3beaf62-f10d-4865-8c56-9a7b6b238d78' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9860da44-7706-4892-a535-0d1dceb866aa' class='xr-var-data-in' type='checkbox'><label for='data-9860da44-7706-4892-a535-0d1dceb866aa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>contact_dermatitis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-eb2a701d-dca1-4bbe-8400-ef2979321dc2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-eb2a701d-dca1-4bbe-8400-ef2979321dc2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0d30722d-53ea-4371-b357-4e379d93d24b' class='xr-var-data-in' type='checkbox'><label for='data-0d30722d-53ea-4371-b357-4e379d93d24b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>indigestion</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-1a11683b-3948-48fa-a005-3cd9ab18122a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1a11683b-3948-48fa-a005-3cd9ab18122a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0859cf30-1db0-4fe5-bc66-ff68a39ec140' class='xr-var-data-in' type='checkbox'><label for='data-0859cf30-1db0-4fe5-bc66-ff68a39ec140' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nicotine_dependence</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... True True</div><input id='attrs-3d39f7c1-2359-4b32-8199-68e6f71cc999' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3d39f7c1-2359-4b32-8199-68e6f71cc999' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a645cd06-a595-4868-9d4b-05f80143b8cd' class='xr-var-data-in' type='checkbox'><label for='data-a645cd06-a595-4868-9d4b-05f80143b8cd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, True, True], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sprain_of_ankle</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-7f090b70-d011-4019-a491-761c82474635' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7f090b70-d011-4019-a491-761c82474635' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c2283c18-4738-49ac-9afa-eb500427e0a3' class='xr-var-data-in' type='checkbox'><label for='data-c2283c18-4738-49ac-9afa-eb500427e0a3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>degenerative_disorder_of_macula</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-2cd0bbec-675e-41f0-a2ff-4989745d4e8e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2cd0bbec-675e-41f0-a2ff-4989745d4e8e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5e98a842-04c9-4b8e-8300-ec754b0184fb' class='xr-var-data-in' type='checkbox'><label for='data-5e98a842-04c9-4b8e-8300-ec754b0184fb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>exacerbation_of_asthma</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-8a04fc81-f44a-487b-948a-94b93d485f58' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8a04fc81-f44a-487b-948a-94b93d485f58' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-458f6af3-bf88-4147-9c35-ae61fdcd4ae5' class='xr-var-data-in' type='checkbox'><label for='data-458f6af3-bf88-4147-9c35-ae61fdcd4ae5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>alcohol_dependence</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-53e21581-82e4-4e3d-9705-81a1d52635e0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-53e21581-82e4-4e3d-9705-81a1d52635e0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e09c5a3b-b948-43d8-9935-b51f6d625394' class='xr-var-data-in' type='checkbox'><label for='data-e09c5a3b-b948-43d8-9935-b51f6d625394' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hypokalemia</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False True</div><input id='attrs-3b30c5a0-1638-46f3-a243-3b4e892cd1e5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3b30c5a0-1638-46f3-a243-3b4e892cd1e5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-03e0a01c-c7b7-4458-a163-4563f8934c88' class='xr-var-data-in' type='checkbox'><label for='data-03e0a01c-c7b7-4458-a163-4563f8934c88' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, True], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mitral_valve_regurgitation</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-393726ec-531b-4f7c-a841-d28d93491ba8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-393726ec-531b-4f7c-a841-d28d93491ba8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e47c4027-df02-44b1-998c-8355d91606c4' class='xr-var-data-in' type='checkbox'><label for='data-e47c4027-df02-44b1-998c-8355d91606c4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hyponatremia</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-84c226ec-7e2c-42d2-8e19-aa910b58fa1b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-84c226ec-7e2c-42d2-8e19-aa910b58fa1b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d5eb5ec6-d1e1-4f04-8392-368fec6957a6' class='xr-var-data-in' type='checkbox'><label for='data-d5eb5ec6-d1e1-4f04-8392-368fec6957a6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>abdominal_aortic_aneurysm</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... True True</div><input id='attrs-480e491c-8453-4f57-9138-2c67c67910f3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-480e491c-8453-4f57-9138-2c67c67910f3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-64140560-fc14-477b-a0b1-339e69324b04' class='xr-var-data-in' type='checkbox'><label for='data-64140560-fc14-477b-a0b1-339e69324b04' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, True, True], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cyst_of_ovary</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-effa6cb3-58f5-44af-88a0-7f98a9b03fdf' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-effa6cb3-58f5-44af-88a0-7f98a9b03fdf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b5d60f6e-8a69-4890-b8e1-8ed015dd0bc1' class='xr-var-data-in' type='checkbox'><label for='data-b5d60f6e-8a69-4890-b8e1-8ed015dd0bc1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>otitis_externa</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-42540aba-208b-4a6c-8b71-767cbe9e124e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-42540aba-208b-4a6c-8b71-767cbe9e124e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-98605d0c-9e95-4596-ba5d-66888d9ee6ad' class='xr-var-data-in' type='checkbox'><label for='data-98605d0c-9e95-4596-ba5d-66888d9ee6ad' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>threatened_abortion</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-c4b86a98-0f39-4032-9024-b663e2a32377' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c4b86a98-0f39-4032-9024-b663e2a32377' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6b509197-491c-48f4-bc77-681f79838b21' class='xr-var-data-in' type='checkbox'><label for='data-6b509197-491c-48f4-bc77-681f79838b21' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>scoliosis_deformity_of_spine</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-5a11ec04-19b7-4eb5-b8c2-ca3652cb9dd8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5a11ec04-19b7-4eb5-b8c2-ca3652cb9dd8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ab633767-f289-4ad2-a364-9f75bc7086c6' class='xr-var-data-in' type='checkbox'><label for='data-ab633767-f289-4ad2-a364-9f75bc7086c6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>seborrheic_dermatitis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-a076dc44-d3ce-4f4d-b032-0939a47bd016' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a076dc44-d3ce-4f4d-b032-0939a47bd016' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d215d244-842a-4c1a-9a74-f3fb1f7f6823' class='xr-var-data-in' type='checkbox'><label for='data-d215d244-842a-4c1a-9a74-f3fb1f7f6823' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spinal_stenosis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-cc83cea4-0da2-4b73-951e-13f399bfc1b4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cc83cea4-0da2-4b73-951e-13f399bfc1b4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3d868872-f87e-419b-af9e-866aee334a91' class='xr-var-data-in' type='checkbox'><label for='data-3d868872-f87e-419b-af9e-866aee334a91' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dysmenorrhea</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-27a91567-3c05-4d80-9f31-3e7c85306473' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-27a91567-3c05-4d80-9f31-3e7c85306473' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7051efaf-13a6-4647-9eb4-7bff6567fa0d' class='xr-var-data-in' type='checkbox'><label for='data-7051efaf-13a6-4647-9eb4-7bff6567fa0d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>acute_otitis_media</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-cdda1532-3242-4b54-bc1a-daf988405352' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cdda1532-3242-4b54-bc1a-daf988405352' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4694667d-8478-4213-a93a-ed8c311c3332' class='xr-var-data-in' type='checkbox'><label for='data-4694667d-8478-4213-a93a-ed8c311c3332' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>alzheimer&#x27;s_disease</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-2ca44c93-a0cc-45bd-8cba-776196fa252a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2ca44c93-a0cc-45bd-8cba-776196fa252a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bdb34c20-8a84-4f65-a686-845476a07d12' class='xr-var-data-in' type='checkbox'><label for='data-bdb34c20-8a84-4f65-a686-845476a07d12' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>neuropathy_due_to_diabetes_mellitus</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-521b6124-d8c8-4e09-86d0-5e4bb4fc6748' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-521b6124-d8c8-4e09-86d0-5e4bb4fc6748' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1cd1af8d-160a-48aa-b37d-bed6e6fc55fb' class='xr-var-data-in' type='checkbox'><label for='data-1cd1af8d-160a-48aa-b37d-bed6e6fc55fb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>acute_pharyngitis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-625483a0-8547-44e1-a551-c8412860a67c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-625483a0-8547-44e1-a551-c8412860a67c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9ef8c4d9-c7c5-4f21-9f7d-f9de511cc73a' class='xr-var-data-in' type='checkbox'><label for='data-9ef8c4d9-c7c5-4f21-9f7d-f9de511cc73a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>degeneration_of_intervertebral_disc</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-40049872-8936-4db6-906b-0ce554691646' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-40049872-8936-4db6-906b-0ce554691646' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d5c2a8f7-506d-4e25-8b2b-eebf0e366a95' class='xr-var-data-in' type='checkbox'><label for='data-d5c2a8f7-506d-4e25-8b2b-eebf0e366a95' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>attention_deficit_hyperactivity_disorder_predominantly_inattentive_type</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-c97ef911-5bcd-4c92-80be-850beab95c02' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c97ef911-5bcd-4c92-80be-850beab95c02' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0d3beb73-1d8a-45ce-89a4-b4663e6f73b9' class='xr-var-data-in' type='checkbox'><label for='data-0d3beb73-1d8a-45ce-89a4-b4663e6f73b9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>unplanned_pregnancy</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-0c7b4098-2235-4a09-861a-95954416bd38' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0c7b4098-2235-4a09-861a-95954416bd38' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6dac04cc-de7a-44f2-bea2-cb10315a8ee7' class='xr-var-data-in' type='checkbox'><label for='data-6dac04cc-de7a-44f2-bea2-cb10315a8ee7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>secondary_erectile_dysfunction</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-7386e7bf-2caf-4427-aad9-17fef3a2da32' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7386e7bf-2caf-4427-aad9-17fef3a2da32' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5f84ae57-4a3e-4ff7-84ec-8877e3d33889' class='xr-var-data-in' type='checkbox'><label for='data-5f84ae57-4a3e-4ff7-84ec-8877e3d33889' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spinal_stenosis_of_lumbar_region</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-1facf2d0-2209-492e-bbf6-59c1dcf6e086' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1facf2d0-2209-492e-bbf6-59c1dcf6e086' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-972b0d2e-3c32-4ef5-a955-975ea9e8231f' class='xr-var-data-in' type='checkbox'><label for='data-972b0d2e-3c32-4ef5-a955-975ea9e8231f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>proteinuria</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-0b5516b5-5c9e-4864-9591-39be76fc6894' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0b5516b5-5c9e-4864-9591-39be76fc6894' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d88da8ab-fe8c-45f5-9db1-f320e9f54a2b' class='xr-var-data-in' type='checkbox'><label for='data-d88da8ab-fe8c-45f5-9db1-f320e9f54a2b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>urticaria</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-3bfa8e55-06fd-4ca5-a056-1c5fc27c7d4f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3bfa8e55-06fd-4ca5-a056-1c5fc27c7d4f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-882dbff3-a5de-4f68-bb55-7a45d744e14d' class='xr-var-data-in' type='checkbox'><label for='data-882dbff3-a5de-4f68-bb55-7a45d744e14d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>genital_herpes_simplex</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-9c97c6cc-9b3c-4cac-b888-e6817163230b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9c97c6cc-9b3c-4cac-b888-e6817163230b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7a5729d3-bd09-4ce7-8ebd-1b9362ffada1' class='xr-var-data-in' type='checkbox'><label for='data-7a5729d3-bd09-4ce7-8ebd-1b9362ffada1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>malignant_neoplasm_of_female_breast</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-f428dd18-ea0a-488b-93c5-f4353ece7882' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f428dd18-ea0a-488b-93c5-f4353ece7882' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2c3d2a37-9a0a-4f45-9179-72625efbe1af' class='xr-var-data-in' type='checkbox'><label for='data-2c3d2a37-9a0a-4f45-9179-72625efbe1af' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nausea</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-6ba34aad-b8db-41ab-bd70-b0cd17f195de' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6ba34aad-b8db-41ab-bd70-b0cd17f195de' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-017aa940-482b-4610-934a-be7bb8b9e560' class='xr-var-data-in' type='checkbox'><label for='data-017aa940-482b-4610-934a-be7bb8b9e560' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>chronic_rhinitis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-45dfdc86-cdf1-4bfc-8518-183caf325a10' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-45dfdc86-cdf1-4bfc-8518-183caf325a10' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-45c37d4b-ea12-4988-a36a-dd41763c44a9' class='xr-var-data-in' type='checkbox'><label for='data-45c37d4b-ea12-4988-a36a-dd41763c44a9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>multiple_sclerosis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-0ead920a-b37a-41cd-9559-1de77627633c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0ead920a-b37a-41cd-9559-1de77627633c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2cba6917-402b-44d3-b30e-986d0d86bee7' class='xr-var-data-in' type='checkbox'><label for='data-2cba6917-402b-44d3-b30e-986d0d86bee7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>chronic_kidney_disease_stage_3</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... True True</div><input id='attrs-5d204f9f-00e6-49ff-afff-fc881fce1de8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5d204f9f-00e6-49ff-afff-fc881fce1de8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-94febe08-ee5b-4dae-8232-435dc5972b08' class='xr-var-data-in' type='checkbox'><label for='data-94febe08-ee5b-4dae-8232-435dc5972b08' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, True, True], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>panic_disorder</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-51a051fd-2333-4111-8ded-ad87c26a5931' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-51a051fd-2333-4111-8ded-ad87c26a5931' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-69cca76f-f11e-4f01-aa1f-32df2d9a3920' class='xr-var-data-in' type='checkbox'><label for='data-69cca76f-f11e-4f01-aa1f-32df2d9a3920' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>attention_deficit_hyperactivity_disorder</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-ab5931c8-7663-43fb-bbea-a5dfe6d66ddb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ab5931c8-7663-43fb-bbea-a5dfe6d66ddb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-58079817-e2b7-474f-8f35-c0b3ec94efbf' class='xr-var-data-in' type='checkbox'><label for='data-58079817-e2b7-474f-8f35-c0b3ec94efbf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>amnesia</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-c0c6aad4-19d4-45c6-8f70-62c36cdec0b2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c0c6aad4-19d4-45c6-8f70-62c36cdec0b2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-182bbad8-5ad8-4c00-ab6c-ad4db632e077' class='xr-var-data-in' type='checkbox'><label for='data-182bbad8-5ad8-4c00-ab6c-ad4db632e077' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>otalgia</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-6aaf2246-a308-4f5c-bfca-07ed0f771a0f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6aaf2246-a308-4f5c-bfca-07ed0f771a0f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-686c28c9-8504-4d54-a88e-9c51565aabd7' class='xr-var-data-in' type='checkbox'><label for='data-686c28c9-8504-4d54-a88e-9c51565aabd7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tremor</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-5b10805d-8f55-4e65-8c18-ba56e9fb4fc6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5b10805d-8f55-4e65-8c18-ba56e9fb4fc6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0ffca9d5-485d-430b-bee6-1436b72d8d06' class='xr-var-data-in' type='checkbox'><label for='data-0ffca9d5-485d-430b-bee6-1436b72d8d06' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>retention_of_urine</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-294c599e-8b37-4ade-bd5d-6a67ed6cdd43' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-294c599e-8b37-4ade-bd5d-6a67ed6cdd43' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0a55beda-5668-4bfd-a98c-fdb0a4ebeb17' class='xr-var-data-in' type='checkbox'><label for='data-0a55beda-5668-4bfd-a98c-fdb0a4ebeb17' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>non-hodgkin&#x27;s_lymphoma</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-bc15a8f0-81fa-4aab-8ee4-b860d82af460' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bc15a8f0-81fa-4aab-8ee4-b860d82af460' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ca07cc40-2ef2-41e2-a369-10d5a257d8d6' class='xr-var-data-in' type='checkbox'><label for='data-ca07cc40-2ef2-41e2-a369-10d5a257d8d6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>alcoholism</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-33627a21-c015-4ab4-b009-0aa64a7694d4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-33627a21-c015-4ab4-b009-0aa64a7694d4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-85999090-142f-42fb-ab21-7ef0c818fa12' class='xr-var-data-in' type='checkbox'><label for='data-85999090-142f-42fb-ab21-7ef0c818fa12' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dysuria</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-b0768762-1d75-452c-aece-8f94eb81820a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b0768762-1d75-452c-aece-8f94eb81820a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8b63289e-b923-4e8a-a51b-54b7c9246710' class='xr-var-data-in' type='checkbox'><label for='data-8b63289e-b923-4e8a-a51b-54b7c9246710' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>generalized_anxiety_disorder</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-6e1d1ee4-2ac6-4fed-9499-7794dad3504e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6e1d1ee4-2ac6-4fed-9499-7794dad3504e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d4821545-4209-4edb-9e45-7440e9997ccb' class='xr-var-data-in' type='checkbox'><label for='data-d4821545-4209-4edb-9e45-7440e9997ccb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>paroxysmal_atrial_fibrillation</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-e958289a-4cdc-4553-ab4b-704beb5ce1cd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e958289a-4cdc-4553-ab4b-704beb5ce1cd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-07d53c68-e28c-4ac4-bf52-50be76d165f0' class='xr-var-data-in' type='checkbox'><label for='data-07d53c68-e28c-4ac4-bf52-50be76d165f0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>peripheral_venous_insufficiency</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-53e40a00-5e7e-445b-8df6-01f5d5dc6a8a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-53e40a00-5e7e-445b-8df6-01f5d5dc6a8a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3fa374a2-ac3a-4ea0-b121-8b1ade94871c' class='xr-var-data-in' type='checkbox'><label for='data-3fa374a2-ac3a-4ea0-b121-8b1ade94871c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nonproliferative_retinopathy_due_to_diabetes_mellitus</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-718a6a55-03e9-4d36-a443-d031ba31171b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-718a6a55-03e9-4d36-a443-d031ba31171b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dec16c21-1e96-4406-b607-13fff5f53588' class='xr-var-data-in' type='checkbox'><label for='data-dec16c21-1e96-4406-b607-13fff5f53588' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>shoulder_joint_pain</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-a379776f-a11b-4b89-98a5-08e7657a9669' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a379776f-a11b-4b89-98a5-08e7657a9669' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b48867ba-77ea-4f61-90ba-c5c4c7cf0b91' class='xr-var-data-in' type='checkbox'><label for='data-b48867ba-77ea-4f61-90ba-c5c4c7cf0b91' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>moderate_recurrent_major_depression</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-fba8ada9-c0a6-4180-a2f1-7af8aea950d0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fba8ada9-c0a6-4180-a2f1-7af8aea950d0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8283b527-5779-4b08-aee3-bcb2515f708a' class='xr-var-data-in' type='checkbox'><label for='data-8283b527-5779-4b08-aee3-bcb2515f708a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diverticulitis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-b37a3478-abcb-444a-8efa-91249036f668' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b37a3478-abcb-444a-8efa-91249036f668' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b1c31bec-23d0-4fac-b7d7-6b48cb2445de' class='xr-var-data-in' type='checkbox'><label for='data-b1c31bec-23d0-4fac-b7d7-6b48cb2445de' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>solitary_nodule_of_lung</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-e5433456-7f0f-4074-be9f-af1638cd9d70' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e5433456-7f0f-4074-be9f-af1638cd9d70' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ac20ee0b-ea3e-4a1d-9326-d99657f9cb78' class='xr-var-data-in' type='checkbox'><label for='data-ac20ee0b-ea3e-4a1d-9326-d99657f9cb78' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hyperkalemia</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False True</div><input id='attrs-ca83bfa4-7dd2-48de-abc9-1bf2de261514' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ca83bfa4-7dd2-48de-abc9-1bf2de261514' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f5ba733b-e70f-44bc-9d96-7eb0e2a02170' class='xr-var-data-in' type='checkbox'><label for='data-f5ba733b-e70f-44bc-9d96-7eb0e2a02170' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, True], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>recurrent_major_depressive_episodes</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-a37862e4-4b26-4edc-a519-9dc9432c63b5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a37862e4-4b26-4edc-a519-9dc9432c63b5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c30cd898-62ca-4b56-8e38-b6af1966aa13' class='xr-var-data-in' type='checkbox'><label for='data-c30cd898-62ca-4b56-8e38-b6af1966aa13' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>multiple_myeloma</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-e16655e5-824e-4c54-8e4b-924378fc4dc4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e16655e5-824e-4c54-8e4b-924378fc4dc4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e9f10352-7c94-454c-80ca-ba3ca4ee0933' class='xr-var-data-in' type='checkbox'><label for='data-e9f10352-7c94-454c-80ca-ba3ca4ee0933' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>regular_astigmatism</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-dcb778d1-97d4-4a77-ad0e-721ab81ef4b6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dcb778d1-97d4-4a77-ad0e-721ab81ef4b6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f3e5e159-e9cb-433f-93e2-7f0ecb537bb0' class='xr-var-data-in' type='checkbox'><label for='data-f3e5e159-e9cb-433f-93e2-7f0ecb537bb0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>secondary_malignant_neoplasm_of_liver</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-d1d40aa3-0d91-43cd-b421-cc0fc0d3a2cc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d1d40aa3-0d91-43cd-b421-cc0fc0d3a2cc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ae88e31d-6da4-4625-9617-94a79b3aee14' class='xr-var-data-in' type='checkbox'><label for='data-ae88e31d-6da4-4625-9617-94a79b3aee14' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ulcerative_colitis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-bc81bb12-8ff3-49cb-a8a2-bc4afd7c7d0b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bc81bb12-8ff3-49cb-a8a2-bc4afd7c7d0b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dda73c68-b30a-4225-8d12-194bc5b28cb1' class='xr-var-data-in' type='checkbox'><label for='data-dda73c68-b30a-4225-8d12-194bc5b28cb1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vaginitis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-4dc34851-4086-423c-b01a-022f2cb5c4f9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4dc34851-4086-423c-b01a-022f2cb5c4f9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-83577876-1189-4dca-a358-a266e450ce30' class='xr-var-data-in' type='checkbox'><label for='data-83577876-1189-4dca-a358-a266e450ce30' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>acute_renal_failure_syndrome</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... True True</div><input id='attrs-cc6249e8-e679-4a38-92a2-5974968c4f02' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cc6249e8-e679-4a38-92a2-5974968c4f02' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f43e708c-dcbc-4259-b730-626105d5d345' class='xr-var-data-in' type='checkbox'><label for='data-f43e708c-dcbc-4259-b730-626105d5d345' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, True, True], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>amenorrhea</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-1d96d138-7214-4754-979d-8f620e47fe42' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1d96d138-7214-4754-979d-8f620e47fe42' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-78f1e2a1-d3c6-4689-86f2-c8438bdadf9d' class='xr-var-data-in' type='checkbox'><label for='data-78f1e2a1-d3c6-4689-86f2-c8438bdadf9d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tendinitis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-8dcf4494-6d2d-44d1-8fc0-d6026f09a7da' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8dcf4494-6d2d-44d1-8fc0-d6026f09a7da' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6cf1ea1d-880e-4aea-8209-99cc5b451a19' class='xr-var-data-in' type='checkbox'><label for='data-6cf1ea1d-880e-4aea-8209-99cc5b451a19' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rhinitis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-f6eb0ff5-ae63-43d6-b055-a7b679e81f71' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f6eb0ff5-ae63-43d6-b055-a7b679e81f71' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5a8c7474-77a7-44eb-8c4e-0035cf76bd61' class='xr-var-data-in' type='checkbox'><label for='data-5a8c7474-77a7-44eb-8c4e-0035cf76bd61' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>bleeding_from_nose</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-54c2ec28-29b3-4f80-91a9-a31b25930a74' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-54c2ec28-29b3-4f80-91a9-a31b25930a74' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8dbe125c-8fa7-4d7f-bf83-af897cd50719' class='xr-var-data-in' type='checkbox'><label for='data-8dbe125c-8fa7-4d7f-bf83-af897cd50719' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>crohn&#x27;s_disease</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-62cfc86f-5ad1-4b0b-97ff-eac67cf23689' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-62cfc86f-5ad1-4b0b-97ff-eac67cf23689' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-977994ee-7474-4c1c-a75b-c916ee030f7d' class='xr-var-data-in' type='checkbox'><label for='data-977994ee-7474-4c1c-a75b-c916ee030f7d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nuclear_senile_cataract</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-24da5a1f-9bdc-452f-9c03-d90ff2b4fdb9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-24da5a1f-9bdc-452f-9c03-d90ff2b4fdb9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-89e2bc88-5df1-4db3-8fb3-f749e54c2e05' class='xr-var-data-in' type='checkbox'><label for='data-89e2bc88-5df1-4db3-8fb3-f749e54c2e05' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>muscle_pain</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-9787e710-7114-4bc9-bba1-05eed3ffed7a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9787e710-7114-4bc9-bba1-05eed3ffed7a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5a3c68bc-4270-4f25-a838-215fe6eb5385' class='xr-var-data-in' type='checkbox'><label for='data-5a3c68bc-4270-4f25-a838-215fe6eb5385' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>epidermoid_cyst</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-5b90cb1a-c035-4b26-bad6-32bb2bdf9581' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5b90cb1a-c035-4b26-bad6-32bb2bdf9581' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c3d6f155-b0f2-4980-9988-c8bc8602adb2' class='xr-var-data-in' type='checkbox'><label for='data-c3d6f155-b0f2-4980-9988-c8bc8602adb2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>impaired_cognition</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-6ae09cf1-73d5-4f8e-8f75-570da7d9e2a9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6ae09cf1-73d5-4f8e-8f75-570da7d9e2a9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9020c96a-af44-46d8-946b-1f3ced51b828' class='xr-var-data-in' type='checkbox'><label for='data-9020c96a-af44-46d8-946b-1f3ced51b828' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>acute_exacerbation_of_chronic_obstructive_airways_disease</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... True True</div><input id='attrs-b5ab05d3-54a0-4a26-b40a-7d93806ec8b7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b5ab05d3-54a0-4a26-b40a-7d93806ec8b7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cab7ba0a-890c-4a9f-92f9-31a21995f1af' class='xr-var-data-in' type='checkbox'><label for='data-cab7ba0a-890c-4a9f-92f9-31a21995f1af' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, True, True], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>eustachian_tube_disorder</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-42dfdbde-588a-45dd-a709-782cc5546480' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-42dfdbde-588a-45dd-a709-782cc5546480' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-788bac68-defc-4bf4-9580-e4986a0e4833' class='xr-var-data-in' type='checkbox'><label for='data-788bac68-defc-4bf4-9580-e4986a0e4833' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>internal_hemorrhoids</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-2f35ff4e-ec5b-4bb0-918e-f3aa3d459e5a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2f35ff4e-ec5b-4bb0-918e-f3aa3d459e5a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e21fd23a-bc5f-4867-92d9-b517b45d6906' class='xr-var-data-in' type='checkbox'><label for='data-e21fd23a-bc5f-4867-92d9-b517b45d6906' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>substance_abuse</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-e3cc58ca-8957-4481-9dee-b9614d12955f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e3cc58ca-8957-4481-9dee-b9614d12955f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-58394fd8-adab-48b8-84f3-27f44bbcdea7' class='xr-var-data-in' type='checkbox'><label for='data-58394fd8-adab-48b8-84f3-27f44bbcdea7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>melanocytic_nevus</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-3587001f-d980-4494-a21e-fb1ae28f3e6d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3587001f-d980-4494-a21e-fb1ae28f3e6d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8db20554-09fc-495f-80a7-0d30ce23f153' class='xr-var-data-in' type='checkbox'><label for='data-8db20554-09fc-495f-80a7-0d30ce23f153' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pain</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-b75d7bfc-bfd5-4524-b218-7dcd04f405a7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b75d7bfc-bfd5-4524-b218-7dcd04f405a7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6d903360-4369-4cf9-b1c7-c8c4df3b76ed' class='xr-var-data-in' type='checkbox'><label for='data-6d903360-4369-4cf9-b1c7-c8c4df3b76ed' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>barrett&#x27;s_esophagus</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-d225589c-0899-4453-9930-b23caa0a4654' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d225589c-0899-4453-9930-b23caa0a4654' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0bd3a83a-7173-4c1a-a035-1c482ffad9ef' class='xr-var-data-in' type='checkbox'><label for='data-0bd3a83a-7173-4c1a-a035-1c482ffad9ef' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cerebrovascular_disease</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-5719f9e9-0755-493c-b20b-cb65ccc1069c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5719f9e9-0755-493c-b20b-cb65ccc1069c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fc9ac6d1-abfd-4999-b2db-6cdb5bfae20e' class='xr-var-data-in' type='checkbox'><label for='data-fc9ac6d1-abfd-4999-b2db-6cdb5bfae20e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>malignant_melanoma</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-c1330440-1512-49e1-a38b-f6e4501aff67' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c1330440-1512-49e1-a38b-f6e4501aff67' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7786f43b-91d4-4819-9bd2-b45e333f82e6' class='xr-var-data-in' type='checkbox'><label for='data-7786f43b-91d4-4819-9bd2-b45e333f82e6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>folliculitis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-0d068ebd-a07a-445d-8119-4fb15da79662' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0d068ebd-a07a-445d-8119-4fb15da79662' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3db7db80-9b53-47a0-8de6-7d171e55b6a2' class='xr-var-data-in' type='checkbox'><label for='data-3db7db80-9b53-47a0-8de6-7d171e55b6a2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mitral_valve_prolapse</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-319fd47f-da33-4b9a-a010-701453bb5a41' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-319fd47f-da33-4b9a-a010-701453bb5a41' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e16be7ae-b895-43e4-bdb3-8f8fa13b4d43' class='xr-var-data-in' type='checkbox'><label for='data-e16be7ae-b895-43e4-bdb3-8f8fa13b4d43' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>chronic_hepatitis_c</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-ddcbb3c6-5839-4ed3-b0bc-f61e99b36e0a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ddcbb3c6-5839-4ed3-b0bc-f61e99b36e0a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d2789071-1453-4975-8cd6-24e7bd88e55f' class='xr-var-data-in' type='checkbox'><label for='data-d2789071-1453-4975-8cd6-24e7bd88e55f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hypermetropia</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-1172623b-95f7-4f5e-9eae-fd7e9a27f25a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1172623b-95f7-4f5e-9eae-fd7e9a27f25a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0190d6e1-33d1-4830-aa69-05e2d481e793' class='xr-var-data-in' type='checkbox'><label for='data-0190d6e1-33d1-4830-aa69-05e2d481e793' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>endometriosis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-5a35f28b-4566-45a6-8901-6c9dc4decbd7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5a35f28b-4566-45a6-8901-6c9dc4decbd7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b4c9d563-de25-41e4-ad30-16433445149e' class='xr-var-data-in' type='checkbox'><label for='data-b4c9d563-de25-41e4-ad30-16433445149e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>gestational_diabetes_mellitus</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-4ed7bb9f-5f26-405b-bcc5-d4cfa04810a6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4ed7bb9f-5f26-405b-bcc5-d4cfa04810a6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fd17784b-58fe-4557-b8b1-2c0f206f37fc' class='xr-var-data-in' type='checkbox'><label for='data-fd17784b-58fe-4557-b8b1-2c0f206f37fc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cirrhosis_of_liver</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-d9163f91-80dc-4211-83ea-a1867e291235' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d9163f91-80dc-4211-83ea-a1867e291235' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-07c39e46-c41b-4ace-ae7c-8307bf41a83d' class='xr-var-data-in' type='checkbox'><label for='data-07c39e46-c41b-4ace-ae7c-8307bf41a83d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>injury_of_head</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False True ... False False</div><input id='attrs-b1590893-907a-49f8-b4d5-ec6459e41614' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b1590893-907a-49f8-b4d5-ec6459e41614' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c0de53ed-4a18-47bc-bbb8-1cb43060d752' class='xr-var-data-in' type='checkbox'><label for='data-c0de53ed-4a18-47bc-bbb8-1cb43060d752' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, True, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dehydration</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-22bfc81d-1a66-4d99-9829-f8aa4306029f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-22bfc81d-1a66-4d99-9829-f8aa4306029f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-db762777-d307-47ae-84f7-d6815a3288ae' class='xr-var-data-in' type='checkbox'><label for='data-db762777-d307-47ae-84f7-d6815a3288ae' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>herpes_simplex</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-5ef33bcc-492c-491b-9358-58f9efab7e98' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5ef33bcc-492c-491b-9358-58f9efab7e98' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e9b7be52-047b-493d-9825-03fcb4c3db57' class='xr-var-data-in' type='checkbox'><label for='data-e9b7be52-047b-493d-9825-03fcb4c3db57' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fracture_of_bone</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False True ... False False</div><input id='attrs-55f948ff-462f-4123-a6e2-2d00b4570f4b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-55f948ff-462f-4123-a6e2-2d00b4570f4b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3c7f9311-8399-47f7-988d-758f72333e0f' class='xr-var-data-in' type='checkbox'><label for='data-3c7f9311-8399-47f7-988d-758f72333e0f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, True, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>overweight</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-d0a4eafe-45c7-4778-a6ac-001c6db6c88e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d0a4eafe-45c7-4778-a6ac-001c6db6c88e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7ff4d1c9-6597-4787-af58-b3fd1a1c589e' class='xr-var-data-in' type='checkbox'><label for='data-7ff4d1c9-6597-4787-af58-b3fd1a1c589e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>right_inguinal_hernia</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-810fc629-1ae9-4485-86c3-729bff3d33c3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-810fc629-1ae9-4485-86c3-729bff3d33c3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-db68fad1-56d0-4f44-8c7b-76f78d657b39' class='xr-var-data-in' type='checkbox'><label for='data-db68fad1-56d0-4f44-8c7b-76f78d657b39' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>adjustment_disorder</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-f54ea0d6-6e4d-4b6d-b670-b19f11fafce4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f54ea0d6-6e4d-4b6d-b670-b19f11fafce4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e1869bfe-f6b2-4809-9ed0-ebdc833e240a' class='xr-var-data-in' type='checkbox'><label for='data-e1869bfe-f6b2-4809-9ed0-ebdc833e240a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tinea_pedis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-041a1928-3c14-4586-9f21-86aa075e6ad6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-041a1928-3c14-4586-9f21-86aa075e6ad6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9e769410-842a-46b9-8a49-604e09eb771b' class='xr-var-data-in' type='checkbox'><label for='data-9e769410-842a-46b9-8a49-604e09eb771b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>aortic_valve_stenosis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-77882bcf-6f8e-4327-9e01-5a9251124bb4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-77882bcf-6f8e-4327-9e01-5a9251124bb4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b2d21650-cb25-4c74-9cad-564690754d74' class='xr-var-data-in' type='checkbox'><label for='data-b2d21650-cb25-4c74-9cad-564690754d74' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>viral_hepatitis_type_b</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-b052bd37-7525-410c-aee9-033f33e90d6d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b052bd37-7525-410c-aee9-033f33e90d6d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e9c28813-eca5-4155-955d-ddd572ad238f' class='xr-var-data-in' type='checkbox'><label for='data-e9c28813-eca5-4155-955d-ddd572ad238f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>umbilical_hernia</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-cee2b487-3e60-4c1b-a151-16b476c52950' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cee2b487-3e60-4c1b-a151-16b476c52950' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6cf85952-b692-418d-b179-07923b2137ba' class='xr-var-data-in' type='checkbox'><label for='data-6cf85952-b692-418d-b179-07923b2137ba' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ingrowing_nail</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-b6fbcfa7-eddc-4497-ba93-958d8ed5d476' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b6fbcfa7-eddc-4497-ba93-958d8ed5d476' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3f1e3d08-3f76-477a-b203-a2e2d50ae3a6' class='xr-var-data-in' type='checkbox'><label for='data-3f1e3d08-3f76-477a-b203-a2e2d50ae3a6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>postmenopausal_bleeding</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-f995acee-147b-44cf-acd1-daee445027b8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f995acee-147b-44cf-acd1-daee445027b8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b221c23f-0c75-41c2-b4d8-c33933711b95' class='xr-var-data-in' type='checkbox'><label for='data-b221c23f-0c75-41c2-b4d8-c33933711b95' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>goiter</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-3dc66ea6-e260-4dc0-b989-be7634d601ec' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3dc66ea6-e260-4dc0-b989-be7634d601ec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8cae91d9-e0de-437b-900e-f7256ff06f15' class='xr-var-data-in' type='checkbox'><label for='data-8cae91d9-e0de-437b-900e-f7256ff06f15' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>secondary_malignant_neoplasm_of_bone</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-4ea73c47-6646-46ce-b51c-c6737ea841db' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4ea73c47-6646-46ce-b51c-c6737ea841db' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3f6aa7fa-4bc3-4980-aed4-8b0c792a4aeb' class='xr-var-data-in' type='checkbox'><label for='data-3f6aa7fa-4bc3-4980-aed4-8b0c792a4aeb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pulmonary_emphysema</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-3ad2ccfe-f7d4-4c51-915b-6a50b0a2ce3c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3ad2ccfe-f7d4-4c51-915b-6a50b0a2ce3c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0c794b1d-debf-47f7-852c-17f2b64bd5f7' class='xr-var-data-in' type='checkbox'><label for='data-0c794b1d-debf-47f7-852c-17f2b64bd5f7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>left_inguinal_hernia</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-6e1b48b9-bf66-47c4-8e49-ffe6952ea1c3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6e1b48b9-bf66-47c4-8e49-ffe6952ea1c3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1a1748da-887a-4b9f-8c51-b22d98de3fb8' class='xr-var-data-in' type='checkbox'><label for='data-1a1748da-887a-4b9f-8c51-b22d98de3fb8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>snoring</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-384e33a6-5e75-42d6-a561-e3fdadfff737' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-384e33a6-5e75-42d6-a561-e3fdadfff737' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1ac39661-24f4-46a4-a40f-e26d9325ef65' class='xr-var-data-in' type='checkbox'><label for='data-1ac39661-24f4-46a4-a40f-e26d9325ef65' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>myocardial_infarction</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-5b0c69c9-9526-4f31-b930-be30e765d04c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5b0c69c9-9526-4f31-b930-be30e765d04c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a9cc9ea0-54b4-4802-b1e4-be3cea609ec3' class='xr-var-data-in' type='checkbox'><label for='data-a9cc9ea0-54b4-4802-b1e4-be3cea609ec3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>polycystic_ovary_syndrome</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-65a2928c-9b50-4795-815f-f41d89c6acfc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-65a2928c-9b50-4795-815f-f41d89c6acfc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5c6fcdd9-0dfa-469d-bb18-ab81901553cd' class='xr-var-data-in' type='checkbox'><label for='data-5c6fcdd9-0dfa-469d-bb18-ab81901553cd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>polycystic_ovary</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-458d23a9-51ab-44b3-9324-d3d750822e03' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-458d23a9-51ab-44b3-9324-d3d750822e03' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-182b6a10-96e4-45eb-b064-b4f46569afd3' class='xr-var-data-in' type='checkbox'><label for='data-182b6a10-96e4-45eb-b064-b4f46569afd3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>microscopic_hematuria</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-8abcfb8e-26a2-4422-bf69-829accd92fbd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8abcfb8e-26a2-4422-bf69-829accd92fbd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5c5d4967-8ea0-4eba-b9ca-a9b720ee8513' class='xr-var-data-in' type='checkbox'><label for='data-5c5d4967-8ea0-4eba-b9ca-a9b720ee8513' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pain_in_wrist</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-2024dd8b-ffb1-482c-a08e-5c589cc7850c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2024dd8b-ffb1-482c-a08e-5c589cc7850c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5b0a847d-19d0-4d06-9297-8d4b6d5fd528' class='xr-var-data-in' type='checkbox'><label for='data-5b0a847d-19d0-4d06-9297-8d4b6d5fd528' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pleural_effusion</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-a34145d5-5479-4508-b87d-fa86aba0c2b4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a34145d5-5479-4508-b87d-fa86aba0c2b4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c55542fd-ae38-424a-a763-69a40f624f81' class='xr-var-data-in' type='checkbox'><label for='data-c55542fd-ae38-424a-a763-69a40f624f81' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>false_labor</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-9c768811-b0e4-4a8d-822a-402ae9dd13c5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9c768811-b0e4-4a8d-822a-402ae9dd13c5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-21b22f49-36e9-4e89-9404-e544d3c7f63d' class='xr-var-data-in' type='checkbox'><label for='data-21b22f49-36e9-4e89-9404-e544d3c7f63d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>bleeding_from_vagina</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-60bdfe31-21ef-489c-ab52-b1470c73056c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-60bdfe31-21ef-489c-ab52-b1470c73056c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a7dfd958-27e3-4050-82a3-fc8197d78fd1' class='xr-var-data-in' type='checkbox'><label for='data-a7dfd958-27e3-4050-82a3-fc8197d78fd1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>acquired_hypothyroidism</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-18692719-1269-4099-87bd-0a0ad3af3057' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-18692719-1269-4099-87bd-0a0ad3af3057' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5941caff-971d-4784-9be5-74d900bbd68c' class='xr-var-data-in' type='checkbox'><label for='data-5941caff-971d-4784-9be5-74d900bbd68c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>premature_rupture_of_membranes</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-e8031b73-eb67-4d02-8871-b9a00ea3b9ed' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e8031b73-eb67-4d02-8871-b9a00ea3b9ed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-032e54f1-f7fd-42de-8eb4-17ed40597cbb' class='xr-var-data-in' type='checkbox'><label for='data-032e54f1-f7fd-42de-8eb4-17ed40597cbb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>human_immunodeficiency_virus_infection</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-faadb8c8-a6aa-43e4-9f15-c7e3e45d701f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-faadb8c8-a6aa-43e4-9f15-c7e3e45d701f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-23882c52-7c44-4a51-b59e-0813916b9cc6' class='xr-var-data-in' type='checkbox'><label for='data-23882c52-7c44-4a51-b59e-0813916b9cc6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>contusion</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-f5660d10-84be-442c-9b25-dcc7ed6e3dab' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f5660d10-84be-442c-9b25-dcc7ed6e3dab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-75d752dd-e811-4bc7-b893-78b3c885b5a5' class='xr-var-data-in' type='checkbox'><label for='data-75d752dd-e811-4bc7-b893-78b3c885b5a5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>secondary_malignant_neoplasm_of_lung</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-984771cc-52d5-4ce7-a1fb-4608614bf301' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-984771cc-52d5-4ce7-a1fb-4608614bf301' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3d92789c-7924-4cc8-885b-bb3f617b56c2' class='xr-var-data-in' type='checkbox'><label for='data-3d92789c-7924-4cc8-885b-bb3f617b56c2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>problem_situation_relating_to_social_and_personal_history</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-5e559fc0-d363-4daf-b8d9-26d844bba6f4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5e559fc0-d363-4daf-b8d9-26d844bba6f4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-41e1f01b-e4a8-40e7-9a1f-e046c33836e9' class='xr-var-data-in' type='checkbox'><label for='data-41e1f01b-e4a8-40e7-9a1f-e046c33836e9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hypercalcemia</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-20b8d15a-305e-46c6-a5db-afa972ae2a6e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-20b8d15a-305e-46c6-a5db-afa972ae2a6e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ef41d45a-7176-4214-bf52-58f079c61137' class='xr-var-data-in' type='checkbox'><label for='data-ef41d45a-7176-4214-bf52-58f079c61137' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>occlusion_of_carotid_artery</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-6aedf2f3-a463-4f2d-a115-906e6256715b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6aedf2f3-a463-4f2d-a115-906e6256715b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f78aaf51-26b1-4257-a2c5-59036c2c8f94' class='xr-var-data-in' type='checkbox'><label for='data-f78aaf51-26b1-4257-a2c5-59036c2c8f94' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>prolonged_second_stage_of_labor</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-1e20b4aa-d518-46cd-8139-aefcef3708d6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1e20b4aa-d518-46cd-8139-aefcef3708d6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5106abf0-95f1-4c80-b3d3-67d4d7531cfe' class='xr-var-data-in' type='checkbox'><label for='data-5106abf0-95f1-4c80-b3d3-67d4d7531cfe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lipoma</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-b2b1d36d-860a-4d1e-9b63-181a6a2c6a19' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b2b1d36d-860a-4d1e-9b63-181a6a2c6a19' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-de9b7a93-eba1-482c-a7db-2c9bdc2e824a' class='xr-var-data-in' type='checkbox'><label for='data-de9b7a93-eba1-482c-a7db-2c9bdc2e824a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>poisoning_caused_by_drug_and/or_medicinal_substance</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-89abc49d-c7e6-4e81-8c02-cbb24ef8fb4e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-89abc49d-c7e6-4e81-8c02-cbb24ef8fb4e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a82d312a-0aba-4016-a8aa-3f4001e5dc51' class='xr-var-data-in' type='checkbox'><label for='data-a82d312a-0aba-4016-a8aa-3f4001e5dc51' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>paranoid_schizophrenia</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-c9c544b0-1456-47f4-af72-7ac676237ccd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c9c544b0-1456-47f4-af72-7ac676237ccd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3c63084b-2ac0-45aa-9f8b-a49adab6ff54' class='xr-var-data-in' type='checkbox'><label for='data-3c63084b-2ac0-45aa-9f8b-a49adab6ff54' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hypersensitivity_reaction</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-77ed9b2a-cd9c-4d93-a746-29b145d569c7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-77ed9b2a-cd9c-4d93-a746-29b145d569c7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6b6d8f28-dedd-44ef-ba1e-dbee5cfbdb46' class='xr-var-data-in' type='checkbox'><label for='data-6b6d8f28-dedd-44ef-ba1e-dbee5cfbdb46' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dysthymia</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-ba1756ae-59f6-490c-8387-3e921fa139e3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ba1756ae-59f6-490c-8387-3e921fa139e3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a53ec304-7c58-48e9-8a8c-9cda6a53e01c' class='xr-var-data-in' type='checkbox'><label for='data-a53ec304-7c58-48e9-8a8c-9cda6a53e01c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fetal_or_neonatal_effect_of_maternal_premature_rupture_of_membrane</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-b89da886-695e-4dca-983f-ccfb1907147e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b89da886-695e-4dca-983f-ccfb1907147e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-716210d2-1bc1-4057-9ed9-e54114c51236' class='xr-var-data-in' type='checkbox'><label for='data-716210d2-1bc1-4057-9ed9-e54114c51236' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>noncompliance_with_treatment</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-9f01fefb-b2ca-46dd-ac7d-7b3504a4af00' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9f01fefb-b2ca-46dd-ac7d-7b3504a4af00' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2ca1acd2-6f0a-4935-bd58-168e1251d868' class='xr-var-data-in' type='checkbox'><label for='data-2ca1acd2-6f0a-4935-bd58-168e1251d868' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>falls</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-26710972-d034-49a0-a60d-08b73fb53f6d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-26710972-d034-49a0-a60d-08b73fb53f6d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e7097ba1-77bf-4023-85d0-a306b8269002' class='xr-var-data-in' type='checkbox'><label for='data-e7097ba1-77bf-4023-85d0-a306b8269002' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>muscle_strain</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-e85e3de9-e832-4f95-8098-c50f55eebb90' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e85e3de9-e832-4f95-8098-c50f55eebb90' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5bbe2990-dae6-49d8-8d72-7e9f6c466a31' class='xr-var-data-in' type='checkbox'><label for='data-5bbe2990-dae6-49d8-8d72-7e9f6c466a31' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>schizophrenia</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-6d982de0-b799-453b-8e64-673e7cc0f588' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6d982de0-b799-453b-8e64-673e7cc0f588' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d10fa355-ec35-4d2a-94cc-ce3c139987e6' class='xr-var-data-in' type='checkbox'><label for='data-d10fa355-ec35-4d2a-94cc-ce3c139987e6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>left_bundle_branch_block</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-d4efacd0-2dc1-4f74-bbfc-585d51e5219a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d4efacd0-2dc1-4f74-bbfc-585d51e5219a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d096e7fe-addb-4d58-9a9b-47d3abb6bc27' class='xr-var-data-in' type='checkbox'><label for='data-d096e7fe-addb-4d58-9a9b-47d3abb6bc27' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>disorder_of_nervous_system_due_to_type_2_diabetes_mellitus</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-61bdec83-60d2-46a5-947e-e78e9ae416b8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-61bdec83-60d2-46a5-947e-e78e9ae416b8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-453bf69f-0fc5-43ed-b5bb-7effcc6cfdc2' class='xr-var-data-in' type='checkbox'><label for='data-453bf69f-0fc5-43ed-b5bb-7effcc6cfdc2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>preinfarction_syndrome</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-e02bdc10-0e2b-4a13-a9e2-e8eb5687a1dc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e02bdc10-0e2b-4a13-a9e2-e8eb5687a1dc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7027df80-7176-4a27-8ffe-905f4296b4e6' class='xr-var-data-in' type='checkbox'><label for='data-7027df80-7176-4a27-8ffe-905f4296b4e6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>conduction_disorder_of_the_heart</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-71edbd4c-22d7-4707-9513-32f64c26a6e9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-71edbd4c-22d7-4707-9513-32f64c26a6e9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2e405055-8913-4057-9184-c6bcbde7d6fd' class='xr-var-data-in' type='checkbox'><label for='data-2e405055-8913-4057-9184-c6bcbde7d6fd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lateral_epicondylitis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-892bad1c-8527-4cb8-ae95-5f5f43778be5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-892bad1c-8527-4cb8-ae95-5f5f43778be5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d3f66e60-0f0d-428c-b8ab-a167b2528302' class='xr-var-data-in' type='checkbox'><label for='data-d3f66e60-0f0d-428c-b8ab-a167b2528302' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>burn</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-184ed66a-2a20-4d33-a99a-20e2a3bdfcb7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-184ed66a-2a20-4d33-a99a-20e2a3bdfcb7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3f154b43-f6e6-495d-8709-3de25d9f38f7' class='xr-var-data-in' type='checkbox'><label for='data-3f154b43-f6e6-495d-8709-3de25d9f38f7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pyelonephritis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... True True</div><input id='attrs-7c0ed3c2-50c2-40e7-ab29-81b799c1136f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7c0ed3c2-50c2-40e7-ab29-81b799c1136f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2cbc9e31-e21a-4e91-a46b-255e0efeeeb2' class='xr-var-data-in' type='checkbox'><label for='data-2cbc9e31-e21a-4e91-a46b-255e0efeeeb2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, True, True], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>intermittent_claudication</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-a4aa1f13-29e6-4078-9b60-fb134aa7daa1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a4aa1f13-29e6-4078-9b60-fb134aa7daa1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e68c21a6-eac5-4f3f-a8f7-483b227cd049' class='xr-var-data-in' type='checkbox'><label for='data-e68c21a6-eac5-4f3f-a8f7-483b227cd049' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>varicose_veins_of_lower_extremity</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-b245a487-2077-4f84-875b-501e47caf731' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b245a487-2077-4f84-875b-501e47caf731' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ea9e2552-3fb9-4f51-b445-3d756be2f1cb' class='xr-var-data-in' type='checkbox'><label for='data-ea9e2552-3fb9-4f51-b445-3d756be2f1cb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hemiplegia</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-95da4eaa-dcb1-4ae2-b247-12ec2dd2e637' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-95da4eaa-dcb1-4ae2-b247-12ec2dd2e637' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c167c443-ea0f-4ec4-ad1b-031e382f4b26' class='xr-var-data-in' type='checkbox'><label for='data-c167c443-ea0f-4ec4-ad1b-031e382f4b26' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>chronic_pain</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-0cbac002-f8d6-48c6-b99e-24f3e6b51e92' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0cbac002-f8d6-48c6-b99e-24f3e6b51e92' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cc149779-33c3-4794-a02d-ef3a1bff65ac' class='xr-var-data-in' type='checkbox'><label for='data-cc149779-33c3-4794-a02d-ef3a1bff65ac' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>bronchiolitis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-b5419573-a71a-4c60-b92e-02a1b23e20ee' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b5419573-a71a-4c60-b92e-02a1b23e20ee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1c410acb-3f7d-4288-9392-7ea0c7319b60' class='xr-var-data-in' type='checkbox'><label for='data-1c410acb-3f7d-4288-9392-7ea0c7319b60' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mild_persistent_asthma</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-83cfe614-59a0-4031-ac79-6eeb462b045d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-83cfe614-59a0-4031-ac79-6eeb462b045d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1cad3105-f50b-46ee-b269-e699120f7327' class='xr-var-data-in' type='checkbox'><label for='data-1cad3105-f50b-46ee-b269-e699120f7327' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>secondary_malignant_neoplasm_of_lymph_node</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-80e180f1-febe-4f22-b657-447392d781b1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-80e180f1-febe-4f22-b657-447392d781b1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4bd2032b-f84e-432a-ad0f-f31451ce0cb3' class='xr-var-data-in' type='checkbox'><label for='data-4bd2032b-f84e-432a-ad0f-f31451ce0cb3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mixed_hyperlipidemia</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... True True</div><input id='attrs-421ddd79-4464-48b3-a17c-c31d40b9270a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-421ddd79-4464-48b3-a17c-c31d40b9270a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ff86068e-20fe-4a73-8cdc-c0b9b75cff62' class='xr-var-data-in' type='checkbox'><label for='data-ff86068e-20fe-4a73-8cdc-c0b9b75cff62' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, True, True], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>female_urinary_stress_incontinence</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-70613a11-c353-4cfb-a5e8-c1e3dbf907d8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-70613a11-c353-4cfb-a5e8-c1e3dbf907d8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3813b098-3a69-41f2-893c-899becc7e884' class='xr-var-data-in' type='checkbox'><label for='data-3813b098-3a69-41f2-893c-899becc7e884' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>localized_primary_osteoarthritis_of_the_ankle_and/or_foot</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-e15826b2-3345-495a-bf81-7795f85a5635' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e15826b2-3345-495a-bf81-7795f85a5635' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7199a0b0-181f-40f2-9d56-ab02de013f4b' class='xr-var-data-in' type='checkbox'><label for='data-7199a0b0-181f-40f2-9d56-ab02de013f4b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hypesthesia</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-31bcca4d-7e1e-413f-afe1-befec2a8e3da' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-31bcca4d-7e1e-413f-afe1-befec2a8e3da' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-62640e0b-4a71-49a4-9e5f-a81daf27ca7b' class='xr-var-data-in' type='checkbox'><label for='data-62640e0b-4a71-49a4-9e5f-a81daf27ca7b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hand_pain</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-bc06fe7b-462a-4de2-a5a1-1a31ecc276d2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bc06fe7b-462a-4de2-a5a1-1a31ecc276d2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3eca9bf8-68d9-4c86-a3df-1deec26f2a77' class='xr-var-data-in' type='checkbox'><label for='data-3eca9bf8-68d9-4c86-a3df-1deec26f2a77' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>psychotic_disorder</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-506fe3a9-bc5d-4aef-8e13-5e911322b396' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-506fe3a9-bc5d-4aef-8e13-5e911322b396' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cb319adf-d154-4664-b97c-98b63ef28eba' class='xr-var-data-in' type='checkbox'><label for='data-cb319adf-d154-4664-b97c-98b63ef28eba' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>menopausal_syndrome</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-cd596084-390f-418d-a1d2-b6995c15940d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cd596084-390f-418d-a1d2-b6995c15940d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-40de6ff3-78c7-4030-ad0f-4910860198d7' class='xr-var-data-in' type='checkbox'><label for='data-40de6ff3-78c7-4030-ad0f-4910860198d7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>acute_myocardial_infarction_of_anterior_wall</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-4b36f068-6306-4a62-b320-95d5fddeec76' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4b36f068-6306-4a62-b320-95d5fddeec76' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a649a8b6-2353-4cc3-b9e9-16c9f32376b8' class='xr-var-data-in' type='checkbox'><label for='data-a649a8b6-2353-4cc3-b9e9-16c9f32376b8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>bronchiectasis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-51e6f033-9fce-4490-ae77-4fcbbc41fd97' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-51e6f033-9fce-4490-ae77-4fcbbc41fd97' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-260efea3-1479-496a-ac7c-51aba4602a76' class='xr-var-data-in' type='checkbox'><label for='data-260efea3-1479-496a-ac7c-51aba4602a76' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lumbar_radiculopathy</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-b1f6c352-3e73-4dc7-b504-3e14adab92a4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b1f6c352-3e73-4dc7-b504-3e14adab92a4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-17a2881e-a82e-435f-93a0-a20adb2faac1' class='xr-var-data-in' type='checkbox'><label for='data-17a2881e-a82e-435f-93a0-a20adb2faac1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>osteoarthritis_of_hip</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-cc219719-f9c4-4ee5-a3b7-6ddc4bca9696' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cc219719-f9c4-4ee5-a3b7-6ddc4bca9696' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9beee05b-af34-48d1-8f9d-2825266614b6' class='xr-var-data-in' type='checkbox'><label for='data-9beee05b-af34-48d1-8f9d-2825266614b6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>deviated_nasal_septum</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-b26adbf8-b935-4724-ba51-3af6ddfb95fe' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b26adbf8-b935-4724-ba51-3af6ddfb95fe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c155b9b2-5163-42de-99c4-318a124405b9' class='xr-var-data-in' type='checkbox'><label for='data-c155b9b2-5163-42de-99c4-318a124405b9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>paresthesia</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-e6ef2c9c-a09d-4f4e-b954-242fd2c1c057' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e6ef2c9c-a09d-4f4e-b954-242fd2c1c057' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d8df9679-1f05-485f-a2f1-2bf7369fccd7' class='xr-var-data-in' type='checkbox'><label for='data-d8df9679-1f05-485f-a2f1-2bf7369fccd7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hypoglycemia</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-afb83099-85a1-4f84-bc71-2c40bed28194' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-afb83099-85a1-4f84-bc71-2c40bed28194' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fe12592b-4ff0-44ad-a189-fa845c6498af' class='xr-var-data-in' type='checkbox'><label for='data-fe12592b-4ff0-44ad-a189-fa845c6498af' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>deep_venous_thrombosis_of_lower_extremity</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-2d00ef3d-13ca-4409-aa83-cf5979b5e5ce' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2d00ef3d-13ca-4409-aa83-cf5979b5e5ce' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-57e2b076-7def-48bb-836b-3ae793073f55' class='xr-var-data-in' type='checkbox'><label for='data-57e2b076-7def-48bb-836b-3ae793073f55' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>complication_of_surgical_procedure</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-f500ddb0-b49d-4eed-b130-9da6085857c9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f500ddb0-b49d-4eed-b130-9da6085857c9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-29eab2d8-d7ad-46e4-b935-d5bcf3aae650' class='xr-var-data-in' type='checkbox'><label for='data-29eab2d8-d7ad-46e4-b935-d5bcf3aae650' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mild_intermittent_asthma</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-a19497c7-20c2-4df7-a1af-018c98b5f377' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a19497c7-20c2-4df7-a1af-018c98b5f377' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-914a182d-0eae-431c-84fa-3bfaa6178162' class='xr-var-data-in' type='checkbox'><label for='data-914a182d-0eae-431c-84fa-3bfaa6178162' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>systemic_lupus_erythematosus</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-25b3bde8-1f30-48db-82e8-0a783f05ef43' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-25b3bde8-1f30-48db-82e8-0a783f05ef43' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-24a122d3-6da8-49c5-b5fd-f7d2860168ec' class='xr-var-data-in' type='checkbox'><label for='data-24a122d3-6da8-49c5-b5fd-f7d2860168ec' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>generalized_ischemic_myocardial_dysfunction</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-2b558e29-c0fd-419d-bb68-579635811ce6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2b558e29-c0fd-419d-bb68-579635811ce6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6c555826-9f84-4cc9-ba62-17f54df53e00' class='xr-var-data-in' type='checkbox'><label for='data-6c555826-9f84-4cc9-ba62-17f54df53e00' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lumbar_spondylosis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-33f79356-73a2-48f3-bcd7-017f73abc2c4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-33f79356-73a2-48f3-bcd7-017f73abc2c4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aa033b8d-3e93-4c33-b5ef-1d2437bb7638' class='xr-var-data-in' type='checkbox'><label for='data-aa033b8d-3e93-4c33-b5ef-1d2437bb7638' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>acute_tonsillitis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-c335c5bd-113b-414e-9912-ebffa4ec7df1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c335c5bd-113b-414e-9912-ebffa4ec7df1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-59cdb393-ec3e-46fa-953c-749ce31cc796' class='xr-var-data-in' type='checkbox'><label for='data-59cdb393-ec3e-46fa-953c-749ce31cc796' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>esophagitis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-6d9cb473-b43d-45cb-8eed-b8ed78bf0572' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6d9cb473-b43d-45cb-8eed-b8ed78bf0572' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8f381981-7d37-433f-88b0-539f72b6f14c' class='xr-var-data-in' type='checkbox'><label for='data-8f381981-7d37-433f-88b0-539f72b6f14c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>aortic_valve_regurgitation</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-c001ba83-5775-4b53-bb5d-12419a986f30' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c001ba83-5775-4b53-bb5d-12419a986f30' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d4eb4b38-3a8b-46fa-913e-56d284e3a0cf' class='xr-var-data-in' type='checkbox'><label for='data-d4eb4b38-3a8b-46fa-913e-56d284e3a0cf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>malignant_neoplasm_of_skin</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-67e676df-5ee0-4e40-b2fa-c1dc8689ce7d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-67e676df-5ee0-4e40-b2fa-c1dc8689ce7d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-09b1cf95-4dbb-45a9-a3ef-baae92a4d456' class='xr-var-data-in' type='checkbox'><label for='data-09b1cf95-4dbb-45a9-a3ef-baae92a4d456' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>altered_mental_status</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-9bcc5237-4d67-45b0-93fd-beb3ee27e410' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9bcc5237-4d67-45b0-93fd-beb3ee27e410' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fa03c68c-f93a-4e61-a0cc-2b5a9ae17981' class='xr-var-data-in' type='checkbox'><label for='data-fa03c68c-f93a-4e61-a0cc-2b5a9ae17981' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>atrial_flutter</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-6489ba62-6c4b-47ef-9073-5a9bf622d417' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6489ba62-6c4b-47ef-9073-5a9bf622d417' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-35c9fa47-9789-48f0-8461-f32594ea21ac' class='xr-var-data-in' type='checkbox'><label for='data-35c9fa47-9789-48f0-8461-f32594ea21ac' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>alopecia</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-396882dd-7280-4f21-bb32-ebe7bb6bd5a4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-396882dd-7280-4f21-bb32-ebe7bb6bd5a4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8601d2f2-0b83-4aa9-81b0-569f73a0cc7b' class='xr-var-data-in' type='checkbox'><label for='data-8601d2f2-0b83-4aa9-81b0-569f73a0cc7b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>high_risk_pregnancy</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-d897efa3-4562-4f44-85be-6b8d3f556d92' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d897efa3-4562-4f44-85be-6b8d3f556d92' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-74147704-3d53-4706-8203-779d0afad575' class='xr-var-data-in' type='checkbox'><label for='data-74147704-3d53-4706-8203-779d0afad575' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>missed_abortion</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-ceb5f5fe-8db0-4e32-bdd7-628020e0e255' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ceb5f5fe-8db0-4e32-bdd7-628020e0e255' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2fdc3636-ca78-4111-9e96-c0a61548e3cb' class='xr-var-data-in' type='checkbox'><label for='data-2fdc3636-ca78-4111-9e96-c0a61548e3cb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>malignant_tumor_of_urinary_bladder</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-edef5d1a-ce4e-488a-b494-cfe6e8582dcd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-edef5d1a-ce4e-488a-b494-cfe6e8582dcd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6a9fea13-73dd-4bd7-888d-99a5fe78305e' class='xr-var-data-in' type='checkbox'><label for='data-6a9fea13-73dd-4bd7-888d-99a5fe78305e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pulmonary_hypertension</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-fc887e9b-dd84-439f-9d74-5918976777a3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fc887e9b-dd84-439f-9d74-5918976777a3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e6744c1f-5122-4d49-9599-212ac7ed07f9' class='xr-var-data-in' type='checkbox'><label for='data-e6744c1f-5122-4d49-9599-212ac7ed07f9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nasal_congestion</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-6c52d3c8-79ca-4f95-8573-0a62f2bcba91' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6c52d3c8-79ca-4f95-8573-0a62f2bcba91' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-89ce1299-dd1c-4d00-b555-6337faff77a5' class='xr-var-data-in' type='checkbox'><label for='data-89ce1299-dd1c-4d00-b555-6337faff77a5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hemoptysis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-66f6309e-b3c7-407b-9723-0ca81619741d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-66f6309e-b3c7-407b-9723-0ca81619741d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d495d420-cf48-4f95-a539-d51f5bf5566e' class='xr-var-data-in' type='checkbox'><label for='data-d495d420-cf48-4f95-a539-d51f5bf5566e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>viral_gastroenteritis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-e80af289-72a0-4106-9227-5676f3a858cb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e80af289-72a0-4106-9227-5676f3a858cb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-32ac3730-7430-424c-a1c2-0b3a1c0ef2a2' class='xr-var-data-in' type='checkbox'><label for='data-32ac3730-7430-424c-a1c2-0b3a1c0ef2a2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>right_upper_quadrant_pain</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-88886109-b5d0-4899-9b9f-c32591d55d98' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-88886109-b5d0-4899-9b9f-c32591d55d98' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9c23e36e-4a3a-4469-8493-e8e4e306a8ed' class='xr-var-data-in' type='checkbox'><label for='data-9c23e36e-4a3a-4469-8493-e8e4e306a8ed' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>primary_open_angle_glaucoma</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-3d597b2a-c2a1-4668-a185-93c15d5e994f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3d597b2a-c2a1-4668-a185-93c15d5e994f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7c0ab476-c878-4fd1-b840-86b49e5f11a8' class='xr-var-data-in' type='checkbox'><label for='data-7c0ab476-c878-4fd1-b840-86b49e5f11a8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>malignant_tumor_of_ovary</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-5b8aefbb-489b-48ea-9a0b-64938a907011' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5b8aefbb-489b-48ea-9a0b-64938a907011' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-38411cc0-4206-4434-93d7-4d1ac04cac06' class='xr-var-data-in' type='checkbox'><label for='data-38411cc0-4206-4434-93d7-4d1ac04cac06' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>incomplete_emptying_of_bladder</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-9cdc6483-6510-41be-8bcb-83a1e8901698' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9cdc6483-6510-41be-8bcb-83a1e8901698' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1a8846a6-ffb3-4530-974b-fe12ae9c59da' class='xr-var-data-in' type='checkbox'><label for='data-1a8846a6-ffb3-4530-974b-fe12ae9c59da' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pruritic_disorder</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-91472817-0214-485c-8944-e38544ea435d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-91472817-0214-485c-8944-e38544ea435d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e4ba1e62-274b-4ef3-aaa7-684f0cb5f5d1' class='xr-var-data-in' type='checkbox'><label for='data-e4ba1e62-274b-4ef3-aaa7-684f0cb5f5d1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fibrocystic_breast_changes</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-b7a54516-1035-48bc-a3b6-aaefc287dc2d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b7a54516-1035-48bc-a3b6-aaefc287dc2d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-012d289a-f18e-4fa6-904e-d4c01e49b905' class='xr-var-data-in' type='checkbox'><label for='data-012d289a-f18e-4fa6-904e-d4c01e49b905' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tinea_corporis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-28aa3656-4de7-42d1-a76e-6eeaf8705c0f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-28aa3656-4de7-42d1-a76e-6eeaf8705c0f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ceb009f1-19e7-4d7c-8c6c-2cd3b6243a24' class='xr-var-data-in' type='checkbox'><label for='data-ceb009f1-19e7-4d7c-8c6c-2cd3b6243a24' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>acute_pancreatitis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-36105ee7-daae-4675-869c-8e3f170e8924' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-36105ee7-daae-4675-869c-8e3f170e8924' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-54204db5-54da-4c34-bb42-34c492bfcce3' class='xr-var-data-in' type='checkbox'><label for='data-54204db5-54da-4c34-bb42-34c492bfcce3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>acute_myocardial_infarction</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-14c2fa70-b4f6-41bc-991f-9650de621908' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-14c2fa70-b4f6-41bc-991f-9650de621908' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-65b016af-4c3a-4e5e-9153-3f61acaed238' class='xr-var-data-in' type='checkbox'><label for='data-65b016af-4c3a-4e5e-9153-3f61acaed238' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cerebral_hemorrhage</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-009f0a76-981e-4aa6-81ee-9431579d2dae' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-009f0a76-981e-4aa6-81ee-9431579d2dae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-34202e54-139a-4599-9de7-f1463ad84c71' class='xr-var-data-in' type='checkbox'><label for='data-34202e54-139a-4599-9de7-f1463ad84c71' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>urge_incontinence_of_urine</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-4bacb8ce-72c1-4e7c-b023-46de5b7b5043' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4bacb8ce-72c1-4e7c-b023-46de5b7b5043' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8be5c87f-d5f8-4d1b-9f6f-a53b89e69c60' class='xr-var-data-in' type='checkbox'><label for='data-8be5c87f-d5f8-4d1b-9f6f-a53b89e69c60' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>megaloblastic_anemia_due_to_vitamin_b&gt;12&lt;_deficiency</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-5d3ef5a2-4f10-48d0-8ed1-28ab98b0d77f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5d3ef5a2-4f10-48d0-8ed1-28ab98b0d77f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ce38b6be-0e0b-45b6-a5a7-30712f840050' class='xr-var-data-in' type='checkbox'><label for='data-ce38b6be-0e0b-45b6-a5a7-30712f840050' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>chronic_lymphoid_leukemia_disease</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-42f08986-a3d9-43bf-9a74-ce0ae21fbcc7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-42f08986-a3d9-43bf-9a74-ce0ae21fbcc7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-07e78215-8055-4dde-be11-56334d7e7e65' class='xr-var-data-in' type='checkbox'><label for='data-07e78215-8055-4dde-be11-56334d7e7e65' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>disorder_of_lymphatic_system</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-0372930f-288d-4a35-aa1d-8c2283042277' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0372930f-288d-4a35-aa1d-8c2283042277' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f26630d4-3b86-4a29-83e9-4e9854f241e8' class='xr-var-data-in' type='checkbox'><label for='data-f26630d4-3b86-4a29-83e9-4e9854f241e8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diverticular_disease</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-965ab215-39bc-4374-b8aa-2ba822a2404d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-965ab215-39bc-4374-b8aa-2ba822a2404d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-beaedf1e-3de6-4512-89ea-e69b623c2123' class='xr-var-data-in' type='checkbox'><label for='data-beaedf1e-3de6-4512-89ea-e69b623c2123' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tonsillitis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-9959bc72-3af3-4167-bf67-fd6b81b243c9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9959bc72-3af3-4167-bf67-fd6b81b243c9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1763af33-b173-4103-a586-9b05efc038f8' class='xr-var-data-in' type='checkbox'><label for='data-1763af33-b173-4103-a586-9b05efc038f8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tear_film_insufficiency</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-30b85fc2-b891-480d-8e49-b56fb665ea09' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-30b85fc2-b891-480d-8e49-b56fb665ea09' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-451c421c-669c-41b0-a353-820842353507' class='xr-var-data-in' type='checkbox'><label for='data-451c421c-669c-41b0-a353-820842353507' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>abnormal_gait</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-260bb3b9-4e24-47e2-9e0b-52b1adf71789' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-260bb3b9-4e24-47e2-9e0b-52b1adf71789' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e0906eb0-fcad-4713-9bb5-d7a72306de8e' class='xr-var-data-in' type='checkbox'><label for='data-e0906eb0-fcad-4713-9bb5-d7a72306de8e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>orthostatic_hypotension</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-dd07a521-907a-4d38-942c-1a7789d281fb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dd07a521-907a-4d38-942c-1a7789d281fb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1955953a-1fac-4131-9e6f-b5109fbddc15' class='xr-var-data-in' type='checkbox'><label for='data-1955953a-1fac-4131-9e6f-b5109fbddc15' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pancreatitis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-16709a00-7dc0-48ee-b6b4-785fa1e5b257' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-16709a00-7dc0-48ee-b6b4-785fa1e5b257' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c0bc07f8-0760-4ffc-b397-aec86270d74d' class='xr-var-data-in' type='checkbox'><label for='data-c0bc07f8-0760-4ffc-b397-aec86270d74d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>closed_fracture_of_distal_end_of_radius</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-a48554b7-1597-48a7-bf4c-019a7d149a84' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a48554b7-1597-48a7-bf4c-019a7d149a84' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d10ca502-796b-4d76-83ad-2f10c3469e5e' class='xr-var-data-in' type='checkbox'><label for='data-d10ca502-796b-4d76-83ad-2f10c3469e5e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>first_degree_perineal_laceration</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-1f455558-50df-4f22-b383-a57d08a7d3b3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1f455558-50df-4f22-b383-a57d08a7d3b3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-18298ee7-b02e-40f3-b7a7-2ffc377ef560' class='xr-var-data-in' type='checkbox'><label for='data-18298ee7-b02e-40f3-b7a7-2ffc377ef560' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>not_for_resuscitation</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-90dfd171-9fac-42c9-a603-356418898fa0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-90dfd171-9fac-42c9-a603-356418898fa0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-95a59034-67b2-418a-ac94-2951ebc85c95' class='xr-var-data-in' type='checkbox'><label for='data-95a59034-67b2-418a-ac94-2951ebc85c95' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>gastroesophageal_reflux_disease_with_esophagitis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-1277eaa5-3801-4020-bce6-a5b17a25534a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1277eaa5-3801-4020-bce6-a5b17a25534a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8f22f4cc-8915-4418-ba74-5e6032405660' class='xr-var-data-in' type='checkbox'><label for='data-8f22f4cc-8915-4418-ba74-5e6032405660' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>intracranial_injury</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-d4ec4c85-c993-47af-a76b-ffb061a65b6f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d4ec4c85-c993-47af-a76b-ffb061a65b6f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3b7ce544-cab5-448e-bb98-843304dd0558' class='xr-var-data-in' type='checkbox'><label for='data-3b7ce544-cab5-448e-bb98-843304dd0558' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>bell&#x27;s_palsy</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-4cae9eb7-6a7e-4d12-9f9e-c9b60dad533a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4cae9eb7-6a7e-4d12-9f9e-c9b60dad533a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4ac7f887-f151-412a-97c8-cdbf02efba5b' class='xr-var-data-in' type='checkbox'><label for='data-4ac7f887-f151-412a-97c8-cdbf02efba5b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>bradycardia</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-94582c31-dadd-49f1-b8e6-8a0df84bfb9b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-94582c31-dadd-49f1-b8e6-8a0df84bfb9b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d10f57e1-6f7c-403d-abb1-3788fe9193b9' class='xr-var-data-in' type='checkbox'><label for='data-d10f57e1-6f7c-403d-abb1-3788fe9193b9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>polymyalgia_rheumatica</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-67cbb2fa-8b9e-4ed6-a7fa-2c88cfb89a95' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-67cbb2fa-8b9e-4ed6-a7fa-2c88cfb89a95' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c6eb344a-eead-4cd7-b425-081c1b0aeadd' class='xr-var-data-in' type='checkbox'><label for='data-c6eb344a-eead-4cd7-b425-081c1b0aeadd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dysplasia_of_cervix</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-57b8eab0-1bc0-49dc-b4f5-f37e8af40c3a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-57b8eab0-1bc0-49dc-b4f5-f37e8af40c3a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f752113e-7a0a-4a3e-b188-4104a325df1d' class='xr-var-data-in' type='checkbox'><label for='data-f752113e-7a0a-4a3e-b188-4104a325df1d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>serous_otitis_media</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-912f5304-a9d4-4d60-90fb-d8ee125ed949' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-912f5304-a9d4-4d60-90fb-d8ee125ed949' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ee6a7be7-12c2-4077-a72b-8ceb228ae42d' class='xr-var-data-in' type='checkbox'><label for='data-ee6a7be7-12c2-4077-a72b-8ceb228ae42d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>angioedema</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-42dd9a47-03e4-4f8c-b226-2afb5eeb4535' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-42dd9a47-03e4-4f8c-b226-2afb5eeb4535' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1c02f3be-1224-43bd-a2a6-4f680786b87e' class='xr-var-data-in' type='checkbox'><label for='data-1c02f3be-1224-43bd-a2a6-4f680786b87e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>venous_varices</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-b8687071-8487-4d2e-baf7-ec55d16fc32f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b8687071-8487-4d2e-baf7-ec55d16fc32f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-166c2180-131e-4053-90d3-599676d1a303' class='xr-var-data-in' type='checkbox'><label for='data-166c2180-131e-4053-90d3-599676d1a303' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spasm</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-57f9b0b3-a8fa-44e7-afcb-c45de65dbc7a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-57f9b0b3-a8fa-44e7-afcb-c45de65dbc7a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d6aa47cd-b72c-49f8-8601-be430717f0b8' class='xr-var-data-in' type='checkbox'><label for='data-d6aa47cd-b72c-49f8-8601-be430717f0b8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sleep_disorder</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-5968dc4c-f66c-4cd2-8546-70cc3aa5de05' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5968dc4c-f66c-4cd2-8546-70cc3aa5de05' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-24ccb98e-5ab1-4cc0-9be0-3eb3a16700ed' class='xr-var-data-in' type='checkbox'><label for='data-24ccb98e-5ab1-4cc0-9be0-3eb3a16700ed' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pain_of_breast</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-acb51f6f-3acc-4888-a141-8f8920891c76' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-acb51f6f-3acc-4888-a141-8f8920891c76' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c37e1928-6bf2-40a5-98df-dd41b67be78a' class='xr-var-data-in' type='checkbox'><label for='data-c37e1928-6bf2-40a5-98df-dd41b67be78a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>malabsorption_syndrome_due_to_intolerance_to_lactose</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-ebfe9e3d-75ce-4345-8fae-ab5a1702324e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ebfe9e3d-75ce-4345-8fae-ab5a1702324e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8434a2cf-9c52-43bc-850b-66490ef26af2' class='xr-var-data-in' type='checkbox'><label for='data-8434a2cf-9c52-43bc-850b-66490ef26af2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tachycardia</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-fd48c603-4f02-4131-981a-ce27402932e9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fd48c603-4f02-4131-981a-ce27402932e9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c0b90269-dfec-4dc7-830b-c976bafd133b' class='xr-var-data-in' type='checkbox'><label for='data-c0b90269-dfec-4dc7-830b-c976bafd133b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>acute_conjunctivitis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-6c72b532-6f07-406c-8cc4-2746322ad286' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6c72b532-6f07-406c-8cc4-2746322ad286' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-10986a0f-d7cb-4bac-8073-35a20003c53b' class='xr-var-data-in' type='checkbox'><label for='data-10986a0f-d7cb-4bac-8073-35a20003c53b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>malignant_lymphoma</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-cbd6088e-1c3b-4f6b-97df-97000027cde3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cbd6088e-1c3b-4f6b-97df-97000027cde3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-83ea32e6-2f6c-4abc-af02-ec2adef987c9' class='xr-var-data-in' type='checkbox'><label for='data-83ea32e6-2f6c-4abc-af02-ec2adef987c9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>supraventricular_tachycardia</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-edf6f10b-8686-46f9-b30e-528d750e2012' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-edf6f10b-8686-46f9-b30e-528d750e2012' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-823ffee0-49c9-4e97-99e7-6e5445c7ff62' class='xr-var-data-in' type='checkbox'><label for='data-823ffee0-49c9-4e97-99e7-6e5445c7ff62' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hyperparathyroidism</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-dc5a9bba-887f-4d4e-8783-e52b18f88f10' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dc5a9bba-887f-4d4e-8783-e52b18f88f10' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2b277b11-1d9d-43ac-9334-84c62850534c' class='xr-var-data-in' type='checkbox'><label for='data-2b277b11-1d9d-43ac-9334-84c62850534c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>periodontitis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-61a0bff9-bc5b-4fa1-ba14-5c5bdf5518f5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-61a0bff9-bc5b-4fa1-ba14-5c5bdf5518f5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-20ae5d40-6998-4c4e-be6d-7bdf16a7ba13' class='xr-var-data-in' type='checkbox'><label for='data-20ae5d40-6998-4c4e-be6d-7bdf16a7ba13' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>renal_disorder_due_to_type_2_diabetes_mellitus</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-ca722f7f-31b7-4b68-a1d5-7cc0d921bcf6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ca722f7f-31b7-4b68-a1d5-7cc0d921bcf6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a9195602-2d6c-44be-bd9c-c07bb5ca9fa3' class='xr-var-data-in' type='checkbox'><label for='data-a9195602-2d6c-44be-bd9c-c07bb5ca9fa3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nerve_root_disorder</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-f163bf74-2fcf-4274-a566-ff655d7f3449' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f163bf74-2fcf-4274-a566-ff655d7f3449' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-de15cdc5-a183-460c-a9d0-fbbd3e5091dd' class='xr-var-data-in' type='checkbox'><label for='data-de15cdc5-a183-460c-a9d0-fbbd3e5091dd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nonexudative_age-related_macular_degeneration</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-e6d21389-69cc-49d8-9dad-5e7fc963e8f6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e6d21389-69cc-49d8-9dad-5e7fc963e8f6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-85f0e175-b6bf-4ac8-9213-7622b39031f7' class='xr-var-data-in' type='checkbox'><label for='data-85f0e175-b6bf-4ac8-9213-7622b39031f7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tension-type_headache</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-dad0dd79-75e2-47e0-be83-0a1af3b8267d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dad0dd79-75e2-47e0-be83-0a1af3b8267d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-80375088-3538-49f8-9fd9-4591a5c9c4af' class='xr-var-data-in' type='checkbox'><label for='data-80375088-3538-49f8-9fd9-4591a5c9c4af' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>chronic_pain_syndrome</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-35eb6f1e-d7f8-46e6-ac0e-f3f65293fb08' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-35eb6f1e-d7f8-46e6-ac0e-f3f65293fb08' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-258dbfd9-fac7-4f29-9122-53aff16e5859' class='xr-var-data-in' type='checkbox'><label for='data-258dbfd9-fac7-4f29-9122-53aff16e5859' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>abnormal_weight_loss</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-cd54884f-b328-4ffa-9dce-8764b95d13cc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cd54884f-b328-4ffa-9dce-8764b95d13cc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e7be82f6-573f-403c-b560-28a0f1732058' class='xr-var-data-in' type='checkbox'><label for='data-e7be82f6-573f-403c-b560-28a0f1732058' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pure_hypercholesterolemia</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... True True</div><input id='attrs-70e598c5-d2ef-4f45-9f62-168d038c1405' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-70e598c5-d2ef-4f45-9f62-168d038c1405' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3ad5c02d-b20e-4edd-9480-100bb8cd37e2' class='xr-var-data-in' type='checkbox'><label for='data-3ad5c02d-b20e-4edd-9480-100bb8cd37e2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, True, True], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>acute_upper_respiratory_infection</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-3e893fe6-c586-4ca5-9b36-91d13a89fbc0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3e893fe6-c586-4ca5-9b36-91d13a89fbc0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1e224625-cfb0-42e5-9d54-7bacfa660716' class='xr-var-data-in' type='checkbox'><label for='data-1e224625-cfb0-42e5-9d54-7bacfa660716' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>inflammatory_disease_of_liver</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-618369ec-2cc8-4e25-a3a7-acac9022ada9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-618369ec-2cc8-4e25-a3a7-acac9022ada9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-550c983b-5fa5-42ac-b76a-d491657e031a' class='xr-var-data-in' type='checkbox'><label for='data-550c983b-5fa5-42ac-b76a-d491657e031a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>colitis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-c5a94aaf-1b9c-4029-841e-d999d50e7e4d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c5a94aaf-1b9c-4029-841e-d999d50e7e4d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dbab8a3b-794d-4c2e-904a-8a461ebf6073' class='xr-var-data-in' type='checkbox'><label for='data-dbab8a3b-794d-4c2e-904a-8a461ebf6073' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>prolonged_pregnancy</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-6d66135b-1761-413b-9877-66d49e7f1b76' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6d66135b-1761-413b-9877-66d49e7f1b76' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-14731b23-f9f1-426a-98ea-e71ad63989f3' class='xr-var-data-in' type='checkbox'><label for='data-14731b23-f9f1-426a-98ea-e71ad63989f3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>major_depression_single_episode</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-47dc354d-faab-4b39-a29f-cdc28077bd94' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-47dc354d-faab-4b39-a29f-cdc28077bd94' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-700025f0-3176-41e5-852f-f1e1b06e5955' class='xr-var-data-in' type='checkbox'><label for='data-700025f0-3176-41e5-852f-f1e1b06e5955' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>croup</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-fc19013e-9acc-4e65-baa3-796f3d8de77d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fc19013e-9acc-4e65-baa3-796f3d8de77d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fa62e3ae-0dda-4716-b057-373c7b59e140' class='xr-var-data-in' type='checkbox'><label for='data-fa62e3ae-0dda-4716-b057-373c7b59e140' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>skin_tag</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-6ec7015f-af2a-49ed-a054-4072748c1cf4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6ec7015f-af2a-49ed-a054-4072748c1cf4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9407bb6c-c7a4-4221-bece-f6c902b13ce8' class='xr-var-data-in' type='checkbox'><label for='data-9407bb6c-c7a4-4221-bece-f6c902b13ce8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>metabolic_syndrome_x</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-4408d9c9-1a25-4eb3-a70e-26e08a2ad451' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4408d9c9-1a25-4eb3-a70e-26e08a2ad451' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-15196f8b-3fcb-4a90-9654-0356e07a92f5' class='xr-var-data-in' type='checkbox'><label for='data-15196f8b-3fcb-4a90-9654-0356e07a92f5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>adverse_reaction_caused_by_drug</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-fa73bf32-da52-4f75-9f6c-d20f9a39e2b7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fa73bf32-da52-4f75-9f6c-d20f9a39e2b7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6703ba80-a2ba-4248-b205-dbb1752e8eda' class='xr-var-data-in' type='checkbox'><label for='data-6703ba80-a2ba-4248-b205-dbb1752e8eda' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>retinopathy_due_to_diabetes_mellitus</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-f037ad73-e70f-4a0d-b8a8-38da7cdc2d61' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f037ad73-e70f-4a0d-b8a8-38da7cdc2d61' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e991db3b-5aab-444b-b866-46ab241ded72' class='xr-var-data-in' type='checkbox'><label for='data-e991db3b-5aab-444b-b866-46ab241ded72' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>female_infertility</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-ab6654d5-2589-4437-91c7-43fd4b20ece2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ab6654d5-2589-4437-91c7-43fd4b20ece2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2a8c844d-5fd1-4870-9fe1-af4967623294' class='xr-var-data-in' type='checkbox'><label for='data-2a8c844d-5fd1-4870-9fe1-af4967623294' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>open-angle_glaucoma</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-54b45608-de50-4955-8b8b-46f45bda73a3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-54b45608-de50-4955-8b8b-46f45bda73a3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-15eefe3c-9614-4f04-a271-1490e8638e91' class='xr-var-data-in' type='checkbox'><label for='data-15eefe3c-9614-4f04-a271-1490e8638e91' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tietze&#x27;s_disease</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-27fdd245-c81c-4333-be52-9169b80b2d25' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-27fdd245-c81c-4333-be52-9169b80b2d25' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a249ce2e-b28a-4581-b6f9-70e550134ef3' class='xr-var-data-in' type='checkbox'><label for='data-a249ce2e-b28a-4581-b6f9-70e550134ef3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>gallbladder_calculus</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-45bdff02-dae2-49b5-b79d-cba20de7ea32' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-45bdff02-dae2-49b5-b79d-cba20de7ea32' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-281a88dc-30ff-497e-a209-33697158120f' class='xr-var-data-in' type='checkbox'><label for='data-281a88dc-30ff-497e-a209-33697158120f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sarcoidosis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-0046d329-2618-478e-a0e3-cf68a399afc8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0046d329-2618-478e-a0e3-cf68a399afc8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-33f4c877-071e-4f39-995b-2ae8cc1eb9dd' class='xr-var-data-in' type='checkbox'><label for='data-33f4c877-071e-4f39-995b-2ae8cc1eb9dd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>neurogenic_bladder</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-bbbad64d-f8e2-4657-9ecb-504246a70a7a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bbbad64d-f8e2-4657-9ecb-504246a70a7a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-303adcdf-94da-4c03-a500-0f2b3448bed2' class='xr-var-data-in' type='checkbox'><label for='data-303adcdf-94da-4c03-a500-0f2b3448bed2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tobacco_dependence_in_remission</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... True True</div><input id='attrs-53028f55-4326-47d6-afd3-c35f088e1919' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-53028f55-4326-47d6-afd3-c35f088e1919' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-64d4c2e6-d74d-4941-90ca-a3578537c37d' class='xr-var-data-in' type='checkbox'><label for='data-64d4c2e6-d74d-4941-90ca-a3578537c37d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, True, True], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wheezing</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-5172d14f-ec5f-4ddf-bd98-35408dd8165f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5172d14f-ec5f-4ddf-bd98-35408dd8165f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-91a3b3c2-978e-41e0-bcda-b7d70c19b1f0' class='xr-var-data-in' type='checkbox'><label for='data-91a3b3c2-978e-41e0-bcda-b7d70c19b1f0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>elevated_levels_of_transaminase_&amp;_lactic_acid_dehydrogenase</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-2f28cb45-c9d4-4be6-98b0-64648d8d0461' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2f28cb45-c9d4-4be6-98b0-64648d8d0461' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bdcb43df-0638-4178-baab-a649468db5eb' class='xr-var-data-in' type='checkbox'><label for='data-bdcb43df-0638-4178-baab-a649468db5eb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ascites</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-03039820-a2a4-45a6-bf9c-53ce57ea90a7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-03039820-a2a4-45a6-bf9c-53ce57ea90a7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fcc33c23-e84f-4868-a659-0e6555e64143' class='xr-var-data-in' type='checkbox'><label for='data-fcc33c23-e84f-4868-a659-0e6555e64143' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>low_blood_pressure</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-90cb168c-a5c7-45f3-a952-3b467299701f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-90cb168c-a5c7-45f3-a952-3b467299701f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f0bf86f9-3645-49df-919d-08900fccaa71' class='xr-var-data-in' type='checkbox'><label for='data-f0bf86f9-3645-49df-919d-08900fccaa71' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>bursitis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-c6cd07b7-cfda-457a-b395-85a378bdb82f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c6cd07b7-cfda-457a-b395-85a378bdb82f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d51f8fa7-b658-4009-a681-a70b1d8a6ae7' class='xr-var-data-in' type='checkbox'><label for='data-d51f8fa7-b658-4009-a681-a70b1d8a6ae7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>miscarriage</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-4fbb5bd6-681b-4bb6-873b-e75db914ead9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4fbb5bd6-681b-4bb6-873b-e75db914ead9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fce4331a-6e7c-4b45-a121-a867647cb814' class='xr-var-data-in' type='checkbox'><label for='data-fce4331a-6e7c-4b45-a121-a867647cb814' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>methicillin_resistant_staphylococcus_aureus_carrier</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-8f6a0f2d-8828-4451-8080-16108f4184ea' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8f6a0f2d-8828-4451-8080-16108f4184ea' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5d974897-7b3a-48c4-943f-ebc71be3a892' class='xr-var-data-in' type='checkbox'><label for='data-5d974897-7b3a-48c4-943f-ebc71be3a892' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>urethritis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-788fc96e-2a96-498c-97e8-94e9612a8633' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-788fc96e-2a96-498c-97e8-94e9612a8633' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-369ea562-50d0-428f-a4dc-c6ff87f5161e' class='xr-var-data-in' type='checkbox'><label for='data-369ea562-50d0-428f-a4dc-c6ff87f5161e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>noninfectious_gastroenteritis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-e59f32ed-50a8-4282-b7c0-d4d459bccc0a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e59f32ed-50a8-4282-b7c0-d4d459bccc0a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f18ee77d-958c-48f9-99a3-46bfc0bfa98e' class='xr-var-data-in' type='checkbox'><label for='data-f18ee77d-958c-48f9-99a3-46bfc0bfa98e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>malignant_tumor_of_thyroid_gland</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-014e9ed3-8b77-49e4-bf59-9942d6317ea4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-014e9ed3-8b77-49e4-bf59-9942d6317ea4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e7093c87-5240-4d91-b1b9-e3292fe2a68d' class='xr-var-data-in' type='checkbox'><label for='data-e7093c87-5240-4d91-b1b9-e3292fe2a68d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pressure_ulcer</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-de820cd0-38b0-4074-b9a7-e1b48058f8c5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-de820cd0-38b0-4074-b9a7-e1b48058f8c5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-109845b1-a090-4a36-94f7-b27066a0f9bc' class='xr-var-data-in' type='checkbox'><label for='data-109845b1-a090-4a36-94f7-b27066a0f9bc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>verruca_plantaris</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-989fde19-ee00-4508-8338-05cc2bda8091' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-989fde19-ee00-4508-8338-05cc2bda8091' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4d16e94a-c2fb-425b-a870-5a8ef8f4bbef' class='xr-var-data-in' type='checkbox'><label for='data-4d16e94a-c2fb-425b-a870-5a8ef8f4bbef' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>anemia_of_chronic_disorder</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-c08dddb2-a686-4c9c-9d3a-8ff5ef82099b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c08dddb2-a686-4c9c-9d3a-8ff5ef82099b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6d7d0b4a-d68e-44d9-9ece-d5a1e13b4a8a' class='xr-var-data-in' type='checkbox'><label for='data-6d7d0b4a-d68e-44d9-9ece-d5a1e13b4a8a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hernia_of_anterior_abdominal_wall</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-14bdfbe6-1cca-482f-8b9c-46eef71898fd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-14bdfbe6-1cca-482f-8b9c-46eef71898fd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-198097b8-0351-4cd6-809d-8bfcea7459be' class='xr-var-data-in' type='checkbox'><label for='data-198097b8-0351-4cd6-809d-8bfcea7459be' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>degeneration_of_lumbar_intervertebral_disc</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-4cd95c41-d464-473a-ad45-46398cf79120' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4cd95c41-d464-473a-ad45-46398cf79120' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8350b44d-634e-4ef7-90ee-adc6f6c090af' class='xr-var-data-in' type='checkbox'><label for='data-8350b44d-634e-4ef7-90ee-adc6f6c090af' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>right_lower_quadrant_pain</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-17a4e601-4720-4d8d-8cdb-3892242302c0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-17a4e601-4720-4d8d-8cdb-3892242302c0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f9a9668d-292b-4a46-a4e9-db8476ed0849' class='xr-var-data-in' type='checkbox'><label for='data-f9a9668d-292b-4a46-a4e9-db8476ed0849' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>infertile</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-770a9548-790b-4a46-b613-8172b4aaf5b8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-770a9548-790b-4a46-b613-8172b4aaf5b8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bee3c018-2c20-4ead-9a17-c82a7cca3be6' class='xr-var-data-in' type='checkbox'><label for='data-bee3c018-2c20-4ead-9a17-c82a7cca3be6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hernia_of_abdominal_wall</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-0cc14ecc-98ca-47a6-a409-26ebd14158ce' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0cc14ecc-98ca-47a6-a409-26ebd14158ce' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e481bc8c-d3a4-47fa-bb3b-57f4fd74a103' class='xr-var-data-in' type='checkbox'><label for='data-e481bc8c-d3a4-47fa-bb3b-57f4fd74a103' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>benign_paroxysmal_positional_vertigo</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-fca7567e-f5c6-4749-9d53-bd295be7a8e0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fca7567e-f5c6-4749-9d53-bd295be7a8e0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9524b62a-bcdc-40dc-83d6-a8eb373d5b57' class='xr-var-data-in' type='checkbox'><label for='data-9524b62a-bcdc-40dc-83d6-a8eb373d5b57' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pityriasis_versicolor</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-e65e133f-2fcc-4cd2-aa54-b174483f13c8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e65e133f-2fcc-4cd2-aa54-b174483f13c8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6fdd8f54-340c-409b-a5a7-2a57e1e8fa97' class='xr-var-data-in' type='checkbox'><label for='data-6fdd8f54-340c-409b-a5a7-2a57e1e8fa97' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>injury_of_lower_leg</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-ef61cdb0-705d-45ef-b0a8-572843ac80d0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ef61cdb0-705d-45ef-b0a8-572843ac80d0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-def08656-ece8-4dfc-8c76-605800b8bb34' class='xr-var-data-in' type='checkbox'><label for='data-def08656-ece8-4dfc-8c76-605800b8bb34' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dry_skin</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-23d86065-7746-478c-93c7-41522aa998e3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-23d86065-7746-478c-93c7-41522aa998e3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d1c790b4-7579-4f30-9281-8bdb872e9b5e' class='xr-var-data-in' type='checkbox'><label for='data-d1c790b4-7579-4f30-9281-8bdb872e9b5e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>impacted_tooth</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-4f8738a5-51ab-4ba4-b43d-920ec438c0cd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4f8738a5-51ab-4ba4-b43d-920ec438c0cd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-981e01e1-2b2c-4e7e-8831-159111c3582a' class='xr-var-data-in' type='checkbox'><label for='data-981e01e1-2b2c-4e7e-8831-159111c3582a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>acquired_trigger_finger</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-49c97fac-b6a2-42e3-8bf1-b3d0d2fac570' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-49c97fac-b6a2-42e3-8bf1-b3d0d2fac570' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-556a62bd-f1d6-4383-82b6-cbde9fd78f67' class='xr-var-data-in' type='checkbox'><label for='data-556a62bd-f1d6-4383-82b6-cbde9fd78f67' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>chronic_osteoarthritis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-96b76c3c-3b3e-4dff-986e-2496a262c6ff' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-96b76c3c-3b3e-4dff-986e-2496a262c6ff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4d916bf7-0a25-42a9-89ca-7eb987b0bcad' class='xr-var-data-in' type='checkbox'><label for='data-4d916bf7-0a25-42a9-89ca-7eb987b0bcad' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>acute_stress_disorder</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-17dbe260-c583-4c08-9e24-84de338dcccf' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-17dbe260-c583-4c08-9e24-84de338dcccf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b428793d-aef6-4bb9-8681-554283dfb498' class='xr-var-data-in' type='checkbox'><label for='data-b428793d-aef6-4bb9-8681-554283dfb498' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>peripheral_circulatory_disorder_due_to_type_2_diabetes_mellitus</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-38aae7da-12a5-4e7c-8161-19ab12239bed' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-38aae7da-12a5-4e7c-8161-19ab12239bed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0177913d-ffb9-46e9-aec4-242ae9c494e6' class='xr-var-data-in' type='checkbox'><label for='data-0177913d-ffb9-46e9-aec4-242ae9c494e6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>respiratory_failure</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... True False</div><input id='attrs-f499cc8e-4108-4f95-8e8d-a7f95b91a9bd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f499cc8e-4108-4f95-8e8d-a7f95b91a9bd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2eef727c-45fe-4c99-a231-ed9571157db9' class='xr-var-data-in' type='checkbox'><label for='data-2eef727c-45fe-4c99-a231-ed9571157db9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, True, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>allergic_disposition</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-0ee20863-98e2-4022-9758-a837ff188834' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0ee20863-98e2-4022-9758-a837ff188834' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8b5b0ab8-f184-426b-adb3-74053c63ec5d' class='xr-var-data-in' type='checkbox'><label for='data-8b5b0ab8-f184-426b-adb3-74053c63ec5d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>stress</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-2e78dac7-139c-4759-a293-feba1f4914d2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2e78dac7-139c-4759-a293-feba1f4914d2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a8e33557-8e7d-4f34-9072-28b7cf81445c' class='xr-var-data-in' type='checkbox'><label for='data-a8e33557-8e7d-4f34-9072-28b7cf81445c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>atrophic_vaginitis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-ac6228fe-dedd-4fad-b520-747b6a3758b8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ac6228fe-dedd-4fad-b520-747b6a3758b8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d488b3ba-20f0-4f94-8e3a-e09ecb7f5321' class='xr-var-data-in' type='checkbox'><label for='data-d488b3ba-20f0-4f94-8e3a-e09ecb7f5321' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>degeneration_of_lumbosacral_intervertebral_disc</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-f0f7c9a3-19ea-4e14-9b25-2858e8e96b71' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f0f7c9a3-19ea-4e14-9b25-2858e8e96b71' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-17eade50-4cfe-44ce-b1df-f825985ea81b' class='xr-var-data-in' type='checkbox'><label for='data-17eade50-4cfe-44ce-b1df-f825985ea81b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>astigmatism</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-56566c87-82db-4455-9fb1-0a4221ebbbb1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-56566c87-82db-4455-9fb1-0a4221ebbbb1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-87e26e52-a636-4cc3-a3e3-56bd026dac56' class='xr-var-data-in' type='checkbox'><label for='data-87e26e52-a636-4cc3-a3e3-56bd026dac56' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sick_sinus_syndrome</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-49512f4c-1440-4810-ac8a-86240687b9fe' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-49512f4c-1440-4810-ac8a-86240687b9fe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-94531dae-1b95-4fd4-9864-3f072da40dd9' class='xr-var-data-in' type='checkbox'><label for='data-94531dae-1b95-4fd4-9864-3f072da40dd9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cocaine_abuse</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-5959e6a0-5192-45c6-8fbe-2387a0a2dede' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5959e6a0-5192-45c6-8fbe-2387a0a2dede' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-75775671-6048-4247-aa7c-55fece710b05' class='xr-var-data-in' type='checkbox'><label for='data-75775671-6048-4247-aa7c-55fece710b05' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>intermittent_asthma</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-aeb72bea-dd56-4b8d-9820-a09dc6eac267' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-aeb72bea-dd56-4b8d-9820-a09dc6eac267' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-08c84eb0-5e1d-479e-b0bf-938c2256da8d' class='xr-var-data-in' type='checkbox'><label for='data-08c84eb0-5e1d-479e-b0bf-938c2256da8d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cervical_radiculopathy</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-8f1c81d2-9124-461f-89ed-84c4878b3ce2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8f1c81d2-9124-461f-89ed-84c4878b3ce2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-367f0f20-67bc-4f6c-a042-8a9d2a9a08cf' class='xr-var-data-in' type='checkbox'><label for='data-367f0f20-67bc-4f6c-a042-8a9d2a9a08cf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>atherosclerosis_of_coronary_artery</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-091ee542-eddc-4354-a2c0-962e19d2c57a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-091ee542-eddc-4354-a2c0-962e19d2c57a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6d26f8b9-c058-45be-a169-4a748601a5e7' class='xr-var-data-in' type='checkbox'><label for='data-6d26f8b9-c058-45be-a169-4a748601a5e7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>methicillin_resistant_staphylococcus_aureus_infection</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... True False</div><input id='attrs-17dcb4d3-aa88-4a57-bbeb-3907fef87514' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-17dcb4d3-aa88-4a57-bbeb-3907fef87514' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a42a34c7-a899-4c8e-9350-2a9cb62755d9' class='xr-var-data-in' type='checkbox'><label for='data-a42a34c7-a899-4c8e-9350-2a9cb62755d9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, True, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>female_pelvic_inflammatory_disease</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-f2f4dd2b-caff-4e4a-a68f-b8b83494d10c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f2f4dd2b-caff-4e4a-a68f-b8b83494d10c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2e3b3243-5e86-4376-8e8d-920b62e128aa' class='xr-var-data-in' type='checkbox'><label for='data-2e3b3243-5e86-4376-8e8d-920b62e128aa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pain_in_eye</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-a5147e9b-d09a-443e-bbd7-e7dd5252e9ac' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a5147e9b-d09a-443e-bbd7-e7dd5252e9ac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f187146a-18a2-4fa9-9842-0e72260177f1' class='xr-var-data-in' type='checkbox'><label for='data-f187146a-18a2-4fa9-9842-0e72260177f1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cervical_spondylosis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-44b26337-2710-4773-bab4-3bcc86f8caba' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-44b26337-2710-4773-bab4-3bcc86f8caba' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-600e78c1-1659-4e60-8104-7e2ec52b37e4' class='xr-var-data-in' type='checkbox'><label for='data-600e78c1-1659-4e60-8104-7e2ec52b37e4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>prematurity_of_infant</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-e813e0cb-5501-41cb-bea4-3735b2fddc16' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e813e0cb-5501-41cb-bea4-3735b2fddc16' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-223fb022-a54a-4ea0-85e0-d0c4c8418caa' class='xr-var-data-in' type='checkbox'><label for='data-223fb022-a54a-4ea0-85e0-d0c4c8418caa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>postmature_infancy</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-3ff3f9df-5c14-461b-a5d7-d9a6e1a2d7e8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3ff3f9df-5c14-461b-a5d7-d9a6e1a2d7e8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fcf479bc-b451-42e2-ac34-de6433645041' class='xr-var-data-in' type='checkbox'><label for='data-fcf479bc-b451-42e2-ac34-de6433645041' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temporomandibular_joint_disorder</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-d183ef21-901b-4392-9940-ffbd663c368b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d183ef21-901b-4392-9940-ffbd663c368b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bbb229dd-858d-4cf0-9bd0-9b6569f0dbd6' class='xr-var-data-in' type='checkbox'><label for='data-bbb229dd-858d-4cf0-9bd0-9b6569f0dbd6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nocturia</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-fed946e9-bf69-4cd3-81b8-de1a9ad3ef00' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fed946e9-bf69-4cd3-81b8-de1a9ad3ef00' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-69623949-e861-4c3c-aa0f-d4b8659a733f' class='xr-var-data-in' type='checkbox'><label for='data-69623949-e861-4c3c-aa0f-d4b8659a733f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>adjustment_disorder_with_depressed_mood</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-3fbac357-30dc-4b81-8bbd-4d01e1b49340' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3fbac357-30dc-4b81-8bbd-4d01e1b49340' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8efff956-5b47-4fde-bc27-34cd39037c80' class='xr-var-data-in' type='checkbox'><label for='data-8efff956-5b47-4fde-bc27-34cd39037c80' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>visual_impairment</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-c9c6b41a-b64c-4bac-8c52-147b27aa0dba' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c9c6b41a-b64c-4bac-8c52-147b27aa0dba' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-05963c0a-6bd9-48a2-94d9-06198f7a746d' class='xr-var-data-in' type='checkbox'><label for='data-05963c0a-6bd9-48a2-94d9-06198f7a746d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>male_hypogonadism</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-0b050af9-0661-4198-837d-008e2d46c41a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0b050af9-0661-4198-837d-008e2d46c41a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4b4169d3-af59-4b48-b765-83e6b857df6b' class='xr-var-data-in' type='checkbox'><label for='data-4b4169d3-af59-4b48-b765-83e6b857df6b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>closed_intertrochanteric_fracture</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-e94fc576-46a1-464f-8784-f8df3728ec83' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e94fc576-46a1-464f-8784-f8df3728ec83' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c83fb5f5-4494-4457-9452-363897a43a4d' class='xr-var-data-in' type='checkbox'><label for='data-c83fb5f5-4494-4457-9452-363897a43a4d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pain_in_limb</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-382bed76-4f72-4f85-bbd3-3ee47ec0d08a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-382bed76-4f72-4f85-bbd3-3ee47ec0d08a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ff1891a5-cc9e-471e-b54d-c9e21749a001' class='xr-var-data-in' type='checkbox'><label for='data-ff1891a5-cc9e-471e-b54d-c9e21749a001' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>complication_occurring_during_pregnancy</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-2bbefa45-d9ba-4822-9b1b-7720bb048512' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2bbefa45-d9ba-4822-9b1b-7720bb048512' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-18001d97-73d1-411f-a10e-b4dae4555540' class='xr-var-data-in' type='checkbox'><label for='data-18001d97-73d1-411f-a10e-b4dae4555540' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sprain_of_knee</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-b972131f-c9c6-4e74-9ba2-1e6df4578253' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b972131f-c9c6-4e74-9ba2-1e6df4578253' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3ae22409-d7b7-41cd-a615-e1361944c5a0' class='xr-var-data-in' type='checkbox'><label for='data-3ae22409-d7b7-41cd-a615-e1361944c5a0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fracture_of_ankle</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-df2c6e80-00be-4044-a5d6-991a6b238b4f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-df2c6e80-00be-4044-a5d6-991a6b238b4f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8b85c048-360d-4cc5-b742-cabf371e2806' class='xr-var-data-in' type='checkbox'><label for='data-8b85c048-360d-4cc5-b742-cabf371e2806' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>injury_of_hand</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-360a478d-c43f-4aae-8a54-a660551f11fb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-360a478d-c43f-4aae-8a54-a660551f11fb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dc06b998-9fd1-437f-9836-271c42723ba4' class='xr-var-data-in' type='checkbox'><label for='data-dc06b998-9fd1-437f-9836-271c42723ba4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>postoperative_wound_infection</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-713ea5a8-ebda-4572-9457-ca9a8b0c894c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-713ea5a8-ebda-4572-9457-ca9a8b0c894c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8b9f2df1-a385-475f-b57f-8e8e49cf172a' class='xr-var-data-in' type='checkbox'><label for='data-8b9f2df1-a385-475f-b57f-8e8e49cf172a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>congestive_cardiomyopathy</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-acf80c3f-02cc-48f2-bd2c-77d8e1198148' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-acf80c3f-02cc-48f2-bd2c-77d8e1198148' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-501da786-424c-44e4-9df7-a8997a2deb1c' class='xr-var-data-in' type='checkbox'><label for='data-501da786-424c-44e4-9df7-a8997a2deb1c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nocturnal_enuresis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-61d5ace8-6064-4137-aad1-78de00791951' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-61d5ace8-6064-4137-aad1-78de00791951' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c513f1f3-cf59-4581-8496-368d448ec5c4' class='xr-var-data-in' type='checkbox'><label for='data-c513f1f3-cf59-4581-8496-368d448ec5c4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>proliferative_retinopathy_due_to_diabetes_mellitus</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-2caca8bc-d0d8-4511-8228-53a69c59263d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2caca8bc-d0d8-4511-8228-53a69c59263d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-38abad34-965c-4f4c-8f37-0ea7a8037316' class='xr-var-data-in' type='checkbox'><label for='data-38abad34-965c-4f4c-8f37-0ea7a8037316' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>major_depressive_disorder</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-d347b05b-e57e-4114-9e23-ba015ccdd47e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d347b05b-e57e-4114-9e23-ba015ccdd47e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d385610e-8195-461d-b23c-f86088e58093' class='xr-var-data-in' type='checkbox'><label for='data-d385610e-8195-461d-b23c-f86088e58093' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>chronic_bronchitis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-002884fb-ff62-4ff6-866c-53558fca102f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-002884fb-ff62-4ff6-866c-53558fca102f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3828b7ad-343f-436f-9e0c-10295584ef2f' class='xr-var-data-in' type='checkbox'><label for='data-3828b7ad-343f-436f-9e0c-10295584ef2f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>advanced_maternal_age_gravida</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-c6f6d35d-18e2-4b3c-9bf7-b091b4054ae8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c6f6d35d-18e2-4b3c-9bf7-b091b4054ae8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ac426166-7f17-4cb0-b2d1-8673a1915cbc' class='xr-var-data-in' type='checkbox'><label for='data-ac426166-7f17-4cb0-b2d1-8673a1915cbc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>recurrent_major_depression_in_full_remission</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-eda4c453-8bdf-48ba-9287-79e23017a58b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-eda4c453-8bdf-48ba-9287-79e23017a58b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6d91fd10-677c-4621-8bd2-68c65ecd5f0b' class='xr-var-data-in' type='checkbox'><label for='data-6d91fd10-677c-4621-8bd2-68c65ecd5f0b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pregnancy-induced_hypertension</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-520fdd2a-26a6-4030-ad6b-f5ba0eed6d1e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-520fdd2a-26a6-4030-ad6b-f5ba0eed6d1e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a4504e64-73c1-4f0f-afa0-93ad60d52542' class='xr-var-data-in' type='checkbox'><label for='data-a4504e64-73c1-4f0f-afa0-93ad60d52542' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>epididymitis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-476b536d-7069-476d-b97c-235c7a68d8aa' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-476b536d-7069-476d-b97c-235c7a68d8aa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-27d6dbd1-01d3-4f4e-91e0-5778f1865260' class='xr-var-data-in' type='checkbox'><label for='data-27d6dbd1-01d3-4f4e-91e0-5778f1865260' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>impetigo</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-7737c7be-e17a-4065-8f6a-6ebd069c832b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7737c7be-e17a-4065-8f6a-6ebd069c832b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cef323cf-cb39-4b5a-9ef2-3ff745c9fad8' class='xr-var-data-in' type='checkbox'><label for='data-cef323cf-cb39-4b5a-9ef2-3ff745c9fad8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>schizoaffective_disorder</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-cc995d64-2b39-45d8-881c-9a6618ffa47d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cc995d64-2b39-45d8-881c-9a6618ffa47d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-278eefff-6fd4-4bf1-87fb-f4feb5b50a0f' class='xr-var-data-in' type='checkbox'><label for='data-278eefff-6fd4-4bf1-87fb-f4feb5b50a0f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>candidiasis_of_mouth</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-c14d22c2-0e26-4762-baa8-7bc4273914b6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c14d22c2-0e26-4762-baa8-7bc4273914b6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-793ce7ba-396f-44b6-883a-658d0e12c189' class='xr-var-data-in' type='checkbox'><label for='data-793ce7ba-396f-44b6-883a-658d0e12c189' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mild_recurrent_major_depression</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-6a7b9010-6174-4691-89d5-6624f4a318ae' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6a7b9010-6174-4691-89d5-6624f4a318ae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c3e39f70-cfaa-4532-b290-06a66f983d8d' class='xr-var-data-in' type='checkbox'><label for='data-c3e39f70-cfaa-4532-b290-06a66f983d8d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cerebral_palsy</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-0c4cbfdc-60b2-4294-9519-063fd1837833' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0c4cbfdc-60b2-4294-9519-063fd1837833' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-25c560d3-e45e-4058-be0f-d61a0bc4e72b' class='xr-var-data-in' type='checkbox'><label for='data-25c560d3-e45e-4058-be0f-d61a0bc4e72b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>squamous_cell_carcinoma_of_skin</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-0b505ba2-27e6-4c66-94a2-f7793d81cd49' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0b505ba2-27e6-4c66-94a2-f7793d81cd49' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6cb1d539-4f9c-41a1-8fb3-7a40c3ea2b22' class='xr-var-data-in' type='checkbox'><label for='data-6cb1d539-4f9c-41a1-8fb3-7a40c3ea2b22' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hypogonadism</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-17d4c509-90fa-4f1b-8865-9902aa2245cc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-17d4c509-90fa-4f1b-8865-9902aa2245cc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cf0888ad-a8c7-4015-a601-47d439aba29f' class='xr-var-data-in' type='checkbox'><label for='data-cf0888ad-a8c7-4015-a601-47d439aba29f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>greater_trochanteric_pain_syndrome</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-a9a98a10-40f7-4744-8a83-159819c7de4e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a9a98a10-40f7-4744-8a83-159819c7de4e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cefca697-b4a2-40a5-bce4-547eb380e9a2' class='xr-var-data-in' type='checkbox'><label for='data-cefca697-b4a2-40a5-bce4-547eb380e9a2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>graves&#x27;_disease</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-53e1a5b4-fee6-4fc6-bb10-c875daa84e5e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-53e1a5b4-fee6-4fc6-bb10-c875daa84e5e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-669a03db-ac28-4230-a8e5-7f94bebe8748' class='xr-var-data-in' type='checkbox'><label for='data-669a03db-ac28-4230-a8e5-7f94bebe8748' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>malignant_neoplastic_disease</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-e5a129b1-3073-4aa1-a05f-cd0744f5f390' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e5a129b1-3073-4aa1-a05f-cd0744f5f390' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3a552d45-a666-4330-bd5a-1890b92255b0' class='xr-var-data-in' type='checkbox'><label for='data-3a552d45-a666-4330-bd5a-1890b92255b0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>moderate_persistent_asthma</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-9fc8882e-a810-42fb-9cdb-b1748a950154' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9fc8882e-a810-42fb-9cdb-b1748a950154' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-760d4b32-ef2a-4e96-928c-09e9329920f5' class='xr-var-data-in' type='checkbox'><label for='data-760d4b32-ef2a-4e96-928c-09e9329920f5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>steatosis_of_liver</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-876788d7-8ca6-4d90-9919-056efe64bc3d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-876788d7-8ca6-4d90-9919-056efe64bc3d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-52a79944-fbac-4bdb-933a-757b7fa28469' class='xr-var-data-in' type='checkbox'><label for='data-52a79944-fbac-4bdb-933a-757b7fa28469' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>obsessive-compulsive_disorder</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-a5d975ad-626b-49f2-982f-92043ac9e023' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a5d975ad-626b-49f2-982f-92043ac9e023' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a286244d-56ec-4afb-9821-ca69dfa79af2' class='xr-var-data-in' type='checkbox'><label for='data-a286244d-56ec-4afb-9821-ca69dfa79af2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mood_disorder</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-4aa9059e-f437-42be-841d-179bf341cf15' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4aa9059e-f437-42be-841d-179bf341cf15' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2b367e75-4e89-4ec4-821e-39437ee0b34d' class='xr-var-data-in' type='checkbox'><label for='data-2b367e75-4e89-4ec4-821e-39437ee0b34d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>degeneration_of_cervical_intervertebral_disc</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-88401351-5f53-48e8-90be-23cc39085d06' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-88401351-5f53-48e8-90be-23cc39085d06' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d1dff65e-6299-4cbb-a084-1c927e3e923a' class='xr-var-data-in' type='checkbox'><label for='data-d1dff65e-6299-4cbb-a084-1c927e3e923a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>corneal_abrasion</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-d62ec2ed-5901-45cd-9060-ce87d1840955' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d62ec2ed-5901-45cd-9060-ce87d1840955' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a6bcbbdd-2dcb-4bbf-8bd9-02c16033224f' class='xr-var-data-in' type='checkbox'><label for='data-a6bcbbdd-2dcb-4bbf-8bd9-02c16033224f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>anal_fissure</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-2e3cee52-384a-4e95-a072-68a2dc2c8a8e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2e3cee52-384a-4e95-a072-68a2dc2c8a8e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6ab2b4cc-a5bd-4fd6-ac53-30b5b8729fb6' class='xr-var-data-in' type='checkbox'><label for='data-6ab2b4cc-a5bd-4fd6-ac53-30b5b8729fb6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>heart_failure</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-cd62640a-22a1-41a5-91a6-e6ee13f8b7d9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cd62640a-22a1-41a5-91a6-e6ee13f8b7d9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c11e30c1-95a7-4547-a86d-80ccf52abd89' class='xr-var-data-in' type='checkbox'><label for='data-c11e30c1-95a7-4547-a86d-80ccf52abd89' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>adjustment_disorder_with_mixed_emotional_features</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-433164ad-967a-4cde-84b9-72a531afba37' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-433164ad-967a-4cde-84b9-72a531afba37' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a0a2de7b-bf61-4ead-8b6b-8f554d4f81ab' class='xr-var-data-in' type='checkbox'><label for='data-a0a2de7b-bf61-4ead-8b6b-8f554d4f81ab' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>febrile_convulsion</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-b011a1f5-69d4-4610-9ae7-0d9d6a72bf61' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b011a1f5-69d4-4610-9ae7-0d9d6a72bf61' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9f0964ff-968e-4113-89f7-d13f67f9acb0' class='xr-var-data-in' type='checkbox'><label for='data-9f0964ff-968e-4113-89f7-d13f67f9acb0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>degenerative_joint_disease_of_hand</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-4b794077-41d6-493b-b2ba-70dd0091641a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4b794077-41d6-493b-b2ba-70dd0091641a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-de643fd1-90d8-43dd-b7c9-e1006c24f1ca' class='xr-var-data-in' type='checkbox'><label for='data-de643fd1-90d8-43dd-b7c9-e1006c24f1ca' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>chronic_type_b_viral_hepatitis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-15bfc1a7-9a91-47e5-92d5-5cca7a51b06f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-15bfc1a7-9a91-47e5-92d5-5cca7a51b06f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f849a7b2-160e-40a1-b504-eca9772f6e63' class='xr-var-data-in' type='checkbox'><label for='data-f849a7b2-160e-40a1-b504-eca9772f6e63' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>blepharitis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-8b4909da-1058-4190-b5c1-555842b8df94' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8b4909da-1058-4190-b5c1-555842b8df94' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a24cf214-ba5c-4340-a6f1-80df74fb36ac' class='xr-var-data-in' type='checkbox'><label for='data-a24cf214-ba5c-4340-a6f1-80df74fb36ac' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cyst</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-e979bd82-c69e-4b1b-91e7-9d7379a9edb5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e979bd82-c69e-4b1b-91e7-9d7379a9edb5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5a3230de-dd28-4f86-adb3-b7b158f2e267' class='xr-var-data-in' type='checkbox'><label for='data-5a3230de-dd28-4f86-adb3-b7b158f2e267' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>heartburn</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-4c94cc9f-0d58-41f0-a9eb-d4830e7314ba' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4c94cc9f-0d58-41f0-a9eb-d4830e7314ba' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d731e180-d2b4-4266-b049-f228812ce946' class='xr-var-data-in' type='checkbox'><label for='data-d731e180-d2b4-4266-b049-f228812ce946' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>intellectual_disability</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-4d428821-559c-417a-8e5e-e1ade7f5953c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4d428821-559c-417a-8e5e-e1ade7f5953c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d967cfcb-ec7d-4f07-944e-67134a2ca7dd' class='xr-var-data-in' type='checkbox'><label for='data-d967cfcb-ec7d-4f07-944e-67134a2ca7dd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>exudative_age-related_macular_degeneration</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-340a16f0-e863-43bf-aa59-f60c421a6269' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-340a16f0-e863-43bf-aa59-f60c421a6269' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e53230c2-2daf-41df-8bf2-1c1bef8d1efc' class='xr-var-data-in' type='checkbox'><label for='data-e53230c2-2daf-41df-8bf2-1c1bef8d1efc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hypoxia</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-e512e149-9a20-4e97-87e7-95ae285e098b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e512e149-9a20-4e97-87e7-95ae285e098b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-02d3fddf-8cdf-42ad-8d8b-3459f0bfd6f2' class='xr-var-data-in' type='checkbox'><label for='data-02d3fddf-8cdf-42ad-8d8b-3459f0bfd6f2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>influenza</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-5c4ed8cd-8bf6-4033-bd2a-e8f9a7f9c5bd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5c4ed8cd-8bf6-4033-bd2a-e8f9a7f9c5bd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bee8a86e-15c2-433c-a7b9-a302ded8da19' class='xr-var-data-in' type='checkbox'><label for='data-bee8a86e-15c2-433c-a7b9-a302ded8da19' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>intervertebral_disc_prolapse</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-a92d089b-6255-4dbb-8df1-5bd48eef4e1e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a92d089b-6255-4dbb-8df1-5bd48eef4e1e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-933fa8c0-cdc6-48d2-8751-06d5b948e013' class='xr-var-data-in' type='checkbox'><label for='data-933fa8c0-cdc6-48d2-8751-06d5b948e013' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>swelling_of_first_metatarsophalangeal_joint_of_hallux</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-0ac93c32-d96a-4a13-a648-5f02fec2ea93' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0ac93c32-d96a-4a13-a648-5f02fec2ea93' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-da9a1745-5895-4f5e-ac27-c5e749b33628' class='xr-var-data-in' type='checkbox'><label for='data-da9a1745-5895-4f5e-ac27-c5e749b33628' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>genuine_stress_incontinence</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-4fa29703-a259-4569-8313-8be160e843c1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4fa29703-a259-4569-8313-8be160e843c1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dee4c5e4-e55b-472f-acaf-072d5993d410' class='xr-var-data-in' type='checkbox'><label for='data-dee4c5e4-e55b-472f-acaf-072d5993d410' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>opioid_dependence</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-e4309789-ff2e-46e5-b898-e4a7fe463e08' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e4309789-ff2e-46e5-b898-e4a7fe463e08' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8bdd6861-bd0d-408a-b76e-cd385931e4d6' class='xr-var-data-in' type='checkbox'><label for='data-8bdd6861-bd0d-408a-b76e-cd385931e4d6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>antepartum_hemorrhage</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-c7dc5444-3270-438f-81e0-fe43aa330c2e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c7dc5444-3270-438f-81e0-fe43aa330c2e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b51133b9-0898-4430-bc6d-f5731e9bdcf1' class='xr-var-data-in' type='checkbox'><label for='data-b51133b9-0898-4430-bc6d-f5731e9bdcf1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pneumothorax</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-1549d72c-2a7b-4422-9d8c-b1338d81ea07' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1549d72c-2a7b-4422-9d8c-b1338d81ea07' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-75407b05-e70a-4e43-aa14-3b20d71dee97' class='xr-var-data-in' type='checkbox'><label for='data-75407b05-e70a-4e43-aa14-3b20d71dee97' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>kidney_disease</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... True True</div><input id='attrs-57a17825-137d-4a19-978d-67cf8921f805' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-57a17825-137d-4a19-978d-67cf8921f805' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d2433dbe-877d-4b79-adb2-c0127716ad9e' class='xr-var-data-in' type='checkbox'><label for='data-d2433dbe-877d-4b79-adb2-c0127716ad9e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, True, True], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>atopic_conjunctivitis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-0b347091-6a83-4244-8965-e8e117410c80' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0b347091-6a83-4244-8965-e8e117410c80' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5a7cc1a0-65e9-49b5-9b4f-5e797d4f726b' class='xr-var-data-in' type='checkbox'><label for='data-5a7cc1a0-65e9-49b5-9b4f-5e797d4f726b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dermatophytosis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-758b8e28-4c4b-4229-b7dd-2faa3e9ab0f5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-758b8e28-4c4b-4229-b7dd-2faa3e9ab0f5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-01f3a1d3-6678-438d-aafe-698eb71eb6e9' class='xr-var-data-in' type='checkbox'><label for='data-01f3a1d3-6678-438d-aafe-698eb71eb6e9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>influenza_caused_by_influenza_a_virus</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-ff20db05-3daa-4b9f-90fe-b8dfcd8989d0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ff20db05-3daa-4b9f-90fe-b8dfcd8989d0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e3e29253-86ee-4679-833e-859c8a9bba1b' class='xr-var-data-in' type='checkbox'><label for='data-e3e29253-86ee-4679-833e-859c8a9bba1b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cystitis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-3a892714-c185-4965-85da-1e318e248d92' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3a892714-c185-4965-85da-1e318e248d92' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0e26cedb-2516-429a-8bfe-6e98c1512188' class='xr-var-data-in' type='checkbox'><label for='data-0e26cedb-2516-429a-8bfe-6e98c1512188' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>moderate_major_depression_single_episode</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-13b46c47-c47d-41ab-8db3-1365a373ba92' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-13b46c47-c47d-41ab-8db3-1365a373ba92' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-97c91ba7-fe3e-4252-a67f-108480974434' class='xr-var-data-in' type='checkbox'><label for='data-97c91ba7-fe3e-4252-a67f-108480974434' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>furuncle</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-bd2d36ef-0596-4c99-a46e-d7f26c536ee6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bd2d36ef-0596-4c99-a46e-d7f26c536ee6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a1e7c3b5-06fc-4ca9-a3c8-f5572dfbeada' class='xr-var-data-in' type='checkbox'><label for='data-a1e7c3b5-06fc-4ca9-a3c8-f5572dfbeada' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cannabis_abuse</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-b906dccd-9e38-477f-88c0-adf18acc0561' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b906dccd-9e38-477f-88c0-adf18acc0561' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-22b00ad4-a305-4576-b037-ae08c2d18a05' class='xr-var-data-in' type='checkbox'><label for='data-22b00ad4-a305-4576-b037-ae08c2d18a05' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>conductive_hearing_loss</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-b0cf3791-d229-4266-9e24-0b783597cbfb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b0cf3791-d229-4266-9e24-0b783597cbfb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4c055146-ab67-4743-925b-31827f6cbd57' class='xr-var-data-in' type='checkbox'><label for='data-4c055146-ab67-4743-925b-31827f6cbd57' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>neutropenic_disorder</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-576c8229-c0a1-4cdc-a037-94eba9eb42d2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-576c8229-c0a1-4cdc-a037-94eba9eb42d2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1220a181-57df-4676-8dfd-464f247dfbf8' class='xr-var-data-in' type='checkbox'><label for='data-1220a181-57df-4676-8dfd-464f247dfbf8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>injury_of_finger</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-33a13b3e-0e2c-4136-ad56-20d17691b9f7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-33a13b3e-0e2c-4136-ad56-20d17691b9f7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7fe0f75e-38e3-44f5-a8bf-a4718aed3eba' class='xr-var-data-in' type='checkbox'><label for='data-7fe0f75e-38e3-44f5-a8bf-a4718aed3eba' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>intestinal_obstruction</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-b393a3d8-212c-430e-8779-5b327d7ae330' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b393a3d8-212c-430e-8779-5b327d7ae330' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f55bd933-3b12-42f7-9d38-1247ca80cbfe' class='xr-var-data-in' type='checkbox'><label for='data-f55bd933-3b12-42f7-9d38-1247ca80cbfe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lymphadenopathy</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-f867e85c-4e8c-449f-bb82-6d7540c63b79' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f867e85c-4e8c-449f-bb82-6d7540c63b79' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1bc726a8-03a6-4145-b61e-a532bfc3e318' class='xr-var-data-in' type='checkbox'><label for='data-1bc726a8-03a6-4145-b61e-a532bfc3e318' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mass_of_pelvic_structure</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-b0aaaae7-2b51-49c8-91af-bc9c8fe047b6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b0aaaae7-2b51-49c8-91af-bc9c8fe047b6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6ddee51e-c435-4ebb-9de5-0d1192b66ab4' class='xr-var-data-in' type='checkbox'><label for='data-6ddee51e-c435-4ebb-9de5-0d1192b66ab4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>myelodysplastic_syndrome</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-5b1fed91-00e8-4005-b435-fba3e773c939' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5b1fed91-00e8-4005-b435-fba3e773c939' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-158620e1-0430-4c2a-813e-9bac381645ba' class='xr-var-data-in' type='checkbox'><label for='data-158620e1-0430-4c2a-813e-9bac381645ba' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>jaundice</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-f8bac823-af01-4267-8087-f0cf314f548f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f8bac823-af01-4267-8087-f0cf314f548f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7bdd6639-e5f1-4551-ad81-0fb58cadbe91' class='xr-var-data-in' type='checkbox'><label for='data-7bdd6639-e5f1-4551-ad81-0fb58cadbe91' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>severe_recurrent_major_depression_without_psychotic_features</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-f662193b-2bd8-4032-b649-c6644b7d86a2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f662193b-2bd8-4032-b649-c6644b7d86a2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0aa9c7fd-dd2a-47f5-af8f-df78fd102d5f' class='xr-var-data-in' type='checkbox'><label for='data-0aa9c7fd-dd2a-47f5-af8f-df78fd102d5f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>inflammation_of_cervix</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-eef87274-2f30-4458-9376-c157878b4af8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-eef87274-2f30-4458-9376-c157878b4af8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d669c9e5-540d-4f7e-b330-cc6a7d7536b6' class='xr-var-data-in' type='checkbox'><label for='data-d669c9e5-540d-4f7e-b330-cc6a7d7536b6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sickle_cell_trait</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-c11c840c-eb2b-48b0-a924-4efb6d72d7ea' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c11c840c-eb2b-48b0-a924-4efb6d72d7ea' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-19f493fb-1e19-4c15-8ec7-7a74fded056f' class='xr-var-data-in' type='checkbox'><label for='data-19f493fb-1e19-4c15-8ec7-7a74fded056f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>squamous_cell_carcinoma</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-0f3a5747-4c59-45bd-bfaf-0b64082933e7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0f3a5747-4c59-45bd-bfaf-0b64082933e7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-24883a49-6fce-4f22-beef-cc7c44586e03' class='xr-var-data-in' type='checkbox'><label for='data-24883a49-6fce-4f22-beef-cc7c44586e03' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>small_bowel_obstruction</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-3ef9b1b9-5e18-456f-8cc2-b6aba5344e38' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3ef9b1b9-5e18-456f-8cc2-b6aba5344e38' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f50cde44-f5e2-457f-b6ba-cfa164f3f960' class='xr-var-data-in' type='checkbox'><label for='data-f50cde44-f5e2-457f-b6ba-cfa164f3f960' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>compression_fracture</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-94ce6afb-d894-4286-8234-de296f63b6b6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-94ce6afb-d894-4286-8234-de296f63b6b6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9fd7ecd4-3a10-4cdc-8a79-83f09008c70a' class='xr-var-data-in' type='checkbox'><label for='data-9fd7ecd4-3a10-4cdc-8a79-83f09008c70a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>premenstrual_tension_syndrome</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-ea465a51-cbec-4f74-9209-abc0ba4c38ee' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ea465a51-cbec-4f74-9209-abc0ba4c38ee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c7352288-5607-4518-8dc3-6fe81c4c16e5' class='xr-var-data-in' type='checkbox'><label for='data-c7352288-5607-4518-8dc3-6fe81c4c16e5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fracture_of_proximal_end_of_femur</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-916bbbd3-2a51-4c5d-989a-86ca0618e80f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-916bbbd3-2a51-4c5d-989a-86ca0618e80f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-73a4fdff-5ac8-484b-93af-b0f1042b2998' class='xr-var-data-in' type='checkbox'><label for='data-73a4fdff-5ac8-484b-93af-b0f1042b2998' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>developmental_delay</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-fec26d3d-b14c-4352-ba5b-3b847cf1f55f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fec26d3d-b14c-4352-ba5b-3b847cf1f55f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-310c64c0-b3ea-4249-b481-be4364a87e83' class='xr-var-data-in' type='checkbox'><label for='data-310c64c0-b3ea-4249-b481-be4364a87e83' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>drug_abuse</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-c9d4e585-6299-4504-8e57-39bc556c47dc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c9d4e585-6299-4504-8e57-39bc556c47dc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-20658ee4-7c28-4c76-8914-6428c1f3a99b' class='xr-var-data-in' type='checkbox'><label for='data-20658ee4-7c28-4c76-8914-6428c1f3a99b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>left_lower_quadrant_pain</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-df9c8051-dc98-4dcf-a686-5a5757dee04c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-df9c8051-dc98-4dcf-a686-5a5757dee04c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3cc309e0-8134-4931-acb9-fc1b0afc9425' class='xr-var-data-in' type='checkbox'><label for='data-3cc309e0-8134-4931-acb9-fc1b0afc9425' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hordeolum</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-ad4538b2-31c6-4f71-9155-89a76a0f3762' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ad4538b2-31c6-4f71-9155-89a76a0f3762' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ec0fd0cc-343f-4f44-8ab9-b24d8afcf20c' class='xr-var-data-in' type='checkbox'><label for='data-ec0fd0cc-343f-4f44-8ab9-b24d8afcf20c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>disorder_of_lung</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... True True</div><input id='attrs-4de085c6-77eb-460e-9dd0-3edb9f5af2cd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4de085c6-77eb-460e-9dd0-3edb9f5af2cd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-41df8c34-e5ca-420d-9536-6d608b3bac45' class='xr-var-data-in' type='checkbox'><label for='data-41df8c34-e5ca-420d-9536-6d608b3bac45' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, True, True], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>migraine_without_aura</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-ca6eb524-0b7a-4834-9d4d-7cbfe8294c6d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ca6eb524-0b7a-4834-9d4d-7cbfe8294c6d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ee898830-1a82-40f7-b311-27a94717f039' class='xr-var-data-in' type='checkbox'><label for='data-ee898830-1a82-40f7-b311-27a94717f039' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>open-angle_glaucoma_-_borderline</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-ecbf117b-b9bf-4f7e-8fe5-3856b12fde3c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ecbf117b-b9bf-4f7e-8fe5-3856b12fde3c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d3cae8d3-6547-45c0-9eaf-b7b2646a01a6' class='xr-var-data-in' type='checkbox'><label for='data-d3cae8d3-6547-45c0-9eaf-b7b2646a01a6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>disorder_of_refraction</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-a26fa8ec-392c-4d4d-b58e-ef22e3d6a8fc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a26fa8ec-392c-4d4d-b58e-ef22e3d6a8fc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a18146c6-e9c9-4204-83d2-59e234bbd7c8' class='xr-var-data-in' type='checkbox'><label for='data-a18146c6-e9c9-4204-83d2-59e234bbd7c8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>acute_appendicitis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-7c5ad4e4-e308-4afa-805e-dbd1c091b61e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7c5ad4e4-e308-4afa-805e-dbd1c091b61e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-81b2d18e-5bb7-46ff-9d17-df2b5084a7ac' class='xr-var-data-in' type='checkbox'><label for='data-81b2d18e-5bb7-46ff-9d17-df2b5084a7ac' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>amyloidosis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-af1dbfdf-17f5-43a3-b701-c7e3a5825898' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-af1dbfdf-17f5-43a3-b701-c7e3a5825898' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1d575827-02ac-4b0c-8b18-3b13d09478e6' class='xr-var-data-in' type='checkbox'><label for='data-1d575827-02ac-4b0c-8b18-3b13d09478e6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hodgkin&#x27;s_disease</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-24085648-fcf1-4020-87e2-fe162324ae30' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-24085648-fcf1-4020-87e2-fe162324ae30' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-35e995bf-6705-4d98-abfc-6731ddc4ac1a' class='xr-var-data-in' type='checkbox'><label for='data-35e995bf-6705-4d98-abfc-6731ddc4ac1a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hydrocele_of_testis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-72b0a63e-2823-4d45-9f3e-3c3c3884cc94' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-72b0a63e-2823-4d45-9f3e-3c3c3884cc94' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7006f9bb-a829-4d3f-981f-b3ad77e764c6' class='xr-var-data-in' type='checkbox'><label for='data-7006f9bb-a829-4d3f-981f-b3ad77e764c6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cellulitis_of_foot</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-77920a5b-7e76-45a2-8aa9-259380670059' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-77920a5b-7e76-45a2-8aa9-259380670059' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8fdb0986-91c8-461f-be22-a3f50f1f9f1e' class='xr-var-data-in' type='checkbox'><label for='data-8fdb0986-91c8-461f-be22-a3f50f1f9f1e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pancytopenia</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-ce80d2c3-33b3-481e-8682-7b1a4566415a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ce80d2c3-33b3-481e-8682-7b1a4566415a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4e9c492f-22cc-421c-9a50-a2b4e047848a' class='xr-var-data-in' type='checkbox'><label for='data-4e9c492f-22cc-421c-9a50-a2b4e047848a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pain_in_elbow</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-71c522e0-66a8-48c5-abbf-301af9c533e0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-71c522e0-66a8-48c5-abbf-301af9c533e0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d225c3ae-1358-478e-a9e4-c8eba107ef25' class='xr-var-data-in' type='checkbox'><label for='data-d225c3ae-1358-478e-a9e4-c8eba107ef25' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>peripheral_nerve_disease</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-6601f2d7-32e9-4b91-81d4-79fe01ab5e27' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6601f2d7-32e9-4b91-81d4-79fe01ab5e27' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-540220e8-e517-4ce6-967b-f657a341b998' class='xr-var-data-in' type='checkbox'><label for='data-540220e8-e517-4ce6-967b-f657a341b998' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>eating_disorder</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-69193f8c-a55e-4b86-bbdb-ebd96034a2a1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-69193f8c-a55e-4b86-bbdb-ebd96034a2a1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d055ca3c-3d50-47a1-98cc-05df60403007' class='xr-var-data-in' type='checkbox'><label for='data-d055ca3c-3d50-47a1-98cc-05df60403007' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hernia_of_abdominal_cavity</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-9368483b-b843-4630-a631-0640a4ef2c19' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9368483b-b843-4630-a631-0640a4ef2c19' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-626cb6bc-27a3-4663-824f-7e45dab4773b' class='xr-var-data-in' type='checkbox'><label for='data-626cb6bc-27a3-4663-824f-7e45dab4773b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>secondary_malignant_neoplasm_of_brain</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-c8232d7f-583f-4d23-b0ca-9adaaa1ad69e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c8232d7f-583f-4d23-b0ca-9adaaa1ad69e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a207a384-7284-4904-b9eb-a71601346f7f' class='xr-var-data-in' type='checkbox'><label for='data-a207a384-7284-4904-b9eb-a71601346f7f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>late_effects_of_respiratory_tuberculosis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-b3d65621-7ffd-4a16-a8d3-28ba613b838c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b3d65621-7ffd-4a16-a8d3-28ba613b838c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ef1f8834-5d10-422f-be81-f8c376d078c5' class='xr-var-data-in' type='checkbox'><label for='data-ef1f8834-5d10-422f-be81-f8c376d078c5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>alcohol_intoxication</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-d403b690-c6dc-4b7d-a5fd-05a1acf4055b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d403b690-c6dc-4b7d-a5fd-05a1acf4055b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0bfbe238-55e9-4ef2-8518-e26e62b9a4e0' class='xr-var-data-in' type='checkbox'><label for='data-0bfbe238-55e9-4ef2-8518-e26e62b9a4e0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>abrasion</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-49ecb104-3f7e-4bae-9d59-e47c82cc6c1a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-49ecb104-3f7e-4bae-9d59-e47c82cc6c1a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-61b7d869-aad0-4d51-90da-310ffedc74e0' class='xr-var-data-in' type='checkbox'><label for='data-61b7d869-aad0-4d51-90da-310ffedc74e0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>breech_presentation</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-517d6851-eb92-4f74-8e42-d3eee353fb07' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-517d6851-eb92-4f74-8e42-d3eee353fb07' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8737ef6d-730b-41da-9d5f-c2ef392e06d2' class='xr-var-data-in' type='checkbox'><label for='data-8737ef6d-730b-41da-9d5f-c2ef392e06d2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>premature_labor</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-4cfa59da-ad5a-4078-a09e-a79a900d78db' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4cfa59da-ad5a-4078-a09e-a79a900d78db' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8528c1ed-db15-46c1-91f2-39a7b186cd2a' class='xr-var-data-in' type='checkbox'><label for='data-8528c1ed-db15-46c1-91f2-39a7b186cd2a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>raynaud&#x27;s_phenomenon</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-84fca2f0-6d4c-4a68-8b0b-85c42da807fc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-84fca2f0-6d4c-4a68-8b0b-85c42da807fc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0152f527-48e2-4d53-a251-348c1d80a2ae' class='xr-var-data-in' type='checkbox'><label for='data-0152f527-48e2-4d53-a251-348c1d80a2ae' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>posterior_rhinorrhea</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-e96fccdd-45dd-4f9f-8808-bef60b616e3b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e96fccdd-45dd-4f9f-8808-bef60b616e3b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6cd2bfa5-7320-47df-b70f-6532e785076c' class='xr-var-data-in' type='checkbox'><label for='data-6cd2bfa5-7320-47df-b70f-6532e785076c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>acute_gastritis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-98431e8c-716b-470e-9eaa-e559dee46557' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-98431e8c-716b-470e-9eaa-e559dee46557' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3b9ee776-44c1-4657-a9f9-c5c47b20e015' class='xr-var-data-in' type='checkbox'><label for='data-3b9ee776-44c1-4657-a9f9-c5c47b20e015' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>polyp_of_nasal_cavity</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-18ce274b-ac0d-45d6-8329-ab2888a626b4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-18ce274b-ac0d-45d6-8329-ab2888a626b4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7c4d1e44-445b-4de6-a5e4-a1f0e70c0d0a' class='xr-var-data-in' type='checkbox'><label for='data-7c4d1e44-445b-4de6-a5e4-a1f0e70c0d0a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>essential_tremor</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-71c058c0-369c-4a77-90df-89aae86bf6f9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-71c058c0-369c-4a77-90df-89aae86bf6f9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2b75b059-333a-4f69-b743-fd67e2df5829' class='xr-var-data-in' type='checkbox'><label for='data-2b75b059-333a-4f69-b743-fd67e2df5829' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>candidiasis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-9e434d51-21a4-45eb-9a1a-0b720fa47072' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9e434d51-21a4-45eb-9a1a-0b720fa47072' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-246969fa-224c-43e3-aa3f-2610ddc36442' class='xr-var-data-in' type='checkbox'><label for='data-246969fa-224c-43e3-aa3f-2610ddc36442' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ocular_hypertension</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-d7312b43-b2ca-45f7-9025-502a58a40794' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d7312b43-b2ca-45f7-9025-502a58a40794' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fdc2ff8d-171f-440a-aab0-01d21031ee93' class='xr-var-data-in' type='checkbox'><label for='data-fdc2ff8d-171f-440a-aab0-01d21031ee93' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>aortic_valve_disorder</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-f4df3d34-216a-40f3-b615-8c3237e4321e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f4df3d34-216a-40f3-b615-8c3237e4321e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-025c1736-2963-494d-8419-66ad3fe2b2d6' class='xr-var-data-in' type='checkbox'><label for='data-025c1736-2963-494d-8419-66ad3fe2b2d6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diaper_rash</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-864d9e26-1b23-40b0-b2e8-cb3005a91bc0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-864d9e26-1b23-40b0-b2e8-cb3005a91bc0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ac8af1fc-6b5f-44d5-a4e2-ac04ee6c288e' class='xr-var-data-in' type='checkbox'><label for='data-ac8af1fc-6b5f-44d5-a4e2-ac04ee6c288e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cholangitis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-fc4781f4-d5c3-4c23-9821-c3596da42504' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fc4781f4-d5c3-4c23-9821-c3596da42504' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-74b34a40-d39d-46f1-a14e-25b526b89e97' class='xr-var-data-in' type='checkbox'><label for='data-74b34a40-d39d-46f1-a14e-25b526b89e97' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>primary_malignant_neoplasm_of_lung</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-fa728297-afce-431e-a308-69f61e667394' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fa728297-afce-431e-a308-69f61e667394' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9606af73-106a-4275-8912-ffa11de10e01' class='xr-var-data-in' type='checkbox'><label for='data-9606af73-106a-4275-8912-ffa11de10e01' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>impingement_syndrome_of_shoulder_region</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-89355b9b-7950-4a5a-b531-ba3711c10444' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-89355b9b-7950-4a5a-b531-ba3711c10444' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-44683186-f594-44af-a99c-c1111ed0b2f7' class='xr-var-data-in' type='checkbox'><label for='data-44683186-f594-44af-a99c-c1111ed0b2f7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>idiopathic_urticaria</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-c5249882-ecba-4930-8468-6d4b6394d4ee' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c5249882-ecba-4930-8468-6d4b6394d4ee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9d96d51c-281e-416c-bdea-608634aed6f8' class='xr-var-data-in' type='checkbox'><label for='data-9d96d51c-281e-416c-bdea-608634aed6f8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>arthritis_of_knee</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-37bd87e9-4c9e-4485-9661-57f9f56a787b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-37bd87e9-4c9e-4485-9661-57f9f56a787b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-adcaf372-6e34-4a79-84eb-0d879ac5f00d' class='xr-var-data-in' type='checkbox'><label for='data-adcaf372-6e34-4a79-84eb-0d879ac5f00d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ulcer_of_duodenum</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-a9282020-2ff6-4ff8-806d-d6d990f26e7e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a9282020-2ff6-4ff8-806d-d6d990f26e7e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c328ffac-9a1e-48a4-9787-e7df577420cd' class='xr-var-data-in' type='checkbox'><label for='data-c328ffac-9a1e-48a4-9787-e7df577420cd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>peripheral_neuropathy_due_to_diabetes_mellitus</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-b97f2ce2-c2e1-48ad-a0f8-0cd426f8a13b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b97f2ce2-c2e1-48ad-a0f8-0cd426f8a13b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1f8ecfb7-008f-4a97-97a7-e9d212969d55' class='xr-var-data-in' type='checkbox'><label for='data-1f8ecfb7-008f-4a97-97a7-e9d212969d55' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hypervolemia</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False True</div><input id='attrs-856df585-503d-4dfa-b352-0e6bd7d04486' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-856df585-503d-4dfa-b352-0e6bd7d04486' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0121cdc2-e6cd-413f-b265-7760e4551e97' class='xr-var-data-in' type='checkbox'><label for='data-0121cdc2-e6cd-413f-b265-7760e4551e97' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, True], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cholecystitis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-72ed056d-28d8-4ea4-bb5f-28aa99f3ca0e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-72ed056d-28d8-4ea4-bb5f-28aa99f3ca0e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-21af68f1-64ee-4cde-aaeb-d11b6d994619' class='xr-var-data-in' type='checkbox'><label for='data-21af68f1-64ee-4cde-aaeb-d11b6d994619' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>deficiency_of_glucose-6-phosphate_dehydrogenase</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-b04770f3-ab70-48ff-862b-570f2b98473d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b04770f3-ab70-48ff-862b-570f2b98473d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-798ceda4-73a5-478e-9274-ad4995b5b029' class='xr-var-data-in' type='checkbox'><label for='data-798ceda4-73a5-478e-9274-ad4995b5b029' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>acute_pyelonephritis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-8325a6e4-e7d5-4102-b910-820a29961c6d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8325a6e4-e7d5-4102-b910-820a29961c6d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6081914d-5245-499e-9c5d-b7af4004b142' class='xr-var-data-in' type='checkbox'><label for='data-6081914d-5245-499e-9c5d-b7af4004b142' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>musculoskeletal_chest_pain</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-cf6dbd66-d1e5-4ae8-af24-557547ac9980' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cf6dbd66-d1e5-4ae8-af24-557547ac9980' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ca50300b-eb56-4877-a585-e9ab89de070b' class='xr-var-data-in' type='checkbox'><label for='data-ca50300b-eb56-4877-a585-e9ab89de070b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>melena</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-1c620925-a329-4bc5-a9c2-796a2a15163b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1c620925-a329-4bc5-a9c2-796a2a15163b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-110cab94-0f78-43ce-9f77-fdc89449a8a7' class='xr-var-data-in' type='checkbox'><label for='data-110cab94-0f78-43ce-9f77-fdc89449a8a7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cigarette_smoker</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... True True</div><input id='attrs-dc326358-80a3-4ff0-bdbf-195721985869' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dc326358-80a3-4ff0-bdbf-195721985869' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b596d4a4-baf5-47eb-b8b0-005eddd90915' class='xr-var-data-in' type='checkbox'><label for='data-b596d4a4-baf5-47eb-b8b0-005eddd90915' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, True, True], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>infectious_mononucleosis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-eb8eda63-9920-4a8c-a9bd-8e8bad8b2473' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-eb8eda63-9920-4a8c-a9bd-8e8bad8b2473' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0f0aeb77-4a0b-4556-8f3c-c481aa40e708' class='xr-var-data-in' type='checkbox'><label for='data-0f0aeb77-4a0b-4556-8f3c-c481aa40e708' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hypertensive_renal_failure</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-8ac815d2-0b3a-4be9-b0d3-af0b77e99e53' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8ac815d2-0b3a-4be9-b0d3-af0b77e99e53' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6f235c08-627b-433f-bcf0-10a375166896' class='xr-var-data-in' type='checkbox'><label for='data-6f235c08-627b-433f-bcf0-10a375166896' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cocaine_dependence</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-91280a61-67ea-42a7-ac2c-5340ec7d8fb1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-91280a61-67ea-42a7-ac2c-5340ec7d8fb1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-006f2685-13c1-477d-9252-1ddc9cea6522' class='xr-var-data-in' type='checkbox'><label for='data-006f2685-13c1-477d-9252-1ddc9cea6522' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>abdominal_aortic_aneurysm_without_rupture</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... True True</div><input id='attrs-7cd5b492-812d-43d9-8b81-ba5aab7f3783' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7cd5b492-812d-43d9-8b81-ba5aab7f3783' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6b118ae3-091f-40b6-bffd-98da9d241868' class='xr-var-data-in' type='checkbox'><label for='data-6b118ae3-091f-40b6-bffd-98da9d241868' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, True, True], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>right_bundle_branch_block</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-2e531a7e-845d-44f1-8081-eae610eb6308' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2e531a7e-845d-44f1-8081-eae610eb6308' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c443207b-16e3-48a7-a4b0-8194233a2d65' class='xr-var-data-in' type='checkbox'><label for='data-c443207b-16e3-48a7-a4b0-8194233a2d65' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>alcoholic_cirrhosis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-44f80ebc-93e2-41a6-b8f4-2e4f3f2af53b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-44f80ebc-93e2-41a6-b8f4-2e4f3f2af53b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-205d9ad4-a7fe-4221-86a1-8c6cc89de36c' class='xr-var-data-in' type='checkbox'><label for='data-205d9ad4-a7fe-4221-86a1-8c6cc89de36c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fibrosis_of_lung</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-748f3425-f161-4808-8d71-b30a486fdce3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-748f3425-f161-4808-8d71-b30a486fdce3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9db17262-aae0-4f73-a35a-543ca865ff1b' class='xr-var-data-in' type='checkbox'><label for='data-9db17262-aae0-4f73-a35a-543ca865ff1b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>neoplasm_of_uncertain_behavior_of_skin</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-9ef31ebb-2237-45a5-b9f1-a7db020c9bad' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9ef31ebb-2237-45a5-b9f1-a7db020c9bad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a6c1ebb5-0bf6-4650-ad46-280267b781ef' class='xr-var-data-in' type='checkbox'><label for='data-a6c1ebb5-0bf6-4650-ad46-280267b781ef' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>malignant_melanoma_of_skin</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-ac8a4f46-0c49-4ef9-962e-e5f6d7f4ce66' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ac8a4f46-0c49-4ef9-962e-e5f6d7f4ce66' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9c1ea805-9e36-4dfb-a5f2-58d819f86a3d' class='xr-var-data-in' type='checkbox'><label for='data-9c1ea805-9e36-4dfb-a5f2-58d819f86a3d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>urgent_desire_to_urinate</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-02ddde88-aeff-418a-817c-813a8a63ebeb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-02ddde88-aeff-418a-817c-813a8a63ebeb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c006fdeb-4a02-4410-9235-c4af2b40be5a' class='xr-var-data-in' type='checkbox'><label for='data-c006fdeb-4a02-4410-9235-c4af2b40be5a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>bursitis_of_hip</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-4ef5d009-0385-4363-9f80-647bfb27027a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4ef5d009-0385-4363-9f80-647bfb27027a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fdda14ce-4eae-4738-95c6-caba364fd8f2' class='xr-var-data-in' type='checkbox'><label for='data-fdda14ce-4eae-4738-95c6-caba364fd8f2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>chronic_alcoholism_in_remission</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-8b37a633-0e81-425c-8b47-4b53b9e03356' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8b37a633-0e81-425c-8b47-4b53b9e03356' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d7048340-7a28-4658-9d7d-7b9d106e71db' class='xr-var-data-in' type='checkbox'><label for='data-d7048340-7a28-4658-9d7d-7b9d106e71db' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>chronic_pancreatitis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-5771f71b-0063-4091-a4e1-9229030a46f5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5771f71b-0063-4091-a4e1-9229030a46f5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a459e012-f09e-4e34-82b3-3037130f7f88' class='xr-var-data-in' type='checkbox'><label for='data-a459e012-f09e-4e34-82b3-3037130f7f88' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>gastroparesis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-c557509e-fffb-4293-8dfb-205abf271594' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c557509e-fffb-4293-8dfb-205abf271594' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e7626aa7-7faf-44a5-9618-0542fd9f5665' class='xr-var-data-in' type='checkbox'><label for='data-e7626aa7-7faf-44a5-9618-0542fd9f5665' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ectopic_pregnancy</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-73c787ee-0825-42a3-bef2-7bc24cf0e3cb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-73c787ee-0825-42a3-bef2-7bc24cf0e3cb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-751fb827-dd96-4769-94a4-2517e663a9b5' class='xr-var-data-in' type='checkbox'><label for='data-751fb827-dd96-4769-94a4-2517e663a9b5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>muscle_weakness</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-7b8c4f89-09d2-4ac6-aa9e-baf3dc536cf0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7b8c4f89-09d2-4ac6-aa9e-baf3dc536cf0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-911989bb-6c16-4fd0-80d5-85bea3e9c3ea' class='xr-var-data-in' type='checkbox'><label for='data-911989bb-6c16-4fd0-80d5-85bea3e9c3ea' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>recurrent_major_depression</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-a7177ed2-efce-489b-bdc8-7afbfee3960b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a7177ed2-efce-489b-bdc8-7afbfee3960b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9729671b-ca27-4b2a-b464-1f78f0b56e01' class='xr-var-data-in' type='checkbox'><label for='data-9729671b-ca27-4b2a-b464-1f78f0b56e01' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pilonidal_cyst</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-e9e04325-d08e-4146-bb9a-dd64536edf2d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e9e04325-d08e-4146-bb9a-dd64536edf2d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9ba707fa-768d-45d6-ad06-2bce6d9cb20e' class='xr-var-data-in' type='checkbox'><label for='data-9ba707fa-768d-45d6-ad06-2bce6d9cb20e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pain_in_toe</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-dab8f45f-4866-45a7-ab51-7be3f1715e3e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dab8f45f-4866-45a7-ab51-7be3f1715e3e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ab6e92dc-d31f-4a4d-bd20-d974ed645a2f' class='xr-var-data-in' type='checkbox'><label for='data-ab6e92dc-d31f-4a4d-bd20-d974ed645a2f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pulmonary_tuberculosis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-a9dc8ab8-0bc4-4336-8dc3-bbb40baa02ac' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a9dc8ab8-0bc4-4336-8dc3-bbb40baa02ac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5b89092e-3813-499c-9dd6-7bd5798a02b9' class='xr-var-data-in' type='checkbox'><label for='data-5b89092e-3813-499c-9dd6-7bd5798a02b9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>celiac_disease</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-4a8ee282-350f-4faf-8706-178f3281f353' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4a8ee282-350f-4faf-8706-178f3281f353' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-217ce433-fc0b-453d-90aa-157b1c77b8e2' class='xr-var-data-in' type='checkbox'><label for='data-217ce433-fc0b-453d-90aa-157b1c77b8e2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cramp_in_lower_leg</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-bcb5aebb-b536-46c4-9653-2739f4704b38' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bcb5aebb-b536-46c4-9653-2739f4704b38' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f3e7f13f-688f-4284-9ffd-804cf004b8a1' class='xr-var-data-in' type='checkbox'><label for='data-f3e7f13f-688f-4284-9ffd-804cf004b8a1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>secondary_malignant_neoplasm_of_pleura</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-7b66ac2e-b0e0-4f4e-8f4e-322ba36140c9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7b66ac2e-b0e0-4f4e-8f4e-322ba36140c9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ee01a30a-f20c-418e-bc49-36df052a9926' class='xr-var-data-in' type='checkbox'><label for='data-ee01a30a-f20c-418e-bc49-36df052a9926' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fracture_of_hand</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-57f76441-2f82-4d8d-b248-666f099d2e11' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-57f76441-2f82-4d8d-b248-666f099d2e11' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-03668633-fc7a-4fa5-bb68-51668611f242' class='xr-var-data-in' type='checkbox'><label for='data-03668633-fc7a-4fa5-bb68-51668611f242' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cyst_of_breast</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-dd68227e-9326-4637-a798-d6ef83ee243c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dd68227e-9326-4637-a798-d6ef83ee243c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-afbec503-20a7-44fb-8fd4-2d9d6a2be3cd' class='xr-var-data-in' type='checkbox'><label for='data-afbec503-20a7-44fb-8fd4-2d9d6a2be3cd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nephrotic_syndrome</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-5aa7eba8-6135-4ad5-a326-41a383ab9421' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5aa7eba8-6135-4ad5-a326-41a383ab9421' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6a3ce783-5e92-4eb2-a67a-7bf52741cdbe' class='xr-var-data-in' type='checkbox'><label for='data-6a3ce783-5e92-4eb2-a67a-7bf52741cdbe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>polyp_of_nasal_sinus</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-bc6576d3-b79e-423a-a3cb-9c284cf9e291' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bc6576d3-b79e-423a-a3cb-9c284cf9e291' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-db3bbaa7-8579-4d1b-ad6d-3bbaf724a54e' class='xr-var-data-in' type='checkbox'><label for='data-db3bbaa7-8579-4d1b-ad6d-3bbaf724a54e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>chondromalacia_of_patella</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-0f52cd36-44f7-4402-9a34-16a7b3600edd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0f52cd36-44f7-4402-9a34-16a7b3600edd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dec3bd2e-b518-4523-9a25-1e7c0c2c1d39' class='xr-var-data-in' type='checkbox'><label for='data-dec3bd2e-b518-4523-9a25-1e7c0c2c1d39' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spinal_stenosis_in_cervical_region</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-344fc11a-0f58-4516-8b6b-b70fd2b4282d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-344fc11a-0f58-4516-8b6b-b70fd2b4282d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c3471c22-94a2-4f7d-b010-58828e87fddc' class='xr-var-data-in' type='checkbox'><label for='data-c3471c22-94a2-4f7d-b010-58828e87fddc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>disorder_of_artery</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... True True</div><input id='attrs-38822c9e-fc80-4dac-bda5-fe3766acc112' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-38822c9e-fc80-4dac-bda5-fe3766acc112' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c6040f83-9384-49a5-8486-38f16217c716' class='xr-var-data-in' type='checkbox'><label for='data-c6040f83-9384-49a5-8486-38f16217c716' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, True, True], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vitiligo</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-52de6462-106f-419e-91a9-a2ac395b8eba' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-52de6462-106f-419e-91a9-a2ac395b8eba' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fc3fefe2-144d-42e9-a7ff-f4b0f607e8cb' class='xr-var-data-in' type='checkbox'><label for='data-fc3fefe2-144d-42e9-a7ff-f4b0f607e8cb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>female_cystocele</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-1e8437bd-fc20-40b7-8e0f-37a526d82633' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1e8437bd-fc20-40b7-8e0f-37a526d82633' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-32656c8f-5b77-4f8e-a0aa-cc16e5971712' class='xr-var-data-in' type='checkbox'><label for='data-32656c8f-5b77-4f8e-a0aa-cc16e5971712' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dysphasia</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-2aa7e45a-c6f1-4cf2-a0a2-148353739362' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2aa7e45a-c6f1-4cf2-a0a2-148353739362' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-20e6bfbc-561c-4766-b9d4-a46e1954704e' class='xr-var-data-in' type='checkbox'><label for='data-20e6bfbc-561c-4766-b9d4-a46e1954704e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>retinal_disorder</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-358922ef-a83a-4e74-9650-172b1752d0c4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-358922ef-a83a-4e74-9650-172b1752d0c4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-660bc7ae-7c37-4e0c-9a41-d5094fcaab97' class='xr-var-data-in' type='checkbox'><label for='data-660bc7ae-7c37-4e0c-9a41-d5094fcaab97' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>epiretinal_membrane</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-6689785e-1474-44bf-b7eb-036ffb862898' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6689785e-1474-44bf-b7eb-036ffb862898' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-df5a86e7-22ac-42fd-8eb1-89daad9cefb1' class='xr-var-data-in' type='checkbox'><label for='data-df5a86e7-22ac-42fd-8eb1-89daad9cefb1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>recurrent_major_depression_in_partial_remission</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-7c6923ee-1f68-49af-8a29-e86639ad1c36' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7c6923ee-1f68-49af-8a29-e86639ad1c36' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a54f9559-c9ef-4a72-bd96-69adb4afe867' class='xr-var-data-in' type='checkbox'><label for='data-a54f9559-c9ef-4a72-bd96-69adb4afe867' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>infection_caused_by_trichomonas</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-65a02905-e152-4361-a0ce-8ade7ff8a37e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-65a02905-e152-4361-a0ce-8ade7ff8a37e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e4d78f17-0a53-404e-a7c8-28dad1af0277' class='xr-var-data-in' type='checkbox'><label for='data-e4d78f17-0a53-404e-a7c8-28dad1af0277' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>osteomyelitis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-4abe44ed-c3e7-4c2c-b463-29a374fc1011' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4abe44ed-c3e7-4c2c-b463-29a374fc1011' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bda65a1c-897b-456e-a92a-55f07d7f01b8' class='xr-var-data-in' type='checkbox'><label for='data-bda65a1c-897b-456e-a92a-55f07d7f01b8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>polyp_of_nasal_cavity_and/or_nasal_sinus</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-4a896e0a-71f0-484e-925b-b1827f314d56' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4a896e0a-71f0-484e-925b-b1827f314d56' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fca79128-39cf-4bd1-9f34-e7608f6a603c' class='xr-var-data-in' type='checkbox'><label for='data-fca79128-39cf-4bd1-9f34-e7608f6a603c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mass_of_neck</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-91fc9c75-b700-43a2-a3b2-60ce37ff0d01' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-91fc9c75-b700-43a2-a3b2-60ce37ff0d01' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6b470bae-0550-46d4-b389-132e49b9318c' class='xr-var-data-in' type='checkbox'><label for='data-6b470bae-0550-46d4-b389-132e49b9318c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>idiopathic_thrombocytopenic_purpura</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-c2f3c819-53a3-4aa6-9ce3-8c2e3d6ede8a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c2f3c819-53a3-4aa6-9ce3-8c2e3d6ede8a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f92019ed-2e7d-42f5-b848-d82f0b1b34ac' class='xr-var-data-in' type='checkbox'><label for='data-f92019ed-2e7d-42f5-b848-d82f0b1b34ac' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>complete_miscarriage</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-57817a90-ea5e-4616-aa24-5e67a364b880' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-57817a90-ea5e-4616-aa24-5e67a364b880' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-36d2ba11-9f33-4cf7-8134-48b60d4f4fde' class='xr-var-data-in' type='checkbox'><label for='data-36d2ba11-9f33-4cf7-8134-48b60d4f4fde' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>gastric_ulcer</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-4c0f9312-43c0-4cd7-a9b0-91ff72de4417' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4c0f9312-43c0-4cd7-a9b0-91ff72de4417' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-131055c7-24f4-4e10-acec-3d75be6d74a5' class='xr-var-data-in' type='checkbox'><label for='data-131055c7-24f4-4e10-acec-3d75be6d74a5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>papilloma_of_skin</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-dcd2528e-361c-4a7a-b65b-aaf3640a1c62' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dcd2528e-361c-4a7a-b65b-aaf3640a1c62' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-90ad0cf6-0d39-470c-9184-f7b906073967' class='xr-var-data-in' type='checkbox'><label for='data-90ad0cf6-0d39-470c-9184-f7b906073967' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fetal_or_neonatal_effect_of_breech_delivery_and_extraction</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-c03763bf-5826-4a0c-bfc9-1d3603d03a87' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c03763bf-5826-4a0c-bfc9-1d3603d03a87' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-92794cb3-1009-4fd7-b99e-392fab71e9a2' class='xr-var-data-in' type='checkbox'><label for='data-92794cb3-1009-4fd7-b99e-392fab71e9a2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>secondary_malignant_neoplastic_disease</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-f899dc44-c716-49fa-8f66-0056b9a85c11' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f899dc44-c716-49fa-8f66-0056b9a85c11' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b3693004-4252-49cb-b9dc-c4a3a47e3b82' class='xr-var-data-in' type='checkbox'><label for='data-b3693004-4252-49cb-b9dc-c4a3a47e3b82' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hypoxemia</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... True False</div><input id='attrs-2f485c25-a563-4f9d-9b32-c9115af8af43' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2f485c25-a563-4f9d-9b32-c9115af8af43' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e6046339-002b-4085-991f-f91f186a0f0d' class='xr-var-data-in' type='checkbox'><label for='data-e6046339-002b-4085-991f-f91f186a0f0d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, True, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>paraplegia</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-8a25d520-bd2b-4172-9d3f-d51e8de147a6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8a25d520-bd2b-4172-9d3f-d51e8de147a6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5d9b8ef5-328d-496f-b92e-ae280ad134c8' class='xr-var-data-in' type='checkbox'><label for='data-5d9b8ef5-328d-496f-b92e-ae280ad134c8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>perforation_of_tympanic_membrane</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-c8190046-37c2-4a54-851c-480f4e99bcfb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c8190046-37c2-4a54-851c-480f4e99bcfb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-21178b5c-ab36-4603-9f3b-d8d012826171' class='xr-var-data-in' type='checkbox'><label for='data-21178b5c-ab36-4603-9f3b-d8d012826171' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ventricular_tachycardia</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-2380448e-b178-4921-89ee-68f1863979d3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2380448e-b178-4921-89ee-68f1863979d3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-23f21be9-bdc8-4752-a1d6-0a099a227337' class='xr-var-data-in' type='checkbox'><label for='data-23f21be9-bdc8-4752-a1d6-0a099a227337' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mixed_incontinence</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-5a652dea-d0cb-48c6-a010-ff7fd7cff022' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5a652dea-d0cb-48c6-a010-ff7fd7cff022' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d08be3a2-bb6b-4a35-909f-187c47d91ff7' class='xr-var-data-in' type='checkbox'><label for='data-d08be3a2-bb6b-4a35-909f-187c47d91ff7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>disorder_of_eye_due_to_type_2_diabetes_mellitus</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-d50730dd-7b8e-4e54-83be-0831707f4275' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d50730dd-7b8e-4e54-83be-0831707f4275' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-35064ff2-66f9-4403-b56f-f142388ac72b' class='xr-var-data-in' type='checkbox'><label for='data-35064ff2-66f9-4403-b56f-f142388ac72b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>trigeminal_neuralgia</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-4bdb412d-33c5-42b0-adc8-1c9ea6f5efc1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4bdb412d-33c5-42b0-adc8-1c9ea6f5efc1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-00a4ab89-c857-40fd-bd98-dc0cfd108fab' class='xr-var-data-in' type='checkbox'><label for='data-00a4ab89-c857-40fd-bd98-dc0cfd108fab' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>retinal_detachment</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-0c2e7989-ff1c-4240-be95-afb344359420' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0c2e7989-ff1c-4240-be95-afb344359420' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-742ca8ba-6cee-4e94-8547-ec8bc32e5c14' class='xr-var-data-in' type='checkbox'><label for='data-742ca8ba-6cee-4e94-8547-ec8bc32e5c14' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>leukopenia</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-eb96c679-a13d-40ea-a617-4735abc78791' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-eb96c679-a13d-40ea-a617-4735abc78791' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e49a837e-adf3-4eb2-8055-436e2dee9086' class='xr-var-data-in' type='checkbox'><label for='data-e49a837e-adf3-4eb2-8055-436e2dee9086' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vitreous_hemorrhage</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-4d633b55-9df5-4274-9d19-29bfb377803b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4d633b55-9df5-4274-9d19-29bfb377803b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6e6b8e73-7c23-4b6b-afb7-837a7b80e644' class='xr-var-data-in' type='checkbox'><label for='data-6e6b8e73-7c23-4b6b-afb7-837a7b80e644' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ischemic_ulcer</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-2143c00e-4e6f-4e12-b693-bb1006bb82f2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2143c00e-4e6f-4e12-b693-bb1006bb82f2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ecb34452-fc3f-4d61-8199-27874be5db48' class='xr-var-data-in' type='checkbox'><label for='data-ecb34452-fc3f-4d61-8199-27874be5db48' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>intramural_leiomyoma_of_uterus</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-402ccba1-65bb-49f0-a2ca-93d3a3c75476' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-402ccba1-65bb-49f0-a2ca-93d3a3c75476' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-98f9e009-2fe5-4663-8277-c7a24dcf882f' class='xr-var-data-in' type='checkbox'><label for='data-98f9e009-2fe5-4663-8277-c7a24dcf882f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>viral_hepatitis_type_a</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-4184e304-faac-409c-99bc-32f0850c7352' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4184e304-faac-409c-99bc-32f0850c7352' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3b2f7e08-3322-48f5-a99c-d8e13f5f80c3' class='xr-var-data-in' type='checkbox'><label for='data-3b2f7e08-3322-48f5-a99c-d8e13f5f80c3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ménière&#x27;s_disease</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-948c40d4-ac12-4fbb-aa2c-83537b6f87b9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-948c40d4-ac12-4fbb-aa2c-83537b6f87b9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-da285fbd-bb76-46f2-91a6-7156f803fc16' class='xr-var-data-in' type='checkbox'><label for='data-da285fbd-bb76-46f2-91a6-7156f803fc16' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fracture_of_phalanx_of_hand</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-56ae5e63-e041-4ed4-b2b1-b77bdb9cc982' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-56ae5e63-e041-4ed4-b2b1-b77bdb9cc982' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0bbd6350-da91-4575-8fe1-57c063468a22' class='xr-var-data-in' type='checkbox'><label for='data-0bbd6350-da91-4575-8fe1-57c063468a22' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>muscle_atrophy</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-989cf75a-9fd5-4b48-b3e1-b8c19670a674' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-989cf75a-9fd5-4b48-b3e1-b8c19670a674' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c8149a15-29ba-4b29-bf9d-4d49b18a4de8' class='xr-var-data-in' type='checkbox'><label for='data-c8149a15-29ba-4b29-bf9d-4d49b18a4de8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>incontinence_of_feces</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-9f36c657-168f-497d-a5d3-3e5f2ad3122d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9f36c657-168f-497d-a5d3-3e5f2ad3122d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a975d0d7-a4fe-4146-bcdd-ed8e621f2e15' class='xr-var-data-in' type='checkbox'><label for='data-a975d0d7-a4fe-4146-bcdd-ed8e621f2e15' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mitral_valve_disorder</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-9dd44c63-1f3d-49f8-bea1-4b586cd4377d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9dd44c63-1f3d-49f8-bea1-4b586cd4377d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-61bad68c-707e-4018-95ab-c58d649c9476' class='xr-var-data-in' type='checkbox'><label for='data-61bad68c-707e-4018-95ab-c58d649c9476' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>atherosclerosis_of_arteries_of_the_extremities</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-4943fd4e-85be-4b9e-8bcc-cb7d120cda0b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4943fd4e-85be-4b9e-8bcc-cb7d120cda0b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e02f32db-04e1-4fee-bc2c-28ff91ee9027' class='xr-var-data-in' type='checkbox'><label for='data-e02f32db-04e1-4fee-bc2c-28ff91ee9027' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spondylosis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-4d5af764-633b-43db-8f45-29f42bfac328' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4d5af764-633b-43db-8f45-29f42bfac328' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f12fdad4-4345-4ade-aefc-19f9f52d5c7d' class='xr-var-data-in' type='checkbox'><label for='data-f12fdad4-4345-4ade-aefc-19f9f52d5c7d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pterygium</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-3691c8f0-0025-455e-963b-cbeffd73d521' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3691c8f0-0025-455e-963b-cbeffd73d521' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6ff08b1f-5d0e-42a3-8d7d-0b819aad42ff' class='xr-var-data-in' type='checkbox'><label for='data-6ff08b1f-5d0e-42a3-8d7d-0b819aad42ff' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ulnar_neuropathy</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-dd482a3c-f989-46db-9e82-f14890a6973f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dd482a3c-f989-46db-9e82-f14890a6973f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f424e67e-a887-4f2c-b7bc-01123c86fe30' class='xr-var-data-in' type='checkbox'><label for='data-f424e67e-a887-4f2c-b7bc-01123c86fe30' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lung_mass</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-59518105-9e05-4a87-bb5f-ceac7fce842a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-59518105-9e05-4a87-bb5f-ceac7fce842a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1d9d9340-4c35-480f-a50b-b953079a06e3' class='xr-var-data-in' type='checkbox'><label for='data-1d9d9340-4c35-480f-a50b-b953079a06e3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>foreign_body_in_respiratory_tract</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False True</div><input id='attrs-8e6a638e-ebb7-443a-8d32-6054df44b94e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8e6a638e-ebb7-443a-8d32-6054df44b94e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8a73e846-710e-41e3-8c4c-d2ccdce79587' class='xr-var-data-in' type='checkbox'><label for='data-8a73e846-710e-41e3-8c4c-d2ccdce79587' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, True], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>chronic_kidney_disease_stage_4</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... True True</div><input id='attrs-e4edb54b-2e36-422d-8c5c-d4297ef66ec4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e4edb54b-2e36-422d-8c5c-d4297ef66ec4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8fe3f9c8-ce61-42a4-8d60-3458e09fca17' class='xr-var-data-in' type='checkbox'><label for='data-8fe3f9c8-ce61-42a4-8d60-3458e09fca17' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, True, True], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>myocardial_ischemia</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-57d32cbe-7837-42ce-b22f-4c7f4f14e41c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-57d32cbe-7837-42ce-b22f-4c7f4f14e41c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b5382922-1c1a-4c9e-87f6-534dd1cdc6f0' class='xr-var-data-in' type='checkbox'><label for='data-b5382922-1c1a-4c9e-87f6-534dd1cdc6f0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>non-toxic_multinodular_goiter</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-cd842b93-39a9-4987-8e98-9b7d3d005de7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cd842b93-39a9-4987-8e98-9b7d3d005de7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6c250b51-c602-486e-a923-1e2f243f5794' class='xr-var-data-in' type='checkbox'><label for='data-6c250b51-c602-486e-a923-1e2f243f5794' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pain_in_finger</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-845964cc-fd20-4e0d-b50d-98b32dcc3b27' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-845964cc-fd20-4e0d-b50d-98b32dcc3b27' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0031f595-0146-4a44-9d7d-daf2bd983cf7' class='xr-var-data-in' type='checkbox'><label for='data-0031f595-0146-4a44-9d7d-daf2bd983cf7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cervical_spondylosis_without_myelopathy</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-53bf8344-381c-443a-815a-f06cd73a07f1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-53bf8344-381c-443a-815a-f06cd73a07f1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7604e7c1-a800-4f95-b7e6-de0b4a03459f' class='xr-var-data-in' type='checkbox'><label for='data-7604e7c1-a800-4f95-b7e6-de0b4a03459f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>body_mass_index_25-29_-_overweight</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-a872fd30-691d-4a4f-8b47-b260d9b00e09' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a872fd30-691d-4a4f-8b47-b260d9b00e09' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-04866979-b77a-4642-9418-2711e33e316e' class='xr-var-data-in' type='checkbox'><label for='data-04866979-b77a-4642-9418-2711e33e316e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>clouded_consciousness</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-53eff496-e5e6-4d19-82a1-0b9eab7dbd28' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-53eff496-e5e6-4d19-82a1-0b9eab7dbd28' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2acc4fd9-9e42-4c7d-a9d5-2e2f304c1033' class='xr-var-data-in' type='checkbox'><label for='data-2acc4fd9-9e42-4c7d-a9d5-2e2f304c1033' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mixed_conductive_and_sensorineural_hearing_loss</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-1cea9bf7-7c20-43ac-8694-c8be766e28b8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1cea9bf7-7c20-43ac-8694-c8be766e28b8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-977d7653-db6c-403a-bd0b-d85bc37ad3f5' class='xr-var-data-in' type='checkbox'><label for='data-977d7653-db6c-403a-bd0b-d85bc37ad3f5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tooth_eruption_disorder</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-0f8ff138-7d17-4742-b436-45b5638f3615' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0f8ff138-7d17-4742-b436-45b5638f3615' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-19989698-d76c-4ea5-abc4-5ac897381537' class='xr-var-data-in' type='checkbox'><label for='data-19989698-d76c-4ea5-abc4-5ac897381537' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hyperuricemia</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-7a7b62eb-22d6-409e-b22b-9102475b15d8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7a7b62eb-22d6-409e-b22b-9102475b15d8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5b1289c6-4ed1-45f1-9b5b-c2de29e3c094' class='xr-var-data-in' type='checkbox'><label for='data-5b1289c6-4ed1-45f1-9b5b-c2de29e3c094' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>closed_fracture_of_neck_of_femur</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-2bb5e96b-ac40-4247-84d0-945352eef807' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2bb5e96b-ac40-4247-84d0-945352eef807' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6b594f5a-ef1e-4296-a4b6-43dc1356a4ec' class='xr-var-data-in' type='checkbox'><label for='data-6b594f5a-ef1e-4296-a4b6-43dc1356a4ec' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>bipolar_ii_disorder</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-184e1eb3-d06f-46f2-8096-996d977b6cdc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-184e1eb3-d06f-46f2-8096-996d977b6cdc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0b23c6c4-7f3d-481b-8cc9-df6a1c4a474f' class='xr-var-data-in' type='checkbox'><label for='data-0b23c6c4-7f3d-481b-8cc9-df6a1c4a474f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>disturbance_in_sleep_behavior</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-a5329e56-60de-446b-8152-665884206ece' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a5329e56-60de-446b-8152-665884206ece' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d2a820fb-8c4d-43e7-9cea-dedc41b8e118' class='xr-var-data-in' type='checkbox'><label for='data-d2a820fb-8c4d-43e7-9cea-dedc41b8e118' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>relationship_problems</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-69049b1c-8d1e-40b6-bcf7-abaf9228af70' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-69049b1c-8d1e-40b6-bcf7-abaf9228af70' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6b88e6a2-fe07-468e-b395-a1e0d7902053' class='xr-var-data-in' type='checkbox'><label for='data-6b88e6a2-fe07-468e-b395-a1e0d7902053' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sprain_of_wrist</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-c4a75e7f-9023-4e71-9f37-f6b29a966ed6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c4a75e7f-9023-4e71-9f37-f6b29a966ed6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-418c738e-85f5-4209-a4b1-a083a90cb085' class='xr-var-data-in' type='checkbox'><label for='data-418c738e-85f5-4209-a4b1-a083a90cb085' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>personality_disorder</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-fe91e03f-dfc8-4338-8297-028a7d9bd49a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fe91e03f-dfc8-4338-8297-028a7d9bd49a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d511ed08-e9ae-45c9-ad5e-b2634f3fd0ef' class='xr-var-data-in' type='checkbox'><label for='data-d511ed08-e9ae-45c9-ad5e-b2634f3fd0ef' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>external_hemorrhoids</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-27112022-c703-4882-96cd-129e29df4f24' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-27112022-c703-4882-96cd-129e29df4f24' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1450f5ea-a485-42ad-ba8f-b0eb7e35d2fe' class='xr-var-data-in' type='checkbox'><label for='data-1450f5ea-a485-42ad-ba8f-b0eb7e35d2fe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>abnormal_vision</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-41dd4eac-2c59-4664-ad9e-a426b31cbb5e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-41dd4eac-2c59-4664-ad9e-a426b31cbb5e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-53bfc3e6-3a3c-4c2c-b46c-a6d6db8d3dea' class='xr-var-data-in' type='checkbox'><label for='data-53bfc3e6-3a3c-4c2c-b46c-a6d6db8d3dea' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hyperprolactinemia</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-ce828ec0-2fb8-4013-8fb1-6b0add3b177d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ce828ec0-2fb8-4013-8fb1-6b0add3b177d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1d698088-ed24-41c5-a9ad-e68fc9bd069a' class='xr-var-data-in' type='checkbox'><label for='data-1d698088-ed24-41c5-a9ad-e68fc9bd069a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hemochromatosis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-145c4895-e3a6-4b63-be58-c8b7c8f4a229' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-145c4895-e3a6-4b63-be58-c8b7c8f4a229' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a92ba455-6d60-4842-863d-44302e6a8ba0' class='xr-var-data-in' type='checkbox'><label for='data-a92ba455-6d60-4842-863d-44302e6a8ba0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lumbosacral_radiculopathy</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-65c6ca66-e692-41ea-8da5-751d7824359b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-65c6ca66-e692-41ea-8da5-751d7824359b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6380a58a-5667-451b-8b19-8b56f0bee097' class='xr-var-data-in' type='checkbox'><label for='data-6380a58a-5667-451b-8b19-8b56f0bee097' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>heart_valve_disorder</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-464f880d-2cf3-486b-9ab3-9fa48a4269cd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-464f880d-2cf3-486b-9ab3-9fa48a4269cd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-be88fa72-d788-47a2-bc62-bd3b8dbabafe' class='xr-var-data-in' type='checkbox'><label for='data-be88fa72-d788-47a2-bc62-bd3b8dbabafe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cardiac_arrest</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False True</div><input id='attrs-2e71a6dc-82b7-463b-a46b-cbe9e7d99d64' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2e71a6dc-82b7-463b-a46b-cbe9e7d99d64' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7f704afc-f34e-4b91-9c25-becfe7479844' class='xr-var-data-in' type='checkbox'><label for='data-7f704afc-f34e-4b91-9c25-becfe7479844' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, True], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>infection_caused_by_molluscum_contagiosum</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-ad09c31d-8d1e-492f-8d5a-8da78d7acc6e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ad09c31d-8d1e-492f-8d5a-8da78d7acc6e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1f0564cc-f91f-4444-aa9f-071fcbe58aef' class='xr-var-data-in' type='checkbox'><label for='data-1f0564cc-f91f-4444-aa9f-071fcbe58aef' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>chronic_kidney_disease_stage_2</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... True True</div><input id='attrs-351b1364-0f19-44d5-bc7e-d23c9da69e5d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-351b1364-0f19-44d5-bc7e-d23c9da69e5d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-417cadd0-11bc-4d30-873f-4570ff014f8a' class='xr-var-data-in' type='checkbox'><label for='data-417cadd0-11bc-4d30-873f-4570ff014f8a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, True, True], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>secondary_malignant_neoplasm_of_peritoneum</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-5bbbe91a-8808-4c8c-a184-8844c30b525f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5bbbe91a-8808-4c8c-a184-8844c30b525f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-33eb3f5b-8efc-4373-8d38-a475afa10732' class='xr-var-data-in' type='checkbox'><label for='data-33eb3f5b-8efc-4373-8d38-a475afa10732' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>thoracic_back_pain</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-6035fe35-fd3f-4583-94eb-da73bce0d6c6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6035fe35-fd3f-4583-94eb-da73bce0d6c6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6b76a707-6799-4a75-863a-3dbe81020cbf' class='xr-var-data-in' type='checkbox'><label for='data-6b76a707-6799-4a75-863a-3dbe81020cbf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>blood_in_urine</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-695914d8-9738-440e-866d-3bf65b745d37' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-695914d8-9738-440e-866d-3bf65b745d37' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-85a6a334-5f72-4a5a-8615-baa0f506dd7d' class='xr-var-data-in' type='checkbox'><label for='data-85a6a334-5f72-4a5a-8615-baa0f506dd7d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>adhesive_capsulitis_of_shoulder</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-21ed3939-960e-44a5-8e08-4a2cc4673fa0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-21ed3939-960e-44a5-8e08-4a2cc4673fa0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0dc68d0f-392e-46fb-b5cd-c35212e6283e' class='xr-var-data-in' type='checkbox'><label for='data-0dc68d0f-392e-46fb-b5cd-c35212e6283e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diplopia</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-d524a40e-2eea-4434-bc05-cf1c713f193a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d524a40e-2eea-4434-bc05-cf1c713f193a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-673cd34f-3c2d-4767-a2a0-c55fa8fc4f74' class='xr-var-data-in' type='checkbox'><label for='data-673cd34f-3c2d-4767-a2a0-c55fa8fc4f74' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sjögren&#x27;s_syndrome</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-8e5ad038-16cd-462f-9a45-e07adbb252e6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8e5ad038-16cd-462f-9a45-e07adbb252e6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ef91d1ac-0257-47c0-b5fb-440abea423b4' class='xr-var-data-in' type='checkbox'><label for='data-ef91d1ac-0257-47c0-b5fb-440abea423b4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ureteric_stone</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... True True</div><input id='attrs-792e986a-e37f-465b-a73b-138d70214942' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-792e986a-e37f-465b-a73b-138d70214942' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-13600cbe-96aa-4a43-b720-d4cdeb45bdfe' class='xr-var-data-in' type='checkbox'><label for='data-13600cbe-96aa-4a43-b720-d4cdeb45bdfe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, True, True], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>bronchospasm</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-d2a72873-0532-4c33-b432-3898f1c2a82b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d2a72873-0532-4c33-b432-3898f1c2a82b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cf38cdf9-f663-4d37-aa24-6eab5bbc8673' class='xr-var-data-in' type='checkbox'><label for='data-cf38cdf9-f663-4d37-aa24-6eab5bbc8673' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>chronic_fatigue_syndrome</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-18a91ff2-7279-463a-9cb5-8a96a5629a84' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-18a91ff2-7279-463a-9cb5-8a96a5629a84' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6a90c827-cffa-409f-aef9-dd063c0e4a98' class='xr-var-data-in' type='checkbox'><label for='data-6a90c827-cffa-409f-aef9-dd063c0e4a98' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cannabis_dependence</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-267b8412-df00-4d79-91ec-559f0db2d372' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-267b8412-df00-4d79-91ec-559f0db2d372' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-04220768-97dd-49ad-96da-088e11956d36' class='xr-var-data-in' type='checkbox'><label for='data-04220768-97dd-49ad-96da-088e11956d36' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>neck_sprain</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-ba891af9-4161-43b3-b779-8c27ed18b508' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ba891af9-4161-43b3-b779-8c27ed18b508' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4a067e4f-9651-48ac-8995-a689e5f9f5ba' class='xr-var-data-in' type='checkbox'><label for='data-4a067e4f-9651-48ac-8995-a689e5f9f5ba' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>multinodular_goiter</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-46188bdb-e263-468f-92e0-349f608a8ccf' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-46188bdb-e263-468f-92e0-349f608a8ccf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-40af6159-e3a7-49a9-b7d0-bd8894c817cc' class='xr-var-data-in' type='checkbox'><label for='data-40af6159-e3a7-49a9-b7d0-bd8894c817cc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ptosis_of_eyelid</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-245f945c-fcfb-474e-8ceb-7b5bc7aa54e3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-245f945c-fcfb-474e-8ceb-7b5bc7aa54e3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c3837994-dc68-4423-8b67-088f054e882a' class='xr-var-data-in' type='checkbox'><label for='data-c3837994-dc68-4423-8b67-088f054e882a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>failure_to_thrive</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-42215227-90a9-49f6-9806-9b555ec58f61' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-42215227-90a9-49f6-9806-9b555ec58f61' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2dd9259b-a160-41e0-82d0-09038bc1b0bd' class='xr-var-data-in' type='checkbox'><label for='data-2dd9259b-a160-41e0-82d0-09038bc1b0bd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>torticollis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-21c607e4-8477-4e42-adce-98e0d1969ef3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-21c607e4-8477-4e42-adce-98e0d1969ef3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f43c517b-82a6-412a-8d97-633354e582f0' class='xr-var-data-in' type='checkbox'><label for='data-f43c517b-82a6-412a-8d97-633354e582f0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>acute_bronchiolitis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-264196fb-c35e-4b3a-90fe-c405c184ce12' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-264196fb-c35e-4b3a-90fe-c405c184ce12' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9fe804b4-b392-4f0e-b626-5afbb5eaa01b' class='xr-var-data-in' type='checkbox'><label for='data-9fe804b4-b392-4f0e-b626-5afbb5eaa01b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>viral_exanthem</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-1acbfe3a-132f-4cd0-b447-8c7b1a33bdb6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1acbfe3a-132f-4cd0-b447-8c7b1a33bdb6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3b2f2868-8d8f-4d34-9bd3-3d6a1efa3903' class='xr-var-data-in' type='checkbox'><label for='data-3b2f2868-8d8f-4d34-9bd3-3d6a1efa3903' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>talipes_planus</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-992a84ce-f7ea-4171-bb84-ae015cfc71e8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-992a84ce-f7ea-4171-bb84-ae015cfc71e8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-969e893b-2930-40af-b97b-917468a5fe6d' class='xr-var-data-in' type='checkbox'><label for='data-969e893b-2930-40af-b97b-917468a5fe6d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>idiopathic_peripheral_neuropathy</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-377b92ca-28e7-47cb-a2a7-8ad4b763334d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-377b92ca-28e7-47cb-a2a7-8ad4b763334d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-64cd9a3a-07a8-4fd4-b57b-b8bf2bb8b59e' class='xr-var-data-in' type='checkbox'><label for='data-64cd9a3a-07a8-4fd4-b57b-b8bf2bb8b59e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>foreign_body_in_pharynx</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False True</div><input id='attrs-d83bc85f-3fa0-4d66-acfb-852ae32028a8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d83bc85f-3fa0-4d66-acfb-852ae32028a8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-02ca9647-a446-4686-b319-2a3035da2cdc' class='xr-var-data-in' type='checkbox'><label for='data-02ca9647-a446-4686-b319-2a3035da2cdc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, True], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>jaw_pain</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-73dd153f-ed67-4e6f-ba4e-6b67cd8f8715' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-73dd153f-ed67-4e6f-ba4e-6b67cd8f8715' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c6c1f58f-7d9a-4013-84f2-ab03329f05f5' class='xr-var-data-in' type='checkbox'><label for='data-c6c1f58f-7d9a-4013-84f2-ab03329f05f5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>renal_impairment</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... True True</div><input id='attrs-75a03846-cde5-442b-9ba2-66ff4feb75a4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-75a03846-cde5-442b-9ba2-66ff4feb75a4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cdfde704-65bc-4fca-97fb-1de1cd85fea2' class='xr-var-data-in' type='checkbox'><label for='data-cdfde704-65bc-4fca-97fb-1de1cd85fea2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, True, True], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ataxia</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-c4929525-1452-4545-bca0-fb7b69df3a55' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c4929525-1452-4545-bca0-fb7b69df3a55' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4dedfd49-8b38-42a1-af21-d57c1e46b6cc' class='xr-var-data-in' type='checkbox'><label for='data-4dedfd49-8b38-42a1-af21-d57c1e46b6cc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>age-related_macular_degeneration</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-1e401bf0-2a4b-48c7-b8a4-2e30f5a71369' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1e401bf0-2a4b-48c7-b8a4-2e30f5a71369' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-be24b78c-8eee-4054-8ec0-d954b0402213' class='xr-var-data-in' type='checkbox'><label for='data-be24b78c-8eee-4054-8ec0-d954b0402213' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>uterine_prolapse</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-20e93ce1-77ee-4889-98e4-14e6b0c01a90' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-20e93ce1-77ee-4889-98e4-14e6b0c01a90' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bbe62ee8-c69f-4d83-9fea-37d3be175be4' class='xr-var-data-in' type='checkbox'><label for='data-bbe62ee8-c69f-4d83-9fea-37d3be175be4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>renal_mass</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-6b9d200f-30d7-446d-b1c5-b324b3f46219' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6b9d200f-30d7-446d-b1c5-b324b3f46219' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c7c39194-3874-461a-a08a-051bb5e43f9a' class='xr-var-data-in' type='checkbox'><label for='data-c7c39194-3874-461a-a08a-051bb5e43f9a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pneumonitis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-d1941238-021a-4f13-96c9-3448019fe7a7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d1941238-021a-4f13-96c9-3448019fe7a7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-45fcbdc2-f0b8-4cd7-9d4a-f775e22fc4ba' class='xr-var-data-in' type='checkbox'><label for='data-45fcbdc2-f0b8-4cd7-9d4a-f775e22fc4ba' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>coordination_problem</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-68a3aa17-f40f-4702-bce8-d05745147aec' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-68a3aa17-f40f-4702-bce8-d05745147aec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-42f1d7de-0c64-4e82-b7af-b7c54a6ecf75' class='xr-var-data-in' type='checkbox'><label for='data-42f1d7de-0c64-4e82-b7af-b7c54a6ecf75' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>blindness_-_both_eyes</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-5d9a54c8-6388-4de4-972e-f68735c3abc9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5d9a54c8-6388-4de4-972e-f68735c3abc9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a50a826d-713c-484c-a946-f815c447c7ef' class='xr-var-data-in' type='checkbox'><label for='data-a50a826d-713c-484c-a946-f815c447c7ef' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>primary_hyperparathyroidism</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-7f5ee981-7195-4a9a-9f67-9b8d19fdae14' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7f5ee981-7195-4a9a-9f67-9b8d19fdae14' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3bbe2d97-a945-46dc-aa9e-3762bd723042' class='xr-var-data-in' type='checkbox'><label for='data-3bbe2d97-a945-46dc-aa9e-3762bd723042' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>musculoskeletal_pain</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-be0c52f9-c7d1-4f4a-be8c-726e0a8459a3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-be0c52f9-c7d1-4f4a-be8c-726e0a8459a3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-22933bcb-bf65-4f64-8d62-3cf3de99fb34' class='xr-var-data-in' type='checkbox'><label for='data-22933bcb-bf65-4f64-8d62-3cf3de99fb34' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mycosis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-f0cc500f-7bde-4216-8087-5aece60eb182' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f0cc500f-7bde-4216-8087-5aece60eb182' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0eb8df0d-6e0e-4b01-9ae8-8fda7892a42a' class='xr-var-data-in' type='checkbox'><label for='data-0eb8df0d-6e0e-4b01-9ae8-8fda7892a42a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>primigravida</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-f9bb8262-3c76-4f43-970f-c1727dd08703' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f9bb8262-3c76-4f43-970f-c1727dd08703' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b02623e2-2fb6-460b-94af-ebd9c2ddaf55' class='xr-var-data-in' type='checkbox'><label for='data-b02623e2-2fb6-460b-94af-ebd9c2ddaf55' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>urethral_stricture</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-e87e7dee-7b0f-4cec-8440-0f4e0485f009' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e87e7dee-7b0f-4cec-8440-0f4e0485f009' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e97b405a-4f8e-4763-bdda-e0ab486fe58a' class='xr-var-data-in' type='checkbox'><label for='data-e97b405a-4f8e-4763-bdda-e0ab486fe58a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>leukocytosis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-b49cb975-faa7-4c9e-b980-06ce33f63148' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b49cb975-faa7-4c9e-b980-06ce33f63148' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b7472ce2-78fe-4e52-9f01-4170e88b43c0' class='xr-var-data-in' type='checkbox'><label for='data-b7472ce2-78fe-4e52-9f01-4170e88b43c0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ventricular_premature_complex</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-3852be33-e44d-4251-8419-c401fe648617' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3852be33-e44d-4251-8419-c401fe648617' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3dcc8e17-db36-403c-ae21-b2692acb4e32' class='xr-var-data-in' type='checkbox'><label for='data-3dcc8e17-db36-403c-ae21-b2692acb4e32' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ulcer_of_foot_due_to_diabetes_mellitus</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-13a2186d-046d-4522-8112-334d81dc82cb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-13a2186d-046d-4522-8112-334d81dc82cb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-56e76a25-34a5-474b-94cf-53b879271f23' class='xr-var-data-in' type='checkbox'><label for='data-56e76a25-34a5-474b-94cf-53b879271f23' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>chronic_headache_disorder</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-29bcac4d-9a3e-4d01-9e78-ebdfd37cee03' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-29bcac4d-9a3e-4d01-9e78-ebdfd37cee03' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-12b721e3-0bda-450c-8e97-c9bc065e56cb' class='xr-var-data-in' type='checkbox'><label for='data-12b721e3-0bda-450c-8e97-c9bc065e56cb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hemangioma</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-8b0c0b99-05d6-4a38-963e-863d990e1335' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8b0c0b99-05d6-4a38-963e-863d990e1335' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e4fcccbf-8289-49ca-b0ae-8bae2c577a6c' class='xr-var-data-in' type='checkbox'><label for='data-e4fcccbf-8289-49ca-b0ae-8bae2c577a6c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lymphedema</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-77e13566-39f2-4ebe-b5af-2e04024a0bc1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-77e13566-39f2-4ebe-b5af-2e04024a0bc1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1c39956a-f6e8-49fa-a686-bb52e3a84153' class='xr-var-data-in' type='checkbox'><label for='data-1c39956a-f6e8-49fa-a686-bb52e3a84153' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>postmenopausal_state</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-be86f6a3-320e-4659-ba5d-29d421843b4d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-be86f6a3-320e-4659-ba5d-29d421843b4d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-28b1fdb6-dd04-4dd4-9662-5440a0b9c601' class='xr-var-data-in' type='checkbox'><label for='data-28b1fdb6-dd04-4dd4-9662-5440a0b9c601' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>chronic_ulcer_of_skin</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-65ae0c3e-3de2-48d2-953d-087a99f70aa7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-65ae0c3e-3de2-48d2-953d-087a99f70aa7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5d522f0b-3de6-41c0-af93-75b88687edcb' class='xr-var-data-in' type='checkbox'><label for='data-5d522f0b-3de6-41c0-af93-75b88687edcb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>left_heart_failure</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-2852a2ba-b253-4fff-8553-8273886503df' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2852a2ba-b253-4fff-8553-8273886503df' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-957bace5-1c89-4a75-afea-56328b0c72fa' class='xr-var-data-in' type='checkbox'><label for='data-957bace5-1c89-4a75-afea-56328b0c72fa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>excessive_and_frequent_menstruation</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-d48a93f6-fd87-4ad0-a311-5616b0ac3639' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d48a93f6-fd87-4ad0-a311-5616b0ac3639' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-50244de4-8a4b-4100-b189-59390f05a2d7' class='xr-var-data-in' type='checkbox'><label for='data-50244de4-8a4b-4100-b189-59390f05a2d7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>thrombocytosis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-183db124-8494-49d1-a6d6-4a7480986c93' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-183db124-8494-49d1-a6d6-4a7480986c93' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-476a4e9b-87c2-4c7b-a654-7e5565a6f754' class='xr-var-data-in' type='checkbox'><label for='data-476a4e9b-87c2-4c7b-a654-7e5565a6f754' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>disorder_of_liver</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-9bc44356-b88e-486d-b3c3-309c067c56f7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9bc44356-b88e-486d-b3c3-309c067c56f7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eab6e7d6-a18e-444c-8bc7-f3ba02073b76' class='xr-var-data-in' type='checkbox'><label for='data-eab6e7d6-a18e-444c-8bc7-f3ba02073b76' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>disorder_of_carotid_artery</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-7b083c90-0ee2-4f64-ac3f-1e33e6952b67' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7b083c90-0ee2-4f64-ac3f-1e33e6952b67' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-acd97b98-6c06-448a-945a-d0c90e8a0fd9' class='xr-var-data-in' type='checkbox'><label for='data-acd97b98-6c06-448a-945a-d0c90e8a0fd9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>altered_bowel_function</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-a703fdd8-ba7d-4815-9f2c-d90ebaa19cc9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a703fdd8-ba7d-4815-9f2c-d90ebaa19cc9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3f2d1c3c-0846-4380-a5d9-03dacf917354' class='xr-var-data-in' type='checkbox'><label for='data-3f2d1c3c-0846-4380-a5d9-03dacf917354' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>abscess_of_foot</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-1b720e30-e6a9-462c-aa1b-4caef82264d3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1b720e30-e6a9-462c-aa1b-4caef82264d3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7ac7a7d7-da13-43d3-94f8-4708532b8e74' class='xr-var-data-in' type='checkbox'><label for='data-7ac7a7d7-da13-43d3-94f8-4708532b8e74' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>malignant_tumor_of_head_and/or_neck</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-57aa57c0-5f40-413e-9428-931c852d75e6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-57aa57c0-5f40-413e-9428-931c852d75e6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1433e3ee-98af-4d69-9100-eead044eacbc' class='xr-var-data-in' type='checkbox'><label for='data-1433e3ee-98af-4d69-9100-eead044eacbc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>streptococcus_group_b_infection_of_the_infant</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-b67aede8-ef6a-485b-b088-de25f42922ac' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b67aede8-ef6a-485b-b088-de25f42922ac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0f7363d2-0eea-4481-b7a5-a1d23497863c' class='xr-var-data-in' type='checkbox'><label for='data-0f7363d2-0eea-4481-b7a5-a1d23497863c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>concussion_injury_of_brain</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-b52fd61f-74c9-4cb3-9ed7-af79a0975ac4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b52fd61f-74c9-4cb3-9ed7-af79a0975ac4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-139d1b15-a58e-4232-9f5c-a735b942ae1f' class='xr-var-data-in' type='checkbox'><label for='data-139d1b15-a58e-4232-9f5c-a735b942ae1f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>feeding_problems_in_newborn</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-4e790288-062f-4d83-80e0-ad3a9763a7bf' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4e790288-062f-4d83-80e0-ad3a9763a7bf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-59a62512-cffc-4d75-b6cf-1513074beae6' class='xr-var-data-in' type='checkbox'><label for='data-59a62512-cffc-4d75-b6cf-1513074beae6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>bipolar_i_disorder</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-eab0334c-b285-4060-aec7-6d1b4d176c81' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-eab0334c-b285-4060-aec7-6d1b4d176c81' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3e2b8efd-5584-4fdb-b22a-df061a812963' class='xr-var-data-in' type='checkbox'><label for='data-3e2b8efd-5584-4fdb-b22a-df061a812963' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>viral_pharyngitis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-ac8fbdd1-8261-42e5-9678-6893462d9570' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ac8fbdd1-8261-42e5-9678-6893462d9570' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0d094c4a-b4e0-460e-87c5-144a61f27e8c' class='xr-var-data-in' type='checkbox'><label for='data-0d094c4a-b4e0-460e-87c5-144a61f27e8c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lower_respiratory_tract_infection</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... True True</div><input id='attrs-7d035a82-5fc9-4bea-9c22-8ab8bc9713dc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7d035a82-5fc9-4bea-9c22-8ab8bc9713dc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6fac4327-24e2-47b5-b930-db2c4c8944f1' class='xr-var-data-in' type='checkbox'><label for='data-6fac4327-24e2-47b5-b930-db2c4c8944f1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, True, True], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hydronephrosis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... True True</div><input id='attrs-ba947235-5136-418d-97da-342742abb564' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ba947235-5136-418d-97da-342742abb564' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-82b5b05a-9722-4932-8886-ffe74d822346' class='xr-var-data-in' type='checkbox'><label for='data-82b5b05a-9722-4932-8886-ffe74d822346' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, True, True], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>borderline_personality_disorder</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-10a727bb-b88f-4c40-80d9-ac3921d0feb3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-10a727bb-b88f-4c40-80d9-ac3921d0feb3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e8c03ca3-e545-4ce9-8afc-5f16df06044b' class='xr-var-data-in' type='checkbox'><label for='data-e8c03ca3-e545-4ce9-8afc-5f16df06044b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>esophageal_varices</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-2c010abb-aaca-40d7-9928-24e2d461d57c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2c010abb-aaca-40d7-9928-24e2d461d57c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-776fb22c-a613-41be-990d-d1a41b821139' class='xr-var-data-in' type='checkbox'><label for='data-776fb22c-a613-41be-990d-d1a41b821139' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hypersomnia</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-a386dbe5-fdf7-4b37-8098-ea742b333386' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a386dbe5-fdf7-4b37-8098-ea742b333386' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f07d35eb-7ee6-4534-ae87-aa9e0a9700e7' class='xr-var-data-in' type='checkbox'><label for='data-f07d35eb-7ee6-4534-ae87-aa9e0a9700e7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sensorineural_hearing_loss_bilateral</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-08ed0b0a-9794-41d8-9cf2-d8d9a870de34' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-08ed0b0a-9794-41d8-9cf2-d8d9a870de34' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-be91028b-7d5c-4d67-89ce-eaaa87de11b7' class='xr-var-data-in' type='checkbox'><label for='data-be91028b-7d5c-4d67-89ce-eaaa87de11b7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>varicocele</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-ccc9c670-7b73-4a58-ac5d-6dec3c87e21e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ccc9c670-7b73-4a58-ac5d-6dec3c87e21e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8e6e02e6-a649-4e93-9b56-aae652acf6d2' class='xr-var-data-in' type='checkbox'><label for='data-8e6e02e6-a649-4e93-9b56-aae652acf6d2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>subarachnoid_intracranial_hemorrhage</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-2923c14a-dcff-4905-a635-343da1fce24b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2923c14a-dcff-4905-a635-343da1fce24b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9887ee7f-9016-4877-91f7-9791003dba97' class='xr-var-data-in' type='checkbox'><label for='data-9887ee7f-9016-4877-91f7-9791003dba97' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>incisional_hernia</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-fa76520a-6303-4a2b-acf4-f3946e33d6f1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fa76520a-6303-4a2b-acf4-f3946e33d6f1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a8a77ecd-8e4c-42e7-a2d7-dc36c3d313af' class='xr-var-data-in' type='checkbox'><label for='data-a8a77ecd-8e4c-42e7-a2d7-dc36c3d313af' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>varicella</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-50092cad-8cab-45b0-b324-115829cf924c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-50092cad-8cab-45b0-b324-115829cf924c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0ad44484-0682-457e-a775-be6bd86fd2de' class='xr-var-data-in' type='checkbox'><label for='data-0ad44484-0682-457e-a775-be6bd86fd2de' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pain_in_testicle</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-d1509f61-c00c-4b69-be79-860a0ee08240' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d1509f61-c00c-4b69-be79-860a0ee08240' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f98aedda-bc16-4288-b8c9-12b78a6d286c' class='xr-var-data-in' type='checkbox'><label for='data-f98aedda-bc16-4288-b8c9-12b78a6d286c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>transplant_follow-up</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-1fbd7441-eecd-44ef-8a3f-1b719e7dc230' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1fbd7441-eecd-44ef-8a3f-1b719e7dc230' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f6099ec6-d575-4663-a7c3-fba10bc362cc' class='xr-var-data-in' type='checkbox'><label for='data-f6099ec6-d575-4663-a7c3-fba10bc362cc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tinea_cruris</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-242d6030-e792-4430-aef3-c9d2f730ee91' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-242d6030-e792-4430-aef3-c9d2f730ee91' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-60fde0e6-06ab-4cc8-bc5a-33c2f3bbd5fc' class='xr-var-data-in' type='checkbox'><label for='data-60fde0e6-06ab-4cc8-bc5a-33c2f3bbd5fc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>laryngitis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-61238277-82f4-4bbf-81dc-a77e8f370a62' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-61238277-82f4-4bbf-81dc-a77e8f370a62' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5c8536b5-189a-4189-88fd-ed7179db58de' class='xr-var-data-in' type='checkbox'><label for='data-5c8536b5-189a-4189-88fd-ed7179db58de' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hypertrophy_of_nail</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-be5b1a28-f37c-4bd2-b773-5bae2beb9f5e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-be5b1a28-f37c-4bd2-b773-5bae2beb9f5e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-06e9e01a-fdca-469d-aab1-cd5024706a33' class='xr-var-data-in' type='checkbox'><label for='data-06e9e01a-fdca-469d-aab1-cd5024706a33' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>amblyopia</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-5d583399-c39f-4ff9-a475-2cb48fed105d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5d583399-c39f-4ff9-a475-2cb48fed105d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4dd74952-3f84-4f45-b9a3-e9de3f62df96' class='xr-var-data-in' type='checkbox'><label for='data-4dd74952-3f84-4f45-b9a3-e9de3f62df96' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>polyp_of_cervix</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-99fe8265-e663-479a-9df2-b31dad589580' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-99fe8265-e663-479a-9df2-b31dad589580' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bf26b06c-98b4-48c0-88a3-2c6e82d56156' class='xr-var-data-in' type='checkbox'><label for='data-bf26b06c-98b4-48c0-88a3-2c6e82d56156' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cyst_of_kidney</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-b067480a-b69c-4d80-93cb-9068f218427f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b067480a-b69c-4d80-93cb-9068f218427f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-913f8892-2a75-41e3-a036-907c3b63d888' class='xr-var-data-in' type='checkbox'><label for='data-913f8892-2a75-41e3-a036-907c3b63d888' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hepatic_encephalopathy</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-cfc80013-a789-4298-829b-6b7dad84e130' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cfc80013-a789-4298-829b-6b7dad84e130' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cc52bead-a3c6-4de4-8cc1-9910d20185b7' class='xr-var-data-in' type='checkbox'><label for='data-cc52bead-a3c6-4de4-8cc1-9910d20185b7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>blood_glucose_abnormal</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-20972f59-129f-4557-a801-6d7f99022f7b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-20972f59-129f-4557-a801-6d7f99022f7b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f997a0a3-4897-4ff1-a4cc-2b1c805b829c' class='xr-var-data-in' type='checkbox'><label for='data-f997a0a3-4897-4ff1-a4cc-2b1c805b829c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>postherpetic_neuralgia</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-e809e1bd-7124-4875-b917-bcd468bb8011' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e809e1bd-7124-4875-b917-bcd468bb8011' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-53d79db7-56f9-4eee-ad5d-61d06ea302e9' class='xr-var-data-in' type='checkbox'><label for='data-53d79db7-56f9-4eee-ad5d-61d06ea302e9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>frank_hematuria</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-0774762d-5f7c-4e08-bfac-4c5821c37dd9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0774762d-5f7c-4e08-bfac-4c5821c37dd9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4f5ab03d-1d8d-4268-b518-8fca9ef7943a' class='xr-var-data-in' type='checkbox'><label for='data-4f5ab03d-1d8d-4268-b518-8fca9ef7943a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cramp</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-cfe8e9ef-a0a8-4284-a88c-54ad7dc734eb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cfe8e9ef-a0a8-4284-a88c-54ad7dc734eb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-44b03509-0fe8-46bc-acf4-ff33e19b0155' class='xr-var-data-in' type='checkbox'><label for='data-44b03509-0fe8-46bc-acf4-ff33e19b0155' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>interstitial_lung_disease</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-fb18d3f2-49c3-4440-8b8b-5347c75a5130' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fb18d3f2-49c3-4440-8b8b-5347c75a5130' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4a3fb953-34c5-44b8-9efd-f3960f399eaf' class='xr-var-data-in' type='checkbox'><label for='data-4a3fb953-34c5-44b8-9efd-f3960f399eaf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>complete_atrioventricular_block</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-752af6ce-bd00-4ef8-9053-4137aabd30e3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-752af6ce-bd00-4ef8-9053-4137aabd30e3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fbfdfbbe-70ac-41a8-8407-8e24c1c0afd9' class='xr-var-data-in' type='checkbox'><label for='data-fbfdfbbe-70ac-41a8-8407-8e24c1c0afd9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>malignant_tumor_of_kidney</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-5beb00b8-1502-48b2-a94c-3ab59af4d014' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5beb00b8-1502-48b2-a94c-3ab59af4d014' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bb4643fc-ab4a-450a-8263-2b140a174493' class='xr-var-data-in' type='checkbox'><label for='data-bb4643fc-ab4a-450a-8263-2b140a174493' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>otitis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-5d6ff214-a3f0-4d21-b87e-91b90beaa1ba' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5d6ff214-a3f0-4d21-b87e-91b90beaa1ba' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6fa1bb6f-e11c-4d80-80d0-609a34fafae1' class='xr-var-data-in' type='checkbox'><label for='data-6fa1bb6f-e11c-4d80-80d0-609a34fafae1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>septic_shock</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-c24e8a28-7f6f-40a2-b7e2-bc65cda047c0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c24e8a28-7f6f-40a2-b7e2-bc65cda047c0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0c126f7b-bf24-428d-abf2-b3edd0840fba' class='xr-var-data-in' type='checkbox'><label for='data-0c126f7b-bf24-428d-abf2-b3edd0840fba' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>disorder_of_thyroid_gland</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-a741ac12-416e-43b6-8604-074edc208072' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a741ac12-416e-43b6-8604-074edc208072' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-74bf33eb-1a1e-49d6-8fab-96d448636153' class='xr-var-data-in' type='checkbox'><label for='data-74bf33eb-1a1e-49d6-8fab-96d448636153' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hypertrophic_cardiomyopathy</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-34a317e5-a2eb-4eac-ae30-92696e73b0ae' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-34a317e5-a2eb-4eac-ae30-92696e73b0ae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-609d16e7-521e-4981-9cdd-424ae4c9ac3c' class='xr-var-data-in' type='checkbox'><label for='data-609d16e7-521e-4981-9cdd-424ae4c9ac3c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>respiratory_distress_syndrome_in_the_newborn</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-6129de9a-4ea3-4833-8ec5-98552456b196' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6129de9a-4ea3-4833-8ec5-98552456b196' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d9181bf7-3616-4dde-ac27-7d04d9b56bd8' class='xr-var-data-in' type='checkbox'><label for='data-d9181bf7-3616-4dde-ac27-7d04d9b56bd8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>infectious_gastroenteritis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-73566dea-0ec2-4d5f-83da-8fc8ea76adde' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-73566dea-0ec2-4d5f-83da-8fc8ea76adde' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9e7f46de-9742-4269-81ca-0b73244c7539' class='xr-var-data-in' type='checkbox'><label for='data-9e7f46de-9742-4269-81ca-0b73244c7539' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>subdural_intracranial_hemorrhage</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-f5dd0174-b541-4f20-828e-0c407bfa2d36' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f5dd0174-b541-4f20-828e-0c407bfa2d36' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0a8d29d8-e91b-4fc2-9429-fcd431f37d93' class='xr-var-data-in' type='checkbox'><label for='data-0a8d29d8-e91b-4fc2-9429-fcd431f37d93' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hepatitis_b_carrier</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-5348285a-31bf-47bf-8d51-8c1eae9ec1c4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5348285a-31bf-47bf-8d51-8c1eae9ec1c4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d5750a08-be02-4d10-b52b-ff9aa9864b68' class='xr-var-data-in' type='checkbox'><label for='data-d5750a08-be02-4d10-b52b-ff9aa9864b68' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>manic_bipolar_i_disorder</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-002decea-b55c-4654-87ed-42c3360753c0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-002decea-b55c-4654-87ed-42c3360753c0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b563ab60-5ca4-4ef9-b2ab-9621a98311ba' class='xr-var-data-in' type='checkbox'><label for='data-b563ab60-5ca4-4ef9-b2ab-9621a98311ba' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>secondary_pulmonary_hypertension</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-26db866e-c44b-443b-8c19-edc8575c3bab' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-26db866e-c44b-443b-8c19-edc8575c3bab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dc1507f5-b373-48dc-9d5c-a5684d7dafb2' class='xr-var-data-in' type='checkbox'><label for='data-dc1507f5-b373-48dc-9d5c-a5684d7dafb2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>gonorrhea</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-0581fd31-8e5f-4391-946a-84789f8d6d1b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0581fd31-8e5f-4391-946a-84789f8d6d1b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-158fb65c-d3d7-4832-85bc-4364f83ac562' class='xr-var-data-in' type='checkbox'><label for='data-158fb65c-d3d7-4832-85bc-4364f83ac562' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>derangement_of_knee</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-10ed64ec-f99e-43ef-a3f3-82eb83317064' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-10ed64ec-f99e-43ef-a3f3-82eb83317064' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d0f56fb7-ce85-4726-9ac0-935a0aab67f7' class='xr-var-data-in' type='checkbox'><label for='data-d0f56fb7-ce85-4726-9ac0-935a0aab67f7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>appendicitis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>True True False ... False False</div><input id='attrs-800b3682-4ec5-4512-83b1-bf04d5dc6af7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-800b3682-4ec5-4512-83b1-bf04d5dc6af7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6f0c7187-c6e5-4b9c-84ed-c82f13d04dae' class='xr-var-data-in' type='checkbox'><label for='data-6f0c7187-c6e5-4b9c-84ed-c82f13d04dae' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([True, True, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>polyneuropathy_due_to_diabetes_mellitus</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-bc8011bc-fd7a-4cc9-8fc6-10514dc4abca' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bc8011bc-fd7a-4cc9-8fc6-10514dc4abca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-027936f2-208e-4d80-9358-f3285380c77e' class='xr-var-data-in' type='checkbox'><label for='data-027936f2-208e-4d80-9358-f3285380c77e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>neonatal_hypoglycemia</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-15c08b11-7e51-45bb-8406-ff795ccdb1bb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-15c08b11-7e51-45bb-8406-ff795ccdb1bb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e679b4f4-7fa9-4a0b-921c-53009232a03a' class='xr-var-data-in' type='checkbox'><label for='data-e679b4f4-7fa9-4a0b-921c-53009232a03a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>prolonged_rupture_of_membranes</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-7763b035-ce27-45d3-bf39-3427133484e5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7763b035-ce27-45d3-bf39-3427133484e5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7c969dee-8ffc-4821-87eb-7a82d20c04e7' class='xr-var-data-in' type='checkbox'><label for='data-7c969dee-8ffc-4821-87eb-7a82d20c04e7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vasomotor_rhinitis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-a8e37a9a-9fc5-493c-94b2-8e51c9af688b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a8e37a9a-9fc5-493c-94b2-8e51c9af688b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b5ba4d39-bc3f-4c1e-8a6f-17c942d6e278' class='xr-var-data-in' type='checkbox'><label for='data-b5ba4d39-bc3f-4c1e-8a6f-17c942d6e278' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>renal_disorder_due_to_type_1_diabetes_mellitus</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-be6515c2-1a05-4bbf-9fd8-1bbf67343250' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-be6515c2-1a05-4bbf-9fd8-1bbf67343250' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-be300d0b-3203-4e54-8377-018e1de849bb' class='xr-var-data-in' type='checkbox'><label for='data-be300d0b-3203-4e54-8377-018e1de849bb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tuberculosis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-723c8e88-29a6-471f-80e2-d725292b7c3f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-723c8e88-29a6-471f-80e2-d725292b7c3f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-02ed28e4-039b-4292-a2bd-fbcec31b5aaa' class='xr-var-data-in' type='checkbox'><label for='data-02ed28e4-039b-4292-a2bd-fbcec31b5aaa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>feeding_problem</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-d55ff3e1-9b55-4ec0-aabd-45252400e23b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d55ff3e1-9b55-4ec0-aabd-45252400e23b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ba300425-8bb9-4d60-9ce4-28d5c4b563c8' class='xr-var-data-in' type='checkbox'><label for='data-ba300425-8bb9-4d60-9ce4-28d5c4b563c8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>chronic_tonsillitis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-169d2810-b3f9-4b30-9d2f-4ad150cf26b7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-169d2810-b3f9-4b30-9d2f-4ad150cf26b7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4a1dc04b-cf4d-4a7b-86ea-2fd966101756' class='xr-var-data-in' type='checkbox'><label for='data-4a1dc04b-cf4d-4a7b-86ea-2fd966101756' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>acute_duodenal_ulcer_with_hemorrhage</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-6a47f474-e072-46ed-b245-8248fc8eca0a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6a47f474-e072-46ed-b245-8248fc8eca0a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-495b9f1f-e832-4221-8858-97fc16909167' class='xr-var-data-in' type='checkbox'><label for='data-495b9f1f-e832-4221-8858-97fc16909167' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hammer_toe</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-2a7c2d17-d722-4107-9e92-072fed32d312' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2a7c2d17-d722-4107-9e92-072fed32d312' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e0109fb8-471b-4fa7-a450-719b6a972696' class='xr-var-data-in' type='checkbox'><label for='data-e0109fb8-471b-4fa7-a450-719b6a972696' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>malignant_tumor_of_cervix</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-d2bafd01-ef52-4d08-9152-78ad370bdb06' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d2bafd01-ef52-4d08-9152-78ad370bdb06' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-798490cc-09ab-4836-a95c-192fb306b45e' class='xr-var-data-in' type='checkbox'><label for='data-798490cc-09ab-4836-a95c-192fb306b45e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>prolapsed_lumbar_intervertebral_disc</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-19e86659-adf6-4915-bb41-40b3537433a8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-19e86659-adf6-4915-bb41-40b3537433a8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-19ea477a-ec23-4de5-965b-2a2fb980b8b4' class='xr-var-data-in' type='checkbox'><label for='data-19ea477a-ec23-4de5-965b-2a2fb980b8b4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hematemesis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-698eca04-2256-43ed-aea4-397fbba05ab9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-698eca04-2256-43ed-aea4-397fbba05ab9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d24dc5f7-5a40-4dc1-bcd9-18fcb720fd83' class='xr-var-data-in' type='checkbox'><label for='data-d24dc5f7-5a40-4dc1-bcd9-18fcb720fd83' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>perianal_abscess</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-5ed196f9-860e-4188-ade7-a96bf62a07e0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5ed196f9-860e-4188-ade7-a96bf62a07e0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-20dd42f1-4e26-4b95-b1c4-42a74f685e00' class='xr-var-data-in' type='checkbox'><label for='data-20dd42f1-4e26-4b95-b1c4-42a74f685e00' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nonvenomous_insect_bite</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-98fa4322-8593-434d-b273-a8d435f0d7cc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-98fa4322-8593-434d-b273-a8d435f0d7cc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-53df7725-068f-49e4-88ab-583b508b1842' class='xr-var-data-in' type='checkbox'><label for='data-53df7725-068f-49e4-88ab-583b508b1842' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spondylolisthesis</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-c6ffbf69-d23c-4e1c-bdd5-d87309c48ac1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c6ffbf69-d23c-4e1c-bdd5-d87309c48ac1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6d4efca6-5da7-4f9b-85c5-3daef84ebbb9' class='xr-var-data-in' type='checkbox'><label for='data-6d4efca6-5da7-4f9b-85c5-3daef84ebbb9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>malignant_tumor_of_esophagus</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-7bea0b6c-ecd3-412c-b4ca-f1f966a3c128' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7bea0b6c-ecd3-412c-b4ca-f1f966a3c128' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ffd3263e-8579-419b-b13f-b3244bac78a5' class='xr-var-data-in' type='checkbox'><label for='data-ffd3263e-8579-419b-b13f-b3244bac78a5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>aphthous_ulcer_of_mouth</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-5ba2252e-7aae-472f-93e9-0dc891d859ed' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5ba2252e-7aae-472f-93e9-0dc891d859ed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-28ba9dad-dc41-46b8-886b-0a6f4f272b9c' class='xr-var-data-in' type='checkbox'><label for='data-28ba9dad-dc41-46b8-886b-0a6f4f272b9c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ventricular_septal_defect</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-f2fc5051-e55a-473c-913c-7d1f21c28a4e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f2fc5051-e55a-473c-913c-7d1f21c28a4e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ffbb9d91-1e8d-47e2-a006-986340f832f2' class='xr-var-data-in' type='checkbox'><label for='data-ffbb9d91-1e8d-47e2-a006-986340f832f2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>oropharyngeal_dysphagia</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-5467ea21-cc04-4335-9b65-3e1893c98b03' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5467ea21-cc04-4335-9b65-3e1893c98b03' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fed5809d-dde3-4e5e-a9f1-bc7a02d65d50' class='xr-var-data-in' type='checkbox'><label for='data-fed5809d-dde3-4e5e-a9f1-bc7a02d65d50' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>injury_of_knee</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-f6a7aeb3-faf7-4303-af7f-c487934dfb79' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f6a7aeb3-faf7-4303-af7f-c487934dfb79' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bb47fef1-64b3-477e-bd5f-c9df2a944665' class='xr-var-data-in' type='checkbox'><label for='data-bb47fef1-64b3-477e-bd5f-c9df2a944665' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>traumatic_brain_injury</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-576b9830-5ff3-4ea0-ad72-5499b37074aa' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-576b9830-5ff3-4ea0-ad72-5499b37074aa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-807cf29d-35a3-42bc-b042-4d0662aa2639' class='xr-var-data-in' type='checkbox'><label for='data-807cf29d-35a3-42bc-b042-4d0662aa2639' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>osteoarthritis_of_glenohumeral_joint</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-8dc773c5-74fb-4323-a1b1-2fe7be70e570' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8dc773c5-74fb-4323-a1b1-2fe7be70e570' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-971cb22d-ddfe-455d-9fb5-1e0b107f5eeb' class='xr-var-data-in' type='checkbox'><label for='data-971cb22d-ddfe-455d-9fb5-1e0b107f5eeb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fetal_or_neonatal_effect_of_maternal_medical_problem</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-a4c8184d-9b0b-416d-91bf-3f6f9155506d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a4c8184d-9b0b-416d-91bf-3f6f9155506d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4648130e-514d-4849-858d-8819c6fd6f8d' class='xr-var-data-in' type='checkbox'><label for='data-4648130e-514d-4849-858d-8819c6fd6f8d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., None, False, False], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9f616075-3730-4964-9b07-f4d3740a8e90' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-9f616075-3730-4964-9b07-f4d3740a8e90' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.Dataset>\n",
-       "Dimensions:                                                                  (index: 2979355)\n",
-       "Coordinates:\n",
-       "  * index                                                                    (index) int64 ...\n",
-       "Data variables:\n",
-       "    eid                                                                      (index) int64 ...\n",
-       "    birthdate                                                                (index) object ...\n",
-       "    t                                                                        (index) float64 ...\n",
-       "    sex_f31                                                                  (index) object ...\n",
-       "    ethnic_background_f21000                                                 (index) object ...\n",
-       "    overall_health_rating_f2178                                              (index) object ...\n",
-       "    smoking_status_f20116                                                    (index) object ...\n",
-       "    alcohol_intake_frequency_f1558                                           (index) object ...\n",
-       "    townsend_deprivation_index_at_recruitment_f189                           (index) float64 ...\n",
-       "    body_mass_index_bmi_f21001                                               (index) float64 ...\n",
-       "    weight_f21002                                                            (index) float64 ...\n",
-       "    systolic_blood_pressure_automated_reading_f4080                          (index) float64 ...\n",
-       "    diastolic_blood_pressure_automated_reading_f4079                         (index) float64 ...\n",
-       "    pulse_rate_automated_reading_f102                                        (index) float64 ...\n",
-       "    pulse_wave_arterial_stiffness_index_f21021                               (index) float64 ...\n",
-       "    pulse_wave_reflection_index_f4195                                        (index) float64 ...\n",
-       "    waist_circumference_f48                                                  (index) float64 ...\n",
-       "    hip_circumference_f49                                                    (index) float64 ...\n",
-       "    standing_height_f50                                                      (index) float64 ...\n",
-       "    trunk_fat_percentage_f23127                                              (index) float64 ...\n",
-       "    body_fat_percentage_f23099                                               (index) float64 ...\n",
-       "    basal_metabolic_rate_f23105                                              (index) float64 ...\n",
-       "    forced_vital_capacity_fvc_best_measure_f20151                            (index) float64 ...\n",
-       "    forced_expiratory_volume_in_1second_fev1_best_measure_f20150             (index) float64 ...\n",
-       "    fev1_fvc_ratio_zscore_f20258                                             (index) float64 ...\n",
-       "    peak_expiratory_flow_pef_f3064                                           (index) float64 ...\n",
-       "    basophill_count_f30160                                                   (index) float64 ...\n",
-       "    basophill_percentage_f30220                                              (index) float64 ...\n",
-       "    eosinophill_count_f30150                                                 (index) float64 ...\n",
-       "    eosinophill_percentage_f30210                                            (index) float64 ...\n",
-       "    haematocrit_percentage_f30030                                            (index) float64 ...\n",
-       "    haemoglobin_concentration_f30020                                         (index) float64 ...\n",
-       "    high_light_scatter_reticulocyte_count_f30300                             (index) float64 ...\n",
-       "    high_light_scatter_reticulocyte_percentage_f30290                        (index) float64 ...\n",
-       "    immature_reticulocyte_fraction_f30280                                    (index) float64 ...\n",
-       "    lymphocyte_count_f30120                                                  (index) float64 ...\n",
-       "    lymphocyte_percentage_f30180                                             (index) float64 ...\n",
-       "    mean_corpuscular_haemoglobin_f30050                                      (index) float64 ...\n",
-       "    mean_corpuscular_haemoglobin_concentration_f30060                        (index) float64 ...\n",
-       "    mean_corpuscular_volume_f30040                                           (index) float64 ...\n",
-       "    mean_platelet_thrombocyte_volume_f30100                                  (index) float64 ...\n",
-       "    mean_reticulocyte_volume_f30260                                          (index) float64 ...\n",
-       "    mean_sphered_cell_volume_f30270                                          (index) float64 ...\n",
-       "    monocyte_count_f30130                                                    (index) float64 ...\n",
-       "    monocyte_percentage_f30190                                               (index) float64 ...\n",
-       "    neutrophill_count_f30140                                                 (index) float64 ...\n",
-       "    neutrophill_percentage_f30200                                            (index) float64 ...\n",
-       "    nucleated_red_blood_cell_count_f30170                                    (index) float64 ...\n",
-       "    nucleated_red_blood_cell_percentage_f30230                               (index) float64 ...\n",
-       "    platelet_count_f30080                                                    (index) float64 ...\n",
-       "    platelet_crit_f30090                                                     (index) float64 ...\n",
-       "    platelet_distribution_width_f30110                                       (index) float64 ...\n",
-       "    red_blood_cell_erythrocyte_count_f30010                                  (index) float64 ...\n",
-       "    red_blood_cell_erythrocyte_distribution_width_f30070                     (index) float64 ...\n",
-       "    reticulocyte_count_f30250                                                (index) float64 ...\n",
-       "    reticulocyte_percentage_f30240                                           (index) float64 ...\n",
-       "    white_blood_cell_leukocyte_count_f30000                                  (index) float64 ...\n",
-       "    alanine_aminotransferase_f30620                                          (index) float64 ...\n",
-       "    albumin_f30600                                                           (index) float64 ...\n",
-       "    alkaline_phosphatase_f30610                                              (index) float64 ...\n",
-       "    apolipoprotein_a_f30630                                                  (index) float64 ...\n",
-       "    apolipoprotein_b_f30640                                                  (index) float64 ...\n",
-       "    aspartate_aminotransferase_f30650                                        (index) float64 ...\n",
-       "    creactive_protein_f30710                                                 (index) float64 ...\n",
-       "    calcium_f30680                                                           (index) float64 ...\n",
-       "    cholesterol_f30690                                                       (index) float64 ...\n",
-       "    creatinine_f30700                                                        (index) float64 ...\n",
-       "    cystatin_c_f30720                                                        (index) float64 ...\n",
-       "    direct_bilirubin_f30660                                                  (index) float64 ...\n",
-       "    gamma_glutamyltransferase_f30730                                         (index) float64 ...\n",
-       "    glucose_f30740                                                           (index) float64 ...\n",
-       "    glycated_haemoglobin_hba1c_f30750                                        (index) float64 ...\n",
-       "    hdl_cholesterol_f30760                                                   (index) float64 ...\n",
-       "    igf1_f30770                                                              (index) float64 ...\n",
-       "    ldl_direct_f30780                                                        (index) float64 ...\n",
-       "    lipoprotein_a_f30790                                                     (index) float64 ...\n",
-       "    oestradiol_f30800                                                        (index) float64 ...\n",
-       "    phosphate_f30810                                                         (index) float64 ...\n",
-       "    rheumatoid_factor_f30820                                                 (index) float64 ...\n",
-       "    shbg_f30830                                                              (index) float64 ...\n",
-       "    testosterone_f30850                                                      (index) float64 ...\n",
-       "    total_bilirubin_f30840                                                   (index) float64 ...\n",
-       "    total_protein_f30860                                                     (index) float64 ...\n",
-       "    triglycerides_f30870                                                     (index) float64 ...\n",
-       "    urate_f30880                                                             (index) float64 ...\n",
-       "    urea_f30670                                                              (index) float64 ...\n",
-       "    vitamin_d_f30890                                                         (index) float64 ...\n",
-       "    diagnosis                                                                (index) object ...\n",
-       "    hypertensive_disorder_systemic_arterial                                  (index) object ...\n",
-       "    hyperlipidemia                                                           (index) object ...\n",
-       "    depressive_disorder                                                      (index) object ...\n",
-       "    gastroesophageal_reflux_disease                                          (index) object ...\n",
-       "    diabetes_mellitus_type_2                                                 (index) object ...\n",
-       "    essential_hypertension                                                   (index) object ...\n",
-       "    obesity                                                                  (index) object ...\n",
-       "    diabetes_mellitus                                                        (index) object ...\n",
-       "    asthma                                                                   (index) object ...\n",
-       "    coronary_arteriosclerosis                                                (index) object ...\n",
-       "    allergic_rhinitis                                                        (index) object ...\n",
-       "    hypothyroidism                                                           (index) object ...\n",
-       "    upper_respiratory_infection                                              (index) object ...\n",
-       "    hypercholesterolemia                                                     (index) object ...\n",
-       "    backache                                                                 (index) object ...\n",
-       "    abdominal_pain                                                           (index) object ...\n",
-       "    osteoarthritis                                                           (index) object ...\n",
-       "    low_back_pain                                                            (index) object ...\n",
-       "    anemia                                                                   (index) object ...\n",
-       "    anxiety                                                                  (index) object ...\n",
-       "    urinary_tract_infectious_disease                                         (index) object ...\n",
-       "    chronic_obstructive_lung_disease                                         (index) object ...\n",
-       "    atrial_fibrillation                                                      (index) object ...\n",
-       "    pneumonia                                                                (index) object ...\n",
-       "    chest_pain                                                               (index) object ...\n",
-       "    congestive_heart_failure                                                 (index) object ...\n",
-       "    headache                                                                 (index) object ...\n",
-       "    migraine                                                                 (index) object ...\n",
-       "    pregnant                                                                 (index) object ...\n",
-       "    knee_pain                                                                (index) object ...\n",
-       "    osteoporosis                                                             (index) object ...\n",
-       "    polyp_of_colon                                                           (index) object ...\n",
-       "    otitis_media                                                             (index) object ...\n",
-       "    sinusitis                                                                (index) object ...\n",
-       "    cough                                                                    (index) object ...\n",
-       "    sleep_apnea                                                              (index) object ...\n",
-       "    insomnia                                                                 (index) object ...\n",
-       "    inflammatory_disorder_due_to_increased_blood_urate_level                 (index) object ...\n",
-       "    tobacco_dependence_syndrome                                              (index) object ...\n",
-       "    malignant_tumor_of_prostate                                              (index) object ...\n",
-       "    constipation                                                             (index) object ...\n",
-       "    hearing_loss                                                             (index) object ...\n",
-       "    fatigue                                                                  (index) object ...\n",
-       "    obstructive_sleep_apnea_syndrome                                         (index) object ...\n",
-       "    malignant_neoplasm_of_breast                                             (index) object ...\n",
-       "    delivery_normal                                                          (index) object ...\n",
-       "    irritable_bowel_syndrome                                                 (index) object ...\n",
-       "    tobacco_user                                                             (index) object ...\n",
-       "    neck_pain                                                                (index) object ...\n",
-       "    cerebrovascular_accident                                                 (index) object ...\n",
-       "    asthenia                                                                 (index) object ...\n",
-       "    shoulder_pain                                                            (index) object ...\n",
-       "    acne_vulgaris                                                            (index) object ...\n",
-       "    benign_prostatic_hyperplasia                                             (index) object ...\n",
-       "    dyspnea                                                                  (index) object ...\n",
-       "    carpal_tunnel_syndrome                                                   (index) object ...\n",
-       "    bronchitis                                                               (index) object ...\n",
-       "    pharyngitis                                                              (index) object ...\n",
-       "    arthritis                                                                (index) object ...\n",
-       "    diarrhea                                                                 (index) object ...\n",
-       "    dizziness                                                                (index) object ...\n",
-       "    alcohol_abuse                                                            (index) object ...\n",
-       "    dementia                                                                 (index) object ...\n",
-       "    eczema                                                                   (index) object ...\n",
-       "    syncope                                                                  (index) object ...\n",
-       "    acute_sinusitis                                                          (index) object ...\n",
-       "    iron_deficiency_anemia                                                   (index) object ...\n",
-       "    allergic_rhinitis_caused_by_pollen                                       (index) object ...\n",
-       "    gastritis                                                                (index) object ...\n",
-       "    cataract                                                                 (index) object ...\n",
-       "    hematuria_syndrome                                                       (index) object ...\n",
-       "    disorder_of_the_peripheral_nervous_system                                (index) object ...\n",
-       "    viral_hepatitis_type_c                                                   (index) object ...\n",
-       "    palpitations                                                             (index) object ...\n",
-       "    eruption_of_skin                                                         (index) object ...\n",
-       "    diabetes_mellitus_type_1                                                 (index) object ...\n",
-       "    renal_failure_syndrome                                                   (index) object ...\n",
-       "    peripheral_vascular_disease                                              (index) object ...\n",
-       "    hyperglycemia                                                            (index) object ...\n",
-       "    seizure_disorder                                                         (index) object ...\n",
-       "    fever                                                                    (index) object ...\n",
-       "    osteoarthritis_of_knee                                                   (index) object ...\n",
-       "    actinic_keratosis                                                        (index) object ...\n",
-       "    urinary_incontinence                                                     (index) object ...\n",
-       "    hemorrhoids                                                              (index) object ...\n",
-       "    seizure                                                                  (index) object ...\n",
-       "    laceration_-_injury                                                      (index) object ...\n",
-       "    glaucoma                                                                 (index) object ...\n",
-       "    body_mass_index_30+_-_obesity                                            (index) object ...\n",
-       "    breast_lump                                                              (index) object ...\n",
-       "    viral_disease                                                            (index) object ...\n",
-       "    abnormal_cervical_smear                                                  (index) object ...\n",
-       "    cellulitis                                                               (index) object ...\n",
-       "    rheumatoid_arthritis                                                     (index) object ...\n",
-       "    senile_hyperkeratosis                                                    (index) object ...\n",
-       "    anxiety_disorder                                                         (index) object ...\n",
-       "    vertigo                                                                  (index) object ...\n",
-       "    chronic_kidney_disease                                                   (index) object ...\n",
-       "    dysphagia                                                                (index) object ...\n",
-       "    edema                                                                    (index) object ...\n",
-       "    malignant_neoplasm_of_colon                                              (index) object ...\n",
-       "    hip_pain                                                                 (index) object ...\n",
-       "    posttraumatic_stress_disorder                                            (index) object ...\n",
-       "    inflammatory_dermatosis                                                  (index) object ...\n",
-       "    psoriasis                                                                (index) object ...\n",
-       "    myopia                                                                   (index) object ...\n",
-       "    senile_cataract                                                          (index) object ...\n",
-       "    heart_murmur                                                             (index) object ...\n",
-       "    liver_function_tests_abnormal                                            (index) object ...\n",
-       "    angina                                                                   (index) object ...\n",
-       "    impaired_fasting_glycemia                                                (index) object ...\n",
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-       "    injury_of_head                                                           (index) object ...\n",
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-       "    acquired_hypothyroidism                                                  (index) object ...\n",
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-       "    hypercalcemia                                                            (index) object ...\n",
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-       "    dysthymia                                                                (index) object ...\n",
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-       "    noncompliance_with_treatment                                             (index) object ...\n",
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-       "    preinfarction_syndrome                                                   (index) object ...\n",
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-       "    bronchiolitis                                                            (index) object ...\n",
-       "    mild_persistent_asthma                                                   (index) object ...\n",
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-       "    mixed_hyperlipidemia                                                     (index) object ...\n",
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-       "    menopausal_syndrome                                                      (index) object ...\n",
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-       "    lumbar_radiculopathy                                                     (index) object ...\n",
-       "    osteoarthritis_of_hip                                                    (index) object ...\n",
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-       "    hypoglycemia                                                             (index) object ...\n",
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-       "    systemic_lupus_erythematosus                                             (index) object ...\n",
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-       "    lumbar_spondylosis                                                       (index) object ...\n",
-       "    acute_tonsillitis                                                        (index) object ...\n",
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-       "    aortic_valve_regurgitation                                               (index) object ...\n",
-       "    malignant_neoplasm_of_skin                                               (index) object ...\n",
-       "    altered_mental_status                                                    (index) object ...\n",
-       "    atrial_flutter                                                           (index) object ...\n",
-       "    alopecia                                                                 (index) object ...\n",
-       "    high_risk_pregnancy                                                      (index) object ...\n",
-       "    missed_abortion                                                          (index) object ...\n",
-       "    malignant_tumor_of_urinary_bladder                                       (index) object ...\n",
-       "    pulmonary_hypertension                                                   (index) object ...\n",
-       "    nasal_congestion                                                         (index) object ...\n",
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-       "    viral_gastroenteritis                                                    (index) object ...\n",
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-       "    primary_open_angle_glaucoma                                              (index) object ...\n",
-       "    malignant_tumor_of_ovary                                                 (index) object ...\n",
-       "    incomplete_emptying_of_bladder                                           (index) object ...\n",
-       "    pruritic_disorder                                                        (index) object ...\n",
-       "    fibrocystic_breast_changes                                               (index) object ...\n",
-       "    tinea_corporis                                                           (index) object ...\n",
-       "    acute_pancreatitis                                                       (index) object ...\n",
-       "    acute_myocardial_infarction                                              (index) object ...\n",
-       "    cerebral_hemorrhage                                                      (index) object ...\n",
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-       "    diverticular_disease                                                     (index) object ...\n",
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-       "    tear_film_insufficiency                                                  (index) object ...\n",
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-       "    pancreatitis                                                             (index) object ...\n",
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-       "    first_degree_perineal_laceration                                         (index) object ...\n",
-       "    not_for_resuscitation                                                    (index) object ...\n",
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-       "    intracranial_injury                                                      (index) object ...\n",
-       "    bell's_palsy                                                             (index) object ...\n",
-       "    bradycardia                                                              (index) object ...\n",
-       "    polymyalgia_rheumatica                                                   (index) object ...\n",
-       "    dysplasia_of_cervix                                                      (index) object ...\n",
-       "    serous_otitis_media                                                      (index) object ...\n",
-       "    angioedema                                                               (index) object ...\n",
-       "    venous_varices                                                           (index) object ...\n",
-       "    spasm                                                                    (index) object ...\n",
-       "    sleep_disorder                                                           (index) object ...\n",
-       "    pain_of_breast                                                           (index) object ...\n",
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-       "    tachycardia                                                              (index) object ...\n",
-       "    acute_conjunctivitis                                                     (index) object ...\n",
-       "    malignant_lymphoma                                                       (index) object ...\n",
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-       "    hyperparathyroidism                                                      (index) object ...\n",
-       "    periodontitis                                                            (index) object ...\n",
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-       "    nerve_root_disorder                                                      (index) object ...\n",
-       "    nonexudative_age-related_macular_degeneration                            (index) object ...\n",
-       "    tension-type_headache                                                    (index) object ...\n",
-       "    chronic_pain_syndrome                                                    (index) object ...\n",
-       "    abnormal_weight_loss                                                     (index) object ...\n",
-       "    pure_hypercholesterolemia                                                (index) object ...\n",
-       "    acute_upper_respiratory_infection                                        (index) object ...\n",
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-       "    colitis                                                                  (index) object ...\n",
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-       "    croup                                                                    (index) object ...\n",
-       "    skin_tag                                                                 (index) object ...\n",
-       "    metabolic_syndrome_x                                                     (index) object ...\n",
-       "    adverse_reaction_caused_by_drug                                          (index) object ...\n",
-       "    retinopathy_due_to_diabetes_mellitus                                     (index) object ...\n",
-       "    female_infertility                                                       (index) object ...\n",
-       "    open-angle_glaucoma                                                      (index) object ...\n",
-       "    tietze's_disease                                                         (index) object ...\n",
-       "    gallbladder_calculus                                                     (index) object ...\n",
-       "    sarcoidosis                                                              (index) object ...\n",
-       "    neurogenic_bladder                                                       (index) object ...\n",
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-       "    wheezing                                                                 (index) object ...\n",
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-       "    miscarriage                                                              (index) object ...\n",
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-       "    urethritis                                                               (index) object ...\n",
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-       "    malignant_tumor_of_thyroid_gland                                         (index) object ...\n",
-       "    pressure_ulcer                                                           (index) object ...\n",
-       "    verruca_plantaris                                                        (index) object ...\n",
-       "    anemia_of_chronic_disorder                                               (index) object ...\n",
-       "    hernia_of_anterior_abdominal_wall                                        (index) object ...\n",
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-       "    infertile                                                                (index) object ...\n",
-       "    hernia_of_abdominal_wall                                                 (index) object ...\n",
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-       "    pityriasis_versicolor                                                    (index) object ...\n",
-       "    injury_of_lower_leg                                                      (index) object ...\n",
-       "    dry_skin                                                                 (index) object ...\n",
-       "    impacted_tooth                                                           (index) object ...\n",
-       "    acquired_trigger_finger                                                  (index) object ...\n",
-       "    chronic_osteoarthritis                                                   (index) object ...\n",
-       "    acute_stress_disorder                                                    (index) object ...\n",
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-       "    respiratory_failure                                                      (index) object ...\n",
-       "    allergic_disposition                                                     (index) object ...\n",
-       "    stress                                                                   (index) object ...\n",
-       "    atrophic_vaginitis                                                       (index) object ...\n",
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-       "    cocaine_abuse                                                            (index) object ...\n",
-       "    intermittent_asthma                                                      (index) object ...\n",
-       "    cervical_radiculopathy                                                   (index) object ...\n",
-       "    atherosclerosis_of_coronary_artery                                       (index) object ...\n",
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-       "    hypervolemia                                                             (index) object ...\n",
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-       "    fibrosis_of_lung                                                         (index) object ...\n",
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-       "    urgent_desire_to_urinate                                                 (index) object ...\n",
-       "    bursitis_of_hip                                                          (index) object ...\n",
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-       "    chronic_pancreatitis                                                     (index) object ...\n",
-       "    gastroparesis                                                            (index) object ...\n",
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-       "    pilonidal_cyst                                                           (index) object ...\n",
-       "    pain_in_toe                                                              (index) object ...\n",
-       "    pulmonary_tuberculosis                                                   (index) object ...\n",
-       "    celiac_disease                                                           (index) object ...\n",
-       "    cramp_in_lower_leg                                                       (index) object ...\n",
-       "    secondary_malignant_neoplasm_of_pleura                                   (index) object ...\n",
-       "    fracture_of_hand                                                         (index) object ...\n",
-       "    cyst_of_breast                                                           (index) object ...\n",
-       "    nephrotic_syndrome                                                       (index) object ...\n",
-       "    polyp_of_nasal_sinus                                                     (index) object ...\n",
-       "    chondromalacia_of_patella                                                (index) object ...\n",
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-       "    female_cystocele                                                         (index) object ...\n",
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-       "    epiretinal_membrane                                                      (index) object ...\n",
-       "    recurrent_major_depression_in_partial_remission                          (index) object ...\n",
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-       "    mass_of_neck                                                             (index) object ...\n",
-       "    idiopathic_thrombocytopenic_purpura                                      (index) object ...\n",
-       "    complete_miscarriage                                                     (index) object ...\n",
-       "    gastric_ulcer                                                            (index) object ...\n",
-       "    papilloma_of_skin                                                        (index) object ...\n",
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-       "    secondary_malignant_neoplastic_disease                                   (index) object ...\n",
-       "    hypoxemia                                                                (index) object ...\n",
-       "    paraplegia                                                               (index) object ...\n",
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-       "    ventricular_tachycardia                                                  (index) object ...\n",
-       "    mixed_incontinence                                                       (index) object ...\n",
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-       "    trigeminal_neuralgia                                                     (index) object ...\n",
-       "    retinal_detachment                                                       (index) object ...\n",
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-       "    ischemic_ulcer                                                           (index) object ...\n",
-       "    intramural_leiomyoma_of_uterus                                           (index) object ...\n",
-       "    viral_hepatitis_type_a                                                   (index) object ...\n",
-       "    ménière's_disease                                                        (index) object ...\n",
-       "    fracture_of_phalanx_of_hand                                              (index) object ...\n",
-       "    muscle_atrophy                                                           (index) object ...\n",
-       "    incontinence_of_feces                                                    (index) object ...\n",
-       "    mitral_valve_disorder                                                    (index) object ...\n",
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-       "    foreign_body_in_respiratory_tract                                        (index) object ...\n",
-       "    chronic_kidney_disease_stage_4                                           (index) object ...\n",
-       "    myocardial_ischemia                                                      (index) object ...\n",
-       "    non-toxic_multinodular_goiter                                            (index) object ...\n",
-       "    pain_in_finger                                                           (index) object ...\n",
-       "    cervical_spondylosis_without_myelopathy                                  (index) object ...\n",
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-       "    clouded_consciousness                                                    (index) object ...\n",
-       "    mixed_conductive_and_sensorineural_hearing_loss                          (index) object ...\n",
-       "    tooth_eruption_disorder                                                  (index) object ...\n",
-       "    hyperuricemia                                                            (index) object ...\n",
-       "    closed_fracture_of_neck_of_femur                                         (index) object ...\n",
-       "    bipolar_ii_disorder                                                      (index) object ...\n",
-       "    disturbance_in_sleep_behavior                                            (index) object ...\n",
-       "    relationship_problems                                                    (index) object ...\n",
-       "    sprain_of_wrist                                                          (index) object ...\n",
-       "    personality_disorder                                                     (index) object ...\n",
-       "    external_hemorrhoids                                                     (index) object ...\n",
-       "    abnormal_vision                                                          (index) object ...\n",
-       "    hyperprolactinemia                                                       (index) object ...\n",
-       "    hemochromatosis                                                          (index) object ...\n",
-       "    lumbosacral_radiculopathy                                                (index) object ...\n",
-       "    heart_valve_disorder                                                     (index) object ...\n",
-       "    cardiac_arrest                                                           (index) object ...\n",
-       "    infection_caused_by_molluscum_contagiosum                                (index) object ...\n",
-       "    chronic_kidney_disease_stage_2                                           (index) object ...\n",
-       "    secondary_malignant_neoplasm_of_peritoneum                               (index) object ...\n",
-       "    thoracic_back_pain                                                       (index) object ...\n",
-       "    blood_in_urine                                                           (index) object ...\n",
-       "    adhesive_capsulitis_of_shoulder                                          (index) object ...\n",
-       "    diplopia                                                                 (index) object ...\n",
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-       "    ureteric_stone                                                           (index) object ...\n",
-       "    bronchospasm                                                             (index) object ...\n",
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-       "    cannabis_dependence                                                      (index) object ...\n",
-       "    neck_sprain                                                              (index) object ...\n",
-       "    multinodular_goiter                                                      (index) object ...\n",
-       "    ptosis_of_eyelid                                                         (index) object ...\n",
-       "    failure_to_thrive                                                        (index) object ...\n",
-       "    torticollis                                                              (index) object ...\n",
-       "    acute_bronchiolitis                                                      (index) object ...\n",
-       "    viral_exanthem                                                           (index) object ...\n",
-       "    talipes_planus                                                           (index) object ...\n",
-       "    idiopathic_peripheral_neuropathy                                         (index) object ...\n",
-       "    foreign_body_in_pharynx                                                  (index) object ...\n",
-       "    jaw_pain                                                                 (index) object ...\n",
-       "    renal_impairment                                                         (index) object ...\n",
-       "    ataxia                                                                   (index) object ...\n",
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-       "    uterine_prolapse                                                         (index) object ...\n",
-       "    renal_mass                                                               (index) object ...\n",
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-       "    musculoskeletal_pain                                                     (index) object ...\n",
-       "    mycosis                                                                  (index) object ...\n",
-       "    primigravida                                                             (index) object ...\n",
-       "    urethral_stricture                                                       (index) object ...\n",
-       "    leukocytosis                                                             (index) object ...\n",
-       "    ventricular_premature_complex                                            (index) object ...\n",
-       "    ulcer_of_foot_due_to_diabetes_mellitus                                   (index) object ...\n",
-       "    chronic_headache_disorder                                                (index) object ...\n",
-       "    hemangioma                                                               (index) object ...\n",
-       "    lymphedema                                                               (index) object ...\n",
-       "    postmenopausal_state                                                     (index) object ...\n",
-       "    chronic_ulcer_of_skin                                                    (index) object ...\n",
-       "    left_heart_failure                                                       (index) object ...\n",
-       "    excessive_and_frequent_menstruation                                      (index) object ...\n",
-       "    thrombocytosis                                                           (index) object ...\n",
-       "    disorder_of_liver                                                        (index) object ...\n",
-       "    disorder_of_carotid_artery                                               (index) object ...\n",
-       "    altered_bowel_function                                                   (index) object ...\n",
-       "    abscess_of_foot                                                          (index) object ...\n",
-       "    malignant_tumor_of_head_and/or_neck                                      (index) object ...\n",
-       "    streptococcus_group_b_infection_of_the_infant                            (index) object ...\n",
-       "    concussion_injury_of_brain                                               (index) object ...\n",
-       "    feeding_problems_in_newborn                                              (index) object ...\n",
-       "    bipolar_i_disorder                                                       (index) object ...\n",
-       "    viral_pharyngitis                                                        (index) object ...\n",
-       "    lower_respiratory_tract_infection                                        (index) object ...\n",
-       "    hydronephrosis                                                           (index) object ...\n",
-       "    borderline_personality_disorder                                          (index) object ...\n",
-       "    esophageal_varices                                                       (index) object ...\n",
-       "    hypersomnia                                                              (index) object ...\n",
-       "    sensorineural_hearing_loss_bilateral                                     (index) object ...\n",
-       "    varicocele                                                               (index) object ...\n",
-       "    subarachnoid_intracranial_hemorrhage                                     (index) object ...\n",
-       "    incisional_hernia                                                        (index) object ...\n",
-       "    varicella                                                                (index) object ...\n",
-       "    pain_in_testicle                                                         (index) object ...\n",
-       "    transplant_follow-up                                                     (index) object ...\n",
-       "    tinea_cruris                                                             (index) object ...\n",
-       "    laryngitis                                                               (index) object ...\n",
-       "    hypertrophy_of_nail                                                      (index) object ...\n",
-       "    amblyopia                                                                (index) object ...\n",
-       "    polyp_of_cervix                                                          (index) object ...\n",
-       "    cyst_of_kidney                                                           (index) object ...\n",
-       "    hepatic_encephalopathy                                                   (index) object ...\n",
-       "    blood_glucose_abnormal                                                   (index) object ...\n",
-       "    postherpetic_neuralgia                                                   (index) object ...\n",
-       "    frank_hematuria                                                          (index) object ...\n",
-       "    cramp                                                                    (index) object ...\n",
-       "    interstitial_lung_disease                                                (index) object ...\n",
-       "    complete_atrioventricular_block                                          (index) object ...\n",
-       "    malignant_tumor_of_kidney                                                (index) object ...\n",
-       "    otitis                                                                   (index) object ...\n",
-       "    septic_shock                                                             (index) object ...\n",
-       "    disorder_of_thyroid_gland                                                (index) object ...\n",
-       "    hypertrophic_cardiomyopathy                                              (index) object ...\n",
-       "    respiratory_distress_syndrome_in_the_newborn                             (index) object ...\n",
-       "    infectious_gastroenteritis                                               (index) object ...\n",
-       "    subdural_intracranial_hemorrhage                                         (index) object ...\n",
-       "    hepatitis_b_carrier                                                      (index) object ...\n",
-       "    manic_bipolar_i_disorder                                                 (index) object ...\n",
-       "    secondary_pulmonary_hypertension                                         (index) object ...\n",
-       "    gonorrhea                                                                (index) object ...\n",
-       "    derangement_of_knee                                                      (index) object ...\n",
-       "    appendicitis                                                             (index) object ...\n",
-       "    polyneuropathy_due_to_diabetes_mellitus                                  (index) object ...\n",
-       "    neonatal_hypoglycemia                                                    (index) object ...\n",
-       "    prolonged_rupture_of_membranes                                           (index) object ...\n",
-       "    vasomotor_rhinitis                                                       (index) object ...\n",
-       "    renal_disorder_due_to_type_1_diabetes_mellitus                           (index) object ...\n",
-       "    tuberculosis                                                             (index) object ...\n",
-       "    feeding_problem                                                          (index) object ...\n",
-       "    chronic_tonsillitis                                                      (index) object ...\n",
-       "    acute_duodenal_ulcer_with_hemorrhage                                     (index) object ...\n",
-       "    hammer_toe                                                               (index) object ...\n",
-       "    malignant_tumor_of_cervix                                                (index) object ...\n",
-       "    prolapsed_lumbar_intervertebral_disc                                     (index) object ...\n",
-       "    hematemesis                                                              (index) object ...\n",
-       "    perianal_abscess                                                         (index) object ...\n",
-       "    nonvenomous_insect_bite                                                  (index) object ...\n",
-       "    spondylolisthesis                                                        (index) object ...\n",
-       "    malignant_tumor_of_esophagus                                             (index) object ...\n",
-       "    aphthous_ulcer_of_mouth                                                  (index) object ...\n",
-       "    ventricular_septal_defect                                                (index) object ...\n",
-       "    oropharyngeal_dysphagia                                                  (index) object ...\n",
-       "    injury_of_knee                                                           (index) object ...\n",
-       "    traumatic_brain_injury                                                   (index) object ...\n",
-       "    osteoarthritis_of_glenohumeral_joint                                     (index) object ...\n",
-       "    fetal_or_neonatal_effect_of_maternal_medical_problem                     (index) object ..."
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "x_data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "basic_data_cols = basic_data.drop(columns=\"birthdate\").columns.to_list()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['t',\n",
-       " 'sex_f31',\n",
-       " 'ethnic_background_f21000',\n",
-       " 'overall_health_rating_f2178',\n",
-       " 'smoking_status_f20116',\n",
-       " 'alcohol_intake_frequency_f1558',\n",
-       " 'townsend_deprivation_index_at_recruitment_f189',\n",
-       " 'body_mass_index_bmi_f21001',\n",
-       " 'weight_f21002',\n",
-       " 'systolic_blood_pressure_automated_reading_f4080',\n",
-       " 'diastolic_blood_pressure_automated_reading_f4079',\n",
-       " 'pulse_rate_automated_reading_f102',\n",
-       " 'pulse_wave_arterial_stiffness_index_f21021',\n",
-       " 'pulse_wave_reflection_index_f4195',\n",
-       " 'waist_circumference_f48',\n",
-       " 'hip_circumference_f49',\n",
-       " 'standing_height_f50',\n",
-       " 'trunk_fat_percentage_f23127',\n",
-       " 'body_fat_percentage_f23099',\n",
-       " 'basal_metabolic_rate_f23105',\n",
-       " 'forced_vital_capacity_fvc_best_measure_f20151',\n",
-       " 'forced_expiratory_volume_in_1second_fev1_best_measure_f20150',\n",
-       " 'fev1_fvc_ratio_zscore_f20258',\n",
-       " 'peak_expiratory_flow_pef_f3064',\n",
-       " 'basophill_count_f30160',\n",
-       " 'basophill_percentage_f30220',\n",
-       " 'eosinophill_count_f30150',\n",
-       " 'eosinophill_percentage_f30210',\n",
-       " 'haematocrit_percentage_f30030',\n",
-       " 'haemoglobin_concentration_f30020',\n",
-       " 'high_light_scatter_reticulocyte_count_f30300',\n",
-       " 'high_light_scatter_reticulocyte_percentage_f30290',\n",
-       " 'immature_reticulocyte_fraction_f30280',\n",
-       " 'lymphocyte_count_f30120',\n",
-       " 'lymphocyte_percentage_f30180',\n",
-       " 'mean_corpuscular_haemoglobin_f30050',\n",
-       " 'mean_corpuscular_haemoglobin_concentration_f30060',\n",
-       " 'mean_corpuscular_volume_f30040',\n",
-       " 'mean_platelet_thrombocyte_volume_f30100',\n",
-       " 'mean_reticulocyte_volume_f30260',\n",
-       " 'mean_sphered_cell_volume_f30270',\n",
-       " 'monocyte_count_f30130',\n",
-       " 'monocyte_percentage_f30190',\n",
-       " 'neutrophill_count_f30140',\n",
-       " 'neutrophill_percentage_f30200',\n",
-       " 'nucleated_red_blood_cell_count_f30170',\n",
-       " 'nucleated_red_blood_cell_percentage_f30230',\n",
-       " 'platelet_count_f30080',\n",
-       " 'platelet_crit_f30090',\n",
-       " 'platelet_distribution_width_f30110',\n",
-       " 'red_blood_cell_erythrocyte_count_f30010',\n",
-       " 'red_blood_cell_erythrocyte_distribution_width_f30070',\n",
-       " 'reticulocyte_count_f30250',\n",
-       " 'reticulocyte_percentage_f30240',\n",
-       " 'white_blood_cell_leukocyte_count_f30000',\n",
-       " 'alanine_aminotransferase_f30620',\n",
-       " 'albumin_f30600',\n",
-       " 'alkaline_phosphatase_f30610',\n",
-       " 'apolipoprotein_a_f30630',\n",
-       " 'apolipoprotein_b_f30640',\n",
-       " 'aspartate_aminotransferase_f30650',\n",
-       " 'creactive_protein_f30710',\n",
-       " 'calcium_f30680',\n",
-       " 'cholesterol_f30690',\n",
-       " 'creatinine_f30700',\n",
-       " 'cystatin_c_f30720',\n",
-       " 'direct_bilirubin_f30660',\n",
-       " 'gamma_glutamyltransferase_f30730',\n",
-       " 'glucose_f30740',\n",
-       " 'glycated_haemoglobin_hba1c_f30750',\n",
-       " 'hdl_cholesterol_f30760',\n",
-       " 'igf1_f30770',\n",
-       " 'ldl_direct_f30780',\n",
-       " 'lipoprotein_a_f30790',\n",
-       " 'oestradiol_f30800',\n",
-       " 'phosphate_f30810',\n",
-       " 'rheumatoid_factor_f30820',\n",
-       " 'shbg_f30830',\n",
-       " 'testosterone_f30850',\n",
-       " 'total_bilirubin_f30840',\n",
-       " 'total_protein_f30860',\n",
-       " 'triglycerides_f30870',\n",
-       " 'urate_f30880',\n",
-       " 'urea_f30670',\n",
-       " 'vitamin_d_f30890',\n",
-       " 'diagnosis',\n",
-       " 'hypertensive_disorder_systemic_arterial',\n",
-       " 'hyperlipidemia',\n",
-       " 'depressive_disorder',\n",
-       " 'gastroesophageal_reflux_disease',\n",
-       " 'diabetes_mellitus_type_2',\n",
-       " 'essential_hypertension',\n",
-       " 'obesity',\n",
-       " 'diabetes_mellitus',\n",
-       " 'asthma',\n",
-       " 'coronary_arteriosclerosis',\n",
-       " 'allergic_rhinitis',\n",
-       " 'hypothyroidism',\n",
-       " 'upper_respiratory_infection',\n",
-       " 'hypercholesterolemia',\n",
-       " 'backache',\n",
-       " 'abdominal_pain',\n",
-       " 'osteoarthritis',\n",
-       " 'low_back_pain',\n",
-       " 'anemia',\n",
-       " 'anxiety',\n",
-       " 'urinary_tract_infectious_disease',\n",
-       " 'chronic_obstructive_lung_disease',\n",
-       " 'atrial_fibrillation',\n",
-       " 'pneumonia',\n",
-       " 'chest_pain',\n",
-       " 'congestive_heart_failure',\n",
-       " 'headache',\n",
-       " 'migraine',\n",
-       " 'pregnant',\n",
-       " 'knee_pain',\n",
-       " 'osteoporosis',\n",
-       " 'polyp_of_colon',\n",
-       " 'otitis_media',\n",
-       " 'sinusitis',\n",
-       " 'cough',\n",
-       " 'sleep_apnea',\n",
-       " 'insomnia',\n",
-       " 'inflammatory_disorder_due_to_increased_blood_urate_level',\n",
-       " 'tobacco_dependence_syndrome',\n",
-       " 'malignant_tumor_of_prostate',\n",
-       " 'constipation',\n",
-       " 'hearing_loss',\n",
-       " 'fatigue',\n",
-       " 'obstructive_sleep_apnea_syndrome',\n",
-       " 'malignant_neoplasm_of_breast',\n",
-       " 'delivery_normal',\n",
-       " 'irritable_bowel_syndrome',\n",
-       " 'tobacco_user',\n",
-       " 'neck_pain',\n",
-       " 'cerebrovascular_accident',\n",
-       " 'asthenia',\n",
-       " 'shoulder_pain',\n",
-       " 'acne_vulgaris',\n",
-       " 'benign_prostatic_hyperplasia',\n",
-       " 'dyspnea',\n",
-       " 'carpal_tunnel_syndrome',\n",
-       " 'bronchitis',\n",
-       " 'pharyngitis',\n",
-       " 'arthritis',\n",
-       " 'diarrhea',\n",
-       " 'dizziness',\n",
-       " 'alcohol_abuse',\n",
-       " 'dementia',\n",
-       " 'eczema',\n",
-       " 'syncope',\n",
-       " 'acute_sinusitis',\n",
-       " 'iron_deficiency_anemia',\n",
-       " 'allergic_rhinitis_caused_by_pollen',\n",
-       " 'gastritis',\n",
-       " 'cataract',\n",
-       " 'hematuria_syndrome',\n",
-       " 'disorder_of_the_peripheral_nervous_system',\n",
-       " 'viral_hepatitis_type_c',\n",
-       " 'palpitations',\n",
-       " 'eruption_of_skin',\n",
-       " 'diabetes_mellitus_type_1',\n",
-       " 'renal_failure_syndrome',\n",
-       " 'peripheral_vascular_disease',\n",
-       " 'hyperglycemia',\n",
-       " 'seizure_disorder',\n",
-       " 'fever',\n",
-       " 'osteoarthritis_of_knee',\n",
-       " 'actinic_keratosis',\n",
-       " 'urinary_incontinence',\n",
-       " 'hemorrhoids',\n",
-       " 'seizure',\n",
-       " 'laceration_-_injury',\n",
-       " 'glaucoma',\n",
-       " 'body_mass_index_30+_-_obesity',\n",
-       " 'breast_lump',\n",
-       " 'viral_disease',\n",
-       " 'abnormal_cervical_smear',\n",
-       " 'cellulitis',\n",
-       " 'rheumatoid_arthritis',\n",
-       " 'senile_hyperkeratosis',\n",
-       " 'anxiety_disorder',\n",
-       " 'vertigo',\n",
-       " 'chronic_kidney_disease',\n",
-       " 'dysphagia',\n",
-       " 'edema',\n",
-       " 'malignant_neoplasm_of_colon',\n",
-       " 'hip_pain',\n",
-       " 'posttraumatic_stress_disorder',\n",
-       " 'inflammatory_dermatosis',\n",
-       " 'psoriasis',\n",
-       " 'myopia',\n",
-       " 'senile_cataract',\n",
-       " 'heart_murmur',\n",
-       " 'liver_function_tests_abnormal',\n",
-       " 'angina',\n",
-       " 'impaired_fasting_glycemia',\n",
-       " 'chronic_ischemic_heart_disease',\n",
-       " 'chronic_sinusitis',\n",
-       " 'menopause_present',\n",
-       " 'basal_cell_carcinoma_of_skin',\n",
-       " 'raised_prostate_specific_antigen',\n",
-       " 'impaired_glucose_tolerance',\n",
-       " 'smoker',\n",
-       " 'hypertriglyceridemia',\n",
-       " 'irregular_periods',\n",
-       " 'herpes_zoster',\n",
-       " 'sensorineural_hearing_loss',\n",
-       " 'rectal_hemorrhage',\n",
-       " 'peptic_ulcer',\n",
-       " 'tinnitus',\n",
-       " 'bipolar_disorder',\n",
-       " 'vitamin_d_deficiency',\n",
-       " 'transient_ischemic_attack',\n",
-       " 'streptococcal_sore_throat',\n",
-       " 'onychomycosis',\n",
-       " 'deep_venous_thrombosis',\n",
-       " 'presbyopia',\n",
-       " 'neonatal_jaundice',\n",
-       " 'bacterial_vaginosis',\n",
-       " 'impacted_cerumen',\n",
-       " 'foot_pain',\n",
-       " 'sciatica',\n",
-       " 'vomiting',\n",
-       " 'benign_prostatic_hypertrophy_with_outflow_obstruction',\n",
-       " 'type_ii_diabetes_mellitus_without_complication',\n",
-       " 'calculus_in_biliary_tract',\n",
-       " 'epigastric_pain',\n",
-       " 'late_effects_of_cerebrovascular_disease',\n",
-       " 'gastroenteritis',\n",
-       " 'pulmonary_embolism',\n",
-       " 'inguinal_hernia',\n",
-       " 'verruca_vulgaris',\n",
-       " 'sepsis',\n",
-       " 'disorder_of_kidney_due_to_diabetes_mellitus',\n",
-       " 'nausea_and_vomiting',\n",
-       " 'hyperthyroidism',\n",
-       " 'abscess',\n",
-       " 'dental_caries',\n",
-       " 'gastrointestinal_hemorrhage',\n",
-       " 'rosacea',\n",
-       " \"parkinson's_disease\",\n",
-       " 'menorrhagia',\n",
-       " 'malignant_tumor_of_lung',\n",
-       " 'joint_pain',\n",
-       " 'morbid_obesity',\n",
-       " 'hiatal_hernia',\n",
-       " 'arthralgia_of_the_ankle_and/or_foot',\n",
-       " 'restless_legs',\n",
-       " 'thrombocytopenic_disorder',\n",
-       " 'old_myocardial_infarction',\n",
-       " 'neuropathy',\n",
-       " 'cardiomyopathy',\n",
-       " 'atopic_dermatitis',\n",
-       " 'pain_in_pelvis',\n",
-       " 'contact_dermatitis',\n",
-       " 'indigestion',\n",
-       " 'nicotine_dependence',\n",
-       " 'sprain_of_ankle',\n",
-       " 'degenerative_disorder_of_macula',\n",
-       " 'exacerbation_of_asthma',\n",
-       " 'alcohol_dependence',\n",
-       " 'hypokalemia',\n",
-       " 'mitral_valve_regurgitation',\n",
-       " 'hyponatremia',\n",
-       " 'abdominal_aortic_aneurysm',\n",
-       " 'cyst_of_ovary',\n",
-       " 'otitis_externa',\n",
-       " 'threatened_abortion',\n",
-       " 'scoliosis_deformity_of_spine',\n",
-       " 'seborrheic_dermatitis',\n",
-       " 'spinal_stenosis',\n",
-       " 'dysmenorrhea',\n",
-       " 'acute_otitis_media',\n",
-       " \"alzheimer's_disease\",\n",
-       " 'neuropathy_due_to_diabetes_mellitus',\n",
-       " 'acute_pharyngitis',\n",
-       " 'degeneration_of_intervertebral_disc',\n",
-       " 'attention_deficit_hyperactivity_disorder_predominantly_inattentive_type',\n",
-       " 'unplanned_pregnancy',\n",
-       " 'secondary_erectile_dysfunction',\n",
-       " 'spinal_stenosis_of_lumbar_region',\n",
-       " 'proteinuria',\n",
-       " 'urticaria',\n",
-       " 'genital_herpes_simplex',\n",
-       " 'malignant_neoplasm_of_female_breast',\n",
-       " 'nausea',\n",
-       " 'chronic_rhinitis',\n",
-       " 'multiple_sclerosis',\n",
-       " 'chronic_kidney_disease_stage_3',\n",
-       " 'panic_disorder',\n",
-       " 'attention_deficit_hyperactivity_disorder',\n",
-       " 'amnesia',\n",
-       " 'otalgia',\n",
-       " 'tremor',\n",
-       " 'retention_of_urine',\n",
-       " \"non-hodgkin's_lymphoma\",\n",
-       " 'alcoholism',\n",
-       " 'dysuria',\n",
-       " 'generalized_anxiety_disorder',\n",
-       " 'paroxysmal_atrial_fibrillation',\n",
-       " 'peripheral_venous_insufficiency',\n",
-       " 'nonproliferative_retinopathy_due_to_diabetes_mellitus',\n",
-       " 'shoulder_joint_pain',\n",
-       " 'moderate_recurrent_major_depression',\n",
-       " 'diverticulitis',\n",
-       " 'solitary_nodule_of_lung',\n",
-       " 'hyperkalemia',\n",
-       " 'recurrent_major_depressive_episodes',\n",
-       " 'multiple_myeloma',\n",
-       " 'regular_astigmatism',\n",
-       " 'secondary_malignant_neoplasm_of_liver',\n",
-       " 'ulcerative_colitis',\n",
-       " 'vaginitis',\n",
-       " 'acute_renal_failure_syndrome',\n",
-       " 'amenorrhea',\n",
-       " 'tendinitis',\n",
-       " 'rhinitis',\n",
-       " 'bleeding_from_nose',\n",
-       " \"crohn's_disease\",\n",
-       " 'nuclear_senile_cataract',\n",
-       " 'muscle_pain',\n",
-       " 'epidermoid_cyst',\n",
-       " 'impaired_cognition',\n",
-       " 'acute_exacerbation_of_chronic_obstructive_airways_disease',\n",
-       " 'eustachian_tube_disorder',\n",
-       " 'internal_hemorrhoids',\n",
-       " 'substance_abuse',\n",
-       " 'melanocytic_nevus',\n",
-       " 'pain',\n",
-       " \"barrett's_esophagus\",\n",
-       " 'cerebrovascular_disease',\n",
-       " 'malignant_melanoma',\n",
-       " 'folliculitis',\n",
-       " 'mitral_valve_prolapse',\n",
-       " 'chronic_hepatitis_c',\n",
-       " 'hypermetropia',\n",
-       " 'endometriosis',\n",
-       " 'gestational_diabetes_mellitus',\n",
-       " 'cirrhosis_of_liver',\n",
-       " 'injury_of_head',\n",
-       " 'dehydration',\n",
-       " 'herpes_simplex',\n",
-       " 'fracture_of_bone',\n",
-       " 'overweight',\n",
-       " 'right_inguinal_hernia',\n",
-       " 'adjustment_disorder',\n",
-       " 'tinea_pedis',\n",
-       " 'aortic_valve_stenosis',\n",
-       " 'viral_hepatitis_type_b',\n",
-       " 'umbilical_hernia',\n",
-       " 'ingrowing_nail',\n",
-       " 'postmenopausal_bleeding',\n",
-       " 'goiter',\n",
-       " 'secondary_malignant_neoplasm_of_bone',\n",
-       " 'pulmonary_emphysema',\n",
-       " 'left_inguinal_hernia',\n",
-       " 'snoring',\n",
-       " 'myocardial_infarction',\n",
-       " 'polycystic_ovary_syndrome',\n",
-       " 'polycystic_ovary',\n",
-       " 'microscopic_hematuria',\n",
-       " 'pain_in_wrist',\n",
-       " 'pleural_effusion',\n",
-       " 'false_labor',\n",
-       " 'bleeding_from_vagina',\n",
-       " 'acquired_hypothyroidism',\n",
-       " 'premature_rupture_of_membranes',\n",
-       " 'human_immunodeficiency_virus_infection',\n",
-       " 'contusion',\n",
-       " 'secondary_malignant_neoplasm_of_lung',\n",
-       " 'problem_situation_relating_to_social_and_personal_history',\n",
-       " 'hypercalcemia',\n",
-       " 'occlusion_of_carotid_artery',\n",
-       " 'prolonged_second_stage_of_labor',\n",
-       " 'lipoma',\n",
-       " 'poisoning_caused_by_drug_and/or_medicinal_substance',\n",
-       " 'paranoid_schizophrenia',\n",
-       " 'hypersensitivity_reaction',\n",
-       " 'dysthymia',\n",
-       " 'fetal_or_neonatal_effect_of_maternal_premature_rupture_of_membrane',\n",
-       " 'noncompliance_with_treatment',\n",
-       " 'falls',\n",
-       " 'muscle_strain',\n",
-       " 'schizophrenia',\n",
-       " 'left_bundle_branch_block',\n",
-       " 'disorder_of_nervous_system_due_to_type_2_diabetes_mellitus',\n",
-       " 'preinfarction_syndrome',\n",
-       " 'conduction_disorder_of_the_heart',\n",
-       " 'lateral_epicondylitis',\n",
-       " 'burn',\n",
-       " 'pyelonephritis',\n",
-       " 'intermittent_claudication',\n",
-       " 'varicose_veins_of_lower_extremity',\n",
-       " 'hemiplegia',\n",
-       " 'chronic_pain',\n",
-       " 'bronchiolitis',\n",
-       " 'mild_persistent_asthma',\n",
-       " 'secondary_malignant_neoplasm_of_lymph_node',\n",
-       " 'mixed_hyperlipidemia',\n",
-       " 'female_urinary_stress_incontinence',\n",
-       " 'localized_primary_osteoarthritis_of_the_ankle_and/or_foot',\n",
-       " 'hypesthesia',\n",
-       " 'hand_pain',\n",
-       " 'psychotic_disorder',\n",
-       " 'menopausal_syndrome',\n",
-       " 'acute_myocardial_infarction_of_anterior_wall',\n",
-       " 'bronchiectasis',\n",
-       " 'lumbar_radiculopathy',\n",
-       " 'osteoarthritis_of_hip',\n",
-       " 'deviated_nasal_septum',\n",
-       " 'paresthesia',\n",
-       " 'hypoglycemia',\n",
-       " 'deep_venous_thrombosis_of_lower_extremity',\n",
-       " 'complication_of_surgical_procedure',\n",
-       " 'mild_intermittent_asthma',\n",
-       " 'systemic_lupus_erythematosus',\n",
-       " 'generalized_ischemic_myocardial_dysfunction',\n",
-       " 'lumbar_spondylosis',\n",
-       " 'acute_tonsillitis',\n",
-       " 'esophagitis',\n",
-       " 'aortic_valve_regurgitation',\n",
-       " 'malignant_neoplasm_of_skin',\n",
-       " 'altered_mental_status',\n",
-       " 'atrial_flutter',\n",
-       " 'alopecia',\n",
-       " 'high_risk_pregnancy',\n",
-       " 'missed_abortion',\n",
-       " 'malignant_tumor_of_urinary_bladder',\n",
-       " 'pulmonary_hypertension',\n",
-       " 'nasal_congestion',\n",
-       " 'hemoptysis',\n",
-       " 'viral_gastroenteritis',\n",
-       " 'right_upper_quadrant_pain',\n",
-       " 'primary_open_angle_glaucoma',\n",
-       " 'malignant_tumor_of_ovary',\n",
-       " 'incomplete_emptying_of_bladder',\n",
-       " 'pruritic_disorder',\n",
-       " 'fibrocystic_breast_changes',\n",
-       " 'tinea_corporis',\n",
-       " 'acute_pancreatitis',\n",
-       " 'acute_myocardial_infarction',\n",
-       " 'cerebral_hemorrhage',\n",
-       " 'urge_incontinence_of_urine',\n",
-       " 'megaloblastic_anemia_due_to_vitamin_b>12<_deficiency',\n",
-       " 'chronic_lymphoid_leukemia_disease',\n",
-       " 'disorder_of_lymphatic_system',\n",
-       " 'diverticular_disease',\n",
-       " 'tonsillitis',\n",
-       " 'tear_film_insufficiency',\n",
-       " 'abnormal_gait',\n",
-       " 'orthostatic_hypotension',\n",
-       " 'pancreatitis',\n",
-       " 'closed_fracture_of_distal_end_of_radius',\n",
-       " 'first_degree_perineal_laceration',\n",
-       " 'not_for_resuscitation',\n",
-       " 'gastroesophageal_reflux_disease_with_esophagitis',\n",
-       " 'intracranial_injury',\n",
-       " \"bell's_palsy\",\n",
-       " 'bradycardia',\n",
-       " 'polymyalgia_rheumatica',\n",
-       " 'dysplasia_of_cervix',\n",
-       " 'serous_otitis_media',\n",
-       " 'angioedema',\n",
-       " 'venous_varices',\n",
-       " 'spasm',\n",
-       " 'sleep_disorder',\n",
-       " 'pain_of_breast',\n",
-       " 'malabsorption_syndrome_due_to_intolerance_to_lactose',\n",
-       " 'tachycardia',\n",
-       " 'acute_conjunctivitis',\n",
-       " 'malignant_lymphoma',\n",
-       " 'supraventricular_tachycardia',\n",
-       " 'hyperparathyroidism',\n",
-       " 'periodontitis',\n",
-       " 'renal_disorder_due_to_type_2_diabetes_mellitus',\n",
-       " 'nerve_root_disorder',\n",
-       " 'nonexudative_age-related_macular_degeneration',\n",
-       " 'tension-type_headache',\n",
-       " 'chronic_pain_syndrome',\n",
-       " 'abnormal_weight_loss',\n",
-       " 'pure_hypercholesterolemia',\n",
-       " 'acute_upper_respiratory_infection',\n",
-       " 'inflammatory_disease_of_liver',\n",
-       " 'colitis',\n",
-       " 'prolonged_pregnancy',\n",
-       " 'major_depression_single_episode',\n",
-       " 'croup',\n",
-       " 'skin_tag',\n",
-       " 'metabolic_syndrome_x',\n",
-       " 'adverse_reaction_caused_by_drug',\n",
-       " 'retinopathy_due_to_diabetes_mellitus',\n",
-       " 'female_infertility',\n",
-       " 'open-angle_glaucoma',\n",
-       " \"tietze's_disease\",\n",
-       " 'gallbladder_calculus',\n",
-       " 'sarcoidosis',\n",
-       " 'neurogenic_bladder',\n",
-       " 'tobacco_dependence_in_remission',\n",
-       " 'wheezing',\n",
-       " 'elevated_levels_of_transaminase_&_lactic_acid_dehydrogenase',\n",
-       " 'ascites',\n",
-       " 'low_blood_pressure',\n",
-       " 'bursitis',\n",
-       " 'miscarriage',\n",
-       " 'methicillin_resistant_staphylococcus_aureus_carrier',\n",
-       " 'urethritis',\n",
-       " 'noninfectious_gastroenteritis',\n",
-       " 'malignant_tumor_of_thyroid_gland',\n",
-       " 'pressure_ulcer',\n",
-       " 'verruca_plantaris',\n",
-       " 'anemia_of_chronic_disorder',\n",
-       " 'hernia_of_anterior_abdominal_wall',\n",
-       " 'degeneration_of_lumbar_intervertebral_disc',\n",
-       " 'right_lower_quadrant_pain',\n",
-       " 'infertile',\n",
-       " 'hernia_of_abdominal_wall',\n",
-       " 'benign_paroxysmal_positional_vertigo',\n",
-       " 'pityriasis_versicolor',\n",
-       " 'injury_of_lower_leg',\n",
-       " 'dry_skin',\n",
-       " 'impacted_tooth',\n",
-       " 'acquired_trigger_finger',\n",
-       " 'chronic_osteoarthritis',\n",
-       " 'acute_stress_disorder',\n",
-       " 'peripheral_circulatory_disorder_due_to_type_2_diabetes_mellitus',\n",
-       " 'respiratory_failure',\n",
-       " 'allergic_disposition',\n",
-       " 'stress',\n",
-       " 'atrophic_vaginitis',\n",
-       " 'degeneration_of_lumbosacral_intervertebral_disc',\n",
-       " 'astigmatism',\n",
-       " 'sick_sinus_syndrome',\n",
-       " 'cocaine_abuse',\n",
-       " 'intermittent_asthma',\n",
-       " 'cervical_radiculopathy',\n",
-       " 'atherosclerosis_of_coronary_artery',\n",
-       " 'methicillin_resistant_staphylococcus_aureus_infection',\n",
-       " 'female_pelvic_inflammatory_disease',\n",
-       " 'pain_in_eye',\n",
-       " 'cervical_spondylosis',\n",
-       " 'prematurity_of_infant',\n",
-       " 'postmature_infancy',\n",
-       " 'temporomandibular_joint_disorder',\n",
-       " 'nocturia',\n",
-       " 'adjustment_disorder_with_depressed_mood',\n",
-       " 'visual_impairment',\n",
-       " 'male_hypogonadism',\n",
-       " 'closed_intertrochanteric_fracture',\n",
-       " 'pain_in_limb',\n",
-       " 'complication_occurring_during_pregnancy',\n",
-       " 'sprain_of_knee',\n",
-       " 'fracture_of_ankle',\n",
-       " 'injury_of_hand',\n",
-       " 'postoperative_wound_infection',\n",
-       " 'congestive_cardiomyopathy',\n",
-       " 'nocturnal_enuresis',\n",
-       " 'proliferative_retinopathy_due_to_diabetes_mellitus',\n",
-       " 'major_depressive_disorder',\n",
-       " 'chronic_bronchitis',\n",
-       " 'advanced_maternal_age_gravida',\n",
-       " 'recurrent_major_depression_in_full_remission',\n",
-       " 'pregnancy-induced_hypertension',\n",
-       " 'epididymitis',\n",
-       " 'impetigo',\n",
-       " 'schizoaffective_disorder',\n",
-       " 'candidiasis_of_mouth',\n",
-       " 'mild_recurrent_major_depression',\n",
-       " 'cerebral_palsy',\n",
-       " 'squamous_cell_carcinoma_of_skin',\n",
-       " 'hypogonadism',\n",
-       " 'greater_trochanteric_pain_syndrome',\n",
-       " \"graves'_disease\",\n",
-       " 'malignant_neoplastic_disease',\n",
-       " 'moderate_persistent_asthma',\n",
-       " 'steatosis_of_liver',\n",
-       " 'obsessive-compulsive_disorder',\n",
-       " 'mood_disorder',\n",
-       " 'degeneration_of_cervical_intervertebral_disc',\n",
-       " 'corneal_abrasion',\n",
-       " 'anal_fissure',\n",
-       " 'heart_failure',\n",
-       " 'adjustment_disorder_with_mixed_emotional_features',\n",
-       " 'febrile_convulsion',\n",
-       " 'degenerative_joint_disease_of_hand',\n",
-       " 'chronic_type_b_viral_hepatitis',\n",
-       " 'blepharitis',\n",
-       " 'cyst',\n",
-       " 'heartburn',\n",
-       " 'intellectual_disability',\n",
-       " 'exudative_age-related_macular_degeneration',\n",
-       " 'hypoxia',\n",
-       " 'influenza',\n",
-       " 'intervertebral_disc_prolapse',\n",
-       " 'swelling_of_first_metatarsophalangeal_joint_of_hallux',\n",
-       " 'genuine_stress_incontinence',\n",
-       " 'opioid_dependence',\n",
-       " 'antepartum_hemorrhage',\n",
-       " 'pneumothorax',\n",
-       " 'kidney_disease',\n",
-       " 'atopic_conjunctivitis',\n",
-       " 'dermatophytosis',\n",
-       " 'influenza_caused_by_influenza_a_virus',\n",
-       " 'cystitis',\n",
-       " 'moderate_major_depression_single_episode',\n",
-       " 'furuncle',\n",
-       " 'cannabis_abuse',\n",
-       " 'conductive_hearing_loss',\n",
-       " 'neutropenic_disorder',\n",
-       " 'injury_of_finger',\n",
-       " 'intestinal_obstruction',\n",
-       " 'lymphadenopathy',\n",
-       " 'mass_of_pelvic_structure',\n",
-       " 'myelodysplastic_syndrome',\n",
-       " 'jaundice',\n",
-       " 'severe_recurrent_major_depression_without_psychotic_features',\n",
-       " 'inflammation_of_cervix',\n",
-       " 'sickle_cell_trait',\n",
-       " 'squamous_cell_carcinoma',\n",
-       " 'small_bowel_obstruction',\n",
-       " 'compression_fracture',\n",
-       " 'premenstrual_tension_syndrome',\n",
-       " 'fracture_of_proximal_end_of_femur',\n",
-       " 'developmental_delay',\n",
-       " 'drug_abuse',\n",
-       " 'left_lower_quadrant_pain',\n",
-       " 'hordeolum',\n",
-       " 'disorder_of_lung',\n",
-       " 'migraine_without_aura',\n",
-       " 'open-angle_glaucoma_-_borderline',\n",
-       " 'disorder_of_refraction',\n",
-       " 'acute_appendicitis',\n",
-       " 'amyloidosis',\n",
-       " \"hodgkin's_disease\",\n",
-       " 'hydrocele_of_testis',\n",
-       " 'cellulitis_of_foot',\n",
-       " 'pancytopenia',\n",
-       " 'pain_in_elbow',\n",
-       " 'peripheral_nerve_disease',\n",
-       " 'eating_disorder',\n",
-       " 'hernia_of_abdominal_cavity',\n",
-       " 'secondary_malignant_neoplasm_of_brain',\n",
-       " 'late_effects_of_respiratory_tuberculosis',\n",
-       " 'alcohol_intoxication',\n",
-       " 'abrasion',\n",
-       " 'breech_presentation',\n",
-       " 'premature_labor',\n",
-       " \"raynaud's_phenomenon\",\n",
-       " 'posterior_rhinorrhea',\n",
-       " 'acute_gastritis',\n",
-       " 'polyp_of_nasal_cavity',\n",
-       " 'essential_tremor',\n",
-       " 'candidiasis',\n",
-       " 'ocular_hypertension',\n",
-       " 'aortic_valve_disorder',\n",
-       " 'diaper_rash',\n",
-       " 'cholangitis',\n",
-       " 'primary_malignant_neoplasm_of_lung',\n",
-       " 'impingement_syndrome_of_shoulder_region',\n",
-       " 'idiopathic_urticaria',\n",
-       " 'arthritis_of_knee',\n",
-       " 'ulcer_of_duodenum',\n",
-       " 'peripheral_neuropathy_due_to_diabetes_mellitus',\n",
-       " 'hypervolemia',\n",
-       " 'cholecystitis',\n",
-       " 'deficiency_of_glucose-6-phosphate_dehydrogenase',\n",
-       " 'acute_pyelonephritis',\n",
-       " 'musculoskeletal_chest_pain',\n",
-       " 'melena',\n",
-       " 'cigarette_smoker',\n",
-       " 'infectious_mononucleosis',\n",
-       " 'hypertensive_renal_failure',\n",
-       " 'cocaine_dependence',\n",
-       " 'abdominal_aortic_aneurysm_without_rupture',\n",
-       " 'right_bundle_branch_block',\n",
-       " 'alcoholic_cirrhosis',\n",
-       " 'fibrosis_of_lung',\n",
-       " 'neoplasm_of_uncertain_behavior_of_skin',\n",
-       " 'malignant_melanoma_of_skin',\n",
-       " 'urgent_desire_to_urinate',\n",
-       " 'bursitis_of_hip',\n",
-       " 'chronic_alcoholism_in_remission',\n",
-       " 'chronic_pancreatitis',\n",
-       " 'gastroparesis',\n",
-       " 'ectopic_pregnancy',\n",
-       " 'muscle_weakness',\n",
-       " 'recurrent_major_depression',\n",
-       " 'pilonidal_cyst',\n",
-       " 'pain_in_toe',\n",
-       " 'pulmonary_tuberculosis',\n",
-       " 'celiac_disease',\n",
-       " 'cramp_in_lower_leg',\n",
-       " 'secondary_malignant_neoplasm_of_pleura',\n",
-       " 'fracture_of_hand',\n",
-       " 'cyst_of_breast',\n",
-       " 'nephrotic_syndrome',\n",
-       " 'polyp_of_nasal_sinus',\n",
-       " 'chondromalacia_of_patella',\n",
-       " 'spinal_stenosis_in_cervical_region',\n",
-       " 'disorder_of_artery',\n",
-       " 'vitiligo',\n",
-       " 'female_cystocele',\n",
-       " 'dysphasia',\n",
-       " 'retinal_disorder',\n",
-       " 'epiretinal_membrane',\n",
-       " 'recurrent_major_depression_in_partial_remission',\n",
-       " 'infection_caused_by_trichomonas',\n",
-       " 'osteomyelitis',\n",
-       " 'polyp_of_nasal_cavity_and/or_nasal_sinus',\n",
-       " 'mass_of_neck',\n",
-       " 'idiopathic_thrombocytopenic_purpura',\n",
-       " 'complete_miscarriage',\n",
-       " 'gastric_ulcer',\n",
-       " 'papilloma_of_skin',\n",
-       " 'fetal_or_neonatal_effect_of_breech_delivery_and_extraction',\n",
-       " 'secondary_malignant_neoplastic_disease',\n",
-       " 'hypoxemia',\n",
-       " 'paraplegia',\n",
-       " 'perforation_of_tympanic_membrane',\n",
-       " 'ventricular_tachycardia',\n",
-       " 'mixed_incontinence',\n",
-       " 'disorder_of_eye_due_to_type_2_diabetes_mellitus',\n",
-       " 'trigeminal_neuralgia',\n",
-       " 'retinal_detachment',\n",
-       " 'leukopenia',\n",
-       " 'vitreous_hemorrhage',\n",
-       " 'ischemic_ulcer',\n",
-       " 'intramural_leiomyoma_of_uterus',\n",
-       " 'viral_hepatitis_type_a',\n",
-       " \"ménière's_disease\",\n",
-       " 'fracture_of_phalanx_of_hand',\n",
-       " 'muscle_atrophy',\n",
-       " 'incontinence_of_feces',\n",
-       " 'mitral_valve_disorder',\n",
-       " 'atherosclerosis_of_arteries_of_the_extremities',\n",
-       " 'spondylosis',\n",
-       " 'pterygium',\n",
-       " 'ulnar_neuropathy',\n",
-       " 'lung_mass',\n",
-       " 'foreign_body_in_respiratory_tract',\n",
-       " 'chronic_kidney_disease_stage_4',\n",
-       " 'myocardial_ischemia',\n",
-       " 'non-toxic_multinodular_goiter',\n",
-       " 'pain_in_finger',\n",
-       " 'cervical_spondylosis_without_myelopathy',\n",
-       " 'body_mass_index_25-29_-_overweight',\n",
-       " 'clouded_consciousness',\n",
-       " 'mixed_conductive_and_sensorineural_hearing_loss',\n",
-       " 'tooth_eruption_disorder',\n",
-       " 'hyperuricemia',\n",
-       " 'closed_fracture_of_neck_of_femur',\n",
-       " 'bipolar_ii_disorder',\n",
-       " 'disturbance_in_sleep_behavior',\n",
-       " 'relationship_problems',\n",
-       " 'sprain_of_wrist',\n",
-       " 'personality_disorder',\n",
-       " 'external_hemorrhoids',\n",
-       " 'abnormal_vision',\n",
-       " 'hyperprolactinemia',\n",
-       " 'hemochromatosis',\n",
-       " 'lumbosacral_radiculopathy',\n",
-       " 'heart_valve_disorder',\n",
-       " 'cardiac_arrest',\n",
-       " 'infection_caused_by_molluscum_contagiosum',\n",
-       " 'chronic_kidney_disease_stage_2',\n",
-       " 'secondary_malignant_neoplasm_of_peritoneum',\n",
-       " 'thoracic_back_pain',\n",
-       " 'blood_in_urine',\n",
-       " 'adhesive_capsulitis_of_shoulder',\n",
-       " 'diplopia',\n",
-       " \"sjögren's_syndrome\",\n",
-       " 'ureteric_stone',\n",
-       " 'bronchospasm',\n",
-       " 'chronic_fatigue_syndrome',\n",
-       " 'cannabis_dependence',\n",
-       " 'neck_sprain',\n",
-       " 'multinodular_goiter',\n",
-       " 'ptosis_of_eyelid',\n",
-       " 'failure_to_thrive',\n",
-       " 'torticollis',\n",
-       " 'acute_bronchiolitis',\n",
-       " 'viral_exanthem',\n",
-       " 'talipes_planus',\n",
-       " 'idiopathic_peripheral_neuropathy',\n",
-       " 'foreign_body_in_pharynx',\n",
-       " 'jaw_pain',\n",
-       " 'renal_impairment',\n",
-       " 'ataxia',\n",
-       " 'age-related_macular_degeneration',\n",
-       " 'uterine_prolapse',\n",
-       " 'renal_mass',\n",
-       " 'pneumonitis',\n",
-       " 'coordination_problem',\n",
-       " 'blindness_-_both_eyes',\n",
-       " 'primary_hyperparathyroidism',\n",
-       " 'musculoskeletal_pain',\n",
-       " 'mycosis',\n",
-       " 'primigravida',\n",
-       " 'urethral_stricture',\n",
-       " 'leukocytosis',\n",
-       " 'ventricular_premature_complex',\n",
-       " 'ulcer_of_foot_due_to_diabetes_mellitus',\n",
-       " 'chronic_headache_disorder',\n",
-       " 'hemangioma',\n",
-       " 'lymphedema',\n",
-       " 'postmenopausal_state',\n",
-       " 'chronic_ulcer_of_skin',\n",
-       " 'left_heart_failure',\n",
-       " 'excessive_and_frequent_menstruation',\n",
-       " 'thrombocytosis',\n",
-       " 'disorder_of_liver',\n",
-       " 'disorder_of_carotid_artery',\n",
-       " 'altered_bowel_function',\n",
-       " 'abscess_of_foot',\n",
-       " 'malignant_tumor_of_head_and/or_neck',\n",
-       " 'streptococcus_group_b_infection_of_the_infant',\n",
-       " 'concussion_injury_of_brain',\n",
-       " 'feeding_problems_in_newborn',\n",
-       " 'bipolar_i_disorder',\n",
-       " 'viral_pharyngitis',\n",
-       " 'lower_respiratory_tract_infection',\n",
-       " 'hydronephrosis',\n",
-       " 'borderline_personality_disorder',\n",
-       " 'esophageal_varices',\n",
-       " 'hypersomnia',\n",
-       " 'sensorineural_hearing_loss_bilateral',\n",
-       " 'varicocele',\n",
-       " 'subarachnoid_intracranial_hemorrhage',\n",
-       " 'incisional_hernia',\n",
-       " 'varicella',\n",
-       " 'pain_in_testicle',\n",
-       " 'transplant_follow-up',\n",
-       " 'tinea_cruris',\n",
-       " 'laryngitis',\n",
-       " 'hypertrophy_of_nail',\n",
-       " 'amblyopia',\n",
-       " 'polyp_of_cervix',\n",
-       " 'cyst_of_kidney',\n",
-       " 'hepatic_encephalopathy',\n",
-       " 'blood_glucose_abnormal',\n",
-       " 'postherpetic_neuralgia',\n",
-       " 'frank_hematuria',\n",
-       " 'cramp',\n",
-       " 'interstitial_lung_disease',\n",
-       " 'complete_atrioventricular_block',\n",
-       " 'malignant_tumor_of_kidney',\n",
-       " 'otitis',\n",
-       " 'septic_shock',\n",
-       " 'disorder_of_thyroid_gland',\n",
-       " 'hypertrophic_cardiomyopathy',\n",
-       " 'respiratory_distress_syndrome_in_the_newborn',\n",
-       " 'infectious_gastroenteritis',\n",
-       " 'subdural_intracranial_hemorrhage',\n",
-       " 'hepatitis_b_carrier',\n",
-       " 'manic_bipolar_i_disorder',\n",
-       " 'secondary_pulmonary_hypertension',\n",
-       " 'gonorrhea',\n",
-       " 'derangement_of_knee',\n",
-       " 'appendicitis',\n",
-       " 'polyneuropathy_due_to_diabetes_mellitus',\n",
-       " 'neonatal_hypoglycemia',\n",
-       " 'prolonged_rupture_of_membranes',\n",
-       " 'vasomotor_rhinitis',\n",
-       " 'renal_disorder_due_to_type_1_diabetes_mellitus',\n",
-       " 'tuberculosis',\n",
-       " 'feeding_problem',\n",
-       " 'chronic_tonsillitis',\n",
-       " 'acute_duodenal_ulcer_with_hemorrhage',\n",
-       " 'hammer_toe',\n",
-       " 'malignant_tumor_of_cervix',\n",
-       " 'prolapsed_lumbar_intervertebral_disc',\n",
-       " 'hematemesis',\n",
-       " 'perianal_abscess',\n",
-       " 'nonvenomous_insect_bite',\n",
-       " 'spondylolisthesis',\n",
-       " 'malignant_tumor_of_esophagus',\n",
-       " 'aphthous_ulcer_of_mouth',\n",
-       " 'ventricular_septal_defect',\n",
-       " 'oropharyngeal_dysphagia',\n",
-       " 'injury_of_knee',\n",
-       " 'traumatic_brain_injury',\n",
-       " 'osteoarthritis_of_glenohumeral_joint',\n",
-       " 'fetal_or_neonatal_effect_of_maternal_medical_problem']"
-      ]
-     },
-     "execution_count": 61,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "basic_data_cols"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Endpoints"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 89,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:39:55.628580Z",
-     "start_time": "2020-11-04T12:33:33.036Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "### define in snomed and get icd codes from there"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 1. Hospital admissions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T12:39:55.629459Z",
-     "start_time": "2020-11-04T12:33:34.764Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'myocardial_infarction': ['I21', 'I22', 'I23', 'I24', 'I25', 'I51'],\n",
-       " 'stroke': ['G45', 'G46', 'I60', 'I67', 'I68', 'I69'],\n",
-       " 'cancer_breast': ['C50'],\n",
-       " 'diabetes': ['E10', 'E11', 'E12', 'E13', 'E14'],\n",
-       " 'atrial_fibrillation': ['I47', 'I48'],\n",
-       " 'copd': ['J44'],\n",
-       " 'dementia': ['F00', 'F01', 'F02', 'F03', 'F09', 'G31', 'R54']}"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "endpoint_list = {\n",
-    "    \"myocardial_infarction\": [\"Myocardial infarction\"],\n",
-    "    \"stroke\": [\"Cerebrovascular disease\"],\n",
-    "    \"cancer_breast\" : [\"Breast Cancer\"],\n",
-    "    \"diabetes\" : [\"Diabetes\"],\n",
-    "    \"atrial_fibrillation\": [\"Atrial fibrillation\", \"Atrial flutter\", \"paroxysmal tachycardia\"],\n",
-    "    \"copd\": [\"COPD\"],\n",
-    "    \"dementia\":[\"dementia\"]\n",
-    "}\n",
-    "\n",
-    "endpoint_list = phenotype_children(phecodes, endpoint_list)\n",
-    "endpoint_list[\"cancer_breast\"] = [\"C50\"]\n",
-    "endpoint_list[\"copd\"] = [\"J44\"]\n",
-    "endpoint_list[\"diabetes\"] = [\"E10\", \"E11\", \"E12\", \"E13\", \"E14\"]\n",
-    "endpoint_list[\"atrial_fibrillation\"] = [\"I47\", \"I48\"]\n",
-    "\n",
-    "\n",
-    "with open(os.path.join(path, dataset_path, 'endpoint_list.yaml'), 'w') as file: yaml.dump(endpoint_list, file, default_flow_style=False)\n",
-    "endpoint_list"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from dateutil.relativedelta import relativedelta\n",
-    "\n",
-    "def extract_endpoints_tte(data, diagnoses_codes, endpoint_list, time0_col, level=None):\n",
-    "    if level is not None: diagnoses_codes = diagnoses_codes.query(\"level==@level\")\n",
-    "    diagnoses_codes_time0 = diagnoses_codes.merge(data[[\"eid\", time0_col]], how=\"left\", on=\"eid\")\n",
-    "    \n",
-    "    cens_time_right = min(diagnoses_codes.sort_values('date').groupby('origin').tail(1).date.to_list())\n",
-    "    print(f\"t_0: {time0_col}\")\n",
-    "    print(f\"t_cens: {cens_time_right}\")\n",
-    "    \n",
-    "    df_interval = diagnoses_codes_time0[(diagnoses_codes_time0.date > diagnoses_codes_time0[time0_col]) & \n",
-    "                                        (diagnoses_codes_time0.date < cens_time_right)]\n",
-    "    \n",
-    "    temp = data[[\"eid\", time0_col]].copy()\n",
-    "    for ph, ph_codes in tqdm(endpoint_list.items()):\n",
-    "        regex = \"|\".join(ph_codes)\n",
-    "        ph_df = df_interval[df_interval.meaning.str.contains(regex, case=False)] \\\n",
-    "            .sort_values('date').groupby('eid').head(1).assign(phenotype=1, date=lambda x: x.date)\n",
-    "        temp_ph = temp.merge(ph_df, how=\"left\", on=\"eid\").fillna(0)\n",
-    "        temp[ph+\"_event\"], temp[ph+\"_event_date\"] = temp_ph.phenotype, temp_ph.date\n",
-    "        \n",
-    "        fill_date = {ph+\"_event_date\" : lambda x: [cens_time_right if event==0 else event_date for event, event_date in zip(x[ph+\"_event\"], x[ph+\"_event_date\"])]}\n",
-    "        calc_tte = {ph+\"_event_time\" : lambda x: [(event_date-time0).days/365.25  for time0, event_date in zip(x[time0_col], x[ph+\"_event_date\"])]}\n",
-    "        \n",
-    "        temp = temp.assign(**fill_date).assign(**calc_tte).drop([ph+\"_event_date\"], axis=1)\n",
-    "        \n",
-    "    temp = temp.drop([time0_col], axis=1)     \n",
-    "    \n",
-    "    return temp.drop_duplicates()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>age_at_recruitment_f21022_0_0</th>\n",
-       "      <th>sex_f31_0_0</th>\n",
-       "      <th>ethnic_background_f21000_0_0</th>\n",
-       "      <th>ethnic_background_f21000_1_0</th>\n",
-       "      <th>ethnic_background_f21000_2_0</th>\n",
-       "      <th>townsend_deprivation_index_at_recruitment_f189_0_0</th>\n",
-       "      <th>date_of_attending_assessment_centre_f53_0_0</th>\n",
-       "      <th>date_of_attending_assessment_centre_f53_1_0</th>\n",
-       "      <th>date_of_attending_assessment_centre_f53_2_0</th>\n",
-       "      <th>date_of_attending_assessment_centre_f53_3_0</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>49.0</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>British</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-1.852930</td>\n",
-       "      <td>2009-11-12</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1000020</td>\n",
-       "      <td>59.0</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>British</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.204248</td>\n",
-       "      <td>2008-02-19</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1000037</td>\n",
-       "      <td>59.0</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>British</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-3.498860</td>\n",
-       "      <td>2008-11-11</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000043</td>\n",
-       "      <td>63.0</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>British</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-5.351150</td>\n",
-       "      <td>2009-06-03</td>\n",
-       "      <td>None</td>\n",
-       "      <td>2018-06-08</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1000051</td>\n",
-       "      <td>51.0</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>British</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-1.799080</td>\n",
-       "      <td>2006-06-10</td>\n",
-       "      <td>None</td>\n",
-       "      <td>2019-09-15</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>502499</th>\n",
-       "      <td>6025150</td>\n",
-       "      <td>43.0</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>British</td>\n",
-       "      <td>British</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.046781</td>\n",
-       "      <td>2007-06-30</td>\n",
-       "      <td>2012-11-17</td>\n",
-       "      <td>2017-08-12</td>\n",
-       "      <td>2019-10-20</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>502500</th>\n",
-       "      <td>6025165</td>\n",
-       "      <td>45.0</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>British</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-2.107040</td>\n",
-       "      <td>2008-09-02</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>502501</th>\n",
-       "      <td>6025173</td>\n",
-       "      <td>57.0</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>British</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-1.827220</td>\n",
-       "      <td>2008-09-17</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>502502</th>\n",
-       "      <td>6025182</td>\n",
-       "      <td>56.0</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>British</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-0.010764</td>\n",
-       "      <td>2010-07-01</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>502503</th>\n",
-       "      <td>6025198</td>\n",
-       "      <td>67.0</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>British</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-1.930650</td>\n",
-       "      <td>2010-01-26</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>502504 rows × 11 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            eid  age_at_recruitment_f21022_0_0 sex_f31_0_0  \\\n",
-       "0       1000018                           49.0      Female   \n",
-       "1       1000020                           59.0        Male   \n",
-       "2       1000037                           59.0      Female   \n",
-       "3       1000043                           63.0        Male   \n",
-       "4       1000051                           51.0      Female   \n",
-       "...         ...                            ...         ...   \n",
-       "502499  6025150                           43.0      Female   \n",
-       "502500  6025165                           45.0      Female   \n",
-       "502501  6025173                           57.0        Male   \n",
-       "502502  6025182                           56.0        Male   \n",
-       "502503  6025198                           67.0        Male   \n",
-       "\n",
-       "       ethnic_background_f21000_0_0 ethnic_background_f21000_1_0  \\\n",
-       "0                           British                          NaN   \n",
-       "1                           British                          NaN   \n",
-       "2                           British                          NaN   \n",
-       "3                           British                          NaN   \n",
-       "4                           British                          NaN   \n",
-       "...                             ...                          ...   \n",
-       "502499                      British                      British   \n",
-       "502500                      British                          NaN   \n",
-       "502501                      British                          NaN   \n",
-       "502502                      British                          NaN   \n",
-       "502503                      British                          NaN   \n",
-       "\n",
-       "       ethnic_background_f21000_2_0  \\\n",
-       "0                               NaN   \n",
-       "1                               NaN   \n",
-       "2                               NaN   \n",
-       "3                               NaN   \n",
-       "4                               NaN   \n",
-       "...                             ...   \n",
-       "502499                          NaN   \n",
-       "502500                          NaN   \n",
-       "502501                          NaN   \n",
-       "502502                          NaN   \n",
-       "502503                          NaN   \n",
-       "\n",
-       "        townsend_deprivation_index_at_recruitment_f189_0_0  \\\n",
-       "0                                               -1.852930    \n",
-       "1                                                0.204248    \n",
-       "2                                               -3.498860    \n",
-       "3                                               -5.351150    \n",
-       "4                                               -1.799080    \n",
-       "...                                                   ...    \n",
-       "502499                                           0.046781    \n",
-       "502500                                          -2.107040    \n",
-       "502501                                          -1.827220    \n",
-       "502502                                          -0.010764    \n",
-       "502503                                          -1.930650    \n",
-       "\n",
-       "       date_of_attending_assessment_centre_f53_0_0  \\\n",
-       "0                                       2009-11-12   \n",
-       "1                                       2008-02-19   \n",
-       "2                                       2008-11-11   \n",
-       "3                                       2009-06-03   \n",
-       "4                                       2006-06-10   \n",
-       "...                                            ...   \n",
-       "502499                                  2007-06-30   \n",
-       "502500                                  2008-09-02   \n",
-       "502501                                  2008-09-17   \n",
-       "502502                                  2010-07-01   \n",
-       "502503                                  2010-01-26   \n",
-       "\n",
-       "       date_of_attending_assessment_centre_f53_1_0  \\\n",
-       "0                                             None   \n",
-       "1                                             None   \n",
-       "2                                             None   \n",
-       "3                                             None   \n",
-       "4                                             None   \n",
-       "...                                            ...   \n",
-       "502499                                  2012-11-17   \n",
-       "502500                                        None   \n",
-       "502501                                        None   \n",
-       "502502                                        None   \n",
-       "502503                                        None   \n",
-       "\n",
-       "       date_of_attending_assessment_centre_f53_2_0  \\\n",
-       "0                                             None   \n",
-       "1                                             None   \n",
-       "2                                             None   \n",
-       "3                                       2018-06-08   \n",
-       "4                                       2019-09-15   \n",
-       "...                                            ...   \n",
-       "502499                                  2017-08-12   \n",
-       "502500                                        None   \n",
-       "502501                                        None   \n",
-       "502502                                        None   \n",
-       "502503                                        None   \n",
-       "\n",
-       "       date_of_attending_assessment_centre_f53_3_0  \n",
-       "0                                             None  \n",
-       "1                                             None  \n",
-       "2                                             None  \n",
-       "3                                             None  \n",
-       "4                                             None  \n",
-       "...                                            ...  \n",
-       "502499                                  2019-10-20  \n",
-       "502500                                        None  \n",
-       "502501                                        None  \n",
-       "502502                                        None  \n",
-       "502503                                        None  \n",
-       "\n",
-       "[502504 rows x 11 columns]"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "basics"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "  <thead>\n",
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-       "      <th></th>\n",
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-       "      <th>date_of_attending_assessment_centre_f53_3_0</th>\n",
-       "      <th>birth_date</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>49.0</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>British</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-1.852930</td>\n",
-       "      <td>2009-11-12</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>1960-11-12</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1000020</td>\n",
-       "      <td>59.0</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>British</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.204248</td>\n",
-       "      <td>2008-02-19</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>1949-02-19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1000037</td>\n",
-       "      <td>59.0</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>British</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-3.498860</td>\n",
-       "      <td>2008-11-11</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>1949-11-11</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000043</td>\n",
-       "      <td>63.0</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>British</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-5.351150</td>\n",
-       "      <td>2009-06-03</td>\n",
-       "      <td>None</td>\n",
-       "      <td>2018-06-08</td>\n",
-       "      <td>None</td>\n",
-       "      <td>1946-06-03</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1000051</td>\n",
-       "      <td>51.0</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>British</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-1.799080</td>\n",
-       "      <td>2006-06-10</td>\n",
-       "      <td>None</td>\n",
-       "      <td>2019-09-15</td>\n",
-       "      <td>None</td>\n",
-       "      <td>1955-06-10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>502499</th>\n",
-       "      <td>6025150</td>\n",
-       "      <td>43.0</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>British</td>\n",
-       "      <td>British</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.046781</td>\n",
-       "      <td>2007-06-30</td>\n",
-       "      <td>2012-11-17</td>\n",
-       "      <td>2017-08-12</td>\n",
-       "      <td>2019-10-20</td>\n",
-       "      <td>1964-06-30</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>502500</th>\n",
-       "      <td>6025165</td>\n",
-       "      <td>45.0</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>British</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-2.107040</td>\n",
-       "      <td>2008-09-02</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>1963-09-02</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>502501</th>\n",
-       "      <td>6025173</td>\n",
-       "      <td>57.0</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>British</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-1.827220</td>\n",
-       "      <td>2008-09-17</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>1951-09-17</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>502502</th>\n",
-       "      <td>6025182</td>\n",
-       "      <td>56.0</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>British</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-0.010764</td>\n",
-       "      <td>2010-07-01</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>1954-07-01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>502503</th>\n",
-       "      <td>6025198</td>\n",
-       "      <td>67.0</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>British</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-1.930650</td>\n",
-       "      <td>2010-01-26</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>1943-01-26</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>502504 rows × 12 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            eid  age_at_recruitment_f21022_0_0 sex_f31_0_0  \\\n",
-       "0       1000018                           49.0      Female   \n",
-       "1       1000020                           59.0        Male   \n",
-       "2       1000037                           59.0      Female   \n",
-       "3       1000043                           63.0        Male   \n",
-       "4       1000051                           51.0      Female   \n",
-       "...         ...                            ...         ...   \n",
-       "502499  6025150                           43.0      Female   \n",
-       "502500  6025165                           45.0      Female   \n",
-       "502501  6025173                           57.0        Male   \n",
-       "502502  6025182                           56.0        Male   \n",
-       "502503  6025198                           67.0        Male   \n",
-       "\n",
-       "       ethnic_background_f21000_0_0 ethnic_background_f21000_1_0  \\\n",
-       "0                           British                          NaN   \n",
-       "1                           British                          NaN   \n",
-       "2                           British                          NaN   \n",
-       "3                           British                          NaN   \n",
-       "4                           British                          NaN   \n",
-       "...                             ...                          ...   \n",
-       "502499                      British                      British   \n",
-       "502500                      British                          NaN   \n",
-       "502501                      British                          NaN   \n",
-       "502502                      British                          NaN   \n",
-       "502503                      British                          NaN   \n",
-       "\n",
-       "       ethnic_background_f21000_2_0  \\\n",
-       "0                               NaN   \n",
-       "1                               NaN   \n",
-       "2                               NaN   \n",
-       "3                               NaN   \n",
-       "4                               NaN   \n",
-       "...                             ...   \n",
-       "502499                          NaN   \n",
-       "502500                          NaN   \n",
-       "502501                          NaN   \n",
-       "502502                          NaN   \n",
-       "502503                          NaN   \n",
-       "\n",
-       "        townsend_deprivation_index_at_recruitment_f189_0_0  \\\n",
-       "0                                               -1.852930    \n",
-       "1                                                0.204248    \n",
-       "2                                               -3.498860    \n",
-       "3                                               -5.351150    \n",
-       "4                                               -1.799080    \n",
-       "...                                                   ...    \n",
-       "502499                                           0.046781    \n",
-       "502500                                          -2.107040    \n",
-       "502501                                          -1.827220    \n",
-       "502502                                          -0.010764    \n",
-       "502503                                          -1.930650    \n",
-       "\n",
-       "       date_of_attending_assessment_centre_f53_0_0  \\\n",
-       "0                                       2009-11-12   \n",
-       "1                                       2008-02-19   \n",
-       "2                                       2008-11-11   \n",
-       "3                                       2009-06-03   \n",
-       "4                                       2006-06-10   \n",
-       "...                                            ...   \n",
-       "502499                                  2007-06-30   \n",
-       "502500                                  2008-09-02   \n",
-       "502501                                  2008-09-17   \n",
-       "502502                                  2010-07-01   \n",
-       "502503                                  2010-01-26   \n",
-       "\n",
-       "       date_of_attending_assessment_centre_f53_1_0  \\\n",
-       "0                                             None   \n",
-       "1                                             None   \n",
-       "2                                             None   \n",
-       "3                                             None   \n",
-       "4                                             None   \n",
-       "...                                            ...   \n",
-       "502499                                  2012-11-17   \n",
-       "502500                                        None   \n",
-       "502501                                        None   \n",
-       "502502                                        None   \n",
-       "502503                                        None   \n",
-       "\n",
-       "       date_of_attending_assessment_centre_f53_2_0  \\\n",
-       "0                                             None   \n",
-       "1                                             None   \n",
-       "2                                             None   \n",
-       "3                                       2018-06-08   \n",
-       "4                                       2019-09-15   \n",
-       "...                                            ...   \n",
-       "502499                                  2017-08-12   \n",
-       "502500                                        None   \n",
-       "502501                                        None   \n",
-       "502502                                        None   \n",
-       "502503                                        None   \n",
-       "\n",
-       "       date_of_attending_assessment_centre_f53_3_0  birth_date  \n",
-       "0                                             None  1960-11-12  \n",
-       "1                                             None  1949-02-19  \n",
-       "2                                             None  1949-11-11  \n",
-       "3                                             None  1946-06-03  \n",
-       "4                                             None  1955-06-10  \n",
-       "...                                            ...         ...  \n",
-       "502499                                  2019-10-20  1964-06-30  \n",
-       "502500                                        None  1963-09-02  \n",
-       "502501                                        None  1951-09-17  \n",
-       "502502                                        None  1954-07-01  \n",
-       "502503                                        None  1943-01-26  \n",
-       "\n",
-       "[502504 rows x 12 columns]"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "basics_birthdate"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "t_0: birthdate\n",
-      "t_cens: 2020-03-31\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "20dd3eaec2c44ae99de7ee70c073218d",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(HTML(value=''), FloatProgress(value=0.0, max=7.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "502504\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>myocardial_infarction_event</th>\n",
-       "      <th>myocardial_infarction_event_time</th>\n",
-       "      <th>stroke_event</th>\n",
-       "      <th>stroke_event_time</th>\n",
-       "      <th>cancer_breast_event</th>\n",
-       "      <th>cancer_breast_event_time</th>\n",
-       "      <th>diabetes_event</th>\n",
-       "      <th>diabetes_event_time</th>\n",
-       "      <th>atrial_fibrillation_event</th>\n",
-       "      <th>atrial_fibrillation_event_time</th>\n",
-       "      <th>copd_event</th>\n",
-       "      <th>copd_event_time</th>\n",
-       "      <th>dementia_event</th>\n",
-       "      <th>dementia_event_time</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>59.381246</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>59.381246</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>59.381246</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>59.381246</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>59.381246</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>59.381246</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>59.381246</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1000020</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>71.110198</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>71.110198</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>71.110198</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>71.110198</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>71.110198</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>71.110198</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>71.110198</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1000037</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>70.384668</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>70.384668</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>70.384668</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>70.384668</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>70.384668</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>70.384668</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>70.384668</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000043</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>68.123203</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>73.826146</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>73.826146</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>73.826146</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>73.826146</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>63.293634</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>73.826146</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1000051</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>64.807666</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>64.807666</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>64.807666</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>55.723477</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>64.807666</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>55.841205</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>64.807666</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       eid  myocardial_infarction_event  myocardial_infarction_event_time  \\\n",
-       "0  1000018                          0.0                         59.381246   \n",
-       "1  1000020                          0.0                         71.110198   \n",
-       "2  1000037                          0.0                         70.384668   \n",
-       "3  1000043                          1.0                         68.123203   \n",
-       "4  1000051                          0.0                         64.807666   \n",
-       "\n",
-       "   stroke_event  stroke_event_time  cancer_breast_event  \\\n",
-       "0           0.0          59.381246                  0.0   \n",
-       "1           0.0          71.110198                  0.0   \n",
-       "2           0.0          70.384668                  0.0   \n",
-       "3           0.0          73.826146                  0.0   \n",
-       "4           0.0          64.807666                  0.0   \n",
-       "\n",
-       "   cancer_breast_event_time  diabetes_event  diabetes_event_time  \\\n",
-       "0                 59.381246             0.0            59.381246   \n",
-       "1                 71.110198             0.0            71.110198   \n",
-       "2                 70.384668             0.0            70.384668   \n",
-       "3                 73.826146             0.0            73.826146   \n",
-       "4                 64.807666             1.0            55.723477   \n",
-       "\n",
-       "   atrial_fibrillation_event  atrial_fibrillation_event_time  copd_event  \\\n",
-       "0                        0.0                       59.381246         0.0   \n",
-       "1                        0.0                       71.110198         0.0   \n",
-       "2                        0.0                       70.384668         0.0   \n",
-       "3                        0.0                       73.826146         1.0   \n",
-       "4                        0.0                       64.807666         1.0   \n",
-       "\n",
-       "   copd_event_time  dementia_event  dementia_event_time  \n",
-       "0        59.381246             0.0            59.381246  \n",
-       "1        71.110198             0.0            71.110198  \n",
-       "2        70.384668             0.0            70.384668  \n",
-       "3        63.293634             0.0            73.826146  \n",
-       "4        55.841205             0.0            64.807666  "
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from dateutil.relativedelta import relativedelta\n",
-    "calc_birth_date = [date_of_attending_assessment_centre - relativedelta(years=age_at_recruitment) \n",
-    "                                                             for date_of_attending_assessment_centre, age_at_recruitment \n",
-    "                                                             in zip(basics[\"date_of_attending_assessment_centre_f53_0_0\"], basics[\"age_at_recruitment_f21022_0_0\"])]\n",
-    "basics_birthdate = basics.assign(birthdate = calc_birth_date)\n",
-    "endpoints_hospital = extract_endpoints_tte(basics_birthdate, diagnoses_codes, endpoint_list, time0_col)\n",
-    "print(len(endpoints_hospital))\n",
-    "endpoints_hospital.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 2. Death registry"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "death_list = {\n",
-    "    \"death_allcause\":[],\n",
-    "    \"death_cvd\":['I{:02}'.format(ID+1) for ID in range(0, 98)],\n",
-    "}\n",
-    "\n",
-    "death_codes = pd.read_feather(f\"{data_path}/1_decoded/codes_death_records.feather\")#.drop(\"level\", axis=1)\n",
-    "\n",
-    "with open(os.path.join(path, dataset_path, 'death_list.yaml'), 'w') as file: yaml.dump(death_list, file, default_flow_style=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "t_0: birthdate\n",
-      "t_cens: 2020-06-28\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b38bd4ceed9a44709826266bc2f93860",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(HTML(value=''), FloatProgress(value=0.0, max=2.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "endpoints_death = extract_endpoints_tte(basics_birthdate, death_codes, death_list, time0_col, level=\"1\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## SCORES"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "scores_list = {\n",
-    "    \"SCORE\":['I{:02}'.format(ID) for ID in [10, 11, 12, 13, 14, 15, 20, 21, 22, 23, 24, 25, 44, 45, 46, 47, 48, 49, 50, 51, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73]],\n",
-    "    \"ASCVD\":['I{:02}'.format(ID) for ID in [20, 21, 22, 23, 24, 25, 63]],\n",
-    "    \"QRISK3\":[\"G45\", \"I20\", \"I21\", \"I22\", \"I23\", \"I24\", \"I25\", \"I63\", \"I64\"],\n",
-    "    \"MACE\":[\"G45\", \"I21\", \"I22\", \"I23\", \"I24\", \"I25\", \"I63\", \"I64\"],    \n",
-    "}\n",
-    "with open(os.path.join(path, dataset_path, 'scores_list.yaml'), 'w') as file: yaml.dump(scores_list, file, default_flow_style=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "scores_list_hospital = {}\n",
-    "scores_list_death = {}\n",
-    "for score, score_codes in scores_list.items():\n",
-    "    scores_list_hospital[\"hospital_\"+score] = score_codes\n",
-    "    scores_list_death[\"death_\"+score] = score_codes"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "t_0: birthdate\n",
-      "t_cens: 2020-03-31\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c2ba870f3b654ffdb85c9127ca23e24f",
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-       "version_minor": 0
-      },
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-       "HBox(children=(HTML(value=''), FloatProgress(value=0.0, max=4.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "t_0: birthdate\n",
-      "t_cens: 2020-06-28\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "713e4fb88e1149fb95e28fdd6398e1ad",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(HTML(value=''), FloatProgress(value=0.0, max=4.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "endpoints_scores = {\n",
-    "    \"hospital\": extract_endpoints_tte(basics_birthdate, diagnoses_codes, scores_list_hospital, time0_col=time0_col),\n",
-    "    \"death\": extract_endpoints_tte(basics_birthdate, death_codes, scores_list_death, time0_col=time0_col, level=1)}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "endpoints_scores_all = endpoints_scores[\"hospital\"].merge(endpoints_scores[\"death\"], on=\"eid\", how=\"left\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### ESC SCORE (Conroy 2003)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "5323\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>SCORE_event</th>\n",
-       "      <th>SCORE_event_time</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>45</th>\n",
-       "      <td>1000463</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>74.165640</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>83</th>\n",
-       "      <td>1000841</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>76.005476</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>102</th>\n",
-       "      <td>1001031</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>75.537303</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>122</th>\n",
-       "      <td>1001237</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>50.132786</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>176</th>\n",
-       "      <td>1001777</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>72.238193</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         eid  SCORE_event  SCORE_event_time\n",
-       "45   1000463          1.0         74.165640\n",
-       "83   1000841          1.0         76.005476\n",
-       "102  1001031          1.0         75.537303\n",
-       "122  1001237          1.0         50.132786\n",
-       "176  1001777          1.0         72.238193"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "score = \"SCORE\"\n",
-    "\n",
-    "temp = endpoints_scores_all.filter(regex=\"eid|\"+score).rename(columns={\"death_SCORE_event\":\"SCORE_event\", \"death_SCORE_event_time\":\"SCORE_event_time\"})\n",
-    "score_SCORE = temp = temp[[\"eid\", \"SCORE_event\", \"SCORE_event_time\"]]\n",
-    "print(len(temp.query(score+\"_event==1\")))\n",
-    "temp.query(score+\"_event==1\").head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### ASCVD (Goff 2014)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "58893\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
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-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>ASCVD_event</th>\n",
-       "      <th>ASCVD_event_time</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1000037</td>\n",
-       "      <td>1</td>\n",
-       "      <td>66.970568</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000043</td>\n",
-       "      <td>1</td>\n",
-       "      <td>68.123203</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>1000066</td>\n",
-       "      <td>1</td>\n",
-       "      <td>65.051335</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>1000079</td>\n",
-       "      <td>1</td>\n",
-       "      <td>61.054073</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>1000233</td>\n",
-       "      <td>1</td>\n",
-       "      <td>68.673511</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        eid  ASCVD_event  ASCVD_event_time\n",
-       "2   1000037            1         66.970568\n",
-       "3   1000043            1         68.123203\n",
-       "5   1000066            1         65.051335\n",
-       "6   1000079            1         61.054073\n",
-       "22  1000233            1         68.673511"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "score = \"ASCVD\"\n",
-    "temp = endpoints_scores_all.filter(regex=\"eid|\"+score)\n",
-    "\n",
-    "aggr_event = {score +\"_event\" : lambda x: [1 if (hospital_event==1) | (death_event == 1) else 0 \n",
-    "                                                    for hospital_event, death_event in zip(x[\"hospital_\"+score+\"_event\"], x[\"death_\"+score+\"_event\"])]}\n",
-    "aggr_date = {score +\"_event_time\" : lambda x: [min(hospital_event_time, death_event_time)\n",
-    "                                                        for hospital_event_time, death_event_time in zip(x[\"hospital_\"+score+\"_event_time\"], x[\"death_\"+score+\"_event_time\"])]}\n",
-    "\n",
-    "score_ASCVD = temp = temp.assign(**aggr_event).assign(**aggr_date)[[\"eid\", score +\"_event\", score +\"_event_time\"]]\n",
-    "print(len(temp.query(score+\"_event==1\")))\n",
-    "temp.query(score+\"_event==1\").head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### UK QRISK3 (2017)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "62625\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>QRISK3_event</th>\n",
-       "      <th>QRISK3_event_time</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1000037</td>\n",
-       "      <td>1</td>\n",
-       "      <td>66.970568</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000043</td>\n",
-       "      <td>1</td>\n",
-       "      <td>68.123203</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>1000066</td>\n",
-       "      <td>1</td>\n",
-       "      <td>65.051335</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>1000079</td>\n",
-       "      <td>1</td>\n",
-       "      <td>61.054073</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>1000233</td>\n",
-       "      <td>1</td>\n",
-       "      <td>68.673511</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        eid  QRISK3_event  QRISK3_event_time\n",
-       "2   1000037             1          66.970568\n",
-       "3   1000043             1          68.123203\n",
-       "5   1000066             1          65.051335\n",
-       "6   1000079             1          61.054073\n",
-       "22  1000233             1          68.673511"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "score = \"QRISK3\"\n",
-    "temp = endpoints_scores_all.filter(regex=\"eid|\"+score)\n",
-    "\n",
-    "aggr_event = {score +\"_event\" : lambda x: [1 if (hospital_event==1) | (death_event == 1) else 0 \n",
-    "                                                    for hospital_event, death_event in zip(x[\"hospital_\"+score+\"_event\"], x[\"death_\"+score+\"_event\"])]}\n",
-    "aggr_date = {score +\"_event_time\" : lambda x: [min(hospital_event_time, death_event_time)\n",
-    "                                                        for hospital_event_time, death_event_time in zip(x[\"hospital_\"+score+\"_event_time\"], x[\"death_\"+score+\"_event_time\"])]}\n",
-    "\n",
-    "score_QRISK3 = temp = temp.assign(**aggr_event).assign(**aggr_date)[[\"eid\", score +\"_event\", score +\"_event_time\"]]\n",
-    "print(len(temp.query(score+\"_event==1\")))\n",
-    "temp.query(score+\"_event==1\").head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### MACE (2020)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "57031\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>MACE_event</th>\n",
-       "      <th>MACE_event_time</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000043</td>\n",
-       "      <td>1</td>\n",
-       "      <td>68.123203</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>1000233</td>\n",
-       "      <td>1</td>\n",
-       "      <td>68.673511</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30</th>\n",
-       "      <td>1000319</td>\n",
-       "      <td>1</td>\n",
-       "      <td>56.922656</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>45</th>\n",
-       "      <td>1000463</td>\n",
-       "      <td>1</td>\n",
-       "      <td>74.069815</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>83</th>\n",
-       "      <td>1000841</td>\n",
-       "      <td>1</td>\n",
-       "      <td>75.980835</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        eid  MACE_event  MACE_event_time\n",
-       "3   1000043           1        68.123203\n",
-       "22  1000233           1        68.673511\n",
-       "30  1000319           1        56.922656\n",
-       "45  1000463           1        74.069815\n",
-       "83  1000841           1        75.980835"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "score = \"MACE\"\n",
-    "temp = endpoints_scores_all.filter(regex=\"eid|\"+score)\n",
-    "\n",
-    "aggr_event = {score +\"_event\" : lambda x: [1 if (hospital_event==1) | (death_event == 1) else 0 \n",
-    "                                                    for hospital_event, death_event in zip(x[\"hospital_\"+score+\"_event\"], x[\"death_\"+score+\"_event\"])]}\n",
-    "aggr_date = {score +\"_event_time\" : lambda x: [min(hospital_event_time, death_event_time)\n",
-    "                                                        for hospital_event_time, death_event_time in zip(x[\"hospital_\"+score+\"_event_time\"], x[\"death_\"+score+\"_event_time\"])]}\n",
-    "\n",
-    "score_MACE = temp = temp.assign(**aggr_event).assign(**aggr_date)[[\"eid\", score +\"_event\", score +\"_event_time\"]]\n",
-    "print(len(temp.query(score+\"_event==1\")))\n",
-    "temp.query(score+\"_event==1\").head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "endpoints_all_list = [df.set_index(\"eid\") for df in [endpoints_hospital, endpoints_death, score_SCORE, score_ASCVD, score_QRISK3, score_MACE]]\n",
-    "endpoints_all = pd.concat(endpoints_all_list, axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>myocardial_infarction_event</th>\n",
-       "      <th>myocardial_infarction_event_time</th>\n",
-       "      <th>stroke_event</th>\n",
-       "      <th>stroke_event_time</th>\n",
-       "      <th>cancer_breast_event</th>\n",
-       "      <th>cancer_breast_event_time</th>\n",
-       "      <th>diabetes_event</th>\n",
-       "      <th>diabetes_event_time</th>\n",
-       "      <th>atrial_fibrillation_event</th>\n",
-       "      <th>atrial_fibrillation_event_time</th>\n",
-       "      <th>...</th>\n",
-       "      <th>death_cvd_event</th>\n",
-       "      <th>death_cvd_event_time</th>\n",
-       "      <th>SCORE_event</th>\n",
-       "      <th>SCORE_event_time</th>\n",
-       "      <th>ASCVD_event</th>\n",
-       "      <th>ASCVD_event_time</th>\n",
-       "      <th>QRISK3_event</th>\n",
-       "      <th>QRISK3_event_time</th>\n",
-       "      <th>MACE_event</th>\n",
-       "      <th>MACE_event_time</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>eid</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1000018</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>59.381246</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>59.381246</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>59.381246</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>59.381246</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>59.381246</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>59.624914</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>59.624914</td>\n",
-       "      <td>0</td>\n",
-       "      <td>59.381246</td>\n",
-       "      <td>0</td>\n",
-       "      <td>59.381246</td>\n",
-       "      <td>0</td>\n",
-       "      <td>59.381246</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1000020</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>71.110198</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>71.110198</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>71.110198</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>71.110198</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>71.110198</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>71.353867</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>71.353867</td>\n",
-       "      <td>0</td>\n",
-       "      <td>71.110198</td>\n",
-       "      <td>0</td>\n",
-       "      <td>71.110198</td>\n",
-       "      <td>0</td>\n",
-       "      <td>71.110198</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1000037</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>70.384668</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>70.384668</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>70.384668</td>\n",
-       "      <td>0.0</td>\n",
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-       "</div>"
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-      "text/plain": [
-       "         myocardial_infarction_event  myocardial_infarction_event_time  \\\n",
-       "eid                                                                      \n",
-       "1000018                          0.0                         59.381246   \n",
-       "1000020                          0.0                         71.110198   \n",
-       "1000037                          0.0                         70.384668   \n",
-       "1000043                          1.0                         68.123203   \n",
-       "1000051                          0.0                         64.807666   \n",
-       "...                              ...                               ...   \n",
-       "6025150                          0.0                         55.750856   \n",
-       "6025165                          0.0                         56.577687   \n",
-       "6025173                          0.0                         68.536619   \n",
-       "6025182                          0.0                         65.749487   \n",
-       "6025198                          0.0                         77.177276   \n",
-       "\n",
-       "         stroke_event  stroke_event_time  cancer_breast_event  \\\n",
-       "eid                                                             \n",
-       "1000018           0.0          59.381246                  0.0   \n",
-       "1000020           0.0          71.110198                  0.0   \n",
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-       "6025182           0.0          65.749487                  0.0   \n",
-       "6025198           0.0          77.177276                  0.0   \n",
-       "\n",
-       "         cancer_breast_event_time  diabetes_event  diabetes_event_time  \\\n",
-       "eid                                                                      \n",
-       "1000018                 59.381246             0.0            59.381246   \n",
-       "1000020                 71.110198             0.0            71.110198   \n",
-       "1000037                 70.384668             0.0            70.384668   \n",
-       "1000043                 73.826146             0.0            73.826146   \n",
-       "1000051                 64.807666             1.0            55.723477   \n",
-       "...                           ...             ...                  ...   \n",
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-       "6025165                 56.577687             0.0            56.577687   \n",
-       "6025173                 68.536619             0.0            68.536619   \n",
-       "6025182                 65.749487             0.0            65.749487   \n",
-       "6025198                 77.177276             0.0            77.177276   \n",
-       "\n",
-       "         atrial_fibrillation_event  atrial_fibrillation_event_time  ...  \\\n",
-       "eid                                                                 ...   \n",
-       "1000018                        0.0                       59.381246  ...   \n",
-       "1000020                        0.0                       71.110198  ...   \n",
-       "1000037                        0.0                       70.384668  ...   \n",
-       "1000043                        0.0                       73.826146  ...   \n",
-       "1000051                        0.0                       64.807666  ...   \n",
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-       "6025173                        0.0                       68.536619  ...   \n",
-       "6025182                        0.0                       65.749487  ...   \n",
-       "6025198                        0.0                       77.177276  ...   \n",
-       "\n",
-       "         death_cvd_event  death_cvd_event_time  SCORE_event  SCORE_event_time  \\\n",
-       "eid                                                                             \n",
-       "1000018              0.0             59.624914          0.0         59.624914   \n",
-       "1000020              0.0             71.353867          0.0         71.353867   \n",
-       "1000037              0.0             70.628337          0.0         70.628337   \n",
-       "1000043              0.0             74.069815          0.0         74.069815   \n",
-       "1000051              0.0             65.051335          0.0         65.051335   \n",
-       "...                  ...                   ...          ...               ...   \n",
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-       "6025182              0.0             65.993155          0.0         65.993155   \n",
-       "6025198              0.0             77.420945          0.0         77.420945   \n",
-       "\n",
-       "         ASCVD_event  ASCVD_event_time  QRISK3_event  QRISK3_event_time  \\\n",
-       "eid                                                                       \n",
-       "1000018            0         59.381246             0          59.381246   \n",
-       "1000020            0         71.110198             0          71.110198   \n",
-       "1000037            1         66.970568             1          66.970568   \n",
-       "1000043            1         68.123203             1          68.123203   \n",
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-       "...              ...               ...           ...                ...   \n",
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-       "6025198            0         77.177276             0          77.177276   \n",
-       "\n",
-       "         MACE_event  MACE_event_time  \n",
-       "eid                                   \n",
-       "1000018           0        59.381246  \n",
-       "1000020           0        71.110198  \n",
-       "1000037           0        70.384668  \n",
-       "1000043           1        68.123203  \n",
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-       "...             ...              ...  \n",
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-       "\n",
-       "[502504 rows x 26 columns]"
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "endpoints_all "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "endpoints_all.reset_index().to_feather(os.path.join(path, dataset_path, 'endpoints_all.feather'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [
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-       "      <th>1000043</th>\n",
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-       "      <td>77.177276</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>502504 rows × 26 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         myocardial_infarction_event  myocardial_infarction_event_time  \\\n",
-       "eid                                                                      \n",
-       "1000018                          0.0                         59.381246   \n",
-       "1000020                          0.0                         71.110198   \n",
-       "1000037                          0.0                         70.384668   \n",
-       "1000043                          1.0                         68.123203   \n",
-       "1000051                          0.0                         64.807666   \n",
-       "...                              ...                               ...   \n",
-       "6025150                          0.0                         55.750856   \n",
-       "6025165                          0.0                         56.577687   \n",
-       "6025173                          0.0                         68.536619   \n",
-       "6025182                          0.0                         65.749487   \n",
-       "6025198                          0.0                         77.177276   \n",
-       "\n",
-       "         stroke_event  stroke_event_time  cancer_breast_event  \\\n",
-       "eid                                                             \n",
-       "1000018           0.0          59.381246                  0.0   \n",
-       "1000020           0.0          71.110198                  0.0   \n",
-       "1000037           0.0          70.384668                  0.0   \n",
-       "1000043           0.0          73.826146                  0.0   \n",
-       "1000051           0.0          64.807666                  0.0   \n",
-       "...               ...                ...                  ...   \n",
-       "6025150           0.0          55.750856                  0.0   \n",
-       "6025165           0.0          56.577687                  0.0   \n",
-       "6025173           0.0          68.536619                  0.0   \n",
-       "6025182           0.0          65.749487                  0.0   \n",
-       "6025198           0.0          77.177276                  0.0   \n",
-       "\n",
-       "         cancer_breast_event_time  diabetes_event  diabetes_event_time  \\\n",
-       "eid                                                                      \n",
-       "1000018                 59.381246             0.0            59.381246   \n",
-       "1000020                 71.110198             0.0            71.110198   \n",
-       "1000037                 70.384668             0.0            70.384668   \n",
-       "1000043                 73.826146             0.0            73.826146   \n",
-       "1000051                 64.807666             1.0            55.723477   \n",
-       "...                           ...             ...                  ...   \n",
-       "6025150                 55.750856             0.0            55.750856   \n",
-       "6025165                 56.577687             0.0            56.577687   \n",
-       "6025173                 68.536619             0.0            68.536619   \n",
-       "6025182                 65.749487             0.0            65.749487   \n",
-       "6025198                 77.177276             0.0            77.177276   \n",
-       "\n",
-       "         atrial_fibrillation_event  atrial_fibrillation_event_time  ...  \\\n",
-       "eid                                                                 ...   \n",
-       "1000018                        0.0                       59.381246  ...   \n",
-       "1000020                        0.0                       71.110198  ...   \n",
-       "1000037                        0.0                       70.384668  ...   \n",
-       "1000043                        0.0                       73.826146  ...   \n",
-       "1000051                        0.0                       64.807666  ...   \n",
-       "...                            ...                             ...  ...   \n",
-       "6025150                        0.0                       55.750856  ...   \n",
-       "6025165                        0.0                       56.577687  ...   \n",
-       "6025173                        0.0                       68.536619  ...   \n",
-       "6025182                        0.0                       65.749487  ...   \n",
-       "6025198                        0.0                       77.177276  ...   \n",
-       "\n",
-       "         death_cvd_event  death_cvd_event_time  SCORE_event  SCORE_event_time  \\\n",
-       "eid                                                                             \n",
-       "1000018              0.0             59.624914          0.0         59.624914   \n",
-       "1000020              0.0             71.353867          0.0         71.353867   \n",
-       "1000037              0.0             70.628337          0.0         70.628337   \n",
-       "1000043              0.0             74.069815          0.0         74.069815   \n",
-       "1000051              0.0             65.051335          0.0         65.051335   \n",
-       "...                  ...                   ...          ...               ...   \n",
-       "6025150              0.0             55.994524          0.0         55.994524   \n",
-       "6025165              0.0             56.821355          0.0         56.821355   \n",
-       "6025173              0.0             68.780287          0.0         68.780287   \n",
-       "6025182              0.0             65.993155          0.0         65.993155   \n",
-       "6025198              0.0             77.420945          0.0         77.420945   \n",
-       "\n",
-       "         ASCVD_event  ASCVD_event_time  QRISK3_event  QRISK3_event_time  \\\n",
-       "eid                                                                       \n",
-       "1000018            0         59.381246             0          59.381246   \n",
-       "1000020            0         71.110198             0          71.110198   \n",
-       "1000037            1         66.970568             1          66.970568   \n",
-       "1000043            1         68.123203             1          68.123203   \n",
-       "1000051            0         64.807666             0          64.807666   \n",
-       "...              ...               ...           ...                ...   \n",
-       "6025150            0         55.750856             0          55.750856   \n",
-       "6025165            0         56.577687             0          56.577687   \n",
-       "6025173            0         68.536619             0          68.536619   \n",
-       "6025182            0         65.749487             0          65.749487   \n",
-       "6025198            0         77.177276             0          77.177276   \n",
-       "\n",
-       "         MACE_event  MACE_event_time  \n",
-       "eid                                   \n",
-       "1000018           0        59.381246  \n",
-       "1000020           0        71.110198  \n",
-       "1000037           0        70.384668  \n",
-       "1000043           1        68.123203  \n",
-       "1000051           0        64.807666  \n",
-       "...             ...              ...  \n",
-       "6025150           0        55.750856  \n",
-       "6025165           0        56.577687  \n",
-       "6025173           0        68.536619  \n",
-       "6025182           0        65.749487  \n",
-       "6025198           0        77.177276  \n",
-       "\n",
-       "[502504 rows x 26 columns]"
-      ]
-     },
-     "execution_count": 44,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "endpoints_all"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Merge Everything"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_dfs_dict = {\"basics\": pd.read_feather(os.path.join(path, dataset_path, 'temp_basics.feather')), \n",
-    "                 \"questionnaire\": pd.read_feather(os.path.join(path, dataset_path, 'temp_questionnaire.feather')), \n",
-    "                 \"measurements\": pd.read_feather(os.path.join(path, dataset_path, 'temp_measurements.feather')), \n",
-    "                 \"labs\": pd.read_feather(os.path.join(path, dataset_path, 'temp_labs.feather')), \n",
-    "                 \"family_history\": pd.read_feather(os.path.join(path, dataset_path, 'temp_family_history.feather')), \n",
-    "                 \"diagnoses\": pd.read_feather(os.path.join(path, dataset_path, 'temp_diagnoses.feather')),\n",
-    "                # \"diagnoses_emb\": pd.read_feather(os.path.join(path, dataset_path, 'temp_diagnoses_emb.feather')), \n",
-    "                 \"medications\": pd.read_feather(os.path.join(path, dataset_path, 'temp_medications.feather')), \n",
-    "                 \"endpoints_hospital\":endpoints_hospital, \n",
-    "                 \"endpoints_death\":endpoints_death, \n",
-    "                 \"score_SCORE\":score_SCORE, \n",
-    "                 \"score_ASCVD\":score_ASCVD, \n",
-    "                 \"score_QRISK3\":score_QRISK3,\n",
-    "                 \"score_MACE\":score_MACE}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_cols_clean(df):\n",
-    "    df.columns = df.columns.str.replace(r'_0_0$', '').str.replace(r'_f[0-9]+$', '').str.replace(\"_automated_reading\", '')\n",
-    "    return df.columns\n",
-    "\n",
-    "def clean_df(df):\n",
-    "    df.columns = get_cols_clean(df)\n",
-    "    return df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "from functools import reduce\n",
-    "\n",
-    "data_baseline = reduce(lambda x, y: pd.merge(x, y, on = 'eid'), list(data_dfs_dict.values()))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_baseline = clean_df(data_baseline)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for col in [col for col in list(data_baseline.columns) if (\"_event\" in col) & (\"_time\" not in col)]:\n",
-    "    data_baseline[col] = data_baseline[col].astype(int)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "covariates = [col for col in list(data_baseline.columns) if not \"_event\" in col]\n",
-    "targets = [col for col in list(data_baseline.columns) if \"_event\" in col]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Exporting"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>age_at_recruitment</th>\n",
-       "      <th>sex</th>\n",
-       "      <th>ethnic_background</th>\n",
-       "      <th>townsend_deprivation_index_at_recruitment</th>\n",
-       "      <th>date_of_attending_assessment_centre</th>\n",
-       "      <th>birth_date</th>\n",
-       "      <th>overall_health_rating</th>\n",
-       "      <th>smoking_status</th>\n",
-       "      <th>alcohol_intake_frequency</th>\n",
-       "      <th>...</th>\n",
-       "      <th>death_cvd_event</th>\n",
-       "      <th>death_cvd_event_time</th>\n",
-       "      <th>SCORE_event</th>\n",
-       "      <th>SCORE_event_time</th>\n",
-       "      <th>ASCVD_event</th>\n",
-       "      <th>ASCVD_event_time</th>\n",
-       "      <th>QRISK3_event</th>\n",
-       "      <th>QRISK3_event_time</th>\n",
-       "      <th>MACE_event</th>\n",
-       "      <th>MACE_event_time</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>49.0</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>White</td>\n",
-       "      <td>-1.852930</td>\n",
-       "      <td>2009-11-12</td>\n",
-       "      <td>1960-11-12</td>\n",
-       "      <td>Fair</td>\n",
-       "      <td>Current</td>\n",
-       "      <td>Once or twice a week</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>59.624914</td>\n",
-       "      <td>0</td>\n",
-       "      <td>59.624914</td>\n",
-       "      <td>0</td>\n",
-       "      <td>59.334702</td>\n",
-       "      <td>0</td>\n",
-       "      <td>59.334702</td>\n",
-       "      <td>0</td>\n",
-       "      <td>59.334702</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1000020</td>\n",
-       "      <td>59.0</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>White</td>\n",
-       "      <td>0.204248</td>\n",
-       "      <td>2008-02-19</td>\n",
-       "      <td>1949-02-19</td>\n",
-       "      <td>Good</td>\n",
-       "      <td>Current</td>\n",
-       "      <td>Once or twice a week</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>71.353867</td>\n",
-       "      <td>0</td>\n",
-       "      <td>71.353867</td>\n",
-       "      <td>0</td>\n",
-       "      <td>71.063655</td>\n",
-       "      <td>0</td>\n",
-       "      <td>71.063655</td>\n",
-       "      <td>0</td>\n",
-       "      <td>71.063655</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1000037</td>\n",
-       "      <td>59.0</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>White</td>\n",
-       "      <td>-3.498860</td>\n",
-       "      <td>2008-11-11</td>\n",
-       "      <td>1949-11-11</td>\n",
-       "      <td>Good</td>\n",
-       "      <td>Previous</td>\n",
-       "      <td>Once or twice a week</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>70.628337</td>\n",
-       "      <td>0</td>\n",
-       "      <td>70.628337</td>\n",
-       "      <td>1</td>\n",
-       "      <td>66.970568</td>\n",
-       "      <td>1</td>\n",
-       "      <td>66.970568</td>\n",
-       "      <td>0</td>\n",
-       "      <td>70.338125</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000043</td>\n",
-       "      <td>63.0</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>White</td>\n",
-       "      <td>-5.351150</td>\n",
-       "      <td>2009-06-03</td>\n",
-       "      <td>1946-06-03</td>\n",
-       "      <td>Fair</td>\n",
-       "      <td>Previous</td>\n",
-       "      <td>Three or four times a week</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>74.069815</td>\n",
-       "      <td>0</td>\n",
-       "      <td>74.069815</td>\n",
-       "      <td>1</td>\n",
-       "      <td>68.123203</td>\n",
-       "      <td>1</td>\n",
-       "      <td>68.123203</td>\n",
-       "      <td>1</td>\n",
-       "      <td>68.123203</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1000051</td>\n",
-       "      <td>51.0</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>White</td>\n",
-       "      <td>-1.799080</td>\n",
-       "      <td>2006-06-10</td>\n",
-       "      <td>1955-06-10</td>\n",
-       "      <td>Poor</td>\n",
-       "      <td>Never</td>\n",
-       "      <td>One to three times a month</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>65.051335</td>\n",
-       "      <td>0</td>\n",
-       "      <td>65.051335</td>\n",
-       "      <td>0</td>\n",
-       "      <td>64.761123</td>\n",
-       "      <td>0</td>\n",
-       "      <td>64.761123</td>\n",
-       "      <td>0</td>\n",
-       "      <td>64.761123</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 3746 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       eid  age_at_recruitment     sex ethnic_background  \\\n",
-       "0  1000018                49.0  Female             White   \n",
-       "1  1000020                59.0    Male             White   \n",
-       "2  1000037                59.0  Female             White   \n",
-       "3  1000043                63.0    Male             White   \n",
-       "4  1000051                51.0  Female             White   \n",
-       "\n",
-       "   townsend_deprivation_index_at_recruitment  \\\n",
-       "0                                  -1.852930   \n",
-       "1                                   0.204248   \n",
-       "2                                  -3.498860   \n",
-       "3                                  -5.351150   \n",
-       "4                                  -1.799080   \n",
-       "\n",
-       "  date_of_attending_assessment_centre  birth_date overall_health_rating  \\\n",
-       "0                          2009-11-12  1960-11-12                  Fair   \n",
-       "1                          2008-02-19  1949-02-19                  Good   \n",
-       "2                          2008-11-11  1949-11-11                  Good   \n",
-       "3                          2009-06-03  1946-06-03                  Fair   \n",
-       "4                          2006-06-10  1955-06-10                  Poor   \n",
-       "\n",
-       "  smoking_status    alcohol_intake_frequency  ...  death_cvd_event  \\\n",
-       "0        Current        Once or twice a week  ...                0   \n",
-       "1        Current        Once or twice a week  ...                0   \n",
-       "2       Previous        Once or twice a week  ...                0   \n",
-       "3       Previous  Three or four times a week  ...                0   \n",
-       "4          Never  One to three times a month  ...                0   \n",
-       "\n",
-       "   death_cvd_event_time  SCORE_event  SCORE_event_time  ASCVD_event  \\\n",
-       "0             59.624914            0         59.624914            0   \n",
-       "1             71.353867            0         71.353867            0   \n",
-       "2             70.628337            0         70.628337            1   \n",
-       "3             74.069815            0         74.069815            1   \n",
-       "4             65.051335            0         65.051335            0   \n",
-       "\n",
-       "   ASCVD_event_time  QRISK3_event  QRISK3_event_time  MACE_event  \\\n",
-       "0         59.334702             0          59.334702           0   \n",
-       "1         71.063655             0          71.063655           0   \n",
-       "2         66.970568             1          66.970568           0   \n",
-       "3         68.123203             1          68.123203           1   \n",
-       "4         64.761123             0          64.761123           0   \n",
-       "\n",
-       "   MACE_event_time  \n",
-       "0        59.334702  \n",
-       "1        71.063655  \n",
-       "2        70.338125  \n",
-       "3        68.123203  \n",
-       "4        64.761123  \n",
-       "\n",
-       "[5 rows x 3746 columns]"
-      ]
-     },
-     "execution_count": 38,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data_baseline.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_cols = {}\n",
-    "for topic, df in data_dfs_dict.items(): \n",
-    "    data_cols[\"eid\"] = [\"admin\"]\n",
-    "    data_cols[topic]=list(get_cols_clean(df))[1:]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_cols_single = {}\n",
-    "for topic, columns in data_cols.items():\n",
-    "    for col in columns:\n",
-    "        data_cols_single[col] = topic"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
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-       "\n",
-       "    .dataframe thead th {\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>covariate</th>\n",
-       "      <th>dtype</th>\n",
-       "      <th>isTarget</th>\n",
-       "      <th>based_on</th>\n",
-       "      <th>aggr_fn</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>eid</td>\n",
-       "      <td>integer</td>\n",
-       "      <td>False</td>\n",
-       "      <td>eid</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2</td>\n",
-       "      <td>age_at_recruitment</td>\n",
-       "      <td>numeric</td>\n",
-       "      <td>False</td>\n",
-       "      <td>basics</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>sex</td>\n",
-       "      <td>category</td>\n",
-       "      <td>False</td>\n",
-       "      <td>basics</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4</td>\n",
-       "      <td>ethnic_background</td>\n",
-       "      <td>category</td>\n",
-       "      <td>False</td>\n",
-       "      <td>basics</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5</td>\n",
-       "      <td>townsend_deprivation_index_at_recruitment</td>\n",
-       "      <td>numeric</td>\n",
-       "      <td>False</td>\n",
-       "      <td>basics</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3741</th>\n",
-       "      <td>3742</td>\n",
-       "      <td>ASCVD_event_time</td>\n",
-       "      <td>numeric</td>\n",
-       "      <td>True</td>\n",
-       "      <td>score_ASCVD</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3742</th>\n",
-       "      <td>3743</td>\n",
-       "      <td>QRISK3_event</td>\n",
-       "      <td>integer</td>\n",
-       "      <td>True</td>\n",
-       "      <td>score_QRISK3</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3743</th>\n",
-       "      <td>3744</td>\n",
-       "      <td>QRISK3_event_time</td>\n",
-       "      <td>numeric</td>\n",
-       "      <td>True</td>\n",
-       "      <td>score_QRISK3</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3744</th>\n",
-       "      <td>3745</td>\n",
-       "      <td>MACE_event</td>\n",
-       "      <td>integer</td>\n",
-       "      <td>True</td>\n",
-       "      <td>score_MACE</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3745</th>\n",
-       "      <td>3746</td>\n",
-       "      <td>MACE_event_time</td>\n",
-       "      <td>numeric</td>\n",
-       "      <td>True</td>\n",
-       "      <td>score_MACE</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>3746 rows × 6 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        id                                  covariate     dtype  isTarget  \\\n",
-       "0        1                                        eid   integer     False   \n",
-       "1        2                         age_at_recruitment   numeric     False   \n",
-       "2        3                                        sex  category     False   \n",
-       "3        4                          ethnic_background  category     False   \n",
-       "4        5  townsend_deprivation_index_at_recruitment   numeric     False   \n",
-       "...    ...                                        ...       ...       ...   \n",
-       "3741  3742                           ASCVD_event_time   numeric      True   \n",
-       "3742  3743                               QRISK3_event   integer      True   \n",
-       "3743  3744                          QRISK3_event_time   numeric      True   \n",
-       "3744  3745                                 MACE_event   integer      True   \n",
-       "3745  3746                            MACE_event_time   numeric      True   \n",
-       "\n",
-       "          based_on  aggr_fn  \n",
-       "0              eid      NaN  \n",
-       "1           basics      NaN  \n",
-       "2           basics      NaN  \n",
-       "3           basics      NaN  \n",
-       "4           basics      NaN  \n",
-       "...            ...      ...  \n",
-       "3741   score_ASCVD      NaN  \n",
-       "3742  score_QRISK3      NaN  \n",
-       "3743  score_QRISK3      NaN  \n",
-       "3744    score_MACE      NaN  \n",
-       "3745    score_MACE      NaN  \n",
-       "\n",
-       "[3746 rows x 6 columns]"
-      ]
-     },
-     "execution_count": 41,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dtypes = {\"int32\":\"integer\", \"int64\":\"integer\", \"float64\":\"numeric\", \"category\":\"category\", \"object\":\"category\", \"bool\":\"logical\"}\n",
-    "desc_dict = {\"id\": [*range(1, len(data_baseline.columns.to_list())+1)] , \n",
-    "             \"covariate\": data_baseline.columns.to_list(), \n",
-    "             \"dtype\":[dtypes[str(col)] for col in data_baseline.dtypes.to_list()], \n",
-    "             \"isTarget\":[True if col in targets else False for col in data_baseline.columns.to_list()],\n",
-    "            \"based_on\":[topic for col, topic in data_cols_single.items()],\n",
-    "            \"aggr_fn\": [np.nan for col in data_baseline.columns.to_list()]}\n",
-    "data_baseline_description = pd.DataFrame.from_dict(desc_dict)\n",
-    "data_baseline_description"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Exclusion Criteria"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_baseline_excl = data_baseline.query(\"myocardial_infarction == False & coronary_heart_disease == False & statins == False\").reset_index(drop=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "<style scoped>\n",
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-       "\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>age_at_recruitment</th>\n",
-       "      <th>sex</th>\n",
-       "      <th>ethnic_background</th>\n",
-       "      <th>townsend_deprivation_index_at_recruitment</th>\n",
-       "      <th>date_of_attending_assessment_centre</th>\n",
-       "      <th>birth_date</th>\n",
-       "      <th>overall_health_rating</th>\n",
-       "      <th>smoking_status</th>\n",
-       "      <th>alcohol_intake_frequency</th>\n",
-       "      <th>...</th>\n",
-       "      <th>death_cvd_event</th>\n",
-       "      <th>death_cvd_event_time</th>\n",
-       "      <th>SCORE_event</th>\n",
-       "      <th>SCORE_event_time</th>\n",
-       "      <th>ASCVD_event</th>\n",
-       "      <th>ASCVD_event_time</th>\n",
-       "      <th>QRISK3_event</th>\n",
-       "      <th>QRISK3_event_time</th>\n",
-       "      <th>MACE_event</th>\n",
-       "      <th>MACE_event_time</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>49.0</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>White</td>\n",
-       "      <td>-1.852930</td>\n",
-       "      <td>2009-11-12</td>\n",
-       "      <td>1960-11-12</td>\n",
-       "      <td>Fair</td>\n",
-       "      <td>Current</td>\n",
-       "      <td>Once or twice a week</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>59.624914</td>\n",
-       "      <td>0</td>\n",
-       "      <td>59.624914</td>\n",
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-       "      <td>59.334702</td>\n",
-       "      <td>0</td>\n",
-       "      <td>59.334702</td>\n",
-       "      <td>0</td>\n",
-       "      <td>59.334702</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1000020</td>\n",
-       "      <td>59.0</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>White</td>\n",
-       "      <td>0.204248</td>\n",
-       "      <td>2008-02-19</td>\n",
-       "      <td>1949-02-19</td>\n",
-       "      <td>Good</td>\n",
-       "      <td>Current</td>\n",
-       "      <td>Once or twice a week</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>71.353867</td>\n",
-       "      <td>0</td>\n",
-       "      <td>71.353867</td>\n",
-       "      <td>0</td>\n",
-       "      <td>71.063655</td>\n",
-       "      <td>0</td>\n",
-       "      <td>71.063655</td>\n",
-       "      <td>0</td>\n",
-       "      <td>71.063655</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1000037</td>\n",
-       "      <td>59.0</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>White</td>\n",
-       "      <td>-3.498860</td>\n",
-       "      <td>2008-11-11</td>\n",
-       "      <td>1949-11-11</td>\n",
-       "      <td>Good</td>\n",
-       "      <td>Previous</td>\n",
-       "      <td>Once or twice a week</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>70.628337</td>\n",
-       "      <td>0</td>\n",
-       "      <td>70.628337</td>\n",
-       "      <td>1</td>\n",
-       "      <td>66.970568</td>\n",
-       "      <td>1</td>\n",
-       "      <td>66.970568</td>\n",
-       "      <td>0</td>\n",
-       "      <td>70.338125</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000043</td>\n",
-       "      <td>63.0</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>White</td>\n",
-       "      <td>-5.351150</td>\n",
-       "      <td>2009-06-03</td>\n",
-       "      <td>1946-06-03</td>\n",
-       "      <td>Fair</td>\n",
-       "      <td>Previous</td>\n",
-       "      <td>Three or four times a week</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>74.069815</td>\n",
-       "      <td>0</td>\n",
-       "      <td>74.069815</td>\n",
-       "      <td>1</td>\n",
-       "      <td>68.123203</td>\n",
-       "      <td>1</td>\n",
-       "      <td>68.123203</td>\n",
-       "      <td>1</td>\n",
-       "      <td>68.123203</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1000079</td>\n",
-       "      <td>60.0</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>White</td>\n",
-       "      <td>-2.708040</td>\n",
-       "      <td>2008-03-18</td>\n",
-       "      <td>1948-03-18</td>\n",
-       "      <td>Fair</td>\n",
-       "      <td>Never</td>\n",
-       "      <td>Once or twice a week</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>72.279261</td>\n",
-       "      <td>0</td>\n",
-       "      <td>72.279261</td>\n",
-       "      <td>1</td>\n",
-       "      <td>61.054073</td>\n",
-       "      <td>1</td>\n",
-       "      <td>61.054073</td>\n",
-       "      <td>0</td>\n",
-       "      <td>71.989049</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>402296</th>\n",
-       "      <td>6025150</td>\n",
-       "      <td>43.0</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>White</td>\n",
-       "      <td>0.046781</td>\n",
-       "      <td>2007-06-30</td>\n",
-       "      <td>1964-06-30</td>\n",
-       "      <td>Excellent</td>\n",
-       "      <td>Never</td>\n",
-       "      <td>Three or four times a week</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>55.994524</td>\n",
-       "      <td>0</td>\n",
-       "      <td>55.994524</td>\n",
-       "      <td>0</td>\n",
-       "      <td>55.704312</td>\n",
-       "      <td>0</td>\n",
-       "      <td>55.704312</td>\n",
-       "      <td>0</td>\n",
-       "      <td>55.704312</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>402297</th>\n",
-       "      <td>6025165</td>\n",
-       "      <td>45.0</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>White</td>\n",
-       "      <td>-2.107040</td>\n",
-       "      <td>2008-09-02</td>\n",
-       "      <td>1963-09-02</td>\n",
-       "      <td>Good</td>\n",
-       "      <td>Never</td>\n",
-       "      <td>Three or four times a week</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>56.821355</td>\n",
-       "      <td>0</td>\n",
-       "      <td>56.821355</td>\n",
-       "      <td>0</td>\n",
-       "      <td>56.531143</td>\n",
-       "      <td>0</td>\n",
-       "      <td>56.531143</td>\n",
-       "      <td>0</td>\n",
-       "      <td>56.531143</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>402298</th>\n",
-       "      <td>6025173</td>\n",
-       "      <td>57.0</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>White</td>\n",
-       "      <td>-1.827220</td>\n",
-       "      <td>2008-09-17</td>\n",
-       "      <td>1951-09-17</td>\n",
-       "      <td>Good</td>\n",
-       "      <td>Never</td>\n",
-       "      <td>Never</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>68.780287</td>\n",
-       "      <td>0</td>\n",
-       "      <td>68.780287</td>\n",
-       "      <td>0</td>\n",
-       "      <td>68.490075</td>\n",
-       "      <td>0</td>\n",
-       "      <td>68.490075</td>\n",
-       "      <td>0</td>\n",
-       "      <td>68.490075</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>402299</th>\n",
-       "      <td>6025182</td>\n",
-       "      <td>56.0</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>White</td>\n",
-       "      <td>-0.010764</td>\n",
-       "      <td>2010-07-01</td>\n",
-       "      <td>1954-07-01</td>\n",
-       "      <td>Excellent</td>\n",
-       "      <td>Previous</td>\n",
-       "      <td>Daily or almost daily</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>65.993155</td>\n",
-       "      <td>0</td>\n",
-       "      <td>65.993155</td>\n",
-       "      <td>0</td>\n",
-       "      <td>65.702943</td>\n",
-       "      <td>0</td>\n",
-       "      <td>65.702943</td>\n",
-       "      <td>0</td>\n",
-       "      <td>65.702943</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>402300</th>\n",
-       "      <td>6025198</td>\n",
-       "      <td>67.0</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>White</td>\n",
-       "      <td>-1.930650</td>\n",
-       "      <td>2010-01-26</td>\n",
-       "      <td>1943-01-26</td>\n",
-       "      <td>Good</td>\n",
-       "      <td>Current</td>\n",
-       "      <td>Daily or almost daily</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>77.420945</td>\n",
-       "      <td>0</td>\n",
-       "      <td>77.420945</td>\n",
-       "      <td>0</td>\n",
-       "      <td>77.130732</td>\n",
-       "      <td>0</td>\n",
-       "      <td>77.130732</td>\n",
-       "      <td>0</td>\n",
-       "      <td>77.130732</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>402301 rows × 3746 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            eid  age_at_recruitment     sex ethnic_background  \\\n",
-       "0       1000018                49.0  Female             White   \n",
-       "1       1000020                59.0    Male             White   \n",
-       "2       1000037                59.0  Female             White   \n",
-       "3       1000043                63.0    Male             White   \n",
-       "4       1000079                60.0  Female             White   \n",
-       "...         ...                 ...     ...               ...   \n",
-       "402296  6025150                43.0  Female             White   \n",
-       "402297  6025165                45.0  Female             White   \n",
-       "402298  6025173                57.0    Male             White   \n",
-       "402299  6025182                56.0    Male             White   \n",
-       "402300  6025198                67.0    Male             White   \n",
-       "\n",
-       "        townsend_deprivation_index_at_recruitment  \\\n",
-       "0                                       -1.852930   \n",
-       "1                                        0.204248   \n",
-       "2                                       -3.498860   \n",
-       "3                                       -5.351150   \n",
-       "4                                       -2.708040   \n",
-       "...                                           ...   \n",
-       "402296                                   0.046781   \n",
-       "402297                                  -2.107040   \n",
-       "402298                                  -1.827220   \n",
-       "402299                                  -0.010764   \n",
-       "402300                                  -1.930650   \n",
-       "\n",
-       "       date_of_attending_assessment_centre  birth_date overall_health_rating  \\\n",
-       "0                               2009-11-12  1960-11-12                  Fair   \n",
-       "1                               2008-02-19  1949-02-19                  Good   \n",
-       "2                               2008-11-11  1949-11-11                  Good   \n",
-       "3                               2009-06-03  1946-06-03                  Fair   \n",
-       "4                               2008-03-18  1948-03-18                  Fair   \n",
-       "...                                    ...         ...                   ...   \n",
-       "402296                          2007-06-30  1964-06-30             Excellent   \n",
-       "402297                          2008-09-02  1963-09-02                  Good   \n",
-       "402298                          2008-09-17  1951-09-17                  Good   \n",
-       "402299                          2010-07-01  1954-07-01             Excellent   \n",
-       "402300                          2010-01-26  1943-01-26                  Good   \n",
-       "\n",
-       "       smoking_status    alcohol_intake_frequency  ...  death_cvd_event  \\\n",
-       "0             Current        Once or twice a week  ...                0   \n",
-       "1             Current        Once or twice a week  ...                0   \n",
-       "2            Previous        Once or twice a week  ...                0   \n",
-       "3            Previous  Three or four times a week  ...                0   \n",
-       "4               Never        Once or twice a week  ...                0   \n",
-       "...               ...                         ...  ...              ...   \n",
-       "402296          Never  Three or four times a week  ...                0   \n",
-       "402297          Never  Three or four times a week  ...                0   \n",
-       "402298          Never                       Never  ...                0   \n",
-       "402299       Previous       Daily or almost daily  ...                0   \n",
-       "402300        Current       Daily or almost daily  ...                0   \n",
-       "\n",
-       "        death_cvd_event_time  SCORE_event  SCORE_event_time  ASCVD_event  \\\n",
-       "0                  59.624914            0         59.624914            0   \n",
-       "1                  71.353867            0         71.353867            0   \n",
-       "2                  70.628337            0         70.628337            1   \n",
-       "3                  74.069815            0         74.069815            1   \n",
-       "4                  72.279261            0         72.279261            1   \n",
-       "...                      ...          ...               ...          ...   \n",
-       "402296             55.994524            0         55.994524            0   \n",
-       "402297             56.821355            0         56.821355            0   \n",
-       "402298             68.780287            0         68.780287            0   \n",
-       "402299             65.993155            0         65.993155            0   \n",
-       "402300             77.420945            0         77.420945            0   \n",
-       "\n",
-       "        ASCVD_event_time  QRISK3_event  QRISK3_event_time  MACE_event  \\\n",
-       "0              59.334702             0          59.334702           0   \n",
-       "1              71.063655             0          71.063655           0   \n",
-       "2              66.970568             1          66.970568           0   \n",
-       "3              68.123203             1          68.123203           1   \n",
-       "4              61.054073             1          61.054073           0   \n",
-       "...                  ...           ...                ...         ...   \n",
-       "402296         55.704312             0          55.704312           0   \n",
-       "402297         56.531143             0          56.531143           0   \n",
-       "402298         68.490075             0          68.490075           0   \n",
-       "402299         65.702943             0          65.702943           0   \n",
-       "402300         77.130732             0          77.130732           0   \n",
-       "\n",
-       "        MACE_event_time  \n",
-       "0             59.334702  \n",
-       "1             71.063655  \n",
-       "2             70.338125  \n",
-       "3             68.123203  \n",
-       "4             71.989049  \n",
-       "...                 ...  \n",
-       "402296        55.704312  \n",
-       "402297        56.531143  \n",
-       "402298        68.490075  \n",
-       "402299        65.702943  \n",
-       "402300        77.130732  \n",
-       "\n",
-       "[402301 rows x 3746 columns]"
-      ]
-     },
-     "execution_count": 43,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data_baseline_excl"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "feature_dict = {}\n",
-    "for group in data_baseline_description.based_on.unique(): feature_dict[group] = data_baseline_description.query(\"based_on==@group\").covariate.to_list()\n",
-    "with open(os.path.join(path, dataset_path, 'feature_list.yaml'), 'w') as file: yaml.dump(feature_dict, file, default_flow_style=False, allow_unicode=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#feature_dict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "### WRITE FEATURES IN YAML!!!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_baseline.to_feather(os.path.join(path, dataset_path, 'baseline_clinical.feather'))\n",
-    "data_baseline_excl.to_feather(os.path.join(path, dataset_path, 'baseline_clinical_excl.feather'))\n",
-    "data_baseline_description.to_feather(os.path.join(path, dataset_path, 'baseline_clinical_description.feather'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#data_baseline.to_csv(os.path.join(path, dataset_path, 'baseline_clinical.csv'), index=False)\n",
-    "#data_baseline_description.to_csv(os.path.join(path, dataset_path, 'baseline_clinical_description.csv'), index=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# !!! REMEMBER IMPUTATION !!!"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python [conda env:miniconda3-pl1.x]",
-   "language": "python",
-   "name": "conda-env-miniconda3-pl1.x-py"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.8"
-  },
-  "toc-autonumbering": true,
-  "toc-showcode": false,
-  "toc-showmarkdowntxt": false
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/neuralcvd/preprocessing/ukbb_tabular/3_preprocessing_pgs.ipynb b/neuralcvd/preprocessing/ukbb_tabular/3_preprocessing_pgs.ipynb
deleted file mode 100644
index 160d750..0000000
--- a/neuralcvd/preprocessing/ukbb_tabular/3_preprocessing_pgs.ipynb
+++ /dev/null
@@ -1,3682 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Preprocessing - Polygenic Risk Scores"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 82,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The autoreload extension is already loaded. To reload it, use:\n",
-      "  %reload_ext autoreload\n"
-     ]
-    }
-   ],
-   "source": [
-    "%load_ext autoreload\n",
-    "%autoreload 2\n",
-    "\n",
-    "import os\n",
-    "from tqdm.auto import tqdm\n",
-    "\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import lifelines\n",
-    "\n",
-    "from joblib import Parallel, delayed\n",
-    "from tqdm.notebook import tqdm\n",
-    "import neptune\n",
-    "import warnings\n",
-    "warnings.filterwarnings(\"ignore\")\n",
-    "import shutil\n",
-    "import pathlib\n",
-    "\n",
-    "dataset_name = \"210212_cvd_gp\"\n",
-    "path = \"/data/analysis/ag-reils/steinfej/code/umbrella/pre/ukbb\"\n",
-    "data_path = \"/data/analysis/ag-reils/ag-reils-shared/cardioRS/data\"\n",
-    "dataset_path = f\"{data_path}/2_datasets_pre/{dataset_name}\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 120,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>age_at_recruitment</th>\n",
-       "      <th>sex</th>\n",
-       "      <th>ethnic_background</th>\n",
-       "      <th>townsend_deprivation_index_at_recruitment</th>\n",
-       "      <th>date_of_attending_assessment_centre</th>\n",
-       "      <th>uk_biobank_assessment_centre</th>\n",
-       "      <th>birth_date</th>\n",
-       "      <th>overall_health_rating</th>\n",
-       "      <th>smoking_status</th>\n",
-       "      <th>...</th>\n",
-       "      <th>general_nutrients</th>\n",
-       "      <th>all_other_non-therapeutic_products</th>\n",
-       "      <th>contrast_media</th>\n",
-       "      <th>diagnostic_radiopharmaceuticals</th>\n",
-       "      <th>therapeutic_radiopharmaceuticals</th>\n",
-       "      <th>surgical_dressings</th>\n",
-       "      <th>statins</th>\n",
-       "      <th>ass</th>\n",
-       "      <th>atypical_antipsychotics</th>\n",
-       "      <th>glucocorticoids</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>49.0</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>White</td>\n",
-       "      <td>-1.852930</td>\n",
-       "      <td>2009-11-12</td>\n",
-       "      <td>Sheffield</td>\n",
-       "      <td>1960-11-12</td>\n",
-       "      <td>Fair</td>\n",
-       "      <td>Current</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1000020</td>\n",
-       "      <td>59.0</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>White</td>\n",
-       "      <td>0.204248</td>\n",
-       "      <td>2008-02-19</td>\n",
-       "      <td>Sheffield</td>\n",
-       "      <td>1949-02-19</td>\n",
-       "      <td>Good</td>\n",
-       "      <td>Current</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1000037</td>\n",
-       "      <td>59.0</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>White</td>\n",
-       "      <td>-3.498860</td>\n",
-       "      <td>2008-11-11</td>\n",
-       "      <td>Sheffield</td>\n",
-       "      <td>1949-11-11</td>\n",
-       "      <td>Good</td>\n",
-       "      <td>Previous</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000043</td>\n",
-       "      <td>63.0</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>White</td>\n",
-       "      <td>-5.351150</td>\n",
-       "      <td>2009-06-03</td>\n",
-       "      <td>Sheffield</td>\n",
-       "      <td>1946-06-03</td>\n",
-       "      <td>Fair</td>\n",
-       "      <td>Previous</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1000051</td>\n",
-       "      <td>51.0</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>White</td>\n",
-       "      <td>-1.799080</td>\n",
-       "      <td>2006-06-10</td>\n",
-       "      <td>Sheffield</td>\n",
-       "      <td>1955-06-10</td>\n",
-       "      <td>Poor</td>\n",
-       "      <td>Never</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 2862 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       eid  age_at_recruitment     sex ethnic_background  \\\n",
-       "0  1000018                49.0  Female             White   \n",
-       "1  1000020                59.0    Male             White   \n",
-       "2  1000037                59.0  Female             White   \n",
-       "3  1000043                63.0    Male             White   \n",
-       "4  1000051                51.0  Female             White   \n",
-       "\n",
-       "   townsend_deprivation_index_at_recruitment  \\\n",
-       "0                                  -1.852930   \n",
-       "1                                   0.204248   \n",
-       "2                                  -3.498860   \n",
-       "3                                  -5.351150   \n",
-       "4                                  -1.799080   \n",
-       "\n",
-       "  date_of_attending_assessment_centre uk_biobank_assessment_centre  \\\n",
-       "0                          2009-11-12                    Sheffield   \n",
-       "1                          2008-02-19                    Sheffield   \n",
-       "2                          2008-11-11                    Sheffield   \n",
-       "3                          2009-06-03                    Sheffield   \n",
-       "4                          2006-06-10                    Sheffield   \n",
-       "\n",
-       "   birth_date overall_health_rating smoking_status  ... general_nutrients  \\\n",
-       "0  1960-11-12                  Fair        Current  ...             False   \n",
-       "1  1949-02-19                  Good        Current  ...             False   \n",
-       "2  1949-11-11                  Good       Previous  ...             False   \n",
-       "3  1946-06-03                  Fair       Previous  ...             False   \n",
-       "4  1955-06-10                  Poor          Never  ...             False   \n",
-       "\n",
-       "   all_other_non-therapeutic_products  contrast_media  \\\n",
-       "0                               False           False   \n",
-       "1                               False           False   \n",
-       "2                               False           False   \n",
-       "3                               False           False   \n",
-       "4                               False           False   \n",
-       "\n",
-       "   diagnostic_radiopharmaceuticals  therapeutic_radiopharmaceuticals  \\\n",
-       "0                            False                             False   \n",
-       "1                            False                             False   \n",
-       "2                            False                             False   \n",
-       "3                            False                             False   \n",
-       "4                            False                             False   \n",
-       "\n",
-       "   surgical_dressings  statins    ass  atypical_antipsychotics  \\\n",
-       "0               False    False  False                    False   \n",
-       "1               False    False  False                    False   \n",
-       "2               False    False  False                    False   \n",
-       "3               False    False  False                    False   \n",
-       "4               False     True  False                     True   \n",
-       "\n",
-       "   glucocorticoids  \n",
-       "0            False  \n",
-       "1            False  \n",
-       "2            False  \n",
-       "3            False  \n",
-       "4            False  \n",
-       "\n",
-       "[5 rows x 2862 columns]"
-      ]
-     },
-     "execution_count": 120,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data = pd.read_feather(f\"{dataset_path}/baseline_covariates.feather\")\n",
-    "data.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 603,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_fp = f\"/data/analysis/ag-reils/steinfej/data/3_datasets_post/210212_cvd_gp/partition_0/train/data_imputed_normalized.feather\"\n",
-    "data = pd.read_feather(data_fp)#.dropna()\n",
-    "description = pd.read_feather(f\"{pathlib.Path(data_fp).parents[2]}/description.feather\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 640,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "14"
-      ]
-     },
-     "execution_count": 640,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "mult = int(1/(len(data[data.MACE_event==True])/len(data)))\n",
-    "mult"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 638,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "284931"
-      ]
-     },
-     "execution_count": 638,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(data)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 642,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>age_at_recruitment</th>\n",
-       "      <th>sex</th>\n",
-       "      <th>ethnic_background</th>\n",
-       "      <th>townsend_deprivation_index_at_recruitment</th>\n",
-       "      <th>date_of_attending_assessment_centre</th>\n",
-       "      <th>uk_biobank_assessment_centre</th>\n",
-       "      <th>birth_date</th>\n",
-       "      <th>overall_health_rating</th>\n",
-       "      <th>smoking_status</th>\n",
-       "      <th>...</th>\n",
-       "      <th>overall_health_rating_3.0</th>\n",
-       "      <th>smoking_status_0.0</th>\n",
-       "      <th>smoking_status_1.0</th>\n",
-       "      <th>smoking_status_2.0</th>\n",
-       "      <th>alcohol_intake_frequency_0.0</th>\n",
-       "      <th>alcohol_intake_frequency_1.0</th>\n",
-       "      <th>alcohol_intake_frequency_2.0</th>\n",
-       "      <th>alcohol_intake_frequency_3.0</th>\n",
-       "      <th>alcohol_intake_frequency_4.0</th>\n",
-       "      <th>alcohol_intake_frequency_5.0</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>-0.786545</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-0.163263</td>\n",
-       "      <td>2009-11-12</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1960-11-12</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1000020</td>\n",
-       "      <td>0.448044</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.509207</td>\n",
-       "      <td>2008-02-19</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1949-02-19</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1000043</td>\n",
-       "      <td>0.941880</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-1.306795</td>\n",
-       "      <td>2009-06-03</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1946-06-03</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000079</td>\n",
-       "      <td>0.571503</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-0.442790</td>\n",
-       "      <td>2008-03-18</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1948-03-18</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1000084</td>\n",
-       "      <td>-1.527299</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2.915721</td>\n",
-       "      <td>2007-10-18</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1964-10-18</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>284805</th>\n",
-       "      <td>5521055</td>\n",
-       "      <td>0.818421</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-0.559274</td>\n",
-       "      <td>2009-11-24</td>\n",
-       "      <td>17.0</td>\n",
-       "      <td>1947-11-24</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>284815</th>\n",
-       "      <td>5521173</td>\n",
-       "      <td>0.448044</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-0.833230</td>\n",
-       "      <td>2008-02-28</td>\n",
-       "      <td>17.0</td>\n",
-       "      <td>1949-02-28</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>284832</th>\n",
-       "      <td>5521549</td>\n",
-       "      <td>-1.774217</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-1.235223</td>\n",
-       "      <td>2009-01-19</td>\n",
-       "      <td>17.0</td>\n",
-       "      <td>1968-01-19</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>284854</th>\n",
-       "      <td>5521845</td>\n",
-       "      <td>0.077668</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-0.847005</td>\n",
-       "      <td>2007-10-30</td>\n",
-       "      <td>17.0</td>\n",
-       "      <td>1951-10-30</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>284878</th>\n",
-       "      <td>5522243</td>\n",
-       "      <td>1.435716</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-0.538690</td>\n",
-       "      <td>2007-09-06</td>\n",
-       "      <td>17.0</td>\n",
-       "      <td>1940-09-06</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>568039 rows × 2996 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            eid  age_at_recruitment  sex  ethnic_background  \\\n",
-       "0       1000018           -0.786545  0.0                0.0   \n",
-       "1       1000020            0.448044  1.0                0.0   \n",
-       "2       1000043            0.941880  1.0                0.0   \n",
-       "3       1000079            0.571503  0.0                0.0   \n",
-       "4       1000084           -1.527299  1.0                0.0   \n",
-       "...         ...                 ...  ...                ...   \n",
-       "284805  5521055            0.818421  0.0                0.0   \n",
-       "284815  5521173            0.448044  1.0                0.0   \n",
-       "284832  5521549           -1.774217  1.0                0.0   \n",
-       "284854  5521845            0.077668  0.0                0.0   \n",
-       "284878  5522243            1.435716  0.0                0.0   \n",
-       "\n",
-       "        townsend_deprivation_index_at_recruitment  \\\n",
-       "0                                       -0.163263   \n",
-       "1                                        0.509207   \n",
-       "2                                       -1.306795   \n",
-       "3                                       -0.442790   \n",
-       "4                                        2.915721   \n",
-       "...                                           ...   \n",
-       "284805                                  -0.559274   \n",
-       "284815                                  -0.833230   \n",
-       "284832                                  -1.235223   \n",
-       "284854                                  -0.847005   \n",
-       "284878                                  -0.538690   \n",
-       "\n",
-       "       date_of_attending_assessment_centre  uk_biobank_assessment_centre  \\\n",
-       "0                               2009-11-12                           0.0   \n",
-       "1                               2008-02-19                           0.0   \n",
-       "2                               2009-06-03                           0.0   \n",
-       "3                               2008-03-18                           0.0   \n",
-       "4                               2007-10-18                           0.0   \n",
-       "...                                    ...                           ...   \n",
-       "284805                          2009-11-24                          17.0   \n",
-       "284815                          2008-02-28                          17.0   \n",
-       "284832                          2009-01-19                          17.0   \n",
-       "284854                          2007-10-30                          17.0   \n",
-       "284878                          2007-09-06                          17.0   \n",
-       "\n",
-       "        birth_date  overall_health_rating  smoking_status  ...  \\\n",
-       "0       1960-11-12                    0.0             0.0  ...   \n",
-       "1       1949-02-19                    1.0             0.0  ...   \n",
-       "2       1946-06-03                    0.0             1.0  ...   \n",
-       "3       1948-03-18                    0.0             2.0  ...   \n",
-       "4       1964-10-18                    0.0             2.0  ...   \n",
-       "...            ...                    ...             ...  ...   \n",
-       "284805  1947-11-24                    1.0             2.0  ...   \n",
-       "284815  1949-02-28                    1.0             2.0  ...   \n",
-       "284832  1968-01-19                    1.0             2.0  ...   \n",
-       "284854  1951-10-30                    1.0             2.0  ...   \n",
-       "284878  1940-09-06                    0.0             2.0  ...   \n",
-       "\n",
-       "        overall_health_rating_3.0  smoking_status_0.0  smoking_status_1.0  \\\n",
-       "0                               0                   1                   0   \n",
-       "1                               0                   1                   0   \n",
-       "2                               0                   0                   1   \n",
-       "3                               0                   0                   0   \n",
-       "4                               0                   0                   0   \n",
-       "...                           ...                 ...                 ...   \n",
-       "284805                          0                   0                   0   \n",
-       "284815                          0                   0                   0   \n",
-       "284832                          0                   0                   0   \n",
-       "284854                          0                   0                   0   \n",
-       "284878                          0                   0                   0   \n",
-       "\n",
-       "        smoking_status_2.0  alcohol_intake_frequency_0.0  \\\n",
-       "0                        0                             1   \n",
-       "1                        0                             1   \n",
-       "2                        0                             0   \n",
-       "3                        1                             1   \n",
-       "4                        1                             1   \n",
-       "...                    ...                           ...   \n",
-       "284805                   1                             1   \n",
-       "284815                   1                             0   \n",
-       "284832                   1                             1   \n",
-       "284854                   1                             1   \n",
-       "284878                   1                             0   \n",
-       "\n",
-       "        alcohol_intake_frequency_1.0  alcohol_intake_frequency_2.0  \\\n",
-       "0                                  0                             0   \n",
-       "1                                  0                             0   \n",
-       "2                                  1                             0   \n",
-       "3                                  0                             0   \n",
-       "4                                  0                             0   \n",
-       "...                              ...                           ...   \n",
-       "284805                             0                             0   \n",
-       "284815                             1                             0   \n",
-       "284832                             0                             0   \n",
-       "284854                             0                             0   \n",
-       "284878                             0                             1   \n",
-       "\n",
-       "        alcohol_intake_frequency_3.0  alcohol_intake_frequency_4.0  \\\n",
-       "0                                  0                             0   \n",
-       "1                                  0                             0   \n",
-       "2                                  0                             0   \n",
-       "3                                  0                             0   \n",
-       "4                                  0                             0   \n",
-       "...                              ...                           ...   \n",
-       "284805                             0                             0   \n",
-       "284815                             0                             0   \n",
-       "284832                             0                             0   \n",
-       "284854                             0                             0   \n",
-       "284878                             0                             0   \n",
-       "\n",
-       "        alcohol_intake_frequency_5.0  \n",
-       "0                                  0  \n",
-       "1                                  0  \n",
-       "2                                  0  \n",
-       "3                                  0  \n",
-       "4                                  0  \n",
-       "...                              ...  \n",
-       "284805                             0  \n",
-       "284815                             0  \n",
-       "284832                             0  \n",
-       "284854                             0  \n",
-       "284878                             0  \n",
-       "\n",
-       "[568039 rows x 2996 columns]"
-      ]
-     },
-     "execution_count": 642,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 621,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "    basics = [\n",
-    "        'age_at_recruitment',\n",
-    "        'ethnic_background_0.0',\n",
-    "        'ethnic_background_1.0',\n",
-    "        'ethnic_background_5.0',#na 2 -> 5\n",
-    "        'ethnic_background_3.0',\n",
-    "        'ethnic_background_4.0',\n",
-    "        'townsend_deprivation_index_at_recruitment',\n",
-    "        'sex'\n",
-    "    ]\n",
-    "    questionnaire = [\n",
-    "        'overall_health_rating_0.0',\n",
-    "        'overall_health_rating_1.0',\n",
-    "        'overall_health_rating_2.0',\n",
-    "        'overall_health_rating_3.0',\n",
-    "        'smoking_status_0.0',\n",
-    "        'smoking_status_1.0',\n",
-    "        'smoking_status_2.0',\n",
-    "    ]\n",
-    "measurements = [\n",
-    "    'body_mass_index_bmi',\n",
-    "    'weight',\n",
-    "    \"standing_height\",\n",
-    "    'systolic_blood_pressure',\n",
-    "    'diastolic_blood_pressure',\n",
-    "]\n",
-    "\n",
-    "labs = [\n",
-    "    \"cholesterol\",\n",
-    "    \"hdl_cholesterol\",\n",
-    "    \"ldl_direct\",\n",
-    "    \"triglycerides\"\n",
-    "]\n",
-    "\n",
-    "family_history = [\n",
-    "    'fh_heart_disease',\n",
-    "]\n",
-    "\n",
-    "diagnoses = [\n",
-    "    'diabetes1',\n",
-    "    'diabetes2',\n",
-    "    'chronic_kidney_disease',\n",
-    "    'atrial_fibrillation',\n",
-    "    'migraine',\n",
-    "    'rheumatoid_arthritis',\n",
-    "    'systemic_lupus_erythematosus',\n",
-    "    'severe_mental_illness',\n",
-    "    'erectile_dysfunction',\n",
-    "]\n",
-    "\n",
-    "medications = [\n",
-    "    \"statins\",\n",
-    "    \"antihypertensives\",\n",
-    "    \"ass\",\n",
-    "    \"atypical_antipsychotics\",\n",
-    "    \"glucocorticoids\"\n",
-    "]\n",
-    "\n",
-    "pgs = [\n",
-    "    'PGS000011',\n",
-    "    'PGS000057',\n",
-    "    'PGS000058',\n",
-    "    'PGS000059']\n",
-    "\n",
-    "feature_dict = {\n",
-    "    \"pgs\": pgs,\n",
-    "    \"basics\": basics,\n",
-    "    \"questionnaire\": questionnaire,\n",
-    "    \"measurements\": measurements,\n",
-    "    \"labs\": labs,\n",
-    "    \"family_history\": family_history,\n",
-    "    \"medications\": medications,\n",
-    "    \"diagnoses\": diagnoses\n",
-    "}\n",
-    "\n",
-    "features = [f for group_list in feature_dict.values() for f in group_list]\n",
-    "target = \"MACE_event\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 613,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Collecting imbalanced_databases\n",
-      "  Downloading imbalanced_databases-0.1.1-py3-none-any.whl (4.3 MB)\n",
-      "\u001b[K     |████████████████████████████████| 4.3 MB 4.6 MB/s eta 0:00:01\n",
-      "\u001b[?25hRequirement already satisfied: numpy in /data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages (from imbalanced_databases) (1.19.1)\n",
-      "Requirement already satisfied: scipy in /data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages (from imbalanced_databases) (1.5.2)\n",
-      "Requirement already satisfied: sklearn in /data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages (from imbalanced_databases) (0.0)\n",
-      "Requirement already satisfied: pandas in /data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages (from imbalanced_databases) (1.1.3)\n",
-      "Requirement already satisfied: scikit-learn in /data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages (from sklearn->imbalanced_databases) (0.23.2)\n",
-      "Requirement already satisfied: python-dateutil>=2.7.3 in /data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages (from pandas->imbalanced_databases) (2.8.1)\n",
-      "Requirement already satisfied: pytz>=2017.2 in /data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages (from pandas->imbalanced_databases) (2020.1)\n",
-      "Requirement already satisfied: threadpoolctl>=2.0.0 in /data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages (from scikit-learn->sklearn->imbalanced_databases) (2.1.0)\n",
-      "Requirement already satisfied: joblib>=0.11 in /data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages (from scikit-learn->sklearn->imbalanced_databases) (0.17.0)\n",
-      "Requirement already satisfied: six>=1.5 in /data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages (from python-dateutil>=2.7.3->pandas->imbalanced_databases) (1.15.0)\n",
-      "Installing collected packages: imbalanced-databases\n",
-      "Successfully installed imbalanced-databases-0.1.1\n"
-     ]
-    }
-   ],
-   "source": [
-    "!pip install imbalanced_databases"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 624,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(150, 4)"
-      ]
-     },
-     "execution_count": 624,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import imbalanced_databases as imbd\n",
-    "dataset= imbd.load_iris0()\n",
-    "dataset[\"data\"].shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 604,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import smote_variants as sv\n",
-    "\n",
-    "oversampler= sv.polynom_fit_SMOTE()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 629,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2021-02-16 22:54:55,131:INFO:polynom_fit_SMOTE: Running sampling via ('polynom_fit_SMOTE', \"{'proportion': 1.0, 'topology': 'star', 'random_state': None}\")\n"
-     ]
-    }
-   ],
-   "source": [
-    "smote_df = data.sample(10000)\n",
-    "for c in smote_df.columns:\n",
-    "    if smote_df[c].dtype==\"bool\":\n",
-    "        #print(smote_df[c].dtype)\n",
-    "        smote_df[c] = smote_df[c].astype(float)\n",
-    "X_samp, y_samp = oversampler.sample(smote_df[features].values, smote_df[target].values)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 631,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 1.78882453,  0.77797397,  0.12463653, ...,  0.        ,\n",
-       "         0.        ,  0.        ],\n",
-       "       [-0.77867311, -1.34328462, -0.38160009, ...,  0.        ,\n",
-       "         0.        ,  0.        ],\n",
-       "       [ 0.2802518 , -0.21876199, -0.43298506, ...,  0.        ,\n",
-       "         0.        ,  0.        ],\n",
-       "       ...,\n",
-       "       [ 0.04400338,  0.01484893,  0.49036118, ...,  0.00221043,\n",
-       "         0.10831123,  0.03868258],\n",
-       "       [ 0.0634923 ,  0.03054282,  0.35859966, ...,  0.00243148,\n",
-       "         0.11914235,  0.04255084],\n",
-       "       [ 0.08298123,  0.04623672,  0.22683815, ...,  0.00265252,\n",
-       "         0.12997347,  0.0464191 ]])"
-      ]
-     },
-     "execution_count": 631,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "X_samp"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 572,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>index</th>\n",
-       "      <th>id</th>\n",
-       "      <th>dtype</th>\n",
-       "      <th>isTarget</th>\n",
-       "      <th>based_on</th>\n",
-       "      <th>aggr_fn</th>\n",
-       "      <th>mapping</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>covariate</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>death_cvd_comp_event</th>\n",
-       "      <td>73.0</td>\n",
-       "      <td>74.0</td>\n",
-       "      <td>int</td>\n",
-       "      <td>True</td>\n",
-       "      <td>endpoints_competing</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>death_cvd_comp_event_time</th>\n",
-       "      <td>74.0</td>\n",
-       "      <td>75.0</td>\n",
-       "      <td>float</td>\n",
-       "      <td>True</td>\n",
-       "      <td>endpoints_competing</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>SCORE_comp_event</th>\n",
-       "      <td>75.0</td>\n",
-       "      <td>76.0</td>\n",
-       "      <td>int</td>\n",
-       "      <td>True</td>\n",
-       "      <td>endpoints_competing</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>SCORE_comp_event_time</th>\n",
-       "      <td>76.0</td>\n",
-       "      <td>77.0</td>\n",
-       "      <td>float</td>\n",
-       "      <td>True</td>\n",
-       "      <td>endpoints_competing</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ASCVD_comp_event</th>\n",
-       "      <td>77.0</td>\n",
-       "      <td>78.0</td>\n",
-       "      <td>int</td>\n",
-       "      <td>True</td>\n",
-       "      <td>endpoints_competing</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ASCVD_comp_event_time</th>\n",
-       "      <td>78.0</td>\n",
-       "      <td>79.0</td>\n",
-       "      <td>float</td>\n",
-       "      <td>True</td>\n",
-       "      <td>endpoints_competing</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>QRISK3_comp_event</th>\n",
-       "      <td>79.0</td>\n",
-       "      <td>80.0</td>\n",
-       "      <td>int</td>\n",
-       "      <td>True</td>\n",
-       "      <td>endpoints_competing</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>QRISK3_comp_event_time</th>\n",
-       "      <td>80.0</td>\n",
-       "      <td>81.0</td>\n",
-       "      <td>float</td>\n",
-       "      <td>True</td>\n",
-       "      <td>endpoints_competing</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>MACE_comp_event</th>\n",
-       "      <td>81.0</td>\n",
-       "      <td>82.0</td>\n",
-       "      <td>int</td>\n",
-       "      <td>True</td>\n",
-       "      <td>endpoints_competing</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>MACE_comp_event_time</th>\n",
-       "      <td>82.0</td>\n",
-       "      <td>83.0</td>\n",
-       "      <td>float</td>\n",
-       "      <td>True</td>\n",
-       "      <td>endpoints_competing</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ethnic_background_0.0</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>bool</td>\n",
-       "      <td>False</td>\n",
-       "      <td>basics</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>{'White': 0, 'Black': 1, nan: -2, 'Asian': 3, ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ethnic_background_1.0</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>bool</td>\n",
-       "      <td>False</td>\n",
-       "      <td>basics</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>{'White': 0, 'Black': 1, nan: -2, 'Asian': 3, ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ethnic_background_3.0</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>bool</td>\n",
-       "      <td>False</td>\n",
-       "      <td>basics</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>{'White': 0, 'Black': 1, nan: -2, 'Asian': 3, ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ethnic_background_4.0</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>bool</td>\n",
-       "      <td>False</td>\n",
-       "      <td>basics</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>{'White': 0, 'Black': 1, nan: -2, 'Asian': 3, ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ethnic_background_5.0</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>bool</td>\n",
-       "      <td>False</td>\n",
-       "      <td>basics</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>{'White': 0, 'Black': 1, nan: -2, 'Asian': 3, ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>uk_biobank_assessment_centre_0.0</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>bool</td>\n",
-       "      <td>False</td>\n",
-       "      <td>basics</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>{'Sheffield': 0, 'Edinburgh': 1, 'Leeds': 2, '...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>uk_biobank_assessment_centre_1.0</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>bool</td>\n",
-       "      <td>False</td>\n",
-       "      <td>basics</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>{'Sheffield': 0, 'Edinburgh': 1, 'Leeds': 2, '...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>uk_biobank_assessment_centre_2.0</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>bool</td>\n",
-       "      <td>False</td>\n",
-       "      <td>basics</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>{'Sheffield': 0, 'Edinburgh': 1, 'Leeds': 2, '...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>uk_biobank_assessment_centre_3.0</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>bool</td>\n",
-       "      <td>False</td>\n",
-       "      <td>basics</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>{'Sheffield': 0, 'Edinburgh': 1, 'Leeds': 2, '...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>uk_biobank_assessment_centre_4.0</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>bool</td>\n",
-       "      <td>False</td>\n",
-       "      <td>basics</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>{'Sheffield': 0, 'Edinburgh': 1, 'Leeds': 2, '...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                  index    id  dtype  isTarget  \\\n",
-       "covariate                                                        \n",
-       "death_cvd_comp_event               73.0  74.0    int      True   \n",
-       "death_cvd_comp_event_time          74.0  75.0  float      True   \n",
-       "SCORE_comp_event                   75.0  76.0    int      True   \n",
-       "SCORE_comp_event_time              76.0  77.0  float      True   \n",
-       "ASCVD_comp_event                   77.0  78.0    int      True   \n",
-       "ASCVD_comp_event_time              78.0  79.0  float      True   \n",
-       "QRISK3_comp_event                  79.0  80.0    int      True   \n",
-       "QRISK3_comp_event_time             80.0  81.0  float      True   \n",
-       "MACE_comp_event                    81.0  82.0    int      True   \n",
-       "MACE_comp_event_time               82.0  83.0  float      True   \n",
-       "ethnic_background_0.0               NaN   NaN   bool     False   \n",
-       "ethnic_background_1.0               NaN   NaN   bool     False   \n",
-       "ethnic_background_3.0               NaN   NaN   bool     False   \n",
-       "ethnic_background_4.0               NaN   NaN   bool     False   \n",
-       "ethnic_background_5.0               NaN   NaN   bool     False   \n",
-       "uk_biobank_assessment_centre_0.0    NaN   NaN   bool     False   \n",
-       "uk_biobank_assessment_centre_1.0    NaN   NaN   bool     False   \n",
-       "uk_biobank_assessment_centre_2.0    NaN   NaN   bool     False   \n",
-       "uk_biobank_assessment_centre_3.0    NaN   NaN   bool     False   \n",
-       "uk_biobank_assessment_centre_4.0    NaN   NaN   bool     False   \n",
-       "\n",
-       "                                             based_on  aggr_fn  \\\n",
-       "covariate                                                        \n",
-       "death_cvd_comp_event              endpoints_competing      NaN   \n",
-       "death_cvd_comp_event_time         endpoints_competing      NaN   \n",
-       "SCORE_comp_event                  endpoints_competing      NaN   \n",
-       "SCORE_comp_event_time             endpoints_competing      NaN   \n",
-       "ASCVD_comp_event                  endpoints_competing      NaN   \n",
-       "ASCVD_comp_event_time             endpoints_competing      NaN   \n",
-       "QRISK3_comp_event                 endpoints_competing      NaN   \n",
-       "QRISK3_comp_event_time            endpoints_competing      NaN   \n",
-       "MACE_comp_event                   endpoints_competing      NaN   \n",
-       "MACE_comp_event_time              endpoints_competing      NaN   \n",
-       "ethnic_background_0.0                          basics      NaN   \n",
-       "ethnic_background_1.0                          basics      NaN   \n",
-       "ethnic_background_3.0                          basics      NaN   \n",
-       "ethnic_background_4.0                          basics      NaN   \n",
-       "ethnic_background_5.0                          basics      NaN   \n",
-       "uk_biobank_assessment_centre_0.0               basics      NaN   \n",
-       "uk_biobank_assessment_centre_1.0               basics      NaN   \n",
-       "uk_biobank_assessment_centre_2.0               basics      NaN   \n",
-       "uk_biobank_assessment_centre_3.0               basics      NaN   \n",
-       "uk_biobank_assessment_centre_4.0               basics      NaN   \n",
-       "\n",
-       "                                                                            mapping  \n",
-       "covariate                                                                            \n",
-       "death_cvd_comp_event                                                           None  \n",
-       "death_cvd_comp_event_time                                                      None  \n",
-       "SCORE_comp_event                                                               None  \n",
-       "SCORE_comp_event_time                                                          None  \n",
-       "ASCVD_comp_event                                                               None  \n",
-       "ASCVD_comp_event_time                                                          None  \n",
-       "QRISK3_comp_event                                                              None  \n",
-       "QRISK3_comp_event_time                                                         None  \n",
-       "MACE_comp_event                                                                None  \n",
-       "MACE_comp_event_time                                                           None  \n",
-       "ethnic_background_0.0             {'White': 0, 'Black': 1, nan: -2, 'Asian': 3, ...  \n",
-       "ethnic_background_1.0             {'White': 0, 'Black': 1, nan: -2, 'Asian': 3, ...  \n",
-       "ethnic_background_3.0             {'White': 0, 'Black': 1, nan: -2, 'Asian': 3, ...  \n",
-       "ethnic_background_4.0             {'White': 0, 'Black': 1, nan: -2, 'Asian': 3, ...  \n",
-       "ethnic_background_5.0             {'White': 0, 'Black': 1, nan: -2, 'Asian': 3, ...  \n",
-       "uk_biobank_assessment_centre_0.0  {'Sheffield': 0, 'Edinburgh': 1, 'Leeds': 2, '...  \n",
-       "uk_biobank_assessment_centre_1.0  {'Sheffield': 0, 'Edinburgh': 1, 'Leeds': 2, '...  \n",
-       "uk_biobank_assessment_centre_2.0  {'Sheffield': 0, 'Edinburgh': 1, 'Leeds': 2, '...  \n",
-       "uk_biobank_assessment_centre_3.0  {'Sheffield': 0, 'Edinburgh': 1, 'Leeds': 2, '...  \n",
-       "uk_biobank_assessment_centre_4.0  {'Sheffield': 0, 'Edinburgh': 1, 'Leeds': 2, '...  "
-      ]
-     },
-     "execution_count": 572,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "description.set_index(\"covariate\")[-50:-30]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 557,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#from sklearn.preprocessing import OrdinalEncoder\n",
-    "from category_encoders.ordinal import OrdinalEncoder\n",
-    "cat_cols = [c for c in description.set_index(\"dtype\").loc[[\"category\"]].covariate.to_list() if \"date\" not in c]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 587,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mapping = [{\"col\": c, \"mapping\": {e: i for i, e in enumerate([v for v in data[c].unique().tolist() if v==v])}} for c in cat_cols]\n",
-    "for i, c in enumerate(cat_cols): mapping[i][\"mapping\"].update({np.nan: -2})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 586,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "AttributeError",
-     "evalue": "module 'pandas' has no attribute 'nan'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-586-b5b301bb25bc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;34m[\u001b[0m\u001b[0mv\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnan\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnan\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/pandas/__init__.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(name)\u001b[0m\n\u001b[1;32m    256\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0m_SparseArray\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    257\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 258\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"module 'pandas' has no attribute '{name}'\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    259\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    260\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mAttributeError\u001b[0m: module 'pandas' has no attribute 'nan'"
-     ]
-    }
-   ],
-   "source": [
-    "[v for v in data[c].unique().tolist() if v is not np.nan][6]==pd.nan"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 588,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'col': 'sex', 'mapping': {0.0: 0, 1.0: 1, nan: -2}},\n",
-       " {'col': 'ethnic_background',\n",
-       "  'mapping': {0.0: 0, 1.0: 1, 3.0: 2, 4.0: 3, 5.0: 4, nan: -2}},\n",
-       " {'col': 'uk_biobank_assessment_centre',\n",
-       "  'mapping': {0.0: 0,\n",
-       "   1.0: 1,\n",
-       "   2.0: 2,\n",
-       "   3.0: 3,\n",
-       "   4.0: 4,\n",
-       "   5.0: 5,\n",
-       "   6.0: 6,\n",
-       "   7.0: 7,\n",
-       "   8.0: 8,\n",
-       "   9.0: 9,\n",
-       "   10.0: 10,\n",
-       "   11.0: 11,\n",
-       "   12.0: 12,\n",
-       "   13.0: 13,\n",
-       "   14.0: 14,\n",
-       "   15.0: 15,\n",
-       "   16.0: 16,\n",
-       "   17.0: 17,\n",
-       "   nan: -2}},\n",
-       " {'col': 'overall_health_rating',\n",
-       "  'mapping': {0.0: 0, 1.0: 1, 2.0: 2, 3.0: 3, nan: -2}},\n",
-       " {'col': 'smoking_status', 'mapping': {0.0: 0, 1.0: 1, 2.0: 2, nan: -2}},\n",
-       " {'col': 'alcohol_intake_frequency',\n",
-       "  'mapping': {0.0: 0, 1.0: 1, 3.0: 2, 4.0: 3, 5.0: 4, 2.0: 5, nan: -2}}]"
-      ]
-     },
-     "execution_count": 588,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "mapping"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 558,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from category_encoders.ordinal import OrdinalEncoder\n",
-    "cat_cols = [c for c in description.set_index(\"dtype\").loc[[\"category\"]].covariate.to_list() if \"date\" not in c]\n",
-    "\n",
-    "mapping = [{\"col\": c, \"mapping\": {e:i for i, e in enumerate(data[c].unique().tolist())}} for c in cat_cols]\n",
-    "for i, c in enumerate(cat_cols): mapping[i][\"mapping\"].update({np.nan:-2})\n",
-    "\n",
-    "enc = OrdinalEncoder(cols=cat_cols, mapping=mapping, handle_missing=\"return_nan\")\n",
-    "data = enc.fit_transform(data)\n",
-    "\n",
-    "description[\"mapping\"] = np.nan\n",
-    "for i, c in enumerate(cat_cols):\n",
-    "    description.loc[description.covariate == c, 'mapping'] = str(enc.mapping[i][\"mapping\"])\n",
-    "    if data[c].nunique()>2: \n",
-    "        ohe_encoded = pd.get_dummies(data[c], prefix=c)\n",
-    "        data[ohe_encoded.columns] = ohe_encoded\n",
-    "        for col in ohe_encoded.columns:\n",
-    "            description = description.append(\n",
-    "                {\"covariate\": col, \"dtype\": \"bool\", \"isTarget\": False, \"based_on\": description.loc[description.covariate == c, \"based_on\"].iloc[0], \n",
-    "                 \"aggr_fn\": np.nan, \"mapping\":str(enc.mapping[i][\"mapping\"])}, ignore_index=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 559,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>age_at_recruitment</th>\n",
-       "      <th>sex</th>\n",
-       "      <th>ethnic_background</th>\n",
-       "      <th>townsend_deprivation_index_at_recruitment</th>\n",
-       "      <th>date_of_attending_assessment_centre</th>\n",
-       "      <th>uk_biobank_assessment_centre</th>\n",
-       "      <th>birth_date</th>\n",
-       "      <th>overall_health_rating</th>\n",
-       "      <th>smoking_status</th>\n",
-       "      <th>...</th>\n",
-       "      <th>smoking_status_0.0</th>\n",
-       "      <th>smoking_status_1.0</th>\n",
-       "      <th>smoking_status_2.0</th>\n",
-       "      <th>alcohol_intake_frequency_0.0</th>\n",
-       "      <th>alcohol_intake_frequency_1.0</th>\n",
-       "      <th>alcohol_intake_frequency_2.0</th>\n",
-       "      <th>alcohol_intake_frequency_3.0</th>\n",
-       "      <th>alcohol_intake_frequency_4.0</th>\n",
-       "      <th>alcohol_intake_frequency_5.0</th>\n",
-       "      <th>ethnic_background_2.0</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>49.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-1.85293</td>\n",
-       "      <td>2009-11-12</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1960-11-12</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1000037</td>\n",
-       "      <td>59.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-3.49886</td>\n",
-       "      <td>2008-11-11</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1949-11-11</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1000043</td>\n",
-       "      <td>63.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-5.35115</td>\n",
-       "      <td>2009-06-03</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1946-06-03</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000084</td>\n",
-       "      <td>43.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>7.56610</td>\n",
-       "      <td>2007-10-18</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1964-10-18</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1000092</td>\n",
-       "      <td>50.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>7.66418</td>\n",
-       "      <td>2009-06-16</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1959-06-16</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
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-       "    <tr>\n",
-       "      <th>...</th>\n",
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-       "      <td>...</td>\n",
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-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
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-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28366</th>\n",
-       "      <td>1450270</td>\n",
-       "      <td>42.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.22516</td>\n",
-       "      <td>2007-08-02</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1965-08-02</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28367</th>\n",
-       "      <td>1450285</td>\n",
-       "      <td>60.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-3.28244</td>\n",
-       "      <td>2008-01-19</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1948-01-19</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28368</th>\n",
-       "      <td>1450301</td>\n",
-       "      <td>67.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-3.45544</td>\n",
-       "      <td>2010-03-16</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1943-03-16</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28369</th>\n",
-       "      <td>1450334</td>\n",
-       "      <td>53.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>5.85141</td>\n",
-       "      <td>2009-06-30</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1956-06-30</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28370</th>\n",
-       "      <td>1450342</td>\n",
-       "      <td>60.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>5.46087</td>\n",
-       "      <td>2008-10-11</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1948-10-11</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>28371 rows × 2978 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "           eid  age_at_recruitment  sex  ethnic_background  \\\n",
-       "0      1000018                49.0  0.0                0.0   \n",
-       "1      1000037                59.0  0.0                0.0   \n",
-       "2      1000043                63.0  1.0                0.0   \n",
-       "3      1000084                43.0  1.0                0.0   \n",
-       "4      1000092                50.0  0.0                1.0   \n",
-       "...        ...                 ...  ...                ...   \n",
-       "28366  1450270                42.0  0.0                1.0   \n",
-       "28367  1450285                60.0  0.0                0.0   \n",
-       "28368  1450301                67.0  1.0                0.0   \n",
-       "28369  1450334                53.0  0.0                0.0   \n",
-       "28370  1450342                60.0  1.0                0.0   \n",
-       "\n",
-       "       townsend_deprivation_index_at_recruitment  \\\n",
-       "0                                       -1.85293   \n",
-       "1                                       -3.49886   \n",
-       "2                                       -5.35115   \n",
-       "3                                        7.56610   \n",
-       "4                                        7.66418   \n",
-       "...                                          ...   \n",
-       "28366                                    1.22516   \n",
-       "28367                                   -3.28244   \n",
-       "28368                                   -3.45544   \n",
-       "28369                                    5.85141   \n",
-       "28370                                    5.46087   \n",
-       "\n",
-       "      date_of_attending_assessment_centre  uk_biobank_assessment_centre  \\\n",
-       "0                              2009-11-12                           0.0   \n",
-       "1                              2008-11-11                           0.0   \n",
-       "2                              2009-06-03                           0.0   \n",
-       "3                              2007-10-18                           0.0   \n",
-       "4                              2009-06-16                           0.0   \n",
-       "...                                   ...                           ...   \n",
-       "28366                          2007-08-02                           1.0   \n",
-       "28367                          2008-01-19                           1.0   \n",
-       "28368                          2010-03-16                           1.0   \n",
-       "28369                          2009-06-30                           1.0   \n",
-       "28370                          2008-10-11                           1.0   \n",
-       "\n",
-       "       birth_date  overall_health_rating  smoking_status  ...  \\\n",
-       "0      1960-11-12                    0.0             0.0  ...   \n",
-       "1      1949-11-11                    1.0             1.0  ...   \n",
-       "2      1946-06-03                    0.0             1.0  ...   \n",
-       "3      1964-10-18                    0.0             2.0  ...   \n",
-       "4      1959-06-16                    2.0             0.0  ...   \n",
-       "...           ...                    ...             ...  ...   \n",
-       "28366  1965-08-02                    1.0             2.0  ...   \n",
-       "28367  1948-01-19                    1.0             2.0  ...   \n",
-       "28368  1943-03-16                    1.0             1.0  ...   \n",
-       "28369  1956-06-30                    1.0             1.0  ...   \n",
-       "28370  1948-10-11                    1.0             2.0  ...   \n",
-       "\n",
-       "       smoking_status_0.0  smoking_status_1.0  smoking_status_2.0  \\\n",
-       "0                       1                   0                   0   \n",
-       "1                       0                   1                   0   \n",
-       "2                       0                   1                   0   \n",
-       "3                       0                   0                   1   \n",
-       "4                       1                   0                   0   \n",
-       "...                   ...                 ...                 ...   \n",
-       "28366                   0                   0                   1   \n",
-       "28367                   0                   0                   1   \n",
-       "28368                   0                   1                   0   \n",
-       "28369                   0                   1                   0   \n",
-       "28370                   0                   0                   1   \n",
-       "\n",
-       "       alcohol_intake_frequency_0.0  alcohol_intake_frequency_1.0  \\\n",
-       "0                                 1                             0   \n",
-       "1                                 1                             0   \n",
-       "2                                 0                             1   \n",
-       "3                                 1                             0   \n",
-       "4                                 0                             0   \n",
-       "...                             ...                           ...   \n",
-       "28366                             0                             0   \n",
-       "28367                             0                             0   \n",
-       "28368                             0                             0   \n",
-       "28369                             0                             0   \n",
-       "28370                             1                             0   \n",
-       "\n",
-       "       alcohol_intake_frequency_2.0  alcohol_intake_frequency_3.0  \\\n",
-       "0                                 0                             0   \n",
-       "1                                 0                             0   \n",
-       "2                                 0                             0   \n",
-       "3                                 0                             0   \n",
-       "4                                 1                             0   \n",
-       "...                             ...                           ...   \n",
-       "28366                             0                             0   \n",
-       "28367                             0                             0   \n",
-       "28368                             0                             0   \n",
-       "28369                             1                             0   \n",
-       "28370                             0                             0   \n",
-       "\n",
-       "       alcohol_intake_frequency_4.0  alcohol_intake_frequency_5.0  \\\n",
-       "0                                 0                             0   \n",
-       "1                                 0                             0   \n",
-       "2                                 0                             0   \n",
-       "3                                 0                             0   \n",
-       "4                                 0                             0   \n",
-       "...                             ...                           ...   \n",
-       "28366                             0                             1   \n",
-       "28367                             1                             0   \n",
-       "28368                             0                             1   \n",
-       "28369                             0                             0   \n",
-       "28370                             0                             0   \n",
-       "\n",
-       "       ethnic_background_2.0  \n",
-       "0                          0  \n",
-       "1                          0  \n",
-       "2                          0  \n",
-       "3                          0  \n",
-       "4                          0  \n",
-       "...                      ...  \n",
-       "28366                      0  \n",
-       "28367                      0  \n",
-       "28368                      0  \n",
-       "28369                      0  \n",
-       "28370                      0  \n",
-       "\n",
-       "[28371 rows x 2978 columns]"
-      ]
-     },
-     "execution_count": 559,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 560,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>index</th>\n",
-       "      <th>id</th>\n",
-       "      <th>dtype</th>\n",
-       "      <th>isTarget</th>\n",
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-       "      <th>covariate</th>\n",
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-       "  </thead>\n",
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-       "      <th>copd_comp_event</th>\n",
-       "      <td>71.0</td>\n",
-       "      <td>72.0</td>\n",
-       "      <td>int</td>\n",
-       "      <td>True</td>\n",
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-       "      <th>copd_comp_event_time</th>\n",
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-       "      <td>73.0</td>\n",
-       "      <td>float</td>\n",
-       "      <td>True</td>\n",
-       "      <td>endpoints_competing</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
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-       "      <th>death_cvd_comp_event</th>\n",
-       "      <td>73.0</td>\n",
-       "      <td>74.0</td>\n",
-       "      <td>int</td>\n",
-       "      <td>True</td>\n",
-       "      <td>endpoints_competing</td>\n",
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-       "      <td>NaN</td>\n",
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-       "      <th>death_cvd_comp_event_time</th>\n",
-       "      <td>74.0</td>\n",
-       "      <td>75.0</td>\n",
-       "      <td>float</td>\n",
-       "      <td>True</td>\n",
-       "      <td>endpoints_competing</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>SCORE_comp_event</th>\n",
-       "      <td>75.0</td>\n",
-       "      <td>76.0</td>\n",
-       "      <td>int</td>\n",
-       "      <td>True</td>\n",
-       "      <td>endpoints_competing</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
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-       "    <tr>\n",
-       "      <th>SCORE_comp_event_time</th>\n",
-       "      <td>76.0</td>\n",
-       "      <td>77.0</td>\n",
-       "      <td>float</td>\n",
-       "      <td>True</td>\n",
-       "      <td>endpoints_competing</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ASCVD_comp_event</th>\n",
-       "      <td>77.0</td>\n",
-       "      <td>78.0</td>\n",
-       "      <td>int</td>\n",
-       "      <td>True</td>\n",
-       "      <td>endpoints_competing</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ASCVD_comp_event_time</th>\n",
-       "      <td>78.0</td>\n",
-       "      <td>79.0</td>\n",
-       "      <td>float</td>\n",
-       "      <td>True</td>\n",
-       "      <td>endpoints_competing</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>QRISK3_comp_event</th>\n",
-       "      <td>79.0</td>\n",
-       "      <td>80.0</td>\n",
-       "      <td>int</td>\n",
-       "      <td>True</td>\n",
-       "      <td>endpoints_competing</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
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-       "    <tr>\n",
-       "      <th>QRISK3_comp_event_time</th>\n",
-       "      <td>80.0</td>\n",
-       "      <td>81.0</td>\n",
-       "      <td>float</td>\n",
-       "      <td>True</td>\n",
-       "      <td>endpoints_competing</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>MACE_comp_event</th>\n",
-       "      <td>81.0</td>\n",
-       "      <td>82.0</td>\n",
-       "      <td>int</td>\n",
-       "      <td>True</td>\n",
-       "      <td>endpoints_competing</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>MACE_comp_event_time</th>\n",
-       "      <td>82.0</td>\n",
-       "      <td>83.0</td>\n",
-       "      <td>float</td>\n",
-       "      <td>True</td>\n",
-       "      <td>endpoints_competing</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ethnic_background_0.0</th>\n",
-       "      <td>NaN</td>\n",
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-       "      <td>bool</td>\n",
-       "      <td>False</td>\n",
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-       "    </tr>\n",
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-       "      <th>ethnic_background_1.0</th>\n",
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-       "      <td>False</td>\n",
-       "      <td>basics</td>\n",
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-       "      <td>{0.0: 0, 1.0: 1, 3.0: 2, 4.0: 3, 5.0: 4, nan: -2}</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ethnic_background_2.0</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>bool</td>\n",
-       "      <td>False</td>\n",
-       "      <td>basics</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>{0.0: 0, 1.0: 1, 3.0: 2, 4.0: 3, 5.0: 4, nan: -2}</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ethnic_background_3.0</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>bool</td>\n",
-       "      <td>False</td>\n",
-       "      <td>basics</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>{0.0: 0, 1.0: 1, 3.0: 2, 4.0: 3, 5.0: 4, nan: -2}</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ethnic_background_4.0</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>bool</td>\n",
-       "      <td>False</td>\n",
-       "      <td>basics</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>{0.0: 0, 1.0: 1, 3.0: 2, 4.0: 3, 5.0: 4, nan: -2}</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>overall_health_rating_0.0</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>bool</td>\n",
-       "      <td>False</td>\n",
-       "      <td>questionnaire</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>{0.0: 0, 1.0: 1, 2.0: 2, 3.0: 3, nan: -2}</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>overall_health_rating_1.0</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>bool</td>\n",
-       "      <td>False</td>\n",
-       "      <td>questionnaire</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>{0.0: 0, 1.0: 1, 2.0: 2, 3.0: 3, nan: -2}</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>overall_health_rating_2.0</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>bool</td>\n",
-       "      <td>False</td>\n",
-       "      <td>questionnaire</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>{0.0: 0, 1.0: 1, 2.0: 2, 3.0: 3, nan: -2}</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                           index    id  dtype  isTarget             based_on  \\\n",
-       "covariate                                                                      \n",
-       "copd_comp_event             71.0  72.0    int      True  endpoints_competing   \n",
-       "copd_comp_event_time        72.0  73.0  float      True  endpoints_competing   \n",
-       "death_cvd_comp_event        73.0  74.0    int      True  endpoints_competing   \n",
-       "death_cvd_comp_event_time   74.0  75.0  float      True  endpoints_competing   \n",
-       "SCORE_comp_event            75.0  76.0    int      True  endpoints_competing   \n",
-       "SCORE_comp_event_time       76.0  77.0  float      True  endpoints_competing   \n",
-       "ASCVD_comp_event            77.0  78.0    int      True  endpoints_competing   \n",
-       "ASCVD_comp_event_time       78.0  79.0  float      True  endpoints_competing   \n",
-       "QRISK3_comp_event           79.0  80.0    int      True  endpoints_competing   \n",
-       "QRISK3_comp_event_time      80.0  81.0  float      True  endpoints_competing   \n",
-       "MACE_comp_event             81.0  82.0    int      True  endpoints_competing   \n",
-       "MACE_comp_event_time        82.0  83.0  float      True  endpoints_competing   \n",
-       "ethnic_background_0.0        NaN   NaN   bool     False               basics   \n",
-       "ethnic_background_1.0        NaN   NaN   bool     False               basics   \n",
-       "ethnic_background_2.0        NaN   NaN   bool     False               basics   \n",
-       "ethnic_background_3.0        NaN   NaN   bool     False               basics   \n",
-       "ethnic_background_4.0        NaN   NaN   bool     False               basics   \n",
-       "overall_health_rating_0.0    NaN   NaN   bool     False        questionnaire   \n",
-       "overall_health_rating_1.0    NaN   NaN   bool     False        questionnaire   \n",
-       "overall_health_rating_2.0    NaN   NaN   bool     False        questionnaire   \n",
-       "\n",
-       "                           aggr_fn  \\\n",
-       "covariate                            \n",
-       "copd_comp_event                NaN   \n",
-       "copd_comp_event_time           NaN   \n",
-       "death_cvd_comp_event           NaN   \n",
-       "death_cvd_comp_event_time      NaN   \n",
-       "SCORE_comp_event               NaN   \n",
-       "SCORE_comp_event_time          NaN   \n",
-       "ASCVD_comp_event               NaN   \n",
-       "ASCVD_comp_event_time          NaN   \n",
-       "QRISK3_comp_event              NaN   \n",
-       "QRISK3_comp_event_time         NaN   \n",
-       "MACE_comp_event                NaN   \n",
-       "MACE_comp_event_time           NaN   \n",
-       "ethnic_background_0.0          NaN   \n",
-       "ethnic_background_1.0          NaN   \n",
-       "ethnic_background_2.0          NaN   \n",
-       "ethnic_background_3.0          NaN   \n",
-       "ethnic_background_4.0          NaN   \n",
-       "overall_health_rating_0.0      NaN   \n",
-       "overall_health_rating_1.0      NaN   \n",
-       "overall_health_rating_2.0      NaN   \n",
-       "\n",
-       "                                                                     mapping  \n",
-       "covariate                                                                     \n",
-       "copd_comp_event                                                          NaN  \n",
-       "copd_comp_event_time                                                     NaN  \n",
-       "death_cvd_comp_event                                                     NaN  \n",
-       "death_cvd_comp_event_time                                                NaN  \n",
-       "SCORE_comp_event                                                         NaN  \n",
-       "SCORE_comp_event_time                                                    NaN  \n",
-       "ASCVD_comp_event                                                         NaN  \n",
-       "ASCVD_comp_event_time                                                    NaN  \n",
-       "QRISK3_comp_event                                                        NaN  \n",
-       "QRISK3_comp_event_time                                                   NaN  \n",
-       "MACE_comp_event                                                          NaN  \n",
-       "MACE_comp_event_time                                                     NaN  \n",
-       "ethnic_background_0.0      {0.0: 0, 1.0: 1, 3.0: 2, 4.0: 3, 5.0: 4, nan: -2}  \n",
-       "ethnic_background_1.0      {0.0: 0, 1.0: 1, 3.0: 2, 4.0: 3, 5.0: 4, nan: -2}  \n",
-       "ethnic_background_2.0      {0.0: 0, 1.0: 1, 3.0: 2, 4.0: 3, 5.0: 4, nan: -2}  \n",
-       "ethnic_background_3.0      {0.0: 0, 1.0: 1, 3.0: 2, 4.0: 3, 5.0: 4, nan: -2}  \n",
-       "ethnic_background_4.0      {0.0: 0, 1.0: 1, 3.0: 2, 4.0: 3, 5.0: 4, nan: -2}  \n",
-       "overall_health_rating_0.0          {0.0: 0, 1.0: 1, 2.0: 2, 3.0: 3, nan: -2}  \n",
-       "overall_health_rating_1.0          {0.0: 0, 1.0: 1, 2.0: 2, 3.0: 3, nan: -2}  \n",
-       "overall_health_rating_2.0          {0.0: 0, 1.0: 1, 2.0: 2, 3.0: 3, nan: -2}  "
-      ]
-     },
-     "execution_count": 560,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "description.set_index(\"covariate\")[-30:-10]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 467,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>index</th>\n",
-       "      <th>id</th>\n",
-       "      <th>dtype</th>\n",
-       "      <th>isTarget</th>\n",
-       "      <th>based_on</th>\n",
-       "      <th>aggr_fn</th>\n",
-       "      <th>mapping</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>covariate</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>sex</th>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>category</td>\n",
-       "      <td>False</td>\n",
-       "      <td>basics</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>{'Female': 0, 'Male': 1, nan: -2}</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "           index  id     dtype  isTarget based_on  aggr_fn  \\\n",
-       "covariate                                                    \n",
-       "sex            2   3  category     False   basics      NaN   \n",
-       "\n",
-       "                                     mapping  \n",
-       "covariate                                     \n",
-       "sex        {'Female': 0, 'Male': 1, nan: -2}  "
-      ]
-     },
-     "execution_count": 467,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "description"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 448,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "description = description.set_index(\"covariate\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 454,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>index</th>\n",
-       "      <th>id</th>\n",
-       "      <th>dtype</th>\n",
-       "      <th>isTarget</th>\n",
-       "      <th>based_on</th>\n",
-       "      <th>aggr_fn</th>\n",
-       "      <th>mapping</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>covariate</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>ethnic_background</th>\n",
-       "      <td>3</td>\n",
-       "      <td>4</td>\n",
-       "      <td>category</td>\n",
-       "      <td>False</td>\n",
-       "      <td>basics</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>test</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                   index  id     dtype  isTarget based_on  aggr_fn mapping\n",
-       "covariate                                                                 \n",
-       "ethnic_background      3   4  category     False   basics      NaN    test"
-      ]
-     },
-     "execution_count": 454,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "description"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "enc.mapping"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data[data.columns[1:20]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "enc.mapping[5][\"mapping\"].loc"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "description[1:20]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 220,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "AttributeError",
-     "evalue": "'OrdinalEncoder' object has no attribute 'categories_'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-220-3b6002a3b60d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0menc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcategories_\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mAttributeError\u001b[0m: 'OrdinalEncoder' object has no attribute 'categories_'"
-     ]
-    }
-   ],
-   "source": [
-    "enc.categories_"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 187,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'OrdinalEncoder' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-187-67f229e689a4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mc\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcat_cols\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m     \u001b[0menc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mOrdinalEncoder\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0menc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m>\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m         \u001b[0mohe_encoded\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_dummies\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprefix\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'OrdinalEncoder' is not defined"
-     ]
-    }
-   ],
-   "source": [
-    "from sklearn.preprocessing import OrdinalEncoder\n",
-    "    for c in cat_cols:\n",
-    "        enc = OrdinalEncoder()\n",
-    "        if data[c]==2: data[c] = enc.fit_transform(data[c])\n",
-    "        if data[c]>2: \n",
-    "            ohe_encoded = pd.get_dummies(data[c], prefix=c)\n",
-    "            data[ohe_encoded.columns] = ohe_encoded\n",
-    "            for col in ohe_encoded.columns:\n",
-    "                description = description.append(\n",
-    "                    {\"covariate\": col, \"dtype\": \"bool\", \"isTarget\": False, \"based_on\": np.nan, \"aggr_fn\": np.nan},\n",
-    "                    ignore_index=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 186,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array(['age_at_recruitment', 'townsend_deprivation_index_at_recruitment',\n",
-       "       'body_mass_index_bmi', 'weight',\n",
-       "       'pulse_wave_arterial_stiffness_index',\n",
-       "       'pulse_wave_reflection_index', 'waist_circumference',\n",
-       "       'hip_circumference', 'standing_height', 'trunk_fat_percentage',\n",
-       "       'body_fat_percentage', 'basal_metabolic_rate',\n",
-       "       'forced_vital_capacity_fvc_best_measure',\n",
-       "       'forced_expiratory_volume_in_1second_fev1_best_measure',\n",
-       "       'fev1_fvc_ratio_zscore', 'peak_expiratory_flow_pef_f3064_0_2',\n",
-       "       'peak_expiratory_flow_pef_f3064_0_1', 'peak_expiratory_flow_pef',\n",
-       "       'systolic_blood_pressure', 'diastolic_blood_pressure',\n",
-       "       'pulse_rate', 'basophill_count', 'basophill_percentage',\n",
-       "       'eosinophill_count', 'eosinophill_percentage',\n",
-       "       'haematocrit_percentage', 'haemoglobin_concentration',\n",
-       "       'high_light_scatter_reticulocyte_count',\n",
-       "       'high_light_scatter_reticulocyte_percentage',\n",
-       "       'immature_reticulocyte_fraction', 'lymphocyte_count',\n",
-       "       'lymphocyte_percentage', 'mean_corpuscular_haemoglobin',\n",
-       "       'mean_corpuscular_haemoglobin_concentration',\n",
-       "       'mean_corpuscular_volume', 'mean_platelet_thrombocyte_volume',\n",
-       "       'mean_reticulocyte_volume', 'mean_sphered_cell_volume',\n",
-       "       'monocyte_count', 'monocyte_percentage', 'neutrophill_count',\n",
-       "       'neutrophill_percentage', 'nucleated_red_blood_cell_count',\n",
-       "       'nucleated_red_blood_cell_percentage', 'platelet_count',\n",
-       "       'platelet_crit', 'platelet_distribution_width',\n",
-       "       'red_blood_cell_erythrocyte_count',\n",
-       "       'red_blood_cell_erythrocyte_distribution_width',\n",
-       "       'reticulocyte_count', 'reticulocyte_percentage',\n",
-       "       'white_blood_cell_leukocyte_count', 'alanine_aminotransferase',\n",
-       "       'albumin', 'alkaline_phosphatase', 'apolipoprotein_a',\n",
-       "       'apolipoprotein_b', 'aspartate_aminotransferase',\n",
-       "       'creactive_protein', 'calcium', 'cholesterol', 'creatinine',\n",
-       "       'cystatin_c', 'direct_bilirubin', 'gamma_glutamyltransferase',\n",
-       "       'glucose', 'glycated_haemoglobin_hba1c', 'hdl_cholesterol', 'igf1',\n",
-       "       'ldl_direct', 'lipoprotein_a', 'oestradiol', 'phosphate',\n",
-       "       'rheumatoid_factor', 'shbg', 'testosterone', 'total_bilirubin',\n",
-       "       'total_protein', 'triglycerides', 'urate', 'urea', 'vitamin_d',\n",
-       "       'PGS000011', 'PGS000013', 'PGS000016', 'PGS000018', 'PGS000039',\n",
-       "       'PGS000057', 'PGS000058', 'PGS000059', 'PGS000116', 'PGS000117',\n",
-       "       'PGS000192', 'PGS000296'], dtype=object)"
-      ]
-     },
-     "execution_count": 186,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "description \\\n",
-    "        .set_index('dtype').loc[['int', \"float\"]] \\\n",
-    "        .query(\"(isTarget == False) & (based_on != 'diagnoses_emb') & (based_on != 'eid')\")['covariate'].values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 105,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>level_0</th>\n",
-       "      <th>index</th>\n",
-       "      <th>id</th>\n",
-       "      <th>covariate</th>\n",
-       "      <th>dtype</th>\n",
-       "      <th>isTarget</th>\n",
-       "      <th>based_on</th>\n",
-       "      <th>aggr_fn</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>eid</td>\n",
-       "      <td>integer</td>\n",
-       "      <td>False</td>\n",
-       "      <td>eid</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>age_at_recruitment</td>\n",
-       "      <td>numeric</td>\n",
-       "      <td>False</td>\n",
-       "      <td>basics</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>sex</td>\n",
-       "      <td>category</td>\n",
-       "      <td>False</td>\n",
-       "      <td>basics</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>3</td>\n",
-       "      <td>3</td>\n",
-       "      <td>4</td>\n",
-       "      <td>ethnic_background</td>\n",
-       "      <td>category</td>\n",
-       "      <td>False</td>\n",
-       "      <td>basics</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>4</td>\n",
-       "      <td>4</td>\n",
-       "      <td>5</td>\n",
-       "      <td>townsend_deprivation_index_at_recruitment</td>\n",
-       "      <td>numeric</td>\n",
-       "      <td>False</td>\n",
-       "      <td>basics</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2951</th>\n",
-       "      <td>2951</td>\n",
-       "      <td>78</td>\n",
-       "      <td>79</td>\n",
-       "      <td>ASCVD_comp_event_time</td>\n",
-       "      <td>numeric</td>\n",
-       "      <td>True</td>\n",
-       "      <td>endpoints_competing</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2952</th>\n",
-       "      <td>2952</td>\n",
-       "      <td>79</td>\n",
-       "      <td>80</td>\n",
-       "      <td>QRISK3_comp_event</td>\n",
-       "      <td>integer</td>\n",
-       "      <td>True</td>\n",
-       "      <td>endpoints_competing</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2953</th>\n",
-       "      <td>2953</td>\n",
-       "      <td>80</td>\n",
-       "      <td>81</td>\n",
-       "      <td>QRISK3_comp_event_time</td>\n",
-       "      <td>numeric</td>\n",
-       "      <td>True</td>\n",
-       "      <td>endpoints_competing</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2954</th>\n",
-       "      <td>2954</td>\n",
-       "      <td>81</td>\n",
-       "      <td>82</td>\n",
-       "      <td>MACE_comp_event</td>\n",
-       "      <td>integer</td>\n",
-       "      <td>True</td>\n",
-       "      <td>endpoints_competing</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2955</th>\n",
-       "      <td>2955</td>\n",
-       "      <td>82</td>\n",
-       "      <td>83</td>\n",
-       "      <td>MACE_comp_event_time</td>\n",
-       "      <td>numeric</td>\n",
-       "      <td>True</td>\n",
-       "      <td>endpoints_competing</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>2956 rows × 8 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      level_0  index  id                                  covariate     dtype  \\\n",
-       "0           0      0   1                                        eid   integer   \n",
-       "1           1      1   2                         age_at_recruitment   numeric   \n",
-       "2           2      2   3                                        sex  category   \n",
-       "3           3      3   4                          ethnic_background  category   \n",
-       "4           4      4   5  townsend_deprivation_index_at_recruitment   numeric   \n",
-       "...       ...    ...  ..                                        ...       ...   \n",
-       "2951     2951     78  79                      ASCVD_comp_event_time   numeric   \n",
-       "2952     2952     79  80                          QRISK3_comp_event   integer   \n",
-       "2953     2953     80  81                     QRISK3_comp_event_time   numeric   \n",
-       "2954     2954     81  82                            MACE_comp_event   integer   \n",
-       "2955     2955     82  83                       MACE_comp_event_time   numeric   \n",
-       "\n",
-       "      isTarget             based_on  aggr_fn  \n",
-       "0        False                  eid      NaN  \n",
-       "1        False               basics      NaN  \n",
-       "2        False               basics      NaN  \n",
-       "3        False               basics      NaN  \n",
-       "4        False               basics      NaN  \n",
-       "...        ...                  ...      ...  \n",
-       "2951      True  endpoints_competing      NaN  \n",
-       "2952      True  endpoints_competing      NaN  \n",
-       "2953      True  endpoints_competing      NaN  \n",
-       "2954      True  endpoints_competing      NaN  \n",
-       "2955      True  endpoints_competing      NaN  \n",
-       "\n",
-       "[2956 rows x 8 columns]"
-      ]
-     },
-     "execution_count": 105,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data.column"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 139,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>covariate</th>\n",
-       "      <th>dtype</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>age_at_recruitment</td>\n",
-       "      <td>numeric</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>standing_height</td>\n",
-       "      <td>numeric</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>102</th>\n",
-       "      <td>myocardial_infarction</td>\n",
-       "      <td>logical</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                 covariate    dtype\n",
-       "1       age_at_recruitment  numeric\n",
-       "17         standing_height  numeric\n",
-       "102  myocardial_infarction  logical"
-      ]
-     },
-     "execution_count": 139,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "description.query(\"covariate ==@features\")[[\"covariate\", \"dtype\"]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 117,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "features = [\"standing_height\", \"age_at_recruitment\", \"myocardial_infarction\", \"ethnicity\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 123,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "continuous = description.query(\"covariate ==@features\").query(\"dtype in ['integer', 'numeric']\").covariate.values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 124,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[1, 17]"
-      ]
-     },
-     "execution_count": 124,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "[data.columns.to_list().index(v) for v in continuous]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 127,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "37"
-      ]
-     },
-     "execution_count": 127,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data.iloc[:,1].nunique()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Score Weights"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "PGS Catalogue searched for [\"Atrial Fibrillation\", \"Coronary Artery Disease\", \"Coronary Heart Disease\", \"Stroke\"] "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 88,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pgs_planned = sorted([p.absolute() for p in list(pathlib.Path(f\"{data_path}/1_genetics/pgs_weights/raw\").rglob('*.txt'))])\n",
-    "pgs_planned_list = [p.name[:-4] for p in pgs_planned]\n",
-    "pgs_planned_dict = dict(zip(pgs_planned_list, pgs_planned))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 89,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'PGS000010': PosixPath('/data/analysis/ag-reils/ag-reils-shared/cardioRS/data/1_genetics/pgs_weights/raw/PGS000010.txt'),\n",
-       " 'PGS000011': PosixPath('/data/analysis/ag-reils/ag-reils-shared/cardioRS/data/1_genetics/pgs_weights/raw/PGS000011.txt'),\n",
-       " 'PGS000012': PosixPath('/data/analysis/ag-reils/ag-reils-shared/cardioRS/data/1_genetics/pgs_weights/raw/PGS000012.txt'),\n",
-       " 'PGS000013': PosixPath('/data/analysis/ag-reils/ag-reils-shared/cardioRS/data/1_genetics/pgs_weights/raw/PGS000013.txt'),\n",
-       " 'PGS000016': PosixPath('/data/analysis/ag-reils/ag-reils-shared/cardioRS/data/1_genetics/pgs_weights/raw/PGS000016.txt'),\n",
-       " 'PGS000018': PosixPath('/data/analysis/ag-reils/ag-reils-shared/cardioRS/data/1_genetics/pgs_weights/raw/PGS000018.txt'),\n",
-       " 'PGS000019': PosixPath('/data/analysis/ag-reils/ag-reils-shared/cardioRS/data/1_genetics/pgs_weights/raw/PGS000019.txt'),\n",
-       " 'PGS000035': PosixPath('/data/analysis/ag-reils/ag-reils-shared/cardioRS/data/1_genetics/pgs_weights/raw/PGS000035.txt'),\n",
-       " 'PGS000038': PosixPath('/data/analysis/ag-reils/ag-reils-shared/cardioRS/data/1_genetics/pgs_weights/raw/PGS000038.txt'),\n",
-       " 'PGS000039': PosixPath('/data/analysis/ag-reils/ag-reils-shared/cardioRS/data/1_genetics/pgs_weights/raw/PGS000039.txt'),\n",
-       " 'PGS000057': PosixPath('/data/analysis/ag-reils/ag-reils-shared/cardioRS/data/1_genetics/pgs_weights/raw/PGS000057.txt'),\n",
-       " 'PGS000058': PosixPath('/data/analysis/ag-reils/ag-reils-shared/cardioRS/data/1_genetics/pgs_weights/raw/PGS000058.txt'),\n",
-       " 'PGS000059': PosixPath('/data/analysis/ag-reils/ag-reils-shared/cardioRS/data/1_genetics/pgs_weights/raw/PGS000059.txt'),\n",
-       " 'PGS000116': PosixPath('/data/analysis/ag-reils/ag-reils-shared/cardioRS/data/1_genetics/pgs_weights/raw/PGS000116.txt'),\n",
-       " 'PGS000117': PosixPath('/data/analysis/ag-reils/ag-reils-shared/cardioRS/data/1_genetics/pgs_weights/raw/PGS000117.txt'),\n",
-       " 'PGS000192': PosixPath('/data/analysis/ag-reils/ag-reils-shared/cardioRS/data/1_genetics/pgs_weights/raw/PGS000192.txt'),\n",
-       " 'PGS000200': PosixPath('/data/analysis/ag-reils/ag-reils-shared/cardioRS/data/1_genetics/pgs_weights/raw/PGS000200.txt'),\n",
-       " 'PGS000296': PosixPath('/data/analysis/ag-reils/ag-reils-shared/cardioRS/data/1_genetics/pgs_weights/raw/PGS000296.txt'),\n",
-       " 'PGS000329': PosixPath('/data/analysis/ag-reils/ag-reils-shared/cardioRS/data/1_genetics/pgs_weights/raw/PGS000329.txt'),\n",
-       " 'PGS000331': PosixPath('/data/analysis/ag-reils/ag-reils-shared/cardioRS/data/1_genetics/pgs_weights/raw/PGS000331.txt')}"
-      ]
-     },
-     "execution_count": 89,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pgs_planned_dict"
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "source": [
-    "from IPython.display import display\n",
-    "\n",
-    "pgs_weights = {}\n",
-    "for pgs_name, pgs_path in tqdm(pgs_planned_dict.items()):\n",
-    "    pgs_weights[pgs_name]=pd.read_csv(pgs_path, sep='\\t', comment='#')\n",
-    "    print(pgs_name + \": \" + str(len(pgs_weights[pgs_name])))\n",
-    "    display(pgs_weights[pgs_name].head())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Get Scores"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 90,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pgs_finished = sorted([p.parent.absolute() for p in list(pathlib.Path(f\"{data_path}/1_genetics\").rglob('*distribution_plot.png'))])\n",
-    "pgs_list = [p.name for p in pgs_finished]\n",
-    "pgs_dict = dict(zip(pgs_list, pgs_finished))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 91,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'PGS000011': PosixPath('/data/analysis/ag-reils/ag-reils-shared/cardioRS/data/1_genetics/pgs_calculated/PGS000011'),\n",
-       " 'PGS000013': PosixPath('/data/analysis/ag-reils/ag-reils-shared/cardioRS/data/1_genetics/pgs_calculated/PGS000013'),\n",
-       " 'PGS000016': PosixPath('/data/analysis/ag-reils/ag-reils-shared/cardioRS/data/1_genetics/pgs_calculated/PGS000016'),\n",
-       " 'PGS000018': PosixPath('/data/analysis/ag-reils/ag-reils-shared/cardioRS/data/1_genetics/pgs_calculated/PGS000018'),\n",
-       " 'PGS000039': PosixPath('/data/analysis/ag-reils/ag-reils-shared/cardioRS/data/1_genetics/pgs_calculated/PGS000039'),\n",
-       " 'PGS000057': PosixPath('/data/analysis/ag-reils/ag-reils-shared/cardioRS/data/1_genetics/pgs_calculated/PGS000057'),\n",
-       " 'PGS000058': PosixPath('/data/analysis/ag-reils/ag-reils-shared/cardioRS/data/1_genetics/pgs_calculated/PGS000058'),\n",
-       " 'PGS000059': PosixPath('/data/analysis/ag-reils/ag-reils-shared/cardioRS/data/1_genetics/pgs_calculated/PGS000059'),\n",
-       " 'PGS000116': PosixPath('/data/analysis/ag-reils/ag-reils-shared/cardioRS/data/1_genetics/pgs_calculated/PGS000116'),\n",
-       " 'PGS000117': PosixPath('/data/analysis/ag-reils/ag-reils-shared/cardioRS/data/1_genetics/pgs_calculated/PGS000117'),\n",
-       " 'PGS000192': PosixPath('/data/analysis/ag-reils/ag-reils-shared/cardioRS/data/1_genetics/pgs_calculated/PGS000192'),\n",
-       " 'PGS000296': PosixPath('/data/analysis/ag-reils/ag-reils-shared/cardioRS/data/1_genetics/pgs_calculated/PGS000296')}"
-      ]
-     },
-     "execution_count": 91,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pgs_dict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 92,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "12"
-      ]
-     },
-     "execution_count": 92,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(pgs_dict)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 93,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['PGS000011',\n",
-       " 'PGS000013',\n",
-       " 'PGS000016',\n",
-       " 'PGS000018',\n",
-       " 'PGS000039',\n",
-       " 'PGS000057',\n",
-       " 'PGS000058',\n",
-       " 'PGS000059',\n",
-       " 'PGS000116',\n",
-       " 'PGS000117',\n",
-       " 'PGS000192',\n",
-       " 'PGS000296']"
-      ]
-     },
-     "execution_count": 93,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "list(pgs_dict.keys())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 94,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# PGS00039: Cardiovascular Disease\n",
-    "# PGS000117: Stroke"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 95,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_pgs = data[[\"eid\"]]\n",
-    "for pgs, pgs_path in pgs_dict.items():\n",
-    "    temp = pd.read_csv(str(pgs_path)+\"/PRSice.all_score\", delim_whitespace=True)\n",
-    "    temp = temp[temp.columns[-2:]]\n",
-    "    temp.columns = [\"eid\", pgs]\n",
-    "    data_pgs = data_pgs.merge(temp, on=\"eid\", how=\"left\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "for pgs in pgs_dict.keys():\n",
-    "    data_pgs[pgs].plot.kde()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Plotting"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "ModuleNotFoundError",
-     "evalue": "No module named 'sklearn_pandas'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-13-61ec3e643020>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpreprocessing\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mStandardScaler\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mLabelEncoder\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpreprocessing\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mOneHotEncoder\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn_pandas\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mDataFrameMapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mleave_eid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcol\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"eid\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'sklearn_pandas'"
-     ]
-    }
-   ],
-   "source": [
-    "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
-    "from sklearn.preprocessing import OneHotEncoder\n",
-    "from sklearn_pandas import DataFrameMapper\n",
-    "        \n",
-    "leave_eid = [(col, None) for col in [\"eid\"]]\n",
-    "standardize = [([col], StandardScaler()) for col in list(pgs_dict.keys())]\n",
-    "\n",
-    "mapper = DataFrameMapper(leave_eid + standardize, df_out=True)\n",
-    "data_pgs_tf = mapper.fit_transform(data_pgs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 97,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>PGS000011</th>\n",
-       "      <th>PGS000013</th>\n",
-       "      <th>PGS000016</th>\n",
-       "      <th>PGS000018</th>\n",
-       "      <th>PGS000039</th>\n",
-       "      <th>PGS000057</th>\n",
-       "      <th>PGS000058</th>\n",
-       "      <th>PGS000059</th>\n",
-       "      <th>PGS000116</th>\n",
-       "      <th>PGS000117</th>\n",
-       "      <th>PGS000192</th>\n",
-       "      <th>PGS000296</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>3.170770</td>\n",
-       "      <td>14.182161</td>\n",
-       "      <td>25.478597</td>\n",
-       "      <td>-7.910004</td>\n",
-       "      <td>0.196099</td>\n",
-       "      <td>3.690275</td>\n",
-       "      <td>8.513230</td>\n",
-       "      <td>4.060235</td>\n",
-       "      <td>2.543097</td>\n",
-       "      <td>15.850781</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>-1.531660</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1000020</td>\n",
-       "      <td>3.935565</td>\n",
-       "      <td>14.203609</td>\n",
-       "      <td>25.508855</td>\n",
-       "      <td>-7.338207</td>\n",
-       "      <td>0.382054</td>\n",
-       "      <td>4.170000</td>\n",
-       "      <td>8.665529</td>\n",
-       "      <td>3.640000</td>\n",
-       "      <td>2.575014</td>\n",
-       "      <td>16.197177</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>-1.529472</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1000037</td>\n",
-       "      <td>4.223654</td>\n",
-       "      <td>14.248176</td>\n",
-       "      <td>25.549722</td>\n",
-       "      <td>-7.533061</td>\n",
-       "      <td>0.189988</td>\n",
-       "      <td>4.288627</td>\n",
-       "      <td>8.540908</td>\n",
-       "      <td>5.541451</td>\n",
-       "      <td>2.441255</td>\n",
-       "      <td>16.068202</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>-1.549326</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000043</td>\n",
-       "      <td>3.529575</td>\n",
-       "      <td>14.176502</td>\n",
-       "      <td>25.394145</td>\n",
-       "      <td>-7.768648</td>\n",
-       "      <td>0.289388</td>\n",
-       "      <td>4.130000</td>\n",
-       "      <td>8.515447</td>\n",
-       "      <td>4.261098</td>\n",
-       "      <td>2.740589</td>\n",
-       "      <td>16.860180</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>-1.565533</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1000051</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>502499</th>\n",
-       "      <td>6025150</td>\n",
-       "      <td>4.181800</td>\n",
-       "      <td>14.053589</td>\n",
-       "      <td>25.402026</td>\n",
-       "      <td>-7.499164</td>\n",
-       "      <td>0.272956</td>\n",
-       "      <td>4.470000</td>\n",
-       "      <td>8.425411</td>\n",
-       "      <td>4.660235</td>\n",
-       "      <td>2.419166</td>\n",
-       "      <td>16.650813</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>-1.504692</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>502500</th>\n",
-       "      <td>6025165</td>\n",
-       "      <td>4.315743</td>\n",
-       "      <td>14.131477</td>\n",
-       "      <td>25.417482</td>\n",
-       "      <td>-7.736421</td>\n",
-       "      <td>0.275103</td>\n",
-       "      <td>3.900941</td>\n",
-       "      <td>8.046961</td>\n",
-       "      <td>4.000196</td>\n",
-       "      <td>2.721753</td>\n",
-       "      <td>16.601598</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>-1.582081</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>502501</th>\n",
-       "      <td>6025173</td>\n",
-       "      <td>3.096671</td>\n",
-       "      <td>14.186225</td>\n",
-       "      <td>25.488755</td>\n",
-       "      <td>-8.079815</td>\n",
-       "      <td>0.352918</td>\n",
-       "      <td>3.507804</td>\n",
-       "      <td>7.994697</td>\n",
-       "      <td>3.690706</td>\n",
-       "      <td>2.687853</td>\n",
-       "      <td>17.412722</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>-1.682143</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>502502</th>\n",
-       "      <td>6025182</td>\n",
-       "      <td>3.699646</td>\n",
-       "      <td>14.134693</td>\n",
-       "      <td>25.439013</td>\n",
-       "      <td>-7.962928</td>\n",
-       "      <td>0.343860</td>\n",
-       "      <td>4.020000</td>\n",
-       "      <td>8.151277</td>\n",
-       "      <td>3.960000</td>\n",
-       "      <td>2.692526</td>\n",
-       "      <td>16.256542</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>-1.611761</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>502503</th>\n",
-       "      <td>6025198</td>\n",
-       "      <td>3.663789</td>\n",
-       "      <td>14.325340</td>\n",
-       "      <td>25.449361</td>\n",
-       "      <td>-7.429906</td>\n",
-       "      <td>0.208367</td>\n",
-       "      <td>3.740000</td>\n",
-       "      <td>8.743372</td>\n",
-       "      <td>4.110000</td>\n",
-       "      <td>2.428418</td>\n",
-       "      <td>15.500291</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>-1.539847</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>502504 rows × 13 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            eid  PGS000011  PGS000013  PGS000016  PGS000018  PGS000039  \\\n",
-       "0       1000018   3.170770  14.182161  25.478597  -7.910004   0.196099   \n",
-       "1       1000020   3.935565  14.203609  25.508855  -7.338207   0.382054   \n",
-       "2       1000037   4.223654  14.248176  25.549722  -7.533061   0.189988   \n",
-       "3       1000043   3.529575  14.176502  25.394145  -7.768648   0.289388   \n",
-       "4       1000051        NaN        NaN        NaN        NaN        NaN   \n",
-       "...         ...        ...        ...        ...        ...        ...   \n",
-       "502499  6025150   4.181800  14.053589  25.402026  -7.499164   0.272956   \n",
-       "502500  6025165   4.315743  14.131477  25.417482  -7.736421   0.275103   \n",
-       "502501  6025173   3.096671  14.186225  25.488755  -8.079815   0.352918   \n",
-       "502502  6025182   3.699646  14.134693  25.439013  -7.962928   0.343860   \n",
-       "502503  6025198   3.663789  14.325340  25.449361  -7.429906   0.208367   \n",
-       "\n",
-       "        PGS000057  PGS000058  PGS000059  PGS000116  PGS000117  PGS000192  \\\n",
-       "0        3.690275   8.513230   4.060235   2.543097  15.850781        3.0   \n",
-       "1        4.170000   8.665529   3.640000   2.575014  16.197177        4.0   \n",
-       "2        4.288627   8.540908   5.541451   2.441255  16.068202        7.0   \n",
-       "3        4.130000   8.515447   4.261098   2.740589  16.860180        9.0   \n",
-       "4             NaN        NaN        NaN        NaN        NaN        NaN   \n",
-       "...           ...        ...        ...        ...        ...        ...   \n",
-       "502499   4.470000   8.425411   4.660235   2.419166  16.650813        3.0   \n",
-       "502500   3.900941   8.046961   4.000196   2.721753  16.601598        2.0   \n",
-       "502501   3.507804   7.994697   3.690706   2.687853  17.412722        5.0   \n",
-       "502502   4.020000   8.151277   3.960000   2.692526  16.256542        4.0   \n",
-       "502503   3.740000   8.743372   4.110000   2.428418  15.500291        2.0   \n",
-       "\n",
-       "        PGS000296  \n",
-       "0       -1.531660  \n",
-       "1       -1.529472  \n",
-       "2       -1.549326  \n",
-       "3       -1.565533  \n",
-       "4             NaN  \n",
-       "...           ...  \n",
-       "502499  -1.504692  \n",
-       "502500  -1.582081  \n",
-       "502501  -1.682143  \n",
-       "502502  -1.611761  \n",
-       "502503  -1.539847  \n",
-       "\n",
-       "[502504 rows x 13 columns]"
-      ]
-     },
-     "execution_count": 97,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data_pgs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for pgs in pgs_dict.keys():\n",
-    "    data_pgs_tf[pgs].plot.kde()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from scipy.stats import pearsonr\n",
-    "import matplotlib.pyplot as plt \n",
-    "\n",
-    "def corrfunc(x,y, ax=None, **kws):\n",
-    "    \"\"\"Plot the correlation coefficient in the top left hand corner of a plot.\"\"\"\n",
-    "    r, _ = pearsonr(x, y)\n",
-    "    ax = ax or plt.gca()\n",
-    "    # Unicode for lowercase rho (ρ)\n",
-    "    rho = '\\u03C1'\n",
-    "    ax.annotate(f'{rho} = {r:.2f}', xy=(.1, .9), xycoords=ax.transAxes)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import seaborn as sns\n",
-    "import matplotlib.pyplot as plt\n",
-    " \n",
-    "# Basic correlogram\n",
-    "g = sns.pairplot(data_pgs_tf[list(pgs_dict.keys())])\n",
-    "g.map_lower(corrfunc)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Write to disk"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 149,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>covariate</th>\n",
-       "      <th>dtype</th>\n",
-       "      <th>isTarget</th>\n",
-       "      <th>based_on</th>\n",
-       "      <th>aggr_fn</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>eid</td>\n",
-       "      <td>int</td>\n",
-       "      <td>False</td>\n",
-       "      <td>eid</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2</td>\n",
-       "      <td>PGS000011</td>\n",
-       "      <td>float</td>\n",
-       "      <td>False</td>\n",
-       "      <td>PGS</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>PGS000013</td>\n",
-       "      <td>float</td>\n",
-       "      <td>False</td>\n",
-       "      <td>PGS</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4</td>\n",
-       "      <td>PGS000016</td>\n",
-       "      <td>float</td>\n",
-       "      <td>False</td>\n",
-       "      <td>PGS</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5</td>\n",
-       "      <td>PGS000018</td>\n",
-       "      <td>float</td>\n",
-       "      <td>False</td>\n",
-       "      <td>PGS</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>6</td>\n",
-       "      <td>PGS000039</td>\n",
-       "      <td>float</td>\n",
-       "      <td>False</td>\n",
-       "      <td>PGS</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>7</td>\n",
-       "      <td>PGS000057</td>\n",
-       "      <td>float</td>\n",
-       "      <td>False</td>\n",
-       "      <td>PGS</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>8</td>\n",
-       "      <td>PGS000058</td>\n",
-       "      <td>float</td>\n",
-       "      <td>False</td>\n",
-       "      <td>PGS</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>9</td>\n",
-       "      <td>PGS000059</td>\n",
-       "      <td>float</td>\n",
-       "      <td>False</td>\n",
-       "      <td>PGS</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>10</td>\n",
-       "      <td>PGS000116</td>\n",
-       "      <td>float</td>\n",
-       "      <td>False</td>\n",
-       "      <td>PGS</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>11</td>\n",
-       "      <td>PGS000117</td>\n",
-       "      <td>float</td>\n",
-       "      <td>False</td>\n",
-       "      <td>PGS</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>12</td>\n",
-       "      <td>PGS000192</td>\n",
-       "      <td>float</td>\n",
-       "      <td>False</td>\n",
-       "      <td>PGS</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>13</td>\n",
-       "      <td>PGS000296</td>\n",
-       "      <td>float</td>\n",
-       "      <td>False</td>\n",
-       "      <td>PGS</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    id  covariate  dtype  isTarget based_on  aggr_fn\n",
-       "0    1        eid    int     False      eid      NaN\n",
-       "1    2  PGS000011  float     False      PGS      NaN\n",
-       "2    3  PGS000013  float     False      PGS      NaN\n",
-       "3    4  PGS000016  float     False      PGS      NaN\n",
-       "4    5  PGS000018  float     False      PGS      NaN\n",
-       "5    6  PGS000039  float     False      PGS      NaN\n",
-       "6    7  PGS000057  float     False      PGS      NaN\n",
-       "7    8  PGS000058  float     False      PGS      NaN\n",
-       "8    9  PGS000059  float     False      PGS      NaN\n",
-       "9   10  PGS000116  float     False      PGS      NaN\n",
-       "10  11  PGS000117  float     False      PGS      NaN\n",
-       "11  12  PGS000192  float     False      PGS      NaN\n",
-       "12  13  PGS000296  float     False      PGS      NaN"
-      ]
-     },
-     "execution_count": 149,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dtypes = {\"int32\":\"int\", \"int64\":\"int\", \"float64\":\"float\", \"category\":\"category\", \"object\":\"category\", \"bool\":\"bool\"}\n",
-    "desc_dict = {\"id\": [*range(1, len(data_pgs.columns.to_list())+1)] , \n",
-    "             \"covariate\": data_pgs.columns.to_list(), \n",
-    "             \"dtype\":[dtypes[str(col)] for col in data_pgs.dtypes.to_list()], \n",
-    "             \"isTarget\":[False for col in data_pgs.columns.to_list()],\n",
-    "            \"based_on\":[\"PGS\" if col!=\"eid\" else \"eid\" for col in data_pgs.columns.to_list()],\n",
-    "            \"aggr_fn\": [np.nan for col in data_pgs.columns.to_list()]}\n",
-    "data_pgs_description = pd.DataFrame.from_dict(desc_dict)\n",
-    "data_pgs_description"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 150,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_pgs.to_feather(f\"{dataset_path}/baseline_pgs.feather\")\n",
-    "data_pgs_description.to_feather(f\"{dataset_path}/baseline_pgs_description.feather\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python [conda env:miniconda3-pl1.x]",
-   "language": "python",
-   "name": "conda-env-miniconda3-pl1.x-py"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.8"
-  },
-  "toc-autonumbering": true,
-  "toc-showcode": true,
-  "toc-showmarkdowntxt": true
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/neuralcvd/preprocessing/ukbb_tabular/4_preprocessing_merge.ipynb b/neuralcvd/preprocessing/ukbb_tabular/4_preprocessing_merge.ipynb
deleted file mode 100644
index fa3eb10..0000000
--- a/neuralcvd/preprocessing/ukbb_tabular/4_preprocessing_merge.ipynb
+++ /dev/null
@@ -1,379 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 1. Preprocessing: Merge Clinical + PGS"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%load_ext autoreload\n",
-    "%autoreload 2\n",
-    "\n",
-    "import os\n",
-    "from tqdm.auto import tqdm\n",
-    "import pathlib\n",
-    "\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import lifelines\n",
-    "from lifelines import CoxPHFitter\n",
-    "from sklearn.model_selection import StratifiedKFold\n",
-    "\n",
-    "from joblib import Parallel, delayed\n",
-    "from tqdm.notebook import tqdm\n",
-    "import neptune\n",
-    "import warnings\n",
-    "warnings.filterwarnings(\"ignore\")\n",
-    "import shutil\n",
-    "\n",
-    "dataset_name = \"cvd_massive_excl_emb\"\n",
-    "path = \"/data/analysis/ag-reils/steinfej/code/umbrella/pre/ukbb\"\n",
-    "data_path = \"/data/analysis/ag-reils/ag-reils-shared/cardioRS/data\"\n",
-    "dataset_path = f\"{data_path}/2_datasets_pre/{dataset_name}\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Read PRS data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_pgs = pd.read_feather(f\"{dataset_path}/baseline_pgs.feather\")\n",
-    "data_pgs_description = pd.read_feather(f\"{dataset_path}/baseline_pgs_description.feather\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>covariate</th>\n",
-       "      <th>dtype</th>\n",
-       "      <th>isTarget</th>\n",
-       "      <th>based_on</th>\n",
-       "      <th>aggr_fn</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>eid</td>\n",
-       "      <td>integer</td>\n",
-       "      <td>False</td>\n",
-       "      <td>admin</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2</td>\n",
-       "      <td>PGS000011</td>\n",
-       "      <td>numeric</td>\n",
-       "      <td>False</td>\n",
-       "      <td>PGS</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>PGS000013</td>\n",
-       "      <td>numeric</td>\n",
-       "      <td>False</td>\n",
-       "      <td>PGS</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4</td>\n",
-       "      <td>PGS000016</td>\n",
-       "      <td>numeric</td>\n",
-       "      <td>False</td>\n",
-       "      <td>PGS</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5</td>\n",
-       "      <td>PGS000018</td>\n",
-       "      <td>numeric</td>\n",
-       "      <td>False</td>\n",
-       "      <td>PGS</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>6</td>\n",
-       "      <td>PGS000039</td>\n",
-       "      <td>numeric</td>\n",
-       "      <td>False</td>\n",
-       "      <td>PGS</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>7</td>\n",
-       "      <td>PGS000057</td>\n",
-       "      <td>numeric</td>\n",
-       "      <td>False</td>\n",
-       "      <td>PGS</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>8</td>\n",
-       "      <td>PGS000058</td>\n",
-       "      <td>numeric</td>\n",
-       "      <td>False</td>\n",
-       "      <td>PGS</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>9</td>\n",
-       "      <td>PGS000059</td>\n",
-       "      <td>numeric</td>\n",
-       "      <td>False</td>\n",
-       "      <td>PGS</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>10</td>\n",
-       "      <td>PGS000116</td>\n",
-       "      <td>numeric</td>\n",
-       "      <td>False</td>\n",
-       "      <td>PGS</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>11</td>\n",
-       "      <td>PGS000117</td>\n",
-       "      <td>numeric</td>\n",
-       "      <td>False</td>\n",
-       "      <td>PGS</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>12</td>\n",
-       "      <td>PGS000192</td>\n",
-       "      <td>numeric</td>\n",
-       "      <td>False</td>\n",
-       "      <td>PGS</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>13</td>\n",
-       "      <td>PGS000296</td>\n",
-       "      <td>numeric</td>\n",
-       "      <td>False</td>\n",
-       "      <td>PGS</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    id  covariate    dtype  isTarget based_on  aggr_fn\n",
-       "0    1        eid  integer     False    admin      NaN\n",
-       "1    2  PGS000011  numeric     False      PGS      NaN\n",
-       "2    3  PGS000013  numeric     False      PGS      NaN\n",
-       "3    4  PGS000016  numeric     False      PGS      NaN\n",
-       "4    5  PGS000018  numeric     False      PGS      NaN\n",
-       "5    6  PGS000039  numeric     False      PGS      NaN\n",
-       "6    7  PGS000057  numeric     False      PGS      NaN\n",
-       "7    8  PGS000058  numeric     False      PGS      NaN\n",
-       "8    9  PGS000059  numeric     False      PGS      NaN\n",
-       "9   10  PGS000116  numeric     False      PGS      NaN\n",
-       "10  11  PGS000117  numeric     False      PGS      NaN\n",
-       "11  12  PGS000192  numeric     False      PGS      NaN\n",
-       "12  13  PGS000296  numeric     False      PGS      NaN"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data_pgs_description"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Read Clinical data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_cv = pd.read_feather(f\"{dataset_path}/baseline_clinical.feather\")\n",
-    "data_cv_description = pd.read_feather(f\"{dataset_path}/baseline_clinical_description.feather\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Merge Genetics + Clinical"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data = data_pgs.merge(data_cv, on=\"eid\")\n",
-    "data_description = pd.concat([data_pgs_description, data_cv_description.tail(-1)]).reset_index()\n",
-    "data_description = data_description.drop(\"index\", axis=1)\n",
-    "data_description = data_description.drop(\"id\", axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "covariate     object\n",
-       "dtype         object\n",
-       "isTarget        bool\n",
-       "based_on      object\n",
-       "aggr_fn      float64\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data_description.dtypes"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "covariate_paths = {\n",
-    "    \"clinical\":(\"baseline_pgs.feather\", \"baseline_pgs_description.feather\"),\n",
-    "    \"pgs\":(\"baseline_clinical.feather\", \"baseline_clinical_description.feather\"),\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "baseline_pgs.feather\n",
-      "baseline_clinical.feather\n"
-     ]
-    }
-   ],
-   "source": [
-    "for paths in covariate_paths:\n",
-    "    print(covariate_paths[paths][0])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Write to disk"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data.to_feather(f\"{dataset_path}/baseline.feather\")\n",
-    "data_description.to_feather(f\"{dataset_path}/baseline_description.feather\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python [conda env:python]",
-   "language": "python",
-   "name": "conda-env-python-py"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/neuralcvd/preprocessing/ukbb_tabular/5_dataset_visualization-template.ipynb b/neuralcvd/preprocessing/ukbb_tabular/5_dataset_visualization-template.ipynb
deleted file mode 100644
index 223b581..0000000
--- a/neuralcvd/preprocessing/ukbb_tabular/5_dataset_visualization-template.ipynb
+++ /dev/null
@@ -1,4430 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 2. Exploration"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T14:16:53.068218Z",
-     "start_time": "2020-11-04T14:16:47.983Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "-- \u001b[1mAttaching packages\u001b[22m --------------------------------------- tidyverse 1.3.0 --\n",
-      "\n",
-      "\u001b[32mv\u001b[39m \u001b[34mggplot2\u001b[39m 3.3.2     \u001b[32mv\u001b[39m \u001b[34mpurrr  \u001b[39m 0.3.4\n",
-      "\u001b[32mv\u001b[39m \u001b[34mtibble \u001b[39m 3.0.3     \u001b[32mv\u001b[39m \u001b[34mdplyr  \u001b[39m 1.0.2\n",
-      "\u001b[32mv\u001b[39m \u001b[34mtidyr  \u001b[39m 1.1.2     \u001b[32mv\u001b[39m \u001b[34mstringr\u001b[39m 1.4.0\n",
-      "\u001b[32mv\u001b[39m \u001b[34mreadr  \u001b[39m 1.3.1     \u001b[32mv\u001b[39m \u001b[34mforcats\u001b[39m 0.5.0\n",
-      "\n",
-      "-- \u001b[1mConflicts\u001b[22m ------------------------------------------ tidyverse_conflicts() --\n",
-      "\u001b[31mx\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mfilter()\u001b[39m  masks \u001b[34mstats\u001b[39m::filter()\n",
-      "\u001b[31mx\u001b[39m \u001b[34mpurrr\u001b[39m::\u001b[32mflatten()\u001b[39m masks \u001b[34mjsonlite\u001b[39m::flatten()\n",
-      "\u001b[31mx\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mlag()\u001b[39m     masks \u001b[34mstats\u001b[39m::lag()\n",
-      "\n",
-      "\n",
-      "Attaching package: 'lubridate'\n",
-      "\n",
-      "\n",
-      "The following objects are masked from 'package:base':\n",
-      "\n",
-      "    date, intersect, setdiff, union\n",
-      "\n",
-      "\n",
-      "\n",
-      "Attaching package: 'glue'\n",
-      "\n",
-      "\n",
-      "The following object is masked from 'package:dplyr':\n",
-      "\n",
-      "    collapse\n",
-      "\n",
-      "\n",
-      "\n",
-      "Attaching package: 'cowplot'\n",
-      "\n",
-      "\n",
-      "The following object is masked from 'package:lubridate':\n",
-      "\n",
-      "    stamp\n",
-      "\n",
-      "\n",
-      "Loading required package: ggpubr\n",
-      "\n",
-      "\n",
-      "Attaching package: 'ggpubr'\n",
-      "\n",
-      "\n",
-      "The following object is masked from 'package:cowplot':\n",
-      "\n",
-      "    get_legend\n",
-      "\n",
-      "\n",
-      "\n",
-      "Attaching package: 'arsenal'\n",
-      "\n",
-      "\n",
-      "The following object is masked from 'package:lubridate':\n",
-      "\n",
-      "    is.Date\n",
-      "\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "try(library(tidyverse), silent=TRUE)\n",
-    "library(lubridate)\n",
-    "library(glue)\n",
-    "library(cowplot)\n",
-    "library(survminer)\n",
-    "library(survival)\n",
-    "library(ggsci)\n",
-    "library(arsenal)\n",
-    "library(yaml)\n",
-    "\n",
-    "#setwd(\"/\")\n",
-    "#path = \"/home/steinfej/projects/uk_biobank/\"\n",
-    "#dataset_path = \"data/datasets/cvd_big_excl\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T14:16:53.090072Z",
-     "start_time": "2020-11-04T14:16:48.270Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "dataset_name = \"cvd_massive_excl\"\n",
-    "path = \"/data/analysis/ag-reils/steinfej/code/umbrella/pre/ukbb\"\n",
-    "data_path = \"/data/analysis/ag-reils/ag-reils-shared/cardioRS/data\"\n",
-    "dataset_path = glue(\"{data_path}/2_datasets_pre/{dataset_name}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T14:16:57.477187Z",
-     "start_time": "2020-11-04T14:16:49.046Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "data = arrow::read_feather(glue(\"{dataset_path}/baseline.feather\")) \n",
-    "data_description = arrow::read_feather(glue(\"{dataset_path}/baseline_description.feather\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T14:16:57.520779Z",
-     "start_time": "2020-11-04T14:16:50.668Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "phenotypes = names(read_yaml(glue(\"{dataset_path}/phenotype_list.yaml\")))\n",
-    "family_history = names(read_yaml(glue(\"{dataset_path}/fh_list.yaml\")))\n",
-    "medications = names(read_yaml(glue(\"{dataset_path}/medication_list.yaml\")))\n",
-    "endpoints_ph = names(read_yaml(glue(\"{dataset_path}/endpoint_list.yaml\")))\n",
-    "endpoints_death = names(read_yaml(glue(\"{dataset_path}/death_list.yaml\")))\n",
-    "endpoints_scores = names(read_yaml(glue(\"{dataset_path}/scores_list.yaml\")))\n",
-    "endpoints = c(endpoints_ph, endpoints_death, endpoints_scores)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T14:16:58.275310Z",
-     "start_time": "2020-11-04T14:16:57.351Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "covariates = (data_description %>% filter(isTarget==FALSE) %>% filter(based_on!=\"PGS\"))$covariate[-1]\n",
-    "targets = (data_description %>% filter(isTarget==TRUE))$covariate[-1]\n",
-    "pgs = (data_description %>% filter(isTarget==FALSE) %>% filter(based_on==\"PGS\") %>% filter(!dtype==\"Date\"))$covariate"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T14:16:58.756284Z",
-     "start_time": "2020-11-04T14:16:57.831Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style>\n",
-       ".list-inline {list-style: none; margin:0; padding: 0}\n",
-       ".list-inline>li {display: inline-block}\n",
-       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
-       "</style>\n",
-       "<ol class=list-inline><li>'age_at_recruitment'</li><li>'sex'</li><li>'ethnic_background'</li><li>'townsend_deprivation_index_at_recruitment'</li><li>'overall_health_rating'</li><li>'smoking_status'</li><li>'alcohol_intake_frequency'</li><li>'body_mass_index_bmi'</li><li>'weight'</li><li>'pulse_wave_arterial_stiffness_index'</li><li>'pulse_wave_reflection_index'</li><li>'waist_circumference'</li><li>'hip_circumference'</li><li>'standing_height'</li><li>'trunk_fat_percentage'</li><li>'body_fat_percentage'</li><li>'basal_metabolic_rate'</li><li>'forced_vital_capacity_fvc_best_measure'</li><li>'forced_expiratory_volume_in_1second_fev1_best_measure'</li><li>'fev1_fvc_ratio_zscore'</li><li>'peak_expiratory_flow_pef_f3064_0_2'</li><li>'peak_expiratory_flow_pef_f3064_0_1'</li><li>'peak_expiratory_flow_pef'</li><li>'systolic_blood_pressure'</li><li>'diastolic_blood_pressure'</li><li>'pulse_rate'</li><li>'basophill_count'</li><li>'basophill_percentage'</li><li>'eosinophill_count'</li><li>'eosinophill_percentage'</li><li>'haematocrit_percentage'</li><li>'haemoglobin_concentration'</li><li>'high_light_scatter_reticulocyte_count'</li><li>'high_light_scatter_reticulocyte_percentage'</li><li>'immature_reticulocyte_fraction'</li><li>'lymphocyte_count'</li><li>'lymphocyte_percentage'</li><li>'mean_corpuscular_haemoglobin'</li><li>'mean_corpuscular_haemoglobin_concentration'</li><li>'mean_corpuscular_volume'</li><li>'mean_platelet_thrombocyte_volume'</li><li>'mean_reticulocyte_volume'</li><li>'mean_sphered_cell_volume'</li><li>'monocyte_count'</li><li>'monocyte_percentage'</li><li>'neutrophill_count'</li><li>'neutrophill_percentage'</li><li>'nucleated_red_blood_cell_count'</li><li>'nucleated_red_blood_cell_percentage'</li><li>'platelet_count'</li><li>'platelet_crit'</li><li>'platelet_distribution_width'</li><li>'red_blood_cell_erythrocyte_count'</li><li>'red_blood_cell_erythrocyte_distribution_width'</li><li>'reticulocyte_count'</li><li>'reticulocyte_percentage'</li><li>'white_blood_cell_leukocyte_count'</li><li>'alanine_aminotransferase'</li><li>'albumin'</li><li>'alkaline_phosphatase'</li><li>'apolipoprotein_a'</li><li>'apolipoprotein_b'</li><li>'aspartate_aminotransferase'</li><li>'creactive_protein'</li><li>'calcium'</li><li>'cholesterol'</li><li>'creatinine'</li><li>'cystatin_c'</li><li>'direct_bilirubin'</li><li>'gamma_glutamyltransferase'</li><li>'glucose'</li><li>'glycated_haemoglobin_hba1c'</li><li>'hdl_cholesterol'</li><li>'igf1'</li><li>'ldl_direct'</li><li>'lipoprotein_a'</li><li>'oestradiol'</li><li>'phosphate'</li><li>'rheumatoid_factor'</li><li>'shbg'</li><li>'testosterone'</li><li>'total_bilirubin'</li><li>'total_protein'</li><li>'triglycerides'</li><li>'urate'</li><li>'urea'</li><li>'vitamin_d'</li><li>'fh_alzheimer\\'s_disease/dementia'</li><li>'fh_bowel_cancer'</li><li>'fh_breast_cancer'</li><li>'fh_chronic_bronchitis/emphysema'</li><li>'fh_diabetes'</li><li>'fh_heart_disease'</li><li>'fh_high_blood_pressure'</li><li>'fh_lung_cancer'</li><li>'fh_parkinson\\'s_disease'</li><li>'fh_severe_depression'</li><li>'fh_stroke'</li><li>'coronary_heart_disease'</li><li>'myocardial_infarction'</li><li>'stroke'</li><li>'diabetes1'</li><li>'diabetes2'</li><li>'chronic_kidney_disease'</li><li>'atrial_fibrillation'</li><li>'migraine'</li><li>'rheumatoid_arthritis'</li><li>'systemic_lupus_erythematosus'</li><li>'severe_mental_illness'</li><li>'erectile_dysfunction'</li><li>'hypertensive_disorder_systemic_arterial'</li><li>'hyperlipidemia'</li><li>'depressive_disorder'</li><li>'gastroesophageal_reflux_disease'</li><li>'diabetes_mellitus_type_2'</li><li>'essential_hypertension'</li><li>'obesity'</li><li>'diabetes_mellitus'</li><li>'asthma'</li><li>'coronary_arteriosclerosis'</li><li>'allergic_rhinitis'</li><li>'hypothyroidism'</li><li>'upper_respiratory_infection'</li><li>'hypercholesterolemia'</li><li>'backache'</li><li>'abdominal_pain'</li><li>'osteoarthritis'</li><li>'low_back_pain'</li><li>'anemia'</li><li>'anxiety'</li><li>'urinary_tract_infectious_disease'</li><li>'chronic_obstructive_lung_disease'</li><li>'pneumonia'</li><li>'chest_pain'</li><li>'congestive_heart_failure'</li><li>'headache'</li><li>'pregnant'</li><li>'knee_pain'</li><li>'osteoporosis'</li><li>'polyp_of_colon'</li><li>'otitis_media'</li><li>'sinusitis'</li><li>'cough'</li><li>'sleep_apnea'</li><li>'insomnia'</li><li>'inflammatory_disorder_due_to_increased_blood_urate_level'</li><li>'tobacco_dependence_syndrome'</li><li>'malignant_tumor_of_prostate'</li><li>'constipation'</li><li>'hearing_loss'</li><li>'fatigue'</li><li>'obstructive_sleep_apnea_syndrome'</li><li>'malignant_neoplasm_of_breast'</li><li>'delivery_normal'</li><li>'irritable_bowel_syndrome'</li><li>'tobacco_user'</li><li>'neck_pain'</li><li>'cerebrovascular_accident'</li><li>'asthenia'</li><li>'shoulder_pain'</li><li>'acne_vulgaris'</li><li>'benign_prostatic_hyperplasia'</li><li>'dyspnea'</li><li>'carpal_tunnel_syndrome'</li><li>'bronchitis'</li><li>'pharyngitis'</li><li>'arthritis'</li><li>'diarrhea'</li><li>'dizziness'</li><li>'alcohol_abuse'</li><li>'dementia'</li><li>'eczema'</li><li>'syncope'</li><li>'acute_sinusitis'</li><li>'iron_deficiency_anemia'</li><li>'allergic_rhinitis_caused_by_pollen'</li><li>'gastritis'</li><li>'cataract'</li><li>'hematuria_syndrome'</li><li>'disorder_of_the_peripheral_nervous_system'</li><li>'viral_hepatitis_type_c'</li><li>'palpitations'</li><li>'eruption_of_skin'</li><li>'diabetes_mellitus_type_1'</li><li>'renal_failure_syndrome'</li><li>'peripheral_vascular_disease'</li><li>'hyperglycemia'</li><li>'seizure_disorder'</li><li>'fever'</li><li>'osteoarthritis_of_knee'</li><li>'actinic_keratosis'</li><li>'urinary_incontinence'</li><li>'hemorrhoids'</li><li>'seizure'</li><li>'laceration_-_injury'</li><li>'glaucoma'</li><li>'body_mass_index_30+_-_obesity'</li><li>'breast_lump'</li><li>'viral_disease'</li><li>'abnormal_cervical_smear'</li><li>⋯</li><li>'viral_exanthem'</li><li>'talipes_planus'</li><li>'idiopathic_peripheral_neuropathy'</li><li>'foreign_body_in_pharynx'</li><li>'jaw_pain'</li><li>'renal_impairment'</li><li>'ataxia'</li><li>'age-related_macular_degeneration'</li><li>'uterine_prolapse'</li><li>'renal_mass'</li><li>'pneumonitis'</li><li>'coordination_problem'</li><li>'blindness_-_both_eyes'</li><li>'primary_hyperparathyroidism'</li><li>'musculoskeletal_pain'</li><li>'mycosis'</li><li>'primigravida'</li><li>'urethral_stricture'</li><li>'leukocytosis'</li><li>'ventricular_premature_complex'</li><li>'ulcer_of_foot_due_to_diabetes_mellitus'</li><li>'chronic_headache_disorder'</li><li>'hemangioma'</li><li>'lymphedema'</li><li>'postmenopausal_state'</li><li>'chronic_ulcer_of_skin'</li><li>'left_heart_failure'</li><li>'excessive_and_frequent_menstruation'</li><li>'thrombocytosis'</li><li>'disorder_of_liver'</li><li>'disorder_of_carotid_artery'</li><li>'altered_bowel_function'</li><li>'abscess_of_foot'</li><li>'malignant_tumor_of_head_and/or_neck'</li><li>'streptococcus_group_b_infection_of_the_infant'</li><li>'concussion_injury_of_brain'</li><li>'feeding_problems_in_newborn'</li><li>'bipolar_i_disorder'</li><li>'viral_pharyngitis'</li><li>'lower_respiratory_tract_infection'</li><li>'hydronephrosis'</li><li>'borderline_personality_disorder'</li><li>'esophageal_varices'</li><li>'hypersomnia'</li><li>'sensorineural_hearing_loss_bilateral'</li><li>'varicocele'</li><li>'subarachnoid_intracranial_hemorrhage'</li><li>'incisional_hernia'</li><li>'varicella'</li><li>'pain_in_testicle'</li><li>'transplant_follow-up'</li><li>'tinea_cruris'</li><li>'laryngitis'</li><li>'hypertrophy_of_nail'</li><li>'amblyopia'</li><li>'polyp_of_cervix'</li><li>'cyst_of_kidney'</li><li>'hepatic_encephalopathy'</li><li>'blood_glucose_abnormal'</li><li>'postherpetic_neuralgia'</li><li>'frank_hematuria'</li><li>'cramp'</li><li>'interstitial_lung_disease'</li><li>'complete_atrioventricular_block'</li><li>'malignant_tumor_of_kidney'</li><li>'otitis'</li><li>'septic_shock'</li><li>'disorder_of_thyroid_gland'</li><li>'hypertrophic_cardiomyopathy'</li><li>'respiratory_distress_syndrome_in_the_newborn'</li><li>'infectious_gastroenteritis'</li><li>'subdural_intracranial_hemorrhage'</li><li>'hepatitis_b_carrier'</li><li>'manic_bipolar_i_disorder'</li><li>'secondary_pulmonary_hypertension'</li><li>'gonorrhea'</li><li>'derangement_of_knee'</li><li>'appendicitis'</li><li>'polyneuropathy_due_to_diabetes_mellitus'</li><li>'neonatal_hypoglycemia'</li><li>'prolonged_rupture_of_membranes'</li><li>'vasomotor_rhinitis'</li><li>'renal_disorder_due_to_type_1_diabetes_mellitus'</li><li>'tuberculosis'</li><li>'feeding_problem'</li><li>'chronic_tonsillitis'</li><li>'acute_duodenal_ulcer_with_hemorrhage'</li><li>'hammer_toe'</li><li>'malignant_tumor_of_cervix'</li><li>'prolapsed_lumbar_intervertebral_disc'</li><li>'hematemesis'</li><li>'perianal_abscess'</li><li>'nonvenomous_insect_bite'</li><li>'spondylolisthesis'</li><li>'malignant_tumor_of_esophagus'</li><li>'aphthous_ulcer_of_mouth'</li><li>'ventricular_septal_defect'</li><li>'oropharyngeal_dysphagia'</li><li>'injury_of_knee'</li><li>'traumatic_brain_injury'</li><li>'osteoarthritis_of_glenohumeral_joint'</li><li>'fetal_or_neonatal_effect_of_maternal_medical_problem'</li><li>'stomatological_preparations'</li><li>'drugs_for_acid_related_disorders'</li><li>'drugs_for_functional_gastrointestinal_disorders'</li><li>'antiemetics_and_antinauseants'</li><li>'bile_and_liver_therapy'</li><li>'drugs_for_constipation'</li><li>'antidiarrheals,_intestinal_antiinflammatory/antiinfective_agents'</li><li>'antiobesity_preparations,_excl._diet_products'</li><li>'digestives,_incl._enzymes'</li><li>'drugs_used_in_diabetes'</li><li>'vitamins'</li><li>'mineral_supplements'</li><li>'tonics'</li><li>'anabolic_agents_for_systemic_use'</li><li>'appetite_stimulants'</li><li>'other_alimentary_tract_and_metabolism_products'</li><li>'antithrombotic_agents'</li><li>'antihemorrhagics'</li><li>'antianemic_preparations'</li><li>'blood_substitutes_and_perfusion_solutions'</li><li>'other_hematological_agents'</li><li>'cardiac_therapy'</li><li>'antihypertensives'</li><li>'diuretics'</li><li>'peripheral_vasodilators'</li><li>'vasoprotectives'</li><li>'beta_blocking_agents'</li><li>'calcium_channel_blockers'</li><li>'agents_acting_on_the_renin-angiotensin_system'</li><li>'lipid_modifying_agents'</li><li>'antifungals_for_dermatological_use'</li><li>'emollients_and_protectives'</li><li>'preparations_for_treatment_of_wounds_and_ulcers'</li><li>'antipruritics,_incl._antihistamines,_anesthetics,_etc.'</li><li>'antipsoriatics'</li><li>'antibiotics_and_chemotherapeutics_for_dermatological_use'</li><li>'corticosteroids,_dermatological_preparations'</li><li>'antiseptics_and_disinfectants'</li><li>'medicated_dressings'</li><li>'anti-acne_preparations'</li><li>'other_dermatological_preparations'</li><li>'gynecological_antiinfectives_and_antiseptics'</li><li>'other_gynecologicals'</li><li>'sex_hormones_and_modulators_of_the_genital_system'</li><li>'urologicals'</li><li>'pituitary_and_hypothalamic_hormones_and_analogues'</li><li>'corticosteroids_for_systemic_use'</li><li>'thyroid_therapy'</li><li>'pancreatic_hormones'</li><li>'calcium_homeostasis'</li><li>'antibacterials_for_systemic_use'</li><li>'antimycotics_for_systemic_use'</li><li>'antimycobacterials'</li><li>'antivirals_for_systemic_use'</li><li>'immune_sera_and_immunoglobulins'</li><li>'vaccines'</li><li>'antineoplastic_agents'</li><li>'endocrine_therapy'</li><li>'immunostimulants'</li><li>'immunosuppressants'</li><li>'antiinflammatory_and_antirheumatic_products'</li><li>'topical_products_for_joint_and_muscular_pain'</li><li>'muscle_relaxants'</li><li>'antigout_preparations'</li><li>'drugs_for_treatment_of_bone_diseases'</li><li>'other_drugs_for_disorders_of_the_musculo-skeletal_system'</li><li>'anesthetics'</li><li>'analgesics'</li><li>'antiepileptics'</li><li>'anti-parkinson_drugs'</li><li>'psycholeptics'</li><li>'psychoanaleptics'</li><li>'other_nervous_system_drugs'</li><li>'antiprotozoals'</li><li>'anthelmintics'</li><li>'ectoparasiticides,_incl._scabicides,_insecticides_and_repellents'</li><li>'nasal_preparations'</li><li>'throat_preparations'</li><li>'drugs_for_obstructive_airway_diseases'</li><li>'cough_and_cold_preparations'</li><li>'antihistamines_for_systemic_use'</li><li>'other_respiratory_system_products'</li><li>'ophthalmologicals'</li><li>'otologicals'</li><li>'ophthalmological_and_otological_preparations'</li><li>'allergens'</li><li>'all_other_therapeutic_products'</li><li>'diagnostic_agents'</li><li>'general_nutrients'</li><li>'all_other_non-therapeutic_products'</li><li>'contrast_media'</li><li>'diagnostic_radiopharmaceuticals'</li><li>'therapeutic_radiopharmaceuticals'</li><li>'surgical_dressings'</li><li>'statins'</li><li>'ass'</li><li>'atypical_antipsychotics'</li><li>'glucocorticoids'</li></ol>\n"
-      ],
-      "text/latex": [
-       "\\begin{enumerate*}\n",
-       "\\item 'age\\_at\\_recruitment'\n",
-       "\\item 'sex'\n",
-       "\\item 'ethnic\\_background'\n",
-       "\\item 'townsend\\_deprivation\\_index\\_at\\_recruitment'\n",
-       "\\item 'overall\\_health\\_rating'\n",
-       "\\item 'smoking\\_status'\n",
-       "\\item 'alcohol\\_intake\\_frequency'\n",
-       "\\item 'body\\_mass\\_index\\_bmi'\n",
-       "\\item 'weight'\n",
-       "\\item 'pulse\\_wave\\_arterial\\_stiffness\\_index'\n",
-       "\\item 'pulse\\_wave\\_reflection\\_index'\n",
-       "\\item 'waist\\_circumference'\n",
-       "\\item 'hip\\_circumference'\n",
-       "\\item 'standing\\_height'\n",
-       "\\item 'trunk\\_fat\\_percentage'\n",
-       "\\item 'body\\_fat\\_percentage'\n",
-       "\\item 'basal\\_metabolic\\_rate'\n",
-       "\\item 'forced\\_vital\\_capacity\\_fvc\\_best\\_measure'\n",
-       "\\item 'forced\\_expiratory\\_volume\\_in\\_1second\\_fev1\\_best\\_measure'\n",
-       "\\item 'fev1\\_fvc\\_ratio\\_zscore'\n",
-       "\\item 'peak\\_expiratory\\_flow\\_pef\\_f3064\\_0\\_2'\n",
-       "\\item 'peak\\_expiratory\\_flow\\_pef\\_f3064\\_0\\_1'\n",
-       "\\item 'peak\\_expiratory\\_flow\\_pef'\n",
-       "\\item 'systolic\\_blood\\_pressure'\n",
-       "\\item 'diastolic\\_blood\\_pressure'\n",
-       "\\item 'pulse\\_rate'\n",
-       "\\item 'basophill\\_count'\n",
-       "\\item 'basophill\\_percentage'\n",
-       "\\item 'eosinophill\\_count'\n",
-       "\\item 'eosinophill\\_percentage'\n",
-       "\\item 'haematocrit\\_percentage'\n",
-       "\\item 'haemoglobin\\_concentration'\n",
-       "\\item 'high\\_light\\_scatter\\_reticulocyte\\_count'\n",
-       "\\item 'high\\_light\\_scatter\\_reticulocyte\\_percentage'\n",
-       "\\item 'immature\\_reticulocyte\\_fraction'\n",
-       "\\item 'lymphocyte\\_count'\n",
-       "\\item 'lymphocyte\\_percentage'\n",
-       "\\item 'mean\\_corpuscular\\_haemoglobin'\n",
-       "\\item 'mean\\_corpuscular\\_haemoglobin\\_concentration'\n",
-       "\\item 'mean\\_corpuscular\\_volume'\n",
-       "\\item 'mean\\_platelet\\_thrombocyte\\_volume'\n",
-       "\\item 'mean\\_reticulocyte\\_volume'\n",
-       "\\item 'mean\\_sphered\\_cell\\_volume'\n",
-       "\\item 'monocyte\\_count'\n",
-       "\\item 'monocyte\\_percentage'\n",
-       "\\item 'neutrophill\\_count'\n",
-       "\\item 'neutrophill\\_percentage'\n",
-       "\\item 'nucleated\\_red\\_blood\\_cell\\_count'\n",
-       "\\item 'nucleated\\_red\\_blood\\_cell\\_percentage'\n",
-       "\\item 'platelet\\_count'\n",
-       "\\item 'platelet\\_crit'\n",
-       "\\item 'platelet\\_distribution\\_width'\n",
-       "\\item 'red\\_blood\\_cell\\_erythrocyte\\_count'\n",
-       "\\item 'red\\_blood\\_cell\\_erythrocyte\\_distribution\\_width'\n",
-       "\\item 'reticulocyte\\_count'\n",
-       "\\item 'reticulocyte\\_percentage'\n",
-       "\\item 'white\\_blood\\_cell\\_leukocyte\\_count'\n",
-       "\\item 'alanine\\_aminotransferase'\n",
-       "\\item 'albumin'\n",
-       "\\item 'alkaline\\_phosphatase'\n",
-       "\\item 'apolipoprotein\\_a'\n",
-       "\\item 'apolipoprotein\\_b'\n",
-       "\\item 'aspartate\\_aminotransferase'\n",
-       "\\item 'creactive\\_protein'\n",
-       "\\item 'calcium'\n",
-       "\\item 'cholesterol'\n",
-       "\\item 'creatinine'\n",
-       "\\item 'cystatin\\_c'\n",
-       "\\item 'direct\\_bilirubin'\n",
-       "\\item 'gamma\\_glutamyltransferase'\n",
-       "\\item 'glucose'\n",
-       "\\item 'glycated\\_haemoglobin\\_hba1c'\n",
-       "\\item 'hdl\\_cholesterol'\n",
-       "\\item 'igf1'\n",
-       "\\item 'ldl\\_direct'\n",
-       "\\item 'lipoprotein\\_a'\n",
-       "\\item 'oestradiol'\n",
-       "\\item 'phosphate'\n",
-       "\\item 'rheumatoid\\_factor'\n",
-       "\\item 'shbg'\n",
-       "\\item 'testosterone'\n",
-       "\\item 'total\\_bilirubin'\n",
-       "\\item 'total\\_protein'\n",
-       "\\item 'triglycerides'\n",
-       "\\item 'urate'\n",
-       "\\item 'urea'\n",
-       "\\item 'vitamin\\_d'\n",
-       "\\item 'fh\\_alzheimer\\textbackslash{}'s\\_disease/dementia'\n",
-       "\\item 'fh\\_bowel\\_cancer'\n",
-       "\\item 'fh\\_breast\\_cancer'\n",
-       "\\item 'fh\\_chronic\\_bronchitis/emphysema'\n",
-       "\\item 'fh\\_diabetes'\n",
-       "\\item 'fh\\_heart\\_disease'\n",
-       "\\item 'fh\\_high\\_blood\\_pressure'\n",
-       "\\item 'fh\\_lung\\_cancer'\n",
-       "\\item 'fh\\_parkinson\\textbackslash{}'s\\_disease'\n",
-       "\\item 'fh\\_severe\\_depression'\n",
-       "\\item 'fh\\_stroke'\n",
-       "\\item 'coronary\\_heart\\_disease'\n",
-       "\\item 'myocardial\\_infarction'\n",
-       "\\item 'stroke'\n",
-       "\\item 'diabetes1'\n",
-       "\\item 'diabetes2'\n",
-       "\\item 'chronic\\_kidney\\_disease'\n",
-       "\\item 'atrial\\_fibrillation'\n",
-       "\\item 'migraine'\n",
-       "\\item 'rheumatoid\\_arthritis'\n",
-       "\\item 'systemic\\_lupus\\_erythematosus'\n",
-       "\\item 'severe\\_mental\\_illness'\n",
-       "\\item 'erectile\\_dysfunction'\n",
-       "\\item 'hypertensive\\_disorder\\_systemic\\_arterial'\n",
-       "\\item 'hyperlipidemia'\n",
-       "\\item 'depressive\\_disorder'\n",
-       "\\item 'gastroesophageal\\_reflux\\_disease'\n",
-       "\\item 'diabetes\\_mellitus\\_type\\_2'\n",
-       "\\item 'essential\\_hypertension'\n",
-       "\\item 'obesity'\n",
-       "\\item 'diabetes\\_mellitus'\n",
-       "\\item 'asthma'\n",
-       "\\item 'coronary\\_arteriosclerosis'\n",
-       "\\item 'allergic\\_rhinitis'\n",
-       "\\item 'hypothyroidism'\n",
-       "\\item 'upper\\_respiratory\\_infection'\n",
-       "\\item 'hypercholesterolemia'\n",
-       "\\item 'backache'\n",
-       "\\item 'abdominal\\_pain'\n",
-       "\\item 'osteoarthritis'\n",
-       "\\item 'low\\_back\\_pain'\n",
-       "\\item 'anemia'\n",
-       "\\item 'anxiety'\n",
-       "\\item 'urinary\\_tract\\_infectious\\_disease'\n",
-       "\\item 'chronic\\_obstructive\\_lung\\_disease'\n",
-       "\\item 'pneumonia'\n",
-       "\\item 'chest\\_pain'\n",
-       "\\item 'congestive\\_heart\\_failure'\n",
-       "\\item 'headache'\n",
-       "\\item 'pregnant'\n",
-       "\\item 'knee\\_pain'\n",
-       "\\item 'osteoporosis'\n",
-       "\\item 'polyp\\_of\\_colon'\n",
-       "\\item 'otitis\\_media'\n",
-       "\\item 'sinusitis'\n",
-       "\\item 'cough'\n",
-       "\\item 'sleep\\_apnea'\n",
-       "\\item 'insomnia'\n",
-       "\\item 'inflammatory\\_disorder\\_due\\_to\\_increased\\_blood\\_urate\\_level'\n",
-       "\\item 'tobacco\\_dependence\\_syndrome'\n",
-       "\\item 'malignant\\_tumor\\_of\\_prostate'\n",
-       "\\item 'constipation'\n",
-       "\\item 'hearing\\_loss'\n",
-       "\\item 'fatigue'\n",
-       "\\item 'obstructive\\_sleep\\_apnea\\_syndrome'\n",
-       "\\item 'malignant\\_neoplasm\\_of\\_breast'\n",
-       "\\item 'delivery\\_normal'\n",
-       "\\item 'irritable\\_bowel\\_syndrome'\n",
-       "\\item 'tobacco\\_user'\n",
-       "\\item 'neck\\_pain'\n",
-       "\\item 'cerebrovascular\\_accident'\n",
-       "\\item 'asthenia'\n",
-       "\\item 'shoulder\\_pain'\n",
-       "\\item 'acne\\_vulgaris'\n",
-       "\\item 'benign\\_prostatic\\_hyperplasia'\n",
-       "\\item 'dyspnea'\n",
-       "\\item 'carpal\\_tunnel\\_syndrome'\n",
-       "\\item 'bronchitis'\n",
-       "\\item 'pharyngitis'\n",
-       "\\item 'arthritis'\n",
-       "\\item 'diarrhea'\n",
-       "\\item 'dizziness'\n",
-       "\\item 'alcohol\\_abuse'\n",
-       "\\item 'dementia'\n",
-       "\\item 'eczema'\n",
-       "\\item 'syncope'\n",
-       "\\item 'acute\\_sinusitis'\n",
-       "\\item 'iron\\_deficiency\\_anemia'\n",
-       "\\item 'allergic\\_rhinitis\\_caused\\_by\\_pollen'\n",
-       "\\item 'gastritis'\n",
-       "\\item 'cataract'\n",
-       "\\item 'hematuria\\_syndrome'\n",
-       "\\item 'disorder\\_of\\_the\\_peripheral\\_nervous\\_system'\n",
-       "\\item 'viral\\_hepatitis\\_type\\_c'\n",
-       "\\item 'palpitations'\n",
-       "\\item 'eruption\\_of\\_skin'\n",
-       "\\item 'diabetes\\_mellitus\\_type\\_1'\n",
-       "\\item 'renal\\_failure\\_syndrome'\n",
-       "\\item 'peripheral\\_vascular\\_disease'\n",
-       "\\item 'hyperglycemia'\n",
-       "\\item 'seizure\\_disorder'\n",
-       "\\item 'fever'\n",
-       "\\item 'osteoarthritis\\_of\\_knee'\n",
-       "\\item 'actinic\\_keratosis'\n",
-       "\\item 'urinary\\_incontinence'\n",
-       "\\item 'hemorrhoids'\n",
-       "\\item 'seizure'\n",
-       "\\item 'laceration\\_-\\_injury'\n",
-       "\\item 'glaucoma'\n",
-       "\\item 'body\\_mass\\_index\\_30+\\_-\\_obesity'\n",
-       "\\item 'breast\\_lump'\n",
-       "\\item 'viral\\_disease'\n",
-       "\\item 'abnormal\\_cervical\\_smear'\n",
-       "\\item ⋯\n",
-       "\\item 'viral\\_exanthem'\n",
-       "\\item 'talipes\\_planus'\n",
-       "\\item 'idiopathic\\_peripheral\\_neuropathy'\n",
-       "\\item 'foreign\\_body\\_in\\_pharynx'\n",
-       "\\item 'jaw\\_pain'\n",
-       "\\item 'renal\\_impairment'\n",
-       "\\item 'ataxia'\n",
-       "\\item 'age-related\\_macular\\_degeneration'\n",
-       "\\item 'uterine\\_prolapse'\n",
-       "\\item 'renal\\_mass'\n",
-       "\\item 'pneumonitis'\n",
-       "\\item 'coordination\\_problem'\n",
-       "\\item 'blindness\\_-\\_both\\_eyes'\n",
-       "\\item 'primary\\_hyperparathyroidism'\n",
-       "\\item 'musculoskeletal\\_pain'\n",
-       "\\item 'mycosis'\n",
-       "\\item 'primigravida'\n",
-       "\\item 'urethral\\_stricture'\n",
-       "\\item 'leukocytosis'\n",
-       "\\item 'ventricular\\_premature\\_complex'\n",
-       "\\item 'ulcer\\_of\\_foot\\_due\\_to\\_diabetes\\_mellitus'\n",
-       "\\item 'chronic\\_headache\\_disorder'\n",
-       "\\item 'hemangioma'\n",
-       "\\item 'lymphedema'\n",
-       "\\item 'postmenopausal\\_state'\n",
-       "\\item 'chronic\\_ulcer\\_of\\_skin'\n",
-       "\\item 'left\\_heart\\_failure'\n",
-       "\\item 'excessive\\_and\\_frequent\\_menstruation'\n",
-       "\\item 'thrombocytosis'\n",
-       "\\item 'disorder\\_of\\_liver'\n",
-       "\\item 'disorder\\_of\\_carotid\\_artery'\n",
-       "\\item 'altered\\_bowel\\_function'\n",
-       "\\item 'abscess\\_of\\_foot'\n",
-       "\\item 'malignant\\_tumor\\_of\\_head\\_and/or\\_neck'\n",
-       "\\item 'streptococcus\\_group\\_b\\_infection\\_of\\_the\\_infant'\n",
-       "\\item 'concussion\\_injury\\_of\\_brain'\n",
-       "\\item 'feeding\\_problems\\_in\\_newborn'\n",
-       "\\item 'bipolar\\_i\\_disorder'\n",
-       "\\item 'viral\\_pharyngitis'\n",
-       "\\item 'lower\\_respiratory\\_tract\\_infection'\n",
-       "\\item 'hydronephrosis'\n",
-       "\\item 'borderline\\_personality\\_disorder'\n",
-       "\\item 'esophageal\\_varices'\n",
-       "\\item 'hypersomnia'\n",
-       "\\item 'sensorineural\\_hearing\\_loss\\_bilateral'\n",
-       "\\item 'varicocele'\n",
-       "\\item 'subarachnoid\\_intracranial\\_hemorrhage'\n",
-       "\\item 'incisional\\_hernia'\n",
-       "\\item 'varicella'\n",
-       "\\item 'pain\\_in\\_testicle'\n",
-       "\\item 'transplant\\_follow-up'\n",
-       "\\item 'tinea\\_cruris'\n",
-       "\\item 'laryngitis'\n",
-       "\\item 'hypertrophy\\_of\\_nail'\n",
-       "\\item 'amblyopia'\n",
-       "\\item 'polyp\\_of\\_cervix'\n",
-       "\\item 'cyst\\_of\\_kidney'\n",
-       "\\item 'hepatic\\_encephalopathy'\n",
-       "\\item 'blood\\_glucose\\_abnormal'\n",
-       "\\item 'postherpetic\\_neuralgia'\n",
-       "\\item 'frank\\_hematuria'\n",
-       "\\item 'cramp'\n",
-       "\\item 'interstitial\\_lung\\_disease'\n",
-       "\\item 'complete\\_atrioventricular\\_block'\n",
-       "\\item 'malignant\\_tumor\\_of\\_kidney'\n",
-       "\\item 'otitis'\n",
-       "\\item 'septic\\_shock'\n",
-       "\\item 'disorder\\_of\\_thyroid\\_gland'\n",
-       "\\item 'hypertrophic\\_cardiomyopathy'\n",
-       "\\item 'respiratory\\_distress\\_syndrome\\_in\\_the\\_newborn'\n",
-       "\\item 'infectious\\_gastroenteritis'\n",
-       "\\item 'subdural\\_intracranial\\_hemorrhage'\n",
-       "\\item 'hepatitis\\_b\\_carrier'\n",
-       "\\item 'manic\\_bipolar\\_i\\_disorder'\n",
-       "\\item 'secondary\\_pulmonary\\_hypertension'\n",
-       "\\item 'gonorrhea'\n",
-       "\\item 'derangement\\_of\\_knee'\n",
-       "\\item 'appendicitis'\n",
-       "\\item 'polyneuropathy\\_due\\_to\\_diabetes\\_mellitus'\n",
-       "\\item 'neonatal\\_hypoglycemia'\n",
-       "\\item 'prolonged\\_rupture\\_of\\_membranes'\n",
-       "\\item 'vasomotor\\_rhinitis'\n",
-       "\\item 'renal\\_disorder\\_due\\_to\\_type\\_1\\_diabetes\\_mellitus'\n",
-       "\\item 'tuberculosis'\n",
-       "\\item 'feeding\\_problem'\n",
-       "\\item 'chronic\\_tonsillitis'\n",
-       "\\item 'acute\\_duodenal\\_ulcer\\_with\\_hemorrhage'\n",
-       "\\item 'hammer\\_toe'\n",
-       "\\item 'malignant\\_tumor\\_of\\_cervix'\n",
-       "\\item 'prolapsed\\_lumbar\\_intervertebral\\_disc'\n",
-       "\\item 'hematemesis'\n",
-       "\\item 'perianal\\_abscess'\n",
-       "\\item 'nonvenomous\\_insect\\_bite'\n",
-       "\\item 'spondylolisthesis'\n",
-       "\\item 'malignant\\_tumor\\_of\\_esophagus'\n",
-       "\\item 'aphthous\\_ulcer\\_of\\_mouth'\n",
-       "\\item 'ventricular\\_septal\\_defect'\n",
-       "\\item 'oropharyngeal\\_dysphagia'\n",
-       "\\item 'injury\\_of\\_knee'\n",
-       "\\item 'traumatic\\_brain\\_injury'\n",
-       "\\item 'osteoarthritis\\_of\\_glenohumeral\\_joint'\n",
-       "\\item 'fetal\\_or\\_neonatal\\_effect\\_of\\_maternal\\_medical\\_problem'\n",
-       "\\item 'stomatological\\_preparations'\n",
-       "\\item 'drugs\\_for\\_acid\\_related\\_disorders'\n",
-       "\\item 'drugs\\_for\\_functional\\_gastrointestinal\\_disorders'\n",
-       "\\item 'antiemetics\\_and\\_antinauseants'\n",
-       "\\item 'bile\\_and\\_liver\\_therapy'\n",
-       "\\item 'drugs\\_for\\_constipation'\n",
-       "\\item 'antidiarrheals,\\_intestinal\\_antiinflammatory/antiinfective\\_agents'\n",
-       "\\item 'antiobesity\\_preparations,\\_excl.\\_diet\\_products'\n",
-       "\\item 'digestives,\\_incl.\\_enzymes'\n",
-       "\\item 'drugs\\_used\\_in\\_diabetes'\n",
-       "\\item 'vitamins'\n",
-       "\\item 'mineral\\_supplements'\n",
-       "\\item 'tonics'\n",
-       "\\item 'anabolic\\_agents\\_for\\_systemic\\_use'\n",
-       "\\item 'appetite\\_stimulants'\n",
-       "\\item 'other\\_alimentary\\_tract\\_and\\_metabolism\\_products'\n",
-       "\\item 'antithrombotic\\_agents'\n",
-       "\\item 'antihemorrhagics'\n",
-       "\\item 'antianemic\\_preparations'\n",
-       "\\item 'blood\\_substitutes\\_and\\_perfusion\\_solutions'\n",
-       "\\item 'other\\_hematological\\_agents'\n",
-       "\\item 'cardiac\\_therapy'\n",
-       "\\item 'antihypertensives'\n",
-       "\\item 'diuretics'\n",
-       "\\item 'peripheral\\_vasodilators'\n",
-       "\\item 'vasoprotectives'\n",
-       "\\item 'beta\\_blocking\\_agents'\n",
-       "\\item 'calcium\\_channel\\_blockers'\n",
-       "\\item 'agents\\_acting\\_on\\_the\\_renin-angiotensin\\_system'\n",
-       "\\item 'lipid\\_modifying\\_agents'\n",
-       "\\item 'antifungals\\_for\\_dermatological\\_use'\n",
-       "\\item 'emollients\\_and\\_protectives'\n",
-       "\\item 'preparations\\_for\\_treatment\\_of\\_wounds\\_and\\_ulcers'\n",
-       "\\item 'antipruritics,\\_incl.\\_antihistamines,\\_anesthetics,\\_etc.'\n",
-       "\\item 'antipsoriatics'\n",
-       "\\item 'antibiotics\\_and\\_chemotherapeutics\\_for\\_dermatological\\_use'\n",
-       "\\item 'corticosteroids,\\_dermatological\\_preparations'\n",
-       "\\item 'antiseptics\\_and\\_disinfectants'\n",
-       "\\item 'medicated\\_dressings'\n",
-       "\\item 'anti-acne\\_preparations'\n",
-       "\\item 'other\\_dermatological\\_preparations'\n",
-       "\\item 'gynecological\\_antiinfectives\\_and\\_antiseptics'\n",
-       "\\item 'other\\_gynecologicals'\n",
-       "\\item 'sex\\_hormones\\_and\\_modulators\\_of\\_the\\_genital\\_system'\n",
-       "\\item 'urologicals'\n",
-       "\\item 'pituitary\\_and\\_hypothalamic\\_hormones\\_and\\_analogues'\n",
-       "\\item 'corticosteroids\\_for\\_systemic\\_use'\n",
-       "\\item 'thyroid\\_therapy'\n",
-       "\\item 'pancreatic\\_hormones'\n",
-       "\\item 'calcium\\_homeostasis'\n",
-       "\\item 'antibacterials\\_for\\_systemic\\_use'\n",
-       "\\item 'antimycotics\\_for\\_systemic\\_use'\n",
-       "\\item 'antimycobacterials'\n",
-       "\\item 'antivirals\\_for\\_systemic\\_use'\n",
-       "\\item 'immune\\_sera\\_and\\_immunoglobulins'\n",
-       "\\item 'vaccines'\n",
-       "\\item 'antineoplastic\\_agents'\n",
-       "\\item 'endocrine\\_therapy'\n",
-       "\\item 'immunostimulants'\n",
-       "\\item 'immunosuppressants'\n",
-       "\\item 'antiinflammatory\\_and\\_antirheumatic\\_products'\n",
-       "\\item 'topical\\_products\\_for\\_joint\\_and\\_muscular\\_pain'\n",
-       "\\item 'muscle\\_relaxants'\n",
-       "\\item 'antigout\\_preparations'\n",
-       "\\item 'drugs\\_for\\_treatment\\_of\\_bone\\_diseases'\n",
-       "\\item 'other\\_drugs\\_for\\_disorders\\_of\\_the\\_musculo-skeletal\\_system'\n",
-       "\\item 'anesthetics'\n",
-       "\\item 'analgesics'\n",
-       "\\item 'antiepileptics'\n",
-       "\\item 'anti-parkinson\\_drugs'\n",
-       "\\item 'psycholeptics'\n",
-       "\\item 'psychoanaleptics'\n",
-       "\\item 'other\\_nervous\\_system\\_drugs'\n",
-       "\\item 'antiprotozoals'\n",
-       "\\item 'anthelmintics'\n",
-       "\\item 'ectoparasiticides,\\_incl.\\_scabicides,\\_insecticides\\_and\\_repellents'\n",
-       "\\item 'nasal\\_preparations'\n",
-       "\\item 'throat\\_preparations'\n",
-       "\\item 'drugs\\_for\\_obstructive\\_airway\\_diseases'\n",
-       "\\item 'cough\\_and\\_cold\\_preparations'\n",
-       "\\item 'antihistamines\\_for\\_systemic\\_use'\n",
-       "\\item 'other\\_respiratory\\_system\\_products'\n",
-       "\\item 'ophthalmologicals'\n",
-       "\\item 'otologicals'\n",
-       "\\item 'ophthalmological\\_and\\_otological\\_preparations'\n",
-       "\\item 'allergens'\n",
-       "\\item 'all\\_other\\_therapeutic\\_products'\n",
-       "\\item 'diagnostic\\_agents'\n",
-       "\\item 'general\\_nutrients'\n",
-       "\\item 'all\\_other\\_non-therapeutic\\_products'\n",
-       "\\item 'contrast\\_media'\n",
-       "\\item 'diagnostic\\_radiopharmaceuticals'\n",
-       "\\item 'therapeutic\\_radiopharmaceuticals'\n",
-       "\\item 'surgical\\_dressings'\n",
-       "\\item 'statins'\n",
-       "\\item 'ass'\n",
-       "\\item 'atypical\\_antipsychotics'\n",
-       "\\item 'glucocorticoids'\n",
-       "\\end{enumerate*}\n"
-      ],
-      "text/markdown": [
-       "1. 'age_at_recruitment'\n",
-       "2. 'sex'\n",
-       "3. 'ethnic_background'\n",
-       "4. 'townsend_deprivation_index_at_recruitment'\n",
-       "5. 'overall_health_rating'\n",
-       "6. 'smoking_status'\n",
-       "7. 'alcohol_intake_frequency'\n",
-       "8. 'body_mass_index_bmi'\n",
-       "9. 'weight'\n",
-       "10. 'pulse_wave_arterial_stiffness_index'\n",
-       "11. 'pulse_wave_reflection_index'\n",
-       "12. 'waist_circumference'\n",
-       "13. 'hip_circumference'\n",
-       "14. 'standing_height'\n",
-       "15. 'trunk_fat_percentage'\n",
-       "16. 'body_fat_percentage'\n",
-       "17. 'basal_metabolic_rate'\n",
-       "18. 'forced_vital_capacity_fvc_best_measure'\n",
-       "19. 'forced_expiratory_volume_in_1second_fev1_best_measure'\n",
-       "20. 'fev1_fvc_ratio_zscore'\n",
-       "21. 'peak_expiratory_flow_pef_f3064_0_2'\n",
-       "22. 'peak_expiratory_flow_pef_f3064_0_1'\n",
-       "23. 'peak_expiratory_flow_pef'\n",
-       "24. 'systolic_blood_pressure'\n",
-       "25. 'diastolic_blood_pressure'\n",
-       "26. 'pulse_rate'\n",
-       "27. 'basophill_count'\n",
-       "28. 'basophill_percentage'\n",
-       "29. 'eosinophill_count'\n",
-       "30. 'eosinophill_percentage'\n",
-       "31. 'haematocrit_percentage'\n",
-       "32. 'haemoglobin_concentration'\n",
-       "33. 'high_light_scatter_reticulocyte_count'\n",
-       "34. 'high_light_scatter_reticulocyte_percentage'\n",
-       "35. 'immature_reticulocyte_fraction'\n",
-       "36. 'lymphocyte_count'\n",
-       "37. 'lymphocyte_percentage'\n",
-       "38. 'mean_corpuscular_haemoglobin'\n",
-       "39. 'mean_corpuscular_haemoglobin_concentration'\n",
-       "40. 'mean_corpuscular_volume'\n",
-       "41. 'mean_platelet_thrombocyte_volume'\n",
-       "42. 'mean_reticulocyte_volume'\n",
-       "43. 'mean_sphered_cell_volume'\n",
-       "44. 'monocyte_count'\n",
-       "45. 'monocyte_percentage'\n",
-       "46. 'neutrophill_count'\n",
-       "47. 'neutrophill_percentage'\n",
-       "48. 'nucleated_red_blood_cell_count'\n",
-       "49. 'nucleated_red_blood_cell_percentage'\n",
-       "50. 'platelet_count'\n",
-       "51. 'platelet_crit'\n",
-       "52. 'platelet_distribution_width'\n",
-       "53. 'red_blood_cell_erythrocyte_count'\n",
-       "54. 'red_blood_cell_erythrocyte_distribution_width'\n",
-       "55. 'reticulocyte_count'\n",
-       "56. 'reticulocyte_percentage'\n",
-       "57. 'white_blood_cell_leukocyte_count'\n",
-       "58. 'alanine_aminotransferase'\n",
-       "59. 'albumin'\n",
-       "60. 'alkaline_phosphatase'\n",
-       "61. 'apolipoprotein_a'\n",
-       "62. 'apolipoprotein_b'\n",
-       "63. 'aspartate_aminotransferase'\n",
-       "64. 'creactive_protein'\n",
-       "65. 'calcium'\n",
-       "66. 'cholesterol'\n",
-       "67. 'creatinine'\n",
-       "68. 'cystatin_c'\n",
-       "69. 'direct_bilirubin'\n",
-       "70. 'gamma_glutamyltransferase'\n",
-       "71. 'glucose'\n",
-       "72. 'glycated_haemoglobin_hba1c'\n",
-       "73. 'hdl_cholesterol'\n",
-       "74. 'igf1'\n",
-       "75. 'ldl_direct'\n",
-       "76. 'lipoprotein_a'\n",
-       "77. 'oestradiol'\n",
-       "78. 'phosphate'\n",
-       "79. 'rheumatoid_factor'\n",
-       "80. 'shbg'\n",
-       "81. 'testosterone'\n",
-       "82. 'total_bilirubin'\n",
-       "83. 'total_protein'\n",
-       "84. 'triglycerides'\n",
-       "85. 'urate'\n",
-       "86. 'urea'\n",
-       "87. 'vitamin_d'\n",
-       "88. 'fh_alzheimer\\'s_disease/dementia'\n",
-       "89. 'fh_bowel_cancer'\n",
-       "90. 'fh_breast_cancer'\n",
-       "91. 'fh_chronic_bronchitis/emphysema'\n",
-       "92. 'fh_diabetes'\n",
-       "93. 'fh_heart_disease'\n",
-       "94. 'fh_high_blood_pressure'\n",
-       "95. 'fh_lung_cancer'\n",
-       "96. 'fh_parkinson\\'s_disease'\n",
-       "97. 'fh_severe_depression'\n",
-       "98. 'fh_stroke'\n",
-       "99. 'coronary_heart_disease'\n",
-       "100. 'myocardial_infarction'\n",
-       "101. 'stroke'\n",
-       "102. 'diabetes1'\n",
-       "103. 'diabetes2'\n",
-       "104. 'chronic_kidney_disease'\n",
-       "105. 'atrial_fibrillation'\n",
-       "106. 'migraine'\n",
-       "107. 'rheumatoid_arthritis'\n",
-       "108. 'systemic_lupus_erythematosus'\n",
-       "109. 'severe_mental_illness'\n",
-       "110. 'erectile_dysfunction'\n",
-       "111. 'hypertensive_disorder_systemic_arterial'\n",
-       "112. 'hyperlipidemia'\n",
-       "113. 'depressive_disorder'\n",
-       "114. 'gastroesophageal_reflux_disease'\n",
-       "115. 'diabetes_mellitus_type_2'\n",
-       "116. 'essential_hypertension'\n",
-       "117. 'obesity'\n",
-       "118. 'diabetes_mellitus'\n",
-       "119. 'asthma'\n",
-       "120. 'coronary_arteriosclerosis'\n",
-       "121. 'allergic_rhinitis'\n",
-       "122. 'hypothyroidism'\n",
-       "123. 'upper_respiratory_infection'\n",
-       "124. 'hypercholesterolemia'\n",
-       "125. 'backache'\n",
-       "126. 'abdominal_pain'\n",
-       "127. 'osteoarthritis'\n",
-       "128. 'low_back_pain'\n",
-       "129. 'anemia'\n",
-       "130. 'anxiety'\n",
-       "131. 'urinary_tract_infectious_disease'\n",
-       "132. 'chronic_obstructive_lung_disease'\n",
-       "133. 'pneumonia'\n",
-       "134. 'chest_pain'\n",
-       "135. 'congestive_heart_failure'\n",
-       "136. 'headache'\n",
-       "137. 'pregnant'\n",
-       "138. 'knee_pain'\n",
-       "139. 'osteoporosis'\n",
-       "140. 'polyp_of_colon'\n",
-       "141. 'otitis_media'\n",
-       "142. 'sinusitis'\n",
-       "143. 'cough'\n",
-       "144. 'sleep_apnea'\n",
-       "145. 'insomnia'\n",
-       "146. 'inflammatory_disorder_due_to_increased_blood_urate_level'\n",
-       "147. 'tobacco_dependence_syndrome'\n",
-       "148. 'malignant_tumor_of_prostate'\n",
-       "149. 'constipation'\n",
-       "150. 'hearing_loss'\n",
-       "151. 'fatigue'\n",
-       "152. 'obstructive_sleep_apnea_syndrome'\n",
-       "153. 'malignant_neoplasm_of_breast'\n",
-       "154. 'delivery_normal'\n",
-       "155. 'irritable_bowel_syndrome'\n",
-       "156. 'tobacco_user'\n",
-       "157. 'neck_pain'\n",
-       "158. 'cerebrovascular_accident'\n",
-       "159. 'asthenia'\n",
-       "160. 'shoulder_pain'\n",
-       "161. 'acne_vulgaris'\n",
-       "162. 'benign_prostatic_hyperplasia'\n",
-       "163. 'dyspnea'\n",
-       "164. 'carpal_tunnel_syndrome'\n",
-       "165. 'bronchitis'\n",
-       "166. 'pharyngitis'\n",
-       "167. 'arthritis'\n",
-       "168. 'diarrhea'\n",
-       "169. 'dizziness'\n",
-       "170. 'alcohol_abuse'\n",
-       "171. 'dementia'\n",
-       "172. 'eczema'\n",
-       "173. 'syncope'\n",
-       "174. 'acute_sinusitis'\n",
-       "175. 'iron_deficiency_anemia'\n",
-       "176. 'allergic_rhinitis_caused_by_pollen'\n",
-       "177. 'gastritis'\n",
-       "178. 'cataract'\n",
-       "179. 'hematuria_syndrome'\n",
-       "180. 'disorder_of_the_peripheral_nervous_system'\n",
-       "181. 'viral_hepatitis_type_c'\n",
-       "182. 'palpitations'\n",
-       "183. 'eruption_of_skin'\n",
-       "184. 'diabetes_mellitus_type_1'\n",
-       "185. 'renal_failure_syndrome'\n",
-       "186. 'peripheral_vascular_disease'\n",
-       "187. 'hyperglycemia'\n",
-       "188. 'seizure_disorder'\n",
-       "189. 'fever'\n",
-       "190. 'osteoarthritis_of_knee'\n",
-       "191. 'actinic_keratosis'\n",
-       "192. 'urinary_incontinence'\n",
-       "193. 'hemorrhoids'\n",
-       "194. 'seizure'\n",
-       "195. 'laceration_-_injury'\n",
-       "196. 'glaucoma'\n",
-       "197. 'body_mass_index_30+_-_obesity'\n",
-       "198. 'breast_lump'\n",
-       "199. 'viral_disease'\n",
-       "200. 'abnormal_cervical_smear'\n",
-       "201. ⋯\n",
-       "202. 'viral_exanthem'\n",
-       "203. 'talipes_planus'\n",
-       "204. 'idiopathic_peripheral_neuropathy'\n",
-       "205. 'foreign_body_in_pharynx'\n",
-       "206. 'jaw_pain'\n",
-       "207. 'renal_impairment'\n",
-       "208. 'ataxia'\n",
-       "209. 'age-related_macular_degeneration'\n",
-       "210. 'uterine_prolapse'\n",
-       "211. 'renal_mass'\n",
-       "212. 'pneumonitis'\n",
-       "213. 'coordination_problem'\n",
-       "214. 'blindness_-_both_eyes'\n",
-       "215. 'primary_hyperparathyroidism'\n",
-       "216. 'musculoskeletal_pain'\n",
-       "217. 'mycosis'\n",
-       "218. 'primigravida'\n",
-       "219. 'urethral_stricture'\n",
-       "220. 'leukocytosis'\n",
-       "221. 'ventricular_premature_complex'\n",
-       "222. 'ulcer_of_foot_due_to_diabetes_mellitus'\n",
-       "223. 'chronic_headache_disorder'\n",
-       "224. 'hemangioma'\n",
-       "225. 'lymphedema'\n",
-       "226. 'postmenopausal_state'\n",
-       "227. 'chronic_ulcer_of_skin'\n",
-       "228. 'left_heart_failure'\n",
-       "229. 'excessive_and_frequent_menstruation'\n",
-       "230. 'thrombocytosis'\n",
-       "231. 'disorder_of_liver'\n",
-       "232. 'disorder_of_carotid_artery'\n",
-       "233. 'altered_bowel_function'\n",
-       "234. 'abscess_of_foot'\n",
-       "235. 'malignant_tumor_of_head_and/or_neck'\n",
-       "236. 'streptococcus_group_b_infection_of_the_infant'\n",
-       "237. 'concussion_injury_of_brain'\n",
-       "238. 'feeding_problems_in_newborn'\n",
-       "239. 'bipolar_i_disorder'\n",
-       "240. 'viral_pharyngitis'\n",
-       "241. 'lower_respiratory_tract_infection'\n",
-       "242. 'hydronephrosis'\n",
-       "243. 'borderline_personality_disorder'\n",
-       "244. 'esophageal_varices'\n",
-       "245. 'hypersomnia'\n",
-       "246. 'sensorineural_hearing_loss_bilateral'\n",
-       "247. 'varicocele'\n",
-       "248. 'subarachnoid_intracranial_hemorrhage'\n",
-       "249. 'incisional_hernia'\n",
-       "250. 'varicella'\n",
-       "251. 'pain_in_testicle'\n",
-       "252. 'transplant_follow-up'\n",
-       "253. 'tinea_cruris'\n",
-       "254. 'laryngitis'\n",
-       "255. 'hypertrophy_of_nail'\n",
-       "256. 'amblyopia'\n",
-       "257. 'polyp_of_cervix'\n",
-       "258. 'cyst_of_kidney'\n",
-       "259. 'hepatic_encephalopathy'\n",
-       "260. 'blood_glucose_abnormal'\n",
-       "261. 'postherpetic_neuralgia'\n",
-       "262. 'frank_hematuria'\n",
-       "263. 'cramp'\n",
-       "264. 'interstitial_lung_disease'\n",
-       "265. 'complete_atrioventricular_block'\n",
-       "266. 'malignant_tumor_of_kidney'\n",
-       "267. 'otitis'\n",
-       "268. 'septic_shock'\n",
-       "269. 'disorder_of_thyroid_gland'\n",
-       "270. 'hypertrophic_cardiomyopathy'\n",
-       "271. 'respiratory_distress_syndrome_in_the_newborn'\n",
-       "272. 'infectious_gastroenteritis'\n",
-       "273. 'subdural_intracranial_hemorrhage'\n",
-       "274. 'hepatitis_b_carrier'\n",
-       "275. 'manic_bipolar_i_disorder'\n",
-       "276. 'secondary_pulmonary_hypertension'\n",
-       "277. 'gonorrhea'\n",
-       "278. 'derangement_of_knee'\n",
-       "279. 'appendicitis'\n",
-       "280. 'polyneuropathy_due_to_diabetes_mellitus'\n",
-       "281. 'neonatal_hypoglycemia'\n",
-       "282. 'prolonged_rupture_of_membranes'\n",
-       "283. 'vasomotor_rhinitis'\n",
-       "284. 'renal_disorder_due_to_type_1_diabetes_mellitus'\n",
-       "285. 'tuberculosis'\n",
-       "286. 'feeding_problem'\n",
-       "287. 'chronic_tonsillitis'\n",
-       "288. 'acute_duodenal_ulcer_with_hemorrhage'\n",
-       "289. 'hammer_toe'\n",
-       "290. 'malignant_tumor_of_cervix'\n",
-       "291. 'prolapsed_lumbar_intervertebral_disc'\n",
-       "292. 'hematemesis'\n",
-       "293. 'perianal_abscess'\n",
-       "294. 'nonvenomous_insect_bite'\n",
-       "295. 'spondylolisthesis'\n",
-       "296. 'malignant_tumor_of_esophagus'\n",
-       "297. 'aphthous_ulcer_of_mouth'\n",
-       "298. 'ventricular_septal_defect'\n",
-       "299. 'oropharyngeal_dysphagia'\n",
-       "300. 'injury_of_knee'\n",
-       "301. 'traumatic_brain_injury'\n",
-       "302. 'osteoarthritis_of_glenohumeral_joint'\n",
-       "303. 'fetal_or_neonatal_effect_of_maternal_medical_problem'\n",
-       "304. 'stomatological_preparations'\n",
-       "305. 'drugs_for_acid_related_disorders'\n",
-       "306. 'drugs_for_functional_gastrointestinal_disorders'\n",
-       "307. 'antiemetics_and_antinauseants'\n",
-       "308. 'bile_and_liver_therapy'\n",
-       "309. 'drugs_for_constipation'\n",
-       "310. 'antidiarrheals,_intestinal_antiinflammatory/antiinfective_agents'\n",
-       "311. 'antiobesity_preparations,_excl._diet_products'\n",
-       "312. 'digestives,_incl._enzymes'\n",
-       "313. 'drugs_used_in_diabetes'\n",
-       "314. 'vitamins'\n",
-       "315. 'mineral_supplements'\n",
-       "316. 'tonics'\n",
-       "317. 'anabolic_agents_for_systemic_use'\n",
-       "318. 'appetite_stimulants'\n",
-       "319. 'other_alimentary_tract_and_metabolism_products'\n",
-       "320. 'antithrombotic_agents'\n",
-       "321. 'antihemorrhagics'\n",
-       "322. 'antianemic_preparations'\n",
-       "323. 'blood_substitutes_and_perfusion_solutions'\n",
-       "324. 'other_hematological_agents'\n",
-       "325. 'cardiac_therapy'\n",
-       "326. 'antihypertensives'\n",
-       "327. 'diuretics'\n",
-       "328. 'peripheral_vasodilators'\n",
-       "329. 'vasoprotectives'\n",
-       "330. 'beta_blocking_agents'\n",
-       "331. 'calcium_channel_blockers'\n",
-       "332. 'agents_acting_on_the_renin-angiotensin_system'\n",
-       "333. 'lipid_modifying_agents'\n",
-       "334. 'antifungals_for_dermatological_use'\n",
-       "335. 'emollients_and_protectives'\n",
-       "336. 'preparations_for_treatment_of_wounds_and_ulcers'\n",
-       "337. 'antipruritics,_incl._antihistamines,_anesthetics,_etc.'\n",
-       "338. 'antipsoriatics'\n",
-       "339. 'antibiotics_and_chemotherapeutics_for_dermatological_use'\n",
-       "340. 'corticosteroids,_dermatological_preparations'\n",
-       "341. 'antiseptics_and_disinfectants'\n",
-       "342. 'medicated_dressings'\n",
-       "343. 'anti-acne_preparations'\n",
-       "344. 'other_dermatological_preparations'\n",
-       "345. 'gynecological_antiinfectives_and_antiseptics'\n",
-       "346. 'other_gynecologicals'\n",
-       "347. 'sex_hormones_and_modulators_of_the_genital_system'\n",
-       "348. 'urologicals'\n",
-       "349. 'pituitary_and_hypothalamic_hormones_and_analogues'\n",
-       "350. 'corticosteroids_for_systemic_use'\n",
-       "351. 'thyroid_therapy'\n",
-       "352. 'pancreatic_hormones'\n",
-       "353. 'calcium_homeostasis'\n",
-       "354. 'antibacterials_for_systemic_use'\n",
-       "355. 'antimycotics_for_systemic_use'\n",
-       "356. 'antimycobacterials'\n",
-       "357. 'antivirals_for_systemic_use'\n",
-       "358. 'immune_sera_and_immunoglobulins'\n",
-       "359. 'vaccines'\n",
-       "360. 'antineoplastic_agents'\n",
-       "361. 'endocrine_therapy'\n",
-       "362. 'immunostimulants'\n",
-       "363. 'immunosuppressants'\n",
-       "364. 'antiinflammatory_and_antirheumatic_products'\n",
-       "365. 'topical_products_for_joint_and_muscular_pain'\n",
-       "366. 'muscle_relaxants'\n",
-       "367. 'antigout_preparations'\n",
-       "368. 'drugs_for_treatment_of_bone_diseases'\n",
-       "369. 'other_drugs_for_disorders_of_the_musculo-skeletal_system'\n",
-       "370. 'anesthetics'\n",
-       "371. 'analgesics'\n",
-       "372. 'antiepileptics'\n",
-       "373. 'anti-parkinson_drugs'\n",
-       "374. 'psycholeptics'\n",
-       "375. 'psychoanaleptics'\n",
-       "376. 'other_nervous_system_drugs'\n",
-       "377. 'antiprotozoals'\n",
-       "378. 'anthelmintics'\n",
-       "379. 'ectoparasiticides,_incl._scabicides,_insecticides_and_repellents'\n",
-       "380. 'nasal_preparations'\n",
-       "381. 'throat_preparations'\n",
-       "382. 'drugs_for_obstructive_airway_diseases'\n",
-       "383. 'cough_and_cold_preparations'\n",
-       "384. 'antihistamines_for_systemic_use'\n",
-       "385. 'other_respiratory_system_products'\n",
-       "386. 'ophthalmologicals'\n",
-       "387. 'otologicals'\n",
-       "388. 'ophthalmological_and_otological_preparations'\n",
-       "389. 'allergens'\n",
-       "390. 'all_other_therapeutic_products'\n",
-       "391. 'diagnostic_agents'\n",
-       "392. 'general_nutrients'\n",
-       "393. 'all_other_non-therapeutic_products'\n",
-       "394. 'contrast_media'\n",
-       "395. 'diagnostic_radiopharmaceuticals'\n",
-       "396. 'therapeutic_radiopharmaceuticals'\n",
-       "397. 'surgical_dressings'\n",
-       "398. 'statins'\n",
-       "399. 'ass'\n",
-       "400. 'atypical_antipsychotics'\n",
-       "401. 'glucocorticoids'\n",
-       "\n",
-       "\n"
-      ],
-      "text/plain": [
-       "  [1] \"age_at_recruitment\"                                                     \n",
-       "  [2] \"sex\"                                                                    \n",
-       "  [3] \"ethnic_background\"                                                      \n",
-       "  [4] \"townsend_deprivation_index_at_recruitment\"                              \n",
-       "  [5] \"overall_health_rating\"                                                  \n",
-       "  [6] \"smoking_status\"                                                         \n",
-       "  [7] \"alcohol_intake_frequency\"                                               \n",
-       "  [8] \"body_mass_index_bmi\"                                                    \n",
-       "  [9] \"weight\"                                                                 \n",
-       " [10] \"pulse_wave_arterial_stiffness_index\"                                    \n",
-       " [11] \"pulse_wave_reflection_index\"                                            \n",
-       " [12] \"waist_circumference\"                                                    \n",
-       " [13] \"hip_circumference\"                                                      \n",
-       " [14] \"standing_height\"                                                        \n",
-       " [15] \"trunk_fat_percentage\"                                                   \n",
-       " [16] \"body_fat_percentage\"                                                    \n",
-       " [17] \"basal_metabolic_rate\"                                                   \n",
-       " [18] \"forced_vital_capacity_fvc_best_measure\"                                 \n",
-       " [19] \"forced_expiratory_volume_in_1second_fev1_best_measure\"                  \n",
-       " [20] \"fev1_fvc_ratio_zscore\"                                                  \n",
-       " [21] \"peak_expiratory_flow_pef_f3064_0_2\"                                     \n",
-       " [22] \"peak_expiratory_flow_pef_f3064_0_1\"                                     \n",
-       " [23] \"peak_expiratory_flow_pef\"                                               \n",
-       " [24] \"systolic_blood_pressure\"                                                \n",
-       " [25] \"diastolic_blood_pressure\"                                               \n",
-       " [26] \"pulse_rate\"                                                             \n",
-       " [27] \"basophill_count\"                                                        \n",
-       " [28] \"basophill_percentage\"                                                   \n",
-       " [29] \"eosinophill_count\"                                                      \n",
-       " [30] \"eosinophill_percentage\"                                                 \n",
-       " [31] \"haematocrit_percentage\"                                                 \n",
-       " [32] \"haemoglobin_concentration\"                                              \n",
-       " [33] \"high_light_scatter_reticulocyte_count\"                                  \n",
-       " [34] \"high_light_scatter_reticulocyte_percentage\"                             \n",
-       " [35] \"immature_reticulocyte_fraction\"                                         \n",
-       " [36] \"lymphocyte_count\"                                                       \n",
-       " [37] \"lymphocyte_percentage\"                                                  \n",
-       " [38] \"mean_corpuscular_haemoglobin\"                                           \n",
-       " [39] \"mean_corpuscular_haemoglobin_concentration\"                             \n",
-       " [40] \"mean_corpuscular_volume\"                                                \n",
-       " [41] \"mean_platelet_thrombocyte_volume\"                                       \n",
-       " [42] \"mean_reticulocyte_volume\"                                               \n",
-       " [43] \"mean_sphered_cell_volume\"                                               \n",
-       " [44] \"monocyte_count\"                                                         \n",
-       " [45] \"monocyte_percentage\"                                                    \n",
-       " [46] \"neutrophill_count\"                                                      \n",
-       " [47] \"neutrophill_percentage\"                                                 \n",
-       " [48] \"nucleated_red_blood_cell_count\"                                         \n",
-       " [49] \"nucleated_red_blood_cell_percentage\"                                    \n",
-       " [50] \"platelet_count\"                                                         \n",
-       " [51] \"platelet_crit\"                                                          \n",
-       " [52] \"platelet_distribution_width\"                                            \n",
-       " [53] \"red_blood_cell_erythrocyte_count\"                                       \n",
-       " [54] \"red_blood_cell_erythrocyte_distribution_width\"                          \n",
-       " [55] \"reticulocyte_count\"                                                     \n",
-       " [56] \"reticulocyte_percentage\"                                                \n",
-       " [57] \"white_blood_cell_leukocyte_count\"                                       \n",
-       " [58] \"alanine_aminotransferase\"                                               \n",
-       " [59] \"albumin\"                                                                \n",
-       " [60] \"alkaline_phosphatase\"                                                   \n",
-       " [61] \"apolipoprotein_a\"                                                       \n",
-       " [62] \"apolipoprotein_b\"                                                       \n",
-       " [63] \"aspartate_aminotransferase\"                                             \n",
-       " [64] \"creactive_protein\"                                                      \n",
-       " [65] \"calcium\"                                                                \n",
-       " [66] \"cholesterol\"                                                            \n",
-       " [67] \"creatinine\"                                                             \n",
-       " [68] \"cystatin_c\"                                                             \n",
-       " [69] \"direct_bilirubin\"                                                       \n",
-       " [70] \"gamma_glutamyltransferase\"                                              \n",
-       " [71] \"glucose\"                                                                \n",
-       " [72] \"glycated_haemoglobin_hba1c\"                                             \n",
-       " [73] \"hdl_cholesterol\"                                                        \n",
-       " [74] \"igf1\"                                                                   \n",
-       " [75] \"ldl_direct\"                                                             \n",
-       " [76] \"lipoprotein_a\"                                                          \n",
-       " [77] \"oestradiol\"                                                             \n",
-       " [78] \"phosphate\"                                                              \n",
-       " [79] \"rheumatoid_factor\"                                                      \n",
-       " [80] \"shbg\"                                                                   \n",
-       " [81] \"testosterone\"                                                           \n",
-       " [82] \"total_bilirubin\"                                                        \n",
-       " [83] \"total_protein\"                                                          \n",
-       " [84] \"triglycerides\"                                                          \n",
-       " [85] \"urate\"                                                                  \n",
-       " [86] \"urea\"                                                                   \n",
-       " [87] \"vitamin_d\"                                                              \n",
-       " [88] \"fh_alzheimer's_disease/dementia\"                                        \n",
-       " [89] \"fh_bowel_cancer\"                                                        \n",
-       " [90] \"fh_breast_cancer\"                                                       \n",
-       " [91] \"fh_chronic_bronchitis/emphysema\"                                        \n",
-       " [92] \"fh_diabetes\"                                                            \n",
-       " [93] \"fh_heart_disease\"                                                       \n",
-       " [94] \"fh_high_blood_pressure\"                                                 \n",
-       " [95] \"fh_lung_cancer\"                                                         \n",
-       " [96] \"fh_parkinson's_disease\"                                                 \n",
-       " [97] \"fh_severe_depression\"                                                   \n",
-       " [98] \"fh_stroke\"                                                              \n",
-       " [99] \"coronary_heart_disease\"                                                 \n",
-       "[100] \"myocardial_infarction\"                                                  \n",
-       "[101] \"stroke\"                                                                 \n",
-       "[102] \"diabetes1\"                                                              \n",
-       "[103] \"diabetes2\"                                                              \n",
-       "[104] \"chronic_kidney_disease\"                                                 \n",
-       "[105] \"atrial_fibrillation\"                                                    \n",
-       "[106] \"migraine\"                                                               \n",
-       "[107] \"rheumatoid_arthritis\"                                                   \n",
-       "[108] \"systemic_lupus_erythematosus\"                                           \n",
-       "[109] \"severe_mental_illness\"                                                  \n",
-       "[110] \"erectile_dysfunction\"                                                   \n",
-       "[111] \"hypertensive_disorder_systemic_arterial\"                                \n",
-       "[112] \"hyperlipidemia\"                                                         \n",
-       "[113] \"depressive_disorder\"                                                    \n",
-       "[114] \"gastroesophageal_reflux_disease\"                                        \n",
-       "[115] \"diabetes_mellitus_type_2\"                                               \n",
-       "[116] \"essential_hypertension\"                                                 \n",
-       "[117] \"obesity\"                                                                \n",
-       "[118] \"diabetes_mellitus\"                                                      \n",
-       "[119] \"asthma\"                                                                 \n",
-       "[120] \"coronary_arteriosclerosis\"                                              \n",
-       "[121] \"allergic_rhinitis\"                                                      \n",
-       "[122] \"hypothyroidism\"                                                         \n",
-       "[123] \"upper_respiratory_infection\"                                            \n",
-       "[124] \"hypercholesterolemia\"                                                   \n",
-       "[125] \"backache\"                                                               \n",
-       "[126] \"abdominal_pain\"                                                         \n",
-       "[127] \"osteoarthritis\"                                                         \n",
-       "[128] \"low_back_pain\"                                                          \n",
-       "[129] \"anemia\"                                                                 \n",
-       "[130] \"anxiety\"                                                                \n",
-       "[131] \"urinary_tract_infectious_disease\"                                       \n",
-       "[132] \"chronic_obstructive_lung_disease\"                                       \n",
-       "[133] \"pneumonia\"                                                              \n",
-       "[134] \"chest_pain\"                                                             \n",
-       "[135] \"congestive_heart_failure\"                                               \n",
-       "[136] \"headache\"                                                               \n",
-       "[137] \"pregnant\"                                                               \n",
-       "[138] \"knee_pain\"                                                              \n",
-       "[139] \"osteoporosis\"                                                           \n",
-       "[140] \"polyp_of_colon\"                                                         \n",
-       "[141] \"otitis_media\"                                                           \n",
-       "[142] \"sinusitis\"                                                              \n",
-       "[143] \"cough\"                                                                  \n",
-       "[144] \"sleep_apnea\"                                                            \n",
-       "[145] \"insomnia\"                                                               \n",
-       "[146] \"inflammatory_disorder_due_to_increased_blood_urate_level\"               \n",
-       "[147] \"tobacco_dependence_syndrome\"                                            \n",
-       "[148] \"malignant_tumor_of_prostate\"                                            \n",
-       "[149] \"constipation\"                                                           \n",
-       "[150] \"hearing_loss\"                                                           \n",
-       "[151] \"fatigue\"                                                                \n",
-       "[152] \"obstructive_sleep_apnea_syndrome\"                                       \n",
-       "[153] \"malignant_neoplasm_of_breast\"                                           \n",
-       "[154] \"delivery_normal\"                                                        \n",
-       "[155] \"irritable_bowel_syndrome\"                                               \n",
-       "[156] \"tobacco_user\"                                                           \n",
-       "[157] \"neck_pain\"                                                              \n",
-       "[158] \"cerebrovascular_accident\"                                               \n",
-       "[159] \"asthenia\"                                                               \n",
-       "[160] \"shoulder_pain\"                                                          \n",
-       "[161] \"acne_vulgaris\"                                                          \n",
-       "[162] \"benign_prostatic_hyperplasia\"                                           \n",
-       "[163] \"dyspnea\"                                                                \n",
-       "[164] \"carpal_tunnel_syndrome\"                                                 \n",
-       "[165] \"bronchitis\"                                                             \n",
-       "[166] \"pharyngitis\"                                                            \n",
-       "[167] \"arthritis\"                                                              \n",
-       "[168] \"diarrhea\"                                                               \n",
-       "[169] \"dizziness\"                                                              \n",
-       "[170] \"alcohol_abuse\"                                                          \n",
-       "[171] \"dementia\"                                                               \n",
-       "[172] \"eczema\"                                                                 \n",
-       "[173] \"syncope\"                                                                \n",
-       "[174] \"acute_sinusitis\"                                                        \n",
-       "[175] \"iron_deficiency_anemia\"                                                 \n",
-       "[176] \"allergic_rhinitis_caused_by_pollen\"                                     \n",
-       "[177] \"gastritis\"                                                              \n",
-       "[178] \"cataract\"                                                               \n",
-       "[179] \"hematuria_syndrome\"                                                     \n",
-       "[180] \"disorder_of_the_peripheral_nervous_system\"                              \n",
-       "[181] \"viral_hepatitis_type_c\"                                                 \n",
-       "[182] \"palpitations\"                                                           \n",
-       "[183] \"eruption_of_skin\"                                                       \n",
-       "[184] \"diabetes_mellitus_type_1\"                                               \n",
-       "[185] \"renal_failure_syndrome\"                                                 \n",
-       "[186] \"peripheral_vascular_disease\"                                            \n",
-       "[187] \"hyperglycemia\"                                                          \n",
-       "[188] \"seizure_disorder\"                                                       \n",
-       "[189] \"fever\"                                                                  \n",
-       "[190] \"osteoarthritis_of_knee\"                                                 \n",
-       "[191] \"actinic_keratosis\"                                                      \n",
-       "[192] \"urinary_incontinence\"                                                   \n",
-       "[193] \"hemorrhoids\"                                                            \n",
-       "[194] \"seizure\"                                                                \n",
-       "[195] \"laceration_-_injury\"                                                    \n",
-       "[196] \"glaucoma\"                                                               \n",
-       "[197] \"body_mass_index_30+_-_obesity\"                                          \n",
-       "[198] \"breast_lump\"                                                            \n",
-       "[199] \"viral_disease\"                                                          \n",
-       "[200] \"abnormal_cervical_smear\"                                                \n",
-       "[201] \"cellulitis\"                                                             \n",
-       "[202] \"senile_hyperkeratosis\"                                                  \n",
-       "[203] \"anxiety_disorder\"                                                       \n",
-       "[204] \"vertigo\"                                                                \n",
-       "[205] \"dysphagia\"                                                              \n",
-       "[206] \"edema\"                                                                  \n",
-       "[207] \"malignant_neoplasm_of_colon\"                                            \n",
-       "[208] \"hip_pain\"                                                               \n",
-       "[209] \"posttraumatic_stress_disorder\"                                          \n",
-       "[210] \"inflammatory_dermatosis\"                                                \n",
-       "[211] \"psoriasis\"                                                              \n",
-       "[212] \"myopia\"                                                                 \n",
-       "[213] \"senile_cataract\"                                                        \n",
-       "[214] \"heart_murmur\"                                                           \n",
-       "[215] \"liver_function_tests_abnormal\"                                          \n",
-       "[216] \"angina\"                                                                 \n",
-       "[217] \"impaired_fasting_glycemia\"                                              \n",
-       "[218] \"chronic_ischemic_heart_disease\"                                         \n",
-       "[219] \"chronic_sinusitis\"                                                      \n",
-       "[220] \"menopause_present\"                                                      \n",
-       "[221] \"basal_cell_carcinoma_of_skin\"                                           \n",
-       "[222] \"raised_prostate_specific_antigen\"                                       \n",
-       "[223] \"impaired_glucose_tolerance\"                                             \n",
-       "[224] \"smoker\"                                                                 \n",
-       "[225] \"hypertriglyceridemia\"                                                   \n",
-       "[226] \"irregular_periods\"                                                      \n",
-       "[227] \"herpes_zoster\"                                                          \n",
-       "[228] \"sensorineural_hearing_loss\"                                             \n",
-       "[229] \"rectal_hemorrhage\"                                                      \n",
-       "[230] \"peptic_ulcer\"                                                           \n",
-       "[231] \"tinnitus\"                                                               \n",
-       "[232] \"bipolar_disorder\"                                                       \n",
-       "[233] \"vitamin_d_deficiency\"                                                   \n",
-       "[234] \"transient_ischemic_attack\"                                              \n",
-       "[235] \"streptococcal_sore_throat\"                                              \n",
-       "[236] \"onychomycosis\"                                                          \n",
-       "[237] \"deep_venous_thrombosis\"                                                 \n",
-       "[238] \"presbyopia\"                                                             \n",
-       "[239] \"neonatal_jaundice\"                                                      \n",
-       "[240] \"bacterial_vaginosis\"                                                    \n",
-       "[241] \"impacted_cerumen\"                                                       \n",
-       "[242] \"foot_pain\"                                                              \n",
-       "[243] \"sciatica\"                                                               \n",
-       "[244] \"vomiting\"                                                               \n",
-       "[245] \"benign_prostatic_hypertrophy_with_outflow_obstruction\"                  \n",
-       "[246] \"type_ii_diabetes_mellitus_without_complication\"                         \n",
-       "[247] \"calculus_in_biliary_tract\"                                              \n",
-       "[248] \"epigastric_pain\"                                                        \n",
-       "[249] \"late_effects_of_cerebrovascular_disease\"                                \n",
-       "[250] \"gastroenteritis\"                                                        \n",
-       "[251] \"pulmonary_embolism\"                                                     \n",
-       "[252] \"inguinal_hernia\"                                                        \n",
-       "[253] \"verruca_vulgaris\"                                                       \n",
-       "[254] \"sepsis\"                                                                 \n",
-       "[255] \"disorder_of_kidney_due_to_diabetes_mellitus\"                            \n",
-       "[256] \"nausea_and_vomiting\"                                                    \n",
-       "[257] \"hyperthyroidism\"                                                        \n",
-       "[258] \"abscess\"                                                                \n",
-       "[259] \"dental_caries\"                                                          \n",
-       "[260] \"gastrointestinal_hemorrhage\"                                            \n",
-       "[261] \"rosacea\"                                                                \n",
-       "[262] \"parkinson's_disease\"                                                    \n",
-       "[263] \"menorrhagia\"                                                            \n",
-       "[264] \"malignant_tumor_of_lung\"                                                \n",
-       "[265] \"joint_pain\"                                                             \n",
-       "[266] \"morbid_obesity\"                                                         \n",
-       "[267] \"hiatal_hernia\"                                                          \n",
-       "[268] \"arthralgia_of_the_ankle_and/or_foot\"                                    \n",
-       "[269] \"restless_legs\"                                                          \n",
-       "[270] \"thrombocytopenic_disorder\"                                              \n",
-       "[271] \"old_myocardial_infarction\"                                              \n",
-       "[272] \"neuropathy\"                                                             \n",
-       "[273] \"cardiomyopathy\"                                                         \n",
-       "[274] \"atopic_dermatitis\"                                                      \n",
-       "[275] \"pain_in_pelvis\"                                                         \n",
-       "[276] \"contact_dermatitis\"                                                     \n",
-       "[277] \"indigestion\"                                                            \n",
-       "[278] \"nicotine_dependence\"                                                    \n",
-       "[279] \"sprain_of_ankle\"                                                        \n",
-       "[280] \"degenerative_disorder_of_macula\"                                        \n",
-       "[281] \"exacerbation_of_asthma\"                                                 \n",
-       "[282] \"alcohol_dependence\"                                                     \n",
-       "[283] \"hypokalemia\"                                                            \n",
-       "[284] \"mitral_valve_regurgitation\"                                             \n",
-       "[285] \"hyponatremia\"                                                           \n",
-       "[286] \"abdominal_aortic_aneurysm\"                                              \n",
-       "[287] \"cyst_of_ovary\"                                                          \n",
-       "[288] \"otitis_externa\"                                                         \n",
-       "[289] \"threatened_abortion\"                                                    \n",
-       "[290] \"scoliosis_deformity_of_spine\"                                           \n",
-       "[291] \"seborrheic_dermatitis\"                                                  \n",
-       "[292] \"spinal_stenosis\"                                                        \n",
-       "[293] \"dysmenorrhea\"                                                           \n",
-       "[294] \"acute_otitis_media\"                                                     \n",
-       "[295] \"alzheimer's_disease\"                                                    \n",
-       "[296] \"neuropathy_due_to_diabetes_mellitus\"                                    \n",
-       "[297] \"acute_pharyngitis\"                                                      \n",
-       "[298] \"degeneration_of_intervertebral_disc\"                                    \n",
-       "[299] \"attention_deficit_hyperactivity_disorder_predominantly_inattentive_type\"\n",
-       "[300] \"unplanned_pregnancy\"                                                    \n",
-       "[301] \"secondary_erectile_dysfunction\"                                         \n",
-       "[302] \"spinal_stenosis_of_lumbar_region\"                                       \n",
-       "[303] \"proteinuria\"                                                            \n",
-       "[304] \"urticaria\"                                                              \n",
-       "[305] \"genital_herpes_simplex\"                                                 \n",
-       "[306] \"malignant_neoplasm_of_female_breast\"                                    \n",
-       "[307] \"nausea\"                                                                 \n",
-       "[308] \"chronic_rhinitis\"                                                       \n",
-       "[309] \"multiple_sclerosis\"                                                     \n",
-       "[310] \"chronic_kidney_disease_stage_3\"                                         \n",
-       "[311] \"panic_disorder\"                                                         \n",
-       "[312] \"attention_deficit_hyperactivity_disorder\"                               \n",
-       "[313] \"amnesia\"                                                                \n",
-       "[314] \"otalgia\"                                                                \n",
-       "[315] \"tremor\"                                                                 \n",
-       "[316] \"retention_of_urine\"                                                     \n",
-       "[317] \"non-hodgkin's_lymphoma\"                                                 \n",
-       "[318] \"alcoholism\"                                                             \n",
-       "[319] \"dysuria\"                                                                \n",
-       "[320] \"generalized_anxiety_disorder\"                                           \n",
-       "[321] \"paroxysmal_atrial_fibrillation\"                                         \n",
-       "[322] \"peripheral_venous_insufficiency\"                                        \n",
-       "[323] \"nonproliferative_retinopathy_due_to_diabetes_mellitus\"                  \n",
-       "[324] \"shoulder_joint_pain\"                                                    \n",
-       "[325] \"moderate_recurrent_major_depression\"                                    \n",
-       "[326] \"diverticulitis\"                                                         \n",
-       "[327] \"solitary_nodule_of_lung\"                                                \n",
-       "[328] \"hyperkalemia\"                                                           \n",
-       "[329] \"recurrent_major_depressive_episodes\"                                    \n",
-       "[330] \"multiple_myeloma\"                                                       \n",
-       "[331] \"regular_astigmatism\"                                                    \n",
-       "[332] \"secondary_malignant_neoplasm_of_liver\"                                  \n",
-       "[333] \"ulcerative_colitis\"                                                     \n",
-       "[334] \"vaginitis\"                                                              \n",
-       "[335] \"acute_renal_failure_syndrome\"                                           \n",
-       "[336] \"amenorrhea\"                                                             \n",
-       "[337] \"tendinitis\"                                                             \n",
-       "[338] \"rhinitis\"                                                               \n",
-       "[339] \"bleeding_from_nose\"                                                     \n",
-       "[340] \"crohn's_disease\"                                                        \n",
-       "[341] \"nuclear_senile_cataract\"                                                \n",
-       "[342] \"muscle_pain\"                                                            \n",
-       "[343] \"epidermoid_cyst\"                                                        \n",
-       "[344] \"impaired_cognition\"                                                     \n",
-       "[345] \"acute_exacerbation_of_chronic_obstructive_airways_disease\"              \n",
-       "[346] \"eustachian_tube_disorder\"                                               \n",
-       "[347] \"internal_hemorrhoids\"                                                   \n",
-       "[348] \"substance_abuse\"                                                        \n",
-       "[349] \"melanocytic_nevus\"                                                      \n",
-       "[350] \"pain\"                                                                   \n",
-       "[351] \"barrett's_esophagus\"                                                    \n",
-       "[352] \"cerebrovascular_disease\"                                                \n",
-       "[353] \"malignant_melanoma\"                                                     \n",
-       "[354] \"folliculitis\"                                                           \n",
-       "[355] \"mitral_valve_prolapse\"                                                  \n",
-       "[356] \"chronic_hepatitis_c\"                                                    \n",
-       "[357] \"hypermetropia\"                                                          \n",
-       "[358] \"endometriosis\"                                                          \n",
-       "[359] \"gestational_diabetes_mellitus\"                                          \n",
-       "[360] \"cirrhosis_of_liver\"                                                     \n",
-       "[361] \"injury_of_head\"                                                         \n",
-       "[362] \"dehydration\"                                                            \n",
-       "[363] \"herpes_simplex\"                                                         \n",
-       "[364] \"fracture_of_bone\"                                                       \n",
-       "[365] \"overweight\"                                                             \n",
-       "[366] \"right_inguinal_hernia\"                                                  \n",
-       "[367] \"adjustment_disorder\"                                                    \n",
-       "[368] \"tinea_pedis\"                                                            \n",
-       "[369] \"aortic_valve_stenosis\"                                                  \n",
-       "[370] \"viral_hepatitis_type_b\"                                                 \n",
-       "[371] \"umbilical_hernia\"                                                       \n",
-       "[372] \"ingrowing_nail\"                                                         \n",
-       "[373] \"postmenopausal_bleeding\"                                                \n",
-       "[374] \"goiter\"                                                                 \n",
-       "[375] \"secondary_malignant_neoplasm_of_bone\"                                   \n",
-       "[376] \"pulmonary_emphysema\"                                                    \n",
-       "[377] \"left_inguinal_hernia\"                                                   \n",
-       "[378] \"snoring\"                                                                \n",
-       "[379] \"polycystic_ovary_syndrome\"                                              \n",
-       "[380] \"polycystic_ovary\"                                                       \n",
-       "[381] \"microscopic_hematuria\"                                                  \n",
-       "[382] \"pain_in_wrist\"                                                          \n",
-       "[383] \"pleural_effusion\"                                                       \n",
-       "[384] \"false_labor\"                                                            \n",
-       "[385] \"bleeding_from_vagina\"                                                   \n",
-       "[386] \"acquired_hypothyroidism\"                                                \n",
-       "[387] \"premature_rupture_of_membranes\"                                         \n",
-       "[388] \"human_immunodeficiency_virus_infection\"                                 \n",
-       "[389] \"contusion\"                                                              \n",
-       "[390] \"secondary_malignant_neoplasm_of_lung\"                                   \n",
-       "[391] \"problem_situation_relating_to_social_and_personal_history\"              \n",
-       "[392] \"hypercalcemia\"                                                          \n",
-       "[393] \"occlusion_of_carotid_artery\"                                            \n",
-       "[394] \"prolonged_second_stage_of_labor\"                                        \n",
-       "[395] \"lipoma\"                                                                 \n",
-       "[396] \"poisoning_caused_by_drug_and/or_medicinal_substance\"                    \n",
-       "[397] \"paranoid_schizophrenia\"                                                 \n",
-       "[398] \"hypersensitivity_reaction\"                                              \n",
-       "[399] \"dysthymia\"                                                              \n",
-       "[400] \"fetal_or_neonatal_effect_of_maternal_premature_rupture_of_membrane\"     \n",
-       "[401] \"noncompliance_with_treatment\"                                           \n",
-       "[402] \"falls\"                                                                  \n",
-       "[403] \"muscle_strain\"                                                          \n",
-       "[404] \"schizophrenia\"                                                          \n",
-       "[405] \"left_bundle_branch_block\"                                               \n",
-       "[406] \"disorder_of_nervous_system_due_to_type_2_diabetes_mellitus\"             \n",
-       "[407] \"preinfarction_syndrome\"                                                 \n",
-       "[408] \"conduction_disorder_of_the_heart\"                                       \n",
-       "[409] \"lateral_epicondylitis\"                                                  \n",
-       "[410] \"burn\"                                                                   \n",
-       "[411] \"pyelonephritis\"                                                         \n",
-       "[412] \"intermittent_claudication\"                                              \n",
-       "[413] \"varicose_veins_of_lower_extremity\"                                      \n",
-       "[414] \"hemiplegia\"                                                             \n",
-       "[415] \"chronic_pain\"                                                           \n",
-       "[416] \"bronchiolitis\"                                                          \n",
-       "[417] \"mild_persistent_asthma\"                                                 \n",
-       "[418] \"secondary_malignant_neoplasm_of_lymph_node\"                             \n",
-       "[419] \"mixed_hyperlipidemia\"                                                   \n",
-       "[420] \"female_urinary_stress_incontinence\"                                     \n",
-       "[421] \"localized_primary_osteoarthritis_of_the_ankle_and/or_foot\"              \n",
-       "[422] \"hypesthesia\"                                                            \n",
-       "[423] \"hand_pain\"                                                              \n",
-       "[424] \"psychotic_disorder\"                                                     \n",
-       "[425] \"menopausal_syndrome\"                                                    \n",
-       "[426] \"acute_myocardial_infarction_of_anterior_wall\"                           \n",
-       "[427] \"bronchiectasis\"                                                         \n",
-       "[428] \"lumbar_radiculopathy\"                                                   \n",
-       "[429] \"osteoarthritis_of_hip\"                                                  \n",
-       "[430] \"deviated_nasal_septum\"                                                  \n",
-       "[431] \"paresthesia\"                                                            \n",
-       "[432] \"hypoglycemia\"                                                           \n",
-       "[433] \"deep_venous_thrombosis_of_lower_extremity\"                              \n",
-       "[434] \"complication_of_surgical_procedure\"                                     \n",
-       "[435] \"mild_intermittent_asthma\"                                               \n",
-       "[436] \"generalized_ischemic_myocardial_dysfunction\"                            \n",
-       "[437] \"lumbar_spondylosis\"                                                     \n",
-       "[438] \"acute_tonsillitis\"                                                      \n",
-       "[439] \"esophagitis\"                                                            \n",
-       "[440] \"aortic_valve_regurgitation\"                                             \n",
-       "[441] \"malignant_neoplasm_of_skin\"                                             \n",
-       "[442] \"altered_mental_status\"                                                  \n",
-       "[443] \"atrial_flutter\"                                                         \n",
-       "[444] \"alopecia\"                                                               \n",
-       "[445] \"high_risk_pregnancy\"                                                    \n",
-       "[446] \"missed_abortion\"                                                        \n",
-       "[447] \"malignant_tumor_of_urinary_bladder\"                                     \n",
-       "[448] \"pulmonary_hypertension\"                                                 \n",
-       "[449] \"nasal_congestion\"                                                       \n",
-       "[450] \"hemoptysis\"                                                             \n",
-       "[451] \"viral_gastroenteritis\"                                                  \n",
-       "[452] \"right_upper_quadrant_pain\"                                              \n",
-       "[453] \"primary_open_angle_glaucoma\"                                            \n",
-       "[454] \"malignant_tumor_of_ovary\"                                               \n",
-       "[455] \"incomplete_emptying_of_bladder\"                                         \n",
-       "[456] \"pruritic_disorder\"                                                      \n",
-       "[457] \"fibrocystic_breast_changes\"                                             \n",
-       "[458] \"tinea_corporis\"                                                         \n",
-       "[459] \"acute_pancreatitis\"                                                     \n",
-       "[460] \"acute_myocardial_infarction\"                                            \n",
-       "[461] \"cerebral_hemorrhage\"                                                    \n",
-       "[462] \"urge_incontinence_of_urine\"                                             \n",
-       "[463] \"megaloblastic_anemia_due_to_vitamin_b>12<_deficiency\"                   \n",
-       "[464] \"chronic_lymphoid_leukemia_disease\"                                      \n",
-       "[465] \"disorder_of_lymphatic_system\"                                           \n",
-       "[466] \"diverticular_disease\"                                                   \n",
-       "[467] \"tonsillitis\"                                                            \n",
-       "[468] \"tear_film_insufficiency\"                                                \n",
-       "[469] \"abnormal_gait\"                                                          \n",
-       "[470] \"orthostatic_hypotension\"                                                \n",
-       "[471] \"pancreatitis\"                                                           \n",
-       "[472] \"closed_fracture_of_distal_end_of_radius\"                                \n",
-       "[473] \"first_degree_perineal_laceration\"                                       \n",
-       "[474] \"not_for_resuscitation\"                                                  \n",
-       "[475] \"gastroesophageal_reflux_disease_with_esophagitis\"                       \n",
-       "[476] \"intracranial_injury\"                                                    \n",
-       "[477] \"bell's_palsy\"                                                           \n",
-       "[478] \"bradycardia\"                                                            \n",
-       "[479] \"polymyalgia_rheumatica\"                                                 \n",
-       "[480] \"dysplasia_of_cervix\"                                                    \n",
-       "[481] \"serous_otitis_media\"                                                    \n",
-       "[482] \"angioedema\"                                                             \n",
-       "[483] \"venous_varices\"                                                         \n",
-       "[484] \"spasm\"                                                                  \n",
-       "[485] \"sleep_disorder\"                                                         \n",
-       "[486] \"pain_of_breast\"                                                         \n",
-       "[487] \"malabsorption_syndrome_due_to_intolerance_to_lactose\"                   \n",
-       "[488] \"tachycardia\"                                                            \n",
-       "[489] \"acute_conjunctivitis\"                                                   \n",
-       "[490] \"malignant_lymphoma\"                                                     \n",
-       "[491] \"supraventricular_tachycardia\"                                           \n",
-       "[492] \"hyperparathyroidism\"                                                    \n",
-       "[493] \"periodontitis\"                                                          \n",
-       "[494] \"renal_disorder_due_to_type_2_diabetes_mellitus\"                         \n",
-       "[495] \"nerve_root_disorder\"                                                    \n",
-       "[496] \"nonexudative_age-related_macular_degeneration\"                          \n",
-       "[497] \"tension-type_headache\"                                                  \n",
-       "[498] \"chronic_pain_syndrome\"                                                  \n",
-       "[499] \"abnormal_weight_loss\"                                                   \n",
-       "[500] \"pure_hypercholesterolemia\"                                              \n",
-       "[501] \"acute_upper_respiratory_infection\"                                      \n",
-       "[502] \"inflammatory_disease_of_liver\"                                          \n",
-       "[503] \"colitis\"                                                                \n",
-       "[504] \"prolonged_pregnancy\"                                                    \n",
-       "[505] \"major_depression_single_episode\"                                        \n",
-       "[506] \"croup\"                                                                  \n",
-       "[507] \"skin_tag\"                                                               \n",
-       "[508] \"metabolic_syndrome_x\"                                                   \n",
-       "[509] \"adverse_reaction_caused_by_drug\"                                        \n",
-       "[510] \"retinopathy_due_to_diabetes_mellitus\"                                   \n",
-       "[511] \"female_infertility\"                                                     \n",
-       "[512] \"open-angle_glaucoma\"                                                    \n",
-       "[513] \"tietze's_disease\"                                                       \n",
-       "[514] \"gallbladder_calculus\"                                                   \n",
-       "[515] \"sarcoidosis\"                                                            \n",
-       "[516] \"neurogenic_bladder\"                                                     \n",
-       "[517] \"tobacco_dependence_in_remission\"                                        \n",
-       "[518] \"wheezing\"                                                               \n",
-       "[519] \"elevated_levels_of_transaminase_&_lactic_acid_dehydrogenase\"            \n",
-       "[520] \"ascites\"                                                                \n",
-       "[521] \"low_blood_pressure\"                                                     \n",
-       "[522] \"bursitis\"                                                               \n",
-       "[523] \"miscarriage\"                                                            \n",
-       "[524] \"methicillin_resistant_staphylococcus_aureus_carrier\"                    \n",
-       "[525] \"urethritis\"                                                             \n",
-       "[526] \"noninfectious_gastroenteritis\"                                          \n",
-       "[527] \"malignant_tumor_of_thyroid_gland\"                                       \n",
-       "[528] \"pressure_ulcer\"                                                         \n",
-       "[529] \"verruca_plantaris\"                                                      \n",
-       "[530] \"anemia_of_chronic_disorder\"                                             \n",
-       "[531] \"hernia_of_anterior_abdominal_wall\"                                      \n",
-       "[532] \"degeneration_of_lumbar_intervertebral_disc\"                             \n",
-       "[533] \"right_lower_quadrant_pain\"                                              \n",
-       "[534] \"infertile\"                                                              \n",
-       "[535] \"hernia_of_abdominal_wall\"                                               \n",
-       "[536] \"benign_paroxysmal_positional_vertigo\"                                   \n",
-       "[537] \"pityriasis_versicolor\"                                                  \n",
-       "[538] \"injury_of_lower_leg\"                                                    \n",
-       "[539] \"dry_skin\"                                                               \n",
-       "[540] \"impacted_tooth\"                                                         \n",
-       "[541] \"acquired_trigger_finger\"                                                \n",
-       "[542] \"chronic_osteoarthritis\"                                                 \n",
-       "[543] \"acute_stress_disorder\"                                                  \n",
-       "[544] \"peripheral_circulatory_disorder_due_to_type_2_diabetes_mellitus\"        \n",
-       "[545] \"respiratory_failure\"                                                    \n",
-       "[546] \"allergic_disposition\"                                                   \n",
-       "[547] \"stress\"                                                                 \n",
-       "[548] \"atrophic_vaginitis\"                                                     \n",
-       "[549] \"degeneration_of_lumbosacral_intervertebral_disc\"                        \n",
-       "[550] \"astigmatism\"                                                            \n",
-       "[551] \"sick_sinus_syndrome\"                                                    \n",
-       "[552] \"cocaine_abuse\"                                                          \n",
-       "[553] \"intermittent_asthma\"                                                    \n",
-       "[554] \"cervical_radiculopathy\"                                                 \n",
-       "[555] \"atherosclerosis_of_coronary_artery\"                                     \n",
-       "[556] \"methicillin_resistant_staphylococcus_aureus_infection\"                  \n",
-       "[557] \"female_pelvic_inflammatory_disease\"                                     \n",
-       "[558] \"pain_in_eye\"                                                            \n",
-       "[559] \"cervical_spondylosis\"                                                   \n",
-       "[560] \"prematurity_of_infant\"                                                  \n",
-       "[561] \"postmature_infancy\"                                                     \n",
-       "[562] \"temporomandibular_joint_disorder\"                                       \n",
-       "[563] \"nocturia\"                                                               \n",
-       "[564] \"adjustment_disorder_with_depressed_mood\"                                \n",
-       "[565] \"visual_impairment\"                                                      \n",
-       "[566] \"male_hypogonadism\"                                                      \n",
-       "[567] \"closed_intertrochanteric_fracture\"                                      \n",
-       "[568] \"pain_in_limb\"                                                           \n",
-       "[569] \"complication_occurring_during_pregnancy\"                                \n",
-       "[570] \"sprain_of_knee\"                                                         \n",
-       "[571] \"fracture_of_ankle\"                                                      \n",
-       "[572] \"injury_of_hand\"                                                         \n",
-       "[573] \"postoperative_wound_infection\"                                          \n",
-       "[574] \"congestive_cardiomyopathy\"                                              \n",
-       "[575] \"nocturnal_enuresis\"                                                     \n",
-       "[576] \"proliferative_retinopathy_due_to_diabetes_mellitus\"                     \n",
-       "[577] \"major_depressive_disorder\"                                              \n",
-       "[578] \"chronic_bronchitis\"                                                     \n",
-       "[579] \"advanced_maternal_age_gravida\"                                          \n",
-       "[580] \"recurrent_major_depression_in_full_remission\"                           \n",
-       "[581] \"pregnancy-induced_hypertension\"                                         \n",
-       "[582] \"epididymitis\"                                                           \n",
-       "[583] \"impetigo\"                                                               \n",
-       "[584] \"schizoaffective_disorder\"                                               \n",
-       "[585] \"candidiasis_of_mouth\"                                                   \n",
-       "[586] \"mild_recurrent_major_depression\"                                        \n",
-       "[587] \"cerebral_palsy\"                                                         \n",
-       "[588] \"squamous_cell_carcinoma_of_skin\"                                        \n",
-       "[589] \"hypogonadism\"                                                           \n",
-       "[590] \"greater_trochanteric_pain_syndrome\"                                     \n",
-       "[591] \"graves'_disease\"                                                        \n",
-       "[592] \"malignant_neoplastic_disease\"                                           \n",
-       "[593] \"moderate_persistent_asthma\"                                             \n",
-       "[594] \"steatosis_of_liver\"                                                     \n",
-       "[595] \"obsessive-compulsive_disorder\"                                          \n",
-       "[596] \"mood_disorder\"                                                          \n",
-       "[597] \"degeneration_of_cervical_intervertebral_disc\"                           \n",
-       "[598] \"corneal_abrasion\"                                                       \n",
-       "[599] \"anal_fissure\"                                                           \n",
-       "[600] \"heart_failure\"                                                          \n",
-       "[601] \"adjustment_disorder_with_mixed_emotional_features\"                      \n",
-       "[602] \"febrile_convulsion\"                                                     \n",
-       "[603] \"degenerative_joint_disease_of_hand\"                                     \n",
-       "[604] \"chronic_type_b_viral_hepatitis\"                                         \n",
-       "[605] \"blepharitis\"                                                            \n",
-       "[606] \"cyst\"                                                                   \n",
-       "[607] \"heartburn\"                                                              \n",
-       "[608] \"intellectual_disability\"                                                \n",
-       "[609] \"exudative_age-related_macular_degeneration\"                             \n",
-       "[610] \"hypoxia\"                                                                \n",
-       "[611] \"influenza\"                                                              \n",
-       "[612] \"intervertebral_disc_prolapse\"                                           \n",
-       "[613] \"swelling_of_first_metatarsophalangeal_joint_of_hallux\"                  \n",
-       "[614] \"genuine_stress_incontinence\"                                            \n",
-       "[615] \"opioid_dependence\"                                                      \n",
-       "[616] \"antepartum_hemorrhage\"                                                  \n",
-       "[617] \"pneumothorax\"                                                           \n",
-       "[618] \"kidney_disease\"                                                         \n",
-       "[619] \"atopic_conjunctivitis\"                                                  \n",
-       "[620] \"dermatophytosis\"                                                        \n",
-       "[621] \"influenza_caused_by_influenza_a_virus\"                                  \n",
-       "[622] \"cystitis\"                                                               \n",
-       "[623] \"moderate_major_depression_single_episode\"                               \n",
-       "[624] \"furuncle\"                                                               \n",
-       "[625] \"cannabis_abuse\"                                                         \n",
-       "[626] \"conductive_hearing_loss\"                                                \n",
-       "[627] \"neutropenic_disorder\"                                                   \n",
-       "[628] \"injury_of_finger\"                                                       \n",
-       "[629] \"intestinal_obstruction\"                                                 \n",
-       "[630] \"lymphadenopathy\"                                                        \n",
-       "[631] \"mass_of_pelvic_structure\"                                               \n",
-       "[632] \"myelodysplastic_syndrome\"                                               \n",
-       "[633] \"jaundice\"                                                               \n",
-       "[634] \"severe_recurrent_major_depression_without_psychotic_features\"           \n",
-       "[635] \"inflammation_of_cervix\"                                                 \n",
-       "[636] \"sickle_cell_trait\"                                                      \n",
-       "[637] \"squamous_cell_carcinoma\"                                                \n",
-       "[638] \"small_bowel_obstruction\"                                                \n",
-       "[639] \"compression_fracture\"                                                   \n",
-       "[640] \"premenstrual_tension_syndrome\"                                          \n",
-       "[641] \"fracture_of_proximal_end_of_femur\"                                      \n",
-       "[642] \"developmental_delay\"                                                    \n",
-       "[643] \"drug_abuse\"                                                             \n",
-       "[644] \"left_lower_quadrant_pain\"                                               \n",
-       "[645] \"hordeolum\"                                                              \n",
-       "[646] \"disorder_of_lung\"                                                       \n",
-       "[647] \"migraine_without_aura\"                                                  \n",
-       "[648] \"open-angle_glaucoma_-_borderline\"                                       \n",
-       "[649] \"disorder_of_refraction\"                                                 \n",
-       "[650] \"acute_appendicitis\"                                                     \n",
-       "[651] \"amyloidosis\"                                                            \n",
-       "[652] \"hodgkin's_disease\"                                                      \n",
-       "[653] \"hydrocele_of_testis\"                                                    \n",
-       "[654] \"cellulitis_of_foot\"                                                     \n",
-       "[655] \"pancytopenia\"                                                           \n",
-       "[656] \"pain_in_elbow\"                                                          \n",
-       "[657] \"peripheral_nerve_disease\"                                               \n",
-       "[658] \"eating_disorder\"                                                        \n",
-       "[659] \"hernia_of_abdominal_cavity\"                                             \n",
-       "[660] \"secondary_malignant_neoplasm_of_brain\"                                  \n",
-       "[661] \"late_effects_of_respiratory_tuberculosis\"                               \n",
-       "[662] \"alcohol_intoxication\"                                                   \n",
-       "[663] \"abrasion\"                                                               \n",
-       "[664] \"breech_presentation\"                                                    \n",
-       "[665] \"premature_labor\"                                                        \n",
-       "[666] \"raynaud's_phenomenon\"                                                   \n",
-       "[667] \"posterior_rhinorrhea\"                                                   \n",
-       "[668] \"acute_gastritis\"                                                        \n",
-       "[669] \"polyp_of_nasal_cavity\"                                                  \n",
-       "[670] \"essential_tremor\"                                                       \n",
-       "[671] \"candidiasis\"                                                            \n",
-       "[672] \"ocular_hypertension\"                                                    \n",
-       "[673] \"aortic_valve_disorder\"                                                  \n",
-       "[674] \"diaper_rash\"                                                            \n",
-       "[675] \"cholangitis\"                                                            \n",
-       "[676] \"primary_malignant_neoplasm_of_lung\"                                     \n",
-       "[677] \"impingement_syndrome_of_shoulder_region\"                                \n",
-       "[678] \"idiopathic_urticaria\"                                                   \n",
-       "[679] \"arthritis_of_knee\"                                                      \n",
-       "[680] \"ulcer_of_duodenum\"                                                      \n",
-       "[681] \"peripheral_neuropathy_due_to_diabetes_mellitus\"                         \n",
-       "[682] \"hypervolemia\"                                                           \n",
-       "[683] \"cholecystitis\"                                                          \n",
-       "[684] \"deficiency_of_glucose-6-phosphate_dehydrogenase\"                        \n",
-       "[685] \"acute_pyelonephritis\"                                                   \n",
-       "[686] \"musculoskeletal_chest_pain\"                                             \n",
-       "[687] \"melena\"                                                                 \n",
-       "[688] \"cigarette_smoker\"                                                       \n",
-       "[689] \"infectious_mononucleosis\"                                               \n",
-       "[690] \"hypertensive_renal_failure\"                                             \n",
-       "[691] \"cocaine_dependence\"                                                     \n",
-       "[692] \"abdominal_aortic_aneurysm_without_rupture\"                              \n",
-       "[693] \"right_bundle_branch_block\"                                              \n",
-       "[694] \"alcoholic_cirrhosis\"                                                    \n",
-       "[695] \"fibrosis_of_lung\"                                                       \n",
-       "[696] \"neoplasm_of_uncertain_behavior_of_skin\"                                 \n",
-       "[697] \"malignant_melanoma_of_skin\"                                             \n",
-       "[698] \"urgent_desire_to_urinate\"                                               \n",
-       "[699] \"bursitis_of_hip\"                                                        \n",
-       "[700] \"chronic_alcoholism_in_remission\"                                        \n",
-       "[701] \"chronic_pancreatitis\"                                                   \n",
-       "[702] \"gastroparesis\"                                                          \n",
-       "[703] \"ectopic_pregnancy\"                                                      \n",
-       "[704] \"muscle_weakness\"                                                        \n",
-       "[705] \"recurrent_major_depression\"                                             \n",
-       "[706] \"pilonidal_cyst\"                                                         \n",
-       "[707] \"pain_in_toe\"                                                            \n",
-       "[708] \"pulmonary_tuberculosis\"                                                 \n",
-       "[709] \"celiac_disease\"                                                         \n",
-       "[710] \"cramp_in_lower_leg\"                                                     \n",
-       "[711] \"secondary_malignant_neoplasm_of_pleura\"                                 \n",
-       "[712] \"fracture_of_hand\"                                                       \n",
-       "[713] \"cyst_of_breast\"                                                         \n",
-       "[714] \"nephrotic_syndrome\"                                                     \n",
-       "[715] \"polyp_of_nasal_sinus\"                                                   \n",
-       "[716] \"chondromalacia_of_patella\"                                              \n",
-       "[717] \"spinal_stenosis_in_cervical_region\"                                     \n",
-       "[718] \"disorder_of_artery\"                                                     \n",
-       "[719] \"vitiligo\"                                                               \n",
-       "[720] \"female_cystocele\"                                                       \n",
-       "[721] \"dysphasia\"                                                              \n",
-       "[722] \"retinal_disorder\"                                                       \n",
-       "[723] \"epiretinal_membrane\"                                                    \n",
-       "[724] \"recurrent_major_depression_in_partial_remission\"                        \n",
-       "[725] \"infection_caused_by_trichomonas\"                                        \n",
-       "[726] \"osteomyelitis\"                                                          \n",
-       "[727] \"polyp_of_nasal_cavity_and/or_nasal_sinus\"                               \n",
-       "[728] \"mass_of_neck\"                                                           \n",
-       "[729] \"idiopathic_thrombocytopenic_purpura\"                                    \n",
-       "[730] \"complete_miscarriage\"                                                   \n",
-       "[731] \"gastric_ulcer\"                                                          \n",
-       "[732] \"papilloma_of_skin\"                                                      \n",
-       "[733] \"fetal_or_neonatal_effect_of_breech_delivery_and_extraction\"             \n",
-       "[734] \"secondary_malignant_neoplastic_disease\"                                 \n",
-       "[735] \"hypoxemia\"                                                              \n",
-       "[736] \"paraplegia\"                                                             \n",
-       "[737] \"perforation_of_tympanic_membrane\"                                       \n",
-       "[738] \"ventricular_tachycardia\"                                                \n",
-       "[739] \"mixed_incontinence\"                                                     \n",
-       "[740] \"disorder_of_eye_due_to_type_2_diabetes_mellitus\"                        \n",
-       "[741] \"trigeminal_neuralgia\"                                                   \n",
-       "[742] \"retinal_detachment\"                                                     \n",
-       "[743] \"leukopenia\"                                                             \n",
-       "[744] \"vitreous_hemorrhage\"                                                    \n",
-       "[745] \"ischemic_ulcer\"                                                         \n",
-       "[746] \"intramural_leiomyoma_of_uterus\"                                         \n",
-       "[747] \"viral_hepatitis_type_a\"                                                 \n",
-       "[748] \"m<U+00E9>ni<U+00E8>re's_disease\"                                        \n",
-       "[749] \"fracture_of_phalanx_of_hand\"                                            \n",
-       "[750] \"muscle_atrophy\"                                                         \n",
-       "[751] \"incontinence_of_feces\"                                                  \n",
-       "[752] \"mitral_valve_disorder\"                                                  \n",
-       "[753] \"atherosclerosis_of_arteries_of_the_extremities\"                         \n",
-       "[754] \"spondylosis\"                                                            \n",
-       "[755] \"pterygium\"                                                              \n",
-       "[756] \"ulnar_neuropathy\"                                                       \n",
-       "[757] \"lung_mass\"                                                              \n",
-       "[758] \"foreign_body_in_respiratory_tract\"                                      \n",
-       "[759] \"chronic_kidney_disease_stage_4\"                                         \n",
-       "[760] \"myocardial_ischemia\"                                                    \n",
-       "[761] \"non-toxic_multinodular_goiter\"                                          \n",
-       "[762] \"pain_in_finger\"                                                         \n",
-       "[763] \"cervical_spondylosis_without_myelopathy\"                                \n",
-       "[764] \"body_mass_index_25-29_-_overweight\"                                     \n",
-       "[765] \"clouded_consciousness\"                                                  \n",
-       "[766] \"mixed_conductive_and_sensorineural_hearing_loss\"                        \n",
-       "[767] \"tooth_eruption_disorder\"                                                \n",
-       "[768] \"hyperuricemia\"                                                          \n",
-       "[769] \"closed_fracture_of_neck_of_femur\"                                       \n",
-       "[770] \"bipolar_ii_disorder\"                                                    \n",
-       "[771] \"disturbance_in_sleep_behavior\"                                          \n",
-       "[772] \"relationship_problems\"                                                  \n",
-       "[773] \"sprain_of_wrist\"                                                        \n",
-       "[774] \"personality_disorder\"                                                   \n",
-       "[775] \"external_hemorrhoids\"                                                   \n",
-       "[776] \"abnormal_vision\"                                                        \n",
-       "[777] \"hyperprolactinemia\"                                                     \n",
-       "[778] \"hemochromatosis\"                                                        \n",
-       "[779] \"lumbosacral_radiculopathy\"                                              \n",
-       "[780] \"heart_valve_disorder\"                                                   \n",
-       "[781] \"cardiac_arrest\"                                                         \n",
-       "[782] \"infection_caused_by_molluscum_contagiosum\"                              \n",
-       "[783] \"chronic_kidney_disease_stage_2\"                                         \n",
-       "[784] \"secondary_malignant_neoplasm_of_peritoneum\"                             \n",
-       "[785] \"thoracic_back_pain\"                                                     \n",
-       "[786] \"blood_in_urine\"                                                         \n",
-       "[787] \"adhesive_capsulitis_of_shoulder\"                                        \n",
-       "[788] \"diplopia\"                                                               \n",
-       "[789] \"sj<U+00F6>gren's_syndrome\"                                              \n",
-       "[790] \"ureteric_stone\"                                                         \n",
-       "[791] \"bronchospasm\"                                                           \n",
-       "[792] \"chronic_fatigue_syndrome\"                                               \n",
-       "[793] \"cannabis_dependence\"                                                    \n",
-       "[794] \"neck_sprain\"                                                            \n",
-       "[795] \"multinodular_goiter\"                                                    \n",
-       "[796] \"ptosis_of_eyelid\"                                                       \n",
-       "[797] \"failure_to_thrive\"                                                      \n",
-       "[798] \"torticollis\"                                                            \n",
-       "[799] \"acute_bronchiolitis\"                                                    \n",
-       "[800] \"viral_exanthem\"                                                         \n",
-       "[801] \"talipes_planus\"                                                         \n",
-       "[802] \"idiopathic_peripheral_neuropathy\"                                       \n",
-       "[803] \"foreign_body_in_pharynx\"                                                \n",
-       "[804] \"jaw_pain\"                                                               \n",
-       "[805] \"renal_impairment\"                                                       \n",
-       "[806] \"ataxia\"                                                                 \n",
-       "[807] \"age-related_macular_degeneration\"                                       \n",
-       "[808] \"uterine_prolapse\"                                                       \n",
-       "[809] \"renal_mass\"                                                             \n",
-       "[810] \"pneumonitis\"                                                            \n",
-       "[811] \"coordination_problem\"                                                   \n",
-       "[812] \"blindness_-_both_eyes\"                                                  \n",
-       "[813] \"primary_hyperparathyroidism\"                                            \n",
-       "[814] \"musculoskeletal_pain\"                                                   \n",
-       "[815] \"mycosis\"                                                                \n",
-       "[816] \"primigravida\"                                                           \n",
-       "[817] \"urethral_stricture\"                                                     \n",
-       "[818] \"leukocytosis\"                                                           \n",
-       "[819] \"ventricular_premature_complex\"                                          \n",
-       "[820] \"ulcer_of_foot_due_to_diabetes_mellitus\"                                 \n",
-       "[821] \"chronic_headache_disorder\"                                              \n",
-       "[822] \"hemangioma\"                                                             \n",
-       "[823] \"lymphedema\"                                                             \n",
-       "[824] \"postmenopausal_state\"                                                   \n",
-       "[825] \"chronic_ulcer_of_skin\"                                                  \n",
-       "[826] \"left_heart_failure\"                                                     \n",
-       "[827] \"excessive_and_frequent_menstruation\"                                    \n",
-       "[828] \"thrombocytosis\"                                                         \n",
-       "[829] \"disorder_of_liver\"                                                      \n",
-       "[830] \"disorder_of_carotid_artery\"                                             \n",
-       "[831] \"altered_bowel_function\"                                                 \n",
-       "[832] \"abscess_of_foot\"                                                        \n",
-       "[833] \"malignant_tumor_of_head_and/or_neck\"                                    \n",
-       "[834] \"streptococcus_group_b_infection_of_the_infant\"                          \n",
-       "[835] \"concussion_injury_of_brain\"                                             \n",
-       "[836] \"feeding_problems_in_newborn\"                                            \n",
-       "[837] \"bipolar_i_disorder\"                                                     \n",
-       "[838] \"viral_pharyngitis\"                                                      \n",
-       "[839] \"lower_respiratory_tract_infection\"                                      \n",
-       "[840] \"hydronephrosis\"                                                         \n",
-       "[841] \"borderline_personality_disorder\"                                        \n",
-       "[842] \"esophageal_varices\"                                                     \n",
-       "[843] \"hypersomnia\"                                                            \n",
-       "[844] \"sensorineural_hearing_loss_bilateral\"                                   \n",
-       "[845] \"varicocele\"                                                             \n",
-       "[846] \"subarachnoid_intracranial_hemorrhage\"                                   \n",
-       "[847] \"incisional_hernia\"                                                      \n",
-       "[848] \"varicella\"                                                              \n",
-       "[849] \"pain_in_testicle\"                                                       \n",
-       "[850] \"transplant_follow-up\"                                                   \n",
-       "[851] \"tinea_cruris\"                                                           \n",
-       "[852] \"laryngitis\"                                                             \n",
-       "[853] \"hypertrophy_of_nail\"                                                    \n",
-       "[854] \"amblyopia\"                                                              \n",
-       "[855] \"polyp_of_cervix\"                                                        \n",
-       "[856] \"cyst_of_kidney\"                                                         \n",
-       "[857] \"hepatic_encephalopathy\"                                                 \n",
-       "[858] \"blood_glucose_abnormal\"                                                 \n",
-       "[859] \"postherpetic_neuralgia\"                                                 \n",
-       "[860] \"frank_hematuria\"                                                        \n",
-       "[861] \"cramp\"                                                                  \n",
-       "[862] \"interstitial_lung_disease\"                                              \n",
-       "[863] \"complete_atrioventricular_block\"                                        \n",
-       "[864] \"malignant_tumor_of_kidney\"                                              \n",
-       "[865] \"otitis\"                                                                 \n",
-       "[866] \"septic_shock\"                                                           \n",
-       "[867] \"disorder_of_thyroid_gland\"                                              \n",
-       "[868] \"hypertrophic_cardiomyopathy\"                                            \n",
-       "[869] \"respiratory_distress_syndrome_in_the_newborn\"                           \n",
-       "[870] \"infectious_gastroenteritis\"                                             \n",
-       "[871] \"subdural_intracranial_hemorrhage\"                                       \n",
-       "[872] \"hepatitis_b_carrier\"                                                    \n",
-       "[873] \"manic_bipolar_i_disorder\"                                               \n",
-       "[874] \"secondary_pulmonary_hypertension\"                                       \n",
-       "[875] \"gonorrhea\"                                                              \n",
-       "[876] \"derangement_of_knee\"                                                    \n",
-       "[877] \"appendicitis\"                                                           \n",
-       "[878] \"polyneuropathy_due_to_diabetes_mellitus\"                                \n",
-       "[879] \"neonatal_hypoglycemia\"                                                  \n",
-       "[880] \"prolonged_rupture_of_membranes\"                                         \n",
-       "[881] \"vasomotor_rhinitis\"                                                     \n",
-       "[882] \"renal_disorder_due_to_type_1_diabetes_mellitus\"                         \n",
-       "[883] \"tuberculosis\"                                                           \n",
-       "[884] \"feeding_problem\"                                                        \n",
-       "[885] \"chronic_tonsillitis\"                                                    \n",
-       "[886] \"acute_duodenal_ulcer_with_hemorrhage\"                                   \n",
-       "[887] \"hammer_toe\"                                                             \n",
-       "[888] \"malignant_tumor_of_cervix\"                                              \n",
-       "[889] \"prolapsed_lumbar_intervertebral_disc\"                                   \n",
-       "[890] \"hematemesis\"                                                            \n",
-       "[891] \"perianal_abscess\"                                                       \n",
-       "[892] \"nonvenomous_insect_bite\"                                                \n",
-       "[893] \"spondylolisthesis\"                                                      \n",
-       "[894] \"malignant_tumor_of_esophagus\"                                           \n",
-       "[895] \"aphthous_ulcer_of_mouth\"                                                \n",
-       "[896] \"ventricular_septal_defect\"                                              \n",
-       "[897] \"oropharyngeal_dysphagia\"                                                \n",
-       "[898] \"injury_of_knee\"                                                         \n",
-       "[899] \"traumatic_brain_injury\"                                                 \n",
-       "[900] \"osteoarthritis_of_glenohumeral_joint\"                                   \n",
-       "[901] \"fetal_or_neonatal_effect_of_maternal_medical_problem\"                   \n",
-       "[902] \"stomatological_preparations\"                                            \n",
-       "[903] \"drugs_for_acid_related_disorders\"                                       \n",
-       "[904] \"drugs_for_functional_gastrointestinal_disorders\"                        \n",
-       "[905] \"antiemetics_and_antinauseants\"                                          \n",
-       "[906] \"bile_and_liver_therapy\"                                                 \n",
-       "[907] \"drugs_for_constipation\"                                                 \n",
-       "[908] \"antidiarrheals,_intestinal_antiinflammatory/antiinfective_agents\"       \n",
-       "[909] \"antiobesity_preparations,_excl._diet_products\"                          \n",
-       "[910] \"digestives,_incl._enzymes\"                                              \n",
-       "[911] \"drugs_used_in_diabetes\"                                                 \n",
-       "[912] \"vitamins\"                                                               \n",
-       "[913] \"mineral_supplements\"                                                    \n",
-       "[914] \"tonics\"                                                                 \n",
-       "[915] \"anabolic_agents_for_systemic_use\"                                       \n",
-       "[916] \"appetite_stimulants\"                                                    \n",
-       "[917] \"other_alimentary_tract_and_metabolism_products\"                         \n",
-       "[918] \"antithrombotic_agents\"                                                  \n",
-       "[919] \"antihemorrhagics\"                                                       \n",
-       "[920] \"antianemic_preparations\"                                                \n",
-       "[921] \"blood_substitutes_and_perfusion_solutions\"                              \n",
-       "[922] \"other_hematological_agents\"                                             \n",
-       "[923] \"cardiac_therapy\"                                                        \n",
-       "[924] \"antihypertensives\"                                                      \n",
-       "[925] \"diuretics\"                                                              \n",
-       "[926] \"peripheral_vasodilators\"                                                \n",
-       "[927] \"vasoprotectives\"                                                        \n",
-       "[928] \"beta_blocking_agents\"                                                   \n",
-       "[929] \"calcium_channel_blockers\"                                               \n",
-       "[930] \"agents_acting_on_the_renin-angiotensin_system\"                          \n",
-       "[931] \"lipid_modifying_agents\"                                                 \n",
-       "[932] \"antifungals_for_dermatological_use\"                                     \n",
-       "[933] \"emollients_and_protectives\"                                             \n",
-       "[934] \"preparations_for_treatment_of_wounds_and_ulcers\"                        \n",
-       "[935] \"antipruritics,_incl._antihistamines,_anesthetics,_etc.\"                 \n",
-       "[936] \"antipsoriatics\"                                                         \n",
-       "[937] \"antibiotics_and_chemotherapeutics_for_dermatological_use\"               \n",
-       "[938] \"corticosteroids,_dermatological_preparations\"                           \n",
-       "[939] \"antiseptics_and_disinfectants\"                                          \n",
-       "[940] \"medicated_dressings\"                                                    \n",
-       "[941] \"anti-acne_preparations\"                                                 \n",
-       "[942] \"other_dermatological_preparations\"                                      \n",
-       "[943] \"gynecological_antiinfectives_and_antiseptics\"                           \n",
-       "[944] \"other_gynecologicals\"                                                   \n",
-       "[945] \"sex_hormones_and_modulators_of_the_genital_system\"                      \n",
-       "[946] \"urologicals\"                                                            \n",
-       "[947] \"pituitary_and_hypothalamic_hormones_and_analogues\"                      \n",
-       "[948] \"corticosteroids_for_systemic_use\"                                       \n",
-       "[949] \"thyroid_therapy\"                                                        \n",
-       "[950] \"pancreatic_hormones\"                                                    \n",
-       "[951] \"calcium_homeostasis\"                                                    \n",
-       "[952] \"antibacterials_for_systemic_use\"                                        \n",
-       "[953] \"antimycotics_for_systemic_use\"                                          \n",
-       "[954] \"antimycobacterials\"                                                     \n",
-       "[955] \"antivirals_for_systemic_use\"                                            \n",
-       "[956] \"immune_sera_and_immunoglobulins\"                                        \n",
-       "[957] \"vaccines\"                                                               \n",
-       "[958] \"antineoplastic_agents\"                                                  \n",
-       "[959] \"endocrine_therapy\"                                                      \n",
-       "[960] \"immunostimulants\"                                                       \n",
-       "[961] \"immunosuppressants\"                                                     \n",
-       "[962] \"antiinflammatory_and_antirheumatic_products\"                            \n",
-       "[963] \"topical_products_for_joint_and_muscular_pain\"                           \n",
-       "[964] \"muscle_relaxants\"                                                       \n",
-       "[965] \"antigout_preparations\"                                                  \n",
-       "[966] \"drugs_for_treatment_of_bone_diseases\"                                   \n",
-       "[967] \"other_drugs_for_disorders_of_the_musculo-skeletal_system\"               \n",
-       "[968] \"anesthetics\"                                                            \n",
-       "[969] \"analgesics\"                                                             \n",
-       "[970] \"antiepileptics\"                                                         \n",
-       "[971] \"anti-parkinson_drugs\"                                                   \n",
-       "[972] \"psycholeptics\"                                                          \n",
-       "[973] \"psychoanaleptics\"                                                       \n",
-       "[974] \"other_nervous_system_drugs\"                                             \n",
-       "[975] \"antiprotozoals\"                                                         \n",
-       "[976] \"anthelmintics\"                                                          \n",
-       "[977] \"ectoparasiticides,_incl._scabicides,_insecticides_and_repellents\"       \n",
-       "[978] \"nasal_preparations\"                                                     \n",
-       "[979] \"throat_preparations\"                                                    \n",
-       "[980] \"drugs_for_obstructive_airway_diseases\"                                  \n",
-       "[981] \"cough_and_cold_preparations\"                                            \n",
-       "[982] \"antihistamines_for_systemic_use\"                                        \n",
-       "[983] \"other_respiratory_system_products\"                                      \n",
-       "[984] \"ophthalmologicals\"                                                      \n",
-       "[985] \"otologicals\"                                                            \n",
-       "[986] \"ophthalmological_and_otological_preparations\"                           \n",
-       "[987] \"allergens\"                                                              \n",
-       "[988] \"all_other_therapeutic_products\"                                         \n",
-       "[989] \"diagnostic_agents\"                                                      \n",
-       "[990] \"general_nutrients\"                                                      \n",
-       "[991] \"all_other_non-therapeutic_products\"                                     \n",
-       "[992] \"contrast_media\"                                                         \n",
-       "[993] \"diagnostic_radiopharmaceuticals\"                                        \n",
-       "[994] \"therapeutic_radiopharmaceuticals\"                                       \n",
-       "[995] \"surgical_dressings\"                                                     \n",
-       "[996] \"statins\"                                                                \n",
-       "[997] \"ass\"                                                                    \n",
-       "[998] \"atypical_antipsychotics\"                                                \n",
-       "[999] \"glucocorticoids\"                                                        "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "covariates"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T14:16:59.684813Z",
-     "start_time": "2020-11-04T14:16:58.773Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style>\n",
-       ".list-inline {list-style: none; margin:0; padding: 0}\n",
-       ".list-inline>li {display: inline-block}\n",
-       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
-       "</style>\n",
-       "<ol class=list-inline><li>'chronic_alcoholism_in_remission'</li><li>'chronic_pancreatitis'</li><li>'gastroparesis'</li><li>'ectopic_pregnancy'</li><li>'muscle_weakness'</li><li>'recurrent_major_depression'</li><li>'pilonidal_cyst'</li><li>'pain_in_toe'</li><li>'pulmonary_tuberculosis'</li><li>'celiac_disease'</li><li>'cramp_in_lower_leg'</li><li>'secondary_malignant_neoplasm_of_pleura'</li><li>'fracture_of_hand'</li><li>'cyst_of_breast'</li><li>'nephrotic_syndrome'</li><li>'polyp_of_nasal_sinus'</li><li>'chondromalacia_of_patella'</li><li>'spinal_stenosis_in_cervical_region'</li><li>'disorder_of_artery'</li><li>'vitiligo'</li><li>'female_cystocele'</li><li>'dysphasia'</li><li>'retinal_disorder'</li><li>'epiretinal_membrane'</li><li>'recurrent_major_depression_in_partial_remission'</li><li>'infection_caused_by_trichomonas'</li><li>'osteomyelitis'</li><li>'polyp_of_nasal_cavity_and/or_nasal_sinus'</li><li>'mass_of_neck'</li><li>'idiopathic_thrombocytopenic_purpura'</li><li>'complete_miscarriage'</li><li>'gastric_ulcer'</li><li>'papilloma_of_skin'</li><li>'fetal_or_neonatal_effect_of_breech_delivery_and_extraction'</li><li>'secondary_malignant_neoplastic_disease'</li><li>'hypoxemia'</li><li>'paraplegia'</li><li>'perforation_of_tympanic_membrane'</li><li>'ventricular_tachycardia'</li><li>'mixed_incontinence'</li><li>'disorder_of_eye_due_to_type_2_diabetes_mellitus'</li><li>'trigeminal_neuralgia'</li><li>'retinal_detachment'</li><li>'leukopenia'</li><li>'vitreous_hemorrhage'</li><li>'ischemic_ulcer'</li><li>'intramural_leiomyoma_of_uterus'</li><li>'viral_hepatitis_type_a'</li><li>'m\\u00e9ni\\u00e8re\\'s_disease'</li><li>'fracture_of_phalanx_of_hand'</li><li>'muscle_atrophy'</li><li>'incontinence_of_feces'</li><li>'mitral_valve_disorder'</li><li>'atherosclerosis_of_arteries_of_the_extremities'</li><li>'spondylosis'</li><li>'pterygium'</li><li>'ulnar_neuropathy'</li><li>'lung_mass'</li><li>'foreign_body_in_respiratory_tract'</li><li>'chronic_kidney_disease_stage_4'</li><li>'myocardial_ischemia'</li><li>'non-toxic_multinodular_goiter'</li><li>'pain_in_finger'</li><li>'cervical_spondylosis_without_myelopathy'</li><li>'body_mass_index_25-29_-_overweight'</li><li>'clouded_consciousness'</li><li>'mixed_conductive_and_sensorineural_hearing_loss'</li><li>'tooth_eruption_disorder'</li><li>'hyperuricemia'</li><li>'closed_fracture_of_neck_of_femur'</li><li>'bipolar_ii_disorder'</li><li>'disturbance_in_sleep_behavior'</li><li>'relationship_problems'</li><li>'sprain_of_wrist'</li><li>'personality_disorder'</li><li>'external_hemorrhoids'</li><li>'abnormal_vision'</li><li>'hyperprolactinemia'</li><li>'hemochromatosis'</li><li>'lumbosacral_radiculopathy'</li><li>'heart_valve_disorder'</li><li>'cardiac_arrest'</li><li>'infection_caused_by_molluscum_contagiosum'</li><li>'chronic_kidney_disease_stage_2'</li><li>'secondary_malignant_neoplasm_of_peritoneum'</li><li>'thoracic_back_pain'</li><li>'blood_in_urine'</li><li>'adhesive_capsulitis_of_shoulder'</li><li>'diplopia'</li><li>'sj\\u00f6gren\\'s_syndrome'</li><li>'ureteric_stone'</li><li>'bronchospasm'</li><li>'chronic_fatigue_syndrome'</li><li>'cannabis_dependence'</li><li>'neck_sprain'</li><li>'multinodular_goiter'</li><li>'ptosis_of_eyelid'</li><li>'failure_to_thrive'</li><li>'torticollis'</li><li>'acute_bronchiolitis'</li><li>'viral_exanthem'</li><li>'talipes_planus'</li><li>'idiopathic_peripheral_neuropathy'</li><li>'foreign_body_in_pharynx'</li><li>'jaw_pain'</li><li>'renal_impairment'</li><li>'ataxia'</li><li>'age-related_macular_degeneration'</li><li>'uterine_prolapse'</li><li>'renal_mass'</li><li>'pneumonitis'</li><li>'coordination_problem'</li><li>'blindness_-_both_eyes'</li><li>'primary_hyperparathyroidism'</li><li>'musculoskeletal_pain'</li><li>'mycosis'</li><li>'primigravida'</li><li>'urethral_stricture'</li><li>'leukocytosis'</li><li>'ventricular_premature_complex'</li><li>'ulcer_of_foot_due_to_diabetes_mellitus'</li><li>'chronic_headache_disorder'</li><li>'hemangioma'</li><li>'lymphedema'</li><li>'postmenopausal_state'</li><li>'chronic_ulcer_of_skin'</li><li>'left_heart_failure'</li><li>'excessive_and_frequent_menstruation'</li><li>'thrombocytosis'</li><li>'disorder_of_liver'</li><li>'disorder_of_carotid_artery'</li><li>'altered_bowel_function'</li><li>'abscess_of_foot'</li><li>'malignant_tumor_of_head_and/or_neck'</li><li>'streptococcus_group_b_infection_of_the_infant'</li><li>'concussion_injury_of_brain'</li><li>'feeding_problems_in_newborn'</li><li>'bipolar_i_disorder'</li><li>'viral_pharyngitis'</li><li>'lower_respiratory_tract_infection'</li><li>'hydronephrosis'</li><li>'borderline_personality_disorder'</li><li>'esophageal_varices'</li><li>'hypersomnia'</li><li>'sensorineural_hearing_loss_bilateral'</li><li>'varicocele'</li><li>'subarachnoid_intracranial_hemorrhage'</li><li>'incisional_hernia'</li><li>'varicella'</li><li>'pain_in_testicle'</li><li>'transplant_follow-up'</li><li>'tinea_cruris'</li><li>'laryngitis'</li><li>'hypertrophy_of_nail'</li><li>'amblyopia'</li><li>'polyp_of_cervix'</li><li>'cyst_of_kidney'</li><li>'hepatic_encephalopathy'</li><li>'blood_glucose_abnormal'</li><li>'postherpetic_neuralgia'</li><li>'frank_hematuria'</li><li>'cramp'</li><li>'interstitial_lung_disease'</li><li>'complete_atrioventricular_block'</li><li>'malignant_tumor_of_kidney'</li><li>'otitis'</li><li>'septic_shock'</li><li>'disorder_of_thyroid_gland'</li><li>'hypertrophic_cardiomyopathy'</li><li>'respiratory_distress_syndrome_in_the_newborn'</li><li>'infectious_gastroenteritis'</li><li>'subdural_intracranial_hemorrhage'</li><li>'hepatitis_b_carrier'</li><li>'manic_bipolar_i_disorder'</li><li>'secondary_pulmonary_hypertension'</li><li>'gonorrhea'</li><li>'derangement_of_knee'</li><li>'appendicitis'</li><li>'polyneuropathy_due_to_diabetes_mellitus'</li><li>'neonatal_hypoglycemia'</li><li>'prolonged_rupture_of_membranes'</li><li>'vasomotor_rhinitis'</li><li>'renal_disorder_due_to_type_1_diabetes_mellitus'</li><li>'tuberculosis'</li><li>'feeding_problem'</li><li>'chronic_tonsillitis'</li><li>'acute_duodenal_ulcer_with_hemorrhage'</li><li>'hammer_toe'</li><li>'malignant_tumor_of_cervix'</li><li>'prolapsed_lumbar_intervertebral_disc'</li><li>'hematemesis'</li><li>'perianal_abscess'</li><li>'nonvenomous_insect_bite'</li><li>'spondylolisthesis'</li><li>'malignant_tumor_of_esophagus'</li><li>'aphthous_ulcer_of_mouth'</li><li>'ventricular_septal_defect'</li><li>'oropharyngeal_dysphagia'</li><li>'injury_of_knee'</li><li>'traumatic_brain_injury'</li><li>'osteoarthritis_of_glenohumeral_joint'</li></ol>\n"
-      ],
-      "text/latex": [
-       "\\begin{enumerate*}\n",
-       "\\item 'chronic\\_alcoholism\\_in\\_remission'\n",
-       "\\item 'chronic\\_pancreatitis'\n",
-       "\\item 'gastroparesis'\n",
-       "\\item 'ectopic\\_pregnancy'\n",
-       "\\item 'muscle\\_weakness'\n",
-       "\\item 'recurrent\\_major\\_depression'\n",
-       "\\item 'pilonidal\\_cyst'\n",
-       "\\item 'pain\\_in\\_toe'\n",
-       "\\item 'pulmonary\\_tuberculosis'\n",
-       "\\item 'celiac\\_disease'\n",
-       "\\item 'cramp\\_in\\_lower\\_leg'\n",
-       "\\item 'secondary\\_malignant\\_neoplasm\\_of\\_pleura'\n",
-       "\\item 'fracture\\_of\\_hand'\n",
-       "\\item 'cyst\\_of\\_breast'\n",
-       "\\item 'nephrotic\\_syndrome'\n",
-       "\\item 'polyp\\_of\\_nasal\\_sinus'\n",
-       "\\item 'chondromalacia\\_of\\_patella'\n",
-       "\\item 'spinal\\_stenosis\\_in\\_cervical\\_region'\n",
-       "\\item 'disorder\\_of\\_artery'\n",
-       "\\item 'vitiligo'\n",
-       "\\item 'female\\_cystocele'\n",
-       "\\item 'dysphasia'\n",
-       "\\item 'retinal\\_disorder'\n",
-       "\\item 'epiretinal\\_membrane'\n",
-       "\\item 'recurrent\\_major\\_depression\\_in\\_partial\\_remission'\n",
-       "\\item 'infection\\_caused\\_by\\_trichomonas'\n",
-       "\\item 'osteomyelitis'\n",
-       "\\item 'polyp\\_of\\_nasal\\_cavity\\_and/or\\_nasal\\_sinus'\n",
-       "\\item 'mass\\_of\\_neck'\n",
-       "\\item 'idiopathic\\_thrombocytopenic\\_purpura'\n",
-       "\\item 'complete\\_miscarriage'\n",
-       "\\item 'gastric\\_ulcer'\n",
-       "\\item 'papilloma\\_of\\_skin'\n",
-       "\\item 'fetal\\_or\\_neonatal\\_effect\\_of\\_breech\\_delivery\\_and\\_extraction'\n",
-       "\\item 'secondary\\_malignant\\_neoplastic\\_disease'\n",
-       "\\item 'hypoxemia'\n",
-       "\\item 'paraplegia'\n",
-       "\\item 'perforation\\_of\\_tympanic\\_membrane'\n",
-       "\\item 'ventricular\\_tachycardia'\n",
-       "\\item 'mixed\\_incontinence'\n",
-       "\\item 'disorder\\_of\\_eye\\_due\\_to\\_type\\_2\\_diabetes\\_mellitus'\n",
-       "\\item 'trigeminal\\_neuralgia'\n",
-       "\\item 'retinal\\_detachment'\n",
-       "\\item 'leukopenia'\n",
-       "\\item 'vitreous\\_hemorrhage'\n",
-       "\\item 'ischemic\\_ulcer'\n",
-       "\\item 'intramural\\_leiomyoma\\_of\\_uterus'\n",
-       "\\item 'viral\\_hepatitis\\_type\\_a'\n",
-       "\\item 'm\\textbackslash{}u00e9ni\\textbackslash{}u00e8re\\textbackslash{}'s\\_disease'\n",
-       "\\item 'fracture\\_of\\_phalanx\\_of\\_hand'\n",
-       "\\item 'muscle\\_atrophy'\n",
-       "\\item 'incontinence\\_of\\_feces'\n",
-       "\\item 'mitral\\_valve\\_disorder'\n",
-       "\\item 'atherosclerosis\\_of\\_arteries\\_of\\_the\\_extremities'\n",
-       "\\item 'spondylosis'\n",
-       "\\item 'pterygium'\n",
-       "\\item 'ulnar\\_neuropathy'\n",
-       "\\item 'lung\\_mass'\n",
-       "\\item 'foreign\\_body\\_in\\_respiratory\\_tract'\n",
-       "\\item 'chronic\\_kidney\\_disease\\_stage\\_4'\n",
-       "\\item 'myocardial\\_ischemia'\n",
-       "\\item 'non-toxic\\_multinodular\\_goiter'\n",
-       "\\item 'pain\\_in\\_finger'\n",
-       "\\item 'cervical\\_spondylosis\\_without\\_myelopathy'\n",
-       "\\item 'body\\_mass\\_index\\_25-29\\_-\\_overweight'\n",
-       "\\item 'clouded\\_consciousness'\n",
-       "\\item 'mixed\\_conductive\\_and\\_sensorineural\\_hearing\\_loss'\n",
-       "\\item 'tooth\\_eruption\\_disorder'\n",
-       "\\item 'hyperuricemia'\n",
-       "\\item 'closed\\_fracture\\_of\\_neck\\_of\\_femur'\n",
-       "\\item 'bipolar\\_ii\\_disorder'\n",
-       "\\item 'disturbance\\_in\\_sleep\\_behavior'\n",
-       "\\item 'relationship\\_problems'\n",
-       "\\item 'sprain\\_of\\_wrist'\n",
-       "\\item 'personality\\_disorder'\n",
-       "\\item 'external\\_hemorrhoids'\n",
-       "\\item 'abnormal\\_vision'\n",
-       "\\item 'hyperprolactinemia'\n",
-       "\\item 'hemochromatosis'\n",
-       "\\item 'lumbosacral\\_radiculopathy'\n",
-       "\\item 'heart\\_valve\\_disorder'\n",
-       "\\item 'cardiac\\_arrest'\n",
-       "\\item 'infection\\_caused\\_by\\_molluscum\\_contagiosum'\n",
-       "\\item 'chronic\\_kidney\\_disease\\_stage\\_2'\n",
-       "\\item 'secondary\\_malignant\\_neoplasm\\_of\\_peritoneum'\n",
-       "\\item 'thoracic\\_back\\_pain'\n",
-       "\\item 'blood\\_in\\_urine'\n",
-       "\\item 'adhesive\\_capsulitis\\_of\\_shoulder'\n",
-       "\\item 'diplopia'\n",
-       "\\item 'sj\\textbackslash{}u00f6gren\\textbackslash{}'s\\_syndrome'\n",
-       "\\item 'ureteric\\_stone'\n",
-       "\\item 'bronchospasm'\n",
-       "\\item 'chronic\\_fatigue\\_syndrome'\n",
-       "\\item 'cannabis\\_dependence'\n",
-       "\\item 'neck\\_sprain'\n",
-       "\\item 'multinodular\\_goiter'\n",
-       "\\item 'ptosis\\_of\\_eyelid'\n",
-       "\\item 'failure\\_to\\_thrive'\n",
-       "\\item 'torticollis'\n",
-       "\\item 'acute\\_bronchiolitis'\n",
-       "\\item 'viral\\_exanthem'\n",
-       "\\item 'talipes\\_planus'\n",
-       "\\item 'idiopathic\\_peripheral\\_neuropathy'\n",
-       "\\item 'foreign\\_body\\_in\\_pharynx'\n",
-       "\\item 'jaw\\_pain'\n",
-       "\\item 'renal\\_impairment'\n",
-       "\\item 'ataxia'\n",
-       "\\item 'age-related\\_macular\\_degeneration'\n",
-       "\\item 'uterine\\_prolapse'\n",
-       "\\item 'renal\\_mass'\n",
-       "\\item 'pneumonitis'\n",
-       "\\item 'coordination\\_problem'\n",
-       "\\item 'blindness\\_-\\_both\\_eyes'\n",
-       "\\item 'primary\\_hyperparathyroidism'\n",
-       "\\item 'musculoskeletal\\_pain'\n",
-       "\\item 'mycosis'\n",
-       "\\item 'primigravida'\n",
-       "\\item 'urethral\\_stricture'\n",
-       "\\item 'leukocytosis'\n",
-       "\\item 'ventricular\\_premature\\_complex'\n",
-       "\\item 'ulcer\\_of\\_foot\\_due\\_to\\_diabetes\\_mellitus'\n",
-       "\\item 'chronic\\_headache\\_disorder'\n",
-       "\\item 'hemangioma'\n",
-       "\\item 'lymphedema'\n",
-       "\\item 'postmenopausal\\_state'\n",
-       "\\item 'chronic\\_ulcer\\_of\\_skin'\n",
-       "\\item 'left\\_heart\\_failure'\n",
-       "\\item 'excessive\\_and\\_frequent\\_menstruation'\n",
-       "\\item 'thrombocytosis'\n",
-       "\\item 'disorder\\_of\\_liver'\n",
-       "\\item 'disorder\\_of\\_carotid\\_artery'\n",
-       "\\item 'altered\\_bowel\\_function'\n",
-       "\\item 'abscess\\_of\\_foot'\n",
-       "\\item 'malignant\\_tumor\\_of\\_head\\_and/or\\_neck'\n",
-       "\\item 'streptococcus\\_group\\_b\\_infection\\_of\\_the\\_infant'\n",
-       "\\item 'concussion\\_injury\\_of\\_brain'\n",
-       "\\item 'feeding\\_problems\\_in\\_newborn'\n",
-       "\\item 'bipolar\\_i\\_disorder'\n",
-       "\\item 'viral\\_pharyngitis'\n",
-       "\\item 'lower\\_respiratory\\_tract\\_infection'\n",
-       "\\item 'hydronephrosis'\n",
-       "\\item 'borderline\\_personality\\_disorder'\n",
-       "\\item 'esophageal\\_varices'\n",
-       "\\item 'hypersomnia'\n",
-       "\\item 'sensorineural\\_hearing\\_loss\\_bilateral'\n",
-       "\\item 'varicocele'\n",
-       "\\item 'subarachnoid\\_intracranial\\_hemorrhage'\n",
-       "\\item 'incisional\\_hernia'\n",
-       "\\item 'varicella'\n",
-       "\\item 'pain\\_in\\_testicle'\n",
-       "\\item 'transplant\\_follow-up'\n",
-       "\\item 'tinea\\_cruris'\n",
-       "\\item 'laryngitis'\n",
-       "\\item 'hypertrophy\\_of\\_nail'\n",
-       "\\item 'amblyopia'\n",
-       "\\item 'polyp\\_of\\_cervix'\n",
-       "\\item 'cyst\\_of\\_kidney'\n",
-       "\\item 'hepatic\\_encephalopathy'\n",
-       "\\item 'blood\\_glucose\\_abnormal'\n",
-       "\\item 'postherpetic\\_neuralgia'\n",
-       "\\item 'frank\\_hematuria'\n",
-       "\\item 'cramp'\n",
-       "\\item 'interstitial\\_lung\\_disease'\n",
-       "\\item 'complete\\_atrioventricular\\_block'\n",
-       "\\item 'malignant\\_tumor\\_of\\_kidney'\n",
-       "\\item 'otitis'\n",
-       "\\item 'septic\\_shock'\n",
-       "\\item 'disorder\\_of\\_thyroid\\_gland'\n",
-       "\\item 'hypertrophic\\_cardiomyopathy'\n",
-       "\\item 'respiratory\\_distress\\_syndrome\\_in\\_the\\_newborn'\n",
-       "\\item 'infectious\\_gastroenteritis'\n",
-       "\\item 'subdural\\_intracranial\\_hemorrhage'\n",
-       "\\item 'hepatitis\\_b\\_carrier'\n",
-       "\\item 'manic\\_bipolar\\_i\\_disorder'\n",
-       "\\item 'secondary\\_pulmonary\\_hypertension'\n",
-       "\\item 'gonorrhea'\n",
-       "\\item 'derangement\\_of\\_knee'\n",
-       "\\item 'appendicitis'\n",
-       "\\item 'polyneuropathy\\_due\\_to\\_diabetes\\_mellitus'\n",
-       "\\item 'neonatal\\_hypoglycemia'\n",
-       "\\item 'prolonged\\_rupture\\_of\\_membranes'\n",
-       "\\item 'vasomotor\\_rhinitis'\n",
-       "\\item 'renal\\_disorder\\_due\\_to\\_type\\_1\\_diabetes\\_mellitus'\n",
-       "\\item 'tuberculosis'\n",
-       "\\item 'feeding\\_problem'\n",
-       "\\item 'chronic\\_tonsillitis'\n",
-       "\\item 'acute\\_duodenal\\_ulcer\\_with\\_hemorrhage'\n",
-       "\\item 'hammer\\_toe'\n",
-       "\\item 'malignant\\_tumor\\_of\\_cervix'\n",
-       "\\item 'prolapsed\\_lumbar\\_intervertebral\\_disc'\n",
-       "\\item 'hematemesis'\n",
-       "\\item 'perianal\\_abscess'\n",
-       "\\item 'nonvenomous\\_insect\\_bite'\n",
-       "\\item 'spondylolisthesis'\n",
-       "\\item 'malignant\\_tumor\\_of\\_esophagus'\n",
-       "\\item 'aphthous\\_ulcer\\_of\\_mouth'\n",
-       "\\item 'ventricular\\_septal\\_defect'\n",
-       "\\item 'oropharyngeal\\_dysphagia'\n",
-       "\\item 'injury\\_of\\_knee'\n",
-       "\\item 'traumatic\\_brain\\_injury'\n",
-       "\\item 'osteoarthritis\\_of\\_glenohumeral\\_joint'\n",
-       "\\end{enumerate*}\n"
-      ],
-      "text/markdown": [
-       "1. 'chronic_alcoholism_in_remission'\n",
-       "2. 'chronic_pancreatitis'\n",
-       "3. 'gastroparesis'\n",
-       "4. 'ectopic_pregnancy'\n",
-       "5. 'muscle_weakness'\n",
-       "6. 'recurrent_major_depression'\n",
-       "7. 'pilonidal_cyst'\n",
-       "8. 'pain_in_toe'\n",
-       "9. 'pulmonary_tuberculosis'\n",
-       "10. 'celiac_disease'\n",
-       "11. 'cramp_in_lower_leg'\n",
-       "12. 'secondary_malignant_neoplasm_of_pleura'\n",
-       "13. 'fracture_of_hand'\n",
-       "14. 'cyst_of_breast'\n",
-       "15. 'nephrotic_syndrome'\n",
-       "16. 'polyp_of_nasal_sinus'\n",
-       "17. 'chondromalacia_of_patella'\n",
-       "18. 'spinal_stenosis_in_cervical_region'\n",
-       "19. 'disorder_of_artery'\n",
-       "20. 'vitiligo'\n",
-       "21. 'female_cystocele'\n",
-       "22. 'dysphasia'\n",
-       "23. 'retinal_disorder'\n",
-       "24. 'epiretinal_membrane'\n",
-       "25. 'recurrent_major_depression_in_partial_remission'\n",
-       "26. 'infection_caused_by_trichomonas'\n",
-       "27. 'osteomyelitis'\n",
-       "28. 'polyp_of_nasal_cavity_and/or_nasal_sinus'\n",
-       "29. 'mass_of_neck'\n",
-       "30. 'idiopathic_thrombocytopenic_purpura'\n",
-       "31. 'complete_miscarriage'\n",
-       "32. 'gastric_ulcer'\n",
-       "33. 'papilloma_of_skin'\n",
-       "34. 'fetal_or_neonatal_effect_of_breech_delivery_and_extraction'\n",
-       "35. 'secondary_malignant_neoplastic_disease'\n",
-       "36. 'hypoxemia'\n",
-       "37. 'paraplegia'\n",
-       "38. 'perforation_of_tympanic_membrane'\n",
-       "39. 'ventricular_tachycardia'\n",
-       "40. 'mixed_incontinence'\n",
-       "41. 'disorder_of_eye_due_to_type_2_diabetes_mellitus'\n",
-       "42. 'trigeminal_neuralgia'\n",
-       "43. 'retinal_detachment'\n",
-       "44. 'leukopenia'\n",
-       "45. 'vitreous_hemorrhage'\n",
-       "46. 'ischemic_ulcer'\n",
-       "47. 'intramural_leiomyoma_of_uterus'\n",
-       "48. 'viral_hepatitis_type_a'\n",
-       "49. 'm\\u00e9ni\\u00e8re\\'s_disease'\n",
-       "50. 'fracture_of_phalanx_of_hand'\n",
-       "51. 'muscle_atrophy'\n",
-       "52. 'incontinence_of_feces'\n",
-       "53. 'mitral_valve_disorder'\n",
-       "54. 'atherosclerosis_of_arteries_of_the_extremities'\n",
-       "55. 'spondylosis'\n",
-       "56. 'pterygium'\n",
-       "57. 'ulnar_neuropathy'\n",
-       "58. 'lung_mass'\n",
-       "59. 'foreign_body_in_respiratory_tract'\n",
-       "60. 'chronic_kidney_disease_stage_4'\n",
-       "61. 'myocardial_ischemia'\n",
-       "62. 'non-toxic_multinodular_goiter'\n",
-       "63. 'pain_in_finger'\n",
-       "64. 'cervical_spondylosis_without_myelopathy'\n",
-       "65. 'body_mass_index_25-29_-_overweight'\n",
-       "66. 'clouded_consciousness'\n",
-       "67. 'mixed_conductive_and_sensorineural_hearing_loss'\n",
-       "68. 'tooth_eruption_disorder'\n",
-       "69. 'hyperuricemia'\n",
-       "70. 'closed_fracture_of_neck_of_femur'\n",
-       "71. 'bipolar_ii_disorder'\n",
-       "72. 'disturbance_in_sleep_behavior'\n",
-       "73. 'relationship_problems'\n",
-       "74. 'sprain_of_wrist'\n",
-       "75. 'personality_disorder'\n",
-       "76. 'external_hemorrhoids'\n",
-       "77. 'abnormal_vision'\n",
-       "78. 'hyperprolactinemia'\n",
-       "79. 'hemochromatosis'\n",
-       "80. 'lumbosacral_radiculopathy'\n",
-       "81. 'heart_valve_disorder'\n",
-       "82. 'cardiac_arrest'\n",
-       "83. 'infection_caused_by_molluscum_contagiosum'\n",
-       "84. 'chronic_kidney_disease_stage_2'\n",
-       "85. 'secondary_malignant_neoplasm_of_peritoneum'\n",
-       "86. 'thoracic_back_pain'\n",
-       "87. 'blood_in_urine'\n",
-       "88. 'adhesive_capsulitis_of_shoulder'\n",
-       "89. 'diplopia'\n",
-       "90. 'sj\\u00f6gren\\'s_syndrome'\n",
-       "91. 'ureteric_stone'\n",
-       "92. 'bronchospasm'\n",
-       "93. 'chronic_fatigue_syndrome'\n",
-       "94. 'cannabis_dependence'\n",
-       "95. 'neck_sprain'\n",
-       "96. 'multinodular_goiter'\n",
-       "97. 'ptosis_of_eyelid'\n",
-       "98. 'failure_to_thrive'\n",
-       "99. 'torticollis'\n",
-       "100. 'acute_bronchiolitis'\n",
-       "101. 'viral_exanthem'\n",
-       "102. 'talipes_planus'\n",
-       "103. 'idiopathic_peripheral_neuropathy'\n",
-       "104. 'foreign_body_in_pharynx'\n",
-       "105. 'jaw_pain'\n",
-       "106. 'renal_impairment'\n",
-       "107. 'ataxia'\n",
-       "108. 'age-related_macular_degeneration'\n",
-       "109. 'uterine_prolapse'\n",
-       "110. 'renal_mass'\n",
-       "111. 'pneumonitis'\n",
-       "112. 'coordination_problem'\n",
-       "113. 'blindness_-_both_eyes'\n",
-       "114. 'primary_hyperparathyroidism'\n",
-       "115. 'musculoskeletal_pain'\n",
-       "116. 'mycosis'\n",
-       "117. 'primigravida'\n",
-       "118. 'urethral_stricture'\n",
-       "119. 'leukocytosis'\n",
-       "120. 'ventricular_premature_complex'\n",
-       "121. 'ulcer_of_foot_due_to_diabetes_mellitus'\n",
-       "122. 'chronic_headache_disorder'\n",
-       "123. 'hemangioma'\n",
-       "124. 'lymphedema'\n",
-       "125. 'postmenopausal_state'\n",
-       "126. 'chronic_ulcer_of_skin'\n",
-       "127. 'left_heart_failure'\n",
-       "128. 'excessive_and_frequent_menstruation'\n",
-       "129. 'thrombocytosis'\n",
-       "130. 'disorder_of_liver'\n",
-       "131. 'disorder_of_carotid_artery'\n",
-       "132. 'altered_bowel_function'\n",
-       "133. 'abscess_of_foot'\n",
-       "134. 'malignant_tumor_of_head_and/or_neck'\n",
-       "135. 'streptococcus_group_b_infection_of_the_infant'\n",
-       "136. 'concussion_injury_of_brain'\n",
-       "137. 'feeding_problems_in_newborn'\n",
-       "138. 'bipolar_i_disorder'\n",
-       "139. 'viral_pharyngitis'\n",
-       "140. 'lower_respiratory_tract_infection'\n",
-       "141. 'hydronephrosis'\n",
-       "142. 'borderline_personality_disorder'\n",
-       "143. 'esophageal_varices'\n",
-       "144. 'hypersomnia'\n",
-       "145. 'sensorineural_hearing_loss_bilateral'\n",
-       "146. 'varicocele'\n",
-       "147. 'subarachnoid_intracranial_hemorrhage'\n",
-       "148. 'incisional_hernia'\n",
-       "149. 'varicella'\n",
-       "150. 'pain_in_testicle'\n",
-       "151. 'transplant_follow-up'\n",
-       "152. 'tinea_cruris'\n",
-       "153. 'laryngitis'\n",
-       "154. 'hypertrophy_of_nail'\n",
-       "155. 'amblyopia'\n",
-       "156. 'polyp_of_cervix'\n",
-       "157. 'cyst_of_kidney'\n",
-       "158. 'hepatic_encephalopathy'\n",
-       "159. 'blood_glucose_abnormal'\n",
-       "160. 'postherpetic_neuralgia'\n",
-       "161. 'frank_hematuria'\n",
-       "162. 'cramp'\n",
-       "163. 'interstitial_lung_disease'\n",
-       "164. 'complete_atrioventricular_block'\n",
-       "165. 'malignant_tumor_of_kidney'\n",
-       "166. 'otitis'\n",
-       "167. 'septic_shock'\n",
-       "168. 'disorder_of_thyroid_gland'\n",
-       "169. 'hypertrophic_cardiomyopathy'\n",
-       "170. 'respiratory_distress_syndrome_in_the_newborn'\n",
-       "171. 'infectious_gastroenteritis'\n",
-       "172. 'subdural_intracranial_hemorrhage'\n",
-       "173. 'hepatitis_b_carrier'\n",
-       "174. 'manic_bipolar_i_disorder'\n",
-       "175. 'secondary_pulmonary_hypertension'\n",
-       "176. 'gonorrhea'\n",
-       "177. 'derangement_of_knee'\n",
-       "178. 'appendicitis'\n",
-       "179. 'polyneuropathy_due_to_diabetes_mellitus'\n",
-       "180. 'neonatal_hypoglycemia'\n",
-       "181. 'prolonged_rupture_of_membranes'\n",
-       "182. 'vasomotor_rhinitis'\n",
-       "183. 'renal_disorder_due_to_type_1_diabetes_mellitus'\n",
-       "184. 'tuberculosis'\n",
-       "185. 'feeding_problem'\n",
-       "186. 'chronic_tonsillitis'\n",
-       "187. 'acute_duodenal_ulcer_with_hemorrhage'\n",
-       "188. 'hammer_toe'\n",
-       "189. 'malignant_tumor_of_cervix'\n",
-       "190. 'prolapsed_lumbar_intervertebral_disc'\n",
-       "191. 'hematemesis'\n",
-       "192. 'perianal_abscess'\n",
-       "193. 'nonvenomous_insect_bite'\n",
-       "194. 'spondylolisthesis'\n",
-       "195. 'malignant_tumor_of_esophagus'\n",
-       "196. 'aphthous_ulcer_of_mouth'\n",
-       "197. 'ventricular_septal_defect'\n",
-       "198. 'oropharyngeal_dysphagia'\n",
-       "199. 'injury_of_knee'\n",
-       "200. 'traumatic_brain_injury'\n",
-       "201. 'osteoarthritis_of_glenohumeral_joint'\n",
-       "\n",
-       "\n"
-      ],
-      "text/plain": [
-       "  [1] \"chronic_alcoholism_in_remission\"                           \n",
-       "  [2] \"chronic_pancreatitis\"                                      \n",
-       "  [3] \"gastroparesis\"                                             \n",
-       "  [4] \"ectopic_pregnancy\"                                         \n",
-       "  [5] \"muscle_weakness\"                                           \n",
-       "  [6] \"recurrent_major_depression\"                                \n",
-       "  [7] \"pilonidal_cyst\"                                            \n",
-       "  [8] \"pain_in_toe\"                                               \n",
-       "  [9] \"pulmonary_tuberculosis\"                                    \n",
-       " [10] \"celiac_disease\"                                            \n",
-       " [11] \"cramp_in_lower_leg\"                                        \n",
-       " [12] \"secondary_malignant_neoplasm_of_pleura\"                    \n",
-       " [13] \"fracture_of_hand\"                                          \n",
-       " [14] \"cyst_of_breast\"                                            \n",
-       " [15] \"nephrotic_syndrome\"                                        \n",
-       " [16] \"polyp_of_nasal_sinus\"                                      \n",
-       " [17] \"chondromalacia_of_patella\"                                 \n",
-       " [18] \"spinal_stenosis_in_cervical_region\"                        \n",
-       " [19] \"disorder_of_artery\"                                        \n",
-       " [20] \"vitiligo\"                                                  \n",
-       " [21] \"female_cystocele\"                                          \n",
-       " [22] \"dysphasia\"                                                 \n",
-       " [23] \"retinal_disorder\"                                          \n",
-       " [24] \"epiretinal_membrane\"                                       \n",
-       " [25] \"recurrent_major_depression_in_partial_remission\"           \n",
-       " [26] \"infection_caused_by_trichomonas\"                           \n",
-       " [27] \"osteomyelitis\"                                             \n",
-       " [28] \"polyp_of_nasal_cavity_and/or_nasal_sinus\"                  \n",
-       " [29] \"mass_of_neck\"                                              \n",
-       " [30] \"idiopathic_thrombocytopenic_purpura\"                       \n",
-       " [31] \"complete_miscarriage\"                                      \n",
-       " [32] \"gastric_ulcer\"                                             \n",
-       " [33] \"papilloma_of_skin\"                                         \n",
-       " [34] \"fetal_or_neonatal_effect_of_breech_delivery_and_extraction\"\n",
-       " [35] \"secondary_malignant_neoplastic_disease\"                    \n",
-       " [36] \"hypoxemia\"                                                 \n",
-       " [37] \"paraplegia\"                                                \n",
-       " [38] \"perforation_of_tympanic_membrane\"                          \n",
-       " [39] \"ventricular_tachycardia\"                                   \n",
-       " [40] \"mixed_incontinence\"                                        \n",
-       " [41] \"disorder_of_eye_due_to_type_2_diabetes_mellitus\"           \n",
-       " [42] \"trigeminal_neuralgia\"                                      \n",
-       " [43] \"retinal_detachment\"                                        \n",
-       " [44] \"leukopenia\"                                                \n",
-       " [45] \"vitreous_hemorrhage\"                                       \n",
-       " [46] \"ischemic_ulcer\"                                            \n",
-       " [47] \"intramural_leiomyoma_of_uterus\"                            \n",
-       " [48] \"viral_hepatitis_type_a\"                                    \n",
-       " [49] \"m<U+00E9>ni<U+00E8>re's_disease\"                           \n",
-       " [50] \"fracture_of_phalanx_of_hand\"                               \n",
-       " [51] \"muscle_atrophy\"                                            \n",
-       " [52] \"incontinence_of_feces\"                                     \n",
-       " [53] \"mitral_valve_disorder\"                                     \n",
-       " [54] \"atherosclerosis_of_arteries_of_the_extremities\"            \n",
-       " [55] \"spondylosis\"                                               \n",
-       " [56] \"pterygium\"                                                 \n",
-       " [57] \"ulnar_neuropathy\"                                          \n",
-       " [58] \"lung_mass\"                                                 \n",
-       " [59] \"foreign_body_in_respiratory_tract\"                         \n",
-       " [60] \"chronic_kidney_disease_stage_4\"                            \n",
-       " [61] \"myocardial_ischemia\"                                       \n",
-       " [62] \"non-toxic_multinodular_goiter\"                             \n",
-       " [63] \"pain_in_finger\"                                            \n",
-       " [64] \"cervical_spondylosis_without_myelopathy\"                   \n",
-       " [65] \"body_mass_index_25-29_-_overweight\"                        \n",
-       " [66] \"clouded_consciousness\"                                     \n",
-       " [67] \"mixed_conductive_and_sensorineural_hearing_loss\"           \n",
-       " [68] \"tooth_eruption_disorder\"                                   \n",
-       " [69] \"hyperuricemia\"                                             \n",
-       " [70] \"closed_fracture_of_neck_of_femur\"                          \n",
-       " [71] \"bipolar_ii_disorder\"                                       \n",
-       " [72] \"disturbance_in_sleep_behavior\"                             \n",
-       " [73] \"relationship_problems\"                                     \n",
-       " [74] \"sprain_of_wrist\"                                           \n",
-       " [75] \"personality_disorder\"                                      \n",
-       " [76] \"external_hemorrhoids\"                                      \n",
-       " [77] \"abnormal_vision\"                                           \n",
-       " [78] \"hyperprolactinemia\"                                        \n",
-       " [79] \"hemochromatosis\"                                           \n",
-       " [80] \"lumbosacral_radiculopathy\"                                 \n",
-       " [81] \"heart_valve_disorder\"                                      \n",
-       " [82] \"cardiac_arrest\"                                            \n",
-       " [83] \"infection_caused_by_molluscum_contagiosum\"                 \n",
-       " [84] \"chronic_kidney_disease_stage_2\"                            \n",
-       " [85] \"secondary_malignant_neoplasm_of_peritoneum\"                \n",
-       " [86] \"thoracic_back_pain\"                                        \n",
-       " [87] \"blood_in_urine\"                                            \n",
-       " [88] \"adhesive_capsulitis_of_shoulder\"                           \n",
-       " [89] \"diplopia\"                                                  \n",
-       " [90] \"sj<U+00F6>gren's_syndrome\"                                 \n",
-       " [91] \"ureteric_stone\"                                            \n",
-       " [92] \"bronchospasm\"                                              \n",
-       " [93] \"chronic_fatigue_syndrome\"                                  \n",
-       " [94] \"cannabis_dependence\"                                       \n",
-       " [95] \"neck_sprain\"                                               \n",
-       " [96] \"multinodular_goiter\"                                       \n",
-       " [97] \"ptosis_of_eyelid\"                                          \n",
-       " [98] \"failure_to_thrive\"                                         \n",
-       " [99] \"torticollis\"                                               \n",
-       "[100] \"acute_bronchiolitis\"                                       \n",
-       "[101] \"viral_exanthem\"                                            \n",
-       "[102] \"talipes_planus\"                                            \n",
-       "[103] \"idiopathic_peripheral_neuropathy\"                          \n",
-       "[104] \"foreign_body_in_pharynx\"                                   \n",
-       "[105] \"jaw_pain\"                                                  \n",
-       "[106] \"renal_impairment\"                                          \n",
-       "[107] \"ataxia\"                                                    \n",
-       "[108] \"age-related_macular_degeneration\"                          \n",
-       "[109] \"uterine_prolapse\"                                          \n",
-       "[110] \"renal_mass\"                                                \n",
-       "[111] \"pneumonitis\"                                               \n",
-       "[112] \"coordination_problem\"                                      \n",
-       "[113] \"blindness_-_both_eyes\"                                     \n",
-       "[114] \"primary_hyperparathyroidism\"                               \n",
-       "[115] \"musculoskeletal_pain\"                                      \n",
-       "[116] \"mycosis\"                                                   \n",
-       "[117] \"primigravida\"                                              \n",
-       "[118] \"urethral_stricture\"                                        \n",
-       "[119] \"leukocytosis\"                                              \n",
-       "[120] \"ventricular_premature_complex\"                             \n",
-       "[121] \"ulcer_of_foot_due_to_diabetes_mellitus\"                    \n",
-       "[122] \"chronic_headache_disorder\"                                 \n",
-       "[123] \"hemangioma\"                                                \n",
-       "[124] \"lymphedema\"                                                \n",
-       "[125] \"postmenopausal_state\"                                      \n",
-       "[126] \"chronic_ulcer_of_skin\"                                     \n",
-       "[127] \"left_heart_failure\"                                        \n",
-       "[128] \"excessive_and_frequent_menstruation\"                       \n",
-       "[129] \"thrombocytosis\"                                            \n",
-       "[130] \"disorder_of_liver\"                                         \n",
-       "[131] \"disorder_of_carotid_artery\"                                \n",
-       "[132] \"altered_bowel_function\"                                    \n",
-       "[133] \"abscess_of_foot\"                                           \n",
-       "[134] \"malignant_tumor_of_head_and/or_neck\"                       \n",
-       "[135] \"streptococcus_group_b_infection_of_the_infant\"             \n",
-       "[136] \"concussion_injury_of_brain\"                                \n",
-       "[137] \"feeding_problems_in_newborn\"                               \n",
-       "[138] \"bipolar_i_disorder\"                                        \n",
-       "[139] \"viral_pharyngitis\"                                         \n",
-       "[140] \"lower_respiratory_tract_infection\"                         \n",
-       "[141] \"hydronephrosis\"                                            \n",
-       "[142] \"borderline_personality_disorder\"                           \n",
-       "[143] \"esophageal_varices\"                                        \n",
-       "[144] \"hypersomnia\"                                               \n",
-       "[145] \"sensorineural_hearing_loss_bilateral\"                      \n",
-       "[146] \"varicocele\"                                                \n",
-       "[147] \"subarachnoid_intracranial_hemorrhage\"                      \n",
-       "[148] \"incisional_hernia\"                                         \n",
-       "[149] \"varicella\"                                                 \n",
-       "[150] \"pain_in_testicle\"                                          \n",
-       "[151] \"transplant_follow-up\"                                      \n",
-       "[152] \"tinea_cruris\"                                              \n",
-       "[153] \"laryngitis\"                                                \n",
-       "[154] \"hypertrophy_of_nail\"                                       \n",
-       "[155] \"amblyopia\"                                                 \n",
-       "[156] \"polyp_of_cervix\"                                           \n",
-       "[157] \"cyst_of_kidney\"                                            \n",
-       "[158] \"hepatic_encephalopathy\"                                    \n",
-       "[159] \"blood_glucose_abnormal\"                                    \n",
-       "[160] \"postherpetic_neuralgia\"                                    \n",
-       "[161] \"frank_hematuria\"                                           \n",
-       "[162] \"cramp\"                                                     \n",
-       "[163] \"interstitial_lung_disease\"                                 \n",
-       "[164] \"complete_atrioventricular_block\"                           \n",
-       "[165] \"malignant_tumor_of_kidney\"                                 \n",
-       "[166] \"otitis\"                                                    \n",
-       "[167] \"septic_shock\"                                              \n",
-       "[168] \"disorder_of_thyroid_gland\"                                 \n",
-       "[169] \"hypertrophic_cardiomyopathy\"                               \n",
-       "[170] \"respiratory_distress_syndrome_in_the_newborn\"              \n",
-       "[171] \"infectious_gastroenteritis\"                                \n",
-       "[172] \"subdural_intracranial_hemorrhage\"                          \n",
-       "[173] \"hepatitis_b_carrier\"                                       \n",
-       "[174] \"manic_bipolar_i_disorder\"                                  \n",
-       "[175] \"secondary_pulmonary_hypertension\"                          \n",
-       "[176] \"gonorrhea\"                                                 \n",
-       "[177] \"derangement_of_knee\"                                       \n",
-       "[178] \"appendicitis\"                                              \n",
-       "[179] \"polyneuropathy_due_to_diabetes_mellitus\"                   \n",
-       "[180] \"neonatal_hypoglycemia\"                                     \n",
-       "[181] \"prolonged_rupture_of_membranes\"                            \n",
-       "[182] \"vasomotor_rhinitis\"                                        \n",
-       "[183] \"renal_disorder_due_to_type_1_diabetes_mellitus\"            \n",
-       "[184] \"tuberculosis\"                                              \n",
-       "[185] \"feeding_problem\"                                           \n",
-       "[186] \"chronic_tonsillitis\"                                       \n",
-       "[187] \"acute_duodenal_ulcer_with_hemorrhage\"                      \n",
-       "[188] \"hammer_toe\"                                                \n",
-       "[189] \"malignant_tumor_of_cervix\"                                 \n",
-       "[190] \"prolapsed_lumbar_intervertebral_disc\"                      \n",
-       "[191] \"hematemesis\"                                               \n",
-       "[192] \"perianal_abscess\"                                          \n",
-       "[193] \"nonvenomous_insect_bite\"                                   \n",
-       "[194] \"spondylolisthesis\"                                         \n",
-       "[195] \"malignant_tumor_of_esophagus\"                              \n",
-       "[196] \"aphthous_ulcer_of_mouth\"                                   \n",
-       "[197] \"ventricular_septal_defect\"                                 \n",
-       "[198] \"oropharyngeal_dysphagia\"                                   \n",
-       "[199] \"injury_of_knee\"                                            \n",
-       "[200] \"traumatic_brain_injury\"                                    \n",
-       "[201] \"osteoarthritis_of_glenohumeral_joint\"                      "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "covariates[700:900]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T14:17:01.632846Z",
-     "start_time": "2020-11-04T14:17:00.584Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "data = data %>% mutate_at(c(\"sex\", \"overall_health_rating\", \"smoking_status\", \"ethnic_background\"), as.factor)\n",
-    "data = data %>% mutate(sex=fct_relevel(sex, c(\"Male\", \"Female\")),\n",
-    "                       overall_health_rating=fct_relevel(overall_health_rating, c(\"Excellent\", \"Good\", \"Fair\", \"Poor\")),\n",
-    "                       smoking_status=fct_relevel(smoking_status, c(\"Current\", \"Previous\", \"Never\")))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Missing values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#na_count <-data.frame(sapply(data %>% as.data.frame(), function(y) sum(length(which(is.na(y))))))\n",
-    "#na_count %>% filter(sapply(data %>% as.data.frame(), function(y) sum(length(which(is.na(y)))))>0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Covariates BIG"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Table 1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mycontrols  <- tableby.control(test=FALSE, total=TRUE,\n",
-    "                               numeric.test=\"kwt\", cat.test=\"chisq\",\n",
-    "                               numeric.stats=c(\"meansd\"), numeric.simplify=TRUE,\n",
-    "                               cat.stats=c(\"countpct\"), digits = 2L,cat.simplify = TRUE,\n",
-    "                               stats.labels=list(N='N', meansd='Median', Nmiss=\"Missing\"))"
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {},
-   "source": [
-    "library(arsenal)\n",
-    "table_one = tableby(sex~., control=mycontrols, data=data %>% select(all_of(c(covariates, targets))))\n",
-    "write2html(table_one, glue(\"{dataset_path}/table1_big.html\"));\n",
-    "#summary(table_one, text=TRUE)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# BASELINE"
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {},
-   "source": [
-    "base_size = 25\n",
-    "title_size = 30\n",
-    "facet_size = 22\n",
-    "geom_text_size=7"
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {},
-   "source": [
-    "library(ggplot2); \n",
-    "theme_set(theme_classic(base_size = base_size) + \n",
-    "          theme(strip.background = element_blank(), plot.title=element_text(size=title_size, hjust=0.5), \n",
-    "                axis.title.y = element_blank(), axis.text.y = element_blank(), axis.ticks.y = element_blank(), axis.line.y = element_blank(), \n",
-    "                strip.text.x = element_text(size = facet_size)))"
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {},
-   "source": [
-    "temp = data %>% select(all_of(c(pgs))) %>% select_if(is.numeric) %>% pivot_longer(everything(), names_to=\"parameter\", values_to=\"value\")\n",
-    "plot_pgs = ggplot(temp, aes(x=value)) + ggtitle(\"Polygenic Scores\") + \n",
-    "    geom_density(adjust=1.5, fill=\"gray70\") +\n",
-    "    scale_y_continuous(expand=c(0,0))+ xlab(\"\") + \n",
-    "    facet_wrap(~parameter, ncol=6, scales = \"free\")"
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {},
-   "source": [
-    "temp = data %>% select(all_of(c(covariates))) %>% select_if(is.numeric) %>% pivot_longer(everything(), names_to=\"parameter\", values_to=\"value\")\n",
-    "plot_cont = ggplot(temp, aes(x=value)) + ggtitle(\"Measurements\") + \n",
-    "    geom_density(adjust=1.5, fill=\"gray70\") +\n",
-    "    scale_y_continuous(expand=c(0,0))+ xlab(\"\") + \n",
-    "    facet_wrap(~parameter, ncol=4, scales = \"free\")"
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {},
-   "source": [
-    "temp = data %>% select(all_of(c(covariates))) %>% select_if(is.factor) %>% pivot_longer(everything(), names_to=\"parameter\", values_to=\"value\",values_ptypes=list(value=\"character\"))%>% \n",
-    "    group_by(parameter, value, .drop = TRUE) %>% summarise(count=n(), ratio=round((100*n()/502504), 1)) %>% ungroup()\n",
-    "plot_cat = ggplot(temp, aes(x=value, y=ratio)) + ggtitle(\"Basic Information\") + \n",
-    "    geom_bar(stat=\"identity\", position=position_dodge(width = 0.8), fill=\"gray70\", width=0.5) +\n",
-    "    geom_text(aes(label=paste0(ratio, \" %\")), vjust=-1, size = geom_text_size) + \n",
-    "    scale_y_continuous(expand=c(0,0), limits=c(0, max(temp$ratio)*1.2))+ xlab(\"\") + \n",
-    "    facet_grid(~parameter, scales = \"free\", space=\"free\")"
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {},
-   "source": [
-    "library(forcats)\n",
-    "library(ggrepel)"
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {},
-   "source": [
-    "options(repr.plot.width=40, repr.plot.height=8)\n",
-    "\n",
-    "plot_cat = ggplot(temp, aes(x=parameter, y=ratio, fill=factor(parameter))) + ggtitle(\"Basic Information\") + \n",
-    "    geom_bar(stat=\"identity\", position=\"fill\", fill=\"gray70\", aes(alpha=ratio)) + coord_flip()+# #, fill=\"gray70\", width=0.5) #+\n",
-    "    geom_text_repel(aes(label=ifelse(is.na(value), \"\", paste0(ratio*100, \" %\")), vjust=1), position = position_stack(vjust = .5), size = geom_text_size) +\n",
-    "    geom_text_repel(aes(label=value, vjust=-0.5), position = position_stack(vjust = .5), size = geom_text_size) +\n",
-    "    scale_y_continuous(limits=c(-0.05, 1.05))+ xlab(\"\") +\n",
-    "    theme_void(base_size = base_size) + theme(strip.background = element_blank(), plot.title=element_text(size=title_size, hjust=0.5), legend.position = \"None\")\n",
-    "\n",
-    "plot_cat "
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {},
-   "source": [
-    "options(repr.plot.width=20, repr.plot.height=8)\n",
-    "\n",
-    "plot_cat = ggplot(temp, aes(x=parameter, y=ratio, fill=fct_rev(value))) + ggtitle(\"Basic Information\") + \n",
-    "    geom_bar(stat=\"identity\", position=\"fill\", alpha=0.5) + coord_flip()+# #, fill=\"gray70\", width=0.5) #+\n",
-    "    geom_text(aes(label=paste0(ratio*100, \" %\"), vjust=1), position = position_stack(vjust = .5), size = geom_text_size) +\n",
-    "    geom_text(aes(label=value, vjust=-0.5), position = position_stack(vjust = .5), size = geom_text_size) +\n",
-    "    scale_y_continuous(limits=c(0, max(temp$ratio)*1.2))+ xlab(\"\") + facet_grid(~parameter, cols=1)+\n",
-    "    theme_classic(base_size = base_size) + theme(strip.background = element_blank(), plot.title=element_text(size=title_size, hjust=0.5), legend.position = \"None\")+\n",
-    "    scale_fill_d3() + theme(axis.line=element_blank(),\n",
-    "      axis.text.x=element_blank(),\n",
-    "      axis.text.y=element_blank(),\n",
-    "      axis.ticks=element_blank(),\n",
-    "      legend.position=\"none\",\n",
-    "      panel.background=element_blank(),\n",
-    "      panel.border=element_blank(),\n",
-    "      panel.grid.major=element_blank(),\n",
-    "      panel.grid.minor=element_blank(),\n",
-    "      plot.background=element_blank())\n",
-    "plot_cat "
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {},
-   "source": [
-    "temp = data %>% select(c(any_of(phenotypes))) %>% \n",
-    "    pivot_longer(everything(),names_to=\"category\", values_to=\"Value\") %>% \n",
-    "    group_by(category) %>% summarise(ratio = sum(Value==TRUE)/n(), .groups = 'drop') %>% mutate(category = fct_reorder(category, ratio)) %>% mutate(ratio=ratio*100)\n",
-    "plot_conditions = ggplot(temp, aes(x=category, y=ratio)) + ggtitle(\"Conditions\") + \n",
-    "    geom_bar(stat=\"identity\", position=position_dodge(width = 0.8), fill=\"gray70\", width=0.5) +\n",
-    "    coord_flip()+xlab(\"\") +\n",
-    "    ylab(\"Prevalence in [%]\") +\n",
-    "    scale_y_continuous(expand=c(0,0), limits=c(0, max(temp$ratio)*1.3))+\n",
-    "    geom_text(aes(x=category, y=ratio, label=glue(\"{round(ratio,1)}%\")), position = position_dodge(0.8), hjust=-0.1, size = geom_text_size)+ #nudge_y=+3000, \n",
-    "    scale_x_discrete()+\n",
-    "    theme_classic(base_size = base_size) + theme(strip.background = element_blank(), plot.title=element_text(size=title_size, hjust=0.5))#, strip.text.x = element_text(size = 16))"
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {},
-   "source": [
-    "temp = data %>% select(c(any_of(medications))) %>% \n",
-    "    pivot_longer(everything(), names_to=\"category\", values_to=\"Value\") %>% \n",
-    "    group_by(category) %>% summarise(ratio = sum(Value==TRUE)/n(), .groups = 'drop') %>% mutate(category = fct_reorder(category, ratio)) %>% mutate(ratio=ratio*100)\n",
-    "plot_medications = ggplot(temp, aes(x=category, y=ratio)) + ggtitle(\"Medications\") + \n",
-    "    geom_bar(stat=\"identity\", position=position_dodge(width = 0.8), fill=\"gray70\", width=0.5) +\n",
-    "    coord_flip()+xlab(\"\") +\n",
-    "    ylab(\"Prevalence in [%]\") +\n",
-    "    scale_y_continuous(expand=c(0,0), limits=c(0, max(temp$ratio)*1.3))+\n",
-    "    geom_text(aes(x=category, y=ratio, label=glue(\"{round(ratio,1)}%\")), position = position_dodge(0.8), hjust=-0.1, size = geom_text_size)+ #nudge_y=+3000, \n",
-    "    scale_x_discrete()"
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {
-    "jupyter": {
-     "outputs_hidden": true
-    }
-   },
-   "source": [
-    "options(repr.plot.width=30, repr.plot.height=100)\n",
-    "plot_1 = plot_grid(plot_pgs, plot_cat, plot_cont, ncol=1, rel_heights=c(1,0.7,10), align=\"v\")\n",
-    "plot_2 = plot_grid(plot_conditions, plot_medications, ncol=2, rel_widths=c(3,2.5), align=\"h\")\n",
-    "plot_grid(plot_1, plot_2, ncol=1, rel_heights=c(5,1.5), align=\"v\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Covariates Union"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T14:17:04.842258Z",
-     "start_time": "2020-11-04T14:17:03.935Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "mycontrols  <- tableby.control(test=FALSE, total=TRUE,\n",
-    "                               numeric.test=\"kwt\", cat.test=\"chisq\",\n",
-    "                               numeric.stats=c(\"meansd\"), numeric.simplify=TRUE,\n",
-    "                               cat.stats=c(\"countpct\"), digits = 2L,cat.simplify = TRUE,\n",
-    "                               stats.labels=list(N='N', meansd='Median', Nmiss=\"Missing\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T14:17:05.316730Z",
-     "start_time": "2020-11-04T14:17:04.385Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "f = list()\n",
-    "f$pgs = c('PGS000011', 'PGS000057', 'PGS000058', 'PGS000059')\n",
-    "f$basics = c('age_at_recruitment','sex', 'ethnic_background',\"townsend_deprivation_index_at_recruitment\")\n",
-    "f$questionnaire = c('overall_health_rating','smoking_status')\n",
-    "f$measurements = c('body_mass_index_bmi','weight',\"standing_height\",'systolic_blood_pressure','diastolic_blood_pressure')\n",
-    "f$labs = c(\"cholesterol\", \"hdl_cholesterol\", \"ldl_direct\",\"triglycerides\")\n",
-    "f$family_history = c('fh_heart_disease')\n",
-    "f$diagnoses = c(\"stroke\", \"diabetes1\", \"diabetes2\", \"chronic_kidney_disease\", \"atrial_fibrillation\", \"migraine\", \n",
-    "                      \"rheumatoid_arthritis\", \"systemic_lupus_erythematosus\", \"severe_mental_illness\", \"erectile_dysfunction\")\n",
-    "f$medications = c(\"antihypertensives\", \"ass\", \"atypical_antipsychotics\", \"glucocorticoids\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T14:17:10.758624Z",
-     "start_time": "2020-11-04T14:17:04.775Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "processing file: table1_union.html.Rmd\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "  |......................................................................| 100%\n",
-      "  ordinary text without R code\n",
-      "\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "output file: table1_union.html.knit.md\n",
-      "\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "/home/steinfej/miniconda3/envs/python/bin/pandoc +RTS -K512m -RTS table1_union.html.utf8.md --to html4 --from markdown+autolink_bare_uris+tex_math_single_backslash --output /data/analysis/ag-reils/ag-reils-shared/cardioRS/data/2_datasets_pre/cvd_massive_excl/table1_union.html --lua-filter /home/steinfej/miniconda3/envs/python/lib/R/library/rmarkdown/rmarkdown/lua/pagebreak.lua --lua-filter /home/steinfej/miniconda3/envs/python/lib/R/library/rmarkdown/rmarkdown/lua/latex-div.lua --email-obfuscation none --self-contained --standalone --section-divs --template /home/steinfej/miniconda3/envs/python/lib/R/library/rmarkdown/rmd/h/default.html --no-highlight --variable highlightjs=1 --variable 'theme:bootstrap' --include-in-header /tmp/RtmpRfvefu/rmarkdown-str11963d037f4.html --mathjax --variable 'mathjax-url:https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML' \n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Output created: /data/analysis/ag-reils/ag-reils-shared/cardioRS/data/2_datasets_pre/cvd_massive_excl/table1_union.html\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "library(arsenal)\n",
-    "table_one = tableby(sex~., control=mycontrols, data=data %>% select(all_of(c(f$pgs, f$basics, f$questionnaire, f$measurements, f$labs, f$family_history, f$medications, f$diagnoses))))\n",
-    "write2html(table_one, glue(\"{dataset_path}/table1_union.html\"));\n",
-    "#summary(table_one, text=TRUE)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# BASELINE"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T14:17:10.778888Z",
-     "start_time": "2020-11-04T14:17:08.887Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "options(repr.plot.width=20, repr.plot.height=8)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T14:17:10.814098Z",
-     "start_time": "2020-11-04T14:17:09.367Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "base_size = 25\n",
-    "title_size = 35\n",
-    "facet_size = 25\n",
-    "geom_text_size=7\n",
-    "library(ggplot2); \n",
-    "theme_set(theme_classic(base_size = base_size) + \n",
-    "          theme(strip.background = element_blank(), plot.title=element_text(size=title_size, hjust=0.5), \n",
-    "                axis.title.y = element_blank(), axis.text.y = element_blank(), axis.ticks.y = element_blank(), axis.line.y = element_blank(), \n",
-    "                strip.text.x = element_text(size = facet_size))) "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T14:17:12.517888Z",
-     "start_time": "2020-11-04T14:17:11.602Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "library(ggplot2); \n",
-    "theme_set(theme_classic(base_size = base_size) + \n",
-    "          theme(strip.background = element_blank(), plot.title=element_text(size=title_size, hjust=0.5), \n",
-    "                axis.title.y = element_blank(), axis.text.y = element_blank(), axis.ticks.y = element_blank(), axis.line.y = element_blank(), \n",
-    "                strip.text.x = element_text(size = facet_size))) "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T14:17:12.887887Z",
-     "start_time": "2020-11-04T14:17:11.969Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style>\n",
-       ".list-inline {list-style: none; margin:0; padding: 0}\n",
-       ".list-inline>li {display: inline-block}\n",
-       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
-       "</style>\n",
-       "<ol class=list-inline><li>White</li><li>Black</li><li>&lt;NA&gt;</li><li>Asian</li><li>Mixed</li><li>Chinese</li></ol>\n",
-       "\n",
-       "<details>\n",
-       "\t<summary style=display:list-item;cursor:pointer>\n",
-       "\t\t<strong>Levels</strong>:\n",
-       "\t</summary>\n",
-       "\t<style>\n",
-       "\t.list-inline {list-style: none; margin:0; padding: 0}\n",
-       "\t.list-inline>li {display: inline-block}\n",
-       "\t.list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
-       "\t</style>\n",
-       "\t<ol class=list-inline><li>'Asian'</li><li>'Black'</li><li>'Chinese'</li><li>'Mixed'</li><li>'White'</li></ol>\n",
-       "</details>"
-      ],
-      "text/latex": [
-       "\\begin{enumerate*}\n",
-       "\\item White\n",
-       "\\item Black\n",
-       "\\item <NA>\n",
-       "\\item Asian\n",
-       "\\item Mixed\n",
-       "\\item Chinese\n",
-       "\\end{enumerate*}\n",
-       "\n",
-       "\\emph{Levels}: \\begin{enumerate*}\n",
-       "\\item 'Asian'\n",
-       "\\item 'Black'\n",
-       "\\item 'Chinese'\n",
-       "\\item 'Mixed'\n",
-       "\\item 'White'\n",
-       "\\end{enumerate*}\n"
-      ],
-      "text/markdown": [
-       "1. White\n",
-       "2. Black\n",
-       "3. &lt;NA&gt;\n",
-       "4. Asian\n",
-       "5. Mixed\n",
-       "6. Chinese\n",
-       "\n",
-       "\n",
-       "\n",
-       "**Levels**: 1. 'Asian'\n",
-       "2. 'Black'\n",
-       "3. 'Chinese'\n",
-       "4. 'Mixed'\n",
-       "5. 'White'\n",
-       "\n",
-       "\n"
-      ],
-      "text/plain": [
-       "[1] White   Black   <NA>    Asian   Mixed   Chinese\n",
-       "Levels: Asian Black Chinese Mixed White"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "unique(data$ethnic_background)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Basic Information"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T14:17:13.873348Z",
-     "start_time": "2020-11-04T14:17:12.784Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "temp = data %>% select(all_of(c(f$basics, f$questionnaire))) %>% select_if(is.numeric) %>% pivot_longer(everything(), names_to=\"parameter\", values_to=\"value\") %>% mutate_at(vars(parameter), list(~ factor(., levels=unique(.))))\n",
-    "labels = c(age_at_recruitment = \"age\", townsend_deprivation_index_at_recruitment=\"townsend\")\n",
-    "plot_age_te = ggplot(temp, aes(x=value))  + \n",
-    "    geom_density(adjust=1.5, fill=\"gray70\")+  facet_wrap(~parameter, ncol=1, scales = \"free\", labeller=labeller(parameter=labels))+\n",
-    "    scale_y_continuous(expand=c(0,0))+ xlab(\"\") "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T14:17:14.547997Z",
-     "start_time": "2020-11-04T14:17:13.377Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "`summarise()` regrouping output by 'parameter' (override with `.groups` argument)\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "temp = data %>% select(all_of(c(f$basics, f$questionnaire))) %>% select_if(is.factor) %>% select_if(is.factor) %>% drop_na()  %>% pivot_longer(everything(), names_to=\"parameter\", values_to=\"value\",values_ptypes=list(value=\"character\")) %>% \n",
-    "    group_by(parameter, value, .drop = TRUE) %>% summarise(count=n(), ratio=round((100*n()/502504), 1)) %>% ungroup() %>% mutate_at(vars(parameter), list(~ factor(., levels=unique(.))))\n",
-    "plot_seth = ggplot(temp, aes(x=value, y=ratio, fill=parameter))+ \n",
-    "    geom_bar(stat=\"identity\", position=position_dodge(width = 0.8), fill=\"gray70\", width=0.5) +\n",
-    "    geom_text(aes(label=paste0(ratio, \" %\")), vjust=-1, size = geom_text_size) + \n",
-    "    scale_y_continuous(expand=c(0,0), limits=c(0, max(temp$ratio)*1.2))+ xlab(\"\") + \n",
-    "    #theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) + \n",
-    "    facet_grid(~parameter, scales = \"free\", space=\"free\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T14:17:16.659027Z",
-     "start_time": "2020-11-04T14:17:14.329Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Warning message:\n",
-      "\"Removed 516 rows containing non-finite values (stat_density).\"\n"
-     ]
-    }
-   ],
-   "source": [
-    "options(repr.plot.width=20, repr.plot.height=8)\n",
-    "plot_title = ggplot(temp, aes(x=value))  + ggtitle(\"Basic Information\")\n",
-    "title <- ggdraw(get_title(plot_title))\n",
-    "plot_basics_raw = plot_grid(plot_age_te, plot_seth, ncol=2, rel_widths=c(1, 4), align=\"h\", axis=\"lr\")\n",
-    "plot_basics = plot_grid(title, plot_basics_raw , ncol=1, rel_heights=c(0.1, 0.9), align=\"v\", axis=\"lr\") "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T14:17:18.391317Z",
-     "start_time": "2020-11-04T14:17:17.305Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "options(repr.plot.width=20, repr.plot.height=8)\n",
-    "temp = data %>% select(all_of(f$pgs)) %>% pivot_longer(everything(), names_to=\"parameter\", values_to=\"value\") %>% mutate_at(vars(parameter), list(~ factor(., levels=unique(.))))\n",
-    "plot_pgs = ggplot(temp, aes(x=value)) + ggtitle(\"Polygenic Scores\") + \n",
-    "    geom_density(adjust=1.5, fill=\"gray70\") +\n",
-    "    scale_y_continuous(expand=c(0,0))+ xlab(\"\") + \n",
-    "    facet_wrap(~parameter, ncol=4, scales = \"free\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T14:17:19.147957Z",
-     "start_time": "2020-11-04T14:17:17.792Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "options(repr.plot.width=20, repr.plot.height=8)\n",
-    "temp = data %>% select(all_of(c(f$measurements, f$labs))) %>% pivot_longer(everything(), names_to=\"parameter\", values_to=\"value\")  %>% mutate_at(vars(parameter), list(~ factor(., levels=unique(.))))\n",
-    "plot_meas = ggplot(temp, aes(x=value)) + ggtitle(\"Measurements\") + \n",
-    "    geom_density(adjust=1.5, fill=\"gray70\") +\n",
-    "    scale_y_continuous(expand=c(0,0))+ xlab(\"\") + \n",
-    "    facet_wrap(~parameter, ncol=5, scales = \"free\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T14:17:19.829906Z",
-     "start_time": "2020-11-04T14:17:18.264Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "temp = data %>% select(c(any_of(f$diagnoses))) %>% \n",
-    "    pivot_longer(everything(),names_to=\"category\", values_to=\"Value\") %>% \n",
-    "    group_by(category) %>% summarise(ratio = sum(Value==TRUE)/n(), .groups = 'drop') %>% mutate(category = fct_reorder(category, ratio)) %>% mutate(ratio=ratio*100)\n",
-    "plot_conditions = ggplot(temp, aes(x=category, y=ratio)) + ggtitle(\"Conditions\") + \n",
-    "    geom_bar(stat=\"identity\", position=position_dodge(width = 0.8), fill=\"gray70\", width=0.5) +\n",
-    "    coord_flip()+xlab(\"\") +\n",
-    "    ylab(\"Prevalence in [%]\") +\n",
-    "    scale_y_continuous(expand=c(0,0), limits=c(0, max(temp$ratio)*1.3))+\n",
-    "    geom_text(aes(x=category, y=ratio, label=glue(\"{round(ratio,1)}%\")), position = position_dodge(0.8), hjust=-0.1, size = geom_text_size)+ #nudge_y=+3000, \n",
-    "    scale_x_discrete()+\n",
-    "    theme_classic(base_size = base_size)+ theme(strip.background = element_blank(), plot.title=element_text(size=title_size, hjust=0.35))#, strip.text.x = element_text(size = 16))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T14:17:20.135005Z",
-     "start_time": "2020-11-04T14:17:18.560Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "temp = data %>% select(c(any_of(f$medications))) %>% \n",
-    "    pivot_longer(everything(), names_to=\"category\", values_to=\"Value\") %>% \n",
-    "    group_by(category) %>% summarise(ratio = sum(Value==TRUE)/n(), .groups = 'drop') %>% mutate(category = fct_reorder(category, ratio)) %>% mutate(ratio=ratio*100)\n",
-    "plot_medications = ggplot(temp, aes(x=category, y=ratio)) + ggtitle(\"Medications\") + \n",
-    "    geom_bar(stat=\"identity\", position=position_dodge(width = 0.8), fill=\"gray70\", width=0.5) +\n",
-    "    coord_flip()+xlab(\"\") +\n",
-    "    ylab(\"Prevalence in [%]\") +\n",
-    "    scale_y_continuous(expand=c(0,0), limits=c(0, max(temp$ratio)*1.3))+\n",
-    "    geom_text(aes(x=category, y=ratio, label=glue(\"{round(ratio,1)}%\")), position = position_dodge(0.8), hjust=-0.1, size = geom_text_size)+ #nudge_y=+3000, \n",
-    "    theme_classic(base_size = base_size) + theme(strip.background = element_blank(), plot.title=element_text(size=title_size, hjust=0.35)) + \n",
-    "    scale_x_discrete()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T14:17:28.906077Z",
-     "start_time": "2020-11-04T14:17:19.409Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Warning message:\n",
-      "\"Removed 48868 rows containing non-finite values (stat_density).\"\n",
-      "Warning message:\n",
-      "\"Removed 192399 rows containing non-finite values (stat_density).\"\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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TApWfpouAOW8w5bsYGmDU4WLuqaaCyX/pXZAi7tXFLAcuihLusutLYi+n6+ehTa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\n1bd0tf2v6aON0DMElnkX6Qs1w7V2X/MO+X58cNf1v9pl+9F6mT0ojtUq/4q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ZdF69IGGhYuJaa11uAmvb7RXxA0y37sPOSA5jn7RI7ZHq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DgAWQlTQB1k4+jedFwfU1wstRAdYM4/ijVWiX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\nCdOUsF5qHQdY1T8LfNjbXPkQeG2xLEXuB/tQKfr6+/nSt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VxInUhingiwDuvfG6E4u/QR6sRBMqayWyzaYFBT9m/qtEKGO+tUg8q3olje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\nJIyABQAAkDACFgAAQMIIWAAAAAn7PxHizNVDi26KAAAAAElFTkSuQmCC",
-      "text/plain": [
-       "plot without title"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 1800,
-       "width": 1200
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_width=20; plot_height=30; plot_dpi=300\n",
-    "options(repr.plot.width=plot_width, repr.plot.height=plot_height)\n",
-    "plot_1 = plot_grid(plot_basics, plot_pgs, plot_meas, ncol=1, rel_heights=c(2,1.3,2), align=\"v\", axis=\"lr\")\n",
-    "plot_2 = plot_grid(plot_conditions, plot_medications, ncol=1, rel_heights=c(3,1.8), align=\"v\", axis=\"lr\")\n",
-    "plot_desc = plot_grid(plot_1, plot_2, ncol=1, rel_heights=c(5,3), align=\"v\", axis=\"lr\")\n",
-    "plot_desc\n",
-    "\n",
-    "plot_name = \"1_dataset_characterization\"\n",
-    "ggsave(filename=glue(\"{dataset_path}/{plot_name}.pdf\"), plot=plot_desc, width=plot_width, height=plot_height, dpi=plot_dpi, device=\"pdf\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# OBSERVATION TIME"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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IaABaA3CeKntL8Ti+y/b3tCum0PWB7n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\njIAFAADgsv8HaRu2/v54gNEAAAAASUVORK5CYII=",
-      "text/plain": [
-       "plot without title"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 360,
-       "width": 1200
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_width=20; plot_height=6; plot_dpi=300\n",
-    "options(repr.plot.width=plot_width, repr.plot.height=plot_height)\n",
-    "temp = data %>% filter(QRISK3_event==0)  %>% select(c(eid, QRISK3_event_time))\n",
-    "mean = round((temp %>% summarise(mean=mean(QRISK3_event_time)))$mean, 1)\n",
-    "obs_time = ggplot(temp, aes(x=QRISK3_event_time)) + ggtitle(\"Observation Time\") + \n",
-    "    geom_density(fill=\"gray70\") +\n",
-    "    xlab(\"Years\") +\n",
-    "    geom_vline(aes(xintercept=mean(QRISK3_event_time)),color=\"black\", linetype=\"dashed\", size=1)+\n",
-    "    #geom_text(x=mean, label=mean, y=0.15, hjust=-0.5)+\n",
-    "    #ylab(\"Prevalence in [%]\") +\n",
-    "    scale_y_continuous(expand=c(0,0))+\n",
-    "   #theme_classic(base_size = 25) + theme(strip.background = element_blank(), plot.title=element_text(size=24, hjust=0.5))+\n",
-    "    annotate(\"text\", x=mean+0.3, y=0.15, label=paste0(\"~ \", mean, \" years\"), size = geom_text_size)\n",
-    "obs_time\n",
-    "plot_name = \"2_observation_time\"\n",
-    "ggsave(filename=glue(\"{dataset_path}/{plot_name}.pdf\"), plot=last_plot(), width=plot_width, height=plot_height, dpi=plot_dpi, device=\"pdf\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# ENDPOINTS"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Frequencies"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plot_width=20; plot_height=6; plot_dpi=300\n",
-    "options(repr.plot.width=plot_width, repr.plot.height=plot_height)\n",
-    "\n",
-    "temp = data %>% select(c(any_of(targets))) %>% select(!contains(\"_time\")) %>% select(!contains(\"cancer_breast\")) %>% \n",
-    "    pivot_longer(everything(), names_to=\"category\", values_to=\"Value\") %>% \n",
-    "    group_by(category) %>% summarise(ratio = sum(Value!=0)/n(), count=sum(Value!=0), .groups = 'drop')  %>% mutate(ratio=ratio*100) %>% arrange(desc(count)) %>% mutate(category = fct_reorder(category, count))\n",
-    "\n",
-    "plot_endpoints = ggplot(temp, aes(x=category, y=count)) + ggtitle(\"Endpoints\") + \n",
-    "    geom_bar(stat=\"identity\", position=position_dodge(width = 0.8), fill=\"gray70\", width=0.5) +\n",
-    "    coord_flip()+xlab(\"\") +\n",
-    "    ylab(\"Number of Events (%)\") +\n",
-    "    scale_y_continuous(expand=c(0,0), limits=c(0, max(temp$count)*1.3))+\n",
-    "    geom_text(aes(x=category, y=count, label=glue(\"{count} ({round(ratio,1)}%)\")), position = position_dodge(0.8), hjust=-0.1, size = geom_text_size)+\n",
-    "    theme_classic(base_size = base_size) + theme(strip.background = element_blank(), plot.title=element_text(size=title_size, hjust=0.35)) + \n",
-    "    scale_x_discrete()\n",
-    "\n",
-    "plot_name = \"3_endpoints\"\n",
-    "ggsave(filename=glue(\"{dataset_path}/{plot_name}.pdf\"), plot=last_plot(), width=plot_width, height=plot_height, dpi=plot_dpi, device=\"pdf\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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ABw/g4unPr66xOX2qf11AOWmqg2zx2R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\nRU5ErkJggg==",
-      "text/plain": [
-       "plot without title"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 360,
-       "width": 1200
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_endpoints"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Event densities"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T14:29:06.050717Z",
-     "start_time": "2020-11-04T14:28:59.615Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "plot_width=20; plot_height=10; plot_dpi=300\n",
-    "options(repr.plot.width=plot_width, repr.plot.height=plot_height)\n",
-    "\n",
-    "plots = list()\n",
-    "for (e in endpoints[-2]){\n",
-    "    temp = data %>% filter(!!sym(paste0(e, \"_event\"))!=0)\n",
-    "    \n",
-    "    plots[[e]] = ggplot(temp, aes(x=!!sym(paste0(e, \"_event_time\")))) + \n",
-    "        geom_density(adjust=1.5, fill=\"gray70\") + \n",
-    "        scale_y_continuous(expand=c(0,0)) +\n",
-    "        scale_x_continuous(expand=c(0,0)) +\n",
-    "    xlab(e)\n",
-    "    #+ \n",
-    "   # theme_classic(base_size = base_size) \n",
-    "    #print(paste0(nrow(temp), \" events in \", nrow(data), \" people in observation time\"))\n",
-    "    #print(paste0(round((nrow(temp)/nrow(data))*100, 1), \" %\"))\n",
-    "}\n",
-    "title <- ggdraw(get_title(ggplot()  + ggtitle(\"Endpoint Densities\")))\n",
-    "plots_density_raw = plot_grid(plotlist = plots, ncol=4)\n",
-    "plots_density = plot_grid(title, plots_density_raw , ncol=1, rel_heights=c(0.1, 0.9), align=\"v\", axis=\"lr\") \n",
-    "\n",
-    "plot_name = \"4_endpoint_densities\"\n",
-    "ggsave(filename=glue(\"{dataset_path}/{plot_name}.pdf\"), plot=last_plot(), width=plot_width, height=plot_height, dpi=plot_dpi, device=\"pdf\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T14:29:06.952215Z",
-     "start_time": "2020-11-04T14:29:00.872Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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93BK1njKWFlQNWWXovmc/cCnDm7s0sUUXyr6SxTMXhjjFT8C+5R8hcClp9ODY+g\nuN57lK+uEVQF80IGuy+GmCi+r/KHXjlYE2PM4O446MGVHpSyJcBq25VoeIm7H1DQma13mYhK\ndl2swIR74oyg0j9wj5SfELD88kU/IyWPUOfX6i7U/Ap3f0FJ790TRiXSxM2g5q1Fpuh3ufsO\n2ne+NVUL/LpzC8NK/sHdE7D37+6OlnRVrveawD9KFNaPavzFPVr+QcDywxd9wqj0BFWORbbI\naEr3cfcYlPNKO6IKk1n+gpwmlfyRu/ugdX/eTI1E/HzUn9rhMCzNuP5k7yJEZfv6dN0jNh3p\ntsvcI+YXBCyffdPfQGUnq3gi66HytJa706CQp1KJaszkOi16IDW4yD0CoG0/Vqc7xFz9qx6u\nmaMVn0y8iSi5xxoR21UNGbdSL12mcwQsH/00PJzKTFH3C3FLbPjr3P0GBWQfbUTUYKGqxeSo\nJfXmHgTQtK/KkVnQx92O2MhPubsDOTnnNzYlim63SEOzXXl0pCrN4h43fyBg+eSvByKp5ETV\nC3OOlPI7d9dBtKz9dUhqukLtYnJwpAqt4B4H0LCPb6LewqptKjW8xt2hkPfh8FiS6k9S/5DP\nwOxKko5wD50fELB8cHF+PCWOUvAacS71ozszuXsPQmXurk5S6hqGYnKwPT78Ne6hAM16O1Ea\nKrDabqc53D0KbVcfb0aU2HOrwG2qlkdNMZ9wD5/vELC8lrmpFEUP5JnnNqMRzebuPwh0bVtl\nMrQUf/lK380LS8E0IODcyzFhY0UW295E08fcfQphf84tSVJ9RWfGVtADVOUs9wj6DAHLWxk1\nydRV5ZmKbthTzPAK9wiAKFc3VCBjm01cxeSoL92ty8NHQXFPRxkniy22GXTzde5ehaovhkVS\n5N36OGvQqc7URXefVAhY3nm3BUkttzEW14KwMn9zDwIIcXltGTK218xe+uN16FHuIQEtSo8I\nnyG62lJpGXe3QtMbncKo+JD9orenmo7VoOXcw+grBCxvnOolUf1VvNXVk+7lHgYQ4MLykmTq\nyJnVC9oRG4HfbaCQfcaIOcKLbXdM9PfcHQs92U81J6o0Wae/DebbEWd6h3sofYSA5dlfE0xU\nYS53cR2rRru4RwIC9c/84hTZeSd3MTl6kGrrcxY/UNBOQ6QSE4iMJjN3z0JN1uEGRPXnKbAx\n1TZLqniOezR9g4DlyaVFRSlpvAamDNkQGa/XCzKBzZ8PFaUi3VW4gKWP2tAk7qEBjdkUFr1U\niVrLqEHHufsWUrL21iLpVt7ZYITpQn25x9M3CFjuXd9SmqIHamPOkDRqpbtj/OCG78cUodi+\nWjwK4kByGGayBXtrpViFvpPXGMpf4u5d6MjaU53CWqxVZlOqL70iPc49pD5BwHIn+1B1zlMH\nC8ioT+u5RwT89VG/cEq8T8Q13RQwX6qKLz24YYUUr9gxp51pJnf3QkXW3hoU1nKDUluSwbqI\noj9xj6ovELDceLohSa00dDjytqhY/EioS9nPtpMoZcxR7gpyqQNN5h4j0I7FVFS5nR77i0Z9\nx93BkJB1oKYlXm1UbEOyuJ/a6Ol3HAQsl164jaRbH+OuJwcjqT33qIDvLm+pQ1T9QQ0cx+fS\nwWTDe9zDBFoxnxKVnC5pLHXn7mEIyD5SJ8j2Xlll1Kd13EPrAwQsF55LJWqktSMDM+rgTELd\n+emh4hTWbAl37Xgwh+rhMnFgNZuSFN3tkVGJMGuy0k40IKm5Fi4VIdq26JhvuQfXewhYzmQf\nb0rUQJGTaAKz0ZT0J/fggA+yn+tqpOjOW7gLx7M7aRH3YIEmPETJm5WttUXUKIu7l8Ht6SYk\nNQuaQ9sdjaE79fMjIQJWYVe31yKpsQbjlcVAGsA9POC1XxZUIiqXptEj2x09HlvkO+7xAg2Y\nRsmKX2XgNtrJ3c1g9nwzkpowT4ytnIwGtIl7hL2GgFXQ6fmlKKy5Vqszvbz0EvcIgVeuHLrb\nQOG3KzFZoyLGUkfuIQN+k6iE8ldx2hxeGmetKuXF5ho8ukWkrZFFf+UeZG8hYDl6f0gkRXbQ\nyGV4nVkqVb/KPUjgUdZLQxOIyg/TyhQfXsioSencwwbcJlLJ7SoUW2f8IK0QOV5p8egWkYbp\n5ywJBCw75zc1JkoetI+7ftxqRwu4xwncyzw5uiRRfKdHuWvFN2sM5S9yDx2wyh5PpdTIVyf2\nRhc9w93ZYPRsKlF9rZ9QE7DjVekE90h7CQErT9aLA6NJajTjOHf1eLA3rggmw9KwC8fvK04U\n3WqO/q6s2plmcI8ecMoeS+pII30AACAASURBVCk71Km1/rg6k3DZGU2IGgR9vLJYZSh/gXu0\nvYOAZZX97sQUoqSeOjjb68Ro6sY9XODCF6vaRRLFtZmp3SlF3TiQEPkN9wgCn+zRlKLWdcgP\nJ0b9zN3f4JK5tx5JjZeptP2YdaGp3OPtHQQsS2W+OqEcUdSd87Q8E+QNGVXoOe4hg8J+2jWo\nDBGldF2o9Z2gLk2gTtyjCGyyR1JptfKVPGnyCO4OB5PL6yuRlKrVc7OEO5QU/hn3kHsl5APW\nX3v7JRFFpk7TxgWdvbFcqok5IbXli62DK1nCVfQtaQrPIKSsjGrI7iErO43K7FKv1tJLhn/N\n3eWgcXpOMhlbKzn7vtZMo5bcg+6VkA5YF1+Y3jiMKL71DP2kK1lrepR76CDPPy88YrZEdIps\nOHCZbndd5Vku1brOPaDAIvt+KrNbzVqbSP24+xwkvrw/iop0VengOa1oQPu5x90bIRuw/n7y\nwVQTUVi13sv08cugnV1RCX9xjx/k5Fx+a82AGpaETonNhq7Q3zHtztypqwt9gTCWfFVW1Xx1\n4ngZw+fcvQ4C2U+3lyhp0H5Vt50GbDCW0cNx7qEYsK5/uHFITYlIKm+eoc+6HEijuQcxxF15\nd9199YyWbGWq0XnKNu56EGdHZPF/uMcW1Cfnqz0q19pU6sndbd07u7IqUZVJ6SpvOi3oRg9x\nj74XQixgXf9kx5hbo+TvxVrdH9bRJJAFHLnJiD/+uFz7z8ZhDcItNWSs3H7s6uDYcXVDH5rM\nPcCgOvX3X1lklA/7lLvj+vbukCJkvD1EThws6EBCpA4u+hw6AevSuxtHNJWzlVTmzvtX6Dzy\nT6W7ucczJH29e8wtkXK2qtRu1Eqd15Bzh4tFfMc9yqAyjv1XFtOpB3fPdezvNfWJkvqqeF6C\nxoyje7i3gWchEbD+fml5v1ry7zlhZVsOXaSLS+96kFGDXuAe1RBz8eT8jsXlGirXOm25Lqe5\n8s446sM91KCu7JEs+epERoWwT7j7rlNZz/aOpLDGmp8WW0kZlekk93bwKNgD1h9Pz7+nAsm/\nCVZpP3LZYe6aEGap1CCLe2xDx9mMSbeEWw9mH7wwGPK5O8fLS+9xjzeoyZKv1D1/MN90HIXl\nl48npxCV6KvKRY00bLHUSPNfgkEcsP565pEu8syPFF2n88S1wXasTCrt5h7g0HDh6UmNDERh\nFc2Tg+hgdjdm053cYw4qyh7Nla9OZJQLw7GkvvrmkdpEUa0W6u7kd/FSaSf31vAkOAPW5ddX\n9JYnfqTYBt2n6XrqR5c2Gctd4R7moJf94aKWJiJDte6zD3BvcPXUo6e5Bx5Ukz2WSrMdxzOF\n+nP3X1/+t7ARkbHxpOD5LSYQm8NLX+LeIh4EX8D6Zs/om+Wfc6Lq3jNND5cW9NfdtIJ7qIPb\nhaNDS1nqqFznmQe5t7W6Vkr1NL/nHUQZr971Bws7nmI8xT0AupH97ozaRGF1R+n39HfRutIj\n3FvFg6AKWJdeXdz5Jst3oqFih/GPBfse1N2RSee4Bzx4/bHp7kiimNRxfF8+fJrj5+eQMYkz\nX504MR5XJPTOP4eGlCQyNhzFcTaCZu2LjfuTe8u4FzQB67fDE6zHISfcMmhRaOw/7UUPcw96\nkPpl9e0GopRuC4PtwD0vbTJWuMq9DUAVU6kk6yVW0pMjfuYeA8279trs24xEsS2m6HNebAUN\noTHcW8e9YAhY2Z9u7F9RPoG+wt0Tg/OAK6cOxMX8wT30QeiPtc3DSKoycAP39mV0F63l3gyg\nhoeoBPOpaGk0kXsQNO3iybltoomkyj0Wheife24dSTZ9w72J3NJ7wLr6xqKOifIRV/V7zwux\nQ2VODKXx3MMfbM7vbGcgqdp9oXG+oEs7I0rq4TpfEKBZdBN3pR9JiMF1VZ3L/HTHyEby9I2l\n2k/FD4MuTKRe3NvJLT0HrHNPP9RCnla7WPPhq0JxwrUjxSN+4N4GwSTz6T5FiCoM3sq9Yfnd\nQwu5twYo7hEqzl/rA2k29zhoz18n141oWkQ+nLiyeWooHgbqtYwK0gfcW8sdvQasP46Ma2iQ\nL3vTfmIwnyro3hgaxr0dgsdnk0sRJfdYx71RNWFvdOJZ7g0CCltESRo4ouJAdBL2lub7+4ND\nC+9LTZKnGAorfcfQxaFxOHEgZlJ77o3mjh4D1i+Pj6ghXw+uWtcZoX3CanrJ8K+5N0Zw+Gd9\nU6IibTB5X56+NIN7m4CyVlDiRu4yk91Lj3IPBbvTnz63Y15ax9pxcrIiKblBl7HLka28U1PT\nF8zRW8D6Zc/QKvKFb+r2fiTYr1nihYmYp0+A7Jf6R5FUdyI+0W44GBer8fOfITBrpKLruavM\napep7DXuweBw5qu3ntixbPKguxuXNpGNKaVB+0HTVx/h3iS6spiacW9KN/QUsE4fSqtmqcLI\n+v0Xp3NvVW04XtrwBfdW0buf51ciSu7DfzCKtgyhSdxbBhS0UYp/jLvGcrUP4mnXLv/9998/\nnTp16pP333/p+YyD2zYsenj8fd1bN6qUKFEeQ2KFm1v3Snv4UaYLFundzXSCezO7ppeAdemZ\nSQ3CiCLq91+CcHXDFOrNvWV07drRuw1kajEPPw0WdCSxyK/cWwcUsz0sdjV3ieXZINXN5h4P\nkc6+vn3WkLtvrZ6SEE4uGOJKVWvUslPftGkL1+3Bh09gVkn1tVs/eghY2R8tbhVpKcoavRYe\n5d6YGpNRLuwz7s2jX59NTCaqODy0D+RzZYTWp/AD/z1uiF7JXWA3NKNnuAdElB829auYG6Ki\nk8tUrla/fv1mqamprdq379S9+8Chox6YPm/Zuu04ukWoVDrIvd1d0nzA+mvfwBKWai3TaSaq\n0olpGp8GRLvOrr/F8iHY4VHuLahVR5Mif+LeRqCMQ8aoZdz1ZWcZteYeESFOr2wsERWpY75/\n1lrsl1LPurCamdzb3hVNB6zs9+fdaiCKbT6Webph7cooH/Yp92bSocynekWSVHcSDid1bSSl\ncW8mUMTx8MjF3NXloCZ9yD0mgfvfkAiSag9ZjWSlupbaPYpPuwHr/JH7ShBJVXovC8VJRL2G\nXVi++/CBkkQl+uK4drfSk02YxzYYPRNhms9dXI5mUD/uQQnUb0MNdNNAzAnKYpOhynXuAnBB\nowHr65WtTfLVLSfiEgEeZJQL+5x7a+nKqUdqylNeLcIfmp6MoeHcGwvEeykqfDZ3aRWQkRKu\n75+jsx+Lo1IPYE8Al9a0nbsEXNBgwLr20gPybAzluy9GwXphKvXl3mL68cPyJkTGxlPw06AX\n0kuYvufeYCDa69HGGdyVVUgaTeEel0D83paih+Lsdj5bjJU0ugtLawHrt23d44hMjdK4L0Kq\nGxllDF9xbzV9OLX0FomkOqNw1qCXxtJQ7m0Ggr0bFzaVu64KOxxb9Dz3yPjv9ZJUH0cJs2pL\n27irwDktBazrrz3USCJKav8w5tT2wQM0iHvLaV/2+zPrEUm1huMoCe8dKxH+LfeGA6E+TJQm\ncpeVMz11fL2cnRFh/XHAAa+txoravByAZgLW1+u6xhMZag1Yw72t9OZ4SXwNunfh+PAUImP9\ntF3c20pnsAsryHxWXBrDXVRO7QqvoNkz7T1YKRXR2jFtIag9beUuBKc0EbC+3zmwHBEltZ22\nn3s76dFYGsG9BTXss+VtIoiim0/ax72d9Ccdu7CCylclaQR3TbnQmg5zj45/llDRVdyDB/Iu\nLE0ehcUdsK69v7ZPWXna2ybD13FvI71KT474mXkzatTvjw9OkWep7TofR6D6Bbuwgsl3ZWgw\nd0W5ska6jXt4/LKaErVxzexQ11abJxIyBqxL720ZdWuUHK4aD1p+jHv76FkaTeDbjFr1T8b4\nOpKluJqNwnxXfksvEY4TCYPFzxWpL3dBuVaf3uEeID/sC4vHjgFN2GKsosUfmTkC1sXPn143\n8e5KYZZsFVa21ag1OEAwQEcSo08zbEft+vfpKU0MRMbafTFLbWDw83PQ+K0adecuJzdm6fG6\n9W9GRK7gHjiwaU17uMvBCSUCVuaZUx+//9rzDp44eGDjmoVTR9zTokZ87rUwq7cbsRjXFxRi\nCM1QYDvq079PT7vVaInuVe6Zg5NRA5Z+E6ZzDw6na5OZu5rcySitv8lGfy4hzeQeN8i1yVAj\ni7sgChMasC6/uX5cx9pJ5FZk6Tp39hq3ECd0CXQotug5kRtSr84/NfUWOVxV6vwwTpcQYzSu\nSBgUzjag9tr+qSCNpnEPko+uNaOB3KMG+VrSAe6KKExYwMp+d0azcDlAmUrWaNyi/d3dHfQZ\nOHD4qCkzFqzejjm0ldCLFonakHp17onJTazhqgvClUDpyRG627EAhZxrSq20na8sfyUmXuQe\nJt9Mols0PqYhZb1UJ5u7JAoRFLC+f7g8kVShw+glu7mHOSTtjSxxWcyW1CVLuGpssISryl1n\nIlwJNpJGc29eCNSF5tRc80cjdqf13OPkk2ekm/BpoyWplM5dE4UICVif9DCQqfkUXIKET2da\nJ2JL6tC/T02x7rmq2m3mAe6tEIyOJkX+wr2NITCXWtEt2p+oZLuhhvb2QLh2ppRhGfeQgb01\n0s3cRVGIgID1c/8wKjsKX26sthsravEkVYVdfmlGMzlcVUa4Us4IGs+9nSEgV9pTI+3nK3kP\nxNPcQ+WDftSbe8DAUVPtFVDAASvr0Rgq+yB+iubWhvaKqAf9yHpvYesoIqliZ/wsqKgjiUV+\n597YEIBrZqqvi0Nfl1B77rHy3pNUQQ+hNaQsp1Tusigo0ID1c0uKTtP8r/shYL1UT0/71wP0\n05YexeQp2u9+EL9LK24YTebe3uC/6/dQbZ1MWFJV+oJ7tLx1oZxhJfdwQUH16RXuwiggwID1\nYjI12sk9qiBrpr3do8rIfHVqHUu4Smg5AZWniiMJMZjHVrcye1ENvUw3OEk/c4JMpi7cowWF\nLKR23IVRQGABa73RMAS/DmrDcmopqCa07FL6wGJExrqDV6PuVDOEpnNvd/BTVn+qqpvjE9MT\no//mHjDvfB6epJfUGlJq0HvcpeEooIA1i2Lnc48o5Kmjy4t5+eLKsV4xRPFtHsRnm6oOxced\n4d724JesQVRZR4co9qel3CPmnTY0jXuswIlZ1I27NBwFErAmU/IG7gGFfLPpHmFloUXvpiUQ\nFe+yGLuuVDeQHube+uCPrPuoop6OUtxjKq+Lk6FPUB3uoQJnMiqGfc5dHA4CCFjTqeQ27vGE\nGzIqhP1PXGFozIX19YjiOy5FuuJwMDb+LHcFgO+yh1H5x7mLxydt6Aj3oHnheg1pFfdIgVNT\naQB3dTjwP2AtphLbuUcT7E2k4QIrQ0t+nZpAYY1n4LRoLv1oDncNgM+yR1D5Pdyl45vV1IJ7\n1LywgVpxDxQ4dzwl/Dvu8rDnd8DaJyVu5h5McJBePDIoJyz6aWQExd6LvaWM9kcnnucuA/CR\nJV+V01m+ko8k/Q/3uHl0qZQJ+xa0aiyN4q4Pe/4GrHcjI7GTVGuG0oNCi0MTzkyMpOLDcVg7\nr160gLsQwDfZw3WYr048SIO4B86jxdSNe5jAlfSkKC3tZvAzYP1RWprBPZJQ0KHYhH/Flge7\nzDWJVGwkfhvktq9I0gXuWgBfZA3VY746cfymyD+5h86Dc8WK6OnEgVAzlKZxV4gd/wJW5p24\nDpMW9aCVguuD2fsNKbK/TqahDm499HICPVhlDdbd8Vc2Q2ge99h5MId6cQ8SuHYoLv4f7hK5\nwb+ANZsa4XQuDdptKntNcIFwujrNSC0wXbsmPB5500XuegCvZfajCvo6fzDP/siSV7lHz61z\nRWN0NLNYCOqrpcMZ/ApYbxiK6fN/3qDXnvaIrhA+X9SnpNncIwq57gm2vaPB7Nq9VFmvP2N1\n0Phn2Hzqwz1E4M7eyJsucRdJPn8C1oXKEiZw16YNUgPhJcJlTwy1xJ+KmrEnouRl7pIA71zp\nRNV0+7/Oeqkx9/i5c7F4kX3cQwRudaHHuKsknz8BazR15h5CcOEWel54jbC4NoYiJ3KPJtjp\nQqu5iwK8crEt1T7IXS7+a0RvcI+gG6uoO/cAgXs7jBWuc5dJHj8C1qthpXDYsVYtobbii4TB\n2daUso57MMHeLlPKFe6yAC/8k0oN9PwJPZt6cg+ha9fKmHZxDxB40EY7vzL7HrCuVJcWcg8g\nuFRD+kiBMlHbT7WpkW5/4whWnWgtd12AZ382pFuOctdKIDJKG3/iHkSXdtJd3OMDnmyQ6mZz\nF0ou3wPWHGrPPX7g2kPUT4EyUdmXZan9Me6RhAJ2mspo+/wusPixOrXU+f87aZqaychBdu2w\nTdzDAx7dRk9wV0ounwPWqch4HOOnYRkp4T8qUShq+uQmzDSjRR1pA3dpgAeflyGz3qfQORRT\nTKtTgjxNt3GPDni2kppzV0ounwNWZxrPPXrgziiapEShqOjTZGko9yiCE9vDy2EXlra9VYz6\ncpdJ4LrRRu6BdKENLeMeHPBCfXqdu1RsfA1Yz1M1vf99FOSOxMdpaCJbP3xdUhrOPYjgVAfa\nxF0d4M6T0VIad5EIsNVQUyvH0Dj6r1SDe2zAG/PIzF0rNj4GrMy6EhK8xvWlJcrUijp+LkdD\nuIcQnNtmLB9MVwoIOluMpmncNSJEKj3LPZZODaWp3EMDXqki/Ze7WKx8DFhb6HbukQMPHo9I\n0fEPOWdrUU/uEQRX7qLN3AUCrmTPoJggOcF7CXXgHk1nTkcV1/n5AyFjukZO9vItYF1MMW3l\nHjnw5G7aoVC1KO9aK5ykqmFbjRWwC0ujrvSj4kEzdVxl6Uvu8XRiIQ3kHhjwTkZK+Pfc5SLz\nLWAtoK7cAwcebQqro80jGLwwnG7G34ga1h67sDTqrxZUOXgujD6J0rgHtLDMsia9XuAx9Iyl\nMdz1IvMpYP1dNBoFpgO30dNK1YvCVlM5HV/jIwRgF5ZGfV6Zmh7irg5x0otFn+Ee0kLSqTX3\nuIC3jhYr8gd3weT4GLAepP7cwwZeWEatlKoXZb1ijN3MPXjgFnZhadKT8dQ1qE7vHkgLuce0\nkNb0KPewgNeG0AzugsnxLWCdjo0Por+Rglkt+kCxilHQLyXCHuEeOnAPJxJqUPZiQ/hY7soQ\na29Eaa3V2ZdSde5RAe8djE04z10yvgWsaTh/XidmUG/FKkY5mbfjGFLtw1xYmnOhJxVdwl0X\nonXQzgV7c42nidyDAj7opYX5inwIWH/FYAeWTmSUNmriFArfzKTGQfUrR3DCdO5a81Udqrqd\nuyyE2yA14h5YRxcT4o5wDwr44PGIUle4i8aXgPUQDeYeMvDSGBqvXM0o5KQhCadQ6EAHWs9d\nKmDncDy1PcpdFApoTCe5h9bBdurGPSTgE7MGdrZ7H7D+iY/FDiy9OJoQ87eCVaOEs2XDgmSW\nxCC33VSG/w9DyHV1LJmC7PCrXAu0crWTXLdKm7iHBHyy1Vglk7tqvA9Yj1A/7gEDr/WnRxSs\nGiX0ph7cgwZeMdMa7mKBXF/fTKVWcxeEQiqFaWmy0f9Sfe4BAR/dSQe4y8brgHWxeJH93OMF\nXtsXWeKyknUj3EGqlM49aOCVnaZSl7jLBax2xtLtB7jrQSmTaAT3+NoZjcsQ6s46qQH3lNte\nB6w11J17uMAHnWmjknUj2p/Fwx/jHjLwUmdazl0vYHGmB0WO4y4G5aQnRf3JPcT5LhWNxx+A\nunMr+5Tb3gas6+XDg+c6DKFgm7FqlqKVI1ZPGsA9YuCt3ZHJF7gLBnKeKkVVNnLXgpKG0Gzu\nMc63E1eJ06Hl1IK5brwNWPuoLfdggU9a0lFFK0eoY1QZlyDUj+60iLtiQt4/95GhV3D/T3Mg\nurhmfotuLm3gHg7wXV16nbduvA1YTaSguVB7iFgjNVa0ckQ6l2Jcwz1e4L290cXOcddMiDtR\nmsqs4K4DpXXVzIwgX0q1uQcD/DCP7uYtHC8D1svUhHuowEeN6FVla0eckTiDUF/60Czumglp\nv/Ukw73BOPmVo+3Gyuzn2dtMpgncgwH+qCp9xFo4XgasjoQ5ivRmAXd499o7YaUwR7Ku7I+N\n+4u7akJX5mNFqfIq7hpQw510mHuwbapHH+YeC/DHDOrJWjjeBawvwypxDxT4rKr0icLVI0Zm\nQ5rHPVbgm4E0hbtsQtZbjShq6HHuClDFGqkJ92jbVLyLeyjALxnlDF9xFo53AWskLnOpQ9Op\nv8LVI8Zaas49VOCjQwlFfuWum9D0ywCJUndwb3+1NKaXuQfcqkTQH+8WrCbRfZyF41XA+js6\nEXOA6E9GSvgPStePAH8mRIXM90XwGEEjuQsnFF2YE01l53NvfPUspLu4h9xqKPdAgJ+OlzRx\nfgt6FbCWUn/uYQI/jKaxStePAPfhIuI6dDTZdIq7ckLO9Y2lKC4tuOdmKKA680HKubiHAfw2\nmkYzFo43ASuzvGkP9yiBH44mFjmteAUF6v2w0tg9qkPjaAB36YSY7IPVyNQtxC5YNoN6cY+7\njHsYwG9Hk6J+4yscbwJWOrXmHiTwy2B6WPEKClD2bTSbe5jAD8fLGPRxDkWQyD5aj8Jab+fe\n7GrLKGP4mnvocxCw9Gw4TeYrHG8C1p0UEqcEB6GDMYnnFS+hwOyjxtyjBH6ZTp24iyd0ZB2u\nT1JqKE72PIGGcw9+DgKWnh2Oj+GbU8aLgPWpVJN7iMBPPWmp8jUUiEvljLgEhT5lVKM3uMsn\nRFzbWYOk29Zwb3EW6ckRP3OPPwKWrg2kGWyF40XASqOp3CMEftoTUfKy8kUUgAXUiXuMwE/z\nKZW7fELCueVlKez2UNx7ZZVGE7i3AAKWrh2MjT/LVTieA9Y/MZijQb/MtEGFKvLbH3Exe7mH\nCPzVkDK4Cyj4fT8pnkx3beLe1nyOJEb/yb0RELB0rS/N4yoczwFrNfXhHh/w23ZjxesqlJG/\nRtJ93CMEflsl1dbIpeKC1kv3GCiuV2ifxD2EpnNvBgQsXdvPdyyyx4CVXd24k3t8wH9tabca\ndeSfL40lsHdUx+6gbdwlFMzOra1FVH50qF+n81Bc3N/cm4J7DCAgvWgBU+F4DFgvUCr36EAA\nNoXVzFKjkPzSjaZwjw8EYEt46UvcNRS03rkvmgzNQmjWdpcG0CzujcE9BBCQ/dHFmHZheQxY\n3Wgh9+hAIG7XygXpC3tTqpLBPTwQiM60iLuIgtPpR+sQJfXGNaRkB2KLsh2knIt7CCAwPWgx\nT+F4Clg/G8tyjw0EZK3UIFuVUvJdc8Lf5/q2N7qo9q8VoDvXMrqZyNBkxnHuzasVfdl3YXGP\nAATm8aji/7IUjqeANYvu5x4bCEwzOqFKKfnsBDXiHhsI0CBdXO5SV94eU5yo9EAc+XrD/hju\nXVjcIwABupdpF5angFUz8gD30EBgVklNVCklX2XVkXCFAL07khz+FXchBZPPZlQiiu2wjHu7\nakw/ms27XbgHAAK0l2kXlqeAVfEu7pGBQDWhp1WpJR/tohbcIwMBm0RduQspaHw5tzaR6baH\ncGZtQQdii/KeSMg9ABCoHjyHi3oKWCVWcw8MBGoF3apKLfnmagVjCM+eGDQyKtOr3KUUFP47\npw6RsdEE/GLgTH96kHXrcPcfArW3SLFzDIXjKWBN5B4XCNzN9KwqxeSTx6g997iAAIvoZu3O\nA6ITma9PqSynq7G4rIELB+NiWKdz5+4/BKwXzWUoHE8Bi3tUQIBl1EyVYvLFxZImnIMeFJrR\nDu5i0rXzhwcXJzI1Hb+Pe0tq2RCayLmRuLsPAeM5UwIBKxQ00t4urMXUlXtUQIhN4aUucFeT\nbn22rJWJKO7O6Ye4N6PGHUmM+olxO3F3HwLXnx5Sv3AQsELBMs0dhXWuWJHHuUcFxLiHHuYu\nJ106vf++skRUvvtiTHjlWRoNY9xW3L2HwB2KZ/iZGQErJDSmp1QpJ6/Not7cYwKCHCga9T13\nPenNuSceaBBGFN1s1HbuzacT6SWN/+PbXty9BwE4fmZGwAoJK6TG2prOvUrsfu4xAVHGUnfu\netKTP49NvNlAZKjRe8kx7k2nI5M5q4y78yDAkWKRqv/MjIAVGppSuir15K0SA7lHBITJqEwv\ncReUPmR9snlwNbKEq6rdZuGoK99kVJLeYdtw3J0HEUbRULULBwErNKyR6mrqZPqGh7lHBMRZ\nItXN5K4ozfvu0JSWsZZwFVG75xyEKz/Mo+ZsG4+77yBCeinjlyoXDgJWiGhBe1UpKC9xDwcI\n1ZLWcFeUhmV+vndK62KWbEUl7xi+Aj8L+qsBHePahNxdByHU/5kZAStEbDBUva5KRXmHezhA\nqJ1RCX9wl5QmnXl59bDGUXK2Kn5Lv9mYRzQgq8OqXWPajtxdByHU/5kZAStUtKFNqlSUd7hH\nA8QaQoO4S0pjLn+wc3K7FDlaGcq0GDh3D/cWCgZtaDXT1uTuOYgxj+5Qt3AQsELFNlPKJVVK\nyivcowFipZeRXueuKa3I/vbYvHurG+RsFV+/87iVR7g3TtDYGVmM6ZrP3D0HQerTE6oWDgJW\nyOhCi1UpKa9wDwYItkCqp6WfoJlcfX/zqNQ4OVpFVm077BHsthKsP43j2bDcHQdBVkm1VD0f\nBwErZOyNTjijSk15g3swQLSWtJK7qFhde2/dffXDLdFKKtmsz/RNGdzbIygdSQ7/jGXrcncc\nRGlJG9UsHASs0DGAHlClprzBPRYg2u7oWM6LxbH6Ye+4WyIs2cpYqe39Sw5yb4lgNo1as2xh\n7n6DKNtMJc6rWDgIWKHjcLFIzVzShHssQLg06spdVQyyPl7dQz6UPaxcm1Er0rm3QfCrS4c4\nNjN3t0GYHvSgioWDgBVCxlJfVYrKC9xDAcJlVOObpohH9kcrzAmWcBXbuP8CTB2qjnXGMhcY\nNjV3t0GYgwlqXjoVASuEHC8vvadKVXnGPRQg3hpjGTV3vjP7dlOPJEu4Smo56jEcb6WirjSV\nYWtz9xrEGUu91Csc1MoRSgAAIABJREFUBKxQMofxYhOOuEcCFNCdRnHXlTouPDG6iiVcFb19\nzCbuMQ85B4uFf6r+FufuNYiTUVF6TbXCQcAKKY14jmAojHsgQAFHSoWFwGRYX61sG0EU0ei+\nNdhzxWE6pWarvtG5Ow0CLZQaqDZVAwJWSFlnqHBZlbryhHsgQAkLpOraKC+lXHtxYlUiKtN1\nHmYPZdOYNqi+4bn7DCK1oPVqFQ4CVmgx03xV6soT7nEARbSnadyVpZyzj/csSmRqnLaVe5hD\n27bI+F/U3vbcfQaRtkcm/qVS4SBghZa9cTE/q1JYHnCPAyjiQJLxXe7SUsb3q1qHEyW2f/gw\n9xjDcOqs9ubn7jIINZCGqVQ4CFghZqSap1C4xj0MoIzZUq0r3LUl3oezGxBRxV4rcNSVFmTU\noL0qVwB3l0Go9NJhb6lTOAhYIeZ4RellNQrLA+5hAIW0oSnctSVW5kvjyhMZ6g3HD4OascGU\n9Ju6VcDdYxBrPql06VQErFCzRKp9TZXSQl2FogPJhje4i0uci+mDihFFNpu4l3tcwd5gMqtb\nCNwdBsFup2WqFA4CVshpRUtUKS3UVUiaL1X6l7u6xDi9rXMUUXzbmThjUGuO16QtqtYCd4dB\nsN0x0arM546AFXL2xEb/oEZpoa5CU2e6j7u6BDi14nYDUcmui49zjyc4sTkq5hs1y4G7vyDa\nGLpLjcJBwAo9Y9Tev466CiVHytFR7vIKTPZ7M+oQSZX7r+UeS3BlLDVV5yAaG+7ugmgZtWmX\nCoWDgBV6MmrSQRVKC3UVotaYimliKhD/XH0mrTSRsUHadu5xBHea0YMqVgV3b0G4jaZivytf\nOAhYIWidscTfypcW6ipUDaM7VLsUhVhn9/aMI4puMXk/9xiCB/uSw55VrzC4ewviDaFuyhcO\nAlYo6k2DlS8t1FWoyriZ5jDXlz9+XCPPJZp099x07gEELywxJqs3oTt3Z0G849VUmE4NASsU\npZeTnla8tFBXIWtPouEkb3357L/zGknyXKIrMZeoXgyhVNUmnOHuKyhgvSlR8WMZELBC0gpD\nmX+ULi3UVehaEFZSheMbRMl6Y1IlorC6wzCXqJ5k3ELj1CoR7r6CEoZS22yFCwcBKzTdS4MU\nrizUVSjrTy3VPMkrAJkvjixJZLplPOYS1ZsDKbRTpSrh7iooIaM+rVS4cBCwQtPR8pSucGmh\nrkJYRmOayllfXsp8fnhxouiW03EJZz1aFxWp0iXluHsKitgRG/mRsoWDgBWiVhuLc/6Iw919\nUNi+EtIhxvryRtbJ+y3pKrbNbBzUrlczpBLqTJrM3VFQxnSqcUHRwkHAClWDqIPSvz+jrkLY\n6sjoj/nqy7P/PFBaTldzj3EPFARgENU9p0a1cPcTFHKXwsfKIGCFqoy6iv/+jLoKZZOl8n/y\nFZh7Py+uRRTVchb2XeldG2qrxqmE3N0EhRwpT9uVLBwErJC1I870npKlhboKcd2pxVW2AnPj\nyoH2BjI2mYJrOAeB9IbUJ0v5muHuJihlQ5GoDxUsHASs0DVTqnhWwdJCXYW4jKY0kKu+XPt8\nQhJRpeF7uEcHxDhUmUYrXzXcvQTFTJcqnFGucBCwQlhX6sJ1GBZ310EFhyrSXKb6cuHqvhZE\nsebV3CMD4uwpTQ8pXjjcnQTldKc2yl3ZCwErhKXXpIWKVRbqCnYkSduYCsyZXx6+iaj2A/hp\nMLhsv4lmK1063H0E5RxvqOCEtQhYoWxnguEZxUoLdQVrosOf5Cmwwt7tHU5FOq7jHhIQbnNx\nmqlw8XB3ERS0L4U2KFU4CFghbYmx6P+UKi3UFZxYaCryOkuBFZCVnkqUcv9B7vEAJWxOpinK\nHuzA3UNQ0oZYo1LX5kXACm2jqKqCR/ihrkLeg2FF/8NRYA6ubKpKVG8WruMcrLaWpGHKHUeT\ng8+rILcwPOZ9ZQoHASvEdaKWHOfSc3cb1DJOSvovQ4HZ+XdpKTK2XMU9EKCgneWoyyUFa4i7\nf6CsydJNXytSOAhYIe54Y+rHcCohd7dBNWlS8qfqF1i+v+cUowjzVu5RAGXtq01NFbz2F3f3\nQGFDqfyPShQOAlaoO1SZJilRWagrsBkmJbPtwzozI46ie2LSq+B3pDmVU+7Cvdy9A6X1pKq/\nKlA4CFghb3dJhskauDsNKhouFXtX9QqTyfEqtt9+7v6DGjJ6SNH7lKok7s6B4rpQdQUSFgIW\nbE6kNeIrC3UFeUZJsS+pXWE5OWdnxlHcAJw4GDKmRtIYhQ4o5e4aKC7DTFXF/0qIgAUn1sVL\nis0DgrqCEycmGSMOqFxh5+clUOyAQ9w9BxWtTaFGykw7w90zUF5GZyr/lejCQcCCEydWx0pr\nRVcW6gpumB0ZtkzN+rq0NImi+x7g7jao62ALit6gxDk73B0DNfSgm94TXDgIWGCxKk5aJLiy\nUFdgZ0VRGqbafCBX15aiqJ77uPsM6ptQhNp9L76iuLsFqhgqxWSILRwELJCtS6QH1Jytgbu/\noLat5ai5gufR27m+rTyZuj7O3WFgsbUuxay8LrqmuHsF6phqMojd046ABVZbSlGvy0JLC3UF\n9g42pdJvKF9ZWfuqkbHDTu7eApeMUdFU/1XBVcXdKVDJknjqd1Fg4SBggc2eqnSrOnsYUFeh\nKaOPFL5U4d2k2UfrUFgrTCsa0nbdTlKPU0LrirtLoJatlajOl+IKBwELch1uRqXfEVdZqCso\naE48tVNiNr8b2pLUYgN3N4HbwkpkGvubwLri7hCo5kgbitkmrHAQsCBPRl8pQq2TCbn7Cix2\n1qOkg0rWVclb13D3ETQgY0JxKjJRXMTi7g+oaGIUdf1DUOEgYMENs2Kp62lBlYW6gsIyhpio\n2y/K1RV+HASbo8MTKfJ+UfMacfcG1LSpOhXbJaZwELDAztYaVOKEmMpCXYEz66pR/GrhZ3mh\nrqCgIyOKU1inF4Qc9cfdF1DV8cEmavW5iMJBwAJ7x/oaqY+o3aOoKygsY3gRqvMC6goUlz6x\nIlG15QJ2ynP3BFS2qT4ZRwsoHAQscLSqIiWsVWwPA+oKTuxsKVGHj1BXoLyFqUYydTt+DXUF\nvpmWTPFzzwX6gYSABQUcGxJFNZX+nZC7k8BqWQ2Sun+MugLl7R6YQlRsxEuZqCvwxZHBMZQ4\n+6/APpAQsKCQnXdKdNtzgRUW6grcmVGBpI6voK5ABUs7xBElD3/mCuoKfLCvdzRFj/wikA8k\nBCxwYlUjosYHA/qbD3UF7mTMqEzUcKvIWZNRV+DCsdmtY4lium35GXUF3jswKJGkOx6/5PcH\nEgIWOLWssUTlFoicqw91BY7mN5Eo/v43UVeggvR5HZOJqO4D/nyocTceuKRPqkkUN/hZPw9L\nRsACF9a0MZHRfEDwLgbUFdywuXtRokoPfoC6AjWsGVTXSP78MM3dcGC0rmsiUeLAw/4c8Y6A\nBS7tHVqOKPrePWf8KCzUFXgjfcZtJqLyY54SE+S5uwNad2jWT6gr8M3xR9rHExmbz3nV18P4\nELDAnVVdk4kMtzz0Io6VAYUcnHRbJFHEHbNfvoC6AsWhrsB3x5fcW1GyfEylTj70nQ+Fg4AF\nHjzau2oYUXiT0bs+FTc9FnenQFOOzu1cjix/IjZM2/KB/6d6oa7AC6gr8M+eyR3KWkIWJbaa\nuPVt734wRMACz/ZON1c0WAorsmHf+Yc/+tefTyjUFbi3e6q5slxk4bV7zNr3/lnUFSgDdQX+\n2z+nX9MkkpW6Y9iig+/96b5wELDAO4cWDm1Z3mitrOQm3cevePzk53/781GFugKXjiwZ0aay\nyVpkiTf/F3UFCsDnFQRo78K0u+omWj+nqEiNNoMeXpv+xrdOf9/xFLBmTQXIN3lo19vrlY0P\ns5UWGRPK1Wra6j0/PrBQV+DKlBHdW9YvF284jroCBXjY54C6Au9MHNzljgaVkky534ZOP688\nBawSBODeKj8+sFBX4MmLqCtQwEnUFShgh7PCCf6AFZ6QkBDL3YhAFbF0IoK7Ea48HaofWPGW\nrWLgbkTgYi3dCOduhBOh+kUYFJ9YRFGWbkRxN8KZEK0ry+ZI4G5DoCQtd8JpXXkKWBW5Wx2w\n+EaNGlXlbkSgylk6kczdCFf8+cDSf11Z1LZsFU1+hfimqqUb8dyNcCJU6yrOsj2qcTcicCmW\nbpTiboQzoVlXkmVzNJK4WxEgg6UPDbkb4YpfAat/a7273bJNbuFuRKCaWTqRyt0IVz724wNL\n/3Vl0diyVe7kbkTgmlq6cTt3I5wI1bq6w7I9mnI3InCa/cwKzbpqJQcs7kYEStOdcFpXngKW\n/r1q2ST3czciUPMtnXicuxFQgNmyVU5xNyJwwy3deJ27EZDvbcv2GMLdiMCtsnRjI3cjIM9V\nOZtkcbciQP/Kf3twN8InCFi6gIClRQhYoAAELBAPAYsFApYuIGBpEQIWKAABC8RDwGKBgKUL\nCFhahIAFCkDAAvEQsFggYOkCApYWIWCBAhCwQDwELBYIWLqAgKVFCFigAAQsEA8BiwUCli4g\nYGkRAhYoAAELxEPAYoGApQsIWFqEgAUKQMAC8RCwWAR/wLp+7ty5i9yNCNRlSyeucjcCCvjX\nslX0/pFlcdHSDadXggcWQfGJlZNzxdKNK9yNgHyWzXGOuw2BytZdJ4I/YAEAAACoDAELAAAA\nQDAELAAAAADBELAAAAAABEPAAgAAABAMAQsAAABAMAQsAAAAAMGCOmB9aLYzgbs1vvtsmNns\nMAPkz5vG9u46YM6zmVwtglx6Ly0UlyahrEA83VeVjosqqAPW67ourOvbO5kd6+pQl9y+pP3O\n1iqw0ndpobg0CmUF4um8qnRdVEEdsJ4xm+fszfMMd2t89O1os7mrQ10ds1TUw4ee2DbEbB58\nnq9hkKPz0kJxaRXKCsTTd1Xpu6iCOmAdMZtf5G6Dv050NXc7ttK+rn67x9zlHXnhyjyzeTVb\nw0Cm59JCcWkWygrE03VV6byogjpg7TKb3+Zug78mmEd+m+NQVxvM5r22pcv9zJ3/ZmoXWOm5\ntFBcmoWyAvF0XVU6L6qgDljrzOZPuNvgrwnrruY41FVmX3PXf3OX95jNR5naBVZ6Li0Ul2ah\nrEA8XVeVzosqqAPWUrP5W+42+MvacPu6+sJsnpa3/JnZ/CBHoyCPnksLxaVZKCsQT9dVpfOi\nCuqANdts/oO7DQGxr6snzOZtectXO5l78rQIbHRfWiguLUJZgXj6ryr9FlVQB6zJZvP5l+cO\n6NJr7LbfuNviF/u62mo2P5F/R39Lz1haBDa6Ly0UlxahrEA8/VeVfosqqANWmtk8MnfCjC77\ns7lb4wf7ulpuf5zfGLP5R5YWgY3uSwvFpUUoKxBP/1Wl36IK6oA1wFJRvZYfOr5hsGVhN3dr\n/GBfV/PN5nfz73jAbP6KpUVgo/vSQnFpEcoKxNN/Vem3qII6YN1jNq+/KC9c32SprK+5m+M7\n+7qaazb/J/+OaWbzFywtAhvdlxaKS4tQViCe/qtKv0UV1AHr4oWLeYvzzOYlnE3xj8vgPlHr\nwT3Y6b60UFxahLIC8fRfVfotqqAOWHa+Mpt76u/XZ/u6WmH/0/Nos/lnlhZBIfosLRSXxqGs\nQDydVpV+iypUAlZ2N7P5HHcjfGZfV9vN5hP5d/Qxmy+wtAgK0Wdpobg0DmUF4um0qvRbVKES\nsHJ6m82nudvgM/u6esZs3pK3fNFs7svTIihMl6WF4tI6lBWIp8+q0m9RhUrAutrJbL7K3Qif\n2dfVN2bzpLzlD8zmOTwtgkL0WVooLo1DWYF4Oq0q/RZVMAest9fOeilv2bIlRnG2xT/2dZU9\n5MaFLdeZzc8yNQlk+i8tFJcGoaxAvCCoKv0WVTAHrOfM5rTctJ49zWzexdsafzhcRHyX2bzV\ntvRXd3P3iy6eAmrQf2mhuDQIZQXiBUFV6beogjlgXelnNi+wXnf76mqzucc/3O3xnUNd/dPL\n3OkVeeH8ZLN5H1ubICcYSgvFpUEoKxAvCKpKv0UVzAEr553OZnPvdceOrx9gNnd6k7s1Pvls\nr2ys2bxI/veodd1Lnczmhw5krLf8/zLxOnP7Qp2OSwvFpV0oKxBPz1Wl96IK6oCV81af3Esw\nmfu9x90W3xwy2+tvW/ncPbm3H9L2qamhQL+lheLSMJQViKfjqtJ7UQV3wMq5cHzmgG73DJ7z\n5BXulvjIaV3l/Ll9XK9ugxe9xdo0sNJtaaG4tAxlBeLpt6r0XlRBHrAAAAAA1IeABQAAACAY\nAhYAAACAYAhYAAAAAIIhYAEAAAAIhoAFAAAAIBgCFgAAAIBgCFgAAAAAgiFgAQAAAAiGgAUA\nAAAgGAIWAAAAgGAIWAAAAACCIWABAAAACIaABQAAACAYAhYAAACAYAhYAAAAAIIFZcB6i4iW\nurn/Q8v9C92/xI8jqxQJT37Y7rW8eJKzR3v1NC/bDSHvhKVEtnM3AtTBurHxYRTs8stLwPcl\nuICA5dRHCSTri4AFmoKAFUIQsEBBCFgqQMBy6hY5XhnCEbBAWxCwQggCFigIAUsFug5Y7aia\n0/XfjRo16gU3z/NYML9bHhBzKDPnot1reVVlhR/t+Wk3euGp3RDyELBCCMPGxodR6Mgvr4C/\nLwtw9b0civQcsLIT/NyQHgvmXcsDxvj6JOeP9vg0v3sBIQgBK4Sov7HxYRRCvC4vHwMWisiO\nngPW/0ipgCWX3mZfn+T80R6f5ncvIAQhYIUQ9Tc2PoxCiFIBC0VkR88Ba5diASvd8oB9vj7J\n+aM9Ps3vXkAIQsAKIepvbHwYhRClAhaKyI6eA9aooAhYfvcCQhACVghRf2PjwyiEKBWwUER2\n9BKwfl7SsXycIa5S900XbCu2U56bLLfetPz7Ss6ZseVNxb8scFZEoWd6Kpj1+a9caJqGRZZ/\nxtcvbkq5c9X53Ee7eGfnActDLxzanXl0eO3k8ITKndf8kbfqPcv9L+XknFnQON6Y2PCBb/0d\nTFDT1b09aieaSrRacsZupZOt+4xl6z6bk/PqgNpJEWXuXH/+xoOzj3SvEJVQa+BLCFjBz+XG\nfnlcw+TwYjW6786vjNzPg88GlzNFVRn2qbwq61DbJGNCk3n/2L+ky2c6fpK4+TBy8iEKuuSs\nvLz5vnTy1WdVsLYci8jpQ6y+mdeubIwhtpJ59Z/KdFQj9BGwrk0Jz99uScesqxw3pFwCT5yr\nId/80KFgnDwzgIC1JPP+vLvKnLzxWk7e2VnA8tgL+0J/rnr+XdEzM23rPrPcOJFzsEju+vBt\nAoYWFPZ8+fztuCZ/pbOt+6rlxv4bBVb+jbwH/9Yyb13bfxCwgpyrjf35LfkVU2J/7jrb58Ec\nybbauCcn59d6uY8p/b/8V3T9zAKfJC4/jJx+iIIeOS0vb74vnXz15TirrUIBy1n55VxOC7vx\nAbg4W/l+s9FFwMo2W7dEXNmi8j/SIXnds61aRRMVadWq1b2WW19a1h+YZH2UQ8By9kxPAetE\nq1Z1LQ+obXnphQUi0/I0+dMowfqJFvOB9dEu3tlJwPLcC7tC32awlnLDaib5327XrCu/tiwe\n3GcpTVOiUV4d9prAQQZF7LJuSEOkdduPz13pdOu+Y1ncPM7ynwjbLLcx/7U9+Lz1SzOhXg3L\n12GzY4SAFcxcbeznYuX1pRtWsVbOIttK6+fBcstnSUKEvNb05d+VLDkr0fqQermx3c0zC36S\nuPowcv4hCjrkvLy8+b508tXntLYci8hF+WW3lW8Yy1SKt77dBPUGQHW6CFiPWTZC8gb5F5av\nR1gW4/+2ra5147feU5bV62KpxtSl03+wLxjnz/TlGCyHyNSTDCM/ys65cqyy5UaDbDfv7CRg\nee7FjXa/bSnHsAd+sixd3H6TZe0s69rv5deKl4Z+lJNz9QU5BLb0bzxBNe9Y/iCMmP1NVs5v\nK2LkKG5d6Xzrvm9Z6keGB77Iybl0oKwc8W1/2U2Qs9gTlu/LKwfLUhsErKDmYmOfsnzhhY39\nzrJ07jH5u++wda38eTAnquhj53KyXq9jWR4xnBo+l5lzcY/8pZaR4+GZzj5JnH4YufjgAv1x\nXl7efF86+epzUVv2ReTiIfJursbPy39X/rpaXvumCl1noouAVdGykf6TuzwqvxjsN+QPlrV3\n0gO5+xpvFIzzZ/odsEjKXXta/gJMd/POTgKW517kPzu7lt336Bdxlj80v5OXfrSsjZb22Fb/\nmWBpTnD/fh0EGlr+UHvZtvhiGFFZeb+Ci61rLTDK/aPx9xJ5n0a/RvyfvfsOjKJa2wD+zm6y\n6YSE0AmE3iIdgdBBiujSq3REkCIiKEWR3kEUkCIISBXpBHu9ls96rVfEXq/XLqJ0SPLNbLKb\nTTKTbTPzzsw+vz+us8tuds45zz3z7uwU8Rvl57nP/lCRUGBZmdJgi9tC9//vcz4RE1PlgrQk\nzQex8R+4nv0+miheyDjverBb/Idbcny8U24mkS2wFCYuMB2FePmzvZTZ9Clkq0CBJf+SzkTl\n/8l79otSRENUbqeBmKHAknYSdXA/+EF80C130WsgpWepvfu3XE9gFN4ZfIF1k/sV0hQ2pJhP\nLlpg+dEKz7tfEBd6eNZmhfRF1fNZ491PTxQfPFtcK4DdS+R1xdox4oMnchRHt2DAHhEf9JcW\n1osL97iffRQFlqUpDPa74sIoz4seFB/tkhZc88GKvGd7isu2U7nLl+OJrs3x9U6ZmURuMlKa\nuMB0FOLlz/ZSZtOnkC3vECm8RPz2OMzz7Oqm/Zeo1D4DMkOBlXPx2zf/43mQSlQrd6lwgfWM\n+yX5Fbn8O4MvsN5xv+JyLFFSMZ8sswfLdys87x6dtynO9audqJ77swTPuYPSftbCV0MFY5EO\nDD3pfvB0pUbX7ctRHF1XwN50P31RDFjJrBzpvhNEp9zPXi2FAsvKFAZ7irjwiedF58Vo9JYW\npPnA4f7B7l7yOmSgOVGFHB/vlJtJZPdgKUxcYDoK8fJneymz6VPIlneIFF6SRNRLzXYZmCkK\nrAKaEJXOXSpUYMVfcb9E4eaVnncGXWCVz39JJ/Hhj8qf7Os6WLKt8Ly7DlHU5fwXNxO/m57N\n+6x0z7PPiY/WFNcKYFfT62xlD4XRlZJSMf/p9uLD73Jc3/bK5D87CAWWlSkMdiOial6vEreT\nydJ/pfmgpftJ6fCZhe4HTqKEHB/vlJtJZAusAvInLjAdhXj5s72U2fQpZMs7RAovaSx+MXg7\nxLaYhPkKrBZEpXKXChVYbT0vUQiM551BF1heO8dvER++oPzJvgos2Va4333WRtTQ68UjxOff\nyPusoZ5n/89XK4DbRXEg2xR+Uml0paT0zH9aCtiLOTmnxf9k5D+7EAWWhSkM9nkxMV28XjZd\nfP6nnNz5YIz7yW3igwPuBwPEGj7HxzvlZhLfBVb+xAVmozSX+LO9LLrpU8qWV4iUXrJa/G/M\n7K9UaJLhmaTAunhgbIuyMZRHvsAa7nm1d2Dk3hl0gTUp/yXz8+YzhU+WLbB8tcL9bukk6t5e\nqyPt/T+a91n596B+AwWW0UkDOVDuSbnRlZIyJf9pKWD7c3JOif8ZkP/sDhRYFqYw2NL/72Or\n5Evy+sI1zfvFnkMyB+UVWMW9U24mUSiwZCcuMBulucSf7WXRTZ9StrxCpPSSy61df73OxEOW\nPyPVHAXW3grkTb7Amux5uVdgZN8ZdIF1d/5LVooPtyt/slyB5bMV7ndLhwbmHwSYk7NcfLwz\n77OmF/gsFFiG9h55H+OZR2l0paTMz396lfhwW+41t0fmP3sABZaFKQz2R1SUdNinNB/MdL9Y\n2ly+5H7gLrCKe6fcTCJfYMlPXGA2SnOJP9vLops+pWx5hUjxJWeH5T2yt15v7RrLFAXWItdY\npLXuOVSUolRgFZwwVhXzzqALrMX5L1krPtyk/MkyBZbvVrjf/Sp5zrJ2eUDxs1BgGdrrhQbS\nRWl0paQszX86L2CvFHw1LjRqZQqD/YbMdupwjj8FVnHv9LvAUpi4wGyU5hJ/tpdFN31K2fIK\nkeJLcnLeGl4i74nEhVdzrMsMBdbz0uVjJ32X90jxGCyZMkfhnUEXWPPyXyKV8fJ7lRQKLD9a\n4X63tOMj/wiJnJyl4uPdsp+FAsvQPhCHaEThJ5VGVyZgj+Re331k/rN7UWBZmMJgf0zehyHk\n811gFfdOfwsspYkLzEZpLvFnezkv/315mz6lbHmFSPElksvPT0vPLbFuuBBkg0zADAWWdMXZ\n+z2PmgVQYCm8M+gCK/8jXD9Ey38XVCiw/GiF+91fU8HTWO+h3Aszo8AymW+owHHruZRGVyZg\nB3PvGud13MRGFFgWpjDY35P8ae2+C6zi3ulvgaU0cYHZKM0l/mwvi276lLLlFSLFl7j9tLWN\nVGEtCqIxJmGCAks666pq/v0gK/hfYCm9M+gCy+uAZelUiv9T/OSiBZY/rXC/+7ydqIHX6gwR\nn39X9rNQYBna5UiiRoWfVBpdmYC9npPzMxU48+cOFFgWpjDYl6OI6su83HeBVdw7/SywFCcu\nMBulucSf7WXRTZ9StrxCpPgSL4ejieLOBdwWszBBgSXdTnm059Fn5H+BpfTOoAssr7BIdyX/\nQ/GTixZY/rTC8+5riByX8j8rPe8hCiyzEQfXcd7z6NNTp6QbECqMbqGAdRAf/iL+t2SBa9d0\nQoFlZQqD3Zwo8kzRV/susIp7p58FluLEBaajEC9/tpcymz6FbHmHSOkl3haIf+7lQFtiGiYo\nsKRfjm/3PJoaQIGl9M7gr+T+vfsVl2KJKil/ctECy59WeN59K3lu1pqTe2fWVvKfhQLL2KT7\neR13P5B2mN+aozi6BQMm3e3EFTDpjGbP1ZfPRKLAsjKFwZ5G7tuQuHyat83yo8Aq5p1+FliK\nExeYjkK8/Nleymz6FLLlHSKll3z3Xf6TzxaYDq3GBAXWZ94/5L7nEB/F5i6LX/1T856WL3OU\n3hl8gVXgRk7MZLYaAAAgAElEQVQTlD+5aIHlTys8736TvC/QNlN8tFn+s1BgGZt03k479wPp\nwgvS/ZsVRtcVsDnup/dT3kUkF3pvRRcTCiwrUxhs6XT3up5zrS5UiuzkOhfLjwKrmHfKzSQy\nk5HixAWmoxAvf7aXMps+hWx5h0j+JdNSvK7NnbOPvO4QZjkmKLCuJhCV+Cl3+eMKcRniePzu\netCMKDLvntzyZY7SO4MvsGLfzX32dA3xwSvKn1y0wPKnFfmny7ai/PsMviEmvdTf8p+FAsvY\nsqWBzDvD+WQiUTnXT4Pyo1sgYGeqUt6O808EoriPc599Mw4FlqUpDfZ14tK4vKNjLg+gvGu2\n+1FgFfNOuZlEZjJSnLjAdBTi5c/2UmbTp5At7xDJv0T6pvmge52uiB9TwrqnEZqgwHLdS6S1\ndF39HxfE0IOzPbNBX2nwfr36zZ+KZY7CO4MqsP4tLnSjkpukjeGbjcUHXV3/7vdZhH60Ir/A\nOhlNZJvxs7j01wMJeTs+UGCZ0DvSfvjBb57P/maFdN2X3N3l8qP7vnfAGpDnvvYDxcWU7eKz\n3yyKo9EosCxNYbC/jhcXO70qbqkuHGjqiYY/BZbyO+VmErnJSGniAvORj5evoVbY9ClkyztE\n8i/5u6y4NOJ16X6sZ5+UyrgZunaCrsxQYH0hDZK9ZpuaNqJR2Y9L5XT9Fp/l5GymXM8pljkK\n7wyqwJIWtowRi/NajaV8UNncm9H7XWD50QqvK+oelHbQCtWaVrdR/j4QFFjm85hrACn3f90H\nOMiOrpSU+4fkB6x03lEP/63kem9JsSqjztJl4LexNAT0oDTYz0mzB8XVKCNdqYjq/eJ60p8C\nS/mdcjOJ3GSkNHGB+cjHy9dQK2z6FLLlHSKFl7wYJT2wV6iS4Hph6/OF19M6zFBg5Tzjvuir\n/d6cnCsNXIv/EWvidF8FlsI7gyqwXpaeuzI27+9R3Y9y/93/K7n7boX3PaFeqe/+JKrsvocr\nCiwTerGGexwTPLvFZUdXSsrqS6Pcz9f72P3iUw3cz/X4W7qI1ia9mwD6URrsD9p4EiOMPp37\nnF8FluI75WYS2clIYeICE5KNl6+hVtj05chnyztESi95q57nSYqYauH6yhwFVs5P9zRNtCc2\nudP1venHQaUiKwz8VVz67daK9uiq/b9ULrDk3xlUgfWkuPBUTs67tzVMcVTsuuVi3kv9L7B8\nt6LAXc2zDt9cr1REcp0Rez2n9KPAMqMLjw2sU9JRrtPKP7yelBldKSkrxAlpWqPSUZW77PA6\nLOHKI87K0SXrjXoxJ+cv8r4UIFiP4mC/OLVJOUdshevmu/cf+FlgKb1TdiaRnYwUJi4wIbl4\n+RpqhU2fS9FseYdI8SXZT01sUTbaXqJaz/t+1LC1/MxRYAFYns+yHwAATAQFFoAhoMACALAS\nFFgAhoACCwDASlBgARgCCiwAACtBgQVgCCiwAACsJIwLrNXd5K3kXjEISyiwAACsJIwLrJEk\nbyj3ikFYQoEFAGAlKLBQYIEhoMACALCSMC6wAIwEBRYAgJWgwAIAAABQGQosAAAAAJWhwAIA\nAABQGQosAAAAAJWhwAIAAABQGQosAAAAAJWhwAIAAABQGQosAAAAAJWhwAIAAABQGQosAAAA\nAJWhwAIAAABQGQosAAAAAJWhwAIAAABQGQosAAAAAJWhwAIAAABQGQosAAAAAJWhwAIAAABQ\nGQosAAAAAJWhwAIAAABQGQosAAAAAJWhwAIAAABQGQosAAAAAJWhwAIAAABQGQosAAAAAJWh\nwAIAAABQGQosAAAAAJWhwAIAAABQGQosAAAAAJWhwAIAAABQGQosAAAAAJWhwAIAAABQGQos\nAAAAAJWhwAIAAABQGQosAAAAAJWhwAIAAABQGQosAAAAAJWhwAIAAABQGQosAAAAAJWhwAIA\nAABQGQosAAAAAJWhwAIAAABQGQosBldOPVfUa/9wrxZYz+V3C+fsW+5VAmv4ssgU9p9s7nUC\n8/nfi0WC9NLP3CulGhRYOvt858QW0SSJrlinaRtRi/TUeOmxvenMl65wrx5YR9bbizrGSMmK\nq9IgQwxao7QS0qOa097gXjMwtyv/t6RHspQloVSNxtIc1rhaovSw9NCD57nXDUzk1PoBFV0b\nw8TK17QSc5TRsJorV1R19L7fuFdOFSiwdPTFpoHlpEqqcrvhs9c/dsLLgfunOmvZiUrd8ir3\nSoIlXHl6bFkxa6mdJ6zcl5+zfUtGNnUQ1brvNPf6gVn98FDvBDFZKRnD7tl8ND9a+1eNbydW\nWSVueY97BcEUrjw3sYqYo4Smg2auO+i1LTyycdbARuIXQ3vHTb9zr2PoUGDp5OzxCVWlSr3l\nzSsOnZB3YE5XcYaqu+Es97qC2f17cmlx6uo4fZdMzA7f0yaCEu60xhdE0NepJU0FojJdpu+Q\nm8EyV/dJJmr/LPdagtFlvzq+FFFsy1s3ZspuC4+tuqkGkaP/c2b/1RkFlh5+2NA9mii6+bgH\n5ePkcXxehp2SF57hXmEwsX82Nxarq26LjinGbM/QRCqx9AL3ioK5fL24AZEtfcymYmawY3df\nQ9TuTe5VBSP7aYlYPZXovvBoMUE6ceLh4RWJ6m27xL22IUGBpbmPF0tf+lL7Lik+Tm6P9I+j\nlLWXudcaTOqr20uQrdlsH2E7PCaeqmNPA/jtj40ZAtmbTNlXfLBEqxuTMPIX7vUFo/q/QZHk\naDdP+fufR+bSNnaqtP4i9xqHAAWWprLfnlVL+tJ38xa/iqtc+wdHU53nuVcdzOit/nZKHLjd\nj5Ttu8EmjMOZq+CPq08MiCIhfZLv6splUWVK3s29zmBEWQdbEFUc+6i/G8NtNzqoyh7z/lCI\nAks7V/81JZXIce3te/1Nk9uuLgKN+pN7/cFsnu9IlDb1iJ8pW51KtXBEMvj09ZxKRBWGbfN/\nAjs2Jor6/8G93mA0l7fVJKHpQh8HyhS084YIam7aE59RYGnkwombSxPFtJl5IJAweaxOo0rP\ncbcBTOXpVkTp8wOYvA7fKETv4F5rMLYrR7rZKLrLioA2iidObK5NlU27VQRNXN6aRhGdHgws\nSKItGSTcYtL9DSiwtPDH7v7xRCWum3s44DC5HR1st83EZbHAX89nEDVdGWDKZsfSnVncaw7G\n9d95FYhqTAriW+KxgYLjIe7VB+PI2lWNIro/HHiSREsqUbkj3A0ICgos1Z1a3T6CqIxzyfGg\nsuSxqgy1x6Gi4Jc3Oorl1X2Bh2xjeeqLa0OCvBf7RVB09weCnMDmx9PtV7mbAAZxIl0sr/w5\nOFTWkSERNMyMF+9DgaWq809Oqk4kVL9pbbBJ8vJoc6r8PneLwAQ+7kXUYFVQIdtbl9r9zb3+\nYEDnHkonSr31Md8ZUvJQReqN6h1E/+5AQvtAzvQqYn01qmLCq3CjwFJN9keru8YQRbeY9Ego\nQfKSOUiIf5y7WWB0P4yxU61FwYbscAtq8Rd3E8BovpuRRLaMJQEeeVXIvnRqY8bdDqCuH0bY\nqFGo+xyO9hcilprudEIUWOr4Yecw6S44lXotDP6wKxl3OezbuJsGhnZ6ZgxVnB3ChvBoW2qB\nK9uCtzcHRVBC/6B/0PE4nEFNLHC/EwjF+YVxVHl+yFk6cWJxEjnNVq+jwArdbwcn1JZuqtRm\ncgAnMvtpeZywhrt9YFyX15aipEl+XLOvGMfaUptz3A0Bw7h6KIModZLSHb0CcrwjNUSFFdYO\np1GJiaHNUG670qnmSe72BAYFVmj+PHZ7A4HI0WjkmtD2pitZX5KWcjcSjOpITYq+6aDvFBXv\nWCvqgVsHgMu5DTWIGgVytY9iZXahxmbb6wDqOdWF7E6/Lyvqy7FeVOJJ7iYFBAVW8P558s6m\ndqKIekOW+XttxyBsTqHF3C0FQ3q7Ldm6yd3POVBHGtAY7saAEfw6L4UiOq9XIVNumZ2oFW4Y\nEKbOznBQg40qpunE1Ej7Ru5WBQIFVnAu/Wtu60gie+3+C0PegeDDlhRaxd1cMJ7vbhKoSeBX\n7ZP1WFVU8ZDzzaRYiuu/U51MuR1vQ92wfzQsHUqllJnqpunE8gSaYaJD3VFgBeE/93WPIxKq\n9ZkX3GXaA/RQsrCVu8lgMGdmRVPaQtUy9kgp4QB3k4DXhzdFUPLoEC7LoOBoYxppok0iqOSr\n6ymij/q7HzaXp5HmuQI3CqwAnT4wphIRlb9+lp83PlXB+gT7Me52g5Fc2ViGkm8L8Uq2BTwQ\nFYtrroWzV3oIlHr7URUj5XGgGs3jbh7o7NKiGErfoEWcdlenXhe5m+cvFFiB+GSldJH2uIxJ\n6p8uWKyVjpj/4247GMfjdSlqsMrfDWcK1Ux6uy8IWfbjrYlq3a3NeTonTuxMEfZyNxF09a86\nlDhVozztT6frzHLaMwosf2W9elcN6XfBgSvV3HHgpzm2lC+5OwAM4r1OJHTeoXrG+tKN+CEn\nLF3d35Co0RLVE5VvXUz0m9ytBP38OkIQumr3E8/hZtTeJCdOoMDyy9XnxpcjcjSfrPIBoH4b\nR3VxuW0QfTfMRg3VuBNTYcfSaTl340B/l7bWJCHjfg0S5WWOUPEn7oaCTrK3laIqgd53PiBH\nW1Dbs9zN9AsKLN+y/nVraaL4Tnercum9IN1I1+PGqXB6RjSlztMmYjtLRr7O3T7Q2dn7K1FE\nZ1XPpJc1lNqa58hkCMXHbShqlCYH8+UTK6wOpviVEAWWLx/cWYkoocsCjRPjy7GGNIu7K4DZ\nhdXJKh/bXsAioSrumRNW/lyYQo4b9TiiNLMFTeduLejg3KxIaq59osQKq5sZjnRHgVWsn9c0\nIIrtNI+5upLsKyMc5u4O4HR1R2WKHablZdf60AjuRoJ+/ndXAsX236NhoLzsLy8c5W4waO6J\nqlRqth55OtKI+pngNx0UWMquHO8ZQfZmd6l6++bgrXUkfMrdJcDnWH2KcGq7NTxajQ5xNxN0\n8tWtUZQ4fL+mgfK21pH0DXebQVs/9CVbL10uDnnixKG6dLPxz8pBgaXkm3sqEFUes1uftPjj\ndrrmPHevAJOXWpHQYavWEXswsvQv3C0FPUhXFS09TtejSidQSxyGZWWXV8dTLS3Ov5G3P41m\nczfZJxRYsrJO9LBRTLfVuoXFL11oPHfHAIu3riNqvl6HiI2ivtxtBe25rio6Ve8DH1rTHO6G\ng3ZeSaf4iVpdSk3OzrL0IHejfUGBJeO35WlE1SdpfZPBgB2qjF9wwtEHvQRK1/S0Z4/jtQm3\nzLG4rGMZWl5VVNmjKfZXuRsPGvlpuCB00ul4PrfNJezHudvtAwqsIt4bE02OzvfpGxX/POhI\n+o67e0BnH/cTqJZ6dx30YWNk2T+4WwwaurStLlETLa8qqmyJUPVv7vaDFi6vSaS05boHaqUj\n7t/cTS8eCqyCrh5qR1RmpH73GQzMeOqQxd1FoKdPhtio2hwd9zYMpZu52wya+WtFBbK30+84\nmUL60FjuHgANPF+P4sYeYwjUTKHCD9yNLxYKLG+nV1UhuuZuhnvh+CmzKa3i7iTQz8nBNkqb\nreuPOUdThZe5mw3a+O9dJSjqhof1jFNBhysLT3J3Aqjt674kdN7Fk6gR1NjQFxxFgZXv80nx\n5OiynicoftpVIuo/3P0EOvlokI2qzNT7WJkVQr1L3C0HDbw/3EGJQ/fqHKeC1tgrnubuB1DV\nP7OjqSbf6WAdaYCRL9aAAitP9vM32ihpqM5H6QVuNjW5zN1XoId3+wiUpnt5dUI6WXUZd9tB\nbdlPXUdUYQL7Nf0G0RjurgAVZT1cnpKmMsxSbodr0yLuTigGCiyX81vTiapPM8AF231qZ+g8\ngUpev0Ggagxneon2JsR9z918UNWFrfWJ6ur7W7O8I5WFZ7l7A1TzbANyDNDpyqIKdibbHufu\nBmUosETfzSxFtoxlrDHx276S+JHQ8l7oRFT7Xq7t4SQazN0BoKKf55UhWxuDXNRvta2aoQ+a\nAf990I2EdnrcybL4REWW/IK7JxShwMp+rred4vuyx8Rvs6mFCe7BBEHLPt6C6JpFfAk7Xg3H\nuVvHeyOjKLaXcea3nnQXd5eAGr4eZqN0I1zOaBI1MGzNHu4F1u/31SJKm6TrLSNClUFruLsN\nNHNldzoJzfW5rKiS5UJj1PCWcPVQe6JyYx9jjVNBB0tHvM/dLRCy/01yUOV7ucOU6zoayd0d\nSsK6wMp+6aZoimhrkt8GPXbFx3/L3XWgjfMbqpKt7TruiLWjrdw9AaH7bXllA1525l5qgYv5\nmdxvM2Kp9O1GCdbhaoadr8K4wPpmQTXxy91IA93N2V+30Y3cnQdaOL20LEV0fYg7XydObHeU\nwyW3ze6dUdHk6MJerBeVQZu4+wZC8dvseEoad4Q7R/m2xMV8yN0p8sK1wPpjS3uBHO0WGeC8\nmsBl1sc9CS3ofzNKUHSfR7jT5TIQ9+U1t/PbryUqM8qQt6TYEZ30K3f/QNB+mRFPiWOMdVTN\nLKr9D3e/yArLAuuvXTc6SKgzcT93LIK1IaKSMeMEQfv8lihKHGqUDeKBxNj/cvcIBO3k7Ukk\nNL3XKD/hFDaaRnP3EATp+ymxlDj6IHeECutBI7h7Rlb4FVi/bu0RRZQ6dAt3JELRn+7k7kdQ\n078H2KnMeAN9K5yAWxKa1dlHWhOV6GvgCe5oqvAGdy9BMD4eGUkpYw00T7kdrkq7uTtHTpgV\nWB8uy7CL1dXg9dx5CNHB0pEnufsSVPPcdURp0zhulqroaEU7EmZGr99SgoRr7jTQATIyFlNT\nHOduPs9fL1CFycaM1qboEl9x94+MMCqwzhwZl0ok1B65kTsLKphNHbn7E9SRdaApUfpcox0N\nOIt6c/cMBOq7xbWJkvsb4DQJH1ob9rQvUHBhe0OiWrOMNk953EatrnD3UVFhUmBdfWNBmwii\n2IwpJjxpUFYjeoy7U0EFFx+qSUKLVdxxKiqzJuFnHFP546H2NorIuNdQe0IVbHOU+Yu7vyAA\n399dmoSWhr6iUSuaz91LRYVDgfXphj6JREL1AcvMcK9BP22MqGzYq9eCv86sKEcRnY25S3UR\ndpKayJndTunEnVuNcpaEL4NpOneXgb+ynu5tp7heBj6qT7IvOeJN7p4qwuoF1q/7xlQmopTO\nd+7hHn+V9TZivQ6B+HlWIkX32sGdJCUN6DnuHgK//LmzV7TJTtw5lOIw7g3kwNuPS6oRpU00\n3ImDRSwQahtup4OVC6yLL8xoLBDFthi/iXvoNbA/MfZ77h6GEHw1IZoSbjLwHodV1JK7j8C3\nHzZ2iSSqOGg9d14CM436cPcc+Hb5qDOCHB1XcMfFLzfSZO7+KsyyBdbn62+II7LXG7rKDIck\nBGMiDeXuZAjaB0MiKOUWY38rbE6Pc3cTFCv73flNxe+QaUPWc2clYJk1CXcUN7r3ppYW0zXO\nwN8CCzhUUXieu8sKsWSBdf7JydWJqHyPOUa6yanajqcJb3H3NATn1R4CpU41+iGB9wvNsrl7\nChT9c/SWikS29Ju3cgclKMupGS7VYGTfL08niu+xhjsoAVhlq3KGu9sKsl6B9e2GG2KIoprf\naqIDEoKziFpj+2dC2SdaE9W+27DnO+drSce5OwvkfbbmuiiiuLbTzbJ3oagM2sPdi6Dk901t\nbRTRfKYxL3qlqB/dwt1zBVmrwLr6ysxriKiCc+Fh7pHWQ3M6yN3jEKir+xoQNVrCnR2/rBWa\noIQ3nkvPTqkhTnOpfZeZ+vCHzRFpF7m7EuScfuT6SOmUVPOdGHY4VXiWu/cKsFCB9cvOQUlE\nkY3GGv8yeyrZaK9xibvXISAXN1cnIcM0e91bYReW0fz+SL8EaQ/9hG3c4QjZDXQfd29CEWd2\nO6OI0kY8zB2PoKy2Vfmbuwe9WaTAuvLavc1sRMld7jb2YcMq605ruXseAvD3yvIUcZ2Jzmld\nKzTFLiwD+fb+9nai0j3mW2IP/Z6Y5NPcPQoF/LVbuuBHpcHGvDSfP/oY60xCKxRYn27onUhk\nqzv8ARMc1qKqndGlDXZMHyj7dU4SRfc07GWvZLWgJ7i7DfJ8t+pagYQaw9Zxh0I1Q2kmd6dC\nvtM7b4wy4QU/CjpU3vYad0d6MXuBdWrr0ArShUS7zHyUe2Q5DKY53CMA/vluSizFD97LnZgA\nraFW3B0Hkj82tharq/Rx5qrPfThYMuZH7o6FXH9sv95BlDp4PXcoQrVUqGOgQ/tMXGCdfXlF\n7zJicRXf8lYT/eiirscS437iHgfww8mRkZQ85gB3XgLXlF7g7ju4eqJfFAl1x+/iToPaJtCt\n3H0Loj+3Xx9JVHnIg9yBUEN3msvdn/nMWWD989qDYxrYxeKqZMa4teH2u2AB42gi92CAT2/0\ntlH5ySY74znXCurM3Xvh7rs5FYgqDjf/Qe1FHS0XiRvmcDuz6wYHUZWh5j3uqqD9yY6T3H3q\nYbYC6/InB+f1rWETa6vIWjdOt/ylrnw6UibyS+4xgWJlP9WBqNrM49xRCVI6Ge8OqmEk+9me\ndorpupI7BhqZRkO4ezi8XTjUT7qN5U1Wqa4ks6iNYU7NMU+BdfnjA/MHpoulNlFs3Rtue8Do\nl8HWyTTcMMfQruxtSNRgAXdMgreQenL3Yfg6/1BdoqoTTfjTsp8yq9g+5O7k8JX1wqhEovLm\nPqpdRnPawt21bmYosLK+PLxwYL1IqbRyVGs/8l5zXqBDI5ihjOzs2jQSMu7jDklIagj/4e7G\nMPXbvBSyt7Hqzqtc96B+5/LJzEpEyb1Mc1E+/22LTvqZu3fzGLzAuvrR9skZ8VJpFVWj48g5\nW8z6O4uG5mCGMqpf5pSiyK6buRMSolk0nLsjw9L3U2Ipts927uHXWGYt3FGVw1+briWK7rTI\nmlvUMYaZswxcYJ0+MbuDVFsJFdoMm7MlrA9lLw5mKIP69JZoiutv/hO/MitGfsvdl+Hnq7EO\nSh5t5ZvV51lIXbn7Ovy8PDyGhIbTLHtV7mNpwkvcfZzLoAXW2cfvaCQdyV6x47iVlk2BSjBD\nGdGLN9qo9FhLHDwzmaZw92a4+XJUBJWbZMrTTgNWj17h7u7w8uf9dYnKDLHiWakeK4V6l7n7\n2cWIBdapVZ0cRBF1+881753i9ZSOGcpgLu1qTFTjLouch3EkOe537h4NK1+PjqCKd5j6Rs4B\nWEoduTs8nLwzJoYiMhZY/RehLrSCu6ddjFZgZb81oyYRpfWZjx1X/lpGHbiHDbz8urg8CS2W\ncsdCPSNpAXefhpHvb4mkCtOseWyMrAb0Inefh4tLe1sSlRlu/uMWfNqbEP89d29LjFVgvXdn\nFSJH80mWuh+E9hriatvG8d7oaIq+4SHuTKhpf2zp89zdGi5+mhJF5aaGy94rlxXUjrvXw8Nv\n0je/xnPCo3afRAO4+1tioALrh6X1iKLbzcKuq0CtpDbcgwcul/e3Eb8hjtnPnQiV9aFN3D0b\nHv6YGUcpkyzy07LfGuMLog4+HRcjfvMz+znNfsusQc9xd3mOcQqsy4eut1NEixmHuMfFlJrQ\ns9wDCOJXhHvFb4gN7rbeN8QdETWzuDs3DJyZV4JKjguPQ9u9raS23F1vea/2tFHKaKt98yvO\naqGuAY5zN0aB9c2sskTVx+3lHhOzWk0Z3EMY9rKe6h1BMT2sdMuJfJ3oCHf/Wt4/S5MpYWRY\n7r9vhF1YmsrObC1uX61y0o2/utBq7o43RIGV/YzTRnE91nKPh5k1pWe4hzG8/bi4KlGVCZa4\nLIOMddSau4ct7tzq0hQ7JAyueyVnJY7C0tDVvelEjRZxD7Lu9sSV4L+eO3uBdW5THaJqk8Py\ni5t6VmMDyOjKcWcEOTpa+Z4mjegN7l62snP3laXoAeF7VZpG9C/uIbCqS1trkNAmLHdfjKXR\n3L3PXWD9dHcpsrdZwT0S5teUnucdyfB18q5yRGm3WHvrON8Y5+RY09/Ly1B03z3cY8xoOXXm\nHgRrurihMkVcFzZHthd0NNX2NvcAsBZYn9wcRXH9cU0GFWAnO4/f1jcniutu7ts5+yGzsv1r\n7r62qN/mJVN0/3Aur0TX0P9xj4MFXVhXkRw9LH3J9mItpFbZzEPAWGC90cdGZcfht0F1YCe7\n/i4c6OkgodH0w9yDr4MpdAd3d1vS15PjKH6ItXd/+mEJdeMeCcu5sLYCOZw7uYeWUwvayzwI\nbAXWcx2Jqt0VVhfU0xR2suvs6rOjEolSR4TJDtjDiSXOcHe59fzfQDslj7LqqRGBqEu4Zb2q\nLkp7r3qFwTXbi/NQROo53mHgKbCyj19LdM0C7u63lGvoNZaxDEtZL08qQ5Tc+wHuUdfPTbSG\nu9ct5uIucRasfHuYnTyvYD45ucfDSi5tqITyStSb5vEOBEeBlbW/AQnNV3H3vcUsou4MYxmO\nsl6dUoEoocti611RtBh7HGlXuXveSr6aVYaEppa/6a7faggfcA+JZVzZWiXcfxzMsz8x9gfW\nodC/wLq6u264njaqrTr0b90HM/xcfWGSWF3FdZwbdjseutAh7s63jIv7u9gozhmmZ3fJuocG\nco+KRVzdWZ0iejzCPaDGMImGsQ6G3gXWlUdqkq2DNS93zWwu9dF5MMPOhcdvTpGqqznhdzuT\nEyfWC7jjpTrenpxMVGsKzu/xllnF/hn3wFhB1v46FNF1O/dwGsXxNIH1Ug36FlhXtlcne+eH\nuDvdmjKrCx/rOpph5vTeAfFEJbrOC7t9V3kaYhepCr5eUpcowbmeezQN5y4DXBbS9LKPXUO2\nTlu4x9JAFvLe6FLPAuvyw9UoostW7h63rNk0VMfRDC/frO0cSVTGuSSsjrsqaC7yFaqf1mUI\nFNFidrjW6MU5Xj7yW+7xMbsnm5HQdhP3SBpLUzrMOCL6FViXt4nlVbeHubvbwjJTI77UbTjD\nSNYbd19DRFUHh9E5g3IyKzh+5B4LM/tlU0c7CfUmhv1FrxRMpsncQ2RuL7QmocV67mE0mo32\nGpf4xivTKqMAACAASURBVESvAuvKdrG86h6+15TVxR00TqfhDB9nDo0uQxTRaByye2IczeEe\nDtP6aWMnO1GNMWFy2bRgHCkVw39zXvN6rRNR0zXcg2hA19P9fKOiT4F15ZHqFNENmyiNHSsb\n9V9dxjNcfHZfZwdRiY4z93MPrSEciCt9gXtITOm/69pL1dVIHB5RrLE0i3ukTOvt7kQNrHy7\n+eDtjkn+k21c9Ciwru6qSRFdUV5pbwLuZ6Ka809NqU5EaQNWhvFhV4X0ou3cw2I+396XIZBQ\nazSOjvDlYELiX9yjZU7v9xSo7hLu8TOq4TSdbWS0L7Cy9tUmexfMLno4nBT3m+YDGg6+fLBH\nLFFU84k43dnbFqER98iYzDcrrxWrq7o34+ulP4bSUu4BM6OP+gpUC/dFUXQoJYrtTvVaF1jZ\nB+vjtFH9jKK5Gg+o9Z1/ckotIirvXBAON3EOTAt6mXt4TOTblc2JhPTxuKS2n/ZFlz3PPWim\nc3KQjarfyz10hjaVhnCNjsYF1uONSWiP6xXr5rG45H+0HVGL+2RNtxgiR9Nx+E4gZzH14x4h\ns/jxgZYC2a6ZgPvBBaAPbeAeN5M5NcRGaXfjhkvFyqzKdrVRTQuslzJIaL2Bu3fDykBareWI\nWtqfB2+pTESVemHXlZLMyhHfcQ+TGfy+ub2NhPrjUV0F5pHIarjhZQA+uclGVWajvPJlIXVg\nGiENC6x3uhI1C/NLB+luT1TFi9oNqXVdfX1+KztRbKtJOFqmOJNoJvdQGd75/U4HUe2xuBtc\n4LrQPu7hM4+TQ2xUeSbKKz80ohM8Y6RZgfXFIIHScdqo7m6krVoNqWV9tblvSSKh5qDlx7iH\nz+gOJZQ6xz1chpb13MgSRKnDcEWGoGwWGmVzD6FJ/GeQWF7NQHnll7VCfZ5doxoVWL/dFklV\n53P3ajjaFlErS5sxtaYzRyfUIKJS1921l3voTKEvbeEeMgM7OaOimKU+a7lHybwy6CnuQTSF\n9/vZKA17r/zWkbaxjJMmBdalVSWpzDQMPouOdEiLMbWiq68vaB1BFNV0LA4U9Nc22zXcw2ZU\nv69vThTdeTEmvhDcx3a0jJm8daNA1XDsVQC2RVZiOUFViwIrsybFjcJxwkweFJprMKbWk/e7\nYLX+S45wD5mptKKXuIfOiK4+3i+KhEbTDnKPj9ml01vcY2l0r3QlqjkH5VVAetNyjrFSv8D6\nsgfZuu/h7s4wdi09r/qgWoznd8E7EdRALaW+3MNnPJ/PrkBUcQRuNBi6+chX8Z5uS1QPh98E\nal9cyT8YRkvtAuvSwmiqv467M8PaSuqq8qBaytU3F+J3wVCk2b/lHkNjOb+rvUAxXXBGjyoy\n02yfcY+ocWUdakLUaCn3IJnRSJYb5qhcYL1RjxLvwL5LXvXoXXVH1Tq+3do/yfW74GL8Lhis\nyTSDexiN5MPJJYnqTcVPg2qZRuO4x9SoLm2vQ0LL+7hHyJwOl4pmuIafqgXWhTvtQpdHuTsy\n7M2lgWqOqlX88/httYkouTN+FwzJoYRkXKkhz/kdLYkS+2ziHhMrOVom+ifucTWkf9ZUInsH\n7HcP1m00Uv9BU7PAer8+lV3M3YtwIrOK/UsVh9UKst5Z0sFB5Gg8Zj336JhfP1xqLdcnU0qS\n0HDmUe4BsZhbaDb3yBrQz/ckk6PHw9yDY2LHK9k+0n3Y1CuwstdEUdcD3J0IojtogmrDagHf\nPjwohYjS+izEqa1q2GZryD2kBnDlUCeBSvTFPStVdzAh6W/u0TWaz8ZHU/wg7HoPyWy6UfeB\nU63AOt2LSszh7kFwOVo65me1xtXk/jwysZZYXJXscMdO7lGxjlb0L+5x5fbLkkpEdafjSD4t\nDKI13ONrLK/0tlHpsTjML0SZtekVvYdOrQLro+pUH/feMoqxdI9K42pm556b1dxOFNVkzDqc\nd6GmJdSPe2x5vTsqiqK64WRpjexxVL7MPcTGceXR5kTV7sQP0aFbRhl6j55KBdaReOqD+7gZ\nxsGEkmG+k/3CS/PaOohstQfiOqKqy6wSwXA+jlFkHWlHVHYMTubRTnfazT3KRvHnilQSmuHQ\nZnU0o+M6j586BdZKm+Mu7r4DL4PoPlUG1pTOPT+3fTSRUMU5Zz/3QFjTJJrJPchc/llXnajB\nPdglqqWHhAa45bPk5K1x5Oi+kXs8LGOd7vd8VqPAyrqNktZwdx142+OoFJ472f/MnJERKRZX\nqTfMxBGhmjkUX4rlxl7sfro7mSI647dBrWXQ09xDze/q0esEKjV8H/dgWEkH2qHvIKpQYF0e\nTKnbuDsOCupBj4Q+sibz3Z4J6TYiWzXnbBRX2uoblldq+HxcFCX0x9kS2ltNnbkHm9uvy6oQ\n1Z2BQ69U9XBElYu6DmPoBdZFJ9VCkW00W231w2kne/bHm4ZWJqLIev3n4mdB7W2zNeAect29\nO8BGZcbhVC5dhPvdKF65KYoc1z3APQzW49T5DNWQC6yLPegaXP3KeNpRphr5MIGs9+/vLV3n\nKq7ZiOW40JVOWobblRpe6UaUNh0n8uhkDg3hHnE+fzxQj6j8GOy20MDu6JQzeo5lqAXW5Z7U\n8BB3p0FRa6m1KgExuC829kuW7n/T9tb1OO5YR0uoL/fQ6+nZdkR15yJhusmsFBGmtxTPfnFo\nNEVkLETYtDGY7tVzOEMssLKGUjrqK0NqRK+qExHDOv/4xOpScdV+8mbuzg4/afbw2QA+2Yqo\n0VLuHg8vU2gq97Bz+G6hOKWVG47j/DTzWGK8nlfhDrHAup1q4vdBY1pETnUiYkynd/WNI4pu\nPg7nMLO4je7ijoBOnmxB1Gw1d3+HmyNJ8X9yj7zezu6+zkaO9oux80pLY2myjmMaWoG1miru\n5e4vUFBdOKlSSAznwmO9osQvej0X4SKiXA4nJJ3ljoEenm1JwrW4CI3+htMS7rHX1dXnRsQT\n1ZqAC9hq7EgZx1f6DWtIBdYRWxKuz2BYM2mUWikxln/fmkhUaTCuRsSqP23mDoL2XmlH1Px+\n7q4OS/uiy+l7Qj2rd6ZVIErpj93xOphKQ/Ub2FAKrA/iHfdxdxYoOl7e8YNqOTGMi480J0rq\nvZa7d8PeDns9q18I5J2uRI3x4yCTnmFzrbVP5tYiiuuMnwb1cbyK7QPdxjaEAuv3NGEGd19B\nMSbSNPWCYgx/LS1HQtO7cfU9A2hDz3DHQVOn+gtUfzl3L4evbfY6WdwZ0MHnixsQOVrNwhVm\ndDOHrtdteIMvsLK6Un/unoLiHC6ZYK3jRE/PTaRo5xbufgWXFdSDOxAa+vGWCKo+n7uPw1oY\nXMrvy2WNiSKaTsXFkXVVV7+r+AVfYM2lRse5OwqKNYIWqRgVbueXJVHCUFx9zzBqCJ9yZ0Ir\nZ+6JpfIz8JMNq7XUljsHmvpiaRMiW8PJmNL0toJa6nV4Q9AF1gu2FNzwzeD2x5axzD15sx9N\npbihuCaIgUyjidyp0MaVjaUpcQJ+h+bWkN7kjoJmPlvSSKquJmEjyqE5HdFpnIMtsH4tb1/B\n3UvgS19ar2pa+HzcjiJ64aueoRxNjrPWT9B5nqpHUYNQyvObT/24s6CNz6Xqyt4I1RWX9ULd\nK/oMdbAFVk8ayt1J4NPOyDSdcqSti/dEUlNcrt1ohtFK7mSo79T1JHR+hLtrQZSZZv+cOw7q\n+3ZFE7G6ajwZV5Bk1Ike1me0gyywtlJdHIBlAt1ot7p5YfFOPSo1m7sroYg9jsqWqN+9nJke\nSfVx4SuDmEa3cgdCZb8+2FogWyNUV8y2OSrpc/RMcAXWNwkxW7m7CPzwkO0a01+s6OqiSKEr\nTrMxom70GHc6VJW9tzylzOTuVXA7mhLzK3cmVHRh/w2RJNS7dTd3v8KJ3rRClzEPqsDK7kS3\ncXcQ+KUNnVA7Mjr7sT0l43R5Y9ogtOSOh5o+6UCRA3HvegMZQ3O5Q6Gat28tSZQ2Evc+MYR9\ncUm6HEAaVIG1iZriDGZzuJ9aqx0ZfT1fhppjf7pRNaHXuQOimotzHdTkIe4eBW+PxZU6x50L\nVfz1YAOikr1wey/DGK7P3eqDKbB+KBGznbt7wE+N6WXVQ6OjVXb7aBTzhrWABnAnRC2v1qEk\n3JnCaPpb4jzoD8fFke3aObjwh4EcSo7+ToehD6bA6k0TuHsH/LVUx9sCqO7iMCq5jLsHQVlm\nZfvX3CFRxdnbbEJ3HOhnODsjq5r9PIqsYx2ISt2EE1MNZjKN1GH0gyiwjlId7FMwj9rCe+rH\nRh+/ZlD1Hdz9B8WZQrdzp0QNr1an8ku5+xJkdKF93OEIycUttYjSZx/j7kco7Fiq7UPtxz/w\nAutsZft67r4B/82hgRrkRg9f1qAMHHJsbIdLJpzmzknILs20C72QNEPaJDTmjkcIzt1fkSI6\nPMDdiSDnHj1+3Am8wJpFfbh7BgKQmWb/TIPgaO/dstQbu0qNbphOpztr6JPGVAa7r4yqBT3L\nHZBgXXygHEU5cdqgUdWlFzTPQMAF1qeOUge5OwYCcSeN0SI5Wns5URjL3XXg076oipe4oxKa\nbbHUEUdfGdZK6sKdkOBk7ahMUX1wNxzjWiU0zdI6BQEXWN0Jp9qYy/HykXqcLqGyp2Ptd3D3\nHPihB+3kzkoo/hlOMdO5+xCKUY/e5Q5JMJ5pQJFOXFLU0DJoj9YxCLTAOkHp3L0CAbqNJmuS\nHS09ERV5D3e/gT+2mPpmAZ/Uo+pbuLsQijOXBnGnJHBf3EhCe/w4aHCbI9IuahyEAAusy7Vt\na7l7BQJ0NCXmJ23So5knoxy4ertJtKYnueMStCMJdP0R7g6EYmVWsX/JnZMAnb83murex91x\n4NONtErjKARYYN1PXbn7BAI2ju7UJj1aeTYa9ZVprKYO3HkJUvZcwYHfoQ3vDprAnZTAvFCD\nkqbh/BwT2BOX9Lu2WQiswPojOQa/KpvPoZLxv2mUH028Ehs5j7vPwG/p9BZ3YoJyth+l4Ax6\n4ztaOvpn7qwE4PRYQbgBZ02Ywwitr+MXWIE1jYZz9wgEYRTdo1F+tPBuov1u7h4D/82nftyR\nCcb/mlJdfF00g7E0mzss/numElVezd1j4KfDpR1faBqHgAqsr6NScDk+MzqQkGiey0F+XkaY\nxt1hEIDMqjYTXmntZGXqiMOvTOFgQuJf3HHx0/nJgn0gYmUe06ivpoEIqMC6iW7n7g8IynBa\noFWC1PZTNbqFu7sgIHfSzdypCdhryTQYh8mYxFBaxp0X/3xUjypi95WZZNagV7VMRCAF1vu2\ntOPc/QFB2R+XfEazDKnq78Y0gLu3IDDHyjn+y52bAGXG2Cdzdxv4a1902fPcifHHpmjqjstw\nm8syaq7lZWYCKbC601zu3oAgDaHFmmVITVe6U2fsWDCbCTSNOziB2RfpmMPdaeC/PrSBOzK+\n/TOE4mZx9xQEKoN2aRiKAAqsf1E97r6AYD0aV+pv7VKknvHU6Ch3X0GgDifHmeo01Ydtscu4\n+wwC8Ehk2hXu0PjyWX2q8TB3R0HAtkRWPKtdKgIosFoTJiXzGkRLtEuRalZTFZzgbEKjaQ53\ndAKwUYjHVSDNpbvhb8j0eCJ1x9HtZtSX5moXC/8LrCeoKXdPQPD2xZpgF9YJW0ncXsKMDiSU\nNMt5Xjk5G4QSuB2FyWyx19X8vrwhWWaLnMLdSRCU/Ymx32uWC78LrOwmwv3cPQEhGGz8o7D+\nk+DAKTjmNNT46XJ7SCixnru7IFAd6CB3cIpxcTglY+oyq0k0RLNk+F1gHaYM7n6AUDwal2zw\nnQy/VyNcAMuk9seV+oc7P/7ZZUtYx91bELANQmPj3lP897ZU/RHuHoJgHa8qvKZVNPwtsLLS\nhQ3c/QAhuYnmaZUiVVzpTP24+wiCNYiWcwfIL0ciYtdw9xUEoRU9zp0dJV/Voha4OoOJLaOm\nWv0A7W+BtY/ac/cChGZ/QuIfGqVIFVOpKS6zZlr7YkpreC6Oap6PilrO3VUQjPupBXd4FPy7\nLPXEpWVMrQ1t1SgcfhZYV+vYNnN3AoRoBN2tUYrUsJcq4ARCE+tPq7gj5Nu/EyLmc3cUBKcZ\nPcsdH1nPJQg3c/cNhGZbVGmN7iXnZ4G1hzpy9wGE6mBi/C/apEgFH8ZGP8jdQRCCvdFlDL8L\n68uywl3c/QRBWk1tufMj52BUxJ3cXQOhGkq3aRMP/wqsq7XtD3F3AYRsLE3VJkWhO12DZnB3\nD4RkgOGPwvqtJmFfg3k1ppe4E1TUVnv0Qu6OgZAdLhvxoSb58K/A2k2duXsAQnc4Jeo7TVIU\nsuye1Iu7dyA0+2JLGft+lxdaI2RmtoI6cEeoiDVCAi7PYAVzqK0mZ6n6VWBdrW3fwt0BoIJJ\ndLMWIQrdcqqPO+SY3WBawJ2j4mQPplY4FtnMGtDL3CEqZBGVXM/dK6CKptrcktCvAmsvdeJu\nPqjhWMWIU1qkKFT/ikjayd03EKr98YY+TXUe1TrM3UUQimXUkTtEBc2lFJz7ZRFbHOW0uE6k\nPwXW1To2HIFlDTOorwYhCtXP5W1LuHsGQjeCZnFHSdljQuld3B0EoWlorKOwZlMZ3N3ZMobQ\nZA0y4k+BtQ+nEFpFZnXhTQ1SFJqrnWk4d8eACg6WjPsfd5iUvBsbjRsQmt0KasedIy8zqBzu\nnGodh8vb31E/JH4UWFn1cA0sy1hE7dUPUYjmURMcG2MJ4+lW7jAp+DlVmM3dOxCyRvQ8d5I8\nZlL5Hdz9ASqaT82uqp4SPwqs/dSBu+mgmsaUqXqIQvO8LWUPd6+AKo6Wi/ycO06yLrelm7g7\nB0K3ijK4o+Q2h8qhvrKWNvSA6jHxXWBlpQsbuVsOqlkr1LuieopC8XM5O25eYhV30gDuPMma\nRC2wk9QKmtMT3FnKtYDKbufuDFDXzriE79XOie8C6xC14244qKgzbVY7RKHI6oIDsKwjs7rw\nBneiZOykSo9xdw2o4QGhsSaXKwrUCkrB8VeWM4F6qR0UnwVWdkNhA3e7QUU7HGWNdD3IxdQI\n+xasYzG14U5UUe/HxGzi7hhQRwYd4I6TaB0l48R668msSwdVTorPAusoteZuNqhqkJFOpn8t\nImk3d4eAiprRIe5MFXa6ujCLu1tAJRtttfkPcXhYSMRhM1a0MbKcypfy81VgZTcR1nO3GlR1\nMDn6G3VDFLw/KguLufsD1LTBXv0Sd6oKyu5Jfbh7BVTTmbZyJ2q/PWEddzeAJm6iUepmxVeB\nlUmtuNsMKrvdMEciZ/eigdy9AerqQSu5Y1XQCqp/jLtTQDXbIyud5w3U45ExuP+gRR1No6dV\nDYuvAqu1gMvzWU1mdaNcEHkd1cO2z2L2xJX4mTtX3l6JKInbMFlJb1rOGqiXYhy474RlrbFX\nVvUQZV8FVrUW3C0G1a0QGqh/RbUgvBeVgEvJWM5YGsMdLC+/VsSP0NayL67k74yBeqdExFzu\nLgDt9KexasbFV4FV/gHuBoP62tN6NUMUpH9qCXO4ewJUdzTV9hZ3tDyyrqch3B0C6hpJd/AF\n6mSKcCd3B4CGDqcKT6mYF18F1gLu9oIGdsYk/aJiiII0nJzcHQEaWETNsriz5bacGh7n7g9Q\n1+GUqC+58vRNRZrA3X7Q1Bp7xT/VC4yvAou7taCJMTRavQwF6RGqdoS7H0ALbWgTd7jyvBZR\nchd3b4Da7qD+THn6uQaN4G49aGww3aReYlBghaWjlYVX1QtRUE7Fx+Bafda0IzrJGMe5/1FZ\nWMTdGaC6zOpMs9fphtSbu/GgtaPV6VHVIoMCKzwtE9IvqxaiYJxvQNO4OwE0MkbN74DBy+6N\nq4BY0jKeH6HPtaHrcNsJ69voKPmdWplBgRWmOtMytTIUlFupM3cXgFaOVaNnWNOVaz2uAmJR\nrWm7/nG63INa4YC+cDCe2qtVwKPAClN7E2K/UilDwXiMUg9ydwFo5j5b1bOM6cr1Pq4CYlUP\nM9xQNWsINcBBo2EhszktVik1KLDC1VTqzHhf+lqO9dwdABrqyXkqfa5/agn3cHcDaGQwTdM7\nTxOo1gHuZoM+9iRF/J86qUGBFa4yG9HD6mQoGOWmcLcftHSwrE2lGSpoI+hG7l4ArRwqHfmx\nvnGaQ6n7uFsNelkkVFHnrs8osMLWw9El/6tKhoIxk7v1oK3FQm3eW8btoqqHuTsBNDOLOui6\nA341lcHvzWFkIDlVyRcKrPA1nm5QI0JB4W47aK0H3c6WLtHnCdGbuLsANNSEdusYp4eFpC3c\nLQYdHUtX55aXKLDCV2Y6x7k4yFV4OFheeJ4rXTk5F5vQVO4eAC1tcZRR50ccv1SLX8fdYNDV\nzpIR/1IhOCiwwtiW6BLfqpChYHA3HTS30paq4i0nAjSFOnK3H7Q1jG7WL09pq7ibCzpbYi+r\nwiE0KLDC2UT1rvcRIO6Wg/YGsd3RJOeoUBFnfFnc0VRBjV0M/sHx7eFnNLW4GHJwUGCFs8zm\n6vzQHDjuloP2jtWmzTzp+jYpci1360FrK4Saup1Hwd1W0F9mWxXu2IsCK6ztTox8O+QMBYO7\n4aCDh+NiPuQI1+VWNJ677aC9G2i6XpHibiowOFiV1oQaHBRY4W2eUEP3SyIjV+FiFtX+myFc\nd1IGd8tBBwfK2PW62Bp3U4HDtkT7EyEGBwVWmOtFQ0KMUFC4mw26cFJ//W8XcFwot5+74aCH\nJUJNne7IxN1SYLEiMuGD0IKDAivMHa1Jm0KLUFC4mw26OFpH/4P8vkmKvJ+73aCPG2mCPqHi\nbijwmC5UCu1UQhRY4W5bfNRbIUUoKNytBn08khTyTvYAXWxGE7hbDTo5VEl4XJdUcTcUmAyl\nBn+FEhwUWGFvrlDp51AiFBTuRoNOVkaWOKlrssZTO+42g27ujyjzkx6p4m4ncOlK7S+EEBwU\nWDCE2l0OIUJB4W4z6GUqVftVx2A9QqkHuZsM+hlJXfS4lB93M4HLsWup55Xgg4MCCzJb0Pjg\nExQc7jaDbvpTy3O65erdmBjcgjCcZDaiRTrkiruZwOZwOg0LvoZHgQUnDlSmB4JOUHC4mwy6\nyWxLN4bwFTAwjYXZ3O0FXe1Jtj+nfa64Wwl8HqtBtwR9LjQKLDhxYmui/XiwCQoOd4tBP0eu\noZF6Xayh3EDu1oLOVkSU/k7zXHE3EhjtS6PxwU5gKLBAtNIR+0aQCQoOd4NBR/ur02SdKqyb\nM7kbC3obS001v2UOdxuB057KNDbIXwlRYIFktq3UJ8ElKDjc7QU97alE05Ar0EhHGqJ1/c7d\nRGC1J42GB3eYAwoscJlIqd8GlaDgcDcXdLWzAk3VZR8Wd0OBweGaNA+5Ag3tq049g7paAwos\nyDWMavwYTIKCw91a0NcjFWkCTqcHbexMEXYgV6Ch/enU9s8ggoMCC/L0o1r6VVjcjQWd7axM\nN11CrkAT6+Mitb1hAHcDgdvhllT/m8CDgwIL3HpRze+DmXyCwd1W0Nu+mtQ5pJtOIFegaKkj\n9lXkCjR0/Hoq+3rAwUGBBW6ZvSnti+Dmn4BxtxV0d7AppWt+mB93I4HJ3fbEd5Ar0NIYW9TO\nQIODAgvyDaay7wY5AwWIu6Wgv2PdqczLyBVoYppQ8m3kCrQ0N5YmB3icAwos8DJWiNf2WAbk\nKpyNtUfer+3JhNwtBDZThETtfiXkbhwYwsaK1OKbgIKDAgu83RVpXxf0LBQA7nYCi8WJ1DeY\nc3GQK/Bpmi1Ws2+H3G0DY3isDSUdDCQ4KLCggBUl6OaLIcxEfuJuJvDYUZdSX0SuQAuzIyO3\nIVegqVsdNDKAk3VQYEFBD6dRs69Dmoz8wd1KYHJsoM025RxyBRpYGkczriJXoKUHq1LqU34H\nBwUWFHKwPZV8LLTpyDfuRgKbFeWp6tPIFWhgYznq/gdyBVo60t9OQ3/2MzgosKCISQ4acTrU\nKQm5AnkHe9qo3zfIFahvX0NKewu5Ak2tqUolH/Tv3oQosKCoDdWo4rHQZyXkCmStqUnRszW5\n6ih3y4DZ8f5C5KLg7suLXIGfjo2JpvpP+hMcFFgg4+igCOqt5VUhuRsIrDKnJFGp5f8gV6C6\nBUnU/D/IFWhqZyeBOvlxYXcUWCBrfR2KuVeDLSByBZKDQ2MpZaHqx8twNwv47WlDkbPOIleg\nqfsbEHV7zVdwUGCBvMwpiVTmgQsqTVDIFRSyb1AsxU38BLkCtd1TiipuV/V0Qu4WgQEtqkvU\n5mhWscFBgQVKHhsYTRXu02YvFnfbwAD2j0wmocPu88gVqOtA/0iqf6D4TR9yBaFa0oio2qrf\nigkOCixQtrtXFJWc/qV68xRyBd6O3lWPqMSop1U7Kpm7QWAQ2zoKVHerajvguZsDBrW2cyRF\nDXhCcQJDgQXF2TO4BNm67FNzJwNyBV429k0mKjXq8N/IFahpY0c7lbrrU1VihVyBkj2jKhCV\nnvCi/E/SKLCgeIen1CJKGHZc3RqLu1VgHMcXd08kcnRc9lboO7K42wIGsq1PHFHLdf/DfAVa\nylzRPYEo5UO54KDAAp/W90khinVu+FyFmQq5gqKOr+ifRmId323+06HdC5q7IWAoh6ZeI5Ct\n1ZJ3Qz0ci7shYGxH53VJfFkuOCiwwA+ZK3qVFzeAFYesf/tyiFMVcgWydk7rLGWMqg9YnPlV\nsBtE7kaA0ewYU0cgSun3wFuhzFzcrQDDy5Td/44CC/y0eVyLeHH752g65v5nvw9hrkKuQMnO\n2X2viZOqrNjGg+8N5qcd7gaAAe25o31JMVPRLcZtfPl3zFegDdngoMAC/2Wun9SlaoRrA3hN\nr6kPHHnrx2CPmuFuCRhW5pbZQ1qniimT3eWOXEEwNk7pUsUuzVwpGcFceo179cH4ZIPjq8Ca\nNxOggDtH39CyZoqrzCISElPTfV7NFrmCQN01fsAp5ArUNH1E92ZpicJx5Ao08KtccHwVWOUI\noHhrg5iwkCvw5V/IFWjgBeQKNCA7X4VFgRWVlJQUy70SKkgU22HnXomiwnbCKimOh8C9EqGT\nRamA7wAAIABJREFUYmXjXgkZ4VhgOcSxiOdeidBITYjjXonihGOuYsRBieZeidBEG30rHlSB\nVY17rdVQpmnTplW4V0IF14jtMOD/S4KZsCyRq8bieERwr0ToGojNiOJeCRnhmKskcSxqcK9E\naEqJTTD0MIRjriqJg1KeeyVCU05sQir3ShQnqAJr+HUW0EYcmgzulVBBc7EdHblXoijZC6yF\nQ66aiePRiXslQmfUZoRjrtqJY9GKeyVCIzWhJfdKFCccc9VaHJTW3CsRmrZG34oHdaFRS9gr\nDs0S7pVQwY1iO77mXgnwkP4v/wf3SoSui9iMH7lXAlyeEcfiDu6VCM3jYhNmcK8EFPSAOChb\nuFciNDvFJqzgXomAocAyERRYxoICC1SGAgu0gAKLCQosE0GBZSwosEBlKLBACyiwmKDAMhEU\nWMaCAgtUhgILtIACiwkKLBNBgWUsKLBAZSiwQAsosJigwDIRFFjGggILVIYCC7SAAosJCiwT\nQYFlLCiwQGUosEALKLCYoMAyERRYxoICC1SGAgu0gAKLSVgUWJfOnDlzgXslVPCP2I4s7pUA\nj7/F8cjmXonQIVbGcVkci3PcKxEaqQnnuVcCCrooDsol7pUIjTm34mFRYAEAAADoCQUWAAAA\ngMpQYAEAAACoDAUWAAAAgMpQYAEAAACoDAUWAAAAgMpQYAEAAACozOoF1vtOL+a8gt/JW5zO\n17yf+O+WKUP6jFjwzFWuNQIL5AqxMhiTRwp5MijkipPVC6zXzJ2unJwrO3o6CybsYO+85kz4\nmW2twp7Zc4VYGY6pI4U8GRZyxcnqBdbTTueCfW5Pc69N4L6e7HT2KZCwY2K27j34+PYxTufo\nv/lWLMyZPFeIlfGYOVLIk3EhV5ysXmAddjpf4F6HEJzo4+x77H7vhP3Uz9n7LWnh4iKncx3b\nioU7c+cKsTIgE0cKeTIw5IqT1QusXU7nm9zrEII7nBO/zimQsM1O577cpQvDnL3+ZFqvsGfu\nXCFWBmTiSCFPBoZccbJ6gbXR6fwP9zqE4I6Nl3IKJOzqUGeff/KW9zidR5jWK+yZO1eIlQGZ\nOFLIk4EhV5ysXmCtcjq/5l6HELjW3Tthp5zOWe7lk07n3RwrBWbPFWJlQCaOFPJkYMgVJ6sX\nWPOdzl+41yFU3gl73Onc7l6+1NM5iGeNwAK5QqyMxeyRQp6MCbniZPUC6y6n8++XFo7oPXjK\n9p+41yVY3gnb5nQ+7vmH4WLjWNYILJArxMpYzB4p5MmYkCtOVi+wJjidE/Oum9F7fzb32gTH\nO2H3eR/xd5vT+T3LGoEFcoVYGYvZI4U8GRNyxcnqBdYIMVaD7zt4fPNocWE399oExzthS5zO\ntz3/MN3p/JxljcACuUKsjMXskUKejAm54mT1Aquf07npnLRwZYsYry+4Vyco3glb6HS+5/mH\nWU7nKZY1AgvkCrEyFrNHCnkyJuSKk9ULrHNnz7kXFzmdKzlXJWiKJfw0M5Tw1mSBXCFWxmL2\nSCFPxoRccbJ6geXlc6dzkAl/gi6YsDXeP0JPdjr/y7JG4MWsuUKsDMuUkUKeDA+50l0YFVjZ\nfZ3OM9wrEQzvhO1wOk94/uEmp/MsyxqBF7PmCrEyLFNGCnkyPORKd2FUYOUMcTp/416HYHgn\n7Gmn82H38jmncyjPGoE3k+YKsTIuM0YKeTI+5EpvYVRgXerpdF7iXolgeCfsS6fzTvfyu07n\nAp41Ai9mzRViZVimjBTyZHjIle4sXmC9+eC8F93L4oBM4lyXoHknLHtM/i0uNzqdzzCtUriz\nQq4QK0MxfaSQJ0NCrlhZvMB61umckFeyZ89yOnfxrk2QCtxOfJfTuS136ff+zv7nFN4C2rJC\nrhArQzF9pJAnQ0KuWFm8wLo4zOlc6rr99qV1TufAv7jXJygFEvbXYGfPl6WFv+9yOh9lW6cw\nZ4VcIVaGYvpIIU+GhFyxsniBlfNWL6dzyMZjxzeNcDp7vs69NoE6uU8yxelcLv33iOu5F3s6\nnfc8lrlJ/P/NtCvM6xe+TJ0rxMqIzBsp5MnIkCtOVi+wct64Ke8+TM5h73CvS8AOOr0Nz33y\n2X55j+8x/kmq1mXmXCFWhmTaSCFPhoZcMbJ8gZVz9vjcEX37jV7wxEXuNQmcbMJyft1x++C+\no5e/wbpqYc/EuUKsjMmskUKejA254mP9AgsAAABAZyiwAAAAAFSGAgsAAABAZSiwAAAAAFSG\nAgsAAABAZSiwAAAAAFSGAgsAAABAZSiwAAAAAFSGAgsAAABAZSiwAAAAAFSGAgsAAABAZSiw\nAAAAAFSGAgsAAABAZSiwAAAAAFSGAgsAAABAZSiwAAAAAFSGAsvlBBHtkBbeEBdWFfPC98V/\nX1bMv/t6v+j7iTVjI8vcG/A6Bv+JYD6+ggZm4plfgveD+Cemh7oenlQhXuHGKBH0SYUVNRAU\nWC46FlgfJJFkaOArGewngglhC2glRtm6ocAKW0aJoE8osCxIxwKrpVRe2SNRYEFxsAW0EqNs\n3VBghS2jRNAnFFgW5BnUbyZNmvR8MS/0NTH5en/Oz+IfiD94NedcEGvppRvV9vcTwRDyR8wv\n2AJaSdAbjfzUoMCCUBglgj6hwLIgvwc15InpbfEP3BbKH3DJTgpscw3cAh0xbAGtJNiNhldq\nUGBBKIwSQZ9QYFmQfgWW9ElbQ/kDLp8RCixzCXTEsAW0kmA3Gl6pQYEFoTBKBH1CgWVB+hVY\nR8U/8Ggof8BlFwoskwl0xLAFtJJgNxq7UGCBOowSQZ9QYFmQ2QqsSSiwTCbQEcMW0EqC3WhM\nQoEF6jBKBH1CgRWKd8TeezEn5+ToKo6Ymrd8LD2VdbBrSkTStYv+cr3gBvEF73q94Wnx8e25\ni1ePjEsvE5lUo9f6X7z/5KV9A9OTHeU6r/zD68n/rrwxrYS9RPX+W866n3pd/Esv5/wxJc1R\n+lPXE9mH+1eNSao/8kWlswhl/ojfZxHmNfSPpc0TI5KbTP/a9a+byGOo/+tZuIU7PH+kbOE1\nluki2RUBTX25qFvleHtCdee6X12PC45Y0QGWGbYCQcu0EaX9L+/BS7c3KRNZqm7/3X/r1h4I\nhtz84iI/gkWmgoKpkbZud4n/md2yoiOl8YxvfX9+cdNXgXj5O4MWPLxBWr2n8pYLB77YhoJu\nuCNYKFm9xD/wjtc/7xEfzy1+Rc1O7wLrpNh7J3IWCLmjFrEnJ+d/DfOGsNJn0gsOi0tTvN4w\nxlNwPVvHM9pxc696XvBcmufZ9e7nLs+I9Lw25Vjek9Kc8viZutJz70uPf+rofknXv+QKLMU/\n4leBldvQA7F5fyByu/Rk4QLLr/Us0kLlAkuui2RXBDR0YYItfxhWZOcUHrEiAyw3bN5B+3ec\nGI/Pcpc/ael5bbn9ercMAiA7v+QojaDMVFB06zY7Z2dM3lPRO318fPHTl3e8/J5BlQqsooEv\npqGgH+YIFknWY+LSLK9/7yk+/qy4FTU/vQusL8TeO3AfkZAUJXWn49M/q4t1VrJdetBQ2rZc\nThFH97Ln9VeSieq7lra7XpPapLZD+m9f90t2uZ62R7tGZ2ruc9lO16MSlUtK/xEO5j77qbj8\n2J2uf5G2a3+7CrukhnXF0iPjGBUpsOT/iN8Flquhj4oTjyM5QvoDtlfFJ0907txAXE7v3Lnz\nMn/Xs2gLn+ncWdzixop/ZECBNZbvItkVAe1kd5W6OSK1eqJrxO7IKTxihQdYdti8gvZtOXGC\neit3+dkE6TWVmtR0vWc5Q/PAP/Lzi9IIyk0FBVMjbd0W7hG/mTqSXOWM7eViP97H9OUVL/9n\nUIUCSybwyg0F/TBHsGiyzscT1fRaP7EGaFbcilqA3gXWt2LvLYgpueFMTtZr14jL48dRk2ev\n5pzbI415pvSK28WFY57XPyU+WiEtvCkOlm36D+LSuR1lxSfn5f77W2LRHTX/y6ycn9bES9st\n15MbxKUym6W9kl+MFxcT/3Q9+5W4uDGB6s5cNfs78eEd0kbtcbGmu3igMnWhIgWW/B/xu8CS\nGrosURj7QU7Opeelqqpj7gu8j8Hyaz1lW1g//4fx/DWW7yKlFQGNSBue5s9JZdL/1klz1euu\nZ71GrNAAyw9bftD+Et8akfdbzFfiH7RN+UZcOrNB+tuH9GsWBEZ+flEaQfmpwDs10tbtjlhh\n1HvZOZeeThcftC72431MX/nxCmAGVSiw5AOPqLJjjqBMsoaK//3I84Ld4qMHillRK9C7wPpe\n7L3Y+A9yl8XSNl7IOO96IHX2LdLCh+JCH8/rR4tZ+FH8b3Z9r24/VUIsor9xLTYRNz4v5T77\nglhVV3b9wFJNfNN7ea+d5Kk/vhOXOtH0vD3Y/xOr5xKf5y7/UJGKFljyf8TvAktqaJywJ/fZ\nX5PErwS5Ryd4F1j+rKd8C+UKLIUuUloR0EhnovL/5C1/UYpoiGvJa8QKDrDCsHmCdrmTOGS7\n8v5dnHzcQ5nzifjaKhe0bAkET2F+URpB+amg8NYtRtid++A3MVfCT8V9vo/pK38eC2AGVSiw\n5AOPqHLjjqBMsh7P3zUichLZfy5mRa1A7wJLGqPcXVI5uT/B2k7lLl8Wi9xrXUviuET+nveK\ny2JB0FVaeEF8aQ/PX1kh7QeTFl4ir8t2SodrPZGTu4egg/cHdsv/6PbuumW9+OAe94seLVpg\nKfwRvwss16eNdz89UXzwrGvJq8Dyaz1lWyhbYCl0kdKKgEbKEQ3zPFjdtP8S10KheSp/gBWG\nzRO0keLCyrx/fVdcHuV57YPio105YEgK84vCCCpMBUVSc6v7NVPFB08X8/G+pi/PQiAzqEKB\nJRt4RJUdcwTlknU5mSjd/dyZqLwPUVhRS+AosBx5Ox9z7iWvH6yaE1VwLUjd7T7Y8klx2VVs\nj3aXFi6/2onqSQu3ik+fdD/7dKVG1+2TFi5+++Z/PK9NJarl+Wh6xv10N/HBKfeDq6WK7sGS\n/yMBFViC55S9HZ6pyXsPlj/rKd9CuQJLoYuUVgQ0In4n6FX02cLzlGeAFYbNHbT5lH9US84U\n8cEnnteejyXqrfbagzoU5helEZSfCgqnxuY5cUs6/+qh4j7fx/TlWQhkBlUosGQDj6iyY46g\nbLLGUd5h7TmuK2zl1nYKK2oJHAVWS/cD6Vffhe4HTqIE18Kf7kPfRKPEJ1137atDFJV/6HtO\nM3GkpTNJa+ae3lCcJkSlPR8df8X9tPi1q0z+iwYVLbDk/0hABZanVs95Tny0xrWkeB0shfWU\nb6FcgaXQRUorAhppLH6FeLvIs4XmqfwBVhi2vKBJk9AQz3lZjYiqef1NcWJKVnvtQR0K84tf\nI+iZCgqnprHnJdJ+z/v8Xpmi05dnIZAZVKHAkg08osqOOYKyyZJ2ay3NWxa3+LH/FLOilsBR\nYI1xP9gmPjjgfjBA3NDkLg30lL7SL4SuvZlnbUQNvf7MCPElb4hFt/h0Gx+f2IKolOej27qf\nPS0+yMh/0UIfBZbnjwRUYA31PP1/nrcpFljy66nQQpkCS6GLFFcENLJa7OKY2V8VerbQPOUZ\nYKVhyw3aSw6i6zzl13nxtV28XjtdfE2xR0EAF4X5xb8R9EwFhVMzwvOSwP5/XHT6ci8ENIMq\nFFhygUdU2TFHUD5ZWRWImuYuSr8QDilmRa2Bo8Ca5n4g/V/Uc0DQIE+BJZ05OMO19IS49JK0\nIF1rwHsXs/Tj4tHcpwfKfczFA2NblHVfr8OrcBnufsEp8cGA/NfvkCuw5P5IQAXWbQWeliuw\nfK6nQgtlCiyFLlJcEdDI5dauwawz8dCfXs8WmqcKDLDcsLmC9klJokb5lwKU3hhbJV9SXjEG\nhqMwvxQzgnJTQeHU3O75g378/7jY6cu9ENAMqlBgyQUeUWXHHEGFZEknDH7jWtpJecdGKG2L\nLYGjwJrpfrDDXT9J8gusrEpEFVwns4wkquL6fUQ6MG+Y159ZLj7emZPzHnkfr5dvbwXyll+4\nTHa/Qrq++cj8NxyQKbBk/0hABdb0Ak/LFFi+11OhhTIFlkIXKa4IaOXssLzxtLde79nkFJqn\nPAOsNGxS0Kamif9T5S/Pv31ERT2TAwakML8oj6DsVFA4NYH8/7j46cu9ENAMqnShUZnAI6rs\nmCOokKy3yf3D4o1Epa8Us6LWYMQCK+duyj0/4XJJ99kFr1LeRRzyPCA+3pR70xHvp/MscgUk\nrXXPoaIU78LFE49XCr5T5kKj8n9E1QLLj/VUaKFMgaXQRSiwGLw1vETeLJW4MO/K7ErzlNKw\nSUHLvT72EM+/vSEzNx7WvjUQOIX5RXEE5aeCEAosH9OXeyGgGVT5VjlFAo+osmOOoEKycmrk\n/R74l8P9NVNpW2wJhiywpL2LN+Xk/kKYe8qBVA4P9fozS8XHu3NyPiDvH4XdnpduxDPpu7xH\nLWQLl7eoQNW8lwoXWAp/RM0Cy5/1lG+hXIGl0EUosFhcfn5aeu7kdUPuVWaU5imlYXvf9eZS\ntSl3h5bLx+T12yIYmcL8ojSCClNB8AWWr+nLvfD/7N0HeBTV2gfwd7ObnhAIIYTQAxKa9N6k\ni+iCVOlIVaQjSFM6UqUI0jtIr0E/Rb323hsXC1exgoqASC/Zb2bTdjfb98y+Mzv/3/Pc6+5s\nss6c8/fMmylnfBpBXRdY+QKPqLJjjqCLPZflcSKDPLOlfIbwPXcrGhpUWWBZmhLFXrFY+ube\ncfgD2d8KPJWs877/KP2jQ75/hzwV7NLcd3WcFi7yE/pszvuuyldgufgSkQWWN+vpfAudFVgu\nmggFFpvT65vIO5zZ1jeuxilX3WYtsCp8/3UUUXzO9cM/k9MZIEB9XIwvrnrQxVDgf4HlafjK\neeHTCGpfYK2xK7BkNoFHVNkxR9DFnsu6Wiulf95LVM7tioYGdRZYm6QP9lmuxhOtylpwxUhU\nzeZrepL1EdA3wolqOP4r5Puyyube2W5JdVq4nCG7OxfGOhZYrr5EYIHl1Xo63UKnBZaLJkKB\nxemAVB/FWucZcTVOueo2OWgN/rZYnpb+WT97TocbkTkP5gSVczG+uOhBV0OB33s3j8NXzguf\nRlC5wMqb+ejJfAWWJS/wiCo75gi62HNZLNWIWmadIZzmdkVDgzoLrH9jibrIV7tF/J295E7p\n9fW8r6ma/Vbq/YgruUu/OXHil6xn6Q7IXfYtOS1cLAXt5t5o6VhgufoSgQWWd+vpbAudz4Pl\noolQYHGaKbW39ZGoLscpF92WG7R7KG+a47pE4f8EY7UhUC7GF+c96Goo8Hvv5nH4yn3hywh6\njLKfHWf1gLMCKzfwiCo73gi62HNZLPOITOctWyhvxlEXKxoS1FlgyROMxlzuRNQ5Z8HD2WdO\nspyS3jWUX8jPTDqSs1Q++Plw1indvJtJx7goXORbi3Nnj/0n3LHAcvUlAgss79bT2RY6L7Bc\nNBEKrGD76ae81y/ldIrLccpFt+UG7UyRvMfWjyO7J458gz2YarkYX5z3oKuhwO+9m8fhK/eF\nLyOoPPPR+JzFVwvnFljOAo+osuONoIs9V9a5wz2WjkR1PaxoSFBpgSXfWLAhkuhwzoL3yXZ6\ntInSuzU5P9csZ+kisj4ZXK6+c08yfxohvYvJ/VfnxWOW7YrMIccCy9WXCCywvFtPZ1toPc5R\n0vHf6KKJUGAF17ikvFlELZadUnu/L7+w6TH7HnHRbXlBk0/MlDxvfSnfYl3pVs7PXi0R3hJ3\nZqmUi/HFeQ+6Ggpcp8bDf8ceh6/cF76OoLk/K9/uai2wnAceUWXHG0EXey5JQ6L+V6KJlntY\n0ZCg0gJLvpkzkSgp7xkiUq/kXmD5ntT/ha3zL2bKi+dkLT2eQJRy3WK5FU9UIHtu2q9TYxtJ\nP3E251+dF4//Gohiv856/X5svgLL1ZcILLC8W09nW2i9BjH8X4d/o4smQoEVXPJQsjLnzU2p\nVwtYbyO06TH7HnHRbTZBG0a5F4G2ll4Ozb5S4kY3snkQAqiLi/HFeQ+6Ggpcp8bDf8ceh6/c\nF76MoJYkorBPspa/FRObXWC5CDyiyo03gi72XBbrZaWp/0dkPONpRUOBWgssax1rMwO55XiU\n9B/3Y3KfXFgWn1cOfyQfUOzx/pXMHxfIM7FYD33KTxtpLN959dvMaFo5OScHDvs1+YE8SZuk\nndmPs2NpgGOB5epLRN5F6N16OttCS2f5v5E/b/14zrbAct5EKLCC62JRqYn7vSv/aXDp/+Rx\nKuuhBDY95tDBzrvNJmhXKkmvN1lf/hAnvWz5ljQ6Xt1bW3rZPIgbBj5xPr646EEXQ4Hr1Hj6\n79jT8JUXLx9GUOvp7GL7Lklb8XhU2HLpzXMWl4FHVNnxRtD5nktyxkjUhqidxxUNBWotsH6x\nTrL4kc1v7pMPWxrSapezfjInZ/GerNkYs/4/6yTy93KAjHc0uUNa+GDmc/IHVep/67hf+7WE\n9VcKSrs3aiXPp71RXppbrrj4EpEFlnfr6WwLrbdIy162m3veaROhwAqyVyPl1jemlo63dlHj\nrAs9bXrMsYOddptt0OQj9nHfWV++LGeGYssny7PWUOU/grdZ4Bvn44uLHnQxFLhOjaf/jj0N\nXzbx8n4EtZyyrntYoRjp/6fJp5qsl3A4Dzyiyo43gs73XLLW1gXbPa9oCFBrgSU/5Jsq2f3q\nm1UoRymbw82vls9ZGp9zoPpYzrTCxicslpvVrC+/yrdfO1Et5xfbX5RnI5Lnz7YpV5x/idCZ\n3L1bT2dbeLVqbuptn57orIlQYAXbB5Vze4FMY7J3NzY9lq+DnXWbXdAWSm/qZJ0u/7xJ7s8a\nBpwPzgaBP5yOLxYXPeh8KHCdGo//HXsYvmzj5fUIarG8lJCzfJ51BrestDoNPKLKjzeCTpMl\n2ygvibnkxYpqn2oLrN35u+/2gUGVC5sSK/Z79rrt4qt7ulcsGJHScuHfuYtOT62dYEyoNd56\nH+hvDxQOT+3+Z/792s0t5lJRBSs/+KrFcoGyZ1mzKVecfonYZxF6t57OtvCvh4sbo8p2PWlf\nYDlrIhRYQZf5wiP1i0YZC6R1eOq33IV5PZa/g510m13QMlva/Gfz6phaKRExqa1n/KDwZkBg\nnI0vVs560OlQ4Do1nv87dj982cXL2xFU8ue0hoVNMZXG/Ci9pNzTPk4D72JDIYh4I+g0WZLz\n8iHPXt6tqNYFu8Dy2ibpj6HfuVcCAAAAwA+qLbDqEt3PvQ4AAAAA/lBrgfUaEb3KvRIAAAAA\n/lBpgXWjBlFt7pUAAAAA8Is6C6zMgUR0jHstAAAAAPyiygLr0zZSfdWJey3cWHy3cwu5VwwA\ndAPjEDBDBN1TX4H1YFHrfHVpf3v+UTb9ybne3CsGALqBcQiYIYLuqa/A6m3tnzt/5l4Pd5Aq\nAOCGcQiYIYLuqa/AGhdBCfWX3/D8gwAAAADqpL4CCwAAAEDjUGABAAAACIYCCwAAAEAwFFgA\nAAAAgqHAAgAAABAMBRYAAACAYCiwAAAAAARDgQUAAAAgGAosAAAAAMFQYAEAAAAIhgILAAAA\nQDAUWAAAAACCocACAAAAEAwFFgAAAIBgKLAAAAAABEOBBQAAACAYCiwAAAAAwVBgAQAAAAiG\nAgsAAABAMBRYAAAAAIKhwAIAAAAQDAUWAAAAgGAosAAAAAAEQ4EFAAAAIBgKLAAAAADBUGAB\nAAAACIYCCwAAAEAwFFgAAAAAgqHAAgAAABAMBRYAAACAYCiwAAAAAARDgQUAAAAgGAosAAAA\nAMFQYAEAAAAIhgILAAAAQDAUWAAAAACCocACAAAAEAwFFgAAAIBgKLAAAAAABEOBBQAAACAY\nCiwAAAAAwVBgBduFd1529N517pUCbcs8+Uq+VL1/jXutQPOufZYvVy9/cpN7rSCk3P4uf8he\n+Yl7rcRAgRVEV95a1L0cycKTK9RsIqlRPtEgvY1u+/Qv3CsHGvXL3nHN4q2pii1Zpb4UqsbV\nyyTIb6NaLPyee+VAs668s7xfVZM1WAml75SD1ahaaWvQ4to/8yv32kFouPbq9FYFrCErWObO\nBvLoVaOcdZ9IKd3X/cy9doFDgRUk5zImNIyQd4JVzcPnbck4muvA8jH3FCcyNN+BIw7goxNr\n+5SSQmVIbdJ74tN780J1dN/SUe1KSp/UWnqWex1Be/63Y0SdcCk+EeXbDJy2+oBNsHYvfqRV\nClFYs02XuFcStO7nZ9rHyKVUk35TV9mG7NDax3vXk/9IrPXkj9zrGCAUWEFwet+o6mHSbrBM\n+3FrbEorGxsGV5RyNvsc95qCdny3tmcxuWKv02f2bqehOrp5eI0wihr8X+41BQ05/9KcDslS\nroxp94xacdh5sNYNTjdQoYm/c68raNivT9U3EKW2n7TdecgyVgysFkaGu3Zq+gIaFFjKuvXl\n2v7lpeHKVKnrdBe7wWyrzNFUcM5l7hUGLfhmXe9UKVXxDYc+7bxiz7G1fzKFdf2ae31BA26d\nPDynm/UShsQGD87f7zZXR9d1jaeoEae51xm06cqONlLxVGXwOvcp2/FIJaKUWX9zr67/UGAp\n5tLHm0Y1jZOvhanea66H4cpqd984KrGLe7VB3a68vbhTUflCmPpDPBRXWQ5PKEvGwWe4VxuY\nnT937tyP0v/OXXX44Orp42/sWjK6Y9VIubaKubPTxI1exOro0f0PJVHcbMdvA/DoxKhCROWH\nbPUmZqvui6a4x/7kXmV/ocASLfOvE2/smP9w2zLylXqG4s0fWubiOLsTuzqZqM2P3FsAKvXv\ne6uH1pIvjUloNGS5N8VVloxJxSlhxW3utYegu33qlfXTBt5br1xiBNlKKFSodJpVUiFTzsLI\nMk16TV7nfayOHj04NJ7KHePeSNCW20dbGyih0yqvY7a7fwLFz9ToJX8osPxw5ft3MrYuf3Li\nyKFDu3fL1r5169aNalcuUyhnvIqvdPfQeXt8GK6s1lSn+A3c2wcqc/vn19aNv7esXLLTR/5X\nAAAgAElEQVSbyrUfu9bXVB0aFEMNTnBvBQTRhVcX960ZlTUUhSUUK1+1Ri35tuUGNWrUSC9f\nLiUlpVBcXEJcUkpq+WoNWnce/NhCrw4nONp1r4EGXODeVtCOq2vTiSqNP+hTzPYPjKfi2zK5\n190fKLB8cfb1tY92rJ5IroTFFS6RXqdllyGTl+z0Z7ySZIyMpq4YskB25vOja6f1b1k+6/hD\nbOX2w5864DlCzmxpQNErNTlCgc9+2DKosrUYL92o6/AZz+zwcyjyylNlqPTb3BsMGnFxYQqZ\n7lrie8x2dwmnZlq8WwcFlpd+O/T4PcWy5rBKrXpXx77DJkyfs3TpmvWybbus9okZstZXoPJf\ncm8uMDr70YGlY7s3Lp1zXic+rVHn4XP9OsSQZ3ws3Y+7VEPeXzsHlJbnV6jccdzKQ2IGJPcO\ndTGYFqB0B8/+mZVIUR03+ZezdXUocuYN7k3wGQoszzK/fqZXKetUaDU6Dp+32ZerFPxyqCPF\nHebeaGBw4+vd03vUTsgqqwyFytVr13v0zGcEFe6bKlHaZ9xbCAq6/eG0umFEMXUHLg5KbZVt\ndkHqdJF720HtLs5JpNge/p7ZkUwsRLWOc2+Fr1BgefD9mu5F5NMztXpMC/AQgg/GR4Qt4d5w\nCKqbn60ZXMt6yMqUWuue/hMWbBS+jzzcyRB7gHs7QSFXMgYVJQqr1HuR9zfVCLK1ElU7xb39\noGpXnypCsb3cz1Tkyc7mFP0M94b4CAWWGxcOPFRWPnDVdNgKxQ9b2VucQONw2F0vrr0xo1Ws\nXFqVbfHgE2sV3D9OiAxbxL2xoIBz2zpLAYpvPiGAAwQBONSWin3C3QagXrc2lqSoBwIrr2SP\nxVFnbV2gjALLhcxP5jY1EUXVHfqMgBHIZ+uK0YO3uNsAguD4U3fLj4tIbTVsifLndZYUopGY\nryHEnF7dJpyoaMcnjyieH5f6G+Jf4W4HUKvnqlB4RyH3WmysROW/4N4cX6DAcub83gEpRIZy\n3eYF81oGO9vTqCcqrBB346UR8iHS4u0nbgtSrDaWoB7au1IUXPrxqcZhRGV6Ph2kALky3hSJ\n60bBmU9bkKGFd5PXenaoI8Xu5t4iH6DAcpT52bxmJqL4pmNcPCMpSHZVQIUV0i4f6F2QKKrB\ncFFjj1eerUAd8FTxEHF8di3p78D0Bz08cCQoZkSG7+NuD1Cf3x4MoxrLBQbtsSjDE9q5fAYF\nlp3z+wamyoeuui9kPNyebXcFelA7QQKfXNnXLZYosf1M32bcE2BvVboXFZb2Zb4/sQJRWLWH\ntwQ7QS7MizKhwgJ7V2bHUcnpYoO2vAj10MwIhgIr1+0PZzc2EcU1GR2s8zUe7EqjUdyNAgq4\nfrR3HFFyp0VBvnUiy75q1AFnCbXt2gsPSX8IRtQd9SxHglyYFxWewd0woCp7S1P8MOF37Wyr\nQE21MqUfCqwsv2zqkURkKN99Af+hq1w7itMc7oYBwW6/NjiRKOn+p1iqK9n+O6k7Tj5r119b\nu0j1edxdEwVNkCbM3Iio17gbB9TjyxZk6rBLgaDtb0B3/sa9dd5BgWWxXHp+TGV5NoYWjyr6\nUAk/bCxs2MbdOiDSx48WJ0q4dwFbdSXbl05DcPJZm76Y19hIlGyezXb7jRvTTAUwWwNkOTfS\nRDW9f6SzT460o7InuTfQK3ovsG5//GSLSKLw6v2Xs+7zXFgRE/EadxOBKN/OSCeKbjEj6DNB\nOtpZhp7gbgzw2aXDQ0sRGSr0EXnNsFDjDSk/cLcSqEHmxmRKeVy5pHWnVE08mlDXBdYvGx9I\nIqLSHWfsVy4JgZllLPw9dzOBCD8vriMV8vUn+vm0ZrG2JtN67gYBn3y1qJX0l2BMY+abmz0Y\nSJW0cnUMKOiTBhTRW9Ghrj8lf8W9lV7QbYF15cVxVeTzgs3HBO8JOP4YRpW0NXUtOPH7003C\nKKz6KCUuSPDLqjjTMe5GAW/9tXtgCfkvwU5z1Xhi0M691Ap3UOjduUeM1EDp2WeGGIpooMLS\nZ4H17fJ20UThNQao8rygvXupA+be1rTfn2keRoZKQ1Vyc2qWJ00Jmntwqi5de3VynTCi2MYj\nN3NnxhuHa9PD3E0GrDK3JFOxGcpHbaghWf1nCfVXYF19cUQ5efZss3rPC9o5VJVmcrcZ+O3U\n0qbS7vGOgZu4c+RoNJX7m7txwL3Mzxa3iyEKS++hgnn5vLSnJK3hbjdg9FUziugVlAshBlOq\n6q9011mB9fv6jrFEkXWHrQ9GAMTYnhiGszna9Pks60zbA4M6V7u37qe2mKxBxU6u7V5Efkhl\n+ymBPyQ3mNbFRbzD3XbA5dKEcKobrCcL9KOyv3JvsAd6KrC+nFPXQFTMPEsV1xl7b6GpiNpj\nBPlcOza8NJGx2lC1ntk5UpMmcjcSOPfb9gGl5StEm41SZW3u3syw1DPcDQg8DpWipCnBi1o3\nqqLyw/B6KbBuvzUujSis8oMKTcyhqIHUDMcaNOXUmg5xRFGNxu7kzo4bO4saDnI3FOTz556H\nK0rFVWy9wSvUf4WoU32pBcYrPTplJlOnoE5/ew81vsK91W7posC6/sKQZKLIhmPUNpGolzLq\n0TTuNgRvXXp+TCVpB1n0vuA/Z9BHyyMSMAeIqvx9cNSdBqKIGv0Wa+aiq/wy6tDj3C0JQXdj\nfgxVWhHcqB1pRPerupgP/QLryqE+CUTxraZq7MSgrZ1Jxje52xG8cPPd2c0j5B3koNXcmfHG\nSKqlmaemhrzzR8bWCCMyVek1T+2VuSc7i4S9zN2cEGRvVaH4UUE/5nqgsrof2BviBda/e7rH\nEiW2n80+eXZg5hpKYzYslbv10aL28USGMh1naKaWb0EjuJsNJOeOjK1plIqrSt1na+PeZg8W\nGlP+4G5TCKazgwyGVhyPHt9ZgpZzb7wboVxgnd/WMYoo+f6FGr2UwVZX6svdnODazfcW3ltA\nPi/YZryqZrvyZF9xwxHuttO7M/tHVQ8jMlbsOlNtT2/2X19qj6dd6kfm5iQqOY8nausSjM9z\nb79rIVtgnVnbLoIotesSnl4X7VAaHeBuUnDqymszW8dKxVVyy9Hau+VrWXjS79wNqGPfbh6c\nTvKRq24hVFzJMqrSCu7GhWA5fhdF9GV7yMDC8AJfc7eAS6FZYJ1c3ET6m7BMzxVcfS7eyvAi\nOOiuOv88P6lRhDxtbZux2iuurAZSWxxq4HDpjQUdk6XoRFXvNTckTgva2xwXfYK7iSEoLk+J\noDobGLM2ltLOcjeCK6FXYN16a2JleXbH/msYu1wBD1Jn7qYFW+czxtUxSkkra56oqdOC9jJq\nqPoShpB049N1Q6qbpOKqUMNBi1X/dEE/TaA6N7kbGoLg+bKUOJE3a52ptVqzFmIF1u9beiQS\nhdd6RN1PcPbHkXTazd28kO3ysYlycWWs0Hmaap7f7KfNcdHfcDenfpx7/elBteVjnqY77ntU\nQ4+T8ENTmsXd2qC4X7tQmHkPc9SO1Kbx3A3hQggVWOePjKkq/1XYZnJoXc6QY1VE8l/cbQzy\nA+Lmt4wkCqvQdcZe7kyIMJ4aqHommdCQ+dMrq0a2TiW5tirT+uHFWp+JwbOdiRGfc7c6KOvm\nkniqsIw7aUeP7kox7OVuC+dCpMD6afco623Od/ZfHgK3DLrQD3cSsvtn34Bi0j6yVIcnuP9q\nE6cRLeBu1tCV+fsH+58aeV/lKLm0osQaHUctDf3aKsvjVOMGd/ODkt6tTrHDVLHDfToy/r/c\nreGU9gusHw/PNKfIfxeG1G3OzhxOoxe5W1vXTi1vHU4U33TMFu4oCLUjIQrXIwv2x5cvbp03\nqlvjMhHWwooiyzTqOmqhmh+cpIAWNIe7H0A5ZweHUXO1XH46jipf4m4QZzRcYF39+tDCQQ0S\n5OEroW7fULwTx9GSsLTL3K2uW1/MqCklrUy3hRp+hokLE6jRbe7mDQlXTry8ZfbwjvWKh1O2\nQuXq3ffghMVq2Q0F17MJkajcQ1XmhiQqMZc7YnnuoT7cTeKM9gqsKz+8s3/ZhB4Ni1kHMEOx\nRj2nbuLu3GDpQJO4m1+fPp50B5Gx+lCNzsXgSX1MWhSYs29vntqjbnJ2VRWWmFa3TY+Hpi7a\nHKq3CHppAjVB5R6aPm1IkXxTXzlxII02cDeKExopsK78753Dq2eO6NI0vUD2IGYoXLlV34nL\nNfNQEiH2JoV/xd0VupP5/viyRBH1x4buCZ6tsfE/cTezRl37eP2olkWsQ5IxuWqL7sOeWLpV\nFVelqEJdWsvdQaCA8yONVF9lf22ui4lR4Xyj6i6wrn93bN0T/dtUTsg54k6xJao27Tjw0Xnr\n1VQ8B89kaoppIYMp891xpYiimjwW2pf3DSczd0trT+aJTQ/Vsp4MTKppHjZrnT6HJLc2RRU6\nzd1NIFrmlqKUMo07W/lMoDuvcjdNPmotsM69sXpk27LGrKoqqkTVZh36jZm2ZLNebsBxqQ5t\n5e4a/bj1xqgSUvqaTg756/syKtF+7tbWlNufLr0/Sb61Ju3uYQt2c3efeg2iXtxdBYJ92ogi\neqrx1FFbGs7dNvmosMA6+3+zOpayFlbx6c26j5i5KiRmGxJkXUTKBe4O0ocrRwcnE0XfNUWN\nQ4lwK00lLnK3uGb8b3WXwvKcC40HL9RFOAJwOI1e5u4uEOnsw0aqp84pcvcVN2RwN48jlRVY\np7YMrGiQS6vqHUfq7aZm7/SgMdydpAO/rrs/Vkphq2m62YF2pUe5G10Trr44qoJcXDUftZa7\nyzRhsSH9OnefgTC3VhWmYuo7O5htmamI2s5Iq6jAurB/aDl5xpiqXSer7PI5NTmQbFLhpXyh\n5NILj94p5bBoh7mHuTs7iPYn4/4Jj06v7yjV3RG1hzzD3V3a0ZbmcXcbiPJ6NYrqp+LrdB6k\ne1R2jbJaCqxvFzU3EUXVGRCyDz8VZTK15u6s0PVXxsRG4USmagNWcfdzsE2lu1Q2NqnMifkN\nw4hS7puhm6OaQjwbH/crd9eBED92JcNdqp5kOeNOWsPdSvZUUWB9PLkSkaFc93korrxQjQ5x\nd1go+uOlhT3Ky/N/pHWcFtq3DLpQm57l7gPVyvxockUpGpX6667uDtwwXOceEi5OjqLyC7nT\n5MHG2NjvuRvKDn+B9fmkNKLwOsNVXRmryUpj+WvcnRZKrnx95KmH7pJvCaPoat2n6faWsDWm\n4v9y94Uq3X57bGlpiKo7cjt3F2nSkTTDm9xdCIG6tSGFCo1W/wxvo6mxqua2ZS6wfphdiSii\n0QTcKOiD9rSQt9dCw5mPDy8f371B1hMBqEjtLhNWq3/8UFJXPCcgv9tvjEiVJ0LDEOW3+Yaa\nqtrnge9eqkYR3TXxn0B9WszdWLY4C6wL65oYyFRvgi5PyATg2diEvxi7TetOv7llev8W5SOz\nH2uSVLlVnwlLNTF2KGxfYuT/uDtHXW6/MTyFKLbFVFx2FYimtJ67JyEQX95DhmYaufNsW3z0\nN9ztZYOtwLp9rGc0GSoNx1wMvhuowgnVNCDzf4fn9qwVl1VYxZet277fuHkbcdlfnjHUmbuP\nVCTz/THFpeqq1TQV3zWlDZsiimKSNe36ZYCRKi/mDpHXxqvq0fVMBdb/Hi9JlNJzHXdnaNPB\noiY8pd43vx2Y0KKgXFiZitfv+NC0Z3DU1ImM8vQad0epxfEpZYliWkxDAS7AAzj5rFnnHoum\n4lO4E+SL+rSUu9HycBRYV59taaColvP0fcVLIB6jDgz9plU/belfVq6tUhr1mrxGT3Nb+WyB\noZaK/vjj8/tTNYkimuhjEv8g2JcYdYq7T8Efl+cXosRHtDVobo2NUc+lDsEvsL4clUhUceQe\n7m7Qsox0eiPoHadJV54fIU+8HV2r9wycjPasCW3m7jF21/a2N1JY7XG4LE+ckZiqQYuuryxG\nsX01d7R/NLVSzZR+QS6wLm1sQFSgIyZCDtACqqeaCKnXn+vNMUSRtQYsOcLdYRqxIbz4Ze5e\n43V8dGGisoO3cfdEaDlSxvARd8+Cj25uKkMRnTX4d2lGDdrI3Xg5glpgff5IATJUfwwXjQau\nAe0LZs9p0O8r7jISFes4C+d5fNCJ5nB3HKPrO5sSxZuXc/dC6JlBLbg7F3xya2t5MrXfyh0c\nv2yISjzD3X7ZgldgXdncgKhgV1zXLsQq4x03gtZ12nN29V1hZLijHybe9tGu+Pg/uDuPy+lp\nRclw5wT8/aeEGvQcd/+C925tSydjG43MzJDfQOrJ3YLZglVgnRhdkAw1JuGeHFHa0KogdZ3m\nXN19XzgZKgzU7PDAaZBepwD578BIirkPFblClhmq3uLuYvDSzS13kLGVho+FHE6jF7gbMUtQ\nCqyb+1oaqEBnDXeY+myOSNX51TLOZb41KIGoZN8N3B2kUQeTw9X1MK/g+Pj+MCo6GHfeKOcu\n2srdyeCVa2vKaru8kiwJS1PH7jEIBdaZWcWJKj+KI+9idab5yved1vw8uxxRoY64iMZ/4+gB\n7l4Mus87GCjtMdwJoaT1ptJ4hKoG/Ls4lcLv1vzfp2aazN2SVooXWB/1jaSodiu42zv07Iwt\ndF7pztOWa7vbhlFE0xnYUQYio4zhE+6eDK4fe4XRHdMwK5/C7qVl3D0Nnvw1LZEiO2zmzkrg\n9haO+C93Y8qULbBu7W9MVGzwbu7WDkm9aaqinacxX8n315d/eBd3t2jeNLqbuy+D6d9JUVT6\nce5G14FtUUXwwBx1+9+IGIrtvoM7KUJMpBZqmMlIyQLr8spyRNWfwJ+GytibEKfbG74cXd7Y\nkCi+wwruPgkJVfT0wJw9xSlxNIaoYOhOs7h7G9z4sLuRCg8Mmfl1a9IO7ha1KFlgnZtVhEyt\nVnA3cwgbTGMU6z1N+XJ4Ahmq4f56QRZSQ+4eDZaf25Opq+Zmqtao3XEJf3N3OLhw+8hdRCVH\nh9Bt/mvDUy5wt6pyBdbZKQUotqs2pynTigOFo35VqPs05NqORphfTay6dJS7V4NjcwJVXc3d\n2vrRD898Vql/V95BVC3ErkN8gEZzt6tSBdb5x+OpQF9ceqWwR2iYIt2nIacmFSFDtYkh9IeX\nCiw31FDD5QtKO9eVooaF1j5F3fYlxKplgm2wcWpCQTK1CLlbrw8km77iblpFCqyrixKpQH8c\neFfcoeQIfT+k/tVORortgGMQojXRw4OYPihN6TjuGVSDaSx3r4Ojt7qaKL5bKJ5smkLNuRtX\ngQIrc3cZiukdMpfKqdooGiK8/zTj2uYaRGVGoJAXb1VY5ZCfdntjpKHrYe6G1pkDidG/cfc7\n2Lq2pRZRyeH7uZOhjJq0h7uBhRdYXzYjkzk0bvRUv0PFwn8Q3YEacX5eMTLUn8vdAyGqhSru\nwFHQrXEUO427lfXnYRrJ3fOQ59epyWSoOztkT5OvNpXkns9dcIF1+TET1cYpm6AZQwPFdqBG\n/D4+nqLMOMGjlLXGCiF9COtKR0pdw93IOnQwCfflqMZb3cIptmNID6L30xPMjSy2wHq9HBWZ\nwt2oenI4Nfyk0B7UhJ+HRVFC353cjR/KWtEW7l5W0PnGVAXx4fAIjeDufJBd2VCTqMSwEL/A\nYnfBaOarlEUWWNfGhxnMId5jajOWBgjsQU349ZFIKvLQAe6WD23rTeVvcne0Ys7WoobID4tD\nRSJ/4e5+sPz4WGEy1A/dc4O5RlE33pYWWGB9V4uS53E3qN4cTjXp6xDW3+OjKXkEpmVQWhva\nxN3VSvm7BrXA8yqZDKfh3P2vd5kvdTRSfGfNP8/ZGxnl6Q3WxhZXYO0vQM33cLen/ujrKqxr\niwpS4jCUV8rbYCoXooew/qlLrUL/T3e1OlQEV2GxuriyElHZkSF632A+Cww1b3M2t6gC6/YU\nQ8Ro7sbUo8N6upHwcDmK6aeXoYFZG9rM3d2KuNqc7sLxKz7DaBR3BHTs21EFyNRETyeamtJG\nzgYXVGBd6UzJITcTrDaM0c1cWCfvIWN7zAASJBtC8yqs292pLqa/YnQwKfo0dwh06vbz7QxU\n8IEt3BEIqo0Rxf5lbHMxBdbZhlQJez4eh1MifhLSiSp3c140VV3B3do60pq2cfe5AiZTOq5v\nZzWUxnGHQJcuLLuDqMI43V1f0Y2mMra6kALr10rUBKMWlxH0iIhOVLkvalKBsbh0JojWGSuG\n3lxYOygZfwnyOlAo9k/uGOjPN8PjydT8Ke7OZ7C3YDTjEQgRBdZPaXQvdn1sDiVFhfzzJ24v\niKDmz3K3tM60pF3c/S7aJ9HRK7mbVfcG0mTuHOhM5v+1M1Bi723cPc9jOPXha3oBBdYvadSF\nuw117WEaE3gvqtpvLSlhKncz686asKqsN+CId66sYTJ3q8K+AgXOcSdBT/5dWYGowqO6OzeY\n40hpw0dsjR94gfVHOnXlbkJ9O5AY84eAKKjXS0Wo9nbuVtahZnSAu+uFyuxInbnbFI4e7Uuz\nuKOgHz+MTSBTs8Xcfc5pBjVja/6AC6x/apOZuwH1bhBNEpEFlcqcZzQNxCloBisMtTK5e1+k\nZ6gybiBUgd2xhTlv7NKT1zoZKaH7Vu4eZ1aTDnN1QKAF1s27qQV2fsz2JYTwMfcrD1DiAu4W\n1qkG9Dx39wt0Ijp2E3eLgqw7LeIOgx5c21yDqMwo3H+2Iqwi15QzgRZYD1FN3Z7bVY++NFNI\nGlToj/qUrvc/wNgsoUbc/S/OzTo0gbtBwWpHVLGr3HEIeWemFyVD/bncfa0KbegZpl4IsMB6\nhkri8Tj8QveY+8k0zADCqDa9yp0AYeZQU+7mhGwdaTV3HELcZ/0jKdq8jrujVWJLZNGLPP0Q\nWIH1dng8ulANHgjRY+5fpVBnnIHmM59ac0dAlOORBTHPh1psCS8bio8JUIvbh5oTpQzezd3N\n6tGdnuDpioAKrD9Sw2ZxtxzIng3NY+4fFzYM5G5afatKH3CHQIzbDWkid2NCrra0gzsRIeuf\nZeWIqk7BAzdt7EmI5ZksMpAC63Yb6s3dcJAlJI+5f1zQMJy7YXVuJnXkToEYa6k+d1tCnrVh\nVUPqBlX1+GFsAQpvuYy7g9XmIRrK0h2BFFgLqAZO36hEKB5z/zLRMIq7XXWvnOEr7hyI8Gdi\n1GbupgQbTSiDOxOh6HV5WoYeOp2y3Z1DxUwnODokgALrs4gEdKRq3E3bxaVCFU4Ww/ErfpOo\nN3cQRBhCD3K3JNhaFko3qKrEtS01MS2DKxOpE0ef+F9gXatKj3M3GuRaF1YltI65/1GecP0V\nv4ziph+4oxC4T8KKYzYZdalFb3KnIrRYp2WoN4e7X9Uq4w56h6FX/C+wplBr7jYDG83okMBc\nsLtcnzpxNylIRtMj3FkIXDOaxt2OYG8u3cudilDycT9My+DeXGrC0C9+F1ifmpJwF6iaPG2o\nLzIYzDK7UlNc4KcGh5KiznCnIVAHqRZ3M4KjdMMX3LkIFTf3NiEqNhhTUrpVm+OyP38LrJu1\n8DehytSh/wiNBqvpVAmXEqjDYM0/6fJGhbCV3K0IjqaExtV9/P5eUIqo2uP4e9SDpw1VbgW9\nc/wtsJZQM+72AnvzqY3QaHA6Ykjazt2ekGVfgYQL3HkIzCpqy92IkE9GiVC4uo/dl0NiKKLt\nCu7e1IIWtCno3eNngfVbfCzuIFSbyvSR2HCwOVkwYgl3a0KOXjSPOxABuVI8AlM0qNAoGsEd\nDa27dbAFUVL/ndxdqQ0bTKWCPh23nwVWL3qIu7XA0TTqKjYcXK7VIkzQoB47o1I0/ZiABdSZ\nuwnBiYOJMX9xZ0PTzi4sQ1Rl0mHujtQMMz0V7D7yr8B6y1AWE/GrTkaZsG8Fx4PHSGrO3ZZg\nQ9uPCbiYFI2HEKrSAJrOHQ4N+3xQNEW0Wc7diVqyI7pwsK928KvAul2H5nG3FeQ3ngaJzgeH\no4bUfdxNCTY2m8oF/+pQYebSA9wNCE7tiU26xJ0OjbqxuylRcn/85eCbnvR4kDvKrwJrGzXi\nbilw4nBKxK+iAxJ8fyablnK3JNhpTbu4U+G3i4VjdnG3HzjXlZZzx0OTfp+ZSlQNj3P22d6E\n2CDPOeNPgXWllAkTmqnSMBonPCFB15n6crcj2FttqMmdCr/NxwEs1doaXjrknqCqvLd6RFDU\nPZh4xB9DaHhwO8ufAms+deRuJ3DqQMG4v4VHJMh2UTr+MlObhvQidy78dDk5CvdYqVZbepY7\nIBpzaV11ouJDMMm3fw4mR/wvqP3lR4F1rlAsTv2qVD+aKT4jQfVXUsQq7lYER4upJXcw/LQc\ntxCq2BpDDe6AaMq3owtSWP1ZmFPUb2OoT1B7zI8CaxJO4ajW7piky+JDEky9qT93I0J+d9KH\n3Mnwy43SEZiwT8Ua0jHuiGjGzYOtDZTQdSN3n2nakVJhQX1Ck+8F1h9xCbjHS7W0ftnoMUrD\ntC4qNIO6cEfDL9upHXfTgRuLqBV3RDTitxnFidLHHeTuMa2bSvcFs9t8L7AepUHcbQQubY0o\nfUOBmATL1XJhmMJdjTLKanKOtczqYWu5mw7cqUwfc4dEAzJf6RpOUW0x6VXgMtLp7SD2nM8F\n1pmYQngKr4q1o61K5CRIZtB93A0ITo2nIdzh8MNLmFBG5Z6gHtwhUb2zT6UTlRyKC9uFeJKa\nBrHvfC6wHqXB3C0EbqwLq5qpRFCC4sfoBIwi6nS4aOTv3PHw3d20kLvhwK2Mknjks3tv940i\nU9MncWG7KLXo+eD1nq8F1l+xBXEAS9WaUoYiSQmGLjSKu/nAhYdoInc8fPa1IZ272cADPPLZ\nnfNPVyUq2m87dy+FkmWGGreD1oG+FlhTaQB3+4Bby6mRIkkJgtepPP5OU6v9CQnBfo5XwAbT\nRO5mAw/wyGfX3u4XTcYGMzAoitUkiA+m8LHAulAwHrcQqlwtekOZrCjtdi0DTuioV2+az50Q\nH52NLoJbUlXvQZrBHRRV+mtJJaLkPlu5+yf0rDGWD9qdYD4WWPOoJ3frgAdz6TFLgp4AACAA\nSURBVB5lsqK0LdSEu+3AtZ1Rxa5yR8Q3c+lB7kYDj/bEFsYjnx3dPtY9kkyNcPBKEXfT6mB1\npG8F1rUUPHdC/dINnyuUFkVdKRm+nrvpwI2OtJY7Iz65WTISo5UGdKaV3FFRmVPTSxMV748p\nchWyOSI1WPNx+1ZgracO3G0DHk2lngqlRVEL8IhLddtsuuMWd0h8cYDacjcZeGGLKU1TuVLY\nle2twiiixTwcvFJO56Bd7uBTgZVZ0Yh5+tUvo6TxpFJ5Uc75xFgcb1C3VrSXOyW+aEGYmFET\nWtMe7qyoxjtDE4gqDMdsNYraGVvoXHD606cC6yg1424Z8MIYelipvChnKvXhbjdwb5WhNndK\nfPBfQyXuBgOvrDLU4Q6LOpyaXYGoUKdnuDsk9PWhScHpUp8KrBaE55howaEiUaeVCoxS/oov\nsJe73cCD+vQyd068N5LGc7cXeKce/Yc7Lfz+2dQ8jMIbT8ONr0Gwr2DMb0HpVV8KrM+oKne7\ngFeG0mOKJUYheMSlBiyk1tw58drlggl4MK5GzKe7uePC7OZzPaKJ0ofhKokgeShIZ3l8KbD6\n01TuZgGv7E8ocF6xyCjidEwinhCgflXpI+6keGsjdeFuLfBWRcNn3Hnh9P7IIkRFe6zh7gYd\nOZQS/l0wutaHAutMZLEj3M0C3ulDc5TLjBLG0lDuNgPPplNX7qR4q65hHXdrgbemUi/uvLD5\ndlp5ovh7FuCuwaB6lB4IRu/6UGDNxGOeNWNXdJFgTfQhxBkcwNKEjDJh33JnxTufUU3uxgKv\nZZQw/cidGBa/LalDFNF46iHuHtCdjDKGT4LQwd4XWNeLReHeUc3oTMsVTI1w42kId4uBN8bT\nIO6seOdhmszdVuC9UTSSOzHBd2FzayMZqo/GfpXDdGobhD72vsDaSe25mwS8tjWiZNAetxS4\ns3EFcQBLEw6nRPzCnRZvXCqQiIMCGnKosN4e+XztUNcoovKD8KxBLlWDce+q9wVWI8Mq7hYB\n77WnTQrGRrAn8NA4rRhGY7nT4o2N1I27pcAXA2k6d2aCKPOthwoRFcNl7ZwWUd1MxXva6wLr\nU6rO3SDgg42mdM08f+JioXjMgaURBwrFneXOixca4hJ3bdmro0c+f/dEWaIE82LuNte7BkF4\nMoXXBdZgXNOgLS218/yJhdSDu7XAW/21cKjhOFXjbifwTXdayp2aoLiwtjFRRDPMJ8pvtfEO\nxa+j8bbAOh+ThERoyipDDeUPgApxLTXyWe7WAm/tiS38L3diPHoUs7hrzXZNXTXqp8xX+0ST\noeqoPdytDbK7aZXSPe5tgbWMenO3BvimET2naHSE2UBm7rYC73WnxdyJ8eR6chxumtCa9rSZ\nOzcKOz2vPFFyjw3cLQ3ZtkamKP3HorcFVhUj7nbQmOWG+opGR5TbFY0budsKvLcjMvUad2Y8\n2E/3crcS+Gq9sdJt7uAoKPPVbuEU3mw25hNVkW40Q+Fu97LAeo0ac7cF+Ko2vaJseMQ4Qndx\ntxT4ogOt4c6MB+1pKXcjgc/uooPcwVHMv6sqE5UYjEcNqsvu+Lgzyna8lwXWAzSHuy3AV4up\npbLhEaMpLeduKfDFZlPaTe7QuPWbsSx3G4HvVhjqcSdHIafGFyRj47k4eKU6g2mYsl3vXYH1\nR0RxhEN7qtHbyqZHhA8w/4fWtKVt3Klxax6eC6BJdbVxyN1Xn/cyUYHum7lbF5w4VDT8G0U7\n37sCawEN5G4J8N0culvR8AjRjWZwtxP4Zp2xoqqvlqlo2sHdROCHxdSaOzrivdXeQMWH454L\nlZpAnRTtfq8KrMw7wnEfvRZVpA8VTY8Ap0wlcXBUa5oHYYY+/71NjbgbCPxSld7nDo9grzcn\nSp+KEU61Msore5rHqwLrFWrG3Q7gjxnUQcnwiPAojeRuJfDVKkN1Fc+xNpimcTcQ+GUWmbnD\nI9S7LYmqzeVuVXBnHjVQcizzqsDqTgiJNlUwfKpgeAS4mJCAo+fa04iOcCfHpcsFEjEnskbd\nYfiCOz7ifN1BKq/mczcpeFBf0UeeeFNg/RmRimOc2jRN4TPMAVuOp+Ro0XJDXe7kuLSNunA3\nD/hpKj3AHR9RTg8xUjoOTKjfamNZBef186bAWkQPcjcC+CejnLr/JLxd3oQJbLWoLr3AnR1X\nWtEq7tYBP2WUNip7U1ewXJ0XT8Un47iEFrSnRcoFwYsCKzMdN+Vo1lTqolx4ApdBLbhbCPzx\nFDXizo4LP4WlczcO+G089ecOkAjPlaP4oYe4GxO8siO24FnFkuBFgfU6ZnHXrow0VR/CakVL\nuFsI/FKbXuYOj3OzaRh324DfjqSa/sedoID91JHC2mPSds3oTyMVy4IXBVYfmsXdAuA3VR/C\n+spQibt9wD+LqAl3epzKLB+xi7ttwH+jaSh3hAJ0a0kcVcTDKTTkQNHwE0qlwXOBdT6mKE4l\na1dGecPnSoUnYENpInf7gJ9q0n+44+PMm9SUu2UgAIdSwk9xZyggX9ej2OHYY2rKRGqvVBw8\nF1grqA/39kMAHlfvjYTnYpJwQ71WLaSm3PlxZhAeDKBtI5V+Opyibj0ZSY23cbch+CajMr2o\nUCA8F1jVw/AUJS3LKKfaubAWUV/u1gG/1VTjk+MuF0g8wt0wEIhDyZE/c6fIb981oITJ3C0I\nPltiqKzQ8+s9FlgfUl3urYeATFPr9Mi3ykbg9lTtWkSNuROU3w7qzN0uEJjhNJw7Rf5aG0uN\nMKRpUSt6WplIeCywHqKp3BsPgUlX6RO+DlMr7qaBANSmY9wRyqctPcPdLBCYQ8mRv3DHyC/n\nOlHMWO7WA79sjU5UZqoGTwXWJTx3QvNmUxtFshOo1rSMu2kgAE9Rfe4I5ZNWnrtVIFDDtXkV\n1tulqNJG7rYDP/VXKHSeCqxN1JV70yFQVel1RcITmOOYo0Hj6tJR7hA5SnmYu1EgUIeSI37i\nzpHPMheZDN1xLEKzDhYzKnK3vacCq5lhLfemQ6Dmq3LOomH0GHfDQECWGWoq+SB6f5TC/I7a\nN1J7c2FduJ8SZnO3GwRgGjVVYjDzVGClVePecAhcbXpegewE5kJcIp4loXGNaS93jBzgHE0I\nOJQSrrHp3L+6gypv4W42CEgd2qFAMjwVWCnjubcbArfMUOO2AuEJyDLqxd0sEKBVYRVvcefI\nHneLgAhjNfZEwv1x1AF/LWrc2vDUi+Kj4anAqnaAe7tBgCa0U3x2ApJZwbSVu1UgUC1pE3eQ\n7HE3CIhwpITxv9xJ8l7mNEMEjkNoX3d6VHw4PBVYuGwvJKwxlr8hPjyBeIHu4m4UCNgGU+lr\n3Emyw90gIMRE6sadJK9d6kxJS7kbDAK3v0j4V8LT4anA4t5oEKMdPSM8OwFpT4u42wQCdy8t\n406SHe72ACEy0gyfcEfJS7/WokrbudsLRJhKzYRf544CSx+2Rhb9V3R2AvF9GCYsCgXbo4oo\ncOGC/7jbA8SYTu24o+SdT4tTi4PcrQVi1KXNovOBAksnutN00dkJxFgaw90iIEIPmsadJVvc\nzQGCVKHXuLPkjefiDH0yuNsKBFkfUeRvwQFBgaUTuwvEnRacnUDckYC7J0LCnoS437nDZIO7\nOUCQhVRfbXOsObHWFD6Bu6VAnD40SHBCUGDpxVBVzd6X1p27PUAMdeWKuzVAlHq0nztMnmQ+\nTvHzudsJBDpU0vCG2IygwNKLQ6nGr8VmJxDFN3O3B4hxqJjpOHea8nC3BojyTFgFld347Ojm\nQEpezd1MINR8Q0Wxt0WjwNKNKWq6bnQFd2uAKBPpXu405eFuDBCmDa3kTpNbl++lNEzlF2ru\npieEpgQFln5UpReEZicQ3G0BwmRUole445SLuzFAmK1RRf7hjpMb5xpRtT3cbQSi7U6MEDoZ\nFgos/VhqqHxTZHYCwd0WIM5iQ3XVPDCHuy1AnB40kTtOrv1+JzXC9AwhaDLVFzmaocDSkVb0\ntMDoBIS7KUCgZrSeO085uJsCxNmXGHWKO0+u/FCO2h3hbiBQQiNaLDAoKLB0ZGt04lmB2QkE\nd1OAQBsjil7gDlQ27qYAgUbRA9x5cuHrVOqK6a9C07b4mO/EJQUFlp70p4fFRScg3C0BIvVQ\n4jGpfuFuCRAoI83wNnegnPooydCfu3FAKeOoyW1hUUGBpScHU42fCotOQLhbAkTalxR+gjtR\nWbhbAkR6kmqL29WJ82YBwyPcTQPKqUdLhGUFBZauTKdG6pgfmbshQKgJ1JY7UVm4GwKEaqKe\nq/vyHIsxjuNuGFDQ1vjo/4oKCwosfakv/nGWfuFuBxDrTjrAHSkr7nYAoTZGFjnHHSlHRyJN\nU7jbBRT1GNUVdb89Cix92SD+cZZ+4W4HEGulsdQl7kzJuNsBxOpNw7kj5WBPeMQM7lYBhTUT\n9gh7FFg600cdj47jbgYQrJM6Ji3ibgYQ60Ax4yfcmbKz1Rg1l7tRQGk7C5veFZMXFFg6c7BE\n2DtiohMQ7mYAwfYmhavhUZfczQCCTaf6arrOfW1Y7ELuJgHlzTakXRQSGBRYevOkocp1IdEJ\nCHcrgGiTqbEK9oTcrQCiNaLV3KHKs9wQv4S7QSAYOlFfIYlBgaU7bWiWkOgEhLsRQLh6atgT\ncjcCiLY5quBp7lTlWEgJT3O3BwTFwTTaJiIyKLB0Z2fBSGE3ofqNuxFAuM3RCb9yxwq5Cj2D\nqTt3qrLNpsRV3K0BQbImKu4bAZlBgaU/E6kh+8kc7jYA8YZSR+5YIVeh50h5yuCOldUUSlrD\n3RgQNGOo+pXAQ4MCS4fq01OBJycw3E0A4mVUol3IFYj2tKm4Ch51mTmOkjdwNwUEUSsaHHhs\nUGDp0Nb4GBFHPwPB3QSggNURSWeQKxDtARrEHCupvnqEUjdzNwQE0/4yAmblRoGlR+OpgaiZ\nav3E3QKghAF0P2+skKtQdLCU4QXmXN0aSCW3crcDBNeamOiAH92LAkuXGtOcQJMTGO4GACUc\nqURbkCsQbYmxOPMTcwZS2R3crQDBNtlQ5myAwUGBpUvPFgr/KMDkBIa7AUAR66ISfkSuQLQH\nqBdrrCxpd+zkbgMIvm7UKsBTPSiw9Gm6oQLrs+O4tx+UMZya3kKuQLBDacz3T3Tcw90EwOBI\nbRoZWHBQYOnUfbwXjnJvPiikPu80ttybD8pYFVHwJ+QKgm13cVoTUHBQYOnUgTKsfxNybz4o\nZEei6S3kCkQbRk0578vh3nxgsiY+/OVAgoMCS69WRcV/G0hyAsO99aCU2YaSgV4YilxBPvXp\ncb5YIVe6NdeU8FUAwUGBpVtj6M7LASQnMNwbD4p5gNrxPSiAe+NBKTuTwgI6lIBcgV/GGEoG\n8AgwFFj61Yb6+R+cAHFvOyjmSHWaiVyBaAtNRX5BriDoetKd5/0ODgos/TqQRiv9Dk6AuLcd\nlLMjKez/kCsQbSA1uI5cQbBltKUmfp/rQYGlYxvjw9/wNzgB4t50UNDi8ILfIVcgWEZjGsoU\nK+RKz440pPb+lvYosPRstrHID34GJ0DcWw5KGkEVmR7Py73loKB9pWkVT6yQK107WIO6+nkP\nKwosXRtKlXn2hNwbDoq6j9rcQK5AsHXx4f9hiRVypW/7KtED/lVYKLD07R5qxbIn5N5uUNTh\nWjSEI1bIVWibayp0ArmCoNuT7meFhQJL3w7Xpn6Z/gQnQNzbDcraU4ZnRnfu7QZljaCyp5Er\nCLrd6dTFn+uwUGDp3N40mujPiBMg7s0GhW1OMqxDrkC07lTzH+QKgm5PJbrHj3sJUWDp3bYU\nmufXmBMQ7q0Gpa2MM+5BrkCwjFZ01xXkCoJuXw1q9LfPwUGBpXvrE2mFf6NOALg3GhS3KCo8\nA7kCwQ7X8/+eeeQK/HewMVU+5WtwUGDBqgRD0O9+5t5mUN7ciMjnkCsQ7EA16hT0G3O4NxpU\n4Eh7SvnAx+CgwIKjTxcwBHtKd+5NhiCYGRFxGLkCwfZVpvuDfQyLe5tBFQYYon287gEFFhw9\nuiLBEOTrsLi3GIJhVkT4DuQKBNtbxa/rjZErCNSUKMPkW74EBwUWSFYVpnFBna2Be4MhKJ6M\nDlsSzFghV7qwrzo1OYdcQfAtT6Y2f/oQHBRYINuQSj2uBTIC+Yh7eyE4liTQmNvIFYh1sCFV\n+SmIsUKuINuzNam4D0/wRYEFVjvSqfEfAY1BPuHeXAiSdcXJfBG5ArGOtKNivl5vjFyBABm9\nwoyPez2rOwosyLK/EZX6JMBhyHvcWwvBsvNOqvwtcgWC9TdEbQtarJAryDM3ieoe9zI4KLAg\nW0Z3Q9SGQAcib3FvLATNoXuoQNCmHOXeWAiax6NpRNBuJuTeWFCTXU0pao53U4WgwIJcU2Ko\nd5BO53BvKgTR6AgaEqS7vrg3FYJnVQmq811wYoVcgZ2JCXTn294EBwUW5FmXRmlvCRiOPOPe\nUgimlSWpwrvIFYi1txnFrQ3Ozc/cmwoq82xLg6Hfb56DgwILbBzsaAgbc0nIkIRcQa4D9xqM\no/9FrkCsMdF0t89PL0GuQIAnS1HsTI/7ShRYYOfJFCodhEfIcW8mBNmcolRiL3IFYm2oRnGL\ngvDgHO7tBPU5/FA8FV121X1wUGCBvf33G+keb++R8Bv3VkKw7e9iomaK31nPvZUQZBkj4ij9\nqNKxQq7Aid3doij1KbdH5lFggaOnq5BpsMLT+HFvIwTfqlpk6PQFcgVC7WhroLveUTZWyBU4\ntb1jFBWa/Kvr4KDAgnwyJqVS5MMnhY5QyBUcnVWeDB0UvYuCewuBwfIaRK3/o2SskCtwYUf3\nODJ1fcXVvRYosMCJQ8OTydjVq/tQ/cO9gcAi4/HyRHU2KzdnA/cGAou5VYlqbfVwOQxyBUrY\nN6wkUdnp3zsNDgoscOrQmFJE1Zb/JX6sQq50bU4dAyUMfVOhu+u5tw6YLKhnoMTRnyqTKuQK\n3HqyeQSR04loUGCBCxmz64dRuHnHBQXGK+RKx9Z1LkhUcvSrXj/PC7kCz9bdH09UecaXCqQK\nuQIPdo+s5vQ6QBRY4NqW/iWJItos/S8GLBDo8LS7ookSOq/6BrkCYQ5OrGciKjv8iPi/Cbk3\nDdTPaXBQYIFbK3qUIaLUnis/FXrAgXuzgNmBae2SpGAld3zylb+RKxBk15hGUUTGWqN2ib1H\nh3u7QP2cBgcFFniy8ZFG8dK+MLrBw6vfOosBC0RZNbRRISlYVKr9Yxvf+QO5AhEOzumabpRS\nVaj5qLVviEkVcgWeOQ0OCizwQsaKR1qVDJN3hkkN+zyx8aX/BvpMaO4NApVYP6FT9QJysCiu\nmnnkgh2vnwjo/A735oAq7HuyX6Nka6oSanQas2z/u6cCvMOQe4NA/ZwGx1OBNX0iQJZxfdvV\nKZtgsI5bFJmSXrdVp77Dxk+f/4sfAxZyBXlG9mhbp1yiMStZFF44rUbTe7oN+Aq5ggCM6X13\n3bxUxaSm121h7jVkzBTn99MjVxCYP50Fx1OBlUIA7i33Y8BCrsAT5AqUgFyBEl53FpzQL7Ai\nChUqFMu9EoGKlzYinHslXNmhywELuVLaAeRKq1Sdq106zFW01CFR3CsRmChpE2K4V8Idvwqs\nNO61Dljh2rVra34rKkgbkcC9Eq44DRZypQXIleogV0rTY65KSB1SjHslApMibUJJ7pVwx68C\nq29rrWsq9UtD7pUIVH1pI5pzr4Qr/jy/F7lSBeRKdZArpekxV42lDmnMvRKBkf/DaMS9Eu44\nzZWnAkv7MqR+mcS9EoEaIm2E0g+MB58gV6AE5ArEWyZ1yDrulQjMVmkTFnCvhM9QYGkCBizV\nQa5ACcgViIcCiwkKLE3AgKU6yBUoAbkC8VBgMUGBpQkYsFQHuQIlIFcgHgosJiiwNAEDluog\nV6AE5ArEQ4HFBAWWJmDAUh3kCpSAXIF4KLCYoMDSBAxYqoNcgRKQKxAPBRYTFFiagAFLdZAr\nUAJyBeKhwGIS+gXWjX/++ecK90oE6rK0ETe5VwJsIVegBOQKxLsmdch17pUIzHVpE65yr4TP\nQr/AAgAAAAgyFFgAAAAAgqHAAgAAABAMBRYAAACAYCiwAAAAAARDgQUAAAAgGAosAAAAAMFC\nusD6zGxjLPfa+O74ELP5bdsFv64b1bNTv5nHbnGtEciQK1ACcgXiaTxVGg9VSBdYb2s6Wjc3\ndzDbZ2vf/dnbMuwM21oBcgXKQK5APE2nSvOhCukC60WzeebOHC9yr42PfhhhNneyy9ZhKVVP\n7Htu00CzecBFvhUD5AqUgFyBeFpOlfZDFdIF1gGz+T/c6+Cvo53MnQ8vtc3W6S7m+z+QX1yb\nbTY/zbZigFyBIpArEE/DqQqBUIV0gbXNbH6fex38Ndb8yA8Wu2ytMZt3Zr262sfc8RzTegFy\nBcpArkA8DacqBEIV0gXWKrP5K+518NfYVdctdtm61dvc6d/s1zvM5oNM6wXIFSgDuQLxNJyq\nEAhVSBdYi8zmH7jXwV/WFbfN1gmzeVLO6+Nm8xSOlQIr5AqUgFyBeBpOVQiEKqQLrBlm8x/c\n6xAQ22w9ZzZvynl9vYP5AZ41AgtyBcpArkA8radK26EK6QJrgtl88bVZ/e7vMWrTae518Ytt\ntjaazc/lftBX2jKWNQILcgXKQK5APK2nStuhCukCa5jZ/Ej2pBn3787kXhs/2GbrKdtr/Uaa\nzT+zrBFYkCtQBnIF4mk9VdoOVUgXWP2kTPV4at+RNQOkF9u518YPttmaazZ/mPvBo2bzdyxr\nBBbkCpSBXIF4Wk+VtkMV0gVWF7N59WX5xc11Ura+514d39lma5bZ/GnuB5PM5hMsawQW5AqU\ngVyBeFpPlbZDFdIF1uVLl3NezjabF3Kuin9cFu/jtFC8hyzkCpSAXIF4Wk+VtkMV0gWWje/M\n5ge0d/7ZNltLbE8/jzCbf2VZI7CHXIESkCsQT5Op0nao9FJgZXY2m//hXgmf2WZrs9l8NPeD\nXmbzJZY1AnvIFSgBuQLxNJkqbYdKLwWWpafZ/Bf3OvjMNlsvms0bcl5fNpt786wROECuQAnI\nFYinxVRpO1R6KbCudzCbr3OvhM9ss3XSbB6f8/oTs3kmzxqBPeQKlIBcgXiaTJW2QxXKBdb7\nK6e/mvNa6o3hnOviH9tsZQ7Me7jlKrP5GNMqAXIFikCuQDzNp0rboQrlAusls3lYdr2eOcls\n3sa7Nv6we5D4NrN5Y9ars13NXS+7+BVQHHIFSkCuQDzNp0rboQrlAutaH7P5Seuzt68/bTZ3\nv8C9Pr6zy9aFHuYOb8gvLk4wm3exrRMgV6AE5ArE03yqtB2qUC6wLB90NJt7rjp8ZHU/s7nD\nu9xr45PjO2WjzOb58j8PWpe92sFsnronY7X0X8y4m8zrp2vIFSgBuQLxtJuqUAhVSBdYlvd6\nZT+EydznI+518c0+s62+WQtf6pL9fqr6b08NacgVKAG5AvE0m6pQCFVoF1iWS0em9evcZcDM\n569xr4mPnGbL8ufm0T06D5j/HuuqAXIFykCuQDytpioUQhXiBRYAAABA8KHAAgAAABAMBRYA\nAACAYCiwAAAAAARDgQUAAAAgGAosAAAAAMFQYAEAAAAIhgILAAAAQDAUWAAAAACCocACAAAA\nEAwFFgAAAIBgKLAAAAAABEOBBQAAACAYCiwAAAAAwVBgAQAAAAiGAgsAAABAMBRYAAAAAIKh\nwAIAAAAQTJcF1gqSrM2/PPOjWZ0qF440JZRo9tDef/J9/MvKntUTIyIKp3ea+2XOsoHSNy3M\n/02dpMVrpH8OpxwxqVXunfniFZGbASpk7fF2jkszy8qLj9ovdBVDi/OoWWzjZOsrkesPqvLr\nqt41kyKN8eXvfuKdTNsP7KMQXazOsIM38j49IS1rZfPTrkY2+VsO2X7tjjDpy17Neffv1h7p\nCaaC5Tov+0vwdgErP/Z0eXIz2Xbqm7dtP3CfSX0OXrossKrKPVs73+LD1Wx7Pm6yfTV0vJvB\n5tPa2cPSh9Lr9Hzf9Ge49PsXLflDVXDUCSU2CFTD2uPG3xyWvklOCiwXMXQVNYtexyj9+tou\nCBV22OzOnESh6MbcTx0KLJcjm2OBddBEFHks+03m8kK5vxM16bpC2wjB58+eLseXXWw/Lbfl\nVt5H7jOpz8FLjwXWO0Qlo4g+tV+aOcqx72v+kffprbFhDp92OWf9oKb08i3Hf8NiaeFQ+UX+\nUJmm3VRw04BbVo/Pd1g62LrUvsByHkM3UdPrGKVXNx4xOPR0lbw/z5xGoVdOBWZXYLkZ2RwK\nrBcjiMJzUnrzAbvfaX5N2a2FYPFzT2d1fahjJtO/zv3QfSb1OXjpscDqSzS2A9HD9ktnyd1d\nZ8m7f1678fdHK+rJ75rl/sX4z93WNFQav/7Isd3TmllDVuOs/Mka6VV/x39DZWnhJ/ILOVRL\nPpO9lbGqXxnrl9T5W9GtA1ZSj0dLQbFfeDWBIsmxwHIeQzdRy4rT7NccXVJua4DP2busQag6\nft3hF7cvuFsOECW8kPNp3sgi+fSNrUMT5c8fzf7UrsByM7LZF1hvSsk1Hch595j0WVjPwydP\nf7P3Xvl3xii9wRAcfu7pZGca5s9k/HM5n7rPpD4HLx0WWOeiiN7ZRlTArm9/MUp/vG3Ke781\nXMrDluw3me2tKfww58OT3eT3LeRU/htPFONwFvtd6cO61lf2A1jmsZby7zW4LHaDQEWkHq9f\ngOgDu4W7iZo4FljOY+guak4umYGQdbOx3PH35F4Dc2GSFBiK/Dj7bb4oXOgjLTEdz3pjW2C5\nG9nsvuVDaSgL25nz7rswKZuvZ7+R/4yMwHVYIcHfPZ3kurUauy8vk1Ni5GDkDHbuM6nPwUuH\nBdZSopKZF6Tie6Pt0jlS9z9uu2CZXKlnv35SztU020/XyKX9UvnVQ5R1PbuNQdKiDdZXjqHK\nXBEhLekuZDtAjaQer9eVaJjdQmnUmuJYYDmPoduo6XSM0qex8gGkZ2yXQCfrDAAAD6xJREFU\nfFFMWlT2fNab/FGw7huzDxfYFljuRjbbb/kykciwJfenxtnsdrPu2tlpgRDg957OYhkmvTSu\nt/306xLSolI2B9hdZ1Kfg5cOC6xKRBMsFqkyr2+7tIfU/SdtF9wqZSjT4YL15W9yVTTd/lvm\nSouKyAcfPs09XJXjUlzucYn8oXpWzmu+i7YgVEg9Xm07USHbK1bOmMjwH8cCy3kM3UZNp2OU\nLn1lyP0rLdfJgtKycVmvnUThw7wdpW2B5W5ks/mW74pK9ZXNPa11iArmXS66S/rBWQFtEKiE\n33s6yydy9bXd/tNT8mnAUVmv3WdSn4OX/gqs16V+/txieU76xxc2i1tI7/+1+8EfLua8miB9\nVv+W3YeW2zUK9TtivbOmnsM3WTZKC4ZnvXQSKvkvw/oWCFFSj1c4ZyLaa7NsiVSCf+5QYLmI\nofuo6XOM0iX55EoXx4XPSgvjsi45dhaFSKKErFe2BZa7kS3vW34qKb182uaHfv70P6/mvXtD\n+nSqf1sC6uL/nk4+XdjT8ev2SQtjsg5huc+kPgcv/RVYPYlqSv+4VYxohM3i+yn7uvT8rheQ\nPnvFcenpnD/vNuaV8FkaSQuyT1M7CdXfCdKyj/xceVA7qcfTLG2J2tssq0H01CcOBZbzGHqI\nmj7HKD06I9XohpP5FteSArDI+spZFIoQhWW9si2w3Ixsed9yunzuNzslH8FyPmUbaIzfe7rf\nwqR8/Zjvl+QDDE9aX7nPpD4HL90VWH9FZv+h9hhRQZv5P+Qz0/fecvorb0kfVXT9jZeliinR\n5oyQPLg1zH7tLFSj8cdgCJN6vIxlO5HxdO6ir+SJsT62L7BcxNBD1PQ5RumRXNDcnX/xJmlx\na+srJ1G4JhVlhbNeOl6D5WJky/2Ws1WkF7PdrM890n7yB182ANTK7z3dNunT+/IvlgY7am59\n5T6T+hy8dFdgLSSKsB7Q/MbuIk7LKfleirb5/2a0ZCVynJuvHCF9vivv7Xjp7dbs185C9Xxu\nICH0SD1e2nKlgO38/uPlqd0/si+wXMTQQ9T0OUbpkXzrzIr8i/+Sz8dYZ8d2EoUXpEUts17a\nFlhuRracb/mntuOFz/ZuTZY+H+DrNoAq+b2nk59asir/4nMGoijrAQb3mdTn4KW3AivzDqKu\nWS8bEjW2+UQOFxnv2/RTvt/pLH2wz813fkW20ybfLEpUKOeYhLNQXZKWpfiz7qAB1gLLMpSo\nSs6SW6lEux0KLFcx9BA1fY5ReiSfC/zYyfJ0afln8ov8Ubghn6tZkvXabh4s1yNb9rdclucQ\nGe9iTW6d+WyF/MyBFv+6+AHQGH/3dDVysuegCtnM+ug6k/ocvPRWYL0s9XL2xGjrpJfHbT5a\nYiSrkj2f+dzuEUvNsq5Hdq0RkSH3+Pkh6adH57xxGqp4aeENx4UQGrIKrPdsrrN7kajgVYcC\ny1UMPUTN2WTIsQpsBHCTLzl3VtHIF9BYH2WTb2Q510FaUjh70m37R+W4HNmyvuVaG+fnI2UT\ns1NWdBEeQBEy/NzTFZc+dTYvqDyFx4vyC/eZ1OfgpbcCqytRsezzz//E2FRCsvfvzu34Al02\n5823LlfoTv76y7PN9gD7fdKb/+a8cVpgyQ/+Pe/3FoCqZRVY8iQM2TeSWnpZZ2u3L7BcxdBD\n1PQ5RumRFAqTs+UDKHs+KjkKK0/k+HD/CPneGcPu7B9zeBahq5HN+i37OsrLTfmubbbKKrCS\nZ14Qs1mgCv7t6aKIwp0tl6d9fFZ+4T6T+hy8dFZgnQkneiznTR/7i9Mln01Kz+37mGE5p6nL\nSO/czmJ8NZGoRPafAr9Jfx00y/3EaYFVydP3gXZlF1gLpGxlPR73orSvfN+hwHIZQw9R0+cY\npUO3KPfudnvyHTLWaY2dRSF8dc6PORRYrkY267ekZS0t+I2zf1/OEay4QY7PLwct82NP5zaT\n1jtM3WdSn4OXzgoseaLa3MNL8tyPOxx/4tfdo2qbsno/Mvsq0+rS6+/dfu0Y6Seez/s3PJv7\ngdMCK1laiKflhKjsAuu0lKH91gWbsm7MsSuwXMbQQ9TkOC14z96Hrn8cNCuKyJjpZLl8oXHu\nESwHzfOikK/Asjgd2bK/JfzpR6X/L3/Wkt//XvvPvmeGFJX3ra86+Ri0y+c9XRxRmLNMPkjZ\n0/65z6Q+By99FVi3y9pO8plZxvZgk41LLz9W0RqPrIn35JnZ3nP7vfJ41jnrZXmipLzDYk4L\nrHA9VO56lV1gyWeKs25pbk4032JfYLmOoYeo6fM6UT1KlXra2Wk5+XoX6wOf8+3MZp6w+TFn\nBZbMYWTL+pYS71lut5P+2ey6q7W5Pk/aE8d9G8D2gCr5tKcrJX16zsly+RTzf+QX7jOpz8FL\nXwXWC/lLbKcHxiX/uVP6MMJ6Qrq39Gq1ix/Ldpf0Z+Af8ovXye5+HGehOk42t5hBiMkpsA4Q\nmc5I//zJQEb57IptgeU6hh6ips8xSo9qudjVVabsSYztorBQetPH9sdcFVgym5HN+i0t/pRe\nnK8gvXrQ9frIE3B18m0TQBu83tM1kD59w8lyeY5a6/F495nU5+ClrwLr/vx7tkdd/exlOU/W\nCUGfll70cv/FOyl7HuS+RIbv8pY7C9VKaVk/PzcA1C6nwLqRRPSUxXpPdDv5vW2B5TqGHqKm\nzzFKj+R5sJbmX/xvGFGc9e4IuyjcrEa51yhYuSuwbEY2m2/5Rr4geb7rFapJZMSV7iHJ2z3d\nGOcJOSctTrSeOnSfSX0OXroqsH4z5d+zJbk8Li5Pa2s9dSM/zjnhYr7PbW9wvV4k60qbC9G5\n86pZOQtVK8qbiBRCTU6BJV/6eafFOnGR9TYamwLLTQw9RE2fY5QebZEPLeVfvD+3cLKPwnsG\nohL/5P2c2wIrb2Sz/Zb/k2o3w0GXvzNW+klchRWavNzTHZA+rZP/tzdTzvUx7jOpz8FLVwXW\nTKmLD/xiYxll7/6cuSYFJF1+cbuE9GPLHD/+pfQ0mxPS8lMy37VY1pL9Y36dhEp+KF00ZmkI\nVbkF1hckzynzgXUSLItdgeUmhh6ips8xSo9+l4vw4/kW35N7DschCvIRr6F5b90XWLkjm923\nLJJex+Q9pO7fk2/b3rE/Vw6tz5sBWuDlnu58DDk7b91YWrrR+sp9JvU5eOmpwLpdkqiU3W0Q\n/+QccLq5ZUSDug4/fpmynsdrscyXD4I63qYsjXXxr+e++17K6FjrVYLJtnOI5g/V7ZbSojGB\nbQioV26BJV9GM03+w/9h67u8AstNDD1FTZ9jlC51JScPfnsnTEpC1hEGhyicL0pkyDvClFdg\nuR/Z7L6lr/SmeE725PmSJ9j8Do5ghYZA9nRyWho4PsVQDkpq1m1d7jOpz8FLTwVWBuV7ynJP\nKQLW+1LlyWBes//sJcq5rvOcPPd6C/sps+S/90rYPCy6lfQ284yRaJLtT+UP1ePyAazTFghR\neQXWCqLa8swy71vf5RVY7mLoIWr6HKN0ST5tQ+vsl12S7/ianPXaMQrPSu/Tcid/sTmC5XZk\ns/uWq/JzTWpnf8dP0us7bP4MqCu9/19AWwSqEMCe7qR8WNXhkZW/pkjLFme9dp9JfQ5eeiqw\n7pV62OFWY/l+ronyi+nSi3J/2n50TR5uNmW9lq+IoOa213jKMxkZ/s9mwT5pwZurbZ+ZI3MM\n1e3H5G9aEvCmgFrlFVh/R1LYm7lPp88rsNzF0EPU9DlG6ZM845Vxk+2S8w3loudq1pt8UWhj\ne2TcpsByO7LZf8tvxeTLabKrqlq5Z35kr8v7yoC3CfgFsqebJ386yfbo+8ky8lGt7JM27jOp\nz8FLRwXWT2FEDR2W3ZKGlKJyPC7KhXiZl/M++a6ptKBkzuPA5PtXKWVjzvO4vmgtv3/C9ptu\nFCUa0dLxmV4OoXpDPl/t6Y5E0LK8AsvSTfpjMPe+m9wCy20MLe6jps8xSp/+kZ+oZej3e+6C\n/fK+LO6D7Hf5ovB9FFHYu9lvbAostyObw7d8EEW5h+C3Sy+jjmR/8Kn8JU8K2jLgFMieLtP6\nrv47Ob96ZU6s9L7wz9lv3WdSn4OXjgos+eTcGseF4yn7+eFvR8rZqTH90OcnTx1/aWk7aTdI\nEcdyfux6H/lTKtR7yc6M7ZPrW9+Ms/+myURFjET29+HIoVrymdWru8dXs/5epysWCFk2BZZ1\nuitj9hUNuQWW+xi6j5ocp9mv5aPsFgGPU+Xkro/pvOXDn858eWyS/IAtin4t59P8u6tZ0pJK\n2Wd3bC9ydzeyOX6L/FRV2mJ9mSnf7Uz3bP38x+MH+oVLL8tj3AoJgezpLt1nXVBh/KbnX9m1\npIN81TuV+jLnU/eZ1OfgpZ8C62YqUWS+u/e+knq9rfXVaynkIPGYzQ/OjrT/MHaLwzf9KAeV\nUu0fOp//4QHGWc6eNgChwqbAui0/fr5d9pucAstTDC3uoubscV4SJbcH2PzWzLGja3yR+2H+\nndl1+QqtKVmv7e4idDOy5fsW+Zk5EW9aX56vZ/c7qSctEBIC2dPdmhDu8Lst/8j90H0m9Tl4\nhfr25TkodWb3/Itr5l429df4WNuOTxj1t90PnupjzPswasjv+b5JftqE/WnD/KEyPej+oYag\ndTYFlnxQM3cWkJwCy2MMLW6ips8xSrcyNxe17ea0JTY3KDs54fK6PMB8an1pP02D65Et37dY\nn5lTOKuWuj4l79fCejp7UiFoUkB7uu8fCLP53Tq21yG7z6Q+B69Q3748d0ud+Vz+xfIcRNkl\ntuXykdGtyxQMjyhcttWjB6/l+9HTq3pWKxQeUeTOBzc6m9NYvl817Cf7ZTahiixRq/+OP5z8\nHoQS2wLru5xJsCx5BZYXMbS4jJo+xygdu75/cK1C8r1bxhaTj9nObOz0ipb+0rIa1kPojvNg\nuRrZ8n+L9Zk5FbMPsZ5d2fWORFOBMvc+eUrYJoEKBLSn+/3/27tjlAaiKAyjRRBiYZE0cQOi\nZSzSCqndgTtwBRY2gksQxBW4HIUsQSwC1lYiOCksbEwGfng3yTntvOIODMP34A3zeDUdHwyO\nTi7vX/5c+P+Z3M+X167fH8A2+550O7f31lMAvQksgMKuu43+XeshgN4EFkBhq79rjZatpwD6\nElgAlc27wrr4XL8OKEVgAVT2ujrnfva8/PpYrF8MVCGwAEp7+P3m6rT1JMDmBBZAbU9DgQVb\nR2ABFPd2cz4+PJ7dtp4D2JzAAgAIE1gAAGECCwAgTGABAIQJLACAMIEFABAmsAAAwgQWAECY\nwAIACBNYAABhAgsAIExgAQCECSwAgDCBBQAQJrAAAMIEFgBAmMACAAgTWAAAYQILACBMYAEA\nhAksAIAwgQUAEPYDmH9cQZZL+RwAAAAASUVORK5CYII=",
-      "text/plain": [
-       "plot without title"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 600,
-       "width": 1200
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plots_density"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Kaplan Meyer estimates"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "options(warn=0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(predvars, data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(predvars, data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(substitute(subset), data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(substitute(subset), data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message:\n",
-      "\"Vectorized input to `element_text()` is not officially supported.\n",
-      "Results may be unexpected or may change in future versions of ggplot2.\"\n",
-      "Scale for 'x' is already present. Adding another scale for 'x', which will\n",
-      "replace the existing scale.\n",
-      "\n",
-      "Scale for 'x' is already present. Adding another scale for 'x', which will\n",
-      "replace the existing scale.\n",
-      "\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(predvars, data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(predvars, data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(substitute(subset), data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(substitute(subset), data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message:\n",
-      "\"Vectorized input to `element_text()` is not officially supported.\n",
-      "Results may be unexpected or may change in future versions of ggplot2.\"\n",
-      "Scale for 'x' is already present. Adding another scale for 'x', which will\n",
-      "replace the existing scale.\n",
-      "\n",
-      "Scale for 'x' is already present. Adding another scale for 'x', which will\n",
-      "replace the existing scale.\n",
-      "\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(predvars, data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(predvars, data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(substitute(subset), data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(substitute(subset), data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message:\n",
-      "\"Vectorized input to `element_text()` is not officially supported.\n",
-      "Results may be unexpected or may change in future versions of ggplot2.\"\n",
-      "Scale for 'x' is already present. Adding another scale for 'x', which will\n",
-      "replace the existing scale.\n",
-      "\n",
-      "Scale for 'x' is already present. Adding another scale for 'x', which will\n",
-      "replace the existing scale.\n",
-      "\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(predvars, data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(predvars, data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(substitute(subset), data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(substitute(subset), data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message:\n",
-      "\"Vectorized input to `element_text()` is not officially supported.\n",
-      "Results may be unexpected or may change in future versions of ggplot2.\"\n",
-      "Scale for 'x' is already present. Adding another scale for 'x', which will\n",
-      "replace the existing scale.\n",
-      "\n",
-      "Scale for 'x' is already present. Adding another scale for 'x', which will\n",
-      "replace the existing scale.\n",
-      "\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(predvars, data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(predvars, data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(substitute(subset), data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(substitute(subset), data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message:\n",
-      "\"Vectorized input to `element_text()` is not officially supported.\n",
-      "Results may be unexpected or may change in future versions of ggplot2.\"\n",
-      "Scale for 'x' is already present. Adding another scale for 'x', which will\n",
-      "replace the existing scale.\n",
-      "\n",
-      "Scale for 'x' is already present. Adding another scale for 'x', which will\n",
-      "replace the existing scale.\n",
-      "\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(predvars, data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(predvars, data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(substitute(subset), data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(substitute(subset), data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message:\n",
-      "\"Vectorized input to `element_text()` is not officially supported.\n",
-      "Results may be unexpected or may change in future versions of ggplot2.\"\n",
-      "Scale for 'x' is already present. Adding another scale for 'x', which will\n",
-      "replace the existing scale.\n",
-      "\n",
-      "Scale for 'x' is already present. Adding another scale for 'x', which will\n",
-      "replace the existing scale.\n",
-      "\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(predvars, data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(predvars, data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(substitute(subset), data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(substitute(subset), data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message:\n",
-      "\"Vectorized input to `element_text()` is not officially supported.\n",
-      "Results may be unexpected or may change in future versions of ggplot2.\"\n",
-      "Scale for 'x' is already present. Adding another scale for 'x', which will\n",
-      "replace the existing scale.\n",
-      "\n",
-      "Scale for 'x' is already present. Adding another scale for 'x', which will\n",
-      "replace the existing scale.\n",
-      "\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(predvars, data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(predvars, data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(substitute(subset), data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(substitute(subset), data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message:\n",
-      "\"Vectorized input to `element_text()` is not officially supported.\n",
-      "Results may be unexpected or may change in future versions of ggplot2.\"\n",
-      "Scale for 'x' is already present. Adding another scale for 'x', which will\n",
-      "replace the existing scale.\n",
-      "\n",
-      "Scale for 'x' is already present. Adding another scale for 'x', which will\n",
-      "replace the existing scale.\n",
-      "\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(predvars, data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(predvars, data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(substitute(subset), data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(substitute(subset), data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message:\n",
-      "\"Vectorized input to `element_text()` is not officially supported.\n",
-      "Results may be unexpected or may change in future versions of ggplot2.\"\n",
-      "Scale for 'x' is already present. Adding another scale for 'x', which will\n",
-      "replace the existing scale.\n",
-      "\n",
-      "Scale for 'x' is already present. Adding another scale for 'x', which will\n",
-      "replace the existing scale.\n",
-      "\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(predvars, data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(predvars, data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(substitute(subset), data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(substitute(subset), data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message:\n",
-      "\"Vectorized input to `element_text()` is not officially supported.\n",
-      "Results may be unexpected or may change in future versions of ggplot2.\"\n",
-      "Scale for 'x' is already present. Adding another scale for 'x', which will\n",
-      "replace the existing scale.\n",
-      "\n",
-      "Scale for 'x' is already present. Adding another scale for 'x', which will\n",
-      "replace the existing scale.\n",
-      "\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(predvars, data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(predvars, data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(substitute(subset), data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(substitute(subset), data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message:\n",
-      "\"Vectorized input to `element_text()` is not officially supported.\n",
-      "Results may be unexpected or may change in future versions of ggplot2.\"\n",
-      "Scale for 'x' is already present. Adding another scale for 'x', which will\n",
-      "replace the existing scale.\n",
-      "\n",
-      "Scale for 'x' is already present. Adding another scale for 'x', which will\n",
-      "replace the existing scale.\n",
-      "\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(predvars, data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(predvars, data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(substitute(subset), data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(substitute(subset), data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message:\n",
-      "\"Vectorized input to `element_text()` is not officially supported.\n",
-      "Results may be unexpected or may change in future versions of ggplot2.\"\n",
-      "Scale for 'x' is already present. Adding another scale for 'x', which will\n",
-      "replace the existing scale.\n",
-      "\n",
-      "Scale for 'x' is already present. Adding another scale for 'x', which will\n",
-      "replace the existing scale.\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "stratum=\"sex\"\n",
-    "plots = list()\n",
-    "tables = list()\n",
-    "plots_tables = list()\n",
-    "expand = c(0.01, 0.8)\n",
-    "for (e in endpoints[-2]){\n",
-    "    #print(e)\n",
-    "    #fit = survfit(Surv(death_cens_time, death_cens)~sex, data = data)\n",
-    "    fit = survfit(as.formula(glue(\"Surv({e}_event_time, {e}_event)~{stratum}\")), data=data)\n",
-    "    plot = ggsurvplot(fit,data, conf.int = TRUE, ylim = c(0.85,1), cumevents=TRUE, cumevents.y.text = FALSE) #, risk.table =\"percentage\") + scale_color_\n",
-    "    plots[[e]] = plot$plot  + ylab(\"\") + theme(legend.position=\"none\") + \n",
-    "        ggtitle(e) + theme(plot.title = element_text(size=facet_size, hjust=0.5)) + \n",
-    "        scale_color_nejm() + scale_fill_nejm() + scale_x_continuous(expand=expand)\n",
-    "\n",
-    "    tables[[e]] = plot$cumevents + ylab(\"\") + scale_color_nejm() + scale_fill_nejm() + scale_x_continuous(expand=expand)#+ scale_x_continuous(expand=c(0,0))\n",
-    "    plots_tables[[e]] = plot_grid(plots[[e]], tables[[e]], align=\"v\", nrow=2, rel_heights=c(4,1))\n",
-    "    legend <- get_legend(plots[[e]] + guides(color = guide_legend(nrow = 1)) + theme(legend.position = \"bottom\", legend.title=element_text(size=base_size), legend.text=element_text(size=base_size)))\n",
-    "}          "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plot_width=20; plot_height=25; plot_dpi=300\n",
-    "options(repr.plot.width=plot_width, repr.plot.height=plot_height)\n",
-    "\n",
-    "plots_f = plot_grid(plotlist = plots_tables, ncol=round(length(plots_tables)/3, 0))\n",
-    "plots_km = plot_grid(legend, plots_f, ncol=1, rel_heights=c(0.05, 1))\n",
-    "\n",
-    "plot_name = \"5_endpoint_km\"\n",
-    "ggsave(filename=glue(\"{dataset_path}/{plot_name}.pdf\"), plot=last_plot(), width=plot_width, height=plot_height, dpi=plot_dpi, device=\"pdf\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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CG90292B7ps3iaTW2v39jh8WrtvsIk1I5Wp4KXLealS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HRAYAxCLIQQCiWbgCIY7QGCw92gtDCKHX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\nLBZmZMW1rSt3XelyTr2NkD8txHXD/MHDvc1p22EAMH3IQTAXb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ewdQgZ5Rs3xe9AY2xMy3iDUvsGEQgAN5CAAWDZZciIhRJZypvTAbr1D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poAAMB0zbkcjPT1dL/2gvfA3uPLVQxBEZ5aq133PfXqMxSLT0FHfEWeSOzLDvf7R61HLIFW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mckDPdfXb/0q3F7v7u5VYN2bN8QutZ23/8O+zQogr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\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lZFHQQAVB4KNAiFELm5ue+9997q1auTk5O9vLwGDRr0/vvv16lTx/jqrYVQCLFn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\nB5w5MeJ83Us1UyZUut0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GhdZyQTaCIG4I93g8NfDBRP8A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\n9Paoj8/I3/yFe6NOizoIAN2Qlnij1dxpd1mP4TcOmP8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-      "text/plain": [
-       "plot without title"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 1500,
-       "width": 1200
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plots_km"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Competing Events"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "competing_endpoints"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fit5 <- survfit(Surv(CompetingEvents_event_time, CompetingEvents_event, type = \"mstate\") ~ 1, data = data)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "options(repr.plot.width=10, repr.plot.height=10)\n",
-    "ggcompetingrisks(fit5, palette = \"jco\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ggsurvplot(fit5,data, conf.int = TRUE, ylim = c(0.70,1), cumevents=TRUE, cumevents.y.text = FALSE)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "R [conda env:python]",
-   "language": "R",
-   "name": "conda-env-python-r"
-  },
-  "language_info": {
-   "codemirror_mode": "r",
-   "file_extension": ".r",
-   "mimetype": "text/x-r-source",
-   "name": "R",
-   "pygments_lexer": "r",
-   "version": "4.0.2"
-  },
-  "toc-autonumbering": true,
-  "toc-showcode": false,
-  "toc-showmarkdowntxt": true
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/neuralcvd/preprocessing/ukbb_tabular/5_dataset_visualization.ipynb b/neuralcvd/preprocessing/ukbb_tabular/5_dataset_visualization.ipynb
deleted file mode 100644
index bc77dec..0000000
--- a/neuralcvd/preprocessing/ukbb_tabular/5_dataset_visualization.ipynb
+++ /dev/null
@@ -1,3037 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 2. Exploration"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T14:16:53.068218Z",
-     "start_time": "2020-11-04T14:16:47.983Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "-- \u001b[1mAttaching packages\u001b[22m --------------------------------------- tidyverse 1.3.0 --\n",
-      "\n",
-      "\u001b[32mv\u001b[39m \u001b[34mggplot2\u001b[39m 3.3.2     \u001b[32mv\u001b[39m \u001b[34mpurrr  \u001b[39m 0.3.4\n",
-      "\u001b[32mv\u001b[39m \u001b[34mtibble \u001b[39m 3.0.3     \u001b[32mv\u001b[39m \u001b[34mdplyr  \u001b[39m 1.0.2\n",
-      "\u001b[32mv\u001b[39m \u001b[34mtidyr  \u001b[39m 1.1.2     \u001b[32mv\u001b[39m \u001b[34mstringr\u001b[39m 1.4.0\n",
-      "\u001b[32mv\u001b[39m \u001b[34mreadr  \u001b[39m 1.3.1     \u001b[32mv\u001b[39m \u001b[34mforcats\u001b[39m 0.5.0\n",
-      "\n",
-      "-- \u001b[1mConflicts\u001b[22m ------------------------------------------ tidyverse_conflicts() --\n",
-      "\u001b[31mx\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mfilter()\u001b[39m masks \u001b[34mstats\u001b[39m::filter()\n",
-      "\u001b[31mx\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mlag()\u001b[39m    masks \u001b[34mstats\u001b[39m::lag()\n",
-      "\n",
-      "\n",
-      "Attaching package: 'lubridate'\n",
-      "\n",
-      "\n",
-      "The following objects are masked from 'package:base':\n",
-      "\n",
-      "    date, intersect, setdiff, union\n",
-      "\n",
-      "\n",
-      "\n",
-      "Attaching package: 'glue'\n",
-      "\n",
-      "\n",
-      "The following object is masked from 'package:dplyr':\n",
-      "\n",
-      "    collapse\n",
-      "\n",
-      "\n",
-      "\n",
-      "Attaching package: 'cowplot'\n",
-      "\n",
-      "\n",
-      "The following object is masked from 'package:lubridate':\n",
-      "\n",
-      "    stamp\n",
-      "\n",
-      "\n",
-      "Loading required package: ggpubr\n",
-      "\n",
-      "\n",
-      "Attaching package: 'ggpubr'\n",
-      "\n",
-      "\n",
-      "The following object is masked from 'package:cowplot':\n",
-      "\n",
-      "    get_legend\n",
-      "\n",
-      "\n",
-      "\n",
-      "Attaching package: 'arsenal'\n",
-      "\n",
-      "\n",
-      "The following object is masked from 'package:lubridate':\n",
-      "\n",
-      "    is.Date\n",
-      "\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "try(library(tidyverse), silent=TRUE)\n",
-    "library(lubridate)\n",
-    "library(glue)\n",
-    "library(cowplot)\n",
-    "library(survminer)\n",
-    "library(survival)\n",
-    "library(ggsci)\n",
-    "library(arsenal)\n",
-    "library(yaml)\n",
-    "\n",
-    "#setwd(\"/\")\n",
-    "#path = \"/home/steinfej/projects/uk_biobank/\"\n",
-    "#dataset_path = \"data/datasets/cvd_big_excl\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T14:16:53.090072Z",
-     "start_time": "2020-11-04T14:16:48.270Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "dataset_name = \"cvd_massive_excl_emb\"\n",
-    "path = \"/data/analysis/ag-reils/steinfej/code/umbrella/pre/ukbb\"\n",
-    "data_path = \"/data/analysis/ag-reils/ag-reils-shared/cardioRS/data\"\n",
-    "dataset_path = glue(\"{data_path}/2_datasets_pre/{dataset_name}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T14:16:57.477187Z",
-     "start_time": "2020-11-04T14:16:49.046Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "data = arrow::read_feather(glue(\"{dataset_path}/baseline_clinical.feather\")) \n",
-    "data_description = arrow::read_feather(glue(\"{dataset_path}/baseline_description.feather\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T14:16:57.520779Z",
-     "start_time": "2020-11-04T14:16:50.668Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "phenotypes = names(read_yaml(glue(\"{dataset_path}/phenotype_list.yaml\")))\n",
-    "family_history = names(read_yaml(glue(\"{dataset_path}/fh_list.yaml\")))\n",
-    "medications = names(read_yaml(glue(\"{dataset_path}/medication_list.yaml\")))\n",
-    "endpoints_ph = names(read_yaml(glue(\"{dataset_path}/endpoint_list.yaml\")))\n",
-    "endpoints_death = names(read_yaml(glue(\"{dataset_path}/death_list.yaml\")))\n",
-    "endpoints_scores = names(read_yaml(glue(\"{dataset_path}/scores_list.yaml\")))\n",
-    "endpoints = c(endpoints_ph, endpoints_death, endpoints_scores)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T14:16:58.275310Z",
-     "start_time": "2020-11-04T14:16:57.351Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "covariates = (data_description %>% filter(isTarget==FALSE) %>% filter(based_on!=\"PGS\"))$covariate[-1]\n",
-    "targets = (data_description %>% filter(isTarget==TRUE))$covariate[-1]\n",
-    "pgs = (data_description %>% filter(isTarget==FALSE) %>% filter(based_on==\"PGS\") %>% filter(!dtype==\"Date\"))$covariate"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style>\n",
-       ".list-inline {list-style: none; margin:0; padding: 0}\n",
-       ".list-inline>li {display: inline-block}\n",
-       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
-       "</style>\n",
-       "<ol class=list-inline><li>'admin'</li><li>'PGS'</li><li>'basics'</li><li>'questionnaire'</li><li>'measurements'</li><li>'labs'</li><li>'family_history'</li><li>'diagnoses'</li><li>'diagnoses_emb'</li><li>'medications'</li><li>'endpoints_hospital'</li><li>'endpoints_death'</li><li>'score_SCORE'</li><li>'score_ASCVD'</li><li>'score_QRISK3'</li><li>'score_MACE'</li></ol>\n"
-      ],
-      "text/latex": [
-       "\\begin{enumerate*}\n",
-       "\\item 'admin'\n",
-       "\\item 'PGS'\n",
-       "\\item 'basics'\n",
-       "\\item 'questionnaire'\n",
-       "\\item 'measurements'\n",
-       "\\item 'labs'\n",
-       "\\item 'family\\_history'\n",
-       "\\item 'diagnoses'\n",
-       "\\item 'diagnoses\\_emb'\n",
-       "\\item 'medications'\n",
-       "\\item 'endpoints\\_hospital'\n",
-       "\\item 'endpoints\\_death'\n",
-       "\\item 'score\\_SCORE'\n",
-       "\\item 'score\\_ASCVD'\n",
-       "\\item 'score\\_QRISK3'\n",
-       "\\item 'score\\_MACE'\n",
-       "\\end{enumerate*}\n"
-      ],
-      "text/markdown": [
-       "1. 'admin'\n",
-       "2. 'PGS'\n",
-       "3. 'basics'\n",
-       "4. 'questionnaire'\n",
-       "5. 'measurements'\n",
-       "6. 'labs'\n",
-       "7. 'family_history'\n",
-       "8. 'diagnoses'\n",
-       "9. 'diagnoses_emb'\n",
-       "10. 'medications'\n",
-       "11. 'endpoints_hospital'\n",
-       "12. 'endpoints_death'\n",
-       "13. 'score_SCORE'\n",
-       "14. 'score_ASCVD'\n",
-       "15. 'score_QRISK3'\n",
-       "16. 'score_MACE'\n",
-       "\n",
-       "\n"
-      ],
-      "text/plain": [
-       " [1] \"admin\"              \"PGS\"                \"basics\"            \n",
-       " [4] \"questionnaire\"      \"measurements\"       \"labs\"              \n",
-       " [7] \"family_history\"     \"diagnoses\"          \"diagnoses_emb\"     \n",
-       "[10] \"medications\"        \"endpoints_hospital\" \"endpoints_death\"   \n",
-       "[13] \"score_SCORE\"        \"score_ASCVD\"        \"score_QRISK3\"      \n",
-       "[16] \"score_MACE\"        "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "unique(data_description$based_on)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 74,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_description %>% filter(based_on %in% c(\"measurements\", \"labs\")) %>% write_csv(\"/data/analysis/ag-reils/ag-reils-shared/cardioRS/data/retina_labels/measurements_labs.csv\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style>\n",
-       ".list-inline {list-style: none; margin:0; padding: 0}\n",
-       ".list-inline>li {display: inline-block}\n",
-       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
-       "</style>\n",
-       "<ol class=list-inline><li>'agents_acting_on_the_renin-angiotensin_system'</li><li>'all_other_non-therapeutic_products'</li><li>'all_other_therapeutic_products'</li><li>'allergens'</li><li>'anabolic_agents_for_systemic_use'</li><li>'analgesics'</li><li>'anesthetics'</li><li>'anthelmintics'</li><li>'anti-acne_preparations'</li><li>'anti-parkinson_drugs'</li><li>'antianemic_preparations'</li><li>'antibacterials_for_systemic_use'</li><li>'antibiotics_and_chemotherapeutics_for_dermatological_use'</li><li>'antidiarrheals,_intestinal_antiinflammatory/antiinfective_agents'</li><li>'antiemetics_and_antinauseants'</li><li>'antiepileptics'</li><li>'antifungals_for_dermatological_use'</li><li>'antigout_preparations'</li><li>'antihemorrhagics'</li><li>'antihistamines_for_systemic_use'</li><li>'antihypertensives'</li><li>'antiinflammatory_and_antirheumatic_products'</li><li>'antimycobacterials'</li><li>'antimycotics_for_systemic_use'</li><li>'antineoplastic_agents'</li><li>'antiobesity_preparations,_excl._diet_products'</li><li>'antiprotozoals'</li><li>'antipruritics,_incl._antihistamines,_anesthetics,_etc.'</li><li>'antipsoriatics'</li><li>'antiseptics_and_disinfectants'</li><li>'antithrombotic_agents'</li><li>'antivirals_for_systemic_use'</li><li>'appetite_stimulants'</li><li>'ass'</li><li>'atypical_antipsychotics'</li><li>'beta_blocking_agents'</li><li>'bile_and_liver_therapy'</li><li>'blood_substitutes_and_perfusion_solutions'</li><li>'calcium_channel_blockers'</li><li>'calcium_homeostasis'</li><li>'cardiac_therapy'</li><li>'contrast_media'</li><li>'corticosteroids,_dermatological_preparations'</li><li>'corticosteroids_for_systemic_use'</li><li>'cough_and_cold_preparations'</li><li>'diagnostic_agents'</li><li>'diagnostic_radiopharmaceuticals'</li><li>'digestives,_incl._enzymes'</li><li>'diuretics'</li><li>'drugs_for_acid_related_disorders'</li><li>'drugs_for_constipation'</li><li>'drugs_for_functional_gastrointestinal_disorders'</li><li>'drugs_for_obstructive_airway_diseases'</li><li>'drugs_for_treatment_of_bone_diseases'</li><li>'drugs_used_in_diabetes'</li><li>'ectoparasiticides,_incl._scabicides,_insecticides_and_repellents'</li><li>'emollients_and_protectives'</li><li>'endocrine_therapy'</li><li>'general_nutrients'</li><li>'glucocorticoids'</li><li>'gynecological_antiinfectives_and_antiseptics'</li><li>'immune_sera_and_immunoglobulins'</li><li>'immunostimulants'</li><li>'immunosuppressants'</li><li>'lipid_modifying_agents'</li><li>'medicated_dressings'</li><li>'mineral_supplements'</li><li>'muscle_relaxants'</li><li>'nasal_preparations'</li><li>'ophthalmological_and_otological_preparations'</li><li>'ophthalmologicals'</li><li>'other_alimentary_tract_and_metabolism_products'</li><li>'other_dermatological_preparations'</li><li>'other_drugs_for_disorders_of_the_musculo-skeletal_system'</li><li>'other_gynecologicals'</li><li>'other_hematological_agents'</li><li>'other_nervous_system_drugs'</li><li>'other_respiratory_system_products'</li><li>'otologicals'</li><li>'pancreatic_hormones'</li><li>'peripheral_vasodilators'</li><li>'pituitary_and_hypothalamic_hormones_and_analogues'</li><li>'preparations_for_treatment_of_wounds_and_ulcers'</li><li>'psychoanaleptics'</li><li>'psycholeptics'</li><li>'sex_hormones_and_modulators_of_the_genital_system'</li><li>'statins'</li><li>'stomatological_preparations'</li><li>'surgical_dressings'</li><li>'therapeutic_radiopharmaceuticals'</li><li>'throat_preparations'</li><li>'thyroid_therapy'</li><li>'tonics'</li><li>'topical_products_for_joint_and_muscular_pain'</li><li>'urologicals'</li><li>'vaccines'</li><li>'vasoprotectives'</li><li>'vitamins'</li></ol>\n"
-      ],
-      "text/latex": [
-       "\\begin{enumerate*}\n",
-       "\\item 'agents\\_acting\\_on\\_the\\_renin-angiotensin\\_system'\n",
-       "\\item 'all\\_other\\_non-therapeutic\\_products'\n",
-       "\\item 'all\\_other\\_therapeutic\\_products'\n",
-       "\\item 'allergens'\n",
-       "\\item 'anabolic\\_agents\\_for\\_systemic\\_use'\n",
-       "\\item 'analgesics'\n",
-       "\\item 'anesthetics'\n",
-       "\\item 'anthelmintics'\n",
-       "\\item 'anti-acne\\_preparations'\n",
-       "\\item 'anti-parkinson\\_drugs'\n",
-       "\\item 'antianemic\\_preparations'\n",
-       "\\item 'antibacterials\\_for\\_systemic\\_use'\n",
-       "\\item 'antibiotics\\_and\\_chemotherapeutics\\_for\\_dermatological\\_use'\n",
-       "\\item 'antidiarrheals,\\_intestinal\\_antiinflammatory/antiinfective\\_agents'\n",
-       "\\item 'antiemetics\\_and\\_antinauseants'\n",
-       "\\item 'antiepileptics'\n",
-       "\\item 'antifungals\\_for\\_dermatological\\_use'\n",
-       "\\item 'antigout\\_preparations'\n",
-       "\\item 'antihemorrhagics'\n",
-       "\\item 'antihistamines\\_for\\_systemic\\_use'\n",
-       "\\item 'antihypertensives'\n",
-       "\\item 'antiinflammatory\\_and\\_antirheumatic\\_products'\n",
-       "\\item 'antimycobacterials'\n",
-       "\\item 'antimycotics\\_for\\_systemic\\_use'\n",
-       "\\item 'antineoplastic\\_agents'\n",
-       "\\item 'antiobesity\\_preparations,\\_excl.\\_diet\\_products'\n",
-       "\\item 'antiprotozoals'\n",
-       "\\item 'antipruritics,\\_incl.\\_antihistamines,\\_anesthetics,\\_etc.'\n",
-       "\\item 'antipsoriatics'\n",
-       "\\item 'antiseptics\\_and\\_disinfectants'\n",
-       "\\item 'antithrombotic\\_agents'\n",
-       "\\item 'antivirals\\_for\\_systemic\\_use'\n",
-       "\\item 'appetite\\_stimulants'\n",
-       "\\item 'ass'\n",
-       "\\item 'atypical\\_antipsychotics'\n",
-       "\\item 'beta\\_blocking\\_agents'\n",
-       "\\item 'bile\\_and\\_liver\\_therapy'\n",
-       "\\item 'blood\\_substitutes\\_and\\_perfusion\\_solutions'\n",
-       "\\item 'calcium\\_channel\\_blockers'\n",
-       "\\item 'calcium\\_homeostasis'\n",
-       "\\item 'cardiac\\_therapy'\n",
-       "\\item 'contrast\\_media'\n",
-       "\\item 'corticosteroids,\\_dermatological\\_preparations'\n",
-       "\\item 'corticosteroids\\_for\\_systemic\\_use'\n",
-       "\\item 'cough\\_and\\_cold\\_preparations'\n",
-       "\\item 'diagnostic\\_agents'\n",
-       "\\item 'diagnostic\\_radiopharmaceuticals'\n",
-       "\\item 'digestives,\\_incl.\\_enzymes'\n",
-       "\\item 'diuretics'\n",
-       "\\item 'drugs\\_for\\_acid\\_related\\_disorders'\n",
-       "\\item 'drugs\\_for\\_constipation'\n",
-       "\\item 'drugs\\_for\\_functional\\_gastrointestinal\\_disorders'\n",
-       "\\item 'drugs\\_for\\_obstructive\\_airway\\_diseases'\n",
-       "\\item 'drugs\\_for\\_treatment\\_of\\_bone\\_diseases'\n",
-       "\\item 'drugs\\_used\\_in\\_diabetes'\n",
-       "\\item 'ectoparasiticides,\\_incl.\\_scabicides,\\_insecticides\\_and\\_repellents'\n",
-       "\\item 'emollients\\_and\\_protectives'\n",
-       "\\item 'endocrine\\_therapy'\n",
-       "\\item 'general\\_nutrients'\n",
-       "\\item 'glucocorticoids'\n",
-       "\\item 'gynecological\\_antiinfectives\\_and\\_antiseptics'\n",
-       "\\item 'immune\\_sera\\_and\\_immunoglobulins'\n",
-       "\\item 'immunostimulants'\n",
-       "\\item 'immunosuppressants'\n",
-       "\\item 'lipid\\_modifying\\_agents'\n",
-       "\\item 'medicated\\_dressings'\n",
-       "\\item 'mineral\\_supplements'\n",
-       "\\item 'muscle\\_relaxants'\n",
-       "\\item 'nasal\\_preparations'\n",
-       "\\item 'ophthalmological\\_and\\_otological\\_preparations'\n",
-       "\\item 'ophthalmologicals'\n",
-       "\\item 'other\\_alimentary\\_tract\\_and\\_metabolism\\_products'\n",
-       "\\item 'other\\_dermatological\\_preparations'\n",
-       "\\item 'other\\_drugs\\_for\\_disorders\\_of\\_the\\_musculo-skeletal\\_system'\n",
-       "\\item 'other\\_gynecologicals'\n",
-       "\\item 'other\\_hematological\\_agents'\n",
-       "\\item 'other\\_nervous\\_system\\_drugs'\n",
-       "\\item 'other\\_respiratory\\_system\\_products'\n",
-       "\\item 'otologicals'\n",
-       "\\item 'pancreatic\\_hormones'\n",
-       "\\item 'peripheral\\_vasodilators'\n",
-       "\\item 'pituitary\\_and\\_hypothalamic\\_hormones\\_and\\_analogues'\n",
-       "\\item 'preparations\\_for\\_treatment\\_of\\_wounds\\_and\\_ulcers'\n",
-       "\\item 'psychoanaleptics'\n",
-       "\\item 'psycholeptics'\n",
-       "\\item 'sex\\_hormones\\_and\\_modulators\\_of\\_the\\_genital\\_system'\n",
-       "\\item 'statins'\n",
-       "\\item 'stomatological\\_preparations'\n",
-       "\\item 'surgical\\_dressings'\n",
-       "\\item 'therapeutic\\_radiopharmaceuticals'\n",
-       "\\item 'throat\\_preparations'\n",
-       "\\item 'thyroid\\_therapy'\n",
-       "\\item 'tonics'\n",
-       "\\item 'topical\\_products\\_for\\_joint\\_and\\_muscular\\_pain'\n",
-       "\\item 'urologicals'\n",
-       "\\item 'vaccines'\n",
-       "\\item 'vasoprotectives'\n",
-       "\\item 'vitamins'\n",
-       "\\end{enumerate*}\n"
-      ],
-      "text/markdown": [
-       "1. 'agents_acting_on_the_renin-angiotensin_system'\n",
-       "2. 'all_other_non-therapeutic_products'\n",
-       "3. 'all_other_therapeutic_products'\n",
-       "4. 'allergens'\n",
-       "5. 'anabolic_agents_for_systemic_use'\n",
-       "6. 'analgesics'\n",
-       "7. 'anesthetics'\n",
-       "8. 'anthelmintics'\n",
-       "9. 'anti-acne_preparations'\n",
-       "10. 'anti-parkinson_drugs'\n",
-       "11. 'antianemic_preparations'\n",
-       "12. 'antibacterials_for_systemic_use'\n",
-       "13. 'antibiotics_and_chemotherapeutics_for_dermatological_use'\n",
-       "14. 'antidiarrheals,_intestinal_antiinflammatory/antiinfective_agents'\n",
-       "15. 'antiemetics_and_antinauseants'\n",
-       "16. 'antiepileptics'\n",
-       "17. 'antifungals_for_dermatological_use'\n",
-       "18. 'antigout_preparations'\n",
-       "19. 'antihemorrhagics'\n",
-       "20. 'antihistamines_for_systemic_use'\n",
-       "21. 'antihypertensives'\n",
-       "22. 'antiinflammatory_and_antirheumatic_products'\n",
-       "23. 'antimycobacterials'\n",
-       "24. 'antimycotics_for_systemic_use'\n",
-       "25. 'antineoplastic_agents'\n",
-       "26. 'antiobesity_preparations,_excl._diet_products'\n",
-       "27. 'antiprotozoals'\n",
-       "28. 'antipruritics,_incl._antihistamines,_anesthetics,_etc.'\n",
-       "29. 'antipsoriatics'\n",
-       "30. 'antiseptics_and_disinfectants'\n",
-       "31. 'antithrombotic_agents'\n",
-       "32. 'antivirals_for_systemic_use'\n",
-       "33. 'appetite_stimulants'\n",
-       "34. 'ass'\n",
-       "35. 'atypical_antipsychotics'\n",
-       "36. 'beta_blocking_agents'\n",
-       "37. 'bile_and_liver_therapy'\n",
-       "38. 'blood_substitutes_and_perfusion_solutions'\n",
-       "39. 'calcium_channel_blockers'\n",
-       "40. 'calcium_homeostasis'\n",
-       "41. 'cardiac_therapy'\n",
-       "42. 'contrast_media'\n",
-       "43. 'corticosteroids,_dermatological_preparations'\n",
-       "44. 'corticosteroids_for_systemic_use'\n",
-       "45. 'cough_and_cold_preparations'\n",
-       "46. 'diagnostic_agents'\n",
-       "47. 'diagnostic_radiopharmaceuticals'\n",
-       "48. 'digestives,_incl._enzymes'\n",
-       "49. 'diuretics'\n",
-       "50. 'drugs_for_acid_related_disorders'\n",
-       "51. 'drugs_for_constipation'\n",
-       "52. 'drugs_for_functional_gastrointestinal_disorders'\n",
-       "53. 'drugs_for_obstructive_airway_diseases'\n",
-       "54. 'drugs_for_treatment_of_bone_diseases'\n",
-       "55. 'drugs_used_in_diabetes'\n",
-       "56. 'ectoparasiticides,_incl._scabicides,_insecticides_and_repellents'\n",
-       "57. 'emollients_and_protectives'\n",
-       "58. 'endocrine_therapy'\n",
-       "59. 'general_nutrients'\n",
-       "60. 'glucocorticoids'\n",
-       "61. 'gynecological_antiinfectives_and_antiseptics'\n",
-       "62. 'immune_sera_and_immunoglobulins'\n",
-       "63. 'immunostimulants'\n",
-       "64. 'immunosuppressants'\n",
-       "65. 'lipid_modifying_agents'\n",
-       "66. 'medicated_dressings'\n",
-       "67. 'mineral_supplements'\n",
-       "68. 'muscle_relaxants'\n",
-       "69. 'nasal_preparations'\n",
-       "70. 'ophthalmological_and_otological_preparations'\n",
-       "71. 'ophthalmologicals'\n",
-       "72. 'other_alimentary_tract_and_metabolism_products'\n",
-       "73. 'other_dermatological_preparations'\n",
-       "74. 'other_drugs_for_disorders_of_the_musculo-skeletal_system'\n",
-       "75. 'other_gynecologicals'\n",
-       "76. 'other_hematological_agents'\n",
-       "77. 'other_nervous_system_drugs'\n",
-       "78. 'other_respiratory_system_products'\n",
-       "79. 'otologicals'\n",
-       "80. 'pancreatic_hormones'\n",
-       "81. 'peripheral_vasodilators'\n",
-       "82. 'pituitary_and_hypothalamic_hormones_and_analogues'\n",
-       "83. 'preparations_for_treatment_of_wounds_and_ulcers'\n",
-       "84. 'psychoanaleptics'\n",
-       "85. 'psycholeptics'\n",
-       "86. 'sex_hormones_and_modulators_of_the_genital_system'\n",
-       "87. 'statins'\n",
-       "88. 'stomatological_preparations'\n",
-       "89. 'surgical_dressings'\n",
-       "90. 'therapeutic_radiopharmaceuticals'\n",
-       "91. 'throat_preparations'\n",
-       "92. 'thyroid_therapy'\n",
-       "93. 'tonics'\n",
-       "94. 'topical_products_for_joint_and_muscular_pain'\n",
-       "95. 'urologicals'\n",
-       "96. 'vaccines'\n",
-       "97. 'vasoprotectives'\n",
-       "98. 'vitamins'\n",
-       "\n",
-       "\n"
-      ],
-      "text/plain": [
-       " [1] \"agents_acting_on_the_renin-angiotensin_system\"                   \n",
-       " [2] \"all_other_non-therapeutic_products\"                              \n",
-       " [3] \"all_other_therapeutic_products\"                                  \n",
-       " [4] \"allergens\"                                                       \n",
-       " [5] \"anabolic_agents_for_systemic_use\"                                \n",
-       " [6] \"analgesics\"                                                      \n",
-       " [7] \"anesthetics\"                                                     \n",
-       " [8] \"anthelmintics\"                                                   \n",
-       " [9] \"anti-acne_preparations\"                                          \n",
-       "[10] \"anti-parkinson_drugs\"                                            \n",
-       "[11] \"antianemic_preparations\"                                         \n",
-       "[12] \"antibacterials_for_systemic_use\"                                 \n",
-       "[13] \"antibiotics_and_chemotherapeutics_for_dermatological_use\"        \n",
-       "[14] \"antidiarrheals,_intestinal_antiinflammatory/antiinfective_agents\"\n",
-       "[15] \"antiemetics_and_antinauseants\"                                   \n",
-       "[16] \"antiepileptics\"                                                  \n",
-       "[17] \"antifungals_for_dermatological_use\"                              \n",
-       "[18] \"antigout_preparations\"                                           \n",
-       "[19] \"antihemorrhagics\"                                                \n",
-       "[20] \"antihistamines_for_systemic_use\"                                 \n",
-       "[21] \"antihypertensives\"                                               \n",
-       "[22] \"antiinflammatory_and_antirheumatic_products\"                     \n",
-       "[23] \"antimycobacterials\"                                              \n",
-       "[24] \"antimycotics_for_systemic_use\"                                   \n",
-       "[25] \"antineoplastic_agents\"                                           \n",
-       "[26] \"antiobesity_preparations,_excl._diet_products\"                   \n",
-       "[27] \"antiprotozoals\"                                                  \n",
-       "[28] \"antipruritics,_incl._antihistamines,_anesthetics,_etc.\"          \n",
-       "[29] \"antipsoriatics\"                                                  \n",
-       "[30] \"antiseptics_and_disinfectants\"                                   \n",
-       "[31] \"antithrombotic_agents\"                                           \n",
-       "[32] \"antivirals_for_systemic_use\"                                     \n",
-       "[33] \"appetite_stimulants\"                                             \n",
-       "[34] \"ass\"                                                             \n",
-       "[35] \"atypical_antipsychotics\"                                         \n",
-       "[36] \"beta_blocking_agents\"                                            \n",
-       "[37] \"bile_and_liver_therapy\"                                          \n",
-       "[38] \"blood_substitutes_and_perfusion_solutions\"                       \n",
-       "[39] \"calcium_channel_blockers\"                                        \n",
-       "[40] \"calcium_homeostasis\"                                             \n",
-       "[41] \"cardiac_therapy\"                                                 \n",
-       "[42] \"contrast_media\"                                                  \n",
-       "[43] \"corticosteroids,_dermatological_preparations\"                    \n",
-       "[44] \"corticosteroids_for_systemic_use\"                                \n",
-       "[45] \"cough_and_cold_preparations\"                                     \n",
-       "[46] \"diagnostic_agents\"                                               \n",
-       "[47] \"diagnostic_radiopharmaceuticals\"                                 \n",
-       "[48] \"digestives,_incl._enzymes\"                                       \n",
-       "[49] \"diuretics\"                                                       \n",
-       "[50] \"drugs_for_acid_related_disorders\"                                \n",
-       "[51] \"drugs_for_constipation\"                                          \n",
-       "[52] \"drugs_for_functional_gastrointestinal_disorders\"                 \n",
-       "[53] \"drugs_for_obstructive_airway_diseases\"                           \n",
-       "[54] \"drugs_for_treatment_of_bone_diseases\"                            \n",
-       "[55] \"drugs_used_in_diabetes\"                                          \n",
-       "[56] \"ectoparasiticides,_incl._scabicides,_insecticides_and_repellents\"\n",
-       "[57] \"emollients_and_protectives\"                                      \n",
-       "[58] \"endocrine_therapy\"                                               \n",
-       "[59] \"general_nutrients\"                                               \n",
-       "[60] \"glucocorticoids\"                                                 \n",
-       "[61] \"gynecological_antiinfectives_and_antiseptics\"                    \n",
-       "[62] \"immune_sera_and_immunoglobulins\"                                 \n",
-       "[63] \"immunostimulants\"                                                \n",
-       "[64] \"immunosuppressants\"                                              \n",
-       "[65] \"lipid_modifying_agents\"                                          \n",
-       "[66] \"medicated_dressings\"                                             \n",
-       "[67] \"mineral_supplements\"                                             \n",
-       "[68] \"muscle_relaxants\"                                                \n",
-       "[69] \"nasal_preparations\"                                              \n",
-       "[70] \"ophthalmological_and_otological_preparations\"                    \n",
-       "[71] \"ophthalmologicals\"                                               \n",
-       "[72] \"other_alimentary_tract_and_metabolism_products\"                  \n",
-       "[73] \"other_dermatological_preparations\"                               \n",
-       "[74] \"other_drugs_for_disorders_of_the_musculo-skeletal_system\"        \n",
-       "[75] \"other_gynecologicals\"                                            \n",
-       "[76] \"other_hematological_agents\"                                      \n",
-       "[77] \"other_nervous_system_drugs\"                                      \n",
-       "[78] \"other_respiratory_system_products\"                               \n",
-       "[79] \"otologicals\"                                                     \n",
-       "[80] \"pancreatic_hormones\"                                             \n",
-       "[81] \"peripheral_vasodilators\"                                         \n",
-       "[82] \"pituitary_and_hypothalamic_hormones_and_analogues\"               \n",
-       "[83] \"preparations_for_treatment_of_wounds_and_ulcers\"                 \n",
-       "[84] \"psychoanaleptics\"                                                \n",
-       "[85] \"psycholeptics\"                                                   \n",
-       "[86] \"sex_hormones_and_modulators_of_the_genital_system\"               \n",
-       "[87] \"statins\"                                                         \n",
-       "[88] \"stomatological_preparations\"                                     \n",
-       "[89] \"surgical_dressings\"                                              \n",
-       "[90] \"therapeutic_radiopharmaceuticals\"                                \n",
-       "[91] \"throat_preparations\"                                             \n",
-       "[92] \"thyroid_therapy\"                                                 \n",
-       "[93] \"tonics\"                                                          \n",
-       "[94] \"topical_products_for_joint_and_muscular_pain\"                    \n",
-       "[95] \"urologicals\"                                                     \n",
-       "[96] \"vaccines\"                                                        \n",
-       "[97] \"vasoprotectives\"                                                 \n",
-       "[98] \"vitamins\"                                                        "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "medications"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ranking_diagnoses = as.data.frame(t(data %>% dplyr::summarise(across(all_of(phenotypes), mean))))\n",
-    "ranking_diagnoses = tibble::rownames_to_column(ranking_diagnoses, var = \"column\")\n",
-    "colnames(ranking_diagnoses) = c(\"column\", \"frequency\")\n",
-    "ranking_diagnoses = ranking_diagnoses %>% arrange(desc(frequency)) %>% mutate(rank=row_number())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ranking_diagnoses %>% filter(frequency>0) %>% write_csv(\"/data/analysis/ag-reils/ag-reils-shared/cardioRS/data/retina_labels/diagnoses.csv\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "ERROR",
-     "evalue": "Error in eval(expr, envir, enclos): object 'measurements' not found\n",
-     "output_type": "error",
-     "traceback": [
-      "Error in eval(expr, envir, enclos): object 'measurements' not found\nTraceback:\n"
-     ]
-    }
-   ],
-   "source": [
-    "measurements"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T14:16:59.684813Z",
-     "start_time": "2020-11-04T14:16:58.773Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style>\n",
-       ".list-inline {list-style: none; margin:0; padding: 0}\n",
-       ".list-inline>li {display: inline-block}\n",
-       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
-       "</style>\n",
-       "<ol class=list-inline><li>'chronic_alcoholism_in_remission'</li><li>'chronic_pancreatitis'</li><li>'gastroparesis'</li><li>'ectopic_pregnancy'</li><li>'muscle_weakness'</li><li>'recurrent_major_depression'</li><li>'pilonidal_cyst'</li><li>'pain_in_toe'</li><li>'pulmonary_tuberculosis'</li><li>'celiac_disease'</li><li>'cramp_in_lower_leg'</li><li>'secondary_malignant_neoplasm_of_pleura'</li><li>'fracture_of_hand'</li><li>'cyst_of_breast'</li><li>'nephrotic_syndrome'</li><li>'polyp_of_nasal_sinus'</li><li>'chondromalacia_of_patella'</li><li>'spinal_stenosis_in_cervical_region'</li><li>'disorder_of_artery'</li><li>'vitiligo'</li><li>'female_cystocele'</li><li>'dysphasia'</li><li>'retinal_disorder'</li><li>'epiretinal_membrane'</li><li>'recurrent_major_depression_in_partial_remission'</li><li>'infection_caused_by_trichomonas'</li><li>'osteomyelitis'</li><li>'polyp_of_nasal_cavity_and/or_nasal_sinus'</li><li>'mass_of_neck'</li><li>'idiopathic_thrombocytopenic_purpura'</li><li>'complete_miscarriage'</li><li>'gastric_ulcer'</li><li>'papilloma_of_skin'</li><li>'fetal_or_neonatal_effect_of_breech_delivery_and_extraction'</li><li>'secondary_malignant_neoplastic_disease'</li><li>'hypoxemia'</li><li>'paraplegia'</li><li>'perforation_of_tympanic_membrane'</li><li>'ventricular_tachycardia'</li><li>'mixed_incontinence'</li><li>'disorder_of_eye_due_to_type_2_diabetes_mellitus'</li><li>'trigeminal_neuralgia'</li><li>'retinal_detachment'</li><li>'leukopenia'</li><li>'vitreous_hemorrhage'</li><li>'ischemic_ulcer'</li><li>'intramural_leiomyoma_of_uterus'</li><li>'viral_hepatitis_type_a'</li><li>'m\\u00e9ni\\u00e8re\\'s_disease'</li><li>'fracture_of_phalanx_of_hand'</li><li>'muscle_atrophy'</li><li>'incontinence_of_feces'</li><li>'mitral_valve_disorder'</li><li>'atherosclerosis_of_arteries_of_the_extremities'</li><li>'spondylosis'</li><li>'pterygium'</li><li>'ulnar_neuropathy'</li><li>'lung_mass'</li><li>'foreign_body_in_respiratory_tract'</li><li>'chronic_kidney_disease_stage_4'</li><li>'myocardial_ischemia'</li><li>'non-toxic_multinodular_goiter'</li><li>'pain_in_finger'</li><li>'cervical_spondylosis_without_myelopathy'</li><li>'body_mass_index_25-29_-_overweight'</li><li>'clouded_consciousness'</li><li>'mixed_conductive_and_sensorineural_hearing_loss'</li><li>'tooth_eruption_disorder'</li><li>'hyperuricemia'</li><li>'closed_fracture_of_neck_of_femur'</li><li>'bipolar_ii_disorder'</li><li>'disturbance_in_sleep_behavior'</li><li>'relationship_problems'</li><li>'sprain_of_wrist'</li><li>'personality_disorder'</li><li>'external_hemorrhoids'</li><li>'abnormal_vision'</li><li>'hyperprolactinemia'</li><li>'hemochromatosis'</li><li>'lumbosacral_radiculopathy'</li><li>'heart_valve_disorder'</li><li>'cardiac_arrest'</li><li>'infection_caused_by_molluscum_contagiosum'</li><li>'chronic_kidney_disease_stage_2'</li><li>'secondary_malignant_neoplasm_of_peritoneum'</li><li>'thoracic_back_pain'</li><li>'blood_in_urine'</li><li>'adhesive_capsulitis_of_shoulder'</li><li>'diplopia'</li><li>'sj\\u00f6gren\\'s_syndrome'</li><li>'ureteric_stone'</li><li>'bronchospasm'</li><li>'chronic_fatigue_syndrome'</li><li>'cannabis_dependence'</li><li>'neck_sprain'</li><li>'multinodular_goiter'</li><li>'ptosis_of_eyelid'</li><li>'failure_to_thrive'</li><li>'torticollis'</li><li>'acute_bronchiolitis'</li><li>'viral_exanthem'</li><li>'talipes_planus'</li><li>'idiopathic_peripheral_neuropathy'</li><li>'foreign_body_in_pharynx'</li><li>'jaw_pain'</li><li>'renal_impairment'</li><li>'ataxia'</li><li>'age-related_macular_degeneration'</li><li>'uterine_prolapse'</li><li>'renal_mass'</li><li>'pneumonitis'</li><li>'coordination_problem'</li><li>'blindness_-_both_eyes'</li><li>'primary_hyperparathyroidism'</li><li>'musculoskeletal_pain'</li><li>'mycosis'</li><li>'primigravida'</li><li>'urethral_stricture'</li><li>'leukocytosis'</li><li>'ventricular_premature_complex'</li><li>'ulcer_of_foot_due_to_diabetes_mellitus'</li><li>'chronic_headache_disorder'</li><li>'hemangioma'</li><li>'lymphedema'</li><li>'postmenopausal_state'</li><li>'chronic_ulcer_of_skin'</li><li>'left_heart_failure'</li><li>'excessive_and_frequent_menstruation'</li><li>'thrombocytosis'</li><li>'disorder_of_liver'</li><li>'disorder_of_carotid_artery'</li><li>'altered_bowel_function'</li><li>'abscess_of_foot'</li><li>'malignant_tumor_of_head_and/or_neck'</li><li>'streptococcus_group_b_infection_of_the_infant'</li><li>'concussion_injury_of_brain'</li><li>'feeding_problems_in_newborn'</li><li>'bipolar_i_disorder'</li><li>'viral_pharyngitis'</li><li>'lower_respiratory_tract_infection'</li><li>'hydronephrosis'</li><li>'borderline_personality_disorder'</li><li>'esophageal_varices'</li><li>'hypersomnia'</li><li>'sensorineural_hearing_loss_bilateral'</li><li>'varicocele'</li><li>'subarachnoid_intracranial_hemorrhage'</li><li>'incisional_hernia'</li><li>'varicella'</li><li>'pain_in_testicle'</li><li>'transplant_follow-up'</li><li>'tinea_cruris'</li><li>'laryngitis'</li><li>'hypertrophy_of_nail'</li><li>'amblyopia'</li><li>'polyp_of_cervix'</li><li>'cyst_of_kidney'</li><li>'hepatic_encephalopathy'</li><li>'blood_glucose_abnormal'</li><li>'postherpetic_neuralgia'</li><li>'frank_hematuria'</li><li>'cramp'</li><li>'interstitial_lung_disease'</li><li>'complete_atrioventricular_block'</li><li>'malignant_tumor_of_kidney'</li><li>'otitis'</li><li>'septic_shock'</li><li>'disorder_of_thyroid_gland'</li><li>'hypertrophic_cardiomyopathy'</li><li>'respiratory_distress_syndrome_in_the_newborn'</li><li>'infectious_gastroenteritis'</li><li>'subdural_intracranial_hemorrhage'</li><li>'hepatitis_b_carrier'</li><li>'manic_bipolar_i_disorder'</li><li>'secondary_pulmonary_hypertension'</li><li>'gonorrhea'</li><li>'derangement_of_knee'</li><li>'appendicitis'</li><li>'polyneuropathy_due_to_diabetes_mellitus'</li><li>'neonatal_hypoglycemia'</li><li>'prolonged_rupture_of_membranes'</li><li>'vasomotor_rhinitis'</li><li>'renal_disorder_due_to_type_1_diabetes_mellitus'</li><li>'tuberculosis'</li><li>'feeding_problem'</li><li>'chronic_tonsillitis'</li><li>'acute_duodenal_ulcer_with_hemorrhage'</li><li>'hammer_toe'</li><li>'malignant_tumor_of_cervix'</li><li>'prolapsed_lumbar_intervertebral_disc'</li><li>'hematemesis'</li><li>'perianal_abscess'</li><li>'nonvenomous_insect_bite'</li><li>'spondylolisthesis'</li><li>'malignant_tumor_of_esophagus'</li><li>'aphthous_ulcer_of_mouth'</li><li>'ventricular_septal_defect'</li><li>'oropharyngeal_dysphagia'</li><li>'injury_of_knee'</li><li>'traumatic_brain_injury'</li><li>'osteoarthritis_of_glenohumeral_joint'</li></ol>\n"
-      ],
-      "text/latex": [
-       "\\begin{enumerate*}\n",
-       "\\item 'chronic\\_alcoholism\\_in\\_remission'\n",
-       "\\item 'chronic\\_pancreatitis'\n",
-       "\\item 'gastroparesis'\n",
-       "\\item 'ectopic\\_pregnancy'\n",
-       "\\item 'muscle\\_weakness'\n",
-       "\\item 'recurrent\\_major\\_depression'\n",
-       "\\item 'pilonidal\\_cyst'\n",
-       "\\item 'pain\\_in\\_toe'\n",
-       "\\item 'pulmonary\\_tuberculosis'\n",
-       "\\item 'celiac\\_disease'\n",
-       "\\item 'cramp\\_in\\_lower\\_leg'\n",
-       "\\item 'secondary\\_malignant\\_neoplasm\\_of\\_pleura'\n",
-       "\\item 'fracture\\_of\\_hand'\n",
-       "\\item 'cyst\\_of\\_breast'\n",
-       "\\item 'nephrotic\\_syndrome'\n",
-       "\\item 'polyp\\_of\\_nasal\\_sinus'\n",
-       "\\item 'chondromalacia\\_of\\_patella'\n",
-       "\\item 'spinal\\_stenosis\\_in\\_cervical\\_region'\n",
-       "\\item 'disorder\\_of\\_artery'\n",
-       "\\item 'vitiligo'\n",
-       "\\item 'female\\_cystocele'\n",
-       "\\item 'dysphasia'\n",
-       "\\item 'retinal\\_disorder'\n",
-       "\\item 'epiretinal\\_membrane'\n",
-       "\\item 'recurrent\\_major\\_depression\\_in\\_partial\\_remission'\n",
-       "\\item 'infection\\_caused\\_by\\_trichomonas'\n",
-       "\\item 'osteomyelitis'\n",
-       "\\item 'polyp\\_of\\_nasal\\_cavity\\_and/or\\_nasal\\_sinus'\n",
-       "\\item 'mass\\_of\\_neck'\n",
-       "\\item 'idiopathic\\_thrombocytopenic\\_purpura'\n",
-       "\\item 'complete\\_miscarriage'\n",
-       "\\item 'gastric\\_ulcer'\n",
-       "\\item 'papilloma\\_of\\_skin'\n",
-       "\\item 'fetal\\_or\\_neonatal\\_effect\\_of\\_breech\\_delivery\\_and\\_extraction'\n",
-       "\\item 'secondary\\_malignant\\_neoplastic\\_disease'\n",
-       "\\item 'hypoxemia'\n",
-       "\\item 'paraplegia'\n",
-       "\\item 'perforation\\_of\\_tympanic\\_membrane'\n",
-       "\\item 'ventricular\\_tachycardia'\n",
-       "\\item 'mixed\\_incontinence'\n",
-       "\\item 'disorder\\_of\\_eye\\_due\\_to\\_type\\_2\\_diabetes\\_mellitus'\n",
-       "\\item 'trigeminal\\_neuralgia'\n",
-       "\\item 'retinal\\_detachment'\n",
-       "\\item 'leukopenia'\n",
-       "\\item 'vitreous\\_hemorrhage'\n",
-       "\\item 'ischemic\\_ulcer'\n",
-       "\\item 'intramural\\_leiomyoma\\_of\\_uterus'\n",
-       "\\item 'viral\\_hepatitis\\_type\\_a'\n",
-       "\\item 'm\\textbackslash{}u00e9ni\\textbackslash{}u00e8re\\textbackslash{}'s\\_disease'\n",
-       "\\item 'fracture\\_of\\_phalanx\\_of\\_hand'\n",
-       "\\item 'muscle\\_atrophy'\n",
-       "\\item 'incontinence\\_of\\_feces'\n",
-       "\\item 'mitral\\_valve\\_disorder'\n",
-       "\\item 'atherosclerosis\\_of\\_arteries\\_of\\_the\\_extremities'\n",
-       "\\item 'spondylosis'\n",
-       "\\item 'pterygium'\n",
-       "\\item 'ulnar\\_neuropathy'\n",
-       "\\item 'lung\\_mass'\n",
-       "\\item 'foreign\\_body\\_in\\_respiratory\\_tract'\n",
-       "\\item 'chronic\\_kidney\\_disease\\_stage\\_4'\n",
-       "\\item 'myocardial\\_ischemia'\n",
-       "\\item 'non-toxic\\_multinodular\\_goiter'\n",
-       "\\item 'pain\\_in\\_finger'\n",
-       "\\item 'cervical\\_spondylosis\\_without\\_myelopathy'\n",
-       "\\item 'body\\_mass\\_index\\_25-29\\_-\\_overweight'\n",
-       "\\item 'clouded\\_consciousness'\n",
-       "\\item 'mixed\\_conductive\\_and\\_sensorineural\\_hearing\\_loss'\n",
-       "\\item 'tooth\\_eruption\\_disorder'\n",
-       "\\item 'hyperuricemia'\n",
-       "\\item 'closed\\_fracture\\_of\\_neck\\_of\\_femur'\n",
-       "\\item 'bipolar\\_ii\\_disorder'\n",
-       "\\item 'disturbance\\_in\\_sleep\\_behavior'\n",
-       "\\item 'relationship\\_problems'\n",
-       "\\item 'sprain\\_of\\_wrist'\n",
-       "\\item 'personality\\_disorder'\n",
-       "\\item 'external\\_hemorrhoids'\n",
-       "\\item 'abnormal\\_vision'\n",
-       "\\item 'hyperprolactinemia'\n",
-       "\\item 'hemochromatosis'\n",
-       "\\item 'lumbosacral\\_radiculopathy'\n",
-       "\\item 'heart\\_valve\\_disorder'\n",
-       "\\item 'cardiac\\_arrest'\n",
-       "\\item 'infection\\_caused\\_by\\_molluscum\\_contagiosum'\n",
-       "\\item 'chronic\\_kidney\\_disease\\_stage\\_2'\n",
-       "\\item 'secondary\\_malignant\\_neoplasm\\_of\\_peritoneum'\n",
-       "\\item 'thoracic\\_back\\_pain'\n",
-       "\\item 'blood\\_in\\_urine'\n",
-       "\\item 'adhesive\\_capsulitis\\_of\\_shoulder'\n",
-       "\\item 'diplopia'\n",
-       "\\item 'sj\\textbackslash{}u00f6gren\\textbackslash{}'s\\_syndrome'\n",
-       "\\item 'ureteric\\_stone'\n",
-       "\\item 'bronchospasm'\n",
-       "\\item 'chronic\\_fatigue\\_syndrome'\n",
-       "\\item 'cannabis\\_dependence'\n",
-       "\\item 'neck\\_sprain'\n",
-       "\\item 'multinodular\\_goiter'\n",
-       "\\item 'ptosis\\_of\\_eyelid'\n",
-       "\\item 'failure\\_to\\_thrive'\n",
-       "\\item 'torticollis'\n",
-       "\\item 'acute\\_bronchiolitis'\n",
-       "\\item 'viral\\_exanthem'\n",
-       "\\item 'talipes\\_planus'\n",
-       "\\item 'idiopathic\\_peripheral\\_neuropathy'\n",
-       "\\item 'foreign\\_body\\_in\\_pharynx'\n",
-       "\\item 'jaw\\_pain'\n",
-       "\\item 'renal\\_impairment'\n",
-       "\\item 'ataxia'\n",
-       "\\item 'age-related\\_macular\\_degeneration'\n",
-       "\\item 'uterine\\_prolapse'\n",
-       "\\item 'renal\\_mass'\n",
-       "\\item 'pneumonitis'\n",
-       "\\item 'coordination\\_problem'\n",
-       "\\item 'blindness\\_-\\_both\\_eyes'\n",
-       "\\item 'primary\\_hyperparathyroidism'\n",
-       "\\item 'musculoskeletal\\_pain'\n",
-       "\\item 'mycosis'\n",
-       "\\item 'primigravida'\n",
-       "\\item 'urethral\\_stricture'\n",
-       "\\item 'leukocytosis'\n",
-       "\\item 'ventricular\\_premature\\_complex'\n",
-       "\\item 'ulcer\\_of\\_foot\\_due\\_to\\_diabetes\\_mellitus'\n",
-       "\\item 'chronic\\_headache\\_disorder'\n",
-       "\\item 'hemangioma'\n",
-       "\\item 'lymphedema'\n",
-       "\\item 'postmenopausal\\_state'\n",
-       "\\item 'chronic\\_ulcer\\_of\\_skin'\n",
-       "\\item 'left\\_heart\\_failure'\n",
-       "\\item 'excessive\\_and\\_frequent\\_menstruation'\n",
-       "\\item 'thrombocytosis'\n",
-       "\\item 'disorder\\_of\\_liver'\n",
-       "\\item 'disorder\\_of\\_carotid\\_artery'\n",
-       "\\item 'altered\\_bowel\\_function'\n",
-       "\\item 'abscess\\_of\\_foot'\n",
-       "\\item 'malignant\\_tumor\\_of\\_head\\_and/or\\_neck'\n",
-       "\\item 'streptococcus\\_group\\_b\\_infection\\_of\\_the\\_infant'\n",
-       "\\item 'concussion\\_injury\\_of\\_brain'\n",
-       "\\item 'feeding\\_problems\\_in\\_newborn'\n",
-       "\\item 'bipolar\\_i\\_disorder'\n",
-       "\\item 'viral\\_pharyngitis'\n",
-       "\\item 'lower\\_respiratory\\_tract\\_infection'\n",
-       "\\item 'hydronephrosis'\n",
-       "\\item 'borderline\\_personality\\_disorder'\n",
-       "\\item 'esophageal\\_varices'\n",
-       "\\item 'hypersomnia'\n",
-       "\\item 'sensorineural\\_hearing\\_loss\\_bilateral'\n",
-       "\\item 'varicocele'\n",
-       "\\item 'subarachnoid\\_intracranial\\_hemorrhage'\n",
-       "\\item 'incisional\\_hernia'\n",
-       "\\item 'varicella'\n",
-       "\\item 'pain\\_in\\_testicle'\n",
-       "\\item 'transplant\\_follow-up'\n",
-       "\\item 'tinea\\_cruris'\n",
-       "\\item 'laryngitis'\n",
-       "\\item 'hypertrophy\\_of\\_nail'\n",
-       "\\item 'amblyopia'\n",
-       "\\item 'polyp\\_of\\_cervix'\n",
-       "\\item 'cyst\\_of\\_kidney'\n",
-       "\\item 'hepatic\\_encephalopathy'\n",
-       "\\item 'blood\\_glucose\\_abnormal'\n",
-       "\\item 'postherpetic\\_neuralgia'\n",
-       "\\item 'frank\\_hematuria'\n",
-       "\\item 'cramp'\n",
-       "\\item 'interstitial\\_lung\\_disease'\n",
-       "\\item 'complete\\_atrioventricular\\_block'\n",
-       "\\item 'malignant\\_tumor\\_of\\_kidney'\n",
-       "\\item 'otitis'\n",
-       "\\item 'septic\\_shock'\n",
-       "\\item 'disorder\\_of\\_thyroid\\_gland'\n",
-       "\\item 'hypertrophic\\_cardiomyopathy'\n",
-       "\\item 'respiratory\\_distress\\_syndrome\\_in\\_the\\_newborn'\n",
-       "\\item 'infectious\\_gastroenteritis'\n",
-       "\\item 'subdural\\_intracranial\\_hemorrhage'\n",
-       "\\item 'hepatitis\\_b\\_carrier'\n",
-       "\\item 'manic\\_bipolar\\_i\\_disorder'\n",
-       "\\item 'secondary\\_pulmonary\\_hypertension'\n",
-       "\\item 'gonorrhea'\n",
-       "\\item 'derangement\\_of\\_knee'\n",
-       "\\item 'appendicitis'\n",
-       "\\item 'polyneuropathy\\_due\\_to\\_diabetes\\_mellitus'\n",
-       "\\item 'neonatal\\_hypoglycemia'\n",
-       "\\item 'prolonged\\_rupture\\_of\\_membranes'\n",
-       "\\item 'vasomotor\\_rhinitis'\n",
-       "\\item 'renal\\_disorder\\_due\\_to\\_type\\_1\\_diabetes\\_mellitus'\n",
-       "\\item 'tuberculosis'\n",
-       "\\item 'feeding\\_problem'\n",
-       "\\item 'chronic\\_tonsillitis'\n",
-       "\\item 'acute\\_duodenal\\_ulcer\\_with\\_hemorrhage'\n",
-       "\\item 'hammer\\_toe'\n",
-       "\\item 'malignant\\_tumor\\_of\\_cervix'\n",
-       "\\item 'prolapsed\\_lumbar\\_intervertebral\\_disc'\n",
-       "\\item 'hematemesis'\n",
-       "\\item 'perianal\\_abscess'\n",
-       "\\item 'nonvenomous\\_insect\\_bite'\n",
-       "\\item 'spondylolisthesis'\n",
-       "\\item 'malignant\\_tumor\\_of\\_esophagus'\n",
-       "\\item 'aphthous\\_ulcer\\_of\\_mouth'\n",
-       "\\item 'ventricular\\_septal\\_defect'\n",
-       "\\item 'oropharyngeal\\_dysphagia'\n",
-       "\\item 'injury\\_of\\_knee'\n",
-       "\\item 'traumatic\\_brain\\_injury'\n",
-       "\\item 'osteoarthritis\\_of\\_glenohumeral\\_joint'\n",
-       "\\end{enumerate*}\n"
-      ],
-      "text/markdown": [
-       "1. 'chronic_alcoholism_in_remission'\n",
-       "2. 'chronic_pancreatitis'\n",
-       "3. 'gastroparesis'\n",
-       "4. 'ectopic_pregnancy'\n",
-       "5. 'muscle_weakness'\n",
-       "6. 'recurrent_major_depression'\n",
-       "7. 'pilonidal_cyst'\n",
-       "8. 'pain_in_toe'\n",
-       "9. 'pulmonary_tuberculosis'\n",
-       "10. 'celiac_disease'\n",
-       "11. 'cramp_in_lower_leg'\n",
-       "12. 'secondary_malignant_neoplasm_of_pleura'\n",
-       "13. 'fracture_of_hand'\n",
-       "14. 'cyst_of_breast'\n",
-       "15. 'nephrotic_syndrome'\n",
-       "16. 'polyp_of_nasal_sinus'\n",
-       "17. 'chondromalacia_of_patella'\n",
-       "18. 'spinal_stenosis_in_cervical_region'\n",
-       "19. 'disorder_of_artery'\n",
-       "20. 'vitiligo'\n",
-       "21. 'female_cystocele'\n",
-       "22. 'dysphasia'\n",
-       "23. 'retinal_disorder'\n",
-       "24. 'epiretinal_membrane'\n",
-       "25. 'recurrent_major_depression_in_partial_remission'\n",
-       "26. 'infection_caused_by_trichomonas'\n",
-       "27. 'osteomyelitis'\n",
-       "28. 'polyp_of_nasal_cavity_and/or_nasal_sinus'\n",
-       "29. 'mass_of_neck'\n",
-       "30. 'idiopathic_thrombocytopenic_purpura'\n",
-       "31. 'complete_miscarriage'\n",
-       "32. 'gastric_ulcer'\n",
-       "33. 'papilloma_of_skin'\n",
-       "34. 'fetal_or_neonatal_effect_of_breech_delivery_and_extraction'\n",
-       "35. 'secondary_malignant_neoplastic_disease'\n",
-       "36. 'hypoxemia'\n",
-       "37. 'paraplegia'\n",
-       "38. 'perforation_of_tympanic_membrane'\n",
-       "39. 'ventricular_tachycardia'\n",
-       "40. 'mixed_incontinence'\n",
-       "41. 'disorder_of_eye_due_to_type_2_diabetes_mellitus'\n",
-       "42. 'trigeminal_neuralgia'\n",
-       "43. 'retinal_detachment'\n",
-       "44. 'leukopenia'\n",
-       "45. 'vitreous_hemorrhage'\n",
-       "46. 'ischemic_ulcer'\n",
-       "47. 'intramural_leiomyoma_of_uterus'\n",
-       "48. 'viral_hepatitis_type_a'\n",
-       "49. 'm\\u00e9ni\\u00e8re\\'s_disease'\n",
-       "50. 'fracture_of_phalanx_of_hand'\n",
-       "51. 'muscle_atrophy'\n",
-       "52. 'incontinence_of_feces'\n",
-       "53. 'mitral_valve_disorder'\n",
-       "54. 'atherosclerosis_of_arteries_of_the_extremities'\n",
-       "55. 'spondylosis'\n",
-       "56. 'pterygium'\n",
-       "57. 'ulnar_neuropathy'\n",
-       "58. 'lung_mass'\n",
-       "59. 'foreign_body_in_respiratory_tract'\n",
-       "60. 'chronic_kidney_disease_stage_4'\n",
-       "61. 'myocardial_ischemia'\n",
-       "62. 'non-toxic_multinodular_goiter'\n",
-       "63. 'pain_in_finger'\n",
-       "64. 'cervical_spondylosis_without_myelopathy'\n",
-       "65. 'body_mass_index_25-29_-_overweight'\n",
-       "66. 'clouded_consciousness'\n",
-       "67. 'mixed_conductive_and_sensorineural_hearing_loss'\n",
-       "68. 'tooth_eruption_disorder'\n",
-       "69. 'hyperuricemia'\n",
-       "70. 'closed_fracture_of_neck_of_femur'\n",
-       "71. 'bipolar_ii_disorder'\n",
-       "72. 'disturbance_in_sleep_behavior'\n",
-       "73. 'relationship_problems'\n",
-       "74. 'sprain_of_wrist'\n",
-       "75. 'personality_disorder'\n",
-       "76. 'external_hemorrhoids'\n",
-       "77. 'abnormal_vision'\n",
-       "78. 'hyperprolactinemia'\n",
-       "79. 'hemochromatosis'\n",
-       "80. 'lumbosacral_radiculopathy'\n",
-       "81. 'heart_valve_disorder'\n",
-       "82. 'cardiac_arrest'\n",
-       "83. 'infection_caused_by_molluscum_contagiosum'\n",
-       "84. 'chronic_kidney_disease_stage_2'\n",
-       "85. 'secondary_malignant_neoplasm_of_peritoneum'\n",
-       "86. 'thoracic_back_pain'\n",
-       "87. 'blood_in_urine'\n",
-       "88. 'adhesive_capsulitis_of_shoulder'\n",
-       "89. 'diplopia'\n",
-       "90. 'sj\\u00f6gren\\'s_syndrome'\n",
-       "91. 'ureteric_stone'\n",
-       "92. 'bronchospasm'\n",
-       "93. 'chronic_fatigue_syndrome'\n",
-       "94. 'cannabis_dependence'\n",
-       "95. 'neck_sprain'\n",
-       "96. 'multinodular_goiter'\n",
-       "97. 'ptosis_of_eyelid'\n",
-       "98. 'failure_to_thrive'\n",
-       "99. 'torticollis'\n",
-       "100. 'acute_bronchiolitis'\n",
-       "101. 'viral_exanthem'\n",
-       "102. 'talipes_planus'\n",
-       "103. 'idiopathic_peripheral_neuropathy'\n",
-       "104. 'foreign_body_in_pharynx'\n",
-       "105. 'jaw_pain'\n",
-       "106. 'renal_impairment'\n",
-       "107. 'ataxia'\n",
-       "108. 'age-related_macular_degeneration'\n",
-       "109. 'uterine_prolapse'\n",
-       "110. 'renal_mass'\n",
-       "111. 'pneumonitis'\n",
-       "112. 'coordination_problem'\n",
-       "113. 'blindness_-_both_eyes'\n",
-       "114. 'primary_hyperparathyroidism'\n",
-       "115. 'musculoskeletal_pain'\n",
-       "116. 'mycosis'\n",
-       "117. 'primigravida'\n",
-       "118. 'urethral_stricture'\n",
-       "119. 'leukocytosis'\n",
-       "120. 'ventricular_premature_complex'\n",
-       "121. 'ulcer_of_foot_due_to_diabetes_mellitus'\n",
-       "122. 'chronic_headache_disorder'\n",
-       "123. 'hemangioma'\n",
-       "124. 'lymphedema'\n",
-       "125. 'postmenopausal_state'\n",
-       "126. 'chronic_ulcer_of_skin'\n",
-       "127. 'left_heart_failure'\n",
-       "128. 'excessive_and_frequent_menstruation'\n",
-       "129. 'thrombocytosis'\n",
-       "130. 'disorder_of_liver'\n",
-       "131. 'disorder_of_carotid_artery'\n",
-       "132. 'altered_bowel_function'\n",
-       "133. 'abscess_of_foot'\n",
-       "134. 'malignant_tumor_of_head_and/or_neck'\n",
-       "135. 'streptococcus_group_b_infection_of_the_infant'\n",
-       "136. 'concussion_injury_of_brain'\n",
-       "137. 'feeding_problems_in_newborn'\n",
-       "138. 'bipolar_i_disorder'\n",
-       "139. 'viral_pharyngitis'\n",
-       "140. 'lower_respiratory_tract_infection'\n",
-       "141. 'hydronephrosis'\n",
-       "142. 'borderline_personality_disorder'\n",
-       "143. 'esophageal_varices'\n",
-       "144. 'hypersomnia'\n",
-       "145. 'sensorineural_hearing_loss_bilateral'\n",
-       "146. 'varicocele'\n",
-       "147. 'subarachnoid_intracranial_hemorrhage'\n",
-       "148. 'incisional_hernia'\n",
-       "149. 'varicella'\n",
-       "150. 'pain_in_testicle'\n",
-       "151. 'transplant_follow-up'\n",
-       "152. 'tinea_cruris'\n",
-       "153. 'laryngitis'\n",
-       "154. 'hypertrophy_of_nail'\n",
-       "155. 'amblyopia'\n",
-       "156. 'polyp_of_cervix'\n",
-       "157. 'cyst_of_kidney'\n",
-       "158. 'hepatic_encephalopathy'\n",
-       "159. 'blood_glucose_abnormal'\n",
-       "160. 'postherpetic_neuralgia'\n",
-       "161. 'frank_hematuria'\n",
-       "162. 'cramp'\n",
-       "163. 'interstitial_lung_disease'\n",
-       "164. 'complete_atrioventricular_block'\n",
-       "165. 'malignant_tumor_of_kidney'\n",
-       "166. 'otitis'\n",
-       "167. 'septic_shock'\n",
-       "168. 'disorder_of_thyroid_gland'\n",
-       "169. 'hypertrophic_cardiomyopathy'\n",
-       "170. 'respiratory_distress_syndrome_in_the_newborn'\n",
-       "171. 'infectious_gastroenteritis'\n",
-       "172. 'subdural_intracranial_hemorrhage'\n",
-       "173. 'hepatitis_b_carrier'\n",
-       "174. 'manic_bipolar_i_disorder'\n",
-       "175. 'secondary_pulmonary_hypertension'\n",
-       "176. 'gonorrhea'\n",
-       "177. 'derangement_of_knee'\n",
-       "178. 'appendicitis'\n",
-       "179. 'polyneuropathy_due_to_diabetes_mellitus'\n",
-       "180. 'neonatal_hypoglycemia'\n",
-       "181. 'prolonged_rupture_of_membranes'\n",
-       "182. 'vasomotor_rhinitis'\n",
-       "183. 'renal_disorder_due_to_type_1_diabetes_mellitus'\n",
-       "184. 'tuberculosis'\n",
-       "185. 'feeding_problem'\n",
-       "186. 'chronic_tonsillitis'\n",
-       "187. 'acute_duodenal_ulcer_with_hemorrhage'\n",
-       "188. 'hammer_toe'\n",
-       "189. 'malignant_tumor_of_cervix'\n",
-       "190. 'prolapsed_lumbar_intervertebral_disc'\n",
-       "191. 'hematemesis'\n",
-       "192. 'perianal_abscess'\n",
-       "193. 'nonvenomous_insect_bite'\n",
-       "194. 'spondylolisthesis'\n",
-       "195. 'malignant_tumor_of_esophagus'\n",
-       "196. 'aphthous_ulcer_of_mouth'\n",
-       "197. 'ventricular_septal_defect'\n",
-       "198. 'oropharyngeal_dysphagia'\n",
-       "199. 'injury_of_knee'\n",
-       "200. 'traumatic_brain_injury'\n",
-       "201. 'osteoarthritis_of_glenohumeral_joint'\n",
-       "\n",
-       "\n"
-      ],
-      "text/plain": [
-       "  [1] \"chronic_alcoholism_in_remission\"                           \n",
-       "  [2] \"chronic_pancreatitis\"                                      \n",
-       "  [3] \"gastroparesis\"                                             \n",
-       "  [4] \"ectopic_pregnancy\"                                         \n",
-       "  [5] \"muscle_weakness\"                                           \n",
-       "  [6] \"recurrent_major_depression\"                                \n",
-       "  [7] \"pilonidal_cyst\"                                            \n",
-       "  [8] \"pain_in_toe\"                                               \n",
-       "  [9] \"pulmonary_tuberculosis\"                                    \n",
-       " [10] \"celiac_disease\"                                            \n",
-       " [11] \"cramp_in_lower_leg\"                                        \n",
-       " [12] \"secondary_malignant_neoplasm_of_pleura\"                    \n",
-       " [13] \"fracture_of_hand\"                                          \n",
-       " [14] \"cyst_of_breast\"                                            \n",
-       " [15] \"nephrotic_syndrome\"                                        \n",
-       " [16] \"polyp_of_nasal_sinus\"                                      \n",
-       " [17] \"chondromalacia_of_patella\"                                 \n",
-       " [18] \"spinal_stenosis_in_cervical_region\"                        \n",
-       " [19] \"disorder_of_artery\"                                        \n",
-       " [20] \"vitiligo\"                                                  \n",
-       " [21] \"female_cystocele\"                                          \n",
-       " [22] \"dysphasia\"                                                 \n",
-       " [23] \"retinal_disorder\"                                          \n",
-       " [24] \"epiretinal_membrane\"                                       \n",
-       " [25] \"recurrent_major_depression_in_partial_remission\"           \n",
-       " [26] \"infection_caused_by_trichomonas\"                           \n",
-       " [27] \"osteomyelitis\"                                             \n",
-       " [28] \"polyp_of_nasal_cavity_and/or_nasal_sinus\"                  \n",
-       " [29] \"mass_of_neck\"                                              \n",
-       " [30] \"idiopathic_thrombocytopenic_purpura\"                       \n",
-       " [31] \"complete_miscarriage\"                                      \n",
-       " [32] \"gastric_ulcer\"                                             \n",
-       " [33] \"papilloma_of_skin\"                                         \n",
-       " [34] \"fetal_or_neonatal_effect_of_breech_delivery_and_extraction\"\n",
-       " [35] \"secondary_malignant_neoplastic_disease\"                    \n",
-       " [36] \"hypoxemia\"                                                 \n",
-       " [37] \"paraplegia\"                                                \n",
-       " [38] \"perforation_of_tympanic_membrane\"                          \n",
-       " [39] \"ventricular_tachycardia\"                                   \n",
-       " [40] \"mixed_incontinence\"                                        \n",
-       " [41] \"disorder_of_eye_due_to_type_2_diabetes_mellitus\"           \n",
-       " [42] \"trigeminal_neuralgia\"                                      \n",
-       " [43] \"retinal_detachment\"                                        \n",
-       " [44] \"leukopenia\"                                                \n",
-       " [45] \"vitreous_hemorrhage\"                                       \n",
-       " [46] \"ischemic_ulcer\"                                            \n",
-       " [47] \"intramural_leiomyoma_of_uterus\"                            \n",
-       " [48] \"viral_hepatitis_type_a\"                                    \n",
-       " [49] \"m<U+00E9>ni<U+00E8>re's_disease\"                           \n",
-       " [50] \"fracture_of_phalanx_of_hand\"                               \n",
-       " [51] \"muscle_atrophy\"                                            \n",
-       " [52] \"incontinence_of_feces\"                                     \n",
-       " [53] \"mitral_valve_disorder\"                                     \n",
-       " [54] \"atherosclerosis_of_arteries_of_the_extremities\"            \n",
-       " [55] \"spondylosis\"                                               \n",
-       " [56] \"pterygium\"                                                 \n",
-       " [57] \"ulnar_neuropathy\"                                          \n",
-       " [58] \"lung_mass\"                                                 \n",
-       " [59] \"foreign_body_in_respiratory_tract\"                         \n",
-       " [60] \"chronic_kidney_disease_stage_4\"                            \n",
-       " [61] \"myocardial_ischemia\"                                       \n",
-       " [62] \"non-toxic_multinodular_goiter\"                             \n",
-       " [63] \"pain_in_finger\"                                            \n",
-       " [64] \"cervical_spondylosis_without_myelopathy\"                   \n",
-       " [65] \"body_mass_index_25-29_-_overweight\"                        \n",
-       " [66] \"clouded_consciousness\"                                     \n",
-       " [67] \"mixed_conductive_and_sensorineural_hearing_loss\"           \n",
-       " [68] \"tooth_eruption_disorder\"                                   \n",
-       " [69] \"hyperuricemia\"                                             \n",
-       " [70] \"closed_fracture_of_neck_of_femur\"                          \n",
-       " [71] \"bipolar_ii_disorder\"                                       \n",
-       " [72] \"disturbance_in_sleep_behavior\"                             \n",
-       " [73] \"relationship_problems\"                                     \n",
-       " [74] \"sprain_of_wrist\"                                           \n",
-       " [75] \"personality_disorder\"                                      \n",
-       " [76] \"external_hemorrhoids\"                                      \n",
-       " [77] \"abnormal_vision\"                                           \n",
-       " [78] \"hyperprolactinemia\"                                        \n",
-       " [79] \"hemochromatosis\"                                           \n",
-       " [80] \"lumbosacral_radiculopathy\"                                 \n",
-       " [81] \"heart_valve_disorder\"                                      \n",
-       " [82] \"cardiac_arrest\"                                            \n",
-       " [83] \"infection_caused_by_molluscum_contagiosum\"                 \n",
-       " [84] \"chronic_kidney_disease_stage_2\"                            \n",
-       " [85] \"secondary_malignant_neoplasm_of_peritoneum\"                \n",
-       " [86] \"thoracic_back_pain\"                                        \n",
-       " [87] \"blood_in_urine\"                                            \n",
-       " [88] \"adhesive_capsulitis_of_shoulder\"                           \n",
-       " [89] \"diplopia\"                                                  \n",
-       " [90] \"sj<U+00F6>gren's_syndrome\"                                 \n",
-       " [91] \"ureteric_stone\"                                            \n",
-       " [92] \"bronchospasm\"                                              \n",
-       " [93] \"chronic_fatigue_syndrome\"                                  \n",
-       " [94] \"cannabis_dependence\"                                       \n",
-       " [95] \"neck_sprain\"                                               \n",
-       " [96] \"multinodular_goiter\"                                       \n",
-       " [97] \"ptosis_of_eyelid\"                                          \n",
-       " [98] \"failure_to_thrive\"                                         \n",
-       " [99] \"torticollis\"                                               \n",
-       "[100] \"acute_bronchiolitis\"                                       \n",
-       "[101] \"viral_exanthem\"                                            \n",
-       "[102] \"talipes_planus\"                                            \n",
-       "[103] \"idiopathic_peripheral_neuropathy\"                          \n",
-       "[104] \"foreign_body_in_pharynx\"                                   \n",
-       "[105] \"jaw_pain\"                                                  \n",
-       "[106] \"renal_impairment\"                                          \n",
-       "[107] \"ataxia\"                                                    \n",
-       "[108] \"age-related_macular_degeneration\"                          \n",
-       "[109] \"uterine_prolapse\"                                          \n",
-       "[110] \"renal_mass\"                                                \n",
-       "[111] \"pneumonitis\"                                               \n",
-       "[112] \"coordination_problem\"                                      \n",
-       "[113] \"blindness_-_both_eyes\"                                     \n",
-       "[114] \"primary_hyperparathyroidism\"                               \n",
-       "[115] \"musculoskeletal_pain\"                                      \n",
-       "[116] \"mycosis\"                                                   \n",
-       "[117] \"primigravida\"                                              \n",
-       "[118] \"urethral_stricture\"                                        \n",
-       "[119] \"leukocytosis\"                                              \n",
-       "[120] \"ventricular_premature_complex\"                             \n",
-       "[121] \"ulcer_of_foot_due_to_diabetes_mellitus\"                    \n",
-       "[122] \"chronic_headache_disorder\"                                 \n",
-       "[123] \"hemangioma\"                                                \n",
-       "[124] \"lymphedema\"                                                \n",
-       "[125] \"postmenopausal_state\"                                      \n",
-       "[126] \"chronic_ulcer_of_skin\"                                     \n",
-       "[127] \"left_heart_failure\"                                        \n",
-       "[128] \"excessive_and_frequent_menstruation\"                       \n",
-       "[129] \"thrombocytosis\"                                            \n",
-       "[130] \"disorder_of_liver\"                                         \n",
-       "[131] \"disorder_of_carotid_artery\"                                \n",
-       "[132] \"altered_bowel_function\"                                    \n",
-       "[133] \"abscess_of_foot\"                                           \n",
-       "[134] \"malignant_tumor_of_head_and/or_neck\"                       \n",
-       "[135] \"streptococcus_group_b_infection_of_the_infant\"             \n",
-       "[136] \"concussion_injury_of_brain\"                                \n",
-       "[137] \"feeding_problems_in_newborn\"                               \n",
-       "[138] \"bipolar_i_disorder\"                                        \n",
-       "[139] \"viral_pharyngitis\"                                         \n",
-       "[140] \"lower_respiratory_tract_infection\"                         \n",
-       "[141] \"hydronephrosis\"                                            \n",
-       "[142] \"borderline_personality_disorder\"                           \n",
-       "[143] \"esophageal_varices\"                                        \n",
-       "[144] \"hypersomnia\"                                               \n",
-       "[145] \"sensorineural_hearing_loss_bilateral\"                      \n",
-       "[146] \"varicocele\"                                                \n",
-       "[147] \"subarachnoid_intracranial_hemorrhage\"                      \n",
-       "[148] \"incisional_hernia\"                                         \n",
-       "[149] \"varicella\"                                                 \n",
-       "[150] \"pain_in_testicle\"                                          \n",
-       "[151] \"transplant_follow-up\"                                      \n",
-       "[152] \"tinea_cruris\"                                              \n",
-       "[153] \"laryngitis\"                                                \n",
-       "[154] \"hypertrophy_of_nail\"                                       \n",
-       "[155] \"amblyopia\"                                                 \n",
-       "[156] \"polyp_of_cervix\"                                           \n",
-       "[157] \"cyst_of_kidney\"                                            \n",
-       "[158] \"hepatic_encephalopathy\"                                    \n",
-       "[159] \"blood_glucose_abnormal\"                                    \n",
-       "[160] \"postherpetic_neuralgia\"                                    \n",
-       "[161] \"frank_hematuria\"                                           \n",
-       "[162] \"cramp\"                                                     \n",
-       "[163] \"interstitial_lung_disease\"                                 \n",
-       "[164] \"complete_atrioventricular_block\"                           \n",
-       "[165] \"malignant_tumor_of_kidney\"                                 \n",
-       "[166] \"otitis\"                                                    \n",
-       "[167] \"septic_shock\"                                              \n",
-       "[168] \"disorder_of_thyroid_gland\"                                 \n",
-       "[169] \"hypertrophic_cardiomyopathy\"                               \n",
-       "[170] \"respiratory_distress_syndrome_in_the_newborn\"              \n",
-       "[171] \"infectious_gastroenteritis\"                                \n",
-       "[172] \"subdural_intracranial_hemorrhage\"                          \n",
-       "[173] \"hepatitis_b_carrier\"                                       \n",
-       "[174] \"manic_bipolar_i_disorder\"                                  \n",
-       "[175] \"secondary_pulmonary_hypertension\"                          \n",
-       "[176] \"gonorrhea\"                                                 \n",
-       "[177] \"derangement_of_knee\"                                       \n",
-       "[178] \"appendicitis\"                                              \n",
-       "[179] \"polyneuropathy_due_to_diabetes_mellitus\"                   \n",
-       "[180] \"neonatal_hypoglycemia\"                                     \n",
-       "[181] \"prolonged_rupture_of_membranes\"                            \n",
-       "[182] \"vasomotor_rhinitis\"                                        \n",
-       "[183] \"renal_disorder_due_to_type_1_diabetes_mellitus\"            \n",
-       "[184] \"tuberculosis\"                                              \n",
-       "[185] \"feeding_problem\"                                           \n",
-       "[186] \"chronic_tonsillitis\"                                       \n",
-       "[187] \"acute_duodenal_ulcer_with_hemorrhage\"                      \n",
-       "[188] \"hammer_toe\"                                                \n",
-       "[189] \"malignant_tumor_of_cervix\"                                 \n",
-       "[190] \"prolapsed_lumbar_intervertebral_disc\"                      \n",
-       "[191] \"hematemesis\"                                               \n",
-       "[192] \"perianal_abscess\"                                          \n",
-       "[193] \"nonvenomous_insect_bite\"                                   \n",
-       "[194] \"spondylolisthesis\"                                         \n",
-       "[195] \"malignant_tumor_of_esophagus\"                              \n",
-       "[196] \"aphthous_ulcer_of_mouth\"                                   \n",
-       "[197] \"ventricular_septal_defect\"                                 \n",
-       "[198] \"oropharyngeal_dysphagia\"                                   \n",
-       "[199] \"injury_of_knee\"                                            \n",
-       "[200] \"traumatic_brain_injury\"                                    \n",
-       "[201] \"osteoarthritis_of_glenohumeral_joint\"                      "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "covariates[700:900]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T14:17:01.632846Z",
-     "start_time": "2020-11-04T14:17:00.584Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "data = data %>% mutate_at(c(\"sex\", \"overall_health_rating\", \"smoking_status\", \"ethnic_background\"), as.factor)\n",
-    "data = data %>% mutate(sex=fct_relevel(sex, c(\"Male\", \"Female\")),\n",
-    "                       overall_health_rating=fct_relevel(overall_health_rating, c(\"Excellent\", \"Good\", \"Fair\", \"Poor\")),\n",
-    "                       smoking_status=fct_relevel(smoking_status, c(\"Current\", \"Previous\", \"Never\")))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Missing values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#na_count <-data.frame(sapply(data %>% as.data.frame(), function(y) sum(length(which(is.na(y))))))\n",
-    "#na_count %>% filter(sapply(data %>% as.data.frame(), function(y) sum(length(which(is.na(y)))))>0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Covariates BIG"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Table 1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mycontrols  <- tableby.control(test=FALSE, total=TRUE,\n",
-    "                               numeric.test=\"kwt\", cat.test=\"chisq\",\n",
-    "                               numeric.stats=c(\"meansd\"), numeric.simplify=TRUE,\n",
-    "                               cat.stats=c(\"countpct\"), digits = 2L,cat.simplify = TRUE,\n",
-    "                               stats.labels=list(N='N', meansd='Median', Nmiss=\"Missing\"))"
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {},
-   "source": [
-    "library(arsenal)\n",
-    "table_one = tableby(sex~., control=mycontrols, data=data %>% select(all_of(c(covariates, targets))))\n",
-    "write2html(table_one, glue(\"{dataset_path}/table1_big.html\"));\n",
-    "#summary(table_one, text=TRUE)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# BASELINE"
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {},
-   "source": [
-    "base_size = 25\n",
-    "title_size = 30\n",
-    "facet_size = 22\n",
-    "geom_text_size=7"
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {},
-   "source": [
-    "library(ggplot2); \n",
-    "theme_set(theme_classic(base_size = base_size) + \n",
-    "          theme(strip.background = element_blank(), plot.title=element_text(size=title_size, hjust=0.5), \n",
-    "                axis.title.y = element_blank(), axis.text.y = element_blank(), axis.ticks.y = element_blank(), axis.line.y = element_blank(), \n",
-    "                strip.text.x = element_text(size = facet_size)))"
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {},
-   "source": [
-    "temp = data %>% select(all_of(c(pgs))) %>% select_if(is.numeric) %>% pivot_longer(everything(), names_to=\"parameter\", values_to=\"value\")\n",
-    "plot_pgs = ggplot(temp, aes(x=value)) + ggtitle(\"Polygenic Scores\") + \n",
-    "    geom_density(adjust=1.5, fill=\"gray70\") +\n",
-    "    scale_y_continuous(expand=c(0,0))+ xlab(\"\") + \n",
-    "    facet_wrap(~parameter, ncol=6, scales = \"free\")"
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {},
-   "source": [
-    "temp = data %>% select(all_of(c(covariates))) %>% select_if(is.numeric) %>% pivot_longer(everything(), names_to=\"parameter\", values_to=\"value\")\n",
-    "plot_cont = ggplot(temp, aes(x=value)) + ggtitle(\"Measurements\") + \n",
-    "    geom_density(adjust=1.5, fill=\"gray70\") +\n",
-    "    scale_y_continuous(expand=c(0,0))+ xlab(\"\") + \n",
-    "    facet_wrap(~parameter, ncol=4, scales = \"free\")"
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {},
-   "source": [
-    "temp = data %>% select(all_of(c(covariates))) %>% select_if(is.factor) %>% pivot_longer(everything(), names_to=\"parameter\", values_to=\"value\",values_ptypes=list(value=\"character\"))%>% \n",
-    "    group_by(parameter, value, .drop = TRUE) %>% summarise(count=n(), ratio=round((100*n()/502504), 1)) %>% ungroup()\n",
-    "plot_cat = ggplot(temp, aes(x=value, y=ratio)) + ggtitle(\"Basic Information\") + \n",
-    "    geom_bar(stat=\"identity\", position=position_dodge(width = 0.8), fill=\"gray70\", width=0.5) +\n",
-    "    geom_text(aes(label=paste0(ratio, \" %\")), vjust=-1, size = geom_text_size) + \n",
-    "    scale_y_continuous(expand=c(0,0), limits=c(0, max(temp$ratio)*1.2))+ xlab(\"\") + \n",
-    "    facet_grid(~parameter, scales = \"free\", space=\"free\")"
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {},
-   "source": [
-    "library(forcats)\n",
-    "library(ggrepel)"
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {},
-   "source": [
-    "options(repr.plot.width=40, repr.plot.height=8)\n",
-    "\n",
-    "plot_cat = ggplot(temp, aes(x=parameter, y=ratio, fill=factor(parameter))) + ggtitle(\"Basic Information\") + \n",
-    "    geom_bar(stat=\"identity\", position=\"fill\", fill=\"gray70\", aes(alpha=ratio)) + coord_flip()+# #, fill=\"gray70\", width=0.5) #+\n",
-    "    geom_text_repel(aes(label=ifelse(is.na(value), \"\", paste0(ratio*100, \" %\")), vjust=1), position = position_stack(vjust = .5), size = geom_text_size) +\n",
-    "    geom_text_repel(aes(label=value, vjust=-0.5), position = position_stack(vjust = .5), size = geom_text_size) +\n",
-    "    scale_y_continuous(limits=c(-0.05, 1.05))+ xlab(\"\") +\n",
-    "    theme_void(base_size = base_size) + theme(strip.background = element_blank(), plot.title=element_text(size=title_size, hjust=0.5), legend.position = \"None\")\n",
-    "\n",
-    "plot_cat "
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {},
-   "source": [
-    "options(repr.plot.width=20, repr.plot.height=8)\n",
-    "\n",
-    "plot_cat = ggplot(temp, aes(x=parameter, y=ratio, fill=fct_rev(value))) + ggtitle(\"Basic Information\") + \n",
-    "    geom_bar(stat=\"identity\", position=\"fill\", alpha=0.5) + coord_flip()+# #, fill=\"gray70\", width=0.5) #+\n",
-    "    geom_text(aes(label=paste0(ratio*100, \" %\"), vjust=1), position = position_stack(vjust = .5), size = geom_text_size) +\n",
-    "    geom_text(aes(label=value, vjust=-0.5), position = position_stack(vjust = .5), size = geom_text_size) +\n",
-    "    scale_y_continuous(limits=c(0, max(temp$ratio)*1.2))+ xlab(\"\") + facet_grid(~parameter, cols=1)+\n",
-    "    theme_classic(base_size = base_size) + theme(strip.background = element_blank(), plot.title=element_text(size=title_size, hjust=0.5), legend.position = \"None\")+\n",
-    "    scale_fill_d3() + theme(axis.line=element_blank(),\n",
-    "      axis.text.x=element_blank(),\n",
-    "      axis.text.y=element_blank(),\n",
-    "      axis.ticks=element_blank(),\n",
-    "      legend.position=\"none\",\n",
-    "      panel.background=element_blank(),\n",
-    "      panel.border=element_blank(),\n",
-    "      panel.grid.major=element_blank(),\n",
-    "      panel.grid.minor=element_blank(),\n",
-    "      plot.background=element_blank())\n",
-    "plot_cat "
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {},
-   "source": [
-    "temp = data %>% select(c(any_of(phenotypes))) %>% \n",
-    "    pivot_longer(everything(),names_to=\"category\", values_to=\"Value\") %>% \n",
-    "    group_by(category) %>% summarise(ratio = sum(Value==TRUE)/n(), .groups = 'drop') %>% mutate(category = fct_reorder(category, ratio)) %>% mutate(ratio=ratio*100)\n",
-    "plot_conditions = ggplot(temp, aes(x=category, y=ratio)) + ggtitle(\"Conditions\") + \n",
-    "    geom_bar(stat=\"identity\", position=position_dodge(width = 0.8), fill=\"gray70\", width=0.5) +\n",
-    "    coord_flip()+xlab(\"\") +\n",
-    "    ylab(\"Prevalence in [%]\") +\n",
-    "    scale_y_continuous(expand=c(0,0), limits=c(0, max(temp$ratio)*1.3))+\n",
-    "    geom_text(aes(x=category, y=ratio, label=glue(\"{round(ratio,1)}%\")), position = position_dodge(0.8), hjust=-0.1, size = geom_text_size)+ #nudge_y=+3000, \n",
-    "    scale_x_discrete()+\n",
-    "    theme_classic(base_size = base_size) + theme(strip.background = element_blank(), plot.title=element_text(size=title_size, hjust=0.5))#, strip.text.x = element_text(size = 16))"
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {},
-   "source": [
-    "temp = data %>% select(c(any_of(medications))) %>% \n",
-    "    pivot_longer(everything(), names_to=\"category\", values_to=\"Value\") %>% \n",
-    "    group_by(category) %>% summarise(ratio = sum(Value==TRUE)/n(), .groups = 'drop') %>% mutate(category = fct_reorder(category, ratio)) %>% mutate(ratio=ratio*100)\n",
-    "plot_medications = ggplot(temp, aes(x=category, y=ratio)) + ggtitle(\"Medications\") + \n",
-    "    geom_bar(stat=\"identity\", position=position_dodge(width = 0.8), fill=\"gray70\", width=0.5) +\n",
-    "    coord_flip()+xlab(\"\") +\n",
-    "    ylab(\"Prevalence in [%]\") +\n",
-    "    scale_y_continuous(expand=c(0,0), limits=c(0, max(temp$ratio)*1.3))+\n",
-    "    geom_text(aes(x=category, y=ratio, label=glue(\"{round(ratio,1)}%\")), position = position_dodge(0.8), hjust=-0.1, size = geom_text_size)+ #nudge_y=+3000, \n",
-    "    scale_x_discrete()"
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {
-    "jupyter": {
-     "outputs_hidden": true
-    }
-   },
-   "source": [
-    "options(repr.plot.width=30, repr.plot.height=100)\n",
-    "plot_1 = plot_grid(plot_pgs, plot_cat, plot_cont, ncol=1, rel_heights=c(1,0.7,10), align=\"v\")\n",
-    "plot_2 = plot_grid(plot_conditions, plot_medications, ncol=2, rel_widths=c(3,2.5), align=\"h\")\n",
-    "plot_grid(plot_1, plot_2, ncol=1, rel_heights=c(5,1.5), align=\"v\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Covariates Union"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T14:17:04.842258Z",
-     "start_time": "2020-11-04T14:17:03.935Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "mycontrols  <- tableby.control(test=FALSE, total=TRUE,\n",
-    "                               numeric.test=\"kwt\", cat.test=\"chisq\",\n",
-    "                               numeric.stats=c(\"meansd\"), numeric.simplify=TRUE,\n",
-    "                               cat.stats=c(\"countpct\"), digits = 2L,cat.simplify = TRUE,\n",
-    "                               stats.labels=list(N='N', meansd='Median', Nmiss=\"Missing\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T14:17:05.316730Z",
-     "start_time": "2020-11-04T14:17:04.385Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "f = list()\n",
-    "f$pgs = c('PGS000011', 'PGS000057', 'PGS000058', 'PGS000059')\n",
-    "f$basics = c('age_at_recruitment','sex', 'ethnic_background',\"townsend_deprivation_index_at_recruitment\")\n",
-    "f$questionnaire = c('overall_health_rating','smoking_status')\n",
-    "f$measurements = c('body_mass_index_bmi','weight',\"standing_height\",'systolic_blood_pressure','diastolic_blood_pressure')\n",
-    "f$labs = c(\"cholesterol\", \"hdl_cholesterol\", \"ldl_direct\",\"triglycerides\")\n",
-    "f$family_history = c('fh_heart_disease')\n",
-    "f$diagnoses = c(\"stroke\", \"diabetes1\", \"diabetes2\", \"chronic_kidney_disease\", \"atrial_fibrillation\", \"migraine\", \n",
-    "                      \"rheumatoid_arthritis\", \"systemic_lupus_erythematosus\", \"severe_mental_illness\", \"erectile_dysfunction\")\n",
-    "f$medications = c(\"antihypertensives\", \"ass\", \"atypical_antipsychotics\", \"glucocorticoids\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T14:17:10.758624Z",
-     "start_time": "2020-11-04T14:17:04.775Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "processing file: table1_union.html.Rmd\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "  |......................................................................| 100%\n",
-      "  ordinary text without R code\n",
-      "\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "output file: table1_union.html.knit.md\n",
-      "\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "/home/steinfej/miniconda3/envs/python/bin/pandoc +RTS -K512m -RTS table1_union.html.utf8.md --to html4 --from markdown+autolink_bare_uris+tex_math_single_backslash --output /data/analysis/ag-reils/ag-reils-shared/cardioRS/data/2_datasets_pre/cvd_massive_excl/table1_union.html --lua-filter /home/steinfej/miniconda3/envs/python/lib/R/library/rmarkdown/rmarkdown/lua/pagebreak.lua --lua-filter /home/steinfej/miniconda3/envs/python/lib/R/library/rmarkdown/rmarkdown/lua/latex-div.lua --email-obfuscation none --self-contained --standalone --section-divs --template /home/steinfej/miniconda3/envs/python/lib/R/library/rmarkdown/rmd/h/default.html --no-highlight --variable highlightjs=1 --variable 'theme:bootstrap' --include-in-header /tmp/RtmpRfvefu/rmarkdown-str11963d037f4.html --mathjax --variable 'mathjax-url:https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML' \n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Output created: /data/analysis/ag-reils/ag-reils-shared/cardioRS/data/2_datasets_pre/cvd_massive_excl/table1_union.html\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "library(arsenal)\n",
-    "table_one = tableby(sex~., control=mycontrols, data=data %>% select(all_of(c(f$pgs, f$basics, f$questionnaire, f$measurements, f$labs, f$family_history, f$medications, f$diagnoses))))\n",
-    "write2html(table_one, glue(\"{dataset_path}/table1_union.html\"));\n",
-    "#summary(table_one, text=TRUE)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# BASELINE"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T14:17:10.778888Z",
-     "start_time": "2020-11-04T14:17:08.887Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "options(repr.plot.width=20, repr.plot.height=8)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T14:17:10.814098Z",
-     "start_time": "2020-11-04T14:17:09.367Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "base_size = 25\n",
-    "title_size = 35\n",
-    "facet_size = 25\n",
-    "geom_text_size=7\n",
-    "library(ggplot2); \n",
-    "theme_set(theme_classic(base_size = base_size) + \n",
-    "          theme(strip.background = element_blank(), plot.title=element_text(size=title_size, hjust=0.5), \n",
-    "                axis.title.y = element_blank(), axis.text.y = element_blank(), axis.ticks.y = element_blank(), axis.line.y = element_blank(), \n",
-    "                strip.text.x = element_text(size = facet_size))) "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T14:17:12.517888Z",
-     "start_time": "2020-11-04T14:17:11.602Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "library(ggplot2); \n",
-    "theme_set(theme_classic(base_size = base_size) + \n",
-    "          theme(strip.background = element_blank(), plot.title=element_text(size=title_size, hjust=0.5), \n",
-    "                axis.title.y = element_blank(), axis.text.y = element_blank(), axis.ticks.y = element_blank(), axis.line.y = element_blank(), \n",
-    "                strip.text.x = element_text(size = facet_size))) "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T14:17:12.887887Z",
-     "start_time": "2020-11-04T14:17:11.969Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style>\n",
-       ".list-inline {list-style: none; margin:0; padding: 0}\n",
-       ".list-inline>li {display: inline-block}\n",
-       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
-       "</style>\n",
-       "<ol class=list-inline><li>White</li><li>Black</li><li>&lt;NA&gt;</li><li>Asian</li><li>Mixed</li><li>Chinese</li></ol>\n",
-       "\n",
-       "<details>\n",
-       "\t<summary style=display:list-item;cursor:pointer>\n",
-       "\t\t<strong>Levels</strong>:\n",
-       "\t</summary>\n",
-       "\t<style>\n",
-       "\t.list-inline {list-style: none; margin:0; padding: 0}\n",
-       "\t.list-inline>li {display: inline-block}\n",
-       "\t.list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
-       "\t</style>\n",
-       "\t<ol class=list-inline><li>'Asian'</li><li>'Black'</li><li>'Chinese'</li><li>'Mixed'</li><li>'White'</li></ol>\n",
-       "</details>"
-      ],
-      "text/latex": [
-       "\\begin{enumerate*}\n",
-       "\\item White\n",
-       "\\item Black\n",
-       "\\item <NA>\n",
-       "\\item Asian\n",
-       "\\item Mixed\n",
-       "\\item Chinese\n",
-       "\\end{enumerate*}\n",
-       "\n",
-       "\\emph{Levels}: \\begin{enumerate*}\n",
-       "\\item 'Asian'\n",
-       "\\item 'Black'\n",
-       "\\item 'Chinese'\n",
-       "\\item 'Mixed'\n",
-       "\\item 'White'\n",
-       "\\end{enumerate*}\n"
-      ],
-      "text/markdown": [
-       "1. White\n",
-       "2. Black\n",
-       "3. &lt;NA&gt;\n",
-       "4. Asian\n",
-       "5. Mixed\n",
-       "6. Chinese\n",
-       "\n",
-       "\n",
-       "\n",
-       "**Levels**: 1. 'Asian'\n",
-       "2. 'Black'\n",
-       "3. 'Chinese'\n",
-       "4. 'Mixed'\n",
-       "5. 'White'\n",
-       "\n",
-       "\n"
-      ],
-      "text/plain": [
-       "[1] White   Black   <NA>    Asian   Mixed   Chinese\n",
-       "Levels: Asian Black Chinese Mixed White"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "unique(data$ethnic_background)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Basic Information"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T14:17:13.873348Z",
-     "start_time": "2020-11-04T14:17:12.784Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "temp = data %>% select(all_of(c(f$basics, f$questionnaire))) %>% select_if(is.numeric) %>% pivot_longer(everything(), names_to=\"parameter\", values_to=\"value\") %>% mutate_at(vars(parameter), list(~ factor(., levels=unique(.))))\n",
-    "labels = c(age_at_recruitment = \"age\", townsend_deprivation_index_at_recruitment=\"townsend\")\n",
-    "plot_age_te = ggplot(temp, aes(x=value))  + \n",
-    "    geom_density(adjust=1.5, fill=\"gray70\")+  facet_wrap(~parameter, ncol=1, scales = \"free\", labeller=labeller(parameter=labels))+\n",
-    "    scale_y_continuous(expand=c(0,0))+ xlab(\"\") "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T14:17:14.547997Z",
-     "start_time": "2020-11-04T14:17:13.377Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "`summarise()` regrouping output by 'parameter' (override with `.groups` argument)\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "temp = data %>% select(all_of(c(f$basics, f$questionnaire))) %>% select_if(is.factor) %>% select_if(is.factor) %>% drop_na()  %>% pivot_longer(everything(), names_to=\"parameter\", values_to=\"value\",values_ptypes=list(value=\"character\")) %>% \n",
-    "    group_by(parameter, value, .drop = TRUE) %>% summarise(count=n(), ratio=round((100*n()/502504), 1)) %>% ungroup() %>% mutate_at(vars(parameter), list(~ factor(., levels=unique(.))))\n",
-    "plot_seth = ggplot(temp, aes(x=value, y=ratio, fill=parameter))+ \n",
-    "    geom_bar(stat=\"identity\", position=position_dodge(width = 0.8), fill=\"gray70\", width=0.5) +\n",
-    "    geom_text(aes(label=paste0(ratio, \" %\")), vjust=-1, size = geom_text_size) + \n",
-    "    scale_y_continuous(expand=c(0,0), limits=c(0, max(temp$ratio)*1.2))+ xlab(\"\") + \n",
-    "    #theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) + \n",
-    "    facet_grid(~parameter, scales = \"free\", space=\"free\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T14:17:16.659027Z",
-     "start_time": "2020-11-04T14:17:14.329Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Warning message:\n",
-      "\"Removed 516 rows containing non-finite values (stat_density).\"\n"
-     ]
-    }
-   ],
-   "source": [
-    "options(repr.plot.width=20, repr.plot.height=8)\n",
-    "plot_title = ggplot(temp, aes(x=value))  + ggtitle(\"Basic Information\")\n",
-    "title <- ggdraw(get_title(plot_title))\n",
-    "plot_basics_raw = plot_grid(plot_age_te, plot_seth, ncol=2, rel_widths=c(1, 4), align=\"h\", axis=\"lr\")\n",
-    "plot_basics = plot_grid(title, plot_basics_raw , ncol=1, rel_heights=c(0.1, 0.9), align=\"v\", axis=\"lr\") "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T14:17:18.391317Z",
-     "start_time": "2020-11-04T14:17:17.305Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "options(repr.plot.width=20, repr.plot.height=8)\n",
-    "temp = data %>% select(all_of(f$pgs)) %>% pivot_longer(everything(), names_to=\"parameter\", values_to=\"value\") %>% mutate_at(vars(parameter), list(~ factor(., levels=unique(.))))\n",
-    "plot_pgs = ggplot(temp, aes(x=value)) + ggtitle(\"Polygenic Scores\") + \n",
-    "    geom_density(adjust=1.5, fill=\"gray70\") +\n",
-    "    scale_y_continuous(expand=c(0,0))+ xlab(\"\") + \n",
-    "    facet_wrap(~parameter, ncol=4, scales = \"free\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T14:17:19.147957Z",
-     "start_time": "2020-11-04T14:17:17.792Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "options(repr.plot.width=20, repr.plot.height=8)\n",
-    "temp = data %>% select(all_of(c(f$measurements, f$labs))) %>% pivot_longer(everything(), names_to=\"parameter\", values_to=\"value\")  %>% mutate_at(vars(parameter), list(~ factor(., levels=unique(.))))\n",
-    "plot_meas = ggplot(temp, aes(x=value)) + ggtitle(\"Measurements\") + \n",
-    "    geom_density(adjust=1.5, fill=\"gray70\") +\n",
-    "    scale_y_continuous(expand=c(0,0))+ xlab(\"\") + \n",
-    "    facet_wrap(~parameter, ncol=5, scales = \"free\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T14:17:19.829906Z",
-     "start_time": "2020-11-04T14:17:18.264Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "temp = data %>% select(c(any_of(f$diagnoses))) %>% \n",
-    "    pivot_longer(everything(),names_to=\"category\", values_to=\"Value\") %>% \n",
-    "    group_by(category) %>% summarise(ratio = sum(Value==TRUE)/n(), .groups = 'drop') %>% mutate(category = fct_reorder(category, ratio)) %>% mutate(ratio=ratio*100)\n",
-    "plot_conditions = ggplot(temp, aes(x=category, y=ratio)) + ggtitle(\"Conditions\") + \n",
-    "    geom_bar(stat=\"identity\", position=position_dodge(width = 0.8), fill=\"gray70\", width=0.5) +\n",
-    "    coord_flip()+xlab(\"\") +\n",
-    "    ylab(\"Prevalence in [%]\") +\n",
-    "    scale_y_continuous(expand=c(0,0), limits=c(0, max(temp$ratio)*1.3))+\n",
-    "    geom_text(aes(x=category, y=ratio, label=glue(\"{round(ratio,1)}%\")), position = position_dodge(0.8), hjust=-0.1, size = geom_text_size)+ #nudge_y=+3000, \n",
-    "    scale_x_discrete()+\n",
-    "    theme_classic(base_size = base_size)+ theme(strip.background = element_blank(), plot.title=element_text(size=title_size, hjust=0.35))#, strip.text.x = element_text(size = 16))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T14:17:20.135005Z",
-     "start_time": "2020-11-04T14:17:18.560Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "temp = data %>% select(c(any_of(f$medications))) %>% \n",
-    "    pivot_longer(everything(), names_to=\"category\", values_to=\"Value\") %>% \n",
-    "    group_by(category) %>% summarise(ratio = sum(Value==TRUE)/n(), .groups = 'drop') %>% mutate(category = fct_reorder(category, ratio)) %>% mutate(ratio=ratio*100)\n",
-    "plot_medications = ggplot(temp, aes(x=category, y=ratio)) + ggtitle(\"Medications\") + \n",
-    "    geom_bar(stat=\"identity\", position=position_dodge(width = 0.8), fill=\"gray70\", width=0.5) +\n",
-    "    coord_flip()+xlab(\"\") +\n",
-    "    ylab(\"Prevalence in [%]\") +\n",
-    "    scale_y_continuous(expand=c(0,0), limits=c(0, max(temp$ratio)*1.3))+\n",
-    "    geom_text(aes(x=category, y=ratio, label=glue(\"{round(ratio,1)}%\")), position = position_dodge(0.8), hjust=-0.1, size = geom_text_size)+ #nudge_y=+3000, \n",
-    "    theme_classic(base_size = base_size) + theme(strip.background = element_blank(), plot.title=element_text(size=title_size, hjust=0.35)) + \n",
-    "    scale_x_discrete()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T14:17:28.906077Z",
-     "start_time": "2020-11-04T14:17:19.409Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Warning message:\n",
-      "\"Removed 48868 rows containing non-finite values (stat_density).\"\n",
-      "Warning message:\n",
-      "\"Removed 192399 rows containing non-finite values (stat_density).\"\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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TApWfpouAOW8w5bsYGmDU4WLuqaaCyX/pXZAi7tXFLAcuihLusutLYi+n6+ehTa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\n1bd0tf2v6aON0DMElnkX6Qs1w7V2X/MO+X58cNf1v9pl+9F6mT0ojtUq/4q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ZdF69IGGhYuJaa11uAmvb7RXxA0y37sPOSA5jn7RI7ZHq\nantS5HSBdVtij03n2qe6GYd8ttJa0f2TGfHng2WBsa/WbpPzXWX8B2h+Iqqt53Fuqy20rMmY\nyd28k5ERdnqRGfbTBFbmDoywkvFw3csY6hlt0BOBZVgblkxDxdWJx3duGh1f2W05f1V+rcA2\nzP6hNivD0gQWs6DcaRoYVeK3DFdPuH3UIxO5wxq8EtPqDPx1pn0ahyOwRDtGisAy8x0XWMyg\nN5qfXslHy7kad1ioRWBlYSp+kQWjEWemKnPozgm5dxH+ufiaXu079Lnu8Tw8zY1fZgzu0r7P\nmNf9tmMhIS6mwJIcZoyrM6aR+kzTuhjDytoFI85p37bLoDvsth5vxl+N61159qD564y2P6tP\n+wte8kJgtetx2d3xuYHiI1emHYO3J/Tr2OH8279O3VXXf+6U+LU9g21vTx/ap32Hc655MPGk\n0dwEluHLtIFdKrsMuStxjUV6clj7pEZqj1QvJq4vTxFYb2nagPhJy+Wa1nsD55Df3da3Q+cB\nM9aYd/W8wIk3A/PqtuQdyWbWR9SeCP2Xm3q0P2dkTW2hZQ1LYLHDTi0yw36awMrcgVVFRzwc\n9zKH+sw26JHA0vWP77uiX6e2nS8aszTlIoQPb72wY7teVy/a6LYqr1ZgwZ0sIwvD0gQWs6Bc\ngcWoEr9luHrC7aMemcgDxuCl629d37Py7MFLNpn6ISYlOAJLtGOkCizzNu/EVOysoMdoqdNj\nuxh3WKhFYGVhKjnRTWYjzkgVa+jOAVXzYAEAAAAARBYILAAAAAAAj4HAAgAAAADwGAgsAAAA\nAACPgcACAAAAAPAYCCwAAAAAAI+BwAIAAAAA8BgILAAAAAAAj4HAAgAAAADwGAgsAAAAAACP\ngcACAAAAAPAYCCwAAAAAAI+BwAIAAAAA8BgILAAAAAAAj4HAAgAAAADwGAgsAAAAAACPgcAC\nAAAAAPAYCCwAAAAAAI+BwAIAAAAA8BgILAAAAAAAj4HAAgAAAADwGAgsAAAAAACPgcACAAAA\nAPAYCCwAAAAAAI+BwAIAAAAA8BgILAAAAAAAj4HAAgAAAADwGAgsAAAAAACPgcACAAAAAPAY\nCCwAAAAAAI+BwAIAAAAA8BgILAAAAAAAj4HAAgAAAADwGAgsAAAAAACPgcACAAAAAPAYCCwA\nAAAAAI+BwAIAAAAA8BgILAAAAAAAj4HAAgAAAADwGAgsAAAAAACPgcACAAAAAPAYCCwAAAAA\nAI+BwAIAAAAA8BgILAAAAAAAj4HAAgAAAADwGAgsAAAAAACPgcACAAAAAPAYCCwAAAAAAI+B\nwAIAAAAA8BgILAAAAAAAj4HAAgAAAADwGAgsAAAAAACPgcACAAAAAPAYCCwAAAAAAI+BwAIA\nAAAA8BgILAAAAAAAj4HAAgAAAADwGAgsAAAAAACPgcACAAAAAPAYCCwAAAAAAI+BwAIAAAAA\n8BgILAAAAAAAj4HAAgAAAADwGAgsAAAAAACPgcACAAAAAPAYCCwAAAAAAI+BwAIAAAAA8BgI\nLAAAAAAAj4HAAgAAAADwGAgsAAAAAACPgcACAAAAAPAYCCwAAAAAAI+BwAIAAAAA8BgILAAA\nAAAAj4HAAgAAAADwGAgsAAAAAACPgcACAAAAAPAYCCwAAAAAAI+BwAIAAAAA8BgILAAAAAAA\nj4HAAgAAAADwGAgsAAAAAACPgcACAAAAAPAYCCwAAAAAAI+BwAIAAAAA8BgILAAAAAAAj4HA\nAgAAAADwGAgsAAAAAACPgcACAAAAAPAYCCwAAAAAAI+BwAIAAAAA8BgILAAAAAAAj4HAAgAA\nAADwGAgsAAAAAACPgcACAAAAAPAYCCwAAAAAAI+BwAIAAAAA8BgILAAAl7Vksla1GwY/xzzZ\npNoNAADIEggsAACXiAmsqg8fvnviDTeMnb7onb+8cQsAADhAYAEQSV6ndBq1PLTTbe/kdpQo\nCaw3rjq4JJmNkn9c94FnzgEAQAYQWABEkkyBZbH/tFzO3URHYD19dGYuKt7z0EEAAEgBAguA\nSMITWET7LMv+KMERWKtKTDbn+debezBTUTbVUx8BACAJBBYAkYQvsKj0nqyPEhyBJcQfx8Rj\nb3706W3/fdJexfH3N6p2DQAQVSCwAIgklsAaMjHBmCt7HWjripInsj1KRARWTyvuNrN+slf8\n9Xiv0tiq4qxTAQAAOQGBBUAksQTW66krv7+mfmx149+yPEo0BNbCWBS7P5ey8rPDY2v33qLI\nKQBAxIHAAiCSMAWWrn/eKrZ+eJZHiYbA+ocZxI5fp63dZP1uOE2JSwCAyAOBBUAk4Qgs/esm\n5vrtsrwdLxIC6/tYEHdkro+dzvuHAo8AAAUABBYAkYQnsPQxsQ3/y+4okRBYi2LXnf2euWFo\n7CqslfI9AgAUABBYAEQSrsBaU2RuuDRt7fdPzJl409QFL21MXZ2zwPr1yTtuGj39VZfpFNYv\nnT1x1IRZr/zJ3+Xnx26/cdzdT62v1VrNBw9OvGHi7Nfcr6OaHLsCi7Hh/VMG3b0sLeJajsnJ\nU5L1j0688e4UzeYe8PdP3D1x1Ngpj3y6zTUGAEDogMACIJJwBZZ+kLmhg3PNl4Nax6ctqHPK\nBKd2SAqsjuyf04abq/eNv3vsJPs+xcYXfm+8fdxc7Orc/efrj4jPpl52yuytji2rzXUnm0sL\nTyiy9ig+2XGLH2Oi0ffPb24fa7v2L7ukYry5S0uXHbI9Jj9P35nrTjfU2U3bm0vjsgjYZFn/\nneIHpCZdn87ORQBAOIDAAiCS8AXWv80NJyXfr7mwlJzsckdNYltSYC2NLX2YfrC9zbWjreXf\nuzuO0uAeXZ9hLlyQ3HnrdQ1SLO37fHLbH+aKww1vznDu0bsqvj1DYK3tW+Tc8z8/c1Nxt7k9\nqzlKXY/plqc15opjdL2/tSkusNwC1vVVnSiV477MwkcAQEiAwAIgkvAFVidbyth8/TdKp0/i\n56qkwKrZx1xK/2VxeUy7WLNL/XFY6lFu0ieaL0MTO/92Qrqh4vGJjdtiAkRfs3/qHj3j29MF\n1let047VnPvcm6dj26fXnjPXY7rm6U/z7QH6YnvDuCwC1r/ZJ+OAjVn1AgCEEwgsACIJX2C1\nMTe0ib/7apfYjqXHXXHbveP772d90HeKb3VcgzXaXNol7Uqhy8yVZ8UWa860/nbPAWNuu/wQ\nc2nR9eb/iRkhbAFWdPzlU+4Ze05La+9bE4cyf1zcvfp04//tzjjv4l6H27+sPWpvThNYq/ay\nNh9ccW7nQ6zfJZvzzv/8ETvz1Pyj2lLmekz3PFWb71rqcXU4LouAt1nzcJWdeOHIMcP7nFAn\n9m4X/nk4AEDIgMACIJLwBda+5obu9pvqE2P7dfjKfv+4NU/WHPutQ2CtKjMXH0891p7muodj\ni/NiuzabZ214+QCinQeba0bE9+0S2+H/2bvvwCiq7Q/gZzcVQgiEvqG30Am9I70XUYrRhLMU\nAAAgAElEQVRSRGlKERAFBGmKFOlF6UjvJSFrf+pTLE98v2cXuyBWLCi9JvnNzGY3u5utycyc\nKd/PHzJls7n3zHH3ZMq9fbJLloxDNnE1wnWOKEZYK/40UclNjirqdG9yrwS9Cqz20ur9p6SV\nH+6V1trkXLLz1N9RLB32szuU9wwSJ6k8LP2a8J+2yw8dWPtmCB3eIBVfjziHfP1rtlRijQvS\nRgDQDRRYAIbkt8ByVCrLs9dWS2sPue2WTs4U+9Ox5v4U4Z3i4gCP93pPKlyk5+1uST9X6CPn\nrkuthTX3Aus56a3muv0m6YLcbc5VcVCqmCJU+2fnhlttxf3Ws+7NdhZY26XqZLfrvZ6U9u73\nE4uPs2+eanP4up9XBH3PIHHKihVX7qP453JeEaTDUu/mu/3+l8VWRv8ToIUAoCcosAAMyW+B\nJV23o08dK7fKiiut3E/9nLC4FWDuBdZL4mKMx3BSU8RNU6RFx41O63P2nS/rqGqcBVY9cWWY\n+09/IZ60ImdJEie9uujPOfsdXch+ktCjwMqUzh+53xAmnX1q5i8Y8ylb/MCNX/l5TcD3DBan\n7PLQ4j68WOAO3xIvgcZ6DN0gRfOgvz4AgM6gwAIwJH8F1umi4vbk7DW79Kr/ebxikLipnmPZ\nvcDKlG5Rch8QPbO8uMXxZKF0OdDmfoboqEeB9Ya4nPC7x28aJ24bnL3iKLA8Jq5JErcsdix7\nFFgviMtx7hMqOuq7M/6i8RDlKNlvxX99DDoV8D2DxSm79fe57Q7S4V/Exaoee7+6Z86zr//h\nrwsAoDMosAAMyU+B9ZN0XsV175D0RGGK50scj8I5yiaPgUala2ZN3V75rtsG6Rm7Bz3eKNm9\nwBojLo/2/E2fitsKZ4+fIJUoxTwGupIGlMi+LOdRYI0Sl+9xf+mtQhRVuo7/kaR2FSN38d1W\nn/J6RcD3DBan7ALL/T76IB2WRs4q4re9AKB7KLAADMlngXXxGUed0dB5Bke68rXI80VXpbGb\nHBf7PAqsX6RbmU7mvHJSzjknaZwCSvd4o6fcC6xy4rL3BD3SxuzxPONyFTiOEz6jHMseBZZ0\n5my7x2t/DjLu+4UnE8lTnYUeI9QHfM9gcXK0vmauvvnv8E3pKcmjgRsNADqGAgvAkBwF1ozN\nLusXP9zOcZ6FSjincpGGTyfvEctbihvHSIueU+XcLq5Mc70uUyw7CjqKkA+lF/7o8T6fuBVY\nv0nLP2d56idu3OBYltq2zmO3NAhE9kjw7gXWr7lOF4Xi/JZuUZ4lVqHJP7n2BnzPoHFytN79\nEcCgHW4qLhZ+IQsADAoFFoAh/Yf8Kn3C+SLpvnX6y+tHh4sb20uLngWWdJuSzXX/0lviavZd\n3AfF5egMj/fJcHuK8FUf+7OyHhW3Zl9XjMt9xmeOuGmQY9m9wDouLV8MJxwOf+8fV8djrPb4\nbc5dAd8zaJwcrX/abWfQDm93tKDHc4GebAQA/UKBBWBI/gusNr+4XiTNZVPQ+0dniVsdt8F7\nFlgZ0mU010mXB8W1fzuWpQmVK3u9UfOcAmu7/3qvj+PFUolywuPn5/opsKQ3y+v9S+fSpzZ3\nm/Pm8ezNAd8zaJwcrXcfJCxohzN7Za8W7rvqA+9CDAD0DwUWgCH5K7CaHHN70SpxSznvH10u\nbi0mLXoWWFmPu1U8WRniwJmVs0cuWCzuqef1Rj1yCqwl/uuNdo4XSyXKpx4/76/AkgalKpuH\noDhdenlyVeev35sV/D2DxsnR+nfcdgbv8IXuOZuK9t+MxwcBDAYFFoAh5S6wCpSt32/NSY8X\nSQVTsvePrhO3Os7XeBVYP0mDN2WvviHuejJ7z2xxxXscqsE5Bdbj/uuNRo4Xh1FgLRQXPYc4\nCN/r2fVNwj/B3zNonByt/9D7RwJ2OCtjaWG3rZFdDvkbiR4A9AgFFoAh+Z8qx8008TW1vLdu\nErdGSYteBVaWNH1N9oNz4jN+Vudt7dI7tfV6o5E5BdZc//VGDceLwyiwpO0ej+zlyUHpMcDs\nhwMDvmfQOOVuffAOC/58ymN66RT/w0wAgO6gwAIwpJAKrJnia6p7b31G3BonLXoXWNKAm44T\nVRmlhMVuzh1SPdHK640G5BRY0uW0XBfZ3IVRYEnjP1QK3LVQvCjd8N44+HsGjVPu1gfvsMPX\nKztFuyosy5wwewAA2oUCC8CQQiqwpKFDc914tFTcWkJa9C6wHFPGfCkuvi4uuSZ2keqTBl5v\n1DmnwNoiLga8Lz2MAku6NlcqcNdCIo07Yb0Q9D2Dxil364N32OXy8w/VcZZYK8JqPwBoGAos\nAEMKqcCSLnLFem+VxhKoLS16F1iOkRPmiUtjhYViriEGnvZ1AqhuToF1SFyMuBmgLWEUWHvE\nxUgf092Ea7/0pl8Hfc+gccrd+uAd9nBqsTQGKcWcCr31AKBpKLAADCmkAssxvtNZr63Sveld\npcVcBdYZKzluVbpZQliY5Nou1ROxnndpX4rIKbDel97I8xZ7T2EUWI6++Z14MHQnpDc6EfQ9\ng8Ypd+uDd9jLtYeln5gS+k8AgKahwAIwpJAKrB+kF73mtTVF3OiYAjBXgeUYekGoJF4U//3Y\ntfkjH/WJdBExu8C6Jt1odCBAW8IosC5K9055T0Pjz4V/Lx1Y9Xefu6TJFOmroO8ZNE65Wx+8\nw7ncL/5ErmcVAUCnUGABGFJIBVZWafFFcz23/SMNw+kYHip3gZUmbpidlTWEcsYbEFyW6pP9\nHm80yq3AymooLt8boClhFFhZlcTlGR6vfWmpwLsEEn0gtewZn79zr/SmF4O/Z7A4+Wh90A7n\nIs3/HIGxGgAMAgUWgCGFVmDdI76ohuc2adhyi2O499wF1i2bdJrlcpxX1SKNNzDS/X3+iXMv\nsB4Sl4td8/xVn7pddQunwJJmga7j8VppYsC1PrqYWUbcU8HnhDTS1ICVQ3jPYHHy0fqgHc46\n7T0xjzRsxBVfDQUA/UGBBWBIoRVYb0ivesdjWztxUxvHcu4CyzFBzEfiuZ9Y9+1TxM3Zg3Y6\nPEDuBdZn0spKj990q7q12ZMfZa+EU2C9Ka286vbSn6X7vT7K8mGy9OLxPvackM5BTQ3hPYPF\nyUfrA3f4+6kdE73rwZvi/W3xvroAADqEAgvAkEIrsLJqia9q6n5ZKlX6wT2OFR8F1mmxDJgp\njjg62H2z44Yrt0ts6RaPAstxOqjYafefWSBucg7/Hk6BlSldz6t3Neel0vmnuj67+FshR9Ny\nzff3lfTgnuWjUN4zSJx8tT5gh38Ro1jukscPvCLuTfHZBwDQHxRYAIYUYoG1U3qZ26NrJ6Xb\njWpmjy/go8DK6ipsqRztdbYnK7O+VK3scK7vi6aCvdwLrH9JBVctt2tkm6Utadlr4RRYWQel\ntUGuC3DbpLfa6ruPixyVXtv3PLZeWxcvbR7q3BDwPYPEyVfrA3dYOv/V0/164CUpgvN99wEA\ndAcFFoAhhVhgZfWRXnf3T461W9uLi6sRx7P3+iqwjjjqFaroeTt2umNr/1evZ2Vdf0F82HDp\n/e4FluOEEJXcn12TfHO3tN7FuTusAstRn1CjV6Q2fHOftNY41zkqh8yB2S1u8tTbjq5kfJ/6\nQCnHtuSczgV8z8Bx8tX6wB1+S1qpcfCGs5EvSSfJCv2YBQDGgAILwJBCLbB+k8Zmp9iuj2/c\nvHhokrRiWePc66vAulnaUZrM83qn4dlljLWETbp5qV+mZ4F1qYFjf4l7Hlu54IFGjpVyrvET\nwiuwfq3q+Hlb+7v71XBcjCzxjb8+Xu1DLglJyVVL50xOU9NtZImA7xk4Tj4LrMAdzr5FrVDH\nsbMXzJvUt6RjdYO/PgCA3qDAAjCkUAusrG+qkJfo3a6dvgqsrBmO6uK01xvd6O7xLgOvZnkW\nWFl/tfb+TVTjlGtveAVW1g8Vvd6q1Pv++5i5IDLXr5YMc78rP/B7BoyTzwIrcIdvDfDRnkX+\n+wAAOoMCC8CQQi6wsv6+z+LxJd/K7Yd8FljfSa/vnOuNMlbG5ZQm4r1LUoE1K+cFN54o4PGb\nIh+6kLMzzAIr6/xYj2Z3/TVgJ7+/N3eJZenytterAr5noDj5LrACdzhrTWGvBtV8KWAfAEBX\nUGABGFLoBVZW1ucTqzu/44sPeMF9j88CK6uNuHGfjzf6a3u/2sUji9Qctl8adkq6aLjA/QVn\nn6jtKieqz/3BfVe4BVZW1vuD47PfKq7v8axgzm7pW9ytmClw26Jvfbwq4Hv6j5OfAitgh4V6\nbnnrnLIvvv/RkGcuBAAdQIEFAFlZZ57btnTRpqMfhjKO+LViYoVxLfgL+4qFg/cY6mdf3rh4\n/spnX/ozL830cvP/di+fv2LncZ+jiPrwx/E9G5Y9sWD5sy985beYCfye4cQpW8AOX/y/g08/\n9cSyTUe/wQjuAAaDAgsAwiMNYT4thBc2FV+4V/H2AABoEAosAAhLZj2hbLJ+F/yF12PEAivA\nvecAAMaFAgsAwiKNuTnQx46f39jmsf6idK/TDR+vBAAwPBRYABCO36VhsP7nvflM/Tjvm+p7\nii/soF7LAAA0BAUWAIThWgexbBqQe0d5cXtrt3vHD0gPx+1Xr2kAABqCAgsAQvd7R7FqivUe\nZFSwRKqn+l90ru+XhoAqj6EHAMCcUGABQChu/Xz1wsdPJJKvoRdEV6pJu0rNe+985pVvdtwm\nrVlfUb2dAACagAILAELxa84gnUN9vuDzIs79OSOez1e5kQAAWoECCwBCkVNgDfFz2e/zyuQp\nZrO6TQQA0A4UWAAQCmeBVWhFhr+XXH480a28ihzymZrtAwDQFBRYABCKc5UjKMLWaukvgV50\nPW3ibeUKRxSu0GjUtp/UahkAgAahwAIAAACQGQosAAAAAJmhwAIAAACQGQosAAAAAJmhwAIA\nAACQGQosAAAAAJmhwAIAAACQGQosAAAAAJmhwAIAAACQGQosAAAAAJmhwAIAAACQGQosAAAA\nAJmhwAIAAACQGQosAAAAAJmhwAIAAACQGQosAAAAAJmhwAIAAACQGQosAAAAAJmhwAIAAACQ\nGQosAAAAAJmhwAIAAACQGQosAAAAAJmhwAIAAACQGQosAAAAAJmhwAIAAACQGQosAAAAAJmh\nwAIAAACQGQosAAAAAJmhwAIAAACQGQosAAAAAJmhwAIAAACQGQosAAAAAJmhwAIAAACQGQos\nAAAAAJmhwAIAAACQGQosAAAAAJmhwAIAAACQGQosAAAAAJmhwAIAAACQGQosAAAAAJmhwAIA\nAACQGQosAAAAAJmhwAIAAACQmZEKrJm2HGVrNB+2+rtcLzm7e1K3+pXKVU/pOXn3b947Mz9a\nObJd7YrlkxvdMc1+wXPfe090Tylfq920l27Jvzkr67seQpO/ydVaP5tBNXpMqRk2T3dKW1vY\ncvkrvFiALIyTUqKrBya0qVm+RotRm5FNXIyVUbeem9KudvmU7otOhh4BDTNqgSVJGvqjxwu+\nHlfWfe99n3rsfamj+89WW3Q1Z9dPd7q2t/tE5s2C7VXEjbkqKT+bQT16TKnxXk1GgaUpxkkp\nwa6arm2VFt7Ib2ggTwyVUa/mfFBN9Sr2dMnQBZbNlvxuzu6bs8t67S23NNO1N3O29892+cO5\n73SK2+aqJ2TdLPx1McSxzauS8rMZ1KTHlBrq9UtRYGmKcVIqK3OKuFK2RacW5cWF26/IHy0I\nzkAZlbUxyW1j90uyx0p1RiuwNn3m8OHrGweIxyr5e+fev/tLB639E7tfPP7ytmnNpbVprh9e\nKR3RTf/35/Wbf3/0rHhtznZHhmPX9Q7CSsttX537Ib2fsFT3dxk3Z2U9V1v4jGpp866k/GwG\nVekxpfrYbJ0+c3NK2vr1Zx5ut9l6ZigaOvDNOCmVtUl4SZ3dF4WlK6lNhOVHFAwb+GWgjHpZ\nbHvftDPnTq5MFpbuUTJs6jBagfWi2/p/GwobRmav3JJOVA753LX3eGdxw7PZa7+Us9nK78/5\n2UPi32QHHcvPiD/o+Ossc66wPEXGzVmThOWG7y72rqT8bAZ16TGl2tlsQ4P16zWhaV8E7z7I\nzzgpdVX4Dkw+lb1yNkX4g/B06GEA2Rgno240Fl6x2LH8cyth+bVw4qBJRi6wsr4QsiXprGN5\nkbAzaYv73psThE2VfnasrBaWn3Lfu1nY0MHxuvo2W4rzcvCtnkJC/ibb5qwsIcFH/J2Vq5Ly\nsxnUpceUamCzjQvSrcvNXJ9joDLjpNRLwi9f5FoTv0k3hhQBkJdxMuqQ8MuHOVdOVbLZuoQW\nAQ0zdIGVNU7YckBaOl3OrWzPdrObzdbgsGN5rLDb4++vW02Smt0rZYb4OfK0a/vLwtozsm0W\nKqnKO4T/5i6wfG8GdekxparYbI8G6Zbwp2SLa0FeA8owTko943GK4b829ytPoB7jZNT9wv6P\nXWviCa/cD0TqjLELrJ3CliWufYO9f+D7Da7LJOKFas9b6n64mL3wsLDrF9fmW8k2W1/ZNmdl\ndflS/G+uSsrPZlCXDlPqpi3o2alPyhnh5LtOGSelxLMfOTdTfy6sTfbZY1CWcTKqsc2WkrP2\nifCiNb46rCfGLrDswpbZ4sKVKh61cW4jhP2f+N7VxmZr6bY63GarcF2uzVlZjv/mqqT8bAZ1\n6TCl/hB+7bqAnbrV1WYbEfAVoBzjpNQR13kS0evC2qoAbQelGCejKthsA3LWblW12e4N0HZd\nMHaBtUvYskxceEtY6BHoZ8W/xoZ5j/8puZZks411W18mvPIzmTa7+KmkUGAx02FKfSf8u1vc\ncMvfRcDNNlulH/3sA6UZJ6V+L2+zjXatLfL/VQ2KMkxGXRe23ue23tazOtMlYxdYDwlbjooL\nS21e9/J5+1F8euJuX0/BfO11NlO8Ee85mTa7oMDSJh2m1IfCv/YrO4fWSrJV67rQxz0M52vb\nbE8GajgoyUApNcPZcMGnVX1ciwI1GCejKtps/d1e3cVmK6f30WsNXWCdrWKzlf1TXBou7Hol\n4A+Lpbyt3D37f/Le8Y6w3f0pjDeE9W0ybXZBgaVNOkwp8d/pdW3Zyj2ca/TH+TZbrfMBGw4K\nMlBKXepmsyVNfPvczfMfLhQ60ezXIF0HRRgno5rYbPXdXi0OUfpHlr4ZucD6Qxw2bby02FtY\n+irwT28q5zjajcdt/9x9CMbnbK6BQST/E9ZXyrTZBQWWNukwpdJtnjr949moX4U/E9cH6zco\nxkgpdXFyziDhSWP0/l2oV8bJKPEpwo9cLz4p7tH70GpGLbBunjuxuI6wXuMHabWNsJhrkksv\nHwx2He/k0Qf+dm4Wb+U85vayT23SWU9ZNrugwNImHaaUeP+FrfLMd8/eOPvSPeLy3ZlZ7uYK\nPTDAFBS6ZayUerFpdmPqbdf7xRzdMk5GHbC539Yunn6z6X3KZ6MVWF6qve3Y1UhYdrss0sbt\nJf/K2fzZwpwdVR7Nrp29b5f6QlifL9NmFxRY2qTDlHpa+PfO7GEEs1LLe70q6++q7qNDguqM\nlFIn+wjLSfVbp4hnQVLcT1WAeoyTUdfrC0urHVtvzpHao/f5JoxdYPX6NnuXOCGS24NTfhJN\n8Oux2d3KO3ZUdIzPdtTmswiXZbMLCixt0mFKXT579mzOY0Ebha093Vuz0marcDbsOIBsDJRS\nb1ex2epsFmeXu5DewesTDVRjoIwSX28b/Nq5m2f2drAliaewct0bpjMGLrCaTX7LtUuckMnt\nGWL/iSa6fPzJttKureLaCzb30V4cIxavkmmzCwosbdJxSjncFP6GTfozZz2jMcbA4mWclDpb\n22Zr4vz6vjZUePHrIUcB5GOcjHKM3p4t6Rnx6++fXK/WF6MVWNu+cfj2t+vuu8Z5HujX0hwW\n+kw00dti7V9BLJ/fFRY2ue15TVjfKdNmFxRY2qTjlMomPkrvNmr7v23ej3SDuoyTUuI1nDdd\nW89Vsdl6Bes8KMA4GSXYmpxdX9V+Rdxc/mYoEdAwoxVYfr48tgq7Hsm9+XW/iZZ1pZfNcTbz\ne5vn7VJ7hPWXZdrsggJLm3ScUtm22Dye47nfZqul988sfTNOSqXYbC3cNo8RNuPiMwPjZJTo\nz40Dm9doMvTQ1aysATZbO9+t1A+TFFjiRFn1rufaHCDRsk4I++4Q/r1Z3vOiyhOOokeWzS4o\nsLRJxymVTXxeZ69r7XoVn7PYg3oMk1JnhX8ecNu8Ulj/t59mgoIMk1He6nkOA69LJimwMpoJ\n+w7l2hwo0a4n2WxtxIXOHjNQZvWx2ZIz5NrshAJLm3ScUtlWCe15yaN1+/20DlRhmJT6Svhn\nitvmdcJ6up9mgoIMk1FexHGwdD9in0kKrKxNwr7GucavdibazYOP9fKesumKsK+LuCDed5cz\nWNsFoSYfJttmJxRY2qTDlDr/+SvujzaLT+J86VqbZXOf3B4YGCalfrZ5zhy3AGeweBgmo7ys\nELae8tMv3TBLgXWptrBztPeslnuclXwLYeFdz31vCptGigv/ERaecG0Wr2ofkG2zEwosbdJf\nSn1i85gT7q8qNlu9nNUONlvDgB0GpRkmpW5UFJLJbRDbIcLLvs0C1Rkmo7Iyf3lr+1/OrVfq\n2WxdA3ZcD8xSYEkPjNruu+q+6ebq8s5EE2f5bvGn+87r4nwD0tWUzDY2W7JzONwLTWy2Gldk\n2+yEAkub9JdSGSk2W9mPXb9zmvArZ7nWbgitGxRG90F+xkmpgTb3gRnOCs1sEHIUQD7GySjx\nIUPX3NRPeJ+F0CXTFFjS1RFb01TXFeCLe1qJW/pdkFbEeSWbHc959ff9xHOrjilFDguLvRzj\ncVwZJiyvyZJvczYUWNqkw5QS72donH2+PVNcqZAzVt/nHuUWcDBOSokTyjV3Pjd4QxwHa0X4\n4YB8M05G3axvs1XOPqO2Tdja2cf9WjpjngIrY56YV7a6k3Y89+4bR5ffXUlafeSaY+/7FcW1\nzste/Pz0j1+9uWmIOIlphTcc+zIHCSuN1n/62xfbxdzsfEPGzd9+JnlEbLpj8UaAzaAyHabU\nNXGwwErT//3D2S92dRFbsCOnweJAye6T24P6jJNSmXcIiyk7xDmer/6ru1htXZA7WBAC42RU\n1rPCYpW5H/7126tivV415ySXbpmnwMrK2lvD5qVFzhCM76Z476z1hnPfhR5um9uelXNze+/f\n6ritz89mUJkeU+p0U7ettqQlbs31GhQLGBgopc51kNbrt2kszbKSknM3M6jIQBmVeY/b1vJG\nmBjATAVW1j8Lq7of1j6H3Idc/OsJj53Js//O2Xfh4bLO7Q/8IetmFFiapseUyjo30bXV1uYV\n99aKQxU9H14EQGZGSqlrcyu5Npcd7fYuoCIjZdSNaUnOrV0+DzsSGmSqAisr63L6jD4NKpet\nktJr8t5c00heeXnOoGY1yleo3Xzg4y94jc52clGP+uXrdp3/kcybUWBpmh5TSnBm1eDGVcvX\n7/ToS57PDz0udOeNLOBkrJT6c8volrXKJzcdvOr7wH0CxRgro754vEet8jXaT3rN+9FHfTJS\ngQUAAACgCSiwAAAAAGSGAgsAAABAZiiwAAAAAGSGAgsAAABAZiiwAAAAAGSGAgsAAABAZiiw\nAAAAAGSGAgsAAABAZiiwAAAAAGSGAgsAAABAZiiwAAAAAGSGAgsAAABAZiiwAAAAAGSGAgsA\nAABAZiiwAAAAAGSGAgsAAABAZiiwAAAAAGSGAgsAAABAZiiwAAAAAGSGAgsAAABAZiiwnH44\ndNDpPe62gEl85ky5D7hbAkbnzDX7Te6WgC58kZ0wb3A3RM9QYEkyj3WyEBVv1Llbu2pRRA0O\nZXK3CIzvmzssVCClS+eUAkQdUdWDgn4dbKW4Rt06VrVQTaQaBHXpwQgq3KJbm5JETd/hbox+\nocASvZJClDxmq11yeFYzC7X4mLtNYHC3FsdSlelHxZRLnVmHLPf+yd0iMKzni1PF6alirm3u\nZInawN0c0LqTyVRmWpqYMEuakXXCZe726BUKrKysPwaRpdUqu5t1TSlqcQZ3u8DIfmlHCVPS\nXSk3vwKVfo67TWBQqyOiRhxzptq8eHqSu0GgbW8kUI8jzoRZYKOaOOGQNyiwst62UdXldi+z\nilDv89wtA+N6tww12eWecalDIi0P4/YYUMACKrLMLdXWF6dl3E0CLXulQMREt4Q53J0KbOdu\nkz6hwNoXYx2c5l1f2e276lC9X7jbBka1N9ZyT7pXyq0sQx3PcTcMjGcNFd/gkWmbEi17uRsF\n2vVmgchZnp9NjxakCfjrLw9MX2BttRaYm7u8Es8odKKqZ7hbB8a0xFJgTu6U29+IaiLlQGbH\nIhI2eGXa6tjYE9zNAq36qHDEY96fTevL4q+/vDB7gXUwolCuy4PZ0vtRtV+52wcGlPkIJa72\nlXLHelCF77lbB8ZyMj56aa5Mm2Upe5a7YaBNP9osD/n+6+80d9P0x+QF1n9iY5flziWnvpSC\n+7BAbhn3U5ktflJuEFX6mbt9YCSXa5GPr0v7YOp4i7tpoEWXGtAwn3/99aSkz7kbpzvmLrB+\ntVlm+cqlbOkdqRsuPIO8Mu6lijv95twAqouiHuQzkrr5/GxrRLO5mwYalDmQOvj5bBpGJT/h\nbp7emLrAyuhAQwPUV3Z7agpN4W4kGEvGcKq8N0DOdaGeGCAE5HKUKhzxmWd7iltf5W4caM9S\nqu47YQRjLCW/5G6fzpi6wFpEjbyf5PKyrwzt524lGEnm/YHrK3tqPZrD3Ugwit9LRK31k2hL\nIsrgNizwcjwyYZv/D6dRVAG3JYfFzAXWR9FFdgeur+z2p2Piv+ZuJxjIdKqwJ3DK7SlufY27\nlWAQ/Wm430QbSj0xIxh4+CPJ8mSgD6eB1OQqdxt1xcQF1s2GFOgGrGyTqNF17paCYaymMjuC\npdxT1rJ/c7cTDCGVqh3zm2fH6tDT3A0ETcnsQ3cH/GxKb0v3cjdSV0xcYD1FbdFic9wAACAA\nSURBVIPXV3Z7W3qMu6VgFMciEjYFT7lBNJy7oWAEF8pGrg2QZ9viCnzB3UTQkvVU239BLjlc\nmbZxt1JPzFtgfV8wPugFQtH+4pEYkw9k8amvIYlyS61EL3I3FQxgEg0ImGiPUFM8JQ0uXxWM\n2xrsw2ljwULfcLdTR8xbYPWgSaHUV3b745Y6uEgIMvinGj0cUsqttFbC9PWQXx9FlvL7QJhD\na1rM3UjQjFvNaUrwD6fJ1AIjqIXMtAVWKtUK8gShSyeaz91aMIIB1DfElOtLj3I3FvQuszXN\nDpJnuxNiv+JuJmjFEmoRyodTC1rO3VL9MGuBdaVixNoQv+zsexNicVIU8m0zJaeGmHIHE6Mx\n4Azkz15qHDTRHqF2eJIQJF/EFg7pppmd8XGnuNuqG2YtsOZR7xC/6wRTqCd3e0H3vo8vEMIN\n7tmmUjfu9oK+XSkf6T3Hsw8NaQd3Q0ETMlrS1NA+nCZSD+7G6oZJC6zTBRP2h/xlZ0+vRc9x\ntxh0LrMjTQg95ex1KJ27xaBrC0O6IL0putQ/3C0FLVhDzUL9PqxNR7lbqxcmLbAG0fgwvuzs\nqy01bnA3GfRtG6WEetOfaI216jXuJoOO/ZFQKOCMAU530SPcTQUNOF0oLugIfU5rIype4W6v\nTpizwHrLUiWcLzu7vTM9w91m0LVzJWI2h5Vy3Wkpd5tBxybTfSHl2aHi0d9ytxX4dQ/nnENv\nPPcVIlMWWBmNaVFYX3b2HTGlLnC3GvRsAg0JL+X2xCVgqjjIqzMxxYMM0eA0hfpzNxbY7aU6\nYZxz2Fc47mfuFuuDKQusHdQyvC87cQ6mx7lbDTp2MrJ0iN93LqNoDHerQbdGh3xCIr2q5T/c\nrQVm50pFhfBERI4H6D7uJuuDGQusy2WjwrtaI9gfX/hP7naDfvWi6eGmXGpSxMfczQad+j6q\nTKhDgtjnUzvu5gKz0TQ4rA+ntLLWj7jbrAtmLLCepNvD/bKz24fTDO52g24dpxrh3fQnmkUd\nudsNOjU61HkqRCn0Mnd7gdXblrJHw/1w6srdaF0wYYH1e+H4feElk+hQQvwf3C0HvWod7k1/\nknp0jLvhoEtnokunhZ5nyy1NMdqomd2sa1kY7odTLXqNu9l6YMIC60EaEW4yiUbQTO6Wg069\nTA3zknJrLMkYHQTyYEJ4w9A0w6BrpraCOoT94bSEUJWHwHwF1vfRJcK93VhyKKHwOe62gz61\npGV5STl7Z3qau+mgQ78XLB7WFZ/Vlob4sjSvXwrH7Qr/w6kZRhsNgfkKrKE0OfxkEt2DBwkh\nT/4VwqRwPu2ILXGeu/GgP7NpZHiJ1pLSuNsMbIbRmDx8OK211Mngbrn2ma7A+iyi3LE8ZJPg\nQFyxi9ytBz26LY8nsOz2u+kx7saD7lxKLHQwvDxbY2mAU1hm9a6lYhg37OW4jfZyN137TFdg\n3Ukz8pJMooG0nLv1oENvU/28ptzBhLhfuJsPerOWBoSbaC3wPIVZZTajJ/P04bQhIvkWd+M1\nz2wF1qfWyuE/L59tb2zSde72g/70pAV5TTn7GBrP3XzQmYwqkSHPKue02tKYu9nAY1/Ikzx7\n60g7uRuveWYrsO6gWXnMJkFP2sbdftCdjy3JeU+5oyWjT3F3APQljdqHn2jN6AXudgOH65Ui\n1ufxw2lTRHWcwgrCZAXWZ/k4gWW3b4mogdv6IExDaGbeU84+kUZxdwD0pT2tCj/PVlBL7nYD\nh7XUPc8fTh1pF3fztc5kBdagfH3Z2VvT89w9AJ05HZWUj5renlYm6hR3F0BPPqU6eUm0RvQ6\nd8tBfZdLR4d9Pdllo7UmzjgEZq4C66uI8vn5srMvpw7cXQCdmUwT8pNy9kn0AHcXQE/G0KN5\nybOnMDGTGS2jO/Lx4dSODnJ3QOPMVWCNoin5yCZBbfqAuw+gK3/HF8nTuLYuqSVj8SAhhOzv\ngsXz9NC9vQ69x912UNvlUrG78/Hh9IwlBcN7BGSqAuuXmJJ5++xxmUXDuDsBurKYhuUv5ez3\n03TuToB+rMhrws2jftxtB7Wtojvz9eHUAjfNBGaqAuvRPI1Y6y69TPTP3L0AHblui92bz5w7\nFF/kAnc3QC8yq0fuzONnW2XrSe7Wg7qul4vOwyQ5bpZTG+4+aJuZCqyLReMP5Sub7OLpBAyt\nDaHbTr3ym3L2u2gVdzdAL/5FbfOaZ1NpBHfrQV3bqWc+P5zq0zvcndA0MxVYa2hgPrPJbj8U\nV/wKdz9ANzLrWTflO+d2RFbBszoQmv60MK95llYqBqfnTSWzbr4/nuZTH+5eaJqJCqyMquEP\ncJzb7bSVuyOgG69Qy/ynnL092bk7AvrwW1TZvD8nPYZmcrcf1PQytcr3h1NVCy4sB2CiAutY\nXgY4zmWrtQ53R0A3utISGXJuOXXj7gjow2Iakfc8O1So2GXuDoCKusvw8TSVRnJ3Q8tMVGB1\nohX5zia7OKfEce6egE58mp9ZctxUs37D3RXQg8xqUXvykWd30gbuHoB6vrJUy/9nU1rJmF+5\nO6Jh5imwTlpq5D+b7OJF54HcXQGdGEHTZcm5yTSNuyugB//O+y3uou2RyRjWyDwm5ndYSMlo\nmsXdEQ0zT4E1gR6RIZvs9vSyUbgVFELxW0zJY7Lk3OG4kte5OwM6MJTm5yvRWtO/uLsAarmY\nkHBUhg8nXFgOxDQF1sWEIqkyZJNdHKlhLndnQBdm0yh5Us7emw5wdwa07+8CJfM1FZh9Ed3O\n3QdQy0YaIMuHU39az90V7TJNgbVBhjEaHA7EJt3k7g3owJUScQdlyrm11Jm7N6B962hwPhOt\nYsQZ7k6AShpYtsry4bQNF5b9M02B1cAqTzYJutER7t6ADmyifnKlnL269Xvu7oDmNcn3V+Y4\nms3dCVDHCWosy0eT3d4W8+X4ZZYC6wQ1kSmb7PbV1IW7O6B9mbUjZKvp7eNpDnd/QOs+o5T8\n5tmBWBtOz5vDaJolxyeTYBm+EP0yS4F1H82RKZvs4umE77j7A5r3ErWWL+UOxJS9xd0h0LhH\nZHiOpxulcXcD1HCxUGKaDB9MkhqWL7i7o1UmKbD+iSsuz/Nckok0g7tDoHk9ZBlk1Kk9nu+C\nwG6WjjuS7zxbQb24+wFq2CrbTcniYKPjubujVSYpsNbR3bJlk91+qEDpG9w9Ao372lpVxpSz\nL6Ch3D0CbXuOusqQaJUifuLuCKigpSX/s6Q6pSYWOs/dH40ySYHV0PqsbNlkF8+jp3L3CDTu\nQVlG8XNJL1kQn2EQyABZTpmOoYXcHQHlnaR6MiSL0920hrtDGmWOAut/1EjGbMJ5dAjqQuEi\ncozil+Mu2sLdJ9Cyc7G2/A2C5bA3qhoeuje+qbL+/bcjsgaSxidzFFjjaIaM2SSoFInR3CGQ\nNXSXvCm3xdKWu0+gZetpqCyJ1pre5u4KKO1mmYKHZckWV9K8yt0lbTJFgXWlaIJMo7g7jaEF\n3J0CTasVsU3elLPXsuDZVfCvuUzjRs6l0dxdAaU9T11kSRanBdSfu0vaZIoCa7eMIz464Dw6\nBPQGtZI55ewTMEUT+PelXDfVpCUmXOHuDChsoKyPOEsz9P7K3SdNMkWB1YHWyZpNdvGU6HHu\nXoGGDaIn5U65/dGVUdSDP4/RZJkSrR/t4+4MKEum+/XcjKJF3J3SJDMUWN9bk+VNJsE8Gsnd\nLdCu36KTZP4AE7Sht7j7BVqVUT72kEx5toZ6cvcGlLWehsiULE578eefT2YosObQeJmzyW4/\nllj4Mne/QLMW00jZU84+BzfHgD+vUXvZEq1i5Fnu7oCimls2y5Yt2W7Dbe6+mKDAyqgQs1/u\nbLLb76Rd3B0DrcqsEr1X/pRLK1LkKnfPQKOG03zZEu1ejGpkbF9THdmSxWkB3cXdLS0yQYH1\nLxn/tsuxjjpxdwy06lVqq0DK2fvSQe6egTZdipdxLrBtlmbc/QElzaaJsiWLU3qZ2L+4+6VB\nJiiwhsh/v7GouvUH7p6BRt1FC5RIuVXUm7tnoE07qb+MiVaPvubuECgns3K0Atd0htJa7o5p\nkPELrPMFS8p/v7FgLIbCAt/+ii2jSMrZy0f9zt030KROsj4o/SDN4+4QKOe4IifYt1kbcndM\ng4xfYG2SdZ7nHHujquOxCfBlDQ1TJOXsw3FzDPhyxlpdzjzbF1Wdu0egnDE0V85scWpIH3P3\nTHuMX2C1lnHWcA8t6T/cfQNNamDdrkzKbbM04e4baNFCekDWRGtB/+XuEijlWtGENFmzJdtU\nmsLdNe0xfIH1jUX+ByYcZtNY7s6BFn0k89TiburTF9y9Aw2qESnvU6vT6WHuLoFSjlAfWZPF\n6UhcqZvcfdMcwxdYsxR4YMIhNaHoNe7egQZNpukKpZx9Mj3G3TvQnveopbx5dqSgLYO7U6CQ\nfrRC3mxx6krPcfdNc4xeYGVUiD2oTDbhqXnw6UbJQkeUSrmDMRVx4x94G0ezZE609vQmd6dA\nGedikmROFqenaBB35zTH6AXW64oMguWwGlNKQG526q5Yytnb4osPvF1LTEiVOc/m0gPcvQJl\nbJR9mhyn9NIF/uHundYYvcCSc4DjXCpGYgZx8NafliqXcvMwXQ54O0y95c6z1PgSuJ/GmNrK\nP02O0120lbt3WmPwAutSoeLKjEgkGUlLuTsIWnMuxqZcxonT5eDGP/DUm1bJnmjd6EXuboES\nfrDUlD1ZnDZSe+7uaY3BC6wdsg5w7G13ZG3uDoLWKHcGXtKHDnP3ELTlbFR5+fNsIQ3n7hco\nYRGNlT9bnJKtZ7j7pzEGL7DkHeA4l2b0PncPQWNaK3cGXrSCbufuIWjLSrpX/jxLT0zAzOJG\nVCdyj/zZ4jSGnuLun8YYu8D60VpVuWQSzMRQWODptKW2oilnLx/9J3cfQVMaWHcokGd9KJW7\nYyC/j6iJAsnitDuyLncHNcbYBdZiGqNgNklDYeHPPHA3n8YrmnL2YbSeu4+gJZ9SQyXybCnd\nxd0zkN9UmqpEtjg1xnQ5noxdYNWO3K1kNolDYe3j7iNoSo0oecfUzmWrpRV3H0FLptHDSuRZ\nesm4y9xdA7lllI09pES2OD1Mj3J3UVsMXWB9oOjpUNFa6szdSdCS/1ILhVPOXtvyHXcvQTsy\nyhZQ5ivzDjrA3TeQ278VHBdSdCi2PEZCdmfoAush5eYscapq/YG7l6Ahk2mG0ik3gZ7g7iVo\nx6vUUZk8W0F3cvcN5Daa5imTLU5t6Th3HzXFyAXWrTJxis1Z4jSWHufuJmjHrdLKp9y+qOrc\n3QTtuFepoZTTSxe4yN05kNf1xIQ0ZbLFaQ7dz91JTTFygfUSdVE2mQT7YypiVlRwepk6K55y\n9pZ0grufoBVXCiceUyjP+tNe7t6BvNKop0LJ4pRauNh17l5qiZELrGG0UOFssouzov6Lu5+g\nGffQAuVTbiY9yN1P0Ir91E+pPFuNIdeMZhAtUSpbnHpQOncvtcTABZay0+Q4LcQM4uCk4OkE\nN6nxJW5w9xQ0og+tVizRbLHnubsHcrpQsKTiX4lPYXgPdwYusPbSHUonkyDdFv07d09BI/ap\nknL2bvQ8d09BG/6MLqdcng2kXdz9AzntogHKZUu29JIFL3D3U0MMXGD1UvBvOzfDaSV3T0Ej\netEaNVJuMd3N3VPQhvU0VLk8W0t9uPsHcupOTyuXLU4DaCd3PzXEuAXWX0r+bedmZ2Qd7q6C\nNvwRVUGVlEsvhT8SQdLWsknBREuK+Ye7gyCfP6IqKpgsTs9QV+6OaohxC6x1NEyFbLKLMz7/\nh7uvoAnraLg6KTeItnH3FbTgR2uyknl2F05GGMl6ukfJbHGqFPkbd0+1w7gFVhvLFjWySRz5\nYyR3X0ETWlqeVSflNlAH7r6CFixVdrLVtdSbu4cgH5W+Eu+lNdw91Q7DFlhnrDXVSCbBseKF\ncMEGsrK+s9RRKeXs1a1nuHsLGtDYukPRPCuLa4TGofDpTpdtlmbcXdUOwxZYS5T9287dINrE\n3VvQgCdoglopdz8t5O4t8PuG6imbZ7hGaCDL1PpKrGf5mruvmmHYAquhdZc62WS3b0HFDoLq\nUfvUSrk9kTW4ewv8nqAHlc0zPEdoIA0typ7udHmQ5nL3VTOMWmB9RSnqJJMohT7h7i+wO0Et\n1Uu5ZvQ+d3+BXe3IvQrnWVIMxho1iK+ovsLJ4rQ/umomd2+1wqgF1uM0UaVsEkylydz9BXYP\n0kz1Um4mTeDuL3D7lJoonWcDaTd3L0EeKn4ltsaD9U5GLbBqRap2ucZuPxpf7Bp3h4HZjRLx\nR9VLOcypClmP0RSl82wN9eXuJcijluKnO11m0Xju3mqFQQusj6mpWskk6k0HuHsMzOzUTd2U\nO8LdY2BWNfqg4nlmi8Uj0obwoYpfiamFi+PPPweDFlgz6WHVssku/p2HwWvNToWJ6t2txBBF\nZvd/atz0N5D2cvcT5DCNpiqfLU496Bh3fzXCoAVWFRX+tnNXJeJH7i4Dq38KlFZ8onoPFSN/\n5e4zsHqEHlU+zVbTHdz9BBlkVow5pHy2OC2l/twd1ghjFljvq/lAl+h+WsDdZ2C1le5SN+VG\n0hLuPgOnzPKxh1XIszIFLnL3FPLvHWqrQrI4pZeJPcfdY20wZoH1CE1XMZsEe6Oq4cFUU2tH\nG9VNud2RNbn7DJzeodvUyLP+uL/UCB6kWWpki9Ng2sjdY20wZIGl0t927lrTW9y9BkanVZqG\nwk0Lepe718DoQZqtRpqtoIHcPYV8u1U6TsVnnO32zZZW3F3WBkMWWO9SGzWTSTSXRnP3Ghgt\noLFqp9wcGsXda+Bzq4w6X5npJQtd4e4r5Ne/qLMayZKjtuVb7j5rgiELrMk0Q91sstvTEhPw\nMWRiNdQbZMbpWLF43B1jXq9RJ3XyrB8d5e4r5NcImq9OtjiNx3Q5EiMWWJllCxxRN5vs4scQ\nHmc2rxPUQvWMsw+krdz9Bjaj1PrKXEpDuPsK+XStaJFj6mSL0/7oyrgrOcuYBZZKd396Wkvd\nufsNbCaoOU2O0yZLc+5+A5friQlp6qRZevHCmKdC59KotzrJkqMNHefutRYYscCaRI+pnU2C\nShiXyLSuF49PZUi5evQpd8+BSRr1UivN+lA6d28hfwbSMrWyxQm3iEoMWGBllC2o/hVCu30E\nreTuOTBJox4MGWefShO5ew5MVJw4YDEN5+4t5MvFgqXUHQVZkFYUdyVnGbLAeovaq51Moh3W\nxtw9ByZ3qP8Houho4aL4DDOni3ElVfvKTE8sgpnldG03DVArWXLcTvu4+60BBiywJqo7pJpL\nCp3k7jqw+CsmiSXjhM+wXdx9Bxa7qL96adaDXuDuL+RHT1qrXrY4rcFdyVlGLLAykliuENrt\nD9Fj3H0HFs/QUJaMs6+3tOXuO7DooeZX5gIayd1fyIc/oyqolyw5MFtqlhELrOM8Vwjt9oMx\nFfBgqik1t2zlSTl7bZw1NaXfoyqqmGXHEord5O4x5N16GqZitriMpGXcPednvAKL6wqh+GDq\n29ydBwZfUl2mjLNPoSncvQcGa+leNdOsG73C3WPIuzaWLWpmi9OuiDrcPednuAIrI6mgqpMu\nuZlF47l7Dwweo0lMGWc/El8MYxSZUDPLNjXTbD7dz91jyLMz1hpqJkuOxvQhd9/ZGa7AYnqG\nUJQaXwJn0s0no0LMQa6Us/fGozom9BXVUzXLUuNL3uLuM+TVUzRG1WxxmUYPcfedneEKrEls\nVwjFM+l42sZ8XqN2bBlnX0sduPsPqptNE9VNs870OnefIa9SInapmy1OR+JK3uDuPDejFVhM\no4w6LKR7uPsPqruXHmfLOLs92fINdwBAZZmVog+om2XzaBx3pyGPPqeG6iZLjq5k5+49N6MV\nWG9znk5ILxaPgR/N5nJ8osrzqHqYSI9yRwBUdpzaqpxlqYVK4xqhTs2iySpni8tTNIC799yM\nVmBNZpmH0KkvHeIOAKhsN93JmHH2QwVLm/40vNmMprlqp1lHeoO715AnmVXUPt2ZI71M7Dnu\n/jMzWIGVWbYA3xVCu30F3ckdAVBZV3qaMePEG//SuEMAqrpapEia2lk2B09I69R71FrtZMkx\nmDZw95+ZwQos1iuEAlvsP9whAFX9HFGZNeOEor43dwxAVfvpdtWzLDW+FK4R6tIkmql6trhs\ntrTi7j8zgxVYvFcI7fZBtIM7BKCqJTSKNePs9kqRP3MHAdTUk1arn2Ud6d/c/YY8uFUmjvOa\nTh2zP4NjrAIrsxzrFUK7fR11444BqKou1zPQLqNpEXcQQEW/Rao5TY7THDxHqEuvUieGbHGZ\nSHO4I8DLWAUW9xVCcYLLs9xBABV9QE2YM86+N6o65sA0kRU0giHLcI1Qn0bQfIZscTlg9gl6\njVVgcV8htNuH0zruIICKHqLpzBlnt7eit7jDAOpJidjJkWUdMdaoDl1PTFD9gQgPt9Gb3DFg\nZagCi/kZQtFWSxvuKIB6bpZmvcPBYS6N4I4DqOZTasySZfPoAe6uQ9iOUS+WbHF5wuQfToYq\nsPivENrtNSynuMMAqnmBunInnN1+rHihi9yBALVMYTpnmhpfHDOt6s7dtIQlW1yOFYu/zB0E\nToYqsPivENrtY2gJdxhANXfTIu6EE/THs6umwXfOtAu9wt15CNPlQiXSebLF5U7azR0FTkYq\nsDL4rxDa7bsiUrjjAGo5X7Ak9+eXaD21544EqOQ56saUZfNpJHfnIUz7eaeZEK2jztxR4GSk\nAus4tefOJkED+ow7EKCSZ2kQd7pJkq2nuEMB6hhAS5mS7FiRote5ew/huZ1jyDQv1axnuMPA\nyEgF1oM0izuZBA/RY9yBAJW0pw3c6SYZR/O5QwGqOBdrYztn2pPs3N2HsPwTm8SVLDkeoAXc\ncWBkoAIro0zcUe5kEhyMqWjukT/M44w1mTvbHPZFV0POmcJ6GsqWZYtpKHf3ISzb6S62bHEx\n9zh9Biqw3qAO3LkkaYthiUxiEd3PnWzZWtM73MEANbSwbGVLsvTi8Ve4+w/h6Ebr2LIlRysz\nfzgZqMAaS3O4U0kyh+7nDgWoolbkHu5ky4acM4evqB5jlvWjQ9wBgDD8Eck8Eb3DHBrDHQk+\nximwbpaMT+VOJUlqQtFr3MEAFfyPmnHnmlNa0QScXDCBmTSZMctW0J3cAYAwbKB7GLPFRfhw\nMu9QWMYpsF6hLtyZlK03HeYOBqhgsgamyXHqS/u5wwGKyygfe4gzy8oUuMAdAghdO9rEmS0u\n/Uw8FJZxCqyRvLNaullJfbiDAcq7pYVpcpxWUw/ueIDiXmW+zXQQRrTVkZ+t1VmzxWUddeSO\nBRvDFFjXixY5xp1IThWifucOByjuRc2cMhVVjPyVOyCgtGG0gDXJ1lE37hBAyFbRKNZsyWHi\ncfoMU2Clc89q6eY+WskdDlDcUFrInWhuRtBy7oCAwi7EcU8cUDESfzrqRjPLdt5scRlH87iD\nwcUwBRb7rJZudkTU5w4HKO0S/zRf7pBzxsc/ccBwWscdBAjRKUsd5mxx2R9dyaxDYRmlwLoU\np6Wvuyb0f9wBAYXtpv7caeahEX3EHRJQ1m2WjcxJttXShjsIEKLFNJY5W3K0o9e4w8HEKAXW\nXk193c2g8dwBAYV1o2e408zDVHqYOySgqO8sNbmTzF7T1BPL6Uq9iN3c2eLypGknATBKgdWL\n1nAnkZvUhKIYlsjYftXGIH45jsSVvskdFFDSHJrAnWT2MbSEOwwQkpPUkDtZcqSXKvA3d0B4\nGKTA+jOqHHcOeTDzyB/msIpGcCeZl270HHdQQEEZFWMOcOeYfZe1AXccICSzWcek9TaEnuEO\nCA+DFFjraBh3CnlYb2nLHRJQVGPrDu4k87KEBnAHBRT0GrXjTjFBCn3BHQgIRdVo/nI8xzZr\nQ+6A8DBIgdXGsoU7hTzVseBzyMi+pBTuFPOWnhTzF3dYQDnD6EnuFBNMorncgYAQnKCW3Kni\noRF9wB0SFsYosE5r4PZPTw/TZO6ggIK0dQLeYZhZT8ObwvmC3INgSfZHV+OOBIRgIs3gThUP\nj5r0uS9jFFgLaRx3Ank5mlDEvBNcGl9mZU2dgHfYZmnMHRdQzCa6izvBJC3pfe5QQFA3S2lo\nHi9Rqkm/EI1RYNWO3MudQN760TbuqIBi3qE23AnmQwp9yh0YUEoLy2bu/JI8Sg9xhwKCeok6\ncyeKl37mnMfSEAXWB9SUO31y2WRpwh0WUMxYmsOdYD48gqGwDOsk1eVOL4cjBW23uIMBwQzT\n1Dxeog2W1txB4WCIAmsKTedOn9wa0QnuuIBCrhcrnMqdXz4ciSt1gzs0oIypNIU7vbJ1pFe5\ngwFBXCpUXAs37HmoQye5w8LACAXWrTIau94smUPDuAMDCkmjntzp5VM3SuMODSjiZumCh7mz\nK9sTNJI7GhDELk1NbOIw1ZTPfRmhwHpZc9ebRelJ0b9yRwaU0Z+WcaeXT8uoD3doQBHHqBt3\ncjkdK1LkGnc4ILDOGpvHS3S0cKIJpzcxQoE1jBZwJ48vo+hx7siAIv6OtWnuBLxD+UgU9YbU\nh5Zz55ZLbzrKHQ4I6KeIKtxJ4oMpb3M3QIF1MU5715tF+2NLX+eODShhEw3mTi4/RmKqOEP6\nJbIid2rlWIYpAzRuMY3hThIfNlhacgdGfQYosHZq8HqzpJcZK3YzaGvZxJ1bfuyOrMEdHFDA\nYhrNnVo50ssUOM8dEAikZuQe7iTxpR59zB0Z1RmgwOqiwevNko2WFO7YgAJOaW7egByt6G3u\n8IDsMqtFaWmgv0G0nTsiEMB71II7RXyaTmO5Q6M6/RdYv2jyerOkGb3GHR2Q33zNzRuQYx7d\nyx0ekN0b1Jo7sdyto87cEYEAxtAs7hTxKbVo/AXu2KhN/wXWMhrFnTj+LKKe3NEB+SVr6nSC\np2PF43D5xnCG0RPcieWhcgSepdCuK0WKaHGUPsFd9DR3cNSm/wIrJWInnmrFqwAAIABJREFU\nd974Vd3yOXd4QG7/0dg89Z7uog3cAQKZ/V1AE/M85xhBK7ljAn7toju4E8SP7RG1MrmjozLd\nF1ifUCPutPFvOt3HHR+Q2wM0mzuvAnjW2og7QCCztTSEO6087bBiWnHtakfruRPEn5ammwVA\n9wXWVHqEO2v8O1Ym+ifuAIG8rhZN0OgJeIdG9D/uEIG8UqzbuLPKSwp9wR0U8OMbDT+Ds5Bu\n5w6PyvReYN2yFdDKFBK+jKVHuCME8jpAfbmzKqAZ9AB3iEBW71MT7qTyNplmcUcF/JhOk7jT\nw7+KEae446MuvRdYL1Mn7pwJ5EjR+L+5QwSy6kGrubMqoNTE+IvcMQI5jdLeQ2EHYyqY7WYa\nvbhZJu4Qd3r4N4GmcgdIXXovsIZqc5ocF6F93CECOf0cUZk7p4IYSJu4gwQyuhifmMadU7m0\npePccQGfDlMP7uQI4Eh84mXuCKlK5wWWVqfJcdlXoKQJZ7g0sIWanIXC3bNWjG9rJBtpIHdK\n5TaHxnDHBXzqou0z7P1pPXeEVKXzAmsHDeDOmCD6mW/oDyPLTNbmLBTuGtMJ7jCBfJpYtnBn\nVG5pRYpc5Q4M+PCtNZk7NwLaFlnDVBeXdV5gdaJ13BkTxI6oije4owSyeYtacWdUULMxOIiB\nfEgp3AnlS186wB0Z8GEaTeROjcDa0nPcMVKTvgusH61VufMlqG6YuMtA7qN53AkV1LGSBc5x\nxwnkMpYe5U4oX56mHtyRgdyulYjT8lP1ghXUgTtIatJ3gbVI8zfE2O2bI2pkcMcJZHI+rvgx\n7oQKbhgt5w4UyORygkbnPakc+Qt3bCCXPdSbOzGCqU0fcEdJRfousGpq/4YYu709HeKOE8hk\nPd3NnU4h2B1dBTW9QTxL/bnTybdR9BR3bCCXVvQMd2IEM4uGckdJRbousE5QC+5sCcEzlgam\nuq3PyBpZtnKnUyja0/PckQJ5NLds4s4m33ZH1uSODXj7mOpx50VQ6UlRP3DHST26LrDGam8E\nPl9amOu2PgP7Py1PfOlmGXXjDhXI4iNt3uIuakH/4Y4OeBmjzRv2PI2nh7jjpB49F1hanxXO\naSU15w4VyGK0Pip6u7265UvuWIEcxmn3G3MOjeaODnj6O66Y9sakzeVIERNNb6LnAmu/xmeF\nc2lEr3DHCmTwTyE9fH6JHqZx3MECGVwqXFSzf0OmJRa+xB0f8LCShnBnRSiGmWh6Ez0XWF1p\nLXeqhGYJteWOFchgLQ3mTqUQpRYv+Cd3tCD/Nmt5IOX+tJU7PuAuo2rkTu6kCMW+AqVMM72J\njgusM9Yq3JkSqvr0Jne0IP9qR2znzqRQDaPF3NGC/NPkKO5Omyy49UFTXqDbuHMiNCaa3kTH\nBdZ8eoA7UUK1kDpxRwvy7d/UkjuRQrY3JgkTCOje/1FD7kQKpB59wh0hcNODlnGnRGi2m2d6\nE/0WWBmVo/dxJ0rIatO73PGC/LqTFnDnUeh60E7ueEF+jaLHuPMokGk0gTtCkONrazXujAhV\nF9N8Oum3wPoXteNOk9DNx8QSuvdDZPl07jwK3UZrXYy+pnP/xBXX9EMVqQkJuM1dOybSQ9wZ\nEaqN1lomGQpZvwXWQFrInSZhSKb/cgcM8mc6jefOonC0pBe4Iwb5s0brD1X0p83cMQKn84WL\nHOVOiJC1paPc8VKHbguss9FJOjqfYJ9HfbgjBvlyOTFe49OoelqOR1d1LrNWxA7uLApsq7UB\nd5DAaRXdxZ0PoVtracQdL3XotsB6ikZwJ0lYqlnMNMWlAa3X6qRw/tSnd7hjBvmhg4cqmuDe\nUq3IqBKp8XLcQzOTnGDXa4GVUSVKB/M8u5lN/bhjBvmQkRy5jTuHwjOfenIHDfJjgPYfqphH\nd3NHCRzSqT13NoRjBbXijpgq9FpgvaCvdBJUtXzMHTTIu3S9jDGTIxknTfXsp6hymr8JIt0W\n/Qt3nEDSgVZxZ0NYGtLr3CFTg14LrD60hDtDwjSTBnAHDfLuNlrJnUHhmk13cEcN8m4O3c+d\nQcGNpjnccQLRx1SHOxfCs4Tac8dMDTotsE5FVOROkHClV7J+xh02yKsTVI87gcKWXtmCgSB1\n63rpAge4Myi4AwVLXuWOFAjuoxncuRCmevQWd9BUoNMCaxpN4M6PsM2gQdxhg7y6k+Zx50/4\ncNJUx3ZTL+78CUVfTEioBb/FlDrGnQphWkhduKOmAn0WWJcTC+nqkXlJegXrSe7AQd58aa2o\n+fthckuvbMV9f3rV1LKBO39CscVaGwPa8ptDo7gzIWymmN5EnwXWRrqDOzvyYDoeudGrEfQI\nd/bkxWN4dFWv3qVG3NkTmlYmed5e066UKKiD68le5lM37rgpT5cFVmbtiK3c2ZEH6eUjvuAO\nHeTFD9Fl9HYCXpJexfI/7thBnvSn+dzZE5oV1IE7VrCRbufOgzyoSf/hDpzidFlgvUStuXMj\nT6bREO7QQV48qK9ZcnLMwRyY+nQqUvtjNGSrg1nAuGXW0OUZhydMcApLlwVWF1rKnRt5kl4u\n4ivu2EH4fi1QXD+zfHmqieHcdWkyPcidOqGaQ/25o2V2dmrLnQV5UpPe4w6d0vRYYH1iqcmd\nGXk0lYZyBw/C9zCN4c6cvFpA7bijB+H7O77IEe7UCVV6ReuX3PEyuXb6G6RPMp+6codOaXos\nsIbrbsgPp2NlI/BZpDu/FUzUzbddLin0Enf8IGwLaRh34oRuKg3njpe5/VeHg/Q51DL8CXYd\nFlhnom26vONY9AgN4w4fhOthHT4C7bLC0iCDO4AQpiulCuzjTpzQHUuK/J47YqY2gOZw50Ae\nPUmduIOnMB0WWA/p9Y5ju3QXFk5h6cyvBYvq9wSW+Bj9Xu4IQpieoX7caROOyTSKO2Jm9m1E\nBb08EJFLHTrOHT5l6a/A+jNOz993U2kwdwAhPJP0eweWaENElevcIYSw3KgQtYM7bcKRVjrq\nFHfMTOwBeog7A/JsMbXlDp+y9FdgzaHh3FmRD+nlrZ9zRxDC8VNscR0X9ILutIY7hhCWLdSd\nO2nCMxGnsPj8GlsilTsB8q4BvcwdQEXprsC6UDROf2PWuplOA7lDCOF4gMZx50z+7Igt8Q93\nECEMNypH6mxUo9QyUd9xR820pun6DPtySxNDT7WkuwJrMd3FnRP5kl7Z+hF3DCF030WV0vHf\nh5K76FHuKEIYtlJX7pQJ12S6hztqZnUuPkF/8/K6aU5HuEOoJL0VWJdLxe7lTon8mU29uIMI\noRuq4xscsh0qWuAH7jBCyK5X0tsJLLv9WLkI3PnAYy7dw3308+UZa61b3DFUkN4KrBV0J3dG\n5FN6suHH/jCQj63ldTsmiMsETNGkI8/o7Q4s0XRMK87jfNG4/dwHP3860FbuICpIZwXWldIx\nu7kTIr8WGP3BCSPpSbO48yX/jlW0GH5KCsO4bIvezp0w4UuvihRj8aTOb5mx27dGlbvCHUXl\n6KzAWqGvAWJ8a0DPc8cRQvMm6XVWJg/zqbmh7yQ1koV0B3e65MV8uo07cmZ0PjFOR0PS+taX\nFnKHUTn6KrAul47ZxZ0O+bfKUtfIV50NJLM5LebOFlk0o93csYSQ/JFQSJ83mTag57hjZ0Lz\ndX8Cy27fG5fwO3ccFaOvAmuZEU5g2e230bPckYRQHKSm3Lkij41RSRe5gwmheJDu406WvFlt\nqY0/G9X2dxH9n8Cy20fQOO5AKkZXBdbFkrG6vwNLtCXadok7lhDc9SoR67hzRSb9MVSDLnwV\nVVKvw9p2oA3c0TOdmTSE+7DL4GiZyM+4I6kUXRVYC6k/dy7Ioz/N4o4lBLeSunFnilwOFYvG\nJJg60JumcqdKXm2LKXWeO3wm81tcwkHuwy6HGcad81lPBdY/iQX1eXtCLvuLxGL+ec37K7GA\nAe74yzbNuJ9hBvIy1dDtvL32QTSdO34mM4FGcR90edQz7Gijeiqw5tBg7kSQy0S6nTuaEMwk\nGsadJzJKob3cAYUgbtSyrODOk7w7lBiDCXPU9E10yaPcB10ez0SWN+hNMzoqsH6PL6zrWQjd\npSfTC9zxhMC+0O/tML5siCp9jjukENhy6sSdJvkxme7gjqCp9Kcp3IdcLv3oEe5oKkNHBdZk\nGsGdBvJZZa1s4NHVDKE7TefOElndTaO4QwoB/VI4TtfXpNOr0evcMTSRdyxV9Xs92cuh4pHG\nnKJXPwXW6ZjiRjqh0Ac3LGhbOtU2zMeX5Gg5y7+5gwqB3E1juJMkf5Za6t7kDqJpZDalhdwH\nXD6zqYkhR/nQT4F1Lz3InQRyOlg88gPukIJ/V6tYV3PniMyWWKpe5g4r+PcqVU7jzpF86kir\nuaNoGjupOffhllNreoo7okrQTYH1aURZvX/6eJpD9W9wBxX8epx6cmeI7HrTJO6wgl/XqluW\ncmdIfu0oWOQsdxxN4qItahP34ZbT7sKxn3PHVAG6KbB60UzuFJBZB5rNHVTw57sCCQYYItnL\noTLWN7kDC/7Mpu7cCZJ/I+le7jiaxHSjDArpNJ0aG/CMg14KrNf1PECMb/uKRZ7gDiv40Z0m\nc+eHAhZZKl/gjiz49ll04n7u/Mi/1AqWt7gjaQpfRhc7xH2wZdbWiGccdFJgZTSmp7iPv+zm\nWaphgjht2k91jFbPS/rRfdyhBZ8ymtOj3Nkhh0W4z10VnfU75L8/+4pHvs0dVtnppMDaRS25\nD78CetEI7sCCL3+VilrPnRyKOFKRDnEHF3xZSS24k0MeHWgJdyxNYD/V4z7Q8ltgqfg3d2Dl\npo8C60r5SEPd0JdN+Lrbxx1a8GG4IeZQ9WVtdNEfuKMLuX1TsNAO7tyQx+74uFPc0TS887ZI\nI/4F2J8GcEdWbvoosOZTX+5jr4h1MYW/5o4t5PI8VUzlTg2l3E+tcAlHczJa00PcmSGXidSd\nO5yGN54Gch9mJaQm0wbu0MpMFwXWT4XijfdIl2QSpVzlji54+btshI5nhAsivTlGuNWepdSU\nOzFkk14Hs14q7D/WMkYadDvHlrgCH3MHV166KLCG0FjuI6+UjjSSO7rg5R5j/nmYbV9Ji507\nwuDpk9jCO7nzQj4bokv+wR1RQ7texzKf+yArZAYlG+s5Zz0UWG9ZKh7jPvBKOVyRNnPHFzwc\nokqGvUAoWhFV9HvuGIO7q/VoBndWyOkeups7pIY2jzpyH2LF9Ka7uMMrKx0UWDfrWxZxH3bl\nbIyLwWhYWvJjYvTT3EmhrLHUEBONa8lEg31hplWlo9wxNbBPohMNeseM4Gg1epo7wHLSQYG1\ngtpxH3UlzbaU+407xOByqx2N5k4JpXWg4dxhhhzHLDaDjRm5NrLk79xRNawbDekx7gOsoK3x\nhjrjoP0C60x8nIHuT/DhbmpznTvI4PQ4NTbkEKPuDlemtdxxBqfTiVEruTNCbsPoDu6wGtZc\nuo378CpqjqW8gW7h036B1ce4d7g7pDej+7mDDNlejyi2mzshlLc1PuoN7kiDw7WmdD93Psgu\nLZm2cwfWoN6PTNzLfXiVNYg63+KOsmw0X2Dtp5pGP6NwsAKt4Q4zSH4uHWHg+/1yPBlR4jR3\nrEEyhtpwZ4MCNsbGf8sdWUO6lGyZx31wFZbe0EAjyWi9wDpbImod9wFX3ObCES9yBxoE11vR\ncO5kUMcoSrnEHW0QrKcKBrsBy2EiNcGNDwoYTb24D63i9payGGaGE60XWP1M8Y23OLLwJ9yR\nhqysB6il0U+XOnWifhnc4Yas16LijTgJmKANPcQdXAM6QBWMOcSoh7WxBf/HHWmZaLzAepZq\nGHYILHcPWcr9xB1rWE/lDnJnglqO1qQZ3PGGk0Ujn+TOBIXsL2PBWA1y+7aw0ceQcZhhKWuQ\n70NtF1hfx8ca9O87b4OpruEmEtebVw17NsGX3SVxHzK3nyrQg9x5oJjV0QmYaFVeV1IMnC8e\nhlIDY4zorukC62oDmsx9pNXSlVpd5o63uX1WJHIBdxao6Zm46Ne5Y25uf9amu7izQEGTqJYx\nviQ1Y7jBRqT1L70jdTbEPXyaLrBGU3vuA62aYy2o6zXugJvZT+VpIncSqOuJyCKfckfdzM41\nou7cOaCo7tQb9/nJaDVVNsENWA6pDWigEQZr0HKBtZEqGvIBG9+ONqCeqLDYnKtLd3OngNom\nWZJ+4I67ef3ViNob+5GK1Lr0MHeUDeRfkYW3ch9S9RxKpvsMUJ5ruMB6I6qQiW6JsdsP16Pu\nmCOOycUW1I07AdQ3lJLPckferH6tR+2N/gDP3jK0jjvOhnGySORC7gOqpn2VaYT+KyztFlhf\nJEY8wX2M1XU4hdrgTncWl9tTa6N/2/nSm+qf4469OX1dhTobP+M2xEekcUfaIH6paLZbGPZU\nortvcIc9vzRbYP1YgcZzH2G1HW1Gdc5wB96MLnekJqncR59DeidqhAqLwdvF6Q5jXx90WBJd\n4Dh3rA3hn/o0kPtgqm1vdep2kTvw+aTVAutsTUM/YONHWjcqY6SpxHXiYjtqfJT72PM41p4a\nGGhqVb3YEWsdw33o1TErIuH/uKNtAJdbUyczFOSeDqVQw5+5Q58/Gi2wztal3txHl8W9ltit\n3ME3m7+aUxOT1ldChdWRauKsqbpuTKSCc7gPvFqmWIpjlor8utKRWqRxH0kGqR0oSd/1uTYL\nrB9rUjfz1euS2QXpPgyIpaYfa1MbU14fdEjvSWU/5j4GpvJjK0oy/gSrLmMtJTEaSP5cam/W\nU+zpQy0FdnOHPz80WWB9UYF6mbS+sts3VKSaH3IfARP5uBz1MG2ySYZY4p/jPgomYi9OLQ5w\nH3M1jbGU+Ig76LomnmI3zQBY3mbE0pSb3Ecg77RYYL1VjAZxH1ZGR7pT9EIdp5S+PF/YMoT7\niHObEmVdkMl9IEziygRL5CiTFfT3W4q+yx13Hfu+JrU28Sn2tWWo3W/cxyDPNFhg7YqxjuU+\nqLxmJVBTnFVXQ+biiKgp3Ieb39JE6vMX97EwhRM1KGkl9+FW3URrHM6R5tXxEtTL+AN6BLC/\nCdne5D4KeaW5AuvWNCpgmhtA/dndmqJnXeU+FMb3z51UdAn3wdaCHbWpPB6nV9zV6ZGW7iaa\nncJlRlQkRhzNm7VR1lHch49Z+jBr5JM6HXNUawXWn12p9NPcB1QDZiZS1Re5D4bRvVeFamzn\nPtLakDbQEjEdMzUp681kKm6ywZOdFsXTWENM3quyv/tTvElTxt2CotRRn+M1aKzA+m9FStnL\nfTQ1YX9PC91xmvt4GNmNuZGW2018b4OXhSWozn+5j4mR/THCYulhqrvb3W0qR81OcR8C3Xmt\nHCWbaP5B/3Y1pOKHuY9GXmirwHo6xtLf1Jeb3a2oTgXnYXJCpbxfnxIf5z7GWrK/E0U+ghFC\nFJKxsRiVf4r7GDM62JoSdnIfBX25MM5iHYg/ASXpI6Pobh0OiaylAuvcnVRoNveB1JD0BxOo\n/G483qWEv8ZaqT3OlXqaV5IqYOo4RbzViGKHm/y7cnwM9fqB+0DoSFo5SsIdoi5rq1CJnbr7\nNtRQgYXTobnsvz2SGuJWLNldX5VINtzakMuh2yOo2+fcR8d4vhtoodbbuI8uu421KG4Bnt0J\nzXe9KaK/aUe/8iXtnmhqq7chIjVTYF2cgNOhPmxqZaGWz+uubte0jF2VqcBwc46MHMzaOhQ5\nEqcZZPXrhGiqsoj7yGpB+vh4Kv/sLe4DogMXZ8ZSzbXcx0trNjWiiBE/ch+bsGilwEqrQDac\nDvVlRWOiuptxb4xcbu2pSRHddnIfVs2aYaPokV9wHyXj+HlyQSo5xWRDi/q1t28kVduG5wkD\nu7mhNBWdjJzJbXYSxU7+hfv4hEEbBdYnXSmi32Hug6dVK1paqci497gPkiFcXl+VrO03ch9S\nLUt9sAxZOh/Cl6AcPrwvhhLH4Gxpji2dIqnsUxjW1r+MPdUoesBB7gOlTanjilHsA19zH6OQ\naaHA+mywleqs5T5yWra1fwJR1cf+x32k9O6HGcUoshPKqyDSpiaTUBa8ivma8uf8lhZEpcei\nvPK0pUcMFRx5gvvoaNSNHTUpojNu2PPryP0lyNrDrpMLzewFVuYrvSxUYRb3UdO61Fkto4nK\nj7Nf5D5gunXrub4RFHcHPrpCsbq3UNIXG37kEvdR063Lh/oXIEu9GRh2Jrc9w4oT1Vn6E/cx\n0p7fF5WliPYbuA+QtqVOqUpkm6qL+92ZC6zTTwqhqjoDV5tDcGBqqwJEUa1mvXSe96Dp0keP\nlCGqON6M85TkTdoTXYQaK7bb099zHzodOru1r/D/aum7NnEfRa06NqtpBFnbrPiO+0hpyXX7\ngBiK6Y6kCW5ZZ+H/r6pT39T8SXbOAuu7FS0tFNV2MffB0o/U+f0qWYgi6o7e/LHmU0s7Mt6f\nWYOoYGc8RRGeY0/dUZaIaj78Msa7Dd2t9x5vZiUqc+dy7uOnbbtGJwufZckTj/3NfcQ04fe9\nQ4oQ2UZgcL7QHJ7aPJoood/aTzX9jD1XgXX55UdqE1lqjUU+hWnvY7fXiBK+9uLaTNn3DdPR\n05MvNw8uSRTVdCoeosiLTaMbCNkW23H+WxjAKLgb/13VL1H4XKtxDyZUDcG2BxoKX5IR9cfv\n/pb7yHHKOLl9bB2h2EzsuQTXcsJwaGaX4sI3YdEe857/lfsY+sNRYP14dGor4f+rqJQHMNNu\n3qQuG9M+ySLmVseHd36Abz7frn6499GuwtcdFb5tmmnngJPB4bm9ywlRjGkxaecnN7gPqlZd\n/fL55aObxApxSuzwyB7uQ6YfR+YPqBkpRK14t5kHvzTfWfnz72wY27KQ0P+o2kOWoboK3/qx\nbcUii0p1mrzh3xqcD1rdAuu3t7c83KWkEA5LxT5zcDtM/ux/cnhLMZZkrdx13NKD75y5puqx\n1K7rp97as3BMp0pWMTjFW41ajQ+ufNsxtUdFMZ7Rte6YtuGlk7j3XZLx26ev7Vk1896eKaVJ\n+j+xQucHcYNy2I4surd5MTF+0XXuFLPLDIP+3Tz16vrJXSuIvbYktR35FMZsz7ttMwY2SpT+\nB4yr23fy6vSPz3Ef3RzBCqznD+bfttXzp44e0LFBuRgpCIWqtBoweTrIYtJdHevbCpBDnC25\n0W09Bwwf+9DsBcuf2bZfhmOXB68EzqgbivzSA1tXL3h0/LDet6WUT8iORoGkeu0HPsh9gAzk\nocEd6pSKcga3VqvuA0ZMfPTxp9Zu3qbIIXXzfuCU+l3p3+9u94alcx8aObBbq9rlEizZqUYR\nCWVrt+o9/GHuQ6RjEwbcVrNUZPYHWbm6bXoNGTvt8eXrdyh0GIOMpPS1Ar/ywLanl8ydOnZY\nvw6NqyVaHf8bVWjUddgU7tAbwqQhXZtWK5adQDFJddr0GjZ++vyVG3cpcCR9+91XIgUrsCoT\nQFhSAmfUee72ge7cFzil3uBuH+jOqsAptYq7faA7b/hKpDALrNiiRYsW5Gm+pIjw+618vz5G\n+PVxfL+eEoTfH8H366OFX18o6Ks0V2BxJ20uUUKD4rkb4c4qNKgIdyM8iP+ju84PqV9gRQq/\n/v/Zuw84qYk9DuBzHFVEFBV9gaN3UEBEBVGKIoKG3qUIYgURUGkiRUQRqdJBRAERqXJrr49n\n7/3ZBbEi+JReDm5fJtuyu6m7s/lnd3/fz0fJJnfJTKb9b5NMThG+VyG8V1YRp0WVmqeUjRk5\nnAVYucpvl2VuKGarjxXBvW6xtHKkEq4cqYxypKKuHEknEhERYJ3VpEmTSq5kQF9D5fjF6Q5/\nhnL4qnSHZw2U45ey/rFUKaccvrrlT3kuwKKutHHKKgmqRZ0IreJKghpSJyJKYyVFkZ7S9QCr\ntHL4esL3KoT3yipCSVoTwr9/zZyrJE07yDsLsEopv93AlXSeqhyppitHOls5Up4rR6qiHOlM\nV45URzmSO3+66kQiCQVY/S+Pcomy22aX02mqHL8N3eGzPPuXKoe/yPKnbjSvUQddSGi0Fkqy\nm7t+VBOtlARdSJ0IrTZKgppSJyLK+VE1/UHzKvWp8MO3Vg5/gfC9CnGZkrTzqROhjyetCXUi\nDJwf03VuNq9Sm6N/u7VrDcReHysC7xYvduVIzZUjXeLKkS5UjtTSlSPx+nRZ9KpP9SqSw6cI\nH1d2O83Zrwh1uXJ8wndpb1YOP4Hu8H5ZOT7h3MfPKYe/ne7wiVqtJPt+6kRovakk6HrqRGj9\nxgdG6kRE4X/KEL4Q+Avl8D3pDm9ml5K01tSJ0HeCB1gefUn4FUrSEn81z/fKb3cUmBpjryhH\nutWVIz2iHGmmK0earBxpvStHGqQcyZ2XXPKQe5eNn0OA5QQCLARYAiDAsoQAywgCrIQgwIqF\nACsZCLBSAQEWAiwBEGBZQoBlBAFWQhBgxUKAlQwEWKmAAAsBlgAIsCwhwDKCACshCLBiIcBK\nBgKsVECAhQBLAARYlhBgGUGAlRAEWLEQYCUDAVYqIMBCgCUAAixLCLCMIMBKCAKsWAiwkoEA\nKxUQYCHAEgABliUEWEYQYCUEAVYsBFjJQICVCgiwEGAJgADLEgIsIwiwEoIAKxYCrGSkKMA6\nunfv3sMJJUiMfcrxT9Adnmf/EN3h/ftps39MOXwavumeutLGKfDaeTyhJGgfdSKi8IZeSHf4\n48rhD9Ad3oz3yipiL22pmUmu6+Qnfb/A1Bhzr29wr1s8pBzJnbj7oHKk464cyW4k4jDAAgAA\nAAArCLAAAAAABEOABQAAACAYAiwAAAAAwRBgAQAAAAiGAAsAAABAMARYAAAAAII5CrC+XzS0\nV+e+d67+I1WpMVf41ozru3fpN3aNnQm+UmVXT1n+D8FxP5Y1RhIkwO//esGN3fsMm/MFycET\n5YlKE4+qGsXxQL3SQVfV3pOjeGo+WKU2vz/rhh5KbV73P+qU6Phi/k09uw2Z8T51OjS+vF6W\n39Cu+GXZ8D5dBkx5wdFkSa6Ne253VqnvhdztXlzpNpz1EA4CrKO71e66AAAgAElEQVTzQ/vs\nsiW5JCbmtxGh43feQHF8VeEEmWZkfIN6ICxY1DF49EUenUtQjycqTTyyahSHvF7poKxqng6w\ndo8OpatbPnVaYh2cGkrbNK/M6luwktcjbYC1oXMwjTfbD5bcG/fc7qxc6IXc7F5c6jZSFWAV\nTlH2NnblpgUDlH9fSDKRCdjdT+lWZjy+ZfkNyvFJIjzuWZloZHxelqesDXne/eMXzpTlHvPy\nN0xRKvFa9w+fIG9Umnhk1SgOdb3SQVrVflkbsUyW73L7+GYOKrV42NOfffXWAiVOeJo6NdGO\n3a6EBQ9syZ/dTZYneeMvsB+HKTFRVID1lNLq7t7w9CODZXmQ3dnw3Rv3XO+sXOiFXOxe3Oo2\nnPUQ9gMs5VR1+4AvHJ4ny33df+PUvbJ8h/rF+AklVz2I3jSyq4d8Lc3IuEmWXyE4bNhLsnzb\nbr7wYTe5ixcvUOjyRKWJR1eN4lDXKx2eqWrz5c47CA8f5zEldglc2/qwo9zDW+/LWSfLA7bz\nhZ8Hk/z9Hc/XRe761BxtgPV7N7mz+qa6I1Nl+SGbu3Fv3HO7s3KjF3Kxe6HoNqx7CPsB1s2y\n/Gxg6bjShD5IIlUJ+V9HudvewOKJ62XZnTc6xiq8S+63gWZkXCXL7xAcNuToQLlX8OW7T0xc\nvpMwJU54otLEI6xGcYjrlQ7PVLXPOspr6I6uQ6nC3wQXx8jyv0nTEuPENbL8YWDxu47yIC98\nhTVSvuVHf1SAtST81cbhfnInm2Owa+Oe252VK72Qe90LRbdho4ewHWD901HuGrq2vkCWtyae\nqsTsnDXl4dDy3HCdd9kzSjz+NM3IuEiWPyc4bMhbsvw44eET5IlKE4+wGsUhrlc6vFLVjt4k\nX+/+F/VmOslyqAteKMvrSNMS4xtZvim0PEWWv6JMS9DIRUrpaQOs49fIXUJvbF4jy5tt7cW9\ncc/tzsqVXsi97oWg27DTQ9j/Buv47nBUuEKWNyaaKBEeJBqc/ughT/ITjYxKnn8kOKzm8L8Q\nHl4AqkoTj7IaxSGuVzq8UtVWhb+T8Yqeshy6cLTQU7cU+v2vyfKc0HK+N+7SVKu1NsD6SpbH\nhpa/lOXx9nZDMu650Fm50wu5170QdBt2eoiE5sG6T5bfTOT3BNnfV+5McWtG4Xi5126qkXGy\nLFNONHCdPED5//4f/vs7YSKSQVVp4pFWozjE9UqHR6razs7yNNIExJsiy58GF8dGrhZ6glKd\nl4WWP5Tl6ZRp0dIGWEoaHwktH+0o93K8M9fGPRc6K5d6Ife6F/e7DVs9RCIB1r5uck/C+4V3\njJLlVRQHflq9e5NoZLxTlve9ds+Azr2HP0Iw8BzuqPzB98UE/hjsoHVH3D9+0sgqTTzSahSH\ntl7p8EpVmyJ3/pXu6Lr+K8sjAx3vex3lCcSJifaK5husT2R5OGVatLQB1grto5f9lXrvcF+u\njXtudFYu9UKudS8E3YatHiKRAGsm2VfAu1YsmzVMlrutpzj4Hz3ku/1kI+PNsnxLaJKUda7f\nQ7pd+aP02U7B49/2t9uHTwpppYlHW43i0NYrHR6pap/J8lKqYxvaLMuDN370xRtzOslD91An\nJspXsjw0tKxU7eso06KlDbBmaW94v1WWnd4I7ca451Zn5VYv5Fr34n63Ya+HSCDAWifLdxQ4\n/zURvuRnr9eKvRTHLhwv9+TfdhKNjHwWlt6zNmxdMkhZWO320ZUTf2vnQS/9dmz30/1keZwX\nBmLbKCtNPOJqFIe2XunwSFUbI3f1xhXlKO+PD4wgg1YdoE5KtIJeshycQPv4UFnuS5uaCG2A\nNU2W3wtvuF2Wv3W2K1fGPZc6K9d6Ide6F/e7DXs9hPMAa7Us30Q1WH0Z6F5ueong2L7ggx1E\nI2M3WV6sfj9dsEw5A9+5fPQP+JS1/6iLv/WW5bdcPnxSKCtNPOJqFIe2XunwRlVTKs0CmiOb\nObhyQKAyd7zdE5VH4xFZHqLOjn54eseO3gyw7pHlj8Ibxjp91NGdcc+lzsq1Xsi17sX1bsNm\nD+E0wDoyXZZv2Z1IgsQ48b+vVit/LM11/cC/95DHq2Ex0ch48ED48v9UWZ7h8tHflyPzmWyR\n5akuHz5JZJUmHnU1ikNbr3R4o6op5+JnmiOb2HOjLM/576GC3a/cIsuLqFMT7eAQWe6x9NVt\nKwfKSzp78xJh1DdYo5x9g+XeuOdGZ+VeL+Ra9+J6t2Gzh3AYYP15myyP2W/9cyn153Xuzz5d\nOFbuEXh9Ff3I+K0s93L5yskXstw59H7U3bLcx92ji0BRaeJ5qRrFIahXOjxR1f7XSb6D5MCm\nxofv0j5yh+eqz66hwRtg5u2T5WHUqQnRBliztfdgDXP0VL/L416KOyuaXii13Yvb3YbdHsJZ\ngPVlPyW0PpZQgkR61/3X0uaHZ36jHxkLu8qyyxdpd8hy//CH7rJMXwccI6g08bxUjeIQ1Csd\nnqhqG7wzK23EN5qn8z6T5dsp06Lj+LPj+3a9ftbn/p0e+opbG2CtlGVfeENfWbZ/G5vr415q\nOyuaXii13Yvb3YbdHsJRgPV2F7mjF6a3OyLLHY9b/5hAu7vLN7wR8JAsL3/jDdLJGfv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DgAWQlTQB1k4+jedFwfU1wstRAdYM4/ijVWiX\nRxZXi99aObR1X/vIytO6L496ms5egPWmZqt1etRf+U7zK9vjDqINsGzuUBuxmQdYphkGgMyH\nAAsgK2kCrEAs85W6qL6bcLG6GBVgTTGOP0I/8nVdva2Vw0c88eApmvVFr9gQmaLKXoClfXW0\ndXocBlg2d+ggwDLLMABkPgRYAFlJG2CpN3uPURevV5ZK/E9djAqwJhnHH3UCP/FhKJooV6f5\n1b24S6IDLL9/zwN1tL/YKPwqaHsBlja4sU6PwwDL5g6dBFgmGQaAzIcACyAraQOsQzw2ko4r\nS4dPVZZ6BNZGBViz+Ic8k/3tra7GEHkPRh7Ti7rJPejbOZcXDwccORO1iXEUYFmmx2mAZXOH\nzgIsv1GGASDzIcACyEraAMs/hH/gMwqs4wvBm8mjAqyH+QezeTNHqBFEL8086boBluLgMyMb\nhCKO2ZrEOAqwLNPjNMCyuUPHAZZfN8MAkPkQYAFkpagAS73zir9HsIPy71kFgZVRAZYai+QW\nGO7uKJ8Pnl0c9QPr9QMsbvt0dQpPVmJ7JDExAVYz0wDLKj2OAyybO0wkwFIPHp1hAMh8CLAA\nslJUgOWvqXw4+Yh/T1EWmf08KsB6Tw0P/mu4O3W2UvZq1Lo5xgGW33/kdvU3RkUSExNg1TYN\nsKzS4zjAsrnDRAOsmAwDQOZDgAWQlaIDLPVFyC/4H+H/fBZcFxVgHVFvJHrScHeL+ebToh+T\n62oWYPn9N/LNtdXFd/hi8aitBSeZBlhW6XEcYNncYeIBVlSGASDzIcACyErRAZY6FdZIfyfl\n/41D66LfRXge/3St4e7UyUrPiVq1t4x5gKW+DjlXjcnUlyqzY9qt7zPTAMsqPY4DLJs7TCbA\n0mQYADIfAiyArBQdYPnbKp/qH+ExxNzQqugAayT/dPqR6J18viu0pE4j1Txq4zQWHWDt2O+P\npn5HdUjdxGKjIf9oiwDLIj3OAyx7O3QSYJlkGAAyHwIsgKwUE2A9zj+uUv4r+mdoVXSA9YUa\n8MyJ2sfxWkUuvPcTdfEhvrWaduPXakDCKqgffrzzsnJsfnQKCvi3ZmXUxYP8BYhso2bjvjMs\nAiyL9DgPsOzt0CrACr2Y0SLDAJD5EGABZKWYAOtQWeUjn4o98mbB6ADL31z9hmeHdh/qd1QX\nqotb1fBke2TbH/WZeomwpHpJ7DceW+QdiErBi3xzo8Ayf0EPu0az8XpmEWBZpMd5gGVvhyYB\nlvoVWP/gB6sMA0DGQ4AFkJViAqxQRMM2hdfEBFgvqd8y1dNcM1uurnlKXd6TE72/T6szpk7T\nwL5UP7fii1dpL48daMhXTQ186MOXcyPvwpnKWHm+alnws05wY56eBAIsWzs0CbAm8g91Q5ss\nMgwAGQ8BFkBWig2w3grEV+WOhtfEBFj+W9QfKL8uOFfUd2pQxK4IblXnBWUjA9/ZfHVrLp9X\nS73Md5265nV1c531oRvZC5+vx1ec/HPgoy+w72cDn764SvnRCXzNkuCP6wQ3FulxHmDZ2qFJ\ngLWchUPCE5YZBoCMhwALICvFBliBeafY0MiK2ADrQONADHbmgLvmTLupSeBDXuiWrZcCn0/r\nfsfwnvX5UvW/A/M0sOZ3DvP5/TcFtp982c13T5t8W6fygY+hAOp44Oisas9hI6/hVyqLvf8A\n//xQcLtegGWengQCLDs7NAmwAjdxsXpdO5x7id8qwwCQ8RBgAWSluADrPjUAeC+yIjbA8v/V\ngsWqsz28dWj0lrwf/f4XQh/mKCFUj7hfZuz+8G+/WzRqQ84q/0L+78zgZr0AyyI9zgMsOzs0\nCbD8TcO/xW/bssgwAGQ6BFgAWSkuwPqF35ZdV7MiLsDyH7unVFS4UHTkvsjGgpu1m9qptzLd\nGAmw/P6HTomJNuo+r9n382U1W8pu9vsf4wvTglt1Ayzz9CQQYNnYoVmA9X4JbYBllWEAyHAI\nsACyUlyA5W+nrJiu+RwfYPn9u+6pHw4Xak36KXrjc5fkBOOS9sGbqfxrWpfLLVO5w2vqh72z\nWkS+pyrTfXP0i/9+Hh6KR866k1+W28IXJwQ36gdYpulJJMCy3qFZgOV/vU7wNzv5bWQYADIb\nAiwAcGLXC0unT53zyPN7dLbt2bpo2gNLX9tr+Mv7P1i/4IF7Zi7b/J3OhObHP149b+rMxz4+\nLiw9CUlihyfeWjj1vsXPaX7TNMMAkMkQYAEAAAAIhgALAAAAQDAEWAAAAACCIcACAAAAEAwB\nFgAAAIBgCLAAAAAABEOABQAAACAYAiwAAAAAwRBgAQAAAAiGAAsAAABAMARYAAAAAIIhwAIA\nAAAQDAEWAAAAgGAIsAAAAAAEQ4AFAAAAIBgCLAAAAADBEGABAAAACIYACwAAAEAwBFgAAAAA\ngiHAAgAAABAMARYAAACAYAiwAAAAAARDgAUAAAAgGAIsAAAAAMEQYAEAAAAIhgALAAAAQDAE\nWAAAAACCIcACAAAAEAwBFgAAAIBgCLAAAAAABEOABQAAACAYAiwAAAAAwRBgAQAAAAiGAAsA\nAABAMARYAAAAAIIhwAIAAAAQDAEWAAAAgGAIsAAAAAAEQ4AFAAAAIBgCLAAAAADBEGABAAAA\nCIYACwAAAEAwBFgAAAAAgiHAAgAAABAMARYAAACAYAiwAAAAAARDgAUAAAAgGAIsAAAAAMEQ\nYAEAAAAIhgALAAAAQDAEWAAAAACCIcACAAAAEMxJgPWFJEnzEzzOS8rvPpng71L4UEnvYts/\nLTJ3iZ9lZ2nOYFYn4jdl+xTXUgPmwo1HbP11VsioMlHCnVBCZWLQhSUzfoSl20CSoBQ1igiU\nsDsQYOlDgJXGMFqmEwRY3oPhlxgCrAyBAEsfAqw0htEynSDA8h4Mv8QQYGUIBFj6do4fP/51\n2z/tjQDLWZozmNWJMBwt+0qXpChJGUnM6Qo3HrH111lIlF1VxjLR4U4ooTLB8Ju0ZBsFStgj\nEGCJ4I0AC2wyGi0L66bjaElG0OlKUdcg9junjKoy1olOrhPC8Ju0JLOJEvYKBFgiIMBKK0aj\n5Q9SGo6WdASdrrQOsNKyylgnGsMvsSSziRL2CgRYIiDASitGo+WGdBwt6Qg6XWkdYKVllbFO\nNIZfYklmEyXsFQiwRECAlVaMRsvx6Tha0hF0utI6wErLKmOdaAy/xJLMJkrYK5wGWAuUfya1\nPafyeT0e3q/ZdPzZ0W3OrVS3+aBHdmvWFj5z/UXV6ra+7c3wWeuv/POZ5ideUz5PNDzgJ8pW\n5Xe/GXlB5WoX3/k1X3Xi6T4NKtXtMHef5sd+XzTgwtp5tZtdv+agZu2OuX2b1syr1Wzgij0W\nK/WEn60IpuHvhzrUrlSv3ZSfzHIX9NbEdudWqt/y+o3BM1RwmSRV/CS8eZ7yw+OMj2xwlm2c\ni4x7irCwviQNCX04VEnJ3g+hT9OVU/p3YDH2dMeciJeHtvTnVlMAACAASURBVKhVpfntyvn/\nR1m9hK/io+VU5Z/7rz6vcoMr7v1Z/bknpZCGKc9YGoptOfGnS6cZOms8NtqcXmka0C3koPSv\nMrHFYdyxWhecTuet/4zZsS03tqlXuVHPRX+bJ86gC4safvVHDNsDiSGe8X/7/e8Mb9Ogyvk9\nVoWO/YGy+m3/33dfWPmc74Or4iuBztkyWMlT8Xh4Oz+lrzo5iiGTRsHZG+lQwsFVXihhpwHW\nouNjQqV3/lvhLdsuDZdpjQePh9bu6h5a2Wdf8Kw9o/xzt2aXo2L6hWjfKFtf8s+uENhJpU3K\nLi8P7rFJeKgtuLdS+OANng+tPTK2YiRJCwtNVuoLV7tAGnzVg79WaZ1J7lTfXh0+RqOtgVWf\nKym88kRw+8/Kri46YHxkg7Ns41xkXIDlv1GSzgktb+NZXRP61EmSOqgLOqdbeyJ29Qltva9w\nh/L/x/hKPlre799QLbil6ga+EgGWifiWE3u6dJuhs8Zjo83plaYB3UJWpX+ViS8Oo47VuuB0\nO2/d4fc/F4Z/7hHT5Bl0YdrhV3/EsD+QGHpH2b41cuwL348c++V9LfmqL9QVepXA/qhhMPza\nO4qJ+GxqCsDuSIcSVle4X8J6qXUaYC0dq/yvUl11nK8ZCo3W5alnud0llfm/QwoCa/erAUDd\ny1sqHWXH5wNnraCBUjcKwnssqCdJrY0PuF35Jd9SSapQtwrfVeXv/2mmHLyeerTLg4VTOFDN\nXO2mdfg/FZ4OrlU7yUrnN6utbp1kvNJAuNqpaXhKKYDK9dTqXfFd49xx22rx9U3aXaymckFg\n5YPK4srgnpX0VnjLb8zgLNs4F5kXYD2u5Cj0h8L9PKe3Bj8cVmradL6ge7ojJ2L/ZeopbNlM\nOZV3fqksruVr+Wg5Z5OyqnJdtWVVfFtZ+e+ePWtIUvWePXve4GYW04JOy4k5XfrN0FnjsW5z\nuqVpQLeQufSvMjrFYdCxWhecfuetN/xuUH8wr6p152nQhWmGX/0Rw8FAYuhjZfvjE5X/Vamr\nJrTmV+rq75XF/HvUNerAqFsJ7I8aBsOvraOYMWkU9kc6lLDhuU9tCeul1mmAdZOUN+7LQv/R\n55srH64IBHsfKTuvOOU3ZenQkw2V1TMDPz6Jn8mXlbH/qK+p1Dt41viJCQff/leVTwuND/iz\nsnl2tTqP7vOfeK+Nsjx6tNRu23H/oU08Vy8GfuZRZfHc1fwbze2jefX7J3xCOvyHF9+uFbw+\nfmC40kC42vE0zK9d4Y4v/f5jr/O+t7tJ7vz+HcqOK969U1na9yg/RqAZFCgVqPYudfFFZeVd\nCZxlG+ci8wKsnzW1/Gqp6vnS+cEPrysb3vEbne7IieB9QEOfUuS/jakgzQqVEh8tJ1WvMOLz\nQv+x1/jJ7BTYa+t0vKHGDfotR3u69Juhs8Zj2eb0S9OAUSFnQJXRKw79jtW64PQ7b53h92Ml\n1q0ya8cJ/65lNfkoY5I+gy4sMvwajBhOBhIjnyrbb5Xypnyn/BWW31T50EY9tvpVZC2p5bTF\n9//iN6oE9kcNg+HX1lHMmDQKJyMdSpikhPVS6zTAkio8FfjwF8/ac3ypsLXmjHynRIGV+QH9\nu6ooleDHwNrfzpOCP/Nf5d/rwnscqSTvD+MD/qr8cPWaXwaWlbi6ZoWOh9UPG5UNdwZ+ppmy\ni8+DPz8+XFd6SlKj0GW47fUl6RbDlQbC1Y6noUaFTYG1e5SYucIe49z5/UrtCP2w/1vlXFxw\nJHDqlLo7lC8cvlCSmh0yObDRWbZxLjIvwPIrbXd4YOlAJenqIZK0M/BphiTV4i1C/3RHCk/5\nO6lG4A8c/3Kpkna0rFZhY2D9X0pNqBAIfhFgGdBvOdrTpd8MnTUeqzZnUJoGjAo5A6qMXnHo\nd6yWBWfQeesMv+2UP/KD37y/UVGSmkau+sQx6MLCOzU4qKOBxPTYoVDmz0ah5V+UhR7SlNBt\nIfqVwP6oYTD82jqKGZNG4WSkQwmTlLBeah0HWENDn/jAriblDWWhX/iHFiqfZvOFR6TgVRzu\nqfBZ46X4v+DaAqXv7GNyQN6rhb/hulZZrvhd8Bdrhm7B4RFlN+3P91WXGkWuJvn9S668fp7h\nSgPhaqemYXRo9TjlwzaT3H2mLIwI72Sl8il468dMZfENv3qhq8I7Jsc1PMs2zkUGBlhjJemi\nwNIrkjRhXvh0dpGka/2Gpzt8IpYoC/eFNg8Ml5J6LseE1vM/n15TlxBgGdBvOZrTZdAMnTUe\nqzZnUJoGDAo5E6qMbnHodqyWBWfQeccPv28pCxNCP8fv8HrFOH0GXVh4pwYHdTaQ2Dr2euXD\n9XxBLb9uoXHRoBLYHzUMhl9bR7FDp1E4GelQwiQlrJdaxwFW+Gm4guqSVJcvjIwqiz15ktSK\nL/RVVn8XWnu8fuis8ZMZuoFOGTSlTX5jPDeV/wl+mCGFLxX4/R0kqXFg6ejPH30V/oXzJamF\nuqB0MIPidqe70kBUZ18h/BzTk6HTbpC7u5WFb8M7OaycosGBRX6R8JJj/u8qmz01qTI4yzbO\nRQYGWPze3d/VpalKVdkmSaPUD0eqBO5pMzjd4RPRS1nYHtr8RdRoWTH8WNkm5dNqdQkBlgH9\nlqM9XfrN0FnjsWpzBqVpwKCQM6HK6BaHbsdqWXAGnXf88MtvKv4m9HOvNWnba4tx+gy6sPBO\nDQ7qbCAxO/ZHoU9HlWPX4Y8XqQPjv0OrDSqB/VHDbPi1OoodOo3CyUiHEiYpYb3UOg2wNHfK\n91A+8q+hL5WkKpHbK/3tlZ6IP0SqxIXnRtbeFDpr/yhjY/vgyhGSVMvschnPzdWhD/wS9JzQ\nB+VPy1o6v9Au9NTZFUo08nHsVt2VBqI6+zbh1f9RPi3jCwa5aytJzTR7UWpOveAiv0g4j5+z\ni3XLIcLgLNs4FxkYYO1V2mDgi2ilWv20t6LUXP3wZnAUNDjd4RPRQArftaVoox0trwiv5n9W\nLVWXEGAZ0G85hqcr3AydNR6rNmdQmgYMCjkTqoxuceh2rJYFZ9B5xw+/Fzt4VNKgCwvv1OCg\nzgYSk2OfF/nYTfnIb5bh5VczvHODSmB/1DAZfi2PYkt8o4hmPtKhhElKWI/TAKtv5OOdknrV\n62BFSbpc81PDldUfKkOj8k/HyNo54bN2YzhQ5l9kj/Cb4LkZFfrwhPLBF/pwg1JSOr9wlSTV\nVxf4F/3V7t8RvVV3pYGozn5YePX7wRpkkLvDyrnordnLFGX9ruDyLOXg85Qa9Z7FkfXPsp1z\nkYEBFi9Rdc6wfXm8UV0eaMf8giu/cmh0ukMnYp8UtX20drQcHl4dKlMEWIb0W47h6Qo3Q0eN\nx6rNGZWmAf1Czogqo18ceh2rVcEZdN7xw+9R5Qc7202fQRcW2qnBQZ0OJMbHvjb62G/6A+XX\nJbTSqBLYHzVMhl/Lo9gS3yiimY90KGGSEtbjNMAaH/k4KzDK82eqtV998stXz/n93yn/aB5e\nfjJ81vgDLveqS/yLbLMJC9TcTNbuYVvow02aAOuo746rGoYmqAlWu4JO6odLxz39T2R3uisN\nRHX2E6JW8xpkkDv+w9UviKgbrM3q0duqR5/st6B/lu2ci0wMsKYHv8t4Sb3sflfw+6xugVt0\njE536ER8qw1KA9/7hUfLyIXaUJkiwDKk33KiT5deM3TUeKzanFFpGtAv5IyoMvrFodexWhWc\nQecdP/zyH7zRbvoMurDQTg0O6nQgMT62ZkIwfmw+CVJUgGxUCeyPGibDr+VRbIlvFH4HIx1K\nmKSE9VLrNMCaHvm4SPm4LnCj162an1ogqTeU8dmYb4us9YXP2okmktRYfUDhNkm6wHS2T56B\naaEPT0qacEwTYG1uLGkFqp3/4K3Bz3mdHgmfNt2V+qI6+ylRq3kNMsjdV1K88AXbL/kjSS0s\nLhAanWU75yITA6y3JKkCL6rJ6k0v+ZI0VvlwtKokPeM3Pt2hE/FJdFN8StKMlvFligDLmG7L\niTpdus3QUeOx0+b0StOA/k4yo8roFodux2pRcAadd/zw+7lkcbVBy6ALC+3U4KBOBxLjY8+K\nfFysfHzCHyi/8Ow4ht207VHDZPi1Pood8Y3C0UiHEqYoYb3UOg2wNE/ePax8XOX3vyuFp0xQ\nLQ+sfid6tWb2sOlS4BGcgjpRhaTDToA1V83ahZ2uHaZoEK52fv/Hw2sH8117znHTlXosOnuD\n3H2oU67PhH7ooNIBSv1NM8zpn+VsDbCO1eCTeqvXyb/z+3cFpk9Uoq48/n4go9MdOhG8lKZG\n9hWeIthbo2V60Gk52tOl3wwdNR47bU6vNA3o7yRTqoxeR6bfsZoWnEHnHT/8fhDzg6YMurDQ\nTg0O6nggMTz2Q/HHjio/k27a5qhhMvzaOYo1nYlGnYx0KGGKEtZLrdMAa2bkIw9bNwQi32Ga\nn3pI+bwxMN+qJizdHDlr/ItC/pQl/yL7B78ZG0HF63wi2fG/BFdfpal2Skfz+uQ2gZz3P2K+\nMp5FZ2+Qu68l7Z0aMe5Sj7rRNMd+o7OcrQGWv5963eOfioHbIJtJFf5SvxVWr9kbne7QifhI\nipqQOF/y6GiZHuJajuZ0GTRDR43Hqs0ZlaYB/Z1kTpWJ78iMOlaTgjPovOOH3y/NerZYBl1Y\naKcGB3U8kNg99np/TPmZdtO2Rg07w6/pUazENwpHIx1K2Csl7DTA0rxRnl/8VKLCn6Topxz5\n31EvBt4nprmw+pjmrHWRpBqH1VvcrvabshFU8Glfl4V/oX1UteN2Pd6Zn7S51iujWXT2Brn7\nVTKeCeJtpYE0laS6Vvc56p/lrA2wlkmS7Pc/FzzdIyTpWb+/e/A7YqPTHToR/Cvc0ZHVq707\nWqaLqJajOV0GzdBR47Fqc0alaUB/J5lVZWI6MpOO1aDgDDrv+OF3pxR1Z7E5gy4stFODgzof\nSGwem89DGVV+Zt20ynLUiB5+V+sOv5ZHMRPfKByNdChhr5Sw0wBLcxscv33/fb//cJ4kXab5\nqVsk9TWjf0pRjwZM0py1deopOVLL/GWtfjtBBX8u4aLIfVyN46qd4pmqSr8TOxuE7koNi87e\nIHcFVQzfrXiouRIp/F5bkgYaH1Slf5azNsBSRrxKh/mEI+ocP0/w7xeOVQu+zsDodIdOxB/R\nfcYEb4+WaSLScqIfVdJrho4aj1WbMypNA/o7ybgqo+3IzDtWvYIz6Lzjh9+CSpLU1m6aDLqw\n0E4NDup8ILF5bN5XRJWfSTcdZj5q8OF3dXj1Q7rDr52jGIprFM5GOpSwV0rYaYCl2V935SN/\nM1IbSap8LLI69LFO1OQWPTRn7UANPvmqT/m5v80PaB1U8Hcvjgz//A+SXrXzz1ZWv21rZYRF\nZ2+Uuw5KOLBPd4cTlS1fqdG5xXS+Bmc5WwMsPknKW7xSqZPs/chnI3pHkuoELpQbnO7QiSis\nHnUq23l+tEwL4ZYTOV1GzdBZ47Foc0alacDg2BlXZTQdmUXHqlNwRp13/CxJyu9Ujjyg8/13\n3/1mnCSDLiy8U4ODOh5IbBybz5K02x9bfsbddITpqPFv5d/l4bU36Q6/to5iJK5ROBvpUMJe\nKWHHM7n/Gvp0TOmKmvAFPgPsi+Ef4i9qlfkCf/YxPD3rvqg3h42QpOqHrpOkIRYHtA4q+OXb\nyNPTkyLV7pdfwmv920IJ1F2pzyrAMsjd5OgA6vvw2X+vonrjaaGsBAcmb1/0G57lrA2wbpWk\nuX9XkOoF/nprKOXtmxd8OYLh6Q6fiCskqWLo9SHqN9FeHy29SrflRE6XUTN01nis2pxBaRow\n2ElGVBmjjiy+Y7UoOKPOO374Ha8svBD6OX5tJPzeoHgGXVh4pwYHdT6QWB6bv0cs3H1Gys+o\nm7Y9avCJ0O4JrTxSX3/4NRwMrMU3CmcjHUqYooT1OA6wot4ZxJ+ZV+8LjUy3NU0KfrU2RxsR\nzIvqDvljA2uraN/+rs86qOCRfPgq6OeVlU/V+dLkBpHZwPz+LZI6tb7uSiNWnb1B7vgtHC3D\nzyYcaVKpR+DZhcMXS1JzfvPcV5WsniQ0OMtZG2BtUE7Ys+FSvl6SXusf/vLW4HSHT8Q9ysKj\noc03WI6WbaJm8YYQg5YTOV0GzdBh47FqcwalacBgJxlQZYw7stiO1bLgjDrv+OGX77pr6Of4\no/FPGyfQoAsL79TgoM4HEqNjPxD6tFUKTmsWXX76lcDBqPGb9mzwh+H0hl+jwcCG+EbhZKRD\nCdOUsF5qHQdY1T8LfNjbXPkQeG2xLEXuB/tQKfr6+/nSt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VxInUhingiwDuvfG6E4u/QR6sRBMqayWyzaYFBT9m/qtEKGO+tUg8q3olje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\nJIyABQAAkDACFgAAQMIIWAAAAAn7PxHizNVDi26KAAAAAElFTkSuQmCC",
-      "text/plain": [
-       "plot without title"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 1800,
-       "width": 1200
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_width=20; plot_height=30; plot_dpi=300\n",
-    "options(repr.plot.width=plot_width, repr.plot.height=plot_height)\n",
-    "plot_1 = plot_grid(plot_basics, plot_pgs, plot_meas, ncol=1, rel_heights=c(2,1.3,2), align=\"v\", axis=\"lr\")\n",
-    "plot_2 = plot_grid(plot_conditions, plot_medications, ncol=1, rel_heights=c(3,1.8), align=\"v\", axis=\"lr\")\n",
-    "plot_desc = plot_grid(plot_1, plot_2, ncol=1, rel_heights=c(5,3), align=\"v\", axis=\"lr\")\n",
-    "plot_desc\n",
-    "\n",
-    "plot_name = \"1_dataset_characterization\"\n",
-    "ggsave(filename=glue(\"{dataset_path}/{plot_name}.pdf\"), plot=plot_desc, width=plot_width, height=plot_height, dpi=plot_dpi, device=\"pdf\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# OBSERVATION TIME"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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IaABaA3CeKntL8Ti+y/b3tCum0PWB7n\nQG0RlyU67lxbm+jy6W/peqpkqqfFvkNCpv0RUup4y/3Jp4jLnLNDSQlncunKDxt5e7baztV+\nApZUjOdVeIp2iEtjXYrRSsAKPHYAENkQsAD0ppX4KZ3m5wEPSp/j30i37QHrf+4PkH7uVvrt\n1rnVDV0DQIbj6sv2eOJdQ/sjvGSaov9zjUzSXOuvlax7w5nlUhq26TVY1F5uwJIuc+g6TYJk\nj7g0ymcx/AJW4LEDgMiGgAWgN3dLH9M/+n6A9KVfJftte8DyODdcOoKV7Lrk442drSUJQFhg\nXyh9/1bmWzlX3gJWkZTWXpFuSifkl07j8Fs96flrri39ylL2Se4PiDdL5nNw2i4ujfNZDL+A\nFXjsACCyIWAB6I00dRTt97n+D+lSOAPtd+wBy2O6gFXisloef/bnY9OaOiOW/do1+8SbPmch\nFXkNWEvFhfbZPWeIN0tnUp8qPflgl8nP5QcsaeL0Gp4FSJfmqeizGH4BK/DYAUBkQ8AC0Juf\npMvRtPS5/mEpGBy037EHrPfcHyH9irDM123FvlglTYVJ0V+I96TAYL7ipxSvAesLceqrmuKZ\nXIW1im+Z/utccUk6e6yt2xOelBuwpC8DYzwLkOY9aOKzGH4BK/DYAUBkQ8AC0B3pcjRuF/xz\nc4u4ttwv9jv2gPV390fcJS7r5PWPL94v/YF0cWb7nPHv+6nEa8CyXwZZ/G3hP8QbXUuWvyg9\n37Nuj90oN2DZ58HynLH0TnFhN5/F8AtYgccOACIbAhaA7ti/I7zVx1p7Esl23LMHLPe5QO0J\naJLnHzpMEFdKP9e7KJ2WdcJPJd4DlnQOvXhVGelKNaWzn+8U7yYXuj32DrkB6yvp9jMeL5Uh\nLpzmsxh+ASvw2AFAZEPAAtCdK3Wlj37v13u+IK00OS/AbA9Yw9weci1ZXOZrqlLp+s9mKQY1\nF2+O9FOJ94D1S7Q9ol2tWPzPxAsly6WTs653e+hvCXIDVlEVLynoV4u48KjPYvgFrMBjBwCR\nDQELQH8elj76E719SVg4UlpXcs1je8BKcrsQjv3rK+dlX748X+ROupqgFIumibcqXHRf/a7L\n13TeA5b9knufFD0n/sPlUsvS3FBt3B65nGQHLOl7TY8pDqTp3YXvfBbDMWAFHDsAiGwIWAD6\nUyjNHkXlXymz5tooaU1qySQO9oBFJ10fNFtcUvFq8a3PZ2aleB7KuiKeQ58g3XxP+tuNbquv\nXmdqtezfjjs+ApY00+kG+48GXS7PLM0O4TaB14fSE1B1530pYJVeq88t4jwv3XGfNv4WcVF7\n38VwDFgBxw4AIhsCFoAOfS59yUcJe91PaCr6qre0XMgvWeIIWPUvlz7oV2m2zzHize/EMFXz\nD7fn+Lu4NsN+u410GOZL19XSMadWjjs+AtYlsbxuReJFceq5LM6XSvmidMH3TUj6ijDGuRnS\nYZ8RJevdI05j8U5L1y3OldY7T/LSVsAKOHYAENkQsAD06Ikoe3Bq6XpE5/cVsfalK0uX/Wxf\nQneVBJNC6bxyx/WfpUNAt11weZI/bhAXLbXfeUqccYEau3yxtVdakue45yNgSSfKl3tdXLnY\nZek56W+zS+6/XY9ImqaBnGeMLRDvlE5M6h5xDkn3ppc+3/vSaVmNnHMhaCxgBRo7AIhsCFgA\nupTrSFh0/QOPfXr+2l/fv7xrcJxjkVuoEReYexenKMfs6T8PlB5zh/2eNJECNTzpPMBV+IR0\nnCj+G8d96XeAVOm4I8R8MlS6XzLzgq+AJT1tVvH/hM9dF2dJfz3NfsTsg3vNREOKUsVFYxwP\n2Cs9QDp7/1qRZ8Qp6iPdHeqYV+vqw9Kfml/0UwzPgBVo7AAgsiFgAejTS1XIu3IHXR/2nbgo\n9vNEIkv7B7c9tHKgPYUlOaOC/bo7FJ81cf7yRff1rWS/u8v553/caF9Q8a4HNy6/O9N+p2bJ\nGV6+AlZhXUcxHd0WP2VfmDxgxpRBTcRb9X6xz9NAbWbeU1DkPHGJGt/Rs5l4ZpVHwPq+hnQ/\nptvi3XtXDa8u3RG2lDy71gJWgLEDgMiGgAWgUz8O85qvOrpPbinFgEpFBVFuD4p+3rn+6kAv\nz+H6FWO7MmsbflGy1lfAKprneKzHBX0muz9Tzc+Lip503pHOB29Rsk48VckjYBV9Us+zFuvh\n0ifXWsAKMHYAENkQsAB065VegucHeMtcj8f8R1zaoKjo8Youj6rjOpv6lkSP52j0hOsTXF5S\nzm2tZdrvpSt9BqwP7Q+O/d198ZWJrk/VTTo/aYJrwPq/aH8Bq+iXUe5b3PZllyfXXMDyP3YA\nENkQsAB07MsV7WNKPr7Nzee8VeYR0pxXNxff+GmO8zvF+gvcfzb42/p2lpInSRhwxvMCej8s\naVKy+rqFX7mu8hmwHIeihpdZ/nh7R0Sy9PibY9HhTinmhNo9n5Pu/KOh45X6FnkJWMWBccp1\nzlpSB/7N7am1F7D8jh0ARDYELAB9u/RO/o61S1ZtPfl/fwR4ZOG7x9Yt3XrqHS+rzr9+ctvq\nJev2nPmk0Mva4pzw5O5VSzfuf+Jc2OUWFZ3L37F89e7nfvOx+tq/ti9dsfNxP6/09aMH1q7c\nc+Yt76VqDsuxAwDtQMACAAAAYAwBCwAAAIAxBCwAAAAAxhCwAAAAABhDwAIAAABgDAELAAAA\ngDEELAAAAADGELAAAAAAGEPAAgAAAGAMAQsAAACAMQQsAAAAAMYQsAAAAAAYQ8ACAAAAYAwB\nCwAAAIAxBCwAAAAAxhCwAAAAABhDwAIAAABgDAELAAAAgDEELAAAAADGELAAAAAAGEPAAgAA\nAGAMAQsAAACAMQQsAAAAAMYQsAAAAAAYQ8ACAAAAYAwBCwAAAIAxBCwAAAAAxhCwAAAAABhD\nwAIAAABgDAELAAAAgDEELAAAAADGELAAlHdhfovMzPr162dmZk79mXcxPp3Ln9GzuE67Hnev\nKPiWd0UAABELAQtAcU+kUUyzoUv3LB7dNZWSN1ziXY83H89oLBCRJSUtI6N+/VoxJKraa/kr\nV3lXBgAQiRCwABR2diiZep8osMu5qxzV/xfvkjxdze0qkLXJoIWHC5yObJo75KbyxSErqc+2\nr3nXBwAQcRCwAJT1bmVK21BQ6pHuprineBfl7m9pROnTcgrKOnB/50pEQuay//CuEQAgsiBg\nASjqnYrC8Dz31DLXEp3HuywX3w8hc5dNXtKVw55x15uIbljzDe9CAQAiCAIWgJLeriiMLxNZ\nFkVbjvAurMS+ZKrnJ17Zvy+cepOZTJ0OXeBdLABApEDAAlDQv1OFu70ElpWxpgO8S7O7NpFi\nxuR5KdHT4fHpROXv+TfvggEAIgMCFoByPqsgTPQaVzbER/8f7+JEFwdSzX0y4pVkZ78korbH\nL/MuGgAgAiBgASjm4k00zkdYmS/U/R/v8oqKfu9M6Ufl5qtiubMzBKq25HvedQMAaB4CFoBi\n7qUOPrNKf+pbyLu+czdR5qkg8pVoR88Yih7zHu/SAQA0DgELQCmnqfpJn0ElrzGt5Vzf5Y7U\nMTfIfFXs+NhKJHTX2EwTAAAag4AFoJDPkqxb/OSUh5OiXvL79wc7d+6s6M/2JlALW/D5qlj+\n7HSizBOY4x0AwCcELABlXMykyX5jylKhzp/+nmAhEf2uYIFbqdYJvwX6s6aVQPV3/qVgeWr5\n9d1H9x7++9vfcv/CFgD0BQELQBkL/JyAZdeX5vt7AoUD1tOWhD0h56tiO7IsVGWNkglQed/v\nE38YaVd1nM1v3gUACAoCFoAiPopOPh4gohxPjvnUzzMoG7A+SbEsDydfFTvQJ4aS559TrESF\nXdjR0kRU8cYud94zbnC3lvFE5YZ+wLsoANANBCwARWTRjIAJZSr19vMMigasqzeT9xm6gnJk\ncDzF3/+dUkUq6afFFcnUKHt7ybbkLb+9KplHfcm7MADQCQSsIPzwG+8KIGIcoYzA+cTWiB7z\n/RSKBqyN1Dr8fFXs5Khkipn4uVJlKuW3GbEU2++A5/6YVZ2sU//gXRwA6AICllxXznQ1mdLv\n3PAx70IgEvxSOWqXjHiyyVT/os/nUDJgfRYXf5BJwCooyLm7ElmGR9TEWIUHq1DKSG/f4OZN\nqUjXaWKOfQCIdAhY8lxcXJ2ofuMYonL7edcCEWAiDZWVTnrScp/PoWDAKsyi+xjlq2K591Un\n0+2vKlOqAt5oTdbBp31sTE4vIWo5JqAAgLAhYMlyuS/FdNtYUJC/fWIsjdHDb9NBUa+aquXI\nyiZHE+J8XndGwYC1jzJCmwHLB9vsNKJOTypTLGNXFlmopb/fTy5KpvZneVcJABEPAUuOq4Op\nqfP7hN116EZ/P/0CKCpqR0tlRpOxNMPXkygXsL4tHyP7Cs8y2RZdT3Tj0SuK1MvSRy0pZYH/\nbTnckupE1FeeAKBFCFgyXBtJ6aWXPDl9K6Xil0bgj40y5QaTnOS4H3w8i3IBa6DPa1CHY11r\ngWqt0/gvQXbEUruAl7e2DaLyz/CuFAAiHAJWYIWTKO2Ya/sdSTf5PjMZ4GpTwd81ctyNoVk+\nnkaxgPWqUJ/pF4QldnazUuL0z5SomY3zgyluupwtmWKxHuBdLABENgSswDZRzcPu3bcjTeBd\nFGjYAeokP5ScLh//k/enUSxgZdGSYGJTMA4PTSJTn6c0etmZDxpRg4fkbcjSOGE173IBIKIh\nYAV0NjHuYY/me6oWHeRdFmjWX7WiZH6KS0bTHO/Po1TAeoqaBVFesHKmpBE12vQ/BQoP18kE\n6nFG7nZsS/HzA08AgIAQsALKprFlmu+ucrFv864LtGod9QkmkZxKSvB+tZl/rFq16hL78gpb\nCuuCqS94q9pZKOaul9iXHpZrcyl6WhBbsasCLeZdMwBEMASsQF4WauWWbb5zhPrneVcG2vRL\nSuzhsv/G+DGSHlCzvtN0c1DlheLgiEpEDZZ8oeZ2BXD+dqok/8w40Z5UWsC7agCIXAhYAVy7\niZZ5a7691f1QhMgxl4YFl0ZOJSb+rF55VxuZtgeuKWy2RW2jSOi428f5Zar7OoMaBZd7Cwr2\nVkLCAoCQIWAFsIfaeu29J1NiMFcDeHEuIelUkB/kI2ilevXtp6wgywvVsYnpRJZu+1VMjz69\nWoWyZJ9+VeKhSrSed+UAEKkQsPz7X8VoH+crT6HBvIsDLZpHI4P9HD9qranaBJ2Xawd1Bn6Y\n9oyoU5yxsrZ9o9bm+ZAXKwS9V0S7ywv7OJcOAJEKAcu/+31+3WNLE/7JuzrQnl+SEoI9gFVQ\n0I1OqFXfUeoeStII3Y5haUTCTQv/j+PUDVvN1rmhVb8l3nySX90AEMkQsPw6n5Tk85JyK6il\nRmf7AY4W04jgP8a3CW3Uqu8mYWdoUSMMD41taiKqOiZHoSsrBnBtBiWuDbX2tTHWx7lUDQCR\nDgHLrx00yHfrbU2HeNcHWvNbcvyJED7GM+g1dep7gVqEGjXCcnRGh3gi663rP1ZnO11cHExV\nd4de+dKouFdUrxkAdAABy5/CJuYDvjvvHkuNP3lXCBqznO4M5VN8Pg1Tp76+tDzUpBGuvJUD\n6hDRddOfUfWK0L/dSunB/nzQzRwh9UM1CwYAnUDA8ucZauOv8/ajNbwrBG05nxp7zN+/Mr7Y\nqlq/U6O+j01poaUMRvZPamElqjDS9pcaWys6eyNlBn9SnJuJVOdbtcoFAP1AwPLnDv//uX8k\nuhou+gyu1vj7Ttmf8TRfjfomUTBzmSvi9PxuSUQJwx9X5TjWJ2l0q5eJgoMzmJr9qkaxAKAr\nCFh+fGWpZfPbeHsRfsMNLi5Ujjka2mf4ydhKKhzU+TkuJey0wUD+yj6pRJWnvKH4Br9Vme7w\n/x6Ww9aFbsF/SgFAkBCw/JhLk/w33n3mhtd4Fwkasp36hfoh3pceVr6+FXRXqPUxZlvZPZ6o\nxUPKnsX4QpJQ9kKiIchrSQPwTgeA4CBg+fZXxbhAJ290oFzeVYJ2XKkb9XCon+G7hHaK13ep\nWqgH2JRwZu6NApWf9rVy25tfzjydTa2nG9E9ytUJALqEgOXbQeoTqO9upta8qwTteIS6hv4Z\n3pTe93i6z5566qmrLOs7TT1Dr08Je/onkXWk53az8rDFuoBVpUdr0nKFygQAnULA8q21sCtg\n372R/sG7TNCKwqamMOZbmk73ezzfQiJiOjVnD9ocen3KyJlclUz93mO5lU7rhbhV7ArdX0HY\nr0SVAKBbCFg+fS00Cdx2l1Ev3nWCVuRTuzA+wXPiK15yfz7WAetrc70w6lNK/ux6ZB71FcPt\nlBTOpeQtLOvcGmexsS4SAPQMAcunjTReRtutLyjyX98QgVqHd4CoF3lc9Y51wFpCE8OpTzG2\nudUpZsZvDLe0qOjqeKqyh22ZK63lXmRaIwDoGwKWT20FOScsz6ZxvAsFbXieMsP6AN9CXd2f\nkHHAulbXejysApWTd08FqnqU3aYWXRxAdQ6yrnKeOenfDGsEAJ1DwPLlv6ZGsj4aKsTzuYIt\naE13WhneB3gD0xduT8g4YD1NncKrT0mnh0TRrR+w2tTfs6hRSDPq+zdVqPIpqxIBQPcQsHzZ\nQvJm0BlCO3mXClrwJjUM8/N7ssds7owD1lBaEWaBitqdQdZFl5ls6Q+ZdNNpJWocTWmqXNEI\nAPQAAcuXDsJ+WT13v+lG3qWCFgymeWF+fJ+MqeE2KwPbgPVzTNXw5zRX1OxkyniLwZZ+3oDB\n5XG860+Nf2RQIQAYAQKWD2dN6TJ7bgt6lXexwN8n5gDXVZKhCz3q+pRsA9YWGhFufUo7egtF\nLQz7INZbVamfUlHS1pMy/sdibwCA/iFg+bCdRsvsufNpDO9igb/xFP6k4Wupv+tTsg1YN5iZ\nn/XN3rwUygzzTKwnE4RRyhVoy6JWOOkSAORAwPKhk7BPZsvNT41j+wtziEDfRVdi8K1U9ehf\nXJ6TacB6nVqEX5/yjnakctsKw9jOh6MsjC6P411+e+qg7AUUAUAnELC8+8FcX3bLHUrbeZcL\nvM2UNWtaIMNoj8tzMg1Y02gugwJVMDOOepwNeTOXCrHLlK0vtxV1+oPVXgEAHUPA8m4XZcvu\nuAdMN/AuV32/vve3PRuf/ZV3GVrxS0ISi5+t7RXauzwpy4B1rVpsDoMC1XCgGVUsCG0rL42m\nCluVru9MC+p4ntFuAQAdQ4ZF8ZkAACAASURBVMDyrjMFMQ10S3qFd73qunq8OUmE9LtwLUbR\nMhrO5MO7kfBF6ZOyDFjP0a1MClSDbZRFmHwhhI38qT3VOaB8fbmtqB3OwwKAQBCwvPrVkhZE\nw11Ao3kXrKa/dtUjoWnnoVOm9W0cQ9ThCd4F8fdnpXJs5rWcRMtKn3VlcnIyq0Ml42kRkwLV\nsakGNXk76G18L41anVKjvNzW1BrnXQJAAAhYXuXSgCD6bX5qvIFOyvigPlk673Bu+vIMoswX\neNfE22a6g81H99GohooUeLlCUh6bCtVxqhtFr70W3DYWJFJ/lWb6ym1LN/2kyI4CAP1AwPJq\nMgV1puwAOsi7YtU8l0zd3b6GWd9KMM1hMwN3pLpYw3qI0Ud3a3pNiQoLqCejAtXyQCLd8mUQ\nW3hlrhA1TbXq8m6hRt8osaMAQD8QsLy6LjqoM4J30q28K1bLw1bzPZ6bv7IiZX7IuzCedlMv\nVp/cc2mKEhUOo1WsKlTLoZsoSf5/t3zXkSqtV7E6221U+yMl9hQA6AYCljdfUvPg2m26KZj/\n2I5gC4TYJWU3/3gHijXOMbwyrqRZmJ1afSa+0hX2Ff4Zn6rxy+R4YZsUQ7d9LW8Dn65MLY6q\nW95gqsziuj4AoFsIWN7skz2Nu8MkWsq7ZlXspIrbvA7A9FhhOe/iuDlIXRl8YDt0d79cDhvH\nqR+7CtWzuyklbpcx6+j5yYI5W/UEOVZIfIb9rgIA3UDA8mYwbQmu1x6z1g9n9ulI8Up0vK/Z\nK7ZVoHuCPCmZvR+fP7ln9dxlO089+18VX/VaQ3MQc3oEspqGsi/xdtrErkIV2SbGUvt/B9q6\np+tStTUcqptmsR5lv68AQC8QsLy4llo+2P8cbkcGmA/qx5rCAp8jsL8mDbzIr7ZLzy7sXYNK\n1Ry86R2VXvoEdQrvg9qNrVIc85+k/hJdnWGFqjrQgsxj/U7s/sN4QbidzySqi2OEdaz3FQDo\nBgKWF29Qx2Bb7UIay7tqxV3NoiF+huBoQ8oKZXZIBv67s298cahKaj5gzJS5S+dPHT0gU7x/\n42Y1fkpf2EzY4WdYgtafjrMucT8NZVmhuuZVp/hlPqcDO784garzOHwl2ViepnE/bgsAGoWA\n5cUqmhpsp81LSeIULtQzh5r7PbB3+ibqxuEY1rldHU1ElXo84HaiuW3HlEwTWQcqfxgrj9oF\n+2+LX5uoH+sSexLTCKiy3PEJVH6W10kR/thamRLGnuFX295qNJjjcVsA0DIELC86U/A/CutH\nR3iXrbBnhEoBfqd1pjn1vqRuUVfyekcRpY/Z6a2eg9k1yDTsE2UruHaDsDXof1v8qh7DeJbw\n36Jrsq1QbccGJ1DUsGc8J1t7bXwiRQ86zrW0I+nU8Re2uwsAdAIBq6wLMSF8Hm2jrrzrVtaV\npkLAb2JymlF/BSYZ8OmTOVWJao3Y67Mg2wO1KGrCOSVrOEHtg/+3xa8hrKetPUKDGJeoutMT\nqxOVH3qs5DjWT/mzbyBKGXiQe2WtqKnMuSQAwFgQsMp6knqH0Gjrm7/lXbiitso5lftUYxqq\n1jkpV/O7mSi2+wb/FdmmVabKpxWsopHJ69GzMGynnmxr7E8bGZfIgW1R9xQiim12x/jBfTs3\nLL5pajFPC5f/ye9O1d9lu8MAQBcQsMqaSfND6LNjSdc/KDqXEiPnYMGJdJqkSj0/rqhNdN19\nMq7tmzs8igb+oFQdBykrhH9Z/Ksd9TPLEi/EVYq8WUa9sW0YcnNtq/Qj0eimgxawubw2AyOE\n5JdY7jAA0AcErLIyLCdD6LKHzBm8C1fSJLpL1jAcrUlLlK/m9ZExZO0S4OBVie3XUWq+MoVc\nrmfx/QVlqIbTXpY15lJf5iXyYzuwbe9RGblaTfeYYhWYHRYAIhwCVhk/Ck1C6rKZpOMvCt6x\nVJX5W60DqbRb2VouH2tDVGV0EAcw8kdahVmKnBy2h7qH9C+LX7ups/TkuzIzM/8Mv8YRtJp9\njeDqAavFwJeKAgDvELDKOON3tiff7qfZvEtXzq30oNxx2JFgPqNgJT8uq05Cxrwgv/TaWJk6\n+p2uMjQXa1qZXYXQRX3z9+KzLySi38Ou8XL5ZH18Q6hlK2KFrWHvKQDQFwSsMmbS4pB67KmY\nmrqdczCXMuQPxNromBeUKuSt0TEU0yOEWZ2OtaCq7Cfb3xTS7yECGkXbxGdnE7CeoB5K1Ahu\nNicJG8PeVQCgKwhYZbQVQpxZpxM9y7t2pbQIaq6nBebk95So4mpOR6JKwXw36MJ2lyn6EcYF\n/a9CzKGQigngIaG9+PRsAtZ4WqpEjeBuW3laG/a+AgA9QcDydDGmdogtdgmN5l28Qp6jFkGN\nxBShJvurLf+6vi7R9XPzQ9w7BQWLygkL2V6SexoND7kavxqaxDk/mASsq5Xjc5UpEtzsSKaV\nDP6dAgDdQMDy9HLIpy3n6/ZyOT1pZXBDcSdd/yvbEj69L4GsXbaEuGvstlakO1le1+Rja0WF\nLjI8hsQzepgErBfpVmVqBA+7Umg1g3+rAEAvELA8rQ/+QoROt9MJ3tUr4l3humCHoht1Yhll\nXh5gpvLDDoe6Y5wO1qf2/2NXVV+aEW5FPuwXOhYxClhTaZ5CRYKH3SnC/vD/rQIAvUDA8tSf\ndoXaYDdTb97VKyKb5gQ7FHktaBCrU/6v5bUjqjOVxSV9T7ei65l9efkspSv287zrzGcZBay6\nMQodZYMytsZZbCz+zQIAXUDA8lQtMfRPzVpRP/EuXwHfWKsGf+LT6XSazOTVrxxqTJSxmFGU\nye9KtT9kUlfRtQxhLZuivBhN29kErHfoZsWKBE+rrOXY/1QVACIUApaHL6hl6P01m/Q4Gc79\nNDGEsThakxaH/9oXt9chU4fNoe8ST7bBlPpq+HUV20cd2JXlaZ/QiU3AWkb3KVcleJpnTtbx\ndMMAEBQELA9HKDv09npAaMW7fvZ+TUwK6UumAxVpZ5gvfWlnTbJ03RP6DvFmghD/dwbD8lNF\n60NsC3NT3/wDk4DV0hT2mWsQhClCbT0exQaAECBgebgn2B/MuWlGH/HeAOZW07DQBmNHgjms\nk/4v761DUb3Zz5Q+yxJ9OvxhGR5OEg8sm3axCFjfCo2UrBLKGERZilyUCQAiDgKWh+aW02F0\n1/toPu8NYK2wvvVIiKOxLiYqN/TXPXkdWXoqcSGagsUx5rCvpvw4peUpUZvTHurMImDtopFK\nVgll2DLp/nD/5QIAXUDAcnfe0iCc7nrCWpftVJb8vUDtQx6O5dHWR0N82advIlMXpb6DWxMv\nrAlvVM7XNm9UqDiHNMtPDAJWT9qpbJng6VhVOhbeTgMAfUDAcvcs9Qmru3agF3lvAmPZIV6a\nUbLUGv1EKC/6bjei1iFccVCurSk0M6wkfB/doVx1khG09/Xdu3dfDqfKovMx1RUuE8rYGhP3\ndlh7DQD0AQHL3TKaFVZzXUDjeW8CW7/HpYZ+cZqCgkVR5Z4O+jXPjjNTk3Vh7YdA9lSlkWGc\nKvOyqWo43yTLsYu6hV6fU47iORDKmiU0+CP8fQcAkQ4By11PCu+sn7ykZJYzmPO3jwaFNSDz\nLDE5wb3ihWXxVO2BsF5UhkNp1DvkCxv92VhYrnSBBXWifg61vhLZtFrxOqGM3jQp7F0HABEP\nActNYUpqmL21DzH4hZqGtBXCnCVhQbQ5mNkaCo/VooSxKlye+HhTahfqZXOyQ75eZRCG0YEQ\nyytxNTUpnMOPEKKc6sKT4e47AIh4CFhu3qd2YfbWDdSX90aw9JHQNNxPm7UJtEj2673cmix9\nj4X7krLk3EyNPg9pUPZTXRUuP7ODeoVUnYsXKUv5OqGsdeYav4S78wAg0iFguTlAY8PtrTWt\n53hvBUNzQr/0dYkdqTT+kqxX+2ywQK1CvhRksPJvo8qvhDAm78TG7lajvprRv4VQnasZpPhX\nreDVIBoR5r4DgIiHgOXmHloTbmu9i7bw3gp2rlYrdyr8T5sDNanll4Ff7Ofp0ZS2LPyXk2+s\nEBvkCWLFfk+n2apUN5iOhLDLXF1nZbD3IAS5aXQmzJ0HAJEOActNa1PYn0gHTDfy3gp2HqMu\nLD5uTrSjFFuAlzq/LJlSpzK6prNcD0SbVgY7XcNQuk2d4jbTHaHuN7sPqYU6lUIZW6Mqhf8b\nBQCIaAhYrq7E1gy/tWbSW7y3g5kBrH6FNiFKmOHvR3t/rq1IcSNUOLPJw/oU6h/cXJ5zqcEZ\nlYqrGhver/3X0mSVKoUyhtM9Ye08AIh4CFiu3qFO4XfW2TSF93aw8ke5qqwOKW2oTLVP+nqd\ncyurUMygo4xeKigHG1HjD4MYkrVU+aBatfWnU2Htvg7Cw2qVCp5yqlreCWvvAUCkQ8BydYDG\nhd9ZzySkyjulW/tOUv/wx8PheF8LdfR6bO/9CbEU0+8ws1cKTm5PSpI/s8Y+ITnMaSuCsJ6G\nhLP3zpnrq1YqlDGPbgln7wFAxEPAcnUvk2/EeoV54EE7BhPL+dR3NCdT1+Me87B+s7GNQKnZ\nXI5eOUy10iiZXxOeNsdvVa8wW8WEv8LYe4foTvVqhTKa04kw9h4ARDwELFdtwj/Hvdgm6sl7\nQ9j4KyGV7UnnC64jSrn3zGeO88q/zltYnK6ExjNVmFfUn611qO5LcgbkWHR02L8yDUZfyg9j\n9w2kzWoWCx52Wmr9GcbuA4BIh4Dl4iqLc9yL1TV/y3tTmMgP88rXXmztm0hEia07d26bmVp8\nS2g0LrxrEzFxpp9gnhXwhPLC+ULMElXrWk13hb73LiUyjscQpH60IPTdBwARDwHLxbssznEv\nNo5W8t4UJu6ilUzGw03ugmFtqgrF2YpSWw55ULUTxgNYnko1jvsfjT/6U6Utqha1cXJcGL8j\n/Dv1VLVa8HSifDkZ078BgF4hYLl4OPx53CVHLOm8N4WFy8nJSh0CyTl2UqFnDtXJOyx0i7+f\nfX18IzV6RN2ahhan0OAnQnW6lxapWy54upfGhbz7ACDiIWC5uJdWsWmsbUjWKT0a9zj1YDMc\nkWHnjWS64zUfY3F+TjRlqTX/lZMYsEK/4kqdGPWnFQM3eVWjQrvaJQDoAQKWi7YCo+MqCymb\n97YwMI6WshmOSDGvLlHWk9fKjkThkepUYYbq9YgBK+VyiHvvbWqtesHgYSqNCestCACRDAGr\n1NW4Goz6qq1yTORf8flqxYQ8RuMRKWwLmxJVu/dF94z1wYIGFDWAw1X9xIBFT4S4+5YxuEw3\nhCmvWtRn4b8TASAyIWCVeo9uYdVYR9Ia3lsTtmepM6vhiCBrOsURVb1jie1LcWqsX189OLc5\nkbXDbh61SAFrbIi7r5XAa+pWKDWNRrF8TwJAJEHAKnWQ0TnuxY5Y07x80xRZ7qEFrIYjopyZ\n3yme7Kzi/5ky7jvOpxIxYCWmXglp7501NeJTNLjKr275hPH7EgAiBQJWqSkMZyXoRI/x3pww\nFVaPVfukbs2w7Z07pFVG4/r1m/Ucv4TfgSAxYGXRUyHtvt00klvdUGq6Lk7HBIBQIGCVasfq\nHPdi66gX780J0xvUntloQEjEgDWPxoe0+3rSLt7lQ7H8GpaPGb8zASBCIGCVYHeOu6ieKcJ/\noL2UpjEcDgiBGLCOJVQM5TvC36PZXJQAwnV/iAkZACIeAlYJhue4F4hzDM7mvUHhaS2oPK0m\neBID1sku9EwIe+84DeJdPUjyUsv9xPzNCQCRAAGrBMNz3IvlJKT+xXuLwvGz+TqGowGhkALW\nYro7hN03lNbzrh7ssmk583cnAEQCBKwS02gFy77alw7x3qJwHKWhLEcDQiAFrNyEyleD3nuX\ny6fgQs8acTS6eqiTxQJAREPAKpFFx1j21V1CRiHvTQrDcFrHcjQgBFLAKuhMzwW99/5urKsc\naVsPOsL+/QkA2oeAVaJiKtu+2jqSZ2q4VjEJh0B4G1elSpVTBYtC+I5wMi3mXTw47RRaKvAO\nBQDNQ8By+pZasO2rG+hm3tsUupepE9vRgFDlJaUG+xVTYY24XN5lQ4lM+qcib1IA0DYELKfH\nmf/uKoNe5L1RIVtAMxmPBoSqOz0e5N57jTrwLhpKLaZBirxJAUDbELCcVjOPFMupB++NClkL\n01HGowGhWhH0ZOAP0izeRYOLOuYInxQPAEKBgOU0nLaz7qvpwpu8typEP+BKdtphS0kMcsaP\nphZOV08EryZH+qR4ABAKBCynG6LyWPfVeTSQ91aF6CCNYD0YELK+lBPU3vuYmvMuGVydjqsS\n2iW7ASCSIWA5XLamMe+rttqmD3lvV2gG0ybmowGhWh9kUF9Gk3mXDG66U75C71QA0C4ELId3\n6Fb2ffV+GsV7u0JyJRnzVGqIrUrs78HsvmbmI7xLBjfrqa9S71UA0CwELIfDNJp9X82rbn6b\n94aF4p/Umf1gQMgG0eEg9t77+IZQc2pbzir2bgUAjULAcphFSxXoq/OpE+8NC8UiTNKgKVup\nVxB7byFN5V0weBhDaxR7twKARiFgOXSnw0o01uZ0hveWhaC9cEiJwYBQ1Y46J3/vNbZgig2t\nORqVHskXzgKAUCBgOVRLVqSx7rCkBfkTew34w1pXkcGAUA2nPbL33tusL0kADLTBbO4AhoOA\nZXeOMpRprLfRKt7bFrTHqJ8ygwEh2iN0kL33HqTpvMuFMhbSGAXfsQCgRQhYds8olSmOJSR8\nx3vjgjWNFikzGBCqRsKncvdeAytmGdWe/NT4oH4JCgCRDwHLbpNiJwaPp9G8Ny5Y11tOKTQY\nEKIpNF/mznuDbuZdLHgxiPYr+qYFAM1BwLIbTZsVaqy5NYUIm2TwrNBUobGAoGTHx8c7ou7J\nmFrX5O292fgFqCbtEdor+7YFAK1BwLJrYT6jVGfdGJXyJe/NC8phGq7UWEAwhhLRScftW+kp\nWTuvsG40Dj9qUiPhK4XfuACgLQhYkquxtZTrrBPo5suyqvj+n7nbFizY89i75xXeXP9G0jrl\nBgPkcw1Yy2mYrJ33CrXjWjP4MoFWK/zGBQBtQcCSfEgdFGytben+gBX88di0puQU1eOhIOY9\nYq1mHPPLXkMoXAOWrXK5X+XsvEn0ANeawZdHzDcq/c4FAE1BwJKcpLsUbK3Hqwo2/6//4oiY\n4ljVrE/21EWLpgztUqf4TveX1dn0Mj7EWdIa4Rqwiu/slrHzLpRPyuVZMvh2I32g+JsXADQE\nAUsyjxYo2Vo3RqW84fvFf1nXkKjS7Ytczp3ZObwuCUP5nLOxje5WcixANreAtU9oLWPnHaI7\neFYMfkylBYq/eQFAQxCwJH3pgKK9dYqQ+KyPl/56egJZ2iy2ef7J8rpUbt4FVUfB7nbaqehY\ngFxuAaugmZwDIB0E7DytOm5toPy7FwC0AwFLUj+2TMBha7olOsfbC781PIqShj/i7U/y7y1P\nzT5WeySKrpRPVXYoQC73gDWNZgfceR8JjTnWC/61pv9T4Q0MAFqBgCW6YE5XurkuiDaXuZzc\ntfxORNUm5/j6mxNdKEn1a0W/TFlKjwXI4x6wTsVWDnhZy1mKzZcL4Zst48cuAKAfCFiiN6mL\n4t11TTz1eMn1Rb9a24Co6YN+D51NsQozr6g7FstxKTutcA9YBX3ooQD77nKVuNP8yoUAcsrV\nkDlbLADoAQKW6DCNVr69bk8n6vTMVekFr3y0uY1Alk6bAv3R5irUQ90TsboofDoayOYRsPaZ\nmhb633dnqAe/aiGgTvS8Om9iANACBCzRA+pc3XhpU6Kourdk90mPIhKa3O311CsPx26gTmrO\nO3optpri4wDyeASsgjb0pP+ddxtt4FYsBLaIJqjzLgYALUDAEt1O+9XpsKtuqZ9Y/KkZU6/D\nWLnHiXJaUOtf1BuKf1A3RUcA5PMMWGuom9999405jVutIENuYqrKX/gDAEcIWKLryin8I0JX\np7YfDOrxuW2p+U+qDcVSXCtYMzwDVkG68J6/fbeIJvAqFWTpLvOKkgCgBwhYxf4yX8e78/qT\n34kyflNrLLIouPgHytk+e/Zst6sWzaYxfnbd+Qpxx3mVCrIsoYlqvZEBgDsErGL/ps68O69f\ntizqKu9y0WG7WK46760Fn/Iqxfzge9+toUG8CwT/chOq4HeEAIaBgFXsiBo/IgxHbnO6M8AP\nyBh5Hr9D07IxtNDnrvurasxh3vVBAFn0oirvYwDQAASsIvFKhAt5N94ATqbRfFWGYhHN4r2t\n4NuJ2Iq/+9p1W6gf7/IgkPl0nyrvYwDQAASsYv3oId6NN5CDlWivGkNxiyBn7gjgZTDN9bHn\nLtWw4uw5zcuJranOoWgA4A8Bq1h6jIo/IgzRjviolwJvSbj+iqnJe0PBn1MpMV9433W7qRfv\n4iCwDvSq8m9jANAEBKyioouWBrzbrgxLhep+TnBm5FnqyXs7wa8pNNjrnruSZlFpLjcIx1ya\npfi7GAC0AQGrqOidyLi88TDKuqr0UCygObw3E/yypQlej2QepK68SwMZTkXXU/pNDAAagYBV\nVHSMRvJuu3LYMulBpYeig4AfomncCmrp5SyeX2qY9/CuDORoQ28p/S4GAG1AwCoqmk8LeHdd\nWY5VFnKVHYk/o+vw3kgIpDU9UnbPDachvOsCWWbQPGXfxACgFQhYRUX9aR/vrivP+qiUrxUd\niaepN+9thED2WGqUmdf/DKXl8q4LZDkR1VjR9zAAaAYCVlFRowj4EaHdeOqs6I+859Fc3psI\nAQ2gHh4n4/1YKWor76pAphb0gZLvYQDQDASsokuW+rx7rly2TNqm5FC0E47w3kQIKPcGmuq+\n3wZQNu+iQK4ptFLJ9zAAaAYCVtG71Il3z5XtQFzsx8qNxAWcghURjlan3a777RA1zOddE8h1\nWLhZubcwAGgIAlbRiUj6z/9p1Fq5uRqexVyVmrI6Ozvb66lVu+KjnindbQdionerXRqErpHp\nO8XewgCgIQhYRQtpPu+WG4SbaYWCI4FZsLRkKBGd9LpmmSXlMcdOu3IfxS5SuTAIxyj3448A\noFcIWEUDKJJmEDqcZH1HqZHoiFmwNMV3wCqYLFBnaT6lc1lUfYe6ZUF4dlFPpd7BAKAlCFhF\njaMj5UeEkrnUVqFfEuJChBrjJ2AVbGpGphGzBrcqT5nHVS0KwlYz+ndl3sEAoCkIWFesabwb\nbnBa0l5lRuJ5XIhQW/wFrIKC+TWKV5tSB+P89kgzkE4p8w4GAE1BwPqIOvBuuMF5KLrCT4qM\nxGKazXvbwJX/gFWQt3D5XkwvGoHW0XBF3sAAoC0IWPl0J++GG6RsGqXISHSig7w3DVwFCFgQ\noWyp5S8r8g4GAE1BwFpNs3g33CDl1RGeU2AgLsXW4L1l4AYBS6e609MKvIEBQGMQsEbTFt79\nNlirhSYK/Bfwi9Sd94aBGwQsnVpE97J//wKA1iBgtRFyePfboGXRWvYDsYxm8t4ucIOApVNn\nytVW9JqiAKAJCFgVKvFut8E7HJfE/jz3LjgFS2MQsPSqHb3J/P0LAFpj+ID1I2Xy7rYhGEn3\nsB6Iy3HVeG8VuEPA0qv7aSHr9y8AaI7hA9YL1Jd3tw1BblXLfxgPxD+pG++tAncIWHp11Hwj\n47cvAGiP4QPWbprMu9uGYib1YTwQK+h+3hsF7hCwdOt6+oLx+xcANMfwAWs6reTdbENhS6fn\n2A5ENzrAe6PA3bSMjIzI+wUGyDCWtrF9+wKA9hg+YPWkR3g325CsEzKusRyHKwlVeW8SgFHs\npW4s370AoEWGD1j14nj32hC1pYMsx+Fl6sJ7iwAMo5b1N5ZvXwDQIKMHrL/M6bxbbYj2WGpf\nYjgQK2k67y0CMIyBdJLhuxcAtMjoAesdyuLdakPVg3YwHIhutJ/3BgEYxlpc8BlA94wesE5S\nNu9WG6qD1qoXmI0DTsECUJEtOeUKs3cvAGiS0QPWEnqQd6sNWW/axGwccAoWgJq6sv4ZMABo\njdED1jDaybvThuxQdJU/WY0DTsECUNM8ms7qzQsA2mT0gJVpyeXdaUPXj901n7vjFCwAFeVE\n12P15gUAbTJ4wCqMr8G70YbhSLnU39mMA07BAlBXK3qPzZsXADTK4AHra2rFu8+GYxAtYzMO\nr+AULABVTaGVbN68AKBRBg9YT9EA3n02HMfiUs4zGYdVNI33tgAYyiPCzUzeuwCgVQYPWFto\nKu8+G5ZBtJ7JOHSnh3hvCoCxNBL+y+TNCwAaZfCANZnW8W6zYTkaU+UvBsNwJaEK7y0BMJhR\ntIvBexcANMvgASuLjvFus+HpTXsYDANmwQJQ2x7qweC9CwCaZfCAVT2Zd5cN0wFLGoMJoVfi\nFCwtGkpEJ3kXAYqpGY0LPgPombED1u9CU95NNlxZdCz8ceiBWbC0CAFL3wbSifDfuwCgWcYO\nWG9Qd95NNlw7hRsKwx2GyzgFS5MQsPRtHd3JoosBgEYZO2AdpdG8m2zY2tBj4Q7DS9SN91aA\nFwhY+mZLTbrEoo0BgDYZO2Atpvm8m2zYNlC7cIdhCc3kvRXgBQKWzvWgp1i0MQDQJmMHrGG0\ni3ePDV8G/SvMYegoPMJ7I8ALBCydW0T3MOljAKBJxg5YN5kj+FLPTotoYHij8Gd0Hd7bAN4g\nYOlcbmz1sE+gBADNMnbASqrOu8WyUMf8WVij8AT15b0J4A0Clt61p9cZtTIA0B5DB6yz1IJ3\nh2VhCk0Naxhm6eBMNF1CwNK7mTSfUS8DAO0xdMB6gW7n3WFZyElK/DWcYcg0n+C9CeANApbe\nHbc0YdXMAEBzDB2w9tBk3h2WiaG0LoxR+MXckPcGgFcIWLp3E73PrJ0BgMYYOmDNpOW8GywT\nh601Loc+Cjk0mPcGgFcIWLo3hZay62cAoC2GDlh96GHeDZaNrnQ89FGYRCt41w9eIWDp3lFz\nBrt+BgDaYuiA1TDGxrvBsrFLuCn0UUi35vCuH7xCwNK/DPqEXUMDAE0xcsC6Yk3j3V5ZyQx9\nstH/Ugbv6sG7gxs3e3WgBAAAIABJREFUbsznXQQoajKtYtnTAEBDjBywPqH2vNsrK4tpSKij\ncIiyeVcPYFSHTS1Y9jQA0BAjB6zHaAjv9sqKrUbUNyGOwl20gXf1AIZ1PYU3TTAAaJaRA9YG\nup93d2VmQsgzFtaIw7dQALxMoPVMuxoAaIaRA9ZEHR27ORlb+WJIg/A+3cy7dgDjOii0YdzX\nAEAjjBywbqXjvLsrO33oYEiDsF4nk60CRKZGwteMGxsAaIORA1aN8rx7K0N7hOYhDUJneoh3\n6QAGNpa2MG5sAKANBg5YfwhNePdWlm4KaaaGP6Jr8S4cwMj2Cx2Y9zYA0AIDB6y3qAvv3srS\nIhoawiDYqB/vwgEMraEJ3xEC6JKBA9YJGsW7tbJkqxH13+AHYaJOLscIEKkmYK5RAH0ycMBa\nSg/ybq1MjacFwQ9C3ZgzvOsGMLTDlqbsuxsA8GfggDWCdvBurUydjKsU9EwNH1Ar3mUDGFwL\n+rcSDQ4AODNwwGpp0tnBm950ONgx2ECTeFcNYHAzaaYSDQ4AODNwwEquyruxMrZbyAx2DLrS\nXt5VAxhcTly1q0p0OADgy7gB6we6iXdjZS2TXg1uDC6Uq8m7ZvBt6YABA3R2lBW8yaJnlGly\nAMCTcQPWS9SHd19lbSEND24MHqXbedcMvg0lopO8iwDlLaVRyjQ5AODJuAFrP03k3VdZs1W3\nng1qDCbTUt41g28IWAZhq5B4QaE2BwD8GDdgzdVhuBhLS4Iag3qYpEHLELCMoh+dUKjNAQA/\nxg1YA3R4Eb7jMdUuBzEEH1EL3hWDHwhYRrGZeivW6ACAF+MGrBusNt5tlb2edCyIIVhFk3kX\nDH4gYBlGrajvFet0AMCJYQNWYawer3K8Q7g5iDFoaTrMu2DwAwHLMMbScsVaHQBwYtiA9Q21\n5t1UldCcXpY9BF8LzXiXC/4gYBnG8ehamAoLQG8MG7Ceof68m6oSFtMg2UOwnu7mXS74g4Bl\nHF3IpmC3AwAeDBuwdtG9vHuqIuqYP5c7BG2Eg7yrBX8QsIxjM/VQst0BAAeGDVj300rePVUR\n99AMmSNw1tSId7HgFwKWgTQQPla04QGA6gwbsHqTPg/fnCmf+Ju8EdhGY3kXC34hYBnIfTRL\n2Y4HAGozbMBqGKPDWRpEg2mLvBHoRPt41wp+IWAZSE5Chb+UbXkAoDKjBqyr1jTeHVUhh611\nZf0e6SdLA96lgn8IWEbSlw4p3fUAQFVGDVifUXveDVUpt1KenBHYQ9m8KwX/Zrdr1y6HdxGg\nkj1Ca6W7HgCoyqgB6wkazLuhKmUzdZAzAt1pN+9KAaBEBr2mdNsDADUZNWBtoWm8+6liMugf\ngQfgf1F1eNcJAKUWUS/lGx8AqMeoAeteWsu7nypmuZwpdfbTMN51AoCLdAGHsAD0xKgBqzsd\n4d1OldNQeCPgALQR8A0hgJYspttUaH0AoBajBqy0eN7dVEELaECg7X+fcB1CAG1pTK+o0fwA\nQB0GDViXzOm8m6mS6pneCzAAU2gm7yIBwM0S6qlK+wMAVRg0YL1PnXg3UyXNprv8b/9fKQn4\n/T+AxjTBISwAHTFowMrX9ynetprmT/xu/yG6nXeNAOBhKXVXqQMCgPIMGrDW6fwbsql0t9/t\nb087eJcIAJ4ay5liBQAig0ED1gTayLuVKiq3kvVTP5v/gdCEd4UAUMZKIf2Cal0QAJRl0IDV\niU7wbqXKmub3h4TTaTrvAgGgrJ40VbUuCADKMmjAqpHMu5EqzFZPeMnn1l9Mjccp7gAadKqq\n6TlZLezn14t9UMioIQKAAowZsP7U/1dkK6iVz+Z7lHrzLg8AvFkt1P09UP/6cMvwBiSpOub0\nb2x7IwAwY8yA9TZ14d1HFdeKjvnY+ms3CNt5VwcyHNu7d6+NdxGgsn40zm/z+uNAu+JkFdO0\nW/fu3dvEEVnHfcu+QwIAA8YMWKcpm3cbVdwuS52/vG/9YWrPuziQY2jxB+lJ3kWAynJqCnm+\nW9fnExNJaDp5a779wXkrB1Sh2AdwFAtAi4wZsFbQXL5NVA29aLXXjb+UZsFlCCMCApYhbbRG\n5/toXB+NjKKUAe5v39y7kyj1YeWaJQCEypgBaxRt5dM71XQkLsnrdwdbqQfv0kAWBCxjWhwd\nddzbO/e9oWaqdl9umcefHBJDY30crgYAfowZsNoJRvgV3Xi69VrZbT9fOfog78pAFgQsg1pZ\nzry/zBv3nYEmqjUj3+sf7K5DzT9XoXECQDCMGbAqp6rcMbmwZdKKstu+mAbyLgzkQcAyqnXx\nwgK33xIWPnuHQHXm+PzJw+lOlPK4Wu0TAOQxZMD6jZqp2S25OZwcVebasT8mJhznXRfIg4Bl\nWJuTKHXleeeb9ofVDYjSHvD7i9KJlqgT6rZRAAjAkAHrdequVqPka7FQz/P3RXfTaN5VgUwI\nWMZ1bEgspU7ZeOqfr+0a3yKKLO2WBfqLFTHmR7i0UwDwwZAB6xiNUaNHakA/Gu6+6ZupmhFO\nP9MHBCwjOzo41j6ZKJnTRh+W8QdrYk0P8WmoAOCVIQPWEpqveHvUhtz6NMt1QvfjpqRdvGsC\nuRCwjO346pmj+/SavEHufxKtjzPt4tZUAaAMQwas4bRT0caoIfuq0KDSH3A/bY3ZwLsikA0B\nC4KyKcHk6/INAKA+QwasVqayc8no1eF0avuTY7vfTLQs5l0PyIeABcHZGBP9PNfWCgAuDBmw\nUqrwboQqymlDDQ6fLd7qfwyPEabzrgaCgIAFQVpkTv4P7/YKAA5GDFjnqDnvPqgm2+3Fn9NN\n725CVGUa71ogGAhYEKwpVBvXfgbQCCMGrJepF+82qK6NI5pFkbn1Yr/z6IDmIGBB0IZQxvnA\nPRAAVGDEgHWIxvPugqo7vQrXx4k4x/bu3YtQDEGxZVH/wsBNEACUZ8SANY8W8e6CAABKyG1C\ny3m3WAAQGTFgDaY9vJsgAIAiDlUwPca7xwJAkTEDVnNLHu8eCACgjHVRyZ/wbrIAYMyAlVid\ndwcEAFDKZLoeJ7oD8GfAgHWWWvBugAAAiulKw3i3WQAwYsB6kfry7n8AAIo5U4/28O6zAGDA\ngPUQTeLd/wAAlLMnLuZN3o0WwPAMGLDm0DLe7Q8AQEFzqP5vvDstgNEZMGDdQft5dz8AACX1\nokG8Oy2A0RkwYDWzYnZsANC1M/VpN+9WC2BwxgtYhbG1efc+AABl7Y0r9x7vZgtgbMYLWF9T\na96tDwBAYXOoyQXe3RbA0IwXsJ6h/rw7HwCA0rrQRN7dFsDQjBewdtG9vBsfgByz27Vrl8O7\nCIhYp2vScd7tFsDIjBew7qeVvBsfgBxDiegk7yIgcm2xJn/Fu98CGJjxAlYfOsi77wHIgYAF\n4ZlA7a/ybrgAxmW8gNWwHGZpgIiAgAXhsbWiRbwbLoBxGS5gXbXW4931AGRBwIIwHatoeo53\nywUwLMMFrE+oA++mByALAhaEa6Wpxs+8ey6AURkuYD1GQ3j3PABZELAgbIOoP++eC2BUhgtY\nG2k675YHIAsCFoQtryHt5d10AQzKcAFrMq3j3fIAZEHAgvDti4v9D++uC2BMhgtYXegY744H\nIAsCFjAwk67/i3fbBTAkwwWs2km8+x2APAhYwEJnmsS77QIYktEC1gVTI97tDkAeBCxg4RQu\nmQPAhdEC1jvUmXe7A5AHAQuY2GYt/yXvzgtgQEYLWDl0F+9uByAPAhawMYHaXeHdegGMx2gB\naxXN5d3sAORZOmDAgDO8iwAdsLWiObxbL4DxGC1gjaatvJsdAICqjlUW8nn3XgDDMVrAaiec\n5t3rAADUtd6SgtOwAFRmtIBVqSLvTgcAoLYxdPNl3t0XwGAMFrB+pQzejQ4AQG221jgNC0Bl\nBgtYr1FP3o0OAEB1xyoJj/LuvwDGYrCAdYTG8u5zAADqWx+V/BnvBgxgKAYLWAtpAe82BwDA\nwWRq9ifvDgxgJAYLWENpN+8uBwDAQxcaxrsDAxiJwQJWpiWXd5MDAOAhpz7t4N2CAQzEYAEr\nsTrvHgcAwMe+hOiXefdgAOMwVsA6Sy14tzgAAE4WCdX+y7sLAxiGsQLWC3Q77w4HAMDLXdQc\nJ7oDqMRYAWsvTeLd4AAAeLF1pOG82zCAURgrYM2i5bwbHIBcBzdu3JjPuwjQl5wGtJZ3HwYw\nCGMFrNvpAO/+BiDXUCI6ybsI0JkD5c1/492IAYzBWAGrcYyNd3sDkAsBCxSwJirxbd6dGMAQ\nDBWwrkan8W5uALIhYIESZgrVvubdiwGMwFAB6xPqwLu3AciGgAWKGEY3nufdjAEMwFAB61Ea\nyru1AciGgAWKsGVRjyu8uzGA/hkqYK2nGbxbG4BsCFigjNxmNJF3NwbQP0MFrPG0kXdnA5AN\nAQsUcqwmLeTdjgF0z1ABq6OAjyuIHAhYoJSHK9FG3v0YQO8MFbCqVODd1gDkQ8ACxewuLxzk\n3ZABdM5IAetXasa7qwHIh4AFytlQLgoTjgIoykgB6zXqybupAciHgAUKWmqJe4F3TwbQNSMF\nrEdoHO+eBiAfAhYoaa45/h+8mzKAnhkpYD1Ii3i3NAD5ELBAUXPMOIYFoCAjBawBtI93RwOQ\nDwELlDXbnPgK77YMoF9GCljNrPm8GxoAgGZMNyW9xLsvA+iWgQLWtXK1ebczAAANmWaKfYJ3\nZwbQKwMFrC+oLe9uBgCgJfOs1pO8WzOAThkoYD1Bg3g3MwAATVlezryXd28G0CcDBazNNI13\nLwMA0JY1ccJy3s0ZQJcMFLAm0TrerQyAgxUVyWOGkrJL5KwCfdpSgUZe4t2eAXTIQAEri47z\n7mQAqsu5XSD3zFR2iZxVoFsH61PbH3n3ZwD9MVDAqlmedx8DUN3mWkQWt8xUdomcVaBjp1pQ\n+ke8GzSA7hgnYP0hNOXdxgDUNtZC1gntXTNT2SVyVoGu5femxNO8WzSA3hgnYL1J3Xh3MQC1\n1aU62wvcMlPZJXJWgc5NsQrTLvNu0gD6YpyAdYxG8+5hAGpL65VT4J6Zyi4JuCqDaGrJneZE\ns6Qbm3vWjo2qcP2YklMbT92dmWq1JDUdecx+P50sBce7J1jGFt/Om966cowpLq3PVlZbBmxt\nrkpt/8u7SwPoinEC1iKaz7uFAahti/h/bpmp7JKAq2YRlXy/ftRMccUxrOBMV3JIcjx8XYpz\nScp6acH1RGcaFd8dWlDwcF3nOmEwu40Dlo63opRjvNs0gJ4YJ2ANpV28OxgAF2Uzk5/vAb2s\nOpNAwh7H7clEPcR/tiFKzV6y6cFOAkWtFBccSiBqMGHhortrF2cu6RiWeOCLrE1vmFhQUJyz\nGkxctnJODyvRfcy2C5iyjbXS4HO8GzWAfhgnYDW35PFuYABchBuwCnoTOQ88NSMSj09NLU5M\n9u8GZwpUPbf4n4OJmon/LMjPJMoWbxT/M73hYfHW1uJHn5EevTmKqoW7OaCUnQ2oqo13pwbQ\nDcMErML4Gry7F0BQpmVkZOTIeeCu5Rv8zvEWdsAqDkipNunWIyaqWfwPWzUyOY9ptSFaUPyP\n7Mw0x98tJcoQ/9mCyHpQWjKLaIjj0fcMn54fcIOAk7xhFurzOe9mDaAThglYX1Jr3s0LIChD\niehkwEfZpqSKpzaljdgv3c0ZVfYhYQesggZES6UbE4hGFv9jO9ENznUPkscPdA8Q1RL/WRyw\nOtiXzCe8/SLElkZUbtFfvNs1gC4YJmA9jks9Q4SRF7CGl5w+3nTk4oV3plYo+5DwA9ZkZ1Zq\nTKaD9vt9neuK89R1JQ/MPX78+F6iquLt4oB1t33pUSvRLZsDbgpogG1qearz8FXeDRtABwwT\nsDbQdN6dCyAo8gLWoJr3HTjz0ANZCY6c1bDsQ8IPWCeiySp+C/mwQDc5S3Nhv0jCss41nUU4\nA9Zcx99PEa/AU63n3KMBNwe4O97bQunHrvFu2QARzzABaxxt4t23AIIiL2BtzbX/M/fBjhUt\nMfXHnin7kPADVkEW0aTif4wlmi3e7e0esKzFi062clngDFgrnX+/NF1absp4wBZwi4C3fVlm\nuv4gLgANEB7DBKy2wineXQsgKPIClgwMAtYq+/eA6RQvBbi+RF1WlBJz1M1EcSM2H84rKDhS\nGrDWlT7DukFp4mEsysA11yPAro4mqrocczYAhMMwASulEu+WBRAcLQWsgupEuwv2EfV0ltbf\nff0mopgd9puPeA1YxY7MamsmujnI6oGLPb1iKDb7hULenRsgchklYH1PmbwbFkBwNBWwRhIN\nLxhBtEG6N63MzwKzibo7bq7zFbCKbUog2hpE6cDP8ZGViBqs/Jp38waIVEYJWM9RP97tCiA4\nmgpYj5ipdkF1+/wLBQU7iRLcJ+7tQzTWcXOAn4BV0J/wc5OIYVvSPopM7bae5d2/ASKSUQLW\ndprCu1kBBEdTAUs8x2o6lVwxvXbp9Z8frNlvlxScHHOJ7itHVFG8URKwbAMzOjmf5s7SnxZC\nBDg6vpFA5ls2fsG7hQNEHqMErHtoDe9OBRActQJW39tue9jfg+0WECWQ6ZDj3lSi+K3SrZ0V\nyLynoKA4fNWRpmh/qEZ6DFnFU+FLj2A1IWGm/dYjVUk4EPYGgZr2j24gEGUs+D+cjwUQFKME\nrCw6xrtNAQSHQcBaPVRUi+gW8Z8TvC4piCLa7P3BrvJTistpUXK3NZG178I1c3pGEw0svn8y\nrvgzeNH2ZbdHx+xKI+q37ZBLwFplIiFz4vzlcwdWIOoa3iYBBwcmZFiIqo2z/cm7kwNEEKME\nrGopvFsUQJAYBKxst+mqqntdUhKwvKxyNYhcv93L7SI4HmgeJE1tNddiv5u4zv5Eo13PwZoZ\nU/K8nbxM1AXad2xmh3ii2D57v+fdzAEihUEC1i/UjHd/AgiSxgLWHqKEXJf7G2+rFWeOb9B/\nl/N++xRzTFr20eLw1TfFWnOh20nuj4xolhJliqt3W9nT3iFS5C3vW5XI1H7TN7wbOkBEMEjA\n+hfdxrs5AQRpdXZ2dm7gh6llE1Ef3jUAbzuy0wUSbt78I++eDqB9BglYDzmvOgsAockiYQfv\nGkADDoxrLFBU71MXebd1AI0zSMCaQct5dyWAiLbV5HKKOxjbgexaRJUewBykAP4YJGDdRocC\nNw0A8OVgDRI28y4CtGNj7zgy93ued2sH0DCDBKy68bzbEUDkWrpgWDzhYgjg5tSk2kTtnuTd\n3AE0yxgB609TI97NCCByJYg/K2ybF/iBYCzLM4haPsa7vwNolDEC1pvUhXcnAohc1Si6wRQb\n7ypAg9a1EKjLe7w7PIAmGSNgHaExvPsQAIAObbyeLJPO8e7xABpkjID1oM8r2wIAQDjmVqHk\nA7ybPID2GCNg3UEP8e5BAAD6dCY7mrp/xbvNA2iNMQJWoxicPwIAoJC9zShhRyHvRg+gLYYI\nWJej6vHuPwAA+mWbHEs9cf0cAFeGCFj/oU682w8AgJ4daEbVnuPd6wG0xBAB6ziN4t18AAB0\nzTbOIky5zLvbA2iHIQLWPFrIu/cABG377NmzMbknRI6VqdTlZ97tHkAzDBGw+tIB3p0HIGhD\niegk7yIA5DvSnOr/h3e/B9AKQwSsunH4ESFEHgQsiDT5Ayj+DO+GD6ARRghY54UmvNsOQPAQ\nsCDyTLeaVvBu+QDa8P/t3XlgVOXVx/Ez2QiQgARkk00WFUSlLGJZrCiotX0iyG5REAVZLLK5\nWwsiGlAUASmiKbLIvkZqq7a1vlq3ape3rrWi+LohigoKyJK8d7JOMhNIZu7MmfvM9/OH3LlD\n9EzOPM/5mZncSYSA9bJcor3pANVHwIIHzcuSa49ob/pAPEiEgPWITNDec4DqI2DBi5afLBfu\n1d71gTiQCAFrkszR3nKA6iNgwZPWniHdPtfe9gF9iRCwzpe12jsOUH0ELHjT5t7S9gPtfR9Q\nlwgB68QG2vsNEAYCFjwqr780e0t74we0JUDA+ky6am83QBgIWPCskZL1svbWDyhLgID1tAzS\n3myAMBCw4F3jfRl/1N77AV0JELDul6naew0QBgIWPGxqcq2ntTd/QFUCBKzRskB7qwHCQMCC\nl92akv477d0f0JQAAatr8mbtnQYIAwELnjYjLY2PzUEisz9gHa3dXHufAcIxMiMjY4N2EUDY\n7k5PXqU9AAA99ges/0hv7W0GABLQ7PSUNdoTAFBjf8DaJCO0dxkASEQ5NZMf1x4BgBb7A9YM\nuV17kwGAhDS3VvJy7RkAKLE/YA2SR7X3GABITPdnJD+mPQQAHfYHrNPS87S3GABIUPfVTlqm\nPQUAFdYHrAPJp2pvMACQsJyE9aj2HAA0WB+wXpcLtfcXAEhc8zN9i7QHAaDA+oD1Wxmrvb0A\nQAJzEtZi7UkAxJ71AeuXkqO9uwBAInsw07dAexQAMWd9wOrpW6e9uQBAQltYV+ZozwIg1mwP\nWEczTtLeWgAgwf2mvtykPQ2AGLM9YL3DB+UAgLbcxjI9X3seADFle8BaIyO1NxYASHi5TWTC\nUe2BAMSS7QHrRpmpva8AAJY3l8EHtScCEEO2B6y+skp7WwHCM6Zx48YbtIsA3LKmvZz3jfZI\nAGLH9oDVoIH2pgKEabiIrNcuAnDNxm7S8WPtmQDEjOUB6yM5W3tPAcJEwIJltpwvrd/UngpA\nrFgesLbJMO0tBQgTAQu2yRskdX+vPRaAGLE8YP1abtfeUYAwEbBgnxvSknO05wIQG5YHrGzJ\n1d5PgDARsGChnLoy9gftyQDEguUBq3mm9m4ChIuABRs92kK67dAeDUAM2B2wdstZ2psJEC4C\nFqy0/lw5YYv2cACiz+6A9bQM0N5LgHARsGCpCWm+yVxzFNazO2DNkenaOwkQLgIWbPVgE+n4\nqvZ8AKLM7oA1TBZrbyRAuAhYsNa6vpJyCz/Egt3sDlinpm/T3keAcBGwYLEZDaTD89ojAogm\nqwPWvqT22psIEDYCFmy2tp/PN3Sn9pQAosfqgPW8/Ex7DwHCRsCC3XLaSM1ffac9J4BosTpg\nLZBJ2jsIELYHJk6cuEW7CCB68iadIA1mf6s9KYDosDpgXSELtTcQAEBl1g2pJfXu+FJ7VgDR\nYHXAOoX3uANAPFszPENqjnpZe1oA7rM5YH3lO0N77wAAHNO6UQ1FOi3mx1iwjc0B6/cySHvn\nAAAcR94d3XyS+tPHvtEeGoCbbA5YM+RW7X0DAHB8uVe2Eqlx4QI+Bhr2sDlg/VSWa28aAIAq\nWTyshYh0mPIkV26AHSwOWPlZJ2pvGACAKnv02h+litToc/ffjmoPECBiFgesd6WX9m4BAKiO\nDTMu9f8gq/7g3M+1ZwgQGYsD1goZrb1VAACqa/mUPvVEks6e9b/aYwSIgMUBa6LM0d4mAABh\nyFs4sn2SyOl3va89SYBwWRywuqZs0t4jAABhenxKtxSRH688qD1MgLDYG7D2p7bV3h4AABFY\nPbGjTxrd/rH2PAHCYG/AekEu0d4bAACRWfLzWpI6jogF77E3YM2TKdobAxCJByZOnLhFuwhA\n3fprG0nNqV9ozxSgmuwNWENkifa2AERiuIis1y4CiANbxmdJxtzD2lMFqBZ7A1bLjDztTQGI\nBAELKLFpdKac9Yr2WAGqw9qA9Zl01t4RgIgQsIAyq86T5F/u1Z4sQNVZG7C2ynDt/QCICAEL\nCHRnY2n3D+3RAlSZtQHrZpmhvRsAESFgAeVsMr70JdqzBagqawNWL98a7c0AiAgBC6jgltpy\n+T7t6QJUja0Ba3+NVto7ARAZAhZQ0SNtpeNO7fkCVImtAetP8nPtjQCIDAELCLL5Qjnpn9oD\nBqgKWwPWHXKL9j4ARIaABYQwxpfxpPaEAarA1oD1E98q7V0AiAwBCwhlSkrqCu0RAxyfpQHr\nQHoL7T0AiBABCwhpVq2kXO0hAxyXpQHrL3zSMzyPgAWE9kBG0mPaUwY4HksD1gy5SXsHACJE\nwAIq8WBmEq8SIt5ZGrD6+FZqbwBAhCa2bdt2o3YRQFyan5G8UnvOAMdmZ8A6WLOZ9vIHAETN\n3Jop27UnDXBMdgas5+Vi7dUPAIiee9IyXtMeNcCx2BmwZskN2osfABBFN/lOfE971gDHYGfA\n6ivLtdc+ACCaRkvbL7SHDVA5KwPWodpNtVc+ACC6jPQ+oD1ugEpZGbD+KhdqL3wAQHRtO0dG\naY8boFJWBqy7ZZr2wgcARNnGk2WR9rwBKmNlwLpIlmmvewBAtD2amfq89sABKmFjwNpfk7dg\nAUACuDOp8cfaIwcIzcaA9Tu5VHvRAwBiYKR0P6g9c4CQbAxY18lM7TUPAIiBvF4yQXvmACHZ\nGLBap2/SXvMAgFjY0Fw2ag8dIBQLA9bb0l17xQMAYmNR2gk7tMcOEIKFAWueTNBe8ACAGJkg\n3X7QnjtAMAsDVl/J1V7vgAtGZmRkbNAuAoh/58qN2nMHCGZfwNpXo4X2agfcMFxE1msXAcS/\ntY18T2pPHiCIfQFrq1ymvdoBNxCwgKqZl9KIj31G3LEvYI2Vu7UXO+AGAhZQRVfKT/O1Zw9Q\ngX0Bq0WtLdprHXADAQuoorwzZKn27AEqsC5g/Ut6ai91wBUELKCqcmvVfld7+gDlWRew7pFJ\n2isdcAUBC6iyydLlkPb4AcqxLmCd61uuvdABVxCwgKrrKTO0xw9Qjm0Ba3dKG+1lDriDgAVU\n3eqslL9pDyAgkG0B6zcyUnuZA+4gYAHVMMt32n7tCQQEsC1g/USWaq9ywB0ELKA6LpZp2hMI\nCGBZwPok+VTtNQ64hIAFVMeGJkl/0Z5BQBnLAtY8GaO9xgGXELCAapmbdPJe7SEElLIsYHXn\ndwhhDQIWUD2XyjjtIQSUsitgve87Q3uBA255ZNasWdu0iwA8ZFNz31PaYwgoYVfAuksmai9w\nAICS+SlN92jPIaCYXQGrY8pq7fUNANAyREZqzyGgmFUB6y3pqr26AQBqtrSVTdqTCChiVcC6\nXaZqr24AgJ5ZNr/9AAAWH0lEQVQFKSfu0h5FQCGrAla7tLXaixsAoGiEDNUeRUAhmwLWi9JD\ne2kDADRtbSdrtYcR4GdTwBohv9Ze2gAAVYvTsj7RnkZAgVUB64saTfK0VzYAQNfV0i9fex4B\nVgWsWXKN9roGACjLO0Me1p5HgE0B63CzGlwECwASXm6t2u9pTyTAooC1QS7WXtUAAH2TpOcR\n7ZEE2BOw+sgC7UUNAIgD3WSO9kgCrAlYb/g6aC9pAEA8WFE37XXtoYSEZ03AGi83ai9pwFVz\nRo4cuUW7CMCTZvjaf689lZDobAlYe+tkMYpgl+Eisl67CMCbLpbx2mMJic6WgJUjw7XXM+Au\nAhYQto0n+Z7QnkuokiNfvv/++1/usfDXEiwJWF9n1eIaDbAMAQsI3wMpDT/Xnkw4pkMvPzr9\n4tZ1pEjKST++bNqy1w5oV+UiSwLW7XK59moGXEbAAiIwUi46qj2aUKl3F5oMf67KbN6+W59z\ne/240ylZSf7byR3GLN+hXZxL7AhYuzLqMIhgGwIWEIG8TnK39mxCaJ/e29HZ3hpeOG72yoCO\nbX1s9tiLTqnh3NPsqvVfa9foAjsC1vVytdoiBqKEgAVEYmW9lOe1hxOCHd3402RJ6XrtktBt\n23Lv6LMzRFJ6zX1fu9JIWRGwdtZosCm2CxeIPgIWEJHZSc12a48nVHBgSTuR1mNWHbNz2+YO\naesT6TTrHe1yI2JFwBot18VovQKxQ8ACIjNcLsnXnk8I9P09DSWlT5U+dmX5+LOSRbrM+1i7\n5vDZELDeSWm6NdrrFIg5AhYQmW1nSo72gEKZww83lZr9l1W5f6snnZUkSRes+E678DBZELDy\nL5QborhCASUELCBCy+slP609olBi82mSNnBNNTt4TTuRzKue8+RPIi0IWEulU1501iagiYAF\nRConJcvzb5W2xLv9JKlv1X96VWbxoPoi7XI+1X4A1ef9gLWzTs1c1xcloI+ABURsnJzp1ReY\nrPL9bTXkzEVhNnHbzF6pknLp77x2XTPPB6z8fjLR1eUIxAkCFhC5fjLEky8v2eUPrSRreiRt\nfPyaFiIt7/xE+4FUi+cD1sO8QAhLTenUqRPXHwEis/lU3uiubc9IScpeF2kn772ghqQMeMpD\nP8byesD6MLMWLxACACrxWL2kDdqTKrFtaiyt7nejlWvHthBpO/dL7QdUVR4PWAd68AIhAKBy\n96enc0V3PV8Ok5TLt7jVzJyfpEr6yFe0H1TVeDtgHR0sPXiBEABQuTuS6nv7iuBetr2JtA33\nze0hrRrZSKRr7n7tB1YF3g5Y0+RU3qMCADiWCXLy59rjKjHtvUZSfuH2lcDz7ujqk6zp8X/9\nDU8HrEXS5NifZwQAwGDp8q32wEpEz7WSFvOj0dBHBmRK0s9+H+dvePdywMpLzqzk07gBACiR\n10d67NUeWQnnwLQk32Wbo9TSTZPaiLR74BvtB3ksHg5Yy9LS5kSpcwAAi2ztKb254Ghs/f10\naZQTzabOPTdFMsa9of04K+fZgHX0Rql9ZzRbBwCwxZZz5LzvtedWIjl8Z6pcGO0rJS+/PEt8\n528+ov1gK+HVgLXvUmmyOMqtAwBYYsvZcgEJK2be7CpZd8SirTe0F2lxz27txxuSRwPW386Q\njqtj0DsAgBU2d5Vz4nMO2+fIvenSO1Yzen6/NEm/6jXtxxyCJwPW3uuT5SLXrlsGALDf5t5y\nSvz/ar8N3ugudW6KYWdXj2wocs6qg9qPuyIvBqwtzaXRjBj2DgDgfXmXSuN4/EGHZQ7NTJMe\nK2Lb2m23d/JJw1t3aj/28jwXsI5u6CopAzfGtncAAO+72pexRXuI2e6FM6TeLQq9XWJqSXL2\nH+Lp0lgeC1j7Hm4nvu4LFXoHxNqmNWvW8ElQgJtuSPNNOaQ9yGy2a5TPd4HSO6Q3TDxZpM09\n8XPRfi8FrINbhtSUlAse0mkdEGPDRSTav+UMJJj5TeScD7WHmbUOL6onLe5RbO/cPmmSNviZ\nOPkxlmcC1o6lQ08QaTRkmWLrgFgiYAHuW9tTsjZoDzRLbesgNUcr/wLa6qubibSaERfvxvJC\nwDr8jyVXtXFmTZa5lxdMkDgIWEA0XJsq2f+nPdcs9PK54jt/uXZ3n3gi754+aZLUb8U+7W9I\nvAesvS8uGdejljNn0rtcvZB0hYRCwAKi4qH2kvlgvF7926teyfZJ5wXarS22dsIpIrWveCZf\n93sStwHr65dyp1/c0ueMGF+z88fdz1WvkHAIWEB05E2oLZ22a085m/yln0i7Wdp9DfTQoAYi\nyr8yGocB66NnFozv01j8Mk+/ZOJ9G7T7BKggYAHRsry3T3o8qz3tLHFwZXeRDnF3dcq82cOU\nr9wfTwHryH/z5ow6u05htMo665Jxs1dq9wdQRMAComd+F5E+W3mhMGIf3HKi+Dpr/upg5ZS/\nNfETsG7slO5PVklNuw+aPG+tdlsAdQQsIJrmninSMoePJ4zE7od6+aR29hLtXlZC+bsTNwEr\nv0VKy17Db1q4WbshQJwgYAHR9UDfNKnx8+XfaM8/j/rgoYtTxdd+Yvy+j0f5GxQ3Aavg4Fbt\nVgBxhYAFRNvqUc3FyViL3tKegF7z1RPTOzg7VMsrcrVbeCzK36T4CVgF2p0A4gsBC4iBRcOa\nOUut6YhFL+3XnoLesP/Vpdee7hNJ7XTNw9rNOw7l7xQBC4hTBCwgNpaM75HpLLeUM4bdsepV\n3pRVmR92PvvITZe1T3a+VakdBt0Rv68MllL+hh0vYGV3iZm2AAJkOdtYa+0igATRvGHddF/h\nL7En1cjIatik+cltT23f8cxOnWM3BONO505nduxwarvWLU9qVL9urdTC74740uuc2KyNdruq\nJnbfqtfDCVitBQAAAJX5CwErnrVq3759Le0i4K5TnaamaBcBV6U4PT1Fuwi4q7bT1JbaRcBd\nWU5Tm8TuP0fAimundenSJVO7CLirk9PUVO0i4Ko0p6dnaRcBd9VxmnqqdhFwV0OnqTFMzWEF\nrH+/htgY6DwbVmsXAXf1cJr6rHYRcNUfnZ721i4C7lrhNHWodhFw1xynqZNj95/bG07AQqxc\n6TwbXtMuAu7q7TT1K+0i4KpdTk/7aBcBd73kNPVq7SLgrtVOU+9SroGAFS8IWBYiYNmHgGUh\nApaFCFgoQ8CyEAHLPgQsCxGwLETAQhkCloUIWPYhYFmIgGUhAhbKELAsRMCyDwHLQgQsCxGw\nUIaAZSECln0IWBYiYFmIgIUyBCwLEbDsQ8CyEAHLQgQslFm3YMGCj7WLgLuWOE39XrsIuGqf\n09Ol2kXAXTudpm7ULgLu+qfT1D8p10DAAgAAcBkBCwAAwGUELAAAAJcRsAAAAFxGwAIAAHAZ\nAQsAAMBlBCwAAACXEbAAAABcRsDS9eYYY14IPPHx0knDB1w586kjWhUhYkFNDXkKnhLUwf8u\nnji0/+U3rPxcqyJErGJT81+cO2bQgBE3r9qlVhIiFXqv3TXEmP+JfTEELE2Hl2Wb8k+GDf1N\nkfHs2x4V3NRQp+ApQR38YWHxQjUDtuiVhUgENfXTySVN7b9BryxEopK9Nv92Q8BKNDuuc7bn\nck+Grc6z4Fcbtv92tDFX7dUrDOELbmqoU/CUoA7mz3RW6s3LNi260vnzKcXKELagpu4eYczA\nuY9veWSs01RisydVttc+aQhYieaJAeayrQ8EPhk+G2j6v+I/ODjLmAVqhSF8wU0NdQqeEtzB\nPzijuPCj2Q88aMzlP6hVhrAFN/UuY6bv8R8cXWrMYD5D1IMq22t3DTajCFgJZoqZsKOg3JNh\niTGri44OjDCX7lGqCxEIbmqoU/CU4A6ON+bJoqMjo415TakuRCCoqXuyzcBviw6PjjHmFa3C\nEL5K9tr828yIDQSsBDNlsfN/voFPhiO/MAP2FR+vMmazUl2IQFBTQ56CpwR18Jtsc9mB4uNF\nxmxTqgsRCGrqR/NmPlpyPL80QMNLKtlrf2fMn7YTsBLMDv8/Ap8Mbxtzc8nxm8bcqlEUIhPU\n1JCn4CnBHTyy+6OSw1xjNirUhAgdc1neqzKNEanQTf18sPl1AQErIQU+GZynwG9Ljn/INkN1\nKkLEQmzbBCyvq6yDdxvz11jXApdU0tR9l5v+vEPDqyo2Nf9WM3Q3ASsxBT4ZnP8X3l56xxXG\n8HuEHkXAslAlHdw70Azh/dBeFbqpH041ZkXsi4E7KjZ1e+Ev+hKwElLgk2Fe4BPjl8Z8FPIr\nEPcIWBaqpIP3lf5iCrwnqKm7cpfOu86YgeuVCkLkKjT188HmVwUErAQV+GSYbcyrpXdMM+Y/\nKhUhYgQsC4Xu4Fpjph+OfTFwR1BT3/RfL2lo7rdK9cAF5Zuaf6sZ4r8wPwErIQU+Ge405u+l\nd9xszNsqFSFiBCwLhezgSmPGMYy9K3TAcpr6jFJBiFyFyxAW/0IoASshVfoTrKn8BMuzCFgW\nCtHBgznGTNitUg1cEaKpR/e8vXKoMfNV6oELyl+8e7C5Nd9/QMBKSIFPhvsDnxjXGfOxSkWI\nGAHLQsEd/OJ6Y27aF/pvwxMqWZZfXG3Mn2JeDNwR2NT8m83goo/1JWAlpMAnwzJjnii943Jj\nvlOpCBEjYFko+NWkEcbMP6RUDVxR2bJ8xZgpsa4FLglsal7pFWMJWAkp8MnwB2NKLyX8vTG/\n0KkIESNgWahiB18aYLL5RGCPq2xZHjQm+0isi4E7Apq6e5AZ+0KRBcY88sILO2JcCwFLWeAK\n/68x00uOXzdmpk5FiBgBy0IVOvhSfzPoZbVi4I7Apv5zU+6bJcf52cYcCP0liHcBTS3+nYUy\nS2NcCwFLWbnXi0eXfcLz4sKLo8GTCFgWKt/BdwaawW/pFQN3BDZ1qTELS44/MWaQTkWIGAEL\npcpt2yuMyS06+nKQGcT1ob2KgGWhch38/moz4F+KxcAdgU193Zihu4qPlxtzh1JJiFTovZb3\nYCWkck+Gb4aZ7Of8B3tvMGaNWk2IEAHLQuU6uNiYzYq1wCXlXkC4zpipXxUePnMpy9W7CFhw\nvLnab5IxOf4/i7brP2cbc9u6vN+McJY614f2oBBNDdVneElwB3f1N9krVpfK064Q1RZiWb43\nyJiBOWs25zpJy9ylXSCq71h7LQErwWwo99rwFUUnnx5YfPs2rtHgRSGaGrLP8JDgDr5Q/o0d\nY7QrRLWFWpb/GVt6YsEPyvUhDMfaawlYCSb0k+GLZdcPu+yqnJdUS0O4CFgWImBZKOSyPPJs\nzjVD+l8+9dEPdYtDeAhYAAAA1iNgAQAAuIyABQAA4DICFgAAgMsIWAAAAC4jYAEAALiMgAUA\nAOAyAhYAAIDLCFgAAAAuI2ABAAC4jIAFAADgMgIWAACAywhYAAAALiNgAQAAuIyABQAA4DIC\nFgAAgMsIWAAAAC4jYAHQk3+eiPy64tmfOSenKVQDAK4hYAFQ9F5NkbS3yp9b5+Srtvt16gEA\ndxCwAGia66Sp3vmBZ75pIuJ7VqkcAHAHAQuApiNdnYT1cOCZa50T12qVAwDuIGABUPWvVJET\nPiu7/VefSLNv9eoBADcQsADoul1EBpfeOnS6c3O7YjkA4AYCFgBdP7R3ItUTJbfucm6MKLnx\n7PWdG6bWbz9o5d7AL/h47s9b1Umu02bQ0u9KTr3ofNVzBV9NapV24juFJ/4766IWGcmZbcyC\nL2LwEACgIgIWAGUvJom02Fd0/F66SMMvi47fOkdKNF5b+rcP3ZhaerrB1uKT//D/2Otbf1KT\nfzg3D4xPKv07teeUews9AMQEAQuAtklODppcdHiBc7iu6PDpTH8+ata5XbL/z5ziv5tvCmNT\nnRYn+P/wbSg6+47/y6ZLccDKv9B/kNK8Td3CM1Ni/oAAgIAFQNt3rUSS/+Y/Wu7kof5FJ993\nElTSpA+co28f8oepjUWnH3IOGy75yjl6z//rhnX3FP1l53BxprS/6d5bdhYULHNudXvmkHP+\n0wX+L30x5o8IQMIjYAFQ97STgjodLij4soHICZ8Wnesn4ltVfP9bdURaHig8bO3Err8Xn57o\nfNm9hUc7naPzZVrxi4EXiDQpfsmx4L36IsNj8iAAIAABC4C+UU5AmltQMNL5I7fozOvO4ajS\n+xc5t1b4D/w/qjqv5Oz/OTcuKj2Sn5S82apxwPvkC+7rMmh2tMsHgIoIWAD07XEyUa0df3ZS\nUt/iM/73ZZV9hM7+WiUvHR788OV/l55uLnJK4UFhwHqq5HQ9kUtjUDQAVI6ABSAObPRnq1NE\nan9QfKKTSOuA+y8SyQr+qs4iJxYe+ANWxuGS0z8SSXs1erUCwPERsADEg8uKLqqwoPjm/iSR\nfgF3T3Pu+yzoi7qL1C888Aes3qWn73Nu1bzl/ShWCwDHQcACEA8+q+fPVz2OFt/0J6ZaLcv4\n732p6K6D66/p3qhmyWWuygLWFaX/rkM9C+86bcLGPTF+FABQjIAFIC74r61Q4+2SW/8rwYre\nY/V403InywLWdWX/ru9GFN+b3HMhGQuABgIWgLjwtZOHziq99VKIgLXJf8eswsNWPbN/4WgQ\nGLCmBf7bXrmiTvFX1b3zSCwfBgAUImABiAvlA9Yb5V7zK/NHn3PHxJ3Ft7pXGrAKCg79cWrH\nooj1swPRqRgAKkfAAhAXygesjyT0pRb6OecfKL3V9RgBy++zR3r5E9Yst2sFgOMhYAGIC+UD\n1qEaIqcH/6XvkkROLvvw5qbHCViOTekitb93t1QAOC4CFoC4UD5gFXQTSf026C/5P9T5qtJb\n78rxA1bBTOeu51ytFACOj4AFIC5UCFhTSz4cp8g7RWHrFefs9aUnJ1cWsHbuLDv2f85hXlQq\nBoDKEbAAxIUKAct/nYb2pb//d6BZ6vn+XyJ8N/CtWX9P818sq/CwXMCa2iDgoqMFq527Xo5m\n4QAQAgELQFyoELAK+jq3xxa/3erQYOfGeufgSKZIneIrur/RtHYP5/SX/uNyAete58aikhuH\nnb9Th18jBBBrBCwAcaFiwNqR4Zw4/3knYh1Y38U5PK/w7JXOUU//h+B8MrOmLLrFuXWP/3S5\ngLW3kXPryhcPOYffPenPYDfG8nEAgB8BC0BcqBiwCp7xJyyp3bah/9JX0mFX4cn3/CeT2/Vq\nlyQyKn+7/57Tu79b4T1Yf65ReBX3pi0zC6+D1XN/bB8JABCwAMSJoIBV8M9epRdx9131dfHJ\np0qu0J78q4KCw2cWHv674pvcX+lQdv33lMnkKwCxR8ACEBeCA1ZBwZ8nd26cVqtp3xk7ys59\ndluXusl1O09/13/jk6H1U5sO+SLoMg35v5/QvVF6cp3W2fM+iX7pABCEgAUAAOAyAhYAAIDL\nCFgAAAAuI2ABAAC4jIAFAADgMgIWAACAywhYAAAALiNgAQAAuIyABQAA4DICFgAAgMsIWAAA\nAC4jYAEAALiMgAUAAOAyAhYAAIDLCFgAAAAuI2ABAAC4jIAFAADgMgIWAACAywhYAAAALiNg\nAQAAuIyABQAA4DICFgAAgMsIWAAAAC4jYAEAALiMgAUAAOAyAhYAAIDLCFgAAAAuI2ABAAC4\njIAFAADgsv8HaRu2/v54gNEAAAAASUVORK5CYII=",
-      "text/plain": [
-       "plot without title"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 360,
-       "width": 1200
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_width=20; plot_height=6; plot_dpi=300\n",
-    "options(repr.plot.width=plot_width, repr.plot.height=plot_height)\n",
-    "temp = data %>% filter(QRISK3_event==0)  %>% select(c(eid, QRISK3_event_time))\n",
-    "mean = round((temp %>% summarise(mean=mean(QRISK3_event_time)))$mean, 1)\n",
-    "obs_time = ggplot(temp, aes(x=QRISK3_event_time)) + ggtitle(\"Observation Time\") + \n",
-    "    geom_density(fill=\"gray70\") +\n",
-    "    xlab(\"Years\") +\n",
-    "    geom_vline(aes(xintercept=mean(QRISK3_event_time)),color=\"black\", linetype=\"dashed\", size=1)+\n",
-    "    #geom_text(x=mean, label=mean, y=0.15, hjust=-0.5)+\n",
-    "    #ylab(\"Prevalence in [%]\") +\n",
-    "    scale_y_continuous(expand=c(0,0))+\n",
-    "   #theme_classic(base_size = 25) + theme(strip.background = element_blank(), plot.title=element_text(size=24, hjust=0.5))+\n",
-    "    annotate(\"text\", x=mean+0.3, y=0.15, label=paste0(\"~ \", mean, \" years\"), size = geom_text_size)\n",
-    "obs_time\n",
-    "plot_name = \"2_observation_time\"\n",
-    "ggsave(filename=glue(\"{dataset_path}/{plot_name}.pdf\"), plot=last_plot(), width=plot_width, height=plot_height, dpi=plot_dpi, device=\"pdf\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# ENDPOINTS"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Frequencies"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plot_width=20; plot_height=6; plot_dpi=300\n",
-    "options(repr.plot.width=plot_width, repr.plot.height=plot_height)\n",
-    "\n",
-    "temp = data %>% select(c(any_of(targets))) %>% select(!contains(\"_time\")) %>% select(!contains(\"cancer_breast\")) %>% \n",
-    "    pivot_longer(everything(), names_to=\"category\", values_to=\"Value\") %>% \n",
-    "    group_by(category) %>% summarise(ratio = sum(Value!=0)/n(), count=sum(Value!=0), .groups = 'drop')  %>% mutate(ratio=ratio*100) %>% arrange(desc(count)) %>% mutate(category = fct_reorder(category, count))\n",
-    "\n",
-    "plot_endpoints = ggplot(temp, aes(x=category, y=count)) + ggtitle(\"Endpoints\") + \n",
-    "    geom_bar(stat=\"identity\", position=position_dodge(width = 0.8), fill=\"gray70\", width=0.5) +\n",
-    "    coord_flip()+xlab(\"\") +\n",
-    "    ylab(\"Number of Events (%)\") +\n",
-    "    scale_y_continuous(expand=c(0,0), limits=c(0, max(temp$count)*1.3))+\n",
-    "    geom_text(aes(x=category, y=count, label=glue(\"{count} ({round(ratio,1)}%)\")), position = position_dodge(0.8), hjust=-0.1, size = geom_text_size)+\n",
-    "    theme_classic(base_size = base_size) + theme(strip.background = element_blank(), plot.title=element_text(size=title_size, hjust=0.35)) + \n",
-    "    scale_x_discrete()\n",
-    "\n",
-    "plot_name = \"3_endpoints\"\n",
-    "ggsave(filename=glue(\"{dataset_path}/{plot_name}.pdf\"), plot=last_plot(), width=plot_width, height=plot_height, dpi=plot_dpi, device=\"pdf\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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ABw/g4unPr66xOX2qf11AOWmqg2zx2R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\nRU5ErkJggg==",
-      "text/plain": [
-       "plot without title"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 360,
-       "width": 1200
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_endpoints"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Event densities"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T14:29:06.050717Z",
-     "start_time": "2020-11-04T14:28:59.615Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "plot_width=20; plot_height=10; plot_dpi=300\n",
-    "options(repr.plot.width=plot_width, repr.plot.height=plot_height)\n",
-    "\n",
-    "plots = list()\n",
-    "for (e in endpoints[-2]){\n",
-    "    temp = data %>% filter(!!sym(paste0(e, \"_event\"))!=0)\n",
-    "    \n",
-    "    plots[[e]] = ggplot(temp, aes(x=!!sym(paste0(e, \"_event_time\")))) + \n",
-    "        geom_density(adjust=1.5, fill=\"gray70\") + \n",
-    "        scale_y_continuous(expand=c(0,0)) +\n",
-    "        scale_x_continuous(expand=c(0,0)) +\n",
-    "    xlab(e)\n",
-    "    #+ \n",
-    "   # theme_classic(base_size = base_size) \n",
-    "    #print(paste0(nrow(temp), \" events in \", nrow(data), \" people in observation time\"))\n",
-    "    #print(paste0(round((nrow(temp)/nrow(data))*100, 1), \" %\"))\n",
-    "}\n",
-    "title <- ggdraw(get_title(ggplot()  + ggtitle(\"Endpoint Densities\")))\n",
-    "plots_density_raw = plot_grid(plotlist = plots, ncol=4)\n",
-    "plots_density = plot_grid(title, plots_density_raw , ncol=1, rel_heights=c(0.1, 0.9), align=\"v\", axis=\"lr\") \n",
-    "\n",
-    "plot_name = \"4_endpoint_densities\"\n",
-    "ggsave(filename=glue(\"{dataset_path}/{plot_name}.pdf\"), plot=last_plot(), width=plot_width, height=plot_height, dpi=plot_dpi, device=\"pdf\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T14:29:06.952215Z",
-     "start_time": "2020-11-04T14:29:00.872Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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93BK1njKWFlQNWWXovmc/cCnDm7s0sUUXyr6SxTMXhjjFT8C+5R8hcClp9ODY+g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\nhAksAIAwgQUAEPYDmH9cQZZL+RwAAAAASUVORK5CYII=",
-      "text/plain": [
-       "plot without title"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 600,
-       "width": 1200
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plots_density"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Kaplan Meyer estimates"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "options(warn=0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(predvars, data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(predvars, data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(substitute(subset), data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(substitute(subset), data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message:\n",
-      "\"Vectorized input to `element_text()` is not officially supported.\n",
-      "Results may be unexpected or may change in future versions of ggplot2.\"\n",
-      "Scale for 'x' is already present. Adding another scale for 'x', which will\n",
-      "replace the existing scale.\n",
-      "\n",
-      "Scale for 'x' is already present. Adding another scale for 'x', which will\n",
-      "replace the existing scale.\n",
-      "\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(predvars, data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(predvars, data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(substitute(subset), data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(substitute(subset), data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message:\n",
-      "\"Vectorized input to `element_text()` is not officially supported.\n",
-      "Results may be unexpected or may change in future versions of ggplot2.\"\n",
-      "Scale for 'x' is already present. Adding another scale for 'x', which will\n",
-      "replace the existing scale.\n",
-      "\n",
-      "Scale for 'x' is already present. Adding another scale for 'x', which will\n",
-      "replace the existing scale.\n",
-      "\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(predvars, data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(predvars, data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(substitute(subset), data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(substitute(subset), data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message:\n",
-      "\"Vectorized input to `element_text()` is not officially supported.\n",
-      "Results may be unexpected or may change in future versions of ggplot2.\"\n",
-      "Scale for 'x' is already present. Adding another scale for 'x', which will\n",
-      "replace the existing scale.\n",
-      "\n",
-      "Scale for 'x' is already present. Adding another scale for 'x', which will\n",
-      "replace the existing scale.\n",
-      "\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(predvars, data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(predvars, data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(substitute(subset), data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(substitute(subset), data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message:\n",
-      "\"Vectorized input to `element_text()` is not officially supported.\n",
-      "Results may be unexpected or may change in future versions of ggplot2.\"\n",
-      "Scale for 'x' is already present. Adding another scale for 'x', which will\n",
-      "replace the existing scale.\n",
-      "\n",
-      "Scale for 'x' is already present. Adding another scale for 'x', which will\n",
-      "replace the existing scale.\n",
-      "\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(predvars, data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(predvars, data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(substitute(subset), data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(substitute(subset), data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message:\n",
-      "\"Vectorized input to `element_text()` is not officially supported.\n",
-      "Results may be unexpected or may change in future versions of ggplot2.\"\n",
-      "Scale for 'x' is already present. Adding another scale for 'x', which will\n",
-      "replace the existing scale.\n",
-      "\n",
-      "Scale for 'x' is already present. Adding another scale for 'x', which will\n",
-      "replace the existing scale.\n",
-      "\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(predvars, data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(predvars, data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(substitute(subset), data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(substitute(subset), data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message:\n",
-      "\"Vectorized input to `element_text()` is not officially supported.\n",
-      "Results may be unexpected or may change in future versions of ggplot2.\"\n",
-      "Scale for 'x' is already present. Adding another scale for 'x', which will\n",
-      "replace the existing scale.\n",
-      "\n",
-      "Scale for 'x' is already present. Adding another scale for 'x', which will\n",
-      "replace the existing scale.\n",
-      "\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(predvars, data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(predvars, data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(substitute(subset), data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(substitute(subset), data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message:\n",
-      "\"Vectorized input to `element_text()` is not officially supported.\n",
-      "Results may be unexpected or may change in future versions of ggplot2.\"\n",
-      "Scale for 'x' is already present. Adding another scale for 'x', which will\n",
-      "replace the existing scale.\n",
-      "\n",
-      "Scale for 'x' is already present. Adding another scale for 'x', which will\n",
-      "replace the existing scale.\n",
-      "\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(predvars, data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(predvars, data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(substitute(subset), data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(substitute(subset), data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message:\n",
-      "\"Vectorized input to `element_text()` is not officially supported.\n",
-      "Results may be unexpected or may change in future versions of ggplot2.\"\n",
-      "Scale for 'x' is already present. Adding another scale for 'x', which will\n",
-      "replace the existing scale.\n",
-      "\n",
-      "Scale for 'x' is already present. Adding another scale for 'x', which will\n",
-      "replace the existing scale.\n",
-      "\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(predvars, data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(predvars, data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(substitute(subset), data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(substitute(subset), data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message:\n",
-      "\"Vectorized input to `element_text()` is not officially supported.\n",
-      "Results may be unexpected or may change in future versions of ggplot2.\"\n",
-      "Scale for 'x' is already present. Adding another scale for 'x', which will\n",
-      "replace the existing scale.\n",
-      "\n",
-      "Scale for 'x' is already present. Adding another scale for 'x', which will\n",
-      "replace the existing scale.\n",
-      "\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(predvars, data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(predvars, data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(substitute(subset), data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(substitute(subset), data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message:\n",
-      "\"Vectorized input to `element_text()` is not officially supported.\n",
-      "Results may be unexpected or may change in future versions of ggplot2.\"\n",
-      "Scale for 'x' is already present. Adding another scale for 'x', which will\n",
-      "replace the existing scale.\n",
-      "\n",
-      "Scale for 'x' is already present. Adding another scale for 'x', which will\n",
-      "replace the existing scale.\n",
-      "\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(predvars, data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(predvars, data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(substitute(subset), data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(substitute(subset), data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message:\n",
-      "\"Vectorized input to `element_text()` is not officially supported.\n",
-      "Results may be unexpected or may change in future versions of ggplot2.\"\n",
-      "Scale for 'x' is already present. Adding another scale for 'x', which will\n",
-      "replace the existing scale.\n",
-      "\n",
-      "Scale for 'x' is already present. Adding another scale for 'x', which will\n",
-      "replace the existing scale.\n",
-      "\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(predvars, data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(predvars, data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(substitute(subset), data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(substitute(subset), data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in terms.formula(formula, data = data):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in FUN(X[[i]], ...):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'm<U+00E9>ni<U+00E8>re's_disease' to native encoding\"\n",
-      "Warning message in eval(extras, data, env):\n",
-      "\"unable to translate 'sj<U+00F6>gren's_syndrome' to native encoding\"\n",
-      "Warning message:\n",
-      "\"Vectorized input to `element_text()` is not officially supported.\n",
-      "Results may be unexpected or may change in future versions of ggplot2.\"\n",
-      "Scale for 'x' is already present. Adding another scale for 'x', which will\n",
-      "replace the existing scale.\n",
-      "\n",
-      "Scale for 'x' is already present. Adding another scale for 'x', which will\n",
-      "replace the existing scale.\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "stratum=\"sex\"\n",
-    "plots = list()\n",
-    "tables = list()\n",
-    "plots_tables = list()\n",
-    "expand = c(0.01, 0.8)\n",
-    "for (e in endpoints[-2]){\n",
-    "    #print(e)\n",
-    "    #fit = survfit(Surv(death_cens_time, death_cens)~sex, data = data)\n",
-    "    fit = survfit(as.formula(glue(\"Surv({e}_event_time, {e}_event)~{stratum}\")), data=data)\n",
-    "    plot = ggsurvplot(fit,data, conf.int = TRUE, ylim = c(0.85,1), cumevents=TRUE, cumevents.y.text = FALSE) #, risk.table =\"percentage\") + scale_color_\n",
-    "    plots[[e]] = plot$plot  + ylab(\"\") + theme(legend.position=\"none\") + \n",
-    "        ggtitle(e) + theme(plot.title = element_text(size=facet_size, hjust=0.5)) + \n",
-    "        scale_color_nejm() + scale_fill_nejm() + scale_x_continuous(expand=expand)\n",
-    "\n",
-    "    tables[[e]] = plot$cumevents + ylab(\"\") + scale_color_nejm() + scale_fill_nejm() + scale_x_continuous(expand=expand)#+ scale_x_continuous(expand=c(0,0))\n",
-    "    plots_tables[[e]] = plot_grid(plots[[e]], tables[[e]], align=\"v\", nrow=2, rel_heights=c(4,1))\n",
-    "    legend <- get_legend(plots[[e]] + guides(color = guide_legend(nrow = 1)) + theme(legend.position = \"bottom\", legend.title=element_text(size=base_size), legend.text=element_text(size=base_size)))\n",
-    "}          "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plot_width=20; plot_height=25; plot_dpi=300\n",
-    "options(repr.plot.width=plot_width, repr.plot.height=plot_height)\n",
-    "\n",
-    "plots_f = plot_grid(plotlist = plots_tables, ncol=round(length(plots_tables)/3, 0))\n",
-    "plots_km = plot_grid(legend, plots_f, ncol=1, rel_heights=c(0.05, 1))\n",
-    "\n",
-    "plot_name = \"5_endpoint_km\"\n",
-    "ggsave(filename=glue(\"{dataset_path}/{plot_name}.pdf\"), plot=last_plot(), width=plot_width, height=plot_height, dpi=plot_dpi, device=\"pdf\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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CG90292B7ps3iaTW2v39jh8WrtvsIk1I5Wp4KXLealS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HRAYAxCLIQQCiWbgCIY7QGCw92gtDCKHX\nU67dqpo1VpcYBphTCCIJcvBcjMfdteWzjk/e8+p7+h/HKEqcX5L9y8eEOQWR7E+nzfvOsc63\nj3a0WcLv0ETi2JVFmquLNZfmKCRcKpJ9AwBMDFAgnIxB2LNza91rz9GOfkuKYUg5f0nOQ0+z\nFarx69dAB5vN/zzSsfFUt58J8680S8G/qlgzN1WyOk9FwCQGAAAYuUmbg7GO8biNhw/odn9j\n/H5vKOwOPRjixicl//RnmmWrSYHwQi4RDJ3ssn9Toz/cat3baAwbxH1kPOqnU+NX56umJ4qV\n8PgqACB2QA4CEP3qXlrfuXXj/74aywJhHwwhhMqFWY/m3jPm1xoEzCkEkQE5OJhQkNHt2d76\n3puu1ub+xzEcE2Tn5z32B2FWROcch0LoRKft45PdO2oN1Tpn2DYcEr+qWLM4U16aIMpTC7gU\n/EwBAMMCBcJJGoRBn7f9s/db/v3XoP/HWXoYScVfflXaLfewlZpx7NsANi+9rUb/zrHO/c1m\nPx3mZiBOxL5jVtLaAvX0pJHtwAQAAJPcZM7BicGr0+q/29H19afuzjYU7gMdRlLSkulJ198i\nKZ52wcuJO33MoVbLrnrj1mp9rT787WgfGY+alyYtjhMtzJAtyZTDEzwAgGgGOQhAbOnZ/nXV\nC49H9JLn2aoQQ2E/gY1yF1Dw1cvG+ipgcoIcPC/9vp3Vzz/JeM5eWwVD6iUr0372AD81I/Jd\najC6tlbrN1foDraYBxvUxzFsebbijlmJawvVLAKPbAcBADEGCoSTOghdbc31rz9vPn64/0EM\nx+NWX5lx14MsmWK8OhaWzuH719GOrdX6I23WsA2SpZwrCtTXT4mfkSyG/AMAgPOCHJwwAjaL\n9Ux524dv22srQnSY1UdxFiUumJJy012y6XOwi/hxt1k8VT3OGp3zZJdtZ73R4AyzGHgfMYdc\nV6Qp0giX5yiK4y5kIiMAAIwpyEEAYlf5XTdYak9F+KK/yH+wRtB/v+dIFAj7vLg657El41CN\nABMY5OBw0E5Hx+cfdG3e6DPqz3oBQ5Ki0uxfPxXh2YR9jC7/5krdm4faT3SG36cQISTkkLdO\nT7h5WsKMJAnsCgEACAsKhJM+CEMh3d5vazc8Qzsc/Q8THE7a7Q8kX38bhkfdX87xDtubh9q+\nONNj89JhG4g55DUlcY8uTs9W8iPcNwAAiCGQgxOP32o2Hdrf/c0mW9XpEB0mJSmhWDptVuqt\n947KfezxDtsnJ7s/LO/WOXxDt0wUc5ZlKxZmyK4q0og45MVfGgAALh7kIAATQ/uH7zS8uSGS\nV3w99dqtqlkRWfD0RxSO+TfAuqNgNEEODl8oyPTs3Nr89l+82q4BL2lWrMl56OkLXq/l4mnt\nvo/Ku98/0VWpdTCDjPPHidgPzEv55SWpQjbciwEAzgIFQghChBCiHfaOTR+1ffQ243L1P85N\nSM5+8HHFnIUoKp8zOaN1vH6g5YszPfZBKoXxIs5vFqZdPzUuQcyJcN8AACD6QQ5OYD6jXrvj\nq64vP/bqesI2oIRiQWa2esUaYVaeKKfgIoO+Vu880Wk/020v77Ifa7cOlssIIQLDspX8Bxek\n/mRKnIRLXcxFAQDgIkEOAjDBNP3ttdaP/xnRS2Jol3z6S+nXR/SiCCGYUAhGA+TgiIVCPbu/\naXjjj36rpf9hgsNNWPeTpOtu4ajGc88mm5c+2WU70+34/EzP9+EWIKUI7NqSuHWF6mXZCinc\niwEAEEJQIIQg7I/xuLU7vmp66w3aYe9/XDF3YeGzrxGcKK2xBZjQgWbzJye7v67S6cOtdYZh\naE6K9LopcWsL1KkybuR7CAAA0QlycDJwd7YZDu7p/PyDwSqFCCFOXELKjXeol6yiRKOzm29Z\nh+2LMz2fnOrutHrpYPiPmiwCK4oT3Toj4ZriuDgRe1SuCwAAIwI5CMAEVvfS+s6tGyN9VQw9\nnHP/KVF6pK8LWxWCCwI5eGGCAb9u9zeNf/+T32zqfxwjSdWiFdkPPsmSysarb33qDa73T3T9\n/VC7yRVmpJQisDX56rUFqmtL4ngs+OkDMKlBgRCCcCDa6Wj6x2tdX30aCv64WgYnLiHnwScV\n8xePY8fOKxgKHWmzbqsxvHWkPeyuSASGFWgEv7wk9coijYwHT8oAACY7yMFJxdlYp/32666v\nP2NczvAtMMRLSMn+1ZPy2ZeM1kXpYGhXvfHrKt32GkO71Rv2YyeBYZlK3p2zkq4q0qTLeaN1\naQAAOC/IQQAmiXEoFmIIIfR6yrVbVbMidsnQq7AAKRgZyMGLEWKY7i2f1//fi0H/Wbst4Cwq\n8ZqbUm+8ixJLxqtvfehg6GCz+eOT3e+VdfnpMIsiC1jkmgLVbTMSlmcr8KhcPQ4AMNagQAhB\nGJ6t8mTNS8+4Whr7H5TNmFPwzAaWZPwfhBmajw7uqDV8VaX78ESXnwnzLxzHsMWZsl/MT12d\nryJxyD8AwCQFOTgJhWjacrqsa/NGR321V9vV/2GgPpy4BGnJtJSb7+anjObz71q778uKng/K\nu4+1WQfbGyNBzLmxNP6qYs2MJDHcoAIAxhrkIACTjaXsSPmvb4/0VTFULsx6NPeeSF5Tyaf0\nzy6P5BVBLIIcvHh+i7lz00cdn/6HPvspTIwkOOr45Otvi7/sSpw9/kuyGV3+j092v7qvpc3i\nCdtAI2TfOiPx7tlJ8MgmAJMNFAghCIfSvfXz2pd/33/0EGexMu/9TeLVN2Kx8Jdm89I76wzv\nHOvcVW8Mu8RZnIh975zkWSmSBekyLhUD3xEAAIwiyMFJjvF6bJWnmv/1F3vtmRDNDHwZQ9z4\nJEnR1Ix7fsVWjuZeGm0Wz9Zq/b+Pdp7utg9WKVTwWdeUaK4oUM9JkcBWhQCAMQI5CMCkNQ67\nFfbBEELoqaw7j0hyx+4iPIpwvXjp2J0fTAyQg6OF8Xq7t37e/PZfBuzZhBCiROK02+5LWPcT\nnBUVuyp02rybK3R/+6Gt3uA6914Mw1CyhHt5gerqYs2iDPm49BAAEGFQIIQgPA97bWXty793\n1Ff3P8hRa1JvvVez4oqo3ZhwALef+ey09tPT2u21xrD/5nks4pfzUx9dkg6b9AIAJg/IQdDL\nbzUbv/+ufeN/Bqwc0AvDcUnJtOQbbpeWTCd4/FG8rsvPfH5a+02NYUuVzhtuxRuEEI5huSr+\nHbOS5qVJZyWP/yo9AICJBHIQAIAQOvXz202nj4zLpbs4iluLHx+LM/MoYuMtUxFCa/JVY3F+\nMDFADo4u2uXs+PyD1vfeDPoHbntE8Hhpt96bdO3NUVImRAh1WL3vn+h681Bbh9UbtkGWgv+L\nS1JuLE2ATZoAmNigQAhBOCydmz9p+PPLQd9ZmUEKBJn3Pxy/5hoMx8erYyNldPk3V+r+U9Z1\nsNl87qssAivQCO+bm7I6Xxkvio3aJwAAXDDIQTCA+fihlnf+Zq+tCPoD576Ks9jS0hnJ190q\nKZk2uuvkOHz0ngbTrnrjhye6bF56sGbpct6ds5JK4kWzksVyPmsUOwAAmJwgBwEA/Y3DVoX9\nPJ9x8155yeidD9tyx7TeP0GNEAwGcnAs0A57z84tPd9usddWhM5ezIwlU+Q+/LRi/pKoGkc9\n3mH715GOD8q73P5zFpVBiMCwDAXvvrnJ98xJhqXXAJiQoEAIQThctMvR/O+/dn72wYD9inhJ\nKWm3P6BeellUxdt5Veucr+5r/vRUj9MfZiCSwLApCaI7ZyXdOiMB8g8AMFFBDoKwGK/XdPRA\n5+cf2qrOBP2+cxuwJFL57AXpdz/IUY3m0qMIISYYKuu0fXKy+7PTPV228I+yIoQ4JH77rKQb\nS+OnJ4lZRCx9/AAARBXIQQDAYMZtDVIM7ZJPfyn9+os/U7qc98Jl2XwW2f8gFAtBf5CDY8qr\n01Y/94T1dNmAcVSOOq5kw5uC9Kzx6lhYbj+zrdaws87wdZVe5whzDyhkkz+ZEverBWkFGkHk\nuwcAGDtQIIQgHBlHXVXtq8866mtC9Fl1NVFeUeEzG7iJyePVsQvDBEO1etcHJ7reONjqCYR5\nUkbIJu+anfTQojSYUAgAmHggB8HQGK9Xu21T89t/Cdis576KUZRs6sykn9wqnzkPYdioX71O\n7zrQbH7neOeJTpt/kAVIBSzyvnnJVxVpZqfA6qMAgBGDHAQADFP5XTdYak9F7noYQgh1sUdt\nAdLZaeLfLvmxGgFlQtALcjACPF0dda89Zy471H/Td4ykxIUleY89y0tKHb+uhRcKoR11hv+U\ndW2q6Al7F5Yi5T69Iuv6KXE8FvyzAWAigAIhBOGF8HS2Vz33uK3yrM/HGEVl/fzRpKtvHK9e\nXQy90/9hedd7xzsrtM7gOf9T4BhWFCd4aU3usiwFgY/+GCgAAIwLyEEwHCGGcbU1t3/0b9Ox\nH/xm47kNCA5HmFOQetNdshlzMZI8t8FF8jPBvY2mF/c0HWm3egPhK4UKPqskXnTbjIRZKZIs\nxWhulAgAmMAgBwEAIzUuGxb+Iv/BGkHSBb0V6/3v5mnx106J6zsKBULQC3IwYtwdrdXPPWGr\nOt3/IM5iKeYvyX7wCbZcOV4dG4LFE/iyQvfe8c7DbZYAM3CklM8ibpuRePP0hBlJYnwMnhYF\nAEQMFAghCC+crfJk3at/cDTWon7/iET5RflPvsBPzRi/fl0UiyewuUL3f9+3VmoddHDg/x0K\nPmtWsuSmafFXF8dRBOQfACC2QQ6CkXI01Na//oK95nTYTQoJDkc+Z2HKjXeIcgvH4uoBJtRo\ndH1dpXv9QGtPuHVvek1LFD+1PHN1ngqSGgAwNMhBAMDFKLv1altzTQQu9K+kNZ/ELbrgt6uF\n7H9dV9T/CBQIQS/IwQiznj5R8cxv/EZD/4MYSclnzs1/6gRpB9AAACAASURBVEVKJB6vjg2t\nxez+97HOt490aMPdgiWKOb+8JHV5tqJAI4T7LwBiERQIIQgvVs+urfV/ej7gsPUdwUgq7/Fn\n41auHcdeXbxWs+dfRzteO9ASdpNeMYecnyb77bKM2SlSeFAGABCjIAfBhWG8ns4vPmz/9D9+\nU5gJhQghbkKSuGBK8vW3CrPzx6IDoRA61mH94kzPm4faHb4wewkjhEQccmqC6Pop8WsL1XEi\n9lh0AwAQ6yAHAQCjwlJ2pPzXt0fscg/n3n9KlD789iwC+/DGqRzqrG2boUYIEOTgeGA87u5t\nX7b+5x8D7qQILk+1cFnOb35H8KJ0NRQmGNpRZ3hxT/ORNsu5EyoQQhwKn58me/bSLBgpBSC2\nQIEQgnAU0C5n/Rt/1G778sdDGJIUleb/9o/chAtbByNaWD2BzZW6F79rqtO7wjaQ8Vgzk8U/\nm5F4aa5SzBn9ddUAAGDsQA6Ci2Q9U97+8TuOxlqvtitsA1IoFOcVp91+v7hw6lh0gAmGDraY\n/+9g68kue6vZE7YNhqFcpeCZS7Muy1MK2ZDUAIAfQQ4CAEZXRCuF/xt/PynMeiT3ngEvEjiu\n5FN9Ky5cWaS5fWai3uF/aEvN2kLVNcVxCGqEAHJw/NAuZ9tHb3d89j7jdvc/Toklmksvz7zv\nIZxijVffzqvD6n33eOdr+1ssnjCLyiCEVELWL+en3jMnWcGP3u8CANAHCoQQhKMkFOrZtbXh\nzy/7Laa+YxhFyWfMK/z9hqh9/mX4jrRZPz7Z/dkpbdgJ9QghLkX8/tKsn06NT5JwItw3AAC4\nMJCDYLQ4m+sb/vyyraKc8XrDNuCo4yQl07J+/ihLphijPugcvoMtlt9tr6/VO8P3gcSnJopv\nmZawMleZKuOOUTcAADEEchAAMKbaP3yn4c0NY32VclHWo+cUCEUcalWuYuMpbe+XSRLOkiy5\nw8tsquhZmat6YF5y73GoEU5ykIPji3G76t54Qb/32wFlQoLPVy1Ynnn/wyypbLz6dl4+Oniw\n2fzid80Hm03+c3YoRAhhGFagEfxyfuraQrVKAJVCAKIXFAghCEdTMOCvWv+Ift+u/gcpoTh+\nzVUZ9/4GmxB/zzU657vHO/99rNPo8p/7Kolj89Kkz67MviRNBhPqAQBRDnIQjK4QTVvPnGj6\n15+d9TWMN8x8PpzFVsxblHnPr7mJyWPXjUaj+72yzs9OaZtM7rCr3yCEshT8wjjh86uy89SC\nsesJACDKQQ4CACJpDPcs/N/gA4YRS/ZX9P754/Luez6vPHcxdhLH3/1pce8CSFAgnOQgB6NB\nwGatfv5Jc9kPA3Z5x3BcNnNu0R9eJ7i88erbcHgCzLF22w+tlr9+39ZtD/O0KEVgc1Olv780\na1GGPPLdAwCcFxQIIQhHWYhhurdtan7rDb/F3P84Jy6h4KkXJSXTxqtjoysUQt+3mN842Hqo\n1aK1h9+kd0mWfP2l2TBHAQAQtSAHwRgJBZm2D/5lr66w1Vb4jYZzG/ASU6TTZguzchPW/WTs\numF0+XfUGl7b31reZQvbAMNQoUZ475zklbnKdHlU33gDAMYC5CAAYBzVvbS+c+vG0T8vhhBC\nFE9g/9PWX3xZFXYN9mdWZE1PEvd9CWXCSQtyMHp4ujuq/vC4rfIkOnucnuBw5bPm5zz0NEsW\n7dW1UAgdbDH/cU/Td40mPx08t0Gmgv/cqux1hWo2iZ/7KgBgvECBEIJwTAR93toNv9ft3Rn0\n/fjwCIZjkpLp+b97iaPSjGPfRp3W7nt1f/Nfvm/znZN/JI7NT5M+uCDtsjwli4D8AwBEF8hB\nMNZCwaCl7HDLe/+w11QE/WGep+HGJ4ryiiTF0xKvvmHsutFq9nxVpfv7obYm46BzCtNkvFkp\nkieXZhTFCceuJwCAqAI5CACIBmMxsxBnc1z/t+P7ZvPrB1vPHakQsMg31uWrhGct+gdlwkkI\ncjDaOJsbmv7xmvn4oaD/rEXLCB5PvXRV1gOPkoIYuFUxOP3bavR/+aHtRKf93LqDmEMuzJC/\nckVuliLmt6MCYGKAAiEE4RjyGXrO/PZBe3VF/4MYReU9uj5u5Vo0sZbgtHnpj8q7X97bFPYB\nPTaJZ8h5uWrBI4vSZ6dIIt89AAA4F+QgiJiA3Vbzwm/NZYcG26SQrVRLp84QF5UmXnn92HXD\n7qW31ej/cbj9jNZpdodZKhwhVJognp4kfmp5JmwqDMCEBzkIAIge1U8/ot37zWidDWdzitb/\nSTFvkeqZ3QZn+M88vV5YnVOkESIoEE5KkIPRiXY5Gv+yoXvHV6HAWYuOEny+ZsllmT9/mOTH\nQJkQIWRy+Tee0r6wp6nLNvAeEMOw0gTRq1fkLcyI3n0WAZgkoEAIQTjmHHVVlesfcbe39j8o\nyi8qefnvLMkEjIFDrZb1Oxu+b7G4/UzYBvEiztJs+QuX5SSKYeQRADCeIAdBhDEed9sH/zIf\nP2SvrwrR4VOSJVeoFizP+uVjODWGW9kzwdA3NfrndjeWd9iZQT4MJ4o5K3KUjy1Jz1bCw60A\nTEyQgwCAKNSz/euqFx6/yJP0FggRQnce9fmEMoQQEwx932zx/O8DmJzPmpEkRgitK1QniDkI\nCoSTEuRgNPP2dDf8dYPpyAHGc9Y8BIwkhVl5mQ88LJ0yY7z6NlJH2633f151sjvMhMKiOOGT\nSzOuLo6jiAk1jQSAGAIFQgjCSAgxTNeWz5rfeiNg/3ETIEosyXviOeX8JePYsbHjpYP/PNL+\n8nfNnec8JtOLReLrCtV/vCwHNj0CAIwXyEEwXmino/2Td03Hvne1NDEe97kNKLFEMWdh7iPP\n4OyxfZjG5qX3NBj/8n1bWafN4aXPbYBhWIFacO/c5PvmJuMTa/EDAADkIAAgyl1ksZCSSDpv\n+4Mor6j3y1Nd9t/tqO979fW1eRn9lviDAuEkBDkY/fwWc/XzT5jLDp37eKVi/uKiP7yOU9S4\ndOwCNJvcj39Tt6veaPUEBrwUJ2L/dlnmDaXxUm7MfDsATBhQIIQgjBzG4z756zttlaf6H1TM\nXpBx/0OC9Kzx6tVYq9E5N+xtrtY76/UuyzkRiGPYtCTRX64smJkM644CACINchCMu1CQaX33\nTWvFKeuZE/33Le5FCkWqBcuyfvFYBDbb8DPB7TWGJ7bVNRhcYfcpVPBZ05PEd89OuqJATeBQ\nKQRgIoAcBADElgtehpSZMt9/33MdVu/9X1T2HXxpTW6+WtC/GdQIJxvIwVjhNxtrXn7GdORg\niD7riUZBeta0v38QKyuO9vLSwU1nev6wq7FW7xzwEolji7Pkf7uqMFMBUykAiBwoEEIQRprh\nwO6q559gXK6+IxiOpd3+87Tb7hvHXkVAKIT2NZn++F3TD+FWHy3UCJ9clnFFgZrPgn+NAIAI\ngRwE0SMYCLS+96at4pTl5NFQMNj/JYwk+CkZ0tJZ6Xf8PAKVQpef2VKle+tIx/ct5gAT5nNy\nnJA9L0326hW5yVLuWHcGADCmIAcBADGn4uEH9Ef3Drf1/55oCqy7k155k9buu/uzir4XH5if\nsjJHOeAdUCOcVCAHY4vfbGr+918tJw67O9r6DhJ8gTivqODpl1ky+Tj27QLsqDU8sa3udLd9\nQGmCwLDSRNFjSzKuKtLAAi4ARAAUCCEIx4HPZDj9yH2O+ur+B1ULlxc++ydsEvwsbF76+d2N\nfz7Y6qWDA14Sc8grizQvrs5RC9nj0jcAwKQCOQiikKe7o3bDesvJo+euokNwOOKCqaL8ovS7\nfoHhY/6P1hNgvq7Sv7qv+USnPXjOB2YMw2anSJ5flb0wQwZLjwIQoyAHAQCxaFjrjoYQQoiX\nkp51/8M7e5jeVUYHFAhzVPxfL0jr3YCwDxQIJxXIwRhV/cJve3Z81f+pSpxFSUpmKuctSrzm\nxnHs2AVoMLru+azyhxaz/5xHM9PlvA2X566F5VsAGGNQIIQgHB8hmm775J22D/5JO3+cUc7R\nxBf87iVJybRx7FjE+OjgJye71+9sbDEP3HsJ/3/27js8imr9A/iZbek9m14JIRA6AoK0JBB6\nUVAUUFEs6L3+vHbxWtGrIIpe1Iuo2ECaIAiEUFMgBIMECCWNkN422U3v2+b3x4bNsimEZHZn\nd+f7eXy8u2dmJ+/zXOGbnXfOORQ1LsDp8wVDJge7sFIbAHAEchBMVmtFedaG96svnacV+qtz\nE0JE7mK38ZOdh4/2WfCgEYq5Lml460j2xeI6SWNb51+cxfaiqQNcX5gcOHUAOoUAZgY5CABm\nraWk6Nyy2V0fu/UbS9hLb5+hvAghmh5hQ6ty+Y6ObV8GuNluuj9c93NoEHIKctB8SZPirr//\nilqu/13JIXSwZ/T8wOWrWKmqz6qa5P8+euPg9YqKhja9Q2J70aww8RcLh4jtRazUBmDx0CBE\nELKJVirTXn22+mJKxxBFxFNmDHt/A8/KuvvPWQ41TR+4VvHJqdy0si5mJwxws31patCzEwKs\nBDxWygMAy4YcBBOnqKvJ/X5Tfea1hpxM0tVvrLZBIZ6Rs+wHDPKInGmEeq6U1b8Zk/VXYW19\nq7LzUVsRP9zT/q3pIXjKFcBcIAcBwKz1pkE48PlX7YJCCCHukyIIIX9er3hk++W2W6sZCfnU\npvuH+jt33H5Bg5BTkINmrU0qubl5o/Rsgqrl9okHFHEYFD5kzUcOoUNYKq2PFCr6x/PFX58t\nyKjQ355QJOCN9nGcMsD17RkhzjZCVsoDsFRoECIIWUar1QW/fpv/87e6U+OtPb2H/2eT45Bh\nLBZmZMW1rSt3XelyTr2NkD8txHXD/MHDvc1p22EAMH3IQTAXb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ewdQgZ5Rs3xe9AY2xMy3iDUvsGEQgAN5CAAWDZZciIhRJZypvTAbr1D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poAAMB0zbkcjPT1dL/2gvfA3uPLVQxBEZ5aq133PfXqMxSLT0FHfEWeSOzLDvf7R61HLIFW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mckDPdfXb/0q3F7v7u5VYN2bN8QutZ23/8O+zQogr\neoNBloUQp9PzpanhoYv2KpYVAADYL/dGV6enyMX6/MS4gvMJxh99Yb6ywQDAyG/YqBZT39V2\nDTaN5MfHnp0fdm7xN8qFsrywgS2T3u71dEdfjdPVr6pkWV66/7z/hxFvhZ924IfbAQAArIUZ\nhDwp46hk+cQH01L/Wm8acK5Zq+nk13z7Pa5gKMeUlqsbsHjf3oQbphK28/XoHegZtu1cyUGN\nWroyq69t0wH2jDoIwEHcfSphysa1aRGbbv/anaesMIPQDlAHUcXI8on330jd/KcwfYsjCe39\nwa3eneXkXl3JYJY2b1fC3F3xx1NySg629fHYPOk+z+oaIcS8HQmTfz/u6e6c9v5DCmUE7AF1\nEADADEI4Kklq9c6sgDGTTbdSRZeyY+aFJf/xi7K5HJBXdc2eF7sNaetjWtFLCHEkOWf29rib\nztQbJO//bZm5Ncam+QAAgF2TVCXuiaQbf+5sywNBpp8dgx60dkgAEJLU6t1PG4+erKlT9+qI\nLDJ2Rf47+rELWzYomszCnuvqd/jV7gOC6pW8QzyakhM0658P/j4rhEi8dEUIkZ5XJE0N5/YQ\nAACg3GgQwqE1Hjul6XNT1a7VjIe67KzTsz8+t+grZVM5IEkSq0d1ePuhpgF13EyDxQbDrWde\nyLky/c/T3AcCAABL8WjRWnK69uC8+aurlOgjVm/S3DrRAOBmAWOnNJnwikfT6792ClPOR389\nM37F9wqmsji1SvpjbKevHwtq5+thGszI0727KfrR7w6cSrs+uXD6n6dd3rCr/igAAIDNsMQo\nU+khElYtif1+TnFu7tVjSWi7hbT75BshsRG6rTm/vqHYYO4vJUkIQ1ioVfMA9o06CMBB3H2J\nUSGELjOjICVR3PiPkIyIv3JTEu50CUuM2gHqIKowWT41638pm9YadDrjgKSSvB8e0PKtj2+Y\nFW0X3t0UPWtrTGHx9edH3TWavGv/4UYsNwqUA3UQAECDkEIIIYRI/HlZ3LKFuqxM04i2a7Dv\nwCc8u4comMoBdft616647DJdohJCT5sQKBfqIABHU2qnsKT47+Znnzl6p1dpENoB6iCqurPz\nwhJWfC+XWHmlVrtOfsPGeD7QU8FU1hB3seDR7/YfvXFXwpI0KmedoUgI0dzT7dT0YNslA6oy\n6iAAwN6eLAPKp+GTI5q99KZHYAvTSMauyMSfl8rFxQqmckA7X+iqLuO8TYMQ0tRwr3f+tk4i\nAAAAAKiMmj43tfmrb7vW8zaNZB85EPv9nNS/1iuYyhr861S7S3dQCGHsDgohTqfns+IoAACA\nmWgQAlfV6x1673e/ej7QyzSSdfDfXU8+nLrxDwVTOaDiz0LlsNCytgnZoB4AAACAo6k/aGiz\nl/6j7RpsGsk5czL66xlxyxYqF8oqVGbfIer0sjQ1PHTRXmvGAQAAsAcsMcpUetxIlo+/99qF\nzdcfOXTxrNd04qvefQYoGMoxbYnO7D3/37JepZZE8WesOAqYhToIwNGUaYnRu9B2C7bI+0BZ\n1EHYk6gPpqdsKvFsqyR8Qx9v+eaHyiWysP+En/54S9meB2XTeuDuqIMAABqEFELcRtQH01P/\nXicbrv7t0NSu22Tci76PPqFsKkdTvgahUd8W2vDx91o2D2B/qIMAYFKm3iENQvtAHYSdObfo\nq4TVS/X5+aaRmq3a+Q0b4xXysIKpLEs1Nbys32GxaT1wJ9RBAABLjAK3EfT2jEYjJqo0zsZD\nXVbmmbmfxiz6StlUjqZXYN0JXfzLd+2GUxnqqeEWjQMAAAAAlVfj8S82e/FNNz9/08ilE0eS\nfvspfftW5UJZmCEs9K1eTcr0TZZBCBX3hgAAALdDgxC4vSbjXwwYPVnt6mo81Oflxi2df2rm\nu8qmcjQLngiSw0LL1yY0CCFNDa/+5kZLhwIAAACAysi3/+Cmz71Wp1MX00jWoX1n536a+tc6\nBVNZ1kehzV8PCSzTJbIQ0tRwr3f+tlIkAACAKooGIXBH/iMnNp001blmravHskj+89fTn9vP\nLg5VhbFNKIeFLhnevqzX5ukMPC4KAAAAwEF4PtCzw+zvfPsPNo3kJ8af+eLjc4u/UTCVZY3r\nUr8cV6XnFXFvCAAAUBINQuBuGgx5utkLb3q0aGU8lA2G82tWnPnyY2VTOaz6NV3KcZUsJGlq\nOCuOAgAAAHAQLad/4NvvcUl19TufopxLcUvmRX30VsaOCGWDWURTrZscFjqsfYOyXshUQgAA\ngJJoEAKl8O4zIGDkJI9mQcZD2SAn/vpjzMLZyqZyTL0C68phoSFNPMt4nSxYcRQAAACAI2n5\n5ocBY19QaZyNh7JBTtmw5ty3X13YYif3RCtGtI1+M7gcF6bnFbm8scHScQAAAKoeSZZlpTMo\nRqvVZmZmFhcXq9VqpbOgsjMU6Q4898zlU8eNh5KTutGwZ5tMekXZVA7rbEZ+4CeR5b7c0905\n7f2HLBcHqKqogwBgkrEz0vyTtd2CrZUDNkQdhCM4v2ZV0u8rcmPOmEZcfeo3GjbG1dvXnn6V\nTVwdtXBPXJkukYQwhIVaJw5QNVAHAQDMIATMonLWdJq7zCOwhfFQLtbHr/oh9vt5yqZyWMYl\nZcqxJaFRel4RC8sAAAAAsHv1Bw1t/OyUul26m0YKU86f+eqT1L/XpW/brGAwy1rwRFBZVxyV\nhXB6jX0oAACAQ2MGIU/KoAyKLmXvHfdEYcp546Gkkjx7POzzyEBt9xBlgzmy6eujZ0ZEl/vy\nvi204ePvtWAeoAqhDgIAHBl1EA7l7LywhFVL5eIi04irT32/J0c1fOIZBVNZ1qcRcW+sjyrr\nVWpJFH/GVEI4IuogAIAGIYUQZaMvLNjzzIDC1GTTSO0OnX0HPun9UD8FU2H4sqMrDyeV71qV\nEHrWloFDog4CABwZdRCOJmHVksRVSwrTUk0j6mrVmk58tcGQpxVMZXEDvj24/mRq6efdaEa/\n5tN6NrFGHqDSog4CAGgQUghRZinha+KXL86LizGNuHp5N3pmfIPHhyuYCkv3J49acbjcl7Mx\nIRwQdRAA4Miog3BA6dv+vhCxMW3rJtlgMI6oXV0bDh3dZPyLygazrPJtWq9RS1dm9bVCHKCS\nog4CAGgQUghRHuk7IlI3rEnbdn0TO5XGufHYFxs9PVbBVBBC9Jy7LyImvXzXMpUQjoY6CABw\nZNRBOKaMnZEF5xPjli3QZV00jkhO6voDhzZ/9b/KBrO4cjxCynKjcCjUQQAADUIKIcopY2dk\n5t4dyet/M1wpNI6o3dz8nxnvP3KissFQwamE7hpV7id9LJgHqLSogwAAR0YdhCNL+nV57LIF\nuozrz1Z6PzygXq++2m7ByoWyinLsRtHc0+3U9GDrxAEqEeogAIAGIYUQFZL487K45d+abqsk\nlcp/1KTGY6comwpCiOnro2dGRJf7cu4J4QiogwAAR0YdhIO7sGXDuUVf5SfFGw8llcr7kYGe\n3Xt69uitbDCLm7g6auGeuDJdwnKjcATUQQAADUIKISoqed0vMQu+0GVnGQ9VGpfGY6ew1mjl\nUZFFR2kTwr5RBwEAjow6CKRH/hW/csml44dMI9XqN2w0/FkXz3r2N5Xw/tl79iRcNP98SQgD\nO1DArlEHAQAqpQMAVZ7vgCGBU6a5NQowHhp0V2K/n3t+zSplU8Fk4ZOtyn3t6fR8r3f+Lv08\nAAAAAKhqPIMfbjR8jJt/E9NIwfnE6G9mXT55NGNnpHK5rGL3S12GtW9g/vmyENLU8NBFe60X\nCQAAQFk0CAEL8O4zMGDUcy5aL+OhvrDg3OKvE1YtUTYVjJpq3eSw0Ald/Mt3eXpekTQ1fObW\nGIuGAgAAAADlefbo3XT8i9ruPSXV1S+I9IUFsUvmJ/68JC3iL2WzWdyKEW1n9Q8q0yUbTmU4\nvRZupTwAAADKYolRptLDYlI2rD077zPdxUzjoeSk9n96fOPxLyqbCiWVY4N6E1aYgf2hDgIA\nHBl1EDDJ2BmZnxgfv/xb0/2sEMK1nk/jsS/4hA5SMJg1bInO7D3/3zJdws0g7BJ1EABAg5BC\nCEtK+nV5zMIvi/NyjYeSSvLp+1jLNz9UNhVuQpsQMKIOAgAcGXUQuMmFzeHxy7/LORNlGnH1\n9m087gVnj5r2tyVhOdqEM/o1n9azSennAVUEdRAAQIOQQggLS/p9ZfzyxYUp568eS6LJ+Jf8\nR05UNBRuo+fcfREx6eW7trmn26npwRaNAyiAOggAcGTUQeBW6Tu2Zu6MTNm01qArMo6oq1Xz\n6ftYnXvu93ywl7LZLG7p/uRRKw6X6RIeGIU9oQ4CAGgQUghheamb1sX/9G3uuWjjodrNrcn4\nlxs+8YyyqXCrsxn5gZ9ElvtyjVq6Mquv5eIAtkYdBAA4MuogcFsZOyPz4s/FLJwtFxeZBj0C\nW/gNHe3dZ6CCwaykrE+O0iOE3aAOAgBUSgcA7JD3IwMaj3/JrVFj46E+Pz/2h7nJf/yibCrc\nqqnWTQ4LndDFv3yXF+llaWp49Tc3WjQUAAAAAChG2y240VPPBox+zsmjhmkwJ/rU2fmfJ6//\nVcFgVrJ1cudZ/YPMP18WQj013Hp5AAAAbIYGIWAVng/0DBgxQVOrtvGw6FJ29NxZSb+tUDYV\nbmvBE0GbJ91XjgtlIQkh8nQGFfeHAAAAAOxIwOhJzV/5b90u3SXV1S+OrmSknVv8TfI6O3zy\n9fUQ/zL1CA1CSFPDZ26NsV4kAAAAG2CJUabSw4ry4mL2P/dUcU6O8VClcW44ZETTya8pmwp3\nMnF11MI9ceW+nBVHUeVQBwEAjow6CJQqY2fkpeOHE1YtMeiuGEfUrq4Boyc3emacssGsZPiy\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lZFHQQAVB4KNAiFELm5ue+9997q1auTk5O9vLwGDRr0/vvv16lTx/jqrYVQCLFn\nz57//e9/+/bty8/PDwoKmjJlypgxY0q+5+7duz/44IPdu3fn5uZ6eXn17t377bffbtq06V1i\nUAiBm2TsjLx88mjcj4vl4iLjiFujgKbjX/IMfljZYLCe8j0xyg1kBVEHAQCOjDoIwEq2PBBk\nzmk1g9rcs2CVtcMoxZwGYaPa1b55vFXJEW7xbIk6CACoJJRpEFYSFELgVhk7Iy9HHY1bvti4\nH6EQws3Pv/HYF+r1YrqYfaJB6MiogwAAR0YdBOySmT1C94CmXZb+Ye0wSvlP+OnM/KL4rII9\n8ZeyC27YndFdo+7euM5jrevVr+lacpxbPAdEHQQA0CCkEAI3y9gZmRN9Kvb7Oaa1Rmu2auc/\nYoK2e4iywWANNAgdGXUQAODIqIOAvdryYCthxpddKmfnkK1HbJBHEdLU8FLPcdFIv4zoZDrk\nLs/RUAcBACqlAwCodLTdgj0CW/gNHS1d+zfipRNHktasyNgRoWwwAAAAAADurtc/J4QklXqa\noajo6Fsv2CCPoiQhbv0/hXFQ8na7YRLh+qi08j0/CgAAqignpQMAqIy03YKFEAbdlcRffjSO\nZO7ZIYQkC+HJPEL7ctNTotwQAgAAAKjqev1zwpy1RtO3b9ka3KZn5DEbRLIxOSzU+If7Z+/Z\nk3DxphcHtqo3vktD26cCAACVCjMIAdyetltwnc5dvR/qZxrJ3LM99a91GTsjlQsFAAAAAEDp\nem2PMuc0Wa/fGtzG2mEUtOzptrcOXi4ssn0SAABQ2dAgBHBH2m7B9Xr38+rxkGkkLWJj+j+b\n6RECAAAAACo583uE+ycOtXYYpTTVurXxrnXt6Opyo/8mXFIqDwAAqDxYYhTA3RjXGpX1+vQd\nW4UQQhbJ4b/J+mLTS7AzrDgK/J+9Ow+Tq67zxX+qu7N2VtJZIQsJCLYEECKLWeykZVBgwG0Y\nRRAURBFFuOAweh9nFJ354TJwvYAoIAoMmQG8XAXmCiNZSCdsIQtZGkLICkkgSWdPp9Pd1ef3\nR4UmJJ2uk6VOVdd5vZ56Hs859aninX9yrLzP9xwAAIpGdU1tlHuNbq1dOP3cMVXPvBJDpPhV\nlHfa58iupvSXHpqf2e7TreybHx9+ypCemd2natfv86sQAChWVhACWVSMrRp83md7f+SUPfth\nsO7pJzbNecE6QgAAAApcxHWE6V31qx6+L9dh8mLqtz725JVjnrxyzIUfeb/529HYnHm9vbXh\nvpdW5zEeAJAvCkIgu/4Tqodf9vXeJ53aemTNn/6z4d11eYwEAAAAUVTX1JZ27ZZlKAze/M1t\nc669NJZE+TG0T9c2j2/Z1RxzEgCgELjFKBBJ/3GTgnTLyofv3Va7MAiClsamN397W5hOD/27\nYv75hHvLAAAARaDqr3NqLvh449Yt7Y9tWTD3pcsvOvOBP8eTKmbnnlCxu7nljQ07W8IgCILX\n1+/YuLMxCIItu5oWrNt+8uD37zK696f8KgSAYmUFIRBV/098cvgXv9q5X0VmN71z58qH7133\nl+L84QQAAEAxGf/U80EqlXVsx/Klqx99MIY8cbqgcsAFlQNSqdRFJw383sSRN08aefOkkWOP\n7ds68MSid1dvbshjQgAgfgpC4CAMmPSpEV++qqznnusKGzduWP2fv9/w3LP5TQUAAABZVc9Y\nHKUjXHrHrUtu/2kMefJr0nH9WrdfWr3lhj/X7mpK5zEPABAzBSFwcIZe/JVRX7++9fkNO5a9\nsebPj2ycNT2voQAAACC76hmLo4y9/fjkzfNeznWY/OrV9QMPHmpMt7yzfff+Y/vccRQAKBoK\nQuCgdR04eOjFXwneu+yybvasTXNe0BECAABQ+KpraqOMzfvuFTkOErfMjUZbdyvKO3929KCy\nkveXVDZnHk4IACSDghA4aBVjq3p/5JQh539+z34YrPnzYw3r38lrKAAAAIgkSkcYhsH0T54W\nQ5g8+toZx/yPTxzbuvvq2m3rLCIEgMRQEAKHomJsVf9xk3qe8JHMbsvuhhUP3P3O00/kNxUA\nAABEEaUjTO9ueO7cj8UQJo8G9erSuv3A7DVXP7rwrpmr9h97qnZ96yvGdABADpVlHwFoS8W4\nic31O5fe+bPGTXVBEDRu3LDs3l+lysoGfvK8fEcDAACALKpraqeMr2x/prl+54zzz57wXy/E\nEyk2Vz6yYP2OxjbfenrJhqeXbPjX808YPahnzKkAgDgpCIFDN+hvLmjcVLfsnttaGpuCIGh4\nd92qyfeXdOrc/xOfzHc0AAAAyCJKR9i0bevMz0wY96cZ8USKR9Woo7btTgdB0BIGM5ZtamhO\n7/3uOR+qOKpbpwN9tp1FhHs/4xAAKHAKQuCwDPvi5S1NjcvvvzNsagqCYPsbtSsfvjcIglRZ\nWcXYqjyHAwAAgHaNuOzqlQ/d0/7M7rqNc6699PS7/j2eSDG4bMwxrdufPqH/DU/UDunVde22\nhsyRr4w5us+BC0IAoDh4BiFwuHqMPH74JVcGqT2722oXrn7kD2Fzc15DAQAAQHajrr6+90mn\nZh3bunBuDGFicEHlgL1fQRCUdykNgmBgz/cfRrijMX3AzwMAxUJBCByuirFVvStPPvpv/661\nI9yyYO6qR/6w4bln85oLAAAAshtz9+SgJMs/kYVhMG3iyfHkyYvUXtvT3qzLWw4AIC4KQuAI\nqBhbVTF24uBzL2o9snXB3GX3/urdKU/nMRUAAABEUf3coqwzLc3NU6tGxxAmLzLrCDPWb9+d\nxyQAQDwUhMCRUTG2asDEcwd/6sLWIztXLnv78ckbZ03PXygAAACIpEv/gVlnwnR62qTs9yPt\nQC6oHDC4V5cnrxzzvaqR5Z33dIQ1yzev39GY32AAQK6V5TsAUDwqxlYFQVDStfvaJx4NW1qC\nINjy6iud+vRpfQsAAAAK07jHp00/92Pp+p3tj7U0NU7/5Eernp0XT6rYpFJBr65lOxvTQRCk\nw/A/568d0qtLp5KS047pPbRP13ynAwCOPAUhcCRlisBUScnbj0/OHNnw3LPpHTuCMKwYNzGf\nyQAAAKBdVc/MnjrxlLC5qf2x9O7dRdkRfu7kQXfNXJXZ/uuSjZmNznPW3PnZjwzu1SV/uQCA\nnHCLUeAIqxhb1e/McRXjJrUe2TTnxXee/S/3GgUAAKDATZr2alCS/Z/L0rt3T6/+aAx5YnBB\n5YDMxjG921gp2Njc8vr6HfEmAgDioCAEjryKsVVHX/D5ARPPbT3y7pT/t/31RTpCAAAAClz1\nc4tSpRE6wsbdNReNjyFPbE4c0GP04J77H39rS0P8YQCAXFMQAjlRMW7i4E9d1H989Z79MFj5\n8O92rl6hIwQAAKDATZq+KMpY46a6Jbf/NNdhYlNWkvqXT5/w+y+efO/Fo2/51Idaj7+4anMe\nUwEAOaIgBHKlYmzVkPM/12PUnh8VLY27V/zuzoZ31ugIAQAAKHDVNbVBKpV17O3HJ79yzSUx\n5Mmp1ruMplJBRXnnQT27jB7cs/W5g2u37v7Tonfzlw4AyAkFIZBDFeMmDv/SlV0HDMrspht2\nvXHnz3csX6ojBAAAoMBVz1gcZWzrovlTP3FSrsPErKwkNaxvt8x2OgwfnrMmv3kAgCNOQQjk\n1qBP/e3Iq77TqVfvzG7Y1LTywd82vLtORwgAAECBi7iOMGxpmVo1OoY8cTpxQHnrdkNzy+y3\ntuYxDABwxKXCMMx3hrypqKioq6trbm4uLS3NdxYocm//n4dXPPCbxk11md0uFQOOu/Z7Zd3L\nK8ZW5TUXJJrzIABJ5jwIRDdlfGWUsfLhI8/696dyHSannqpd37odhuHPp6+YuXxTZveo7p0e\n+NIp67c3Xv3Ya+mwqc2P9y/vtP6Wc+IIymFzHgTACkIgDl0HHX3cNTd17tM3s7t74/oVv/91\ny+4G6wgBAAAocNU1tamysqxj9auXxxAmNqlU6pTBPVt3N+9q+tnU5Xc/v/pA7WAQBLvTsSQD\nAI4EBSEQh4qxVZ169T72q98q7do1c6R+9Yqlv/7l7o3r2/8gAAAA5N2kaQuyzoRhMGV85eZ5\nL8eQJx5/c0LF6ce898SQMJi5YtMrb2/JbyQA4EhREAIxqRhb1XXQ0cMv/XqqbM/NKxreWbv8\n93e989//ld9gAAAAkFV1TW2UsbnXXfHS5RflOkw8SlKpykE98p0CAMgJBSEQn4qxVT2P//Dw\nL32tpHOnzJHGjRtWP/KHDTOm5DcYAAAAZBWxI9yxfOmcay/NdZh4nP/h/mNH9B3Us8ugnl3K\nO3tYHQAUj+z3Twc4girGVgVBMKp33+X33ZFu2BUEwfYli9c88WiqtDTzFgAAABSs6praqZ84\nKWxpaX9sy4K5qx99cNjFX4kn1ZFyQeWAp2o/8CiQ8s5lSzfuXL+jMcrHtzU0pW78f627nxk9\n4P9eMeYIRwQAjhArCIG4VYyt6j50xNCLvxKk9hype7Fm3dN/3jhrej5jAQAAQASTnlsUpFJZ\nx97640MxhIlB1aijIs+mWl8lJakzhvbNYSwA4PBYQQjkQWax4OBzL1z39BOZI+unP1M+7NjW\ntwAAAKBgVc9YPGV8ZfszDevWzDj/7An/9UI8kXLnsjHHXDbmmMz2um27r35s4YEme3Ut2/ov\n58SVCwA4LFYQAvlRMbZqQNW5/c4Yu2c/DFY+fO+21xZaRwgAAEDh69J/YNaZpm1bi+ZhhABA\nkVEQAnlTMW7i0Z/7Uvfhx2Z2w+b0yn+/b/O8l3WEAAAAFLhxj08bcdnVWe81unXhvHjyAAAc\nFAUhkE/9x00a+dVruw8bkdkNm5tWP/KHzfNm6wgBAAAocKOuvr56xuL2Z8IwnDK+cvWjD8YT\nCQAgIs8gBPJs4CfPC1vSy+75VcM7a4MgCMLgrT8+1LnvUfWr3lz629uDMOx3xvhTf/nbfMcE\nAACANpR2L0/X72x/ZuUffj3s4q/Ek+cwXVA5YO/dp2rX7707uFeXJ68cE/GzAEAhs4IQyL+y\n8p7HXXNTzxP2POA9bG5edu//3rro1aAlDMJg08s1+Y0HAAAAB1L1zOysM03btz1/8TkxhAEA\niEhBCORfxdiq0q5dR1x6ddeBgzNHwuam9TOm7NkOg6kTKvOXDgAAANrT+6RTs87sWrdGRwgA\nFA4FIVAQKsZWlXTuPPKq67oOGrL/u2EYTJlQuerh++IPBgAAAO0bc/fkoCT7P7LpCAGAwqEg\nBApFxdiqTr16H3/tP7R96WUYLL/vf8ceCgAAALKrfm5RaffyrGO71q1Z/eiDMeQBAGifghAo\nIAv+53cW/tP1WxfPD1JBkNr33Zbm5injK/2UAgAAoABVPTO7S/+BWceW3nHr5nkvx5DniLig\nckC+IwAAOaEgBApIrw+PzjqzarIbjQIAAFCIxj0+LcrY3Ouu6EAdIQBQlBSEQAEZc/fkILXf\nysEPaqzbOHVCZTx5AAAA4KBU19RGGVvwg2/nOgkAQDsUhEBhqZ6xOOtMGAZTxle63BIAAIAC\nFKUjbN6x46XLL4ohzOFzl1EAKEoKQqDgRLzccv5NV+c6CQAAAByCEZdl/8W6Y/nSBT/4Tgxh\nAAD2pyAEClF1TW2qtLT9mZamxpoLxsaTBwAAAKIbdfX1pd3Ls45tW/paDGEAAPanIAQK1KTp\nCwdPPP8Dh8Jgn93GbZt1hAAAABSgqmdmB6lU+zO731k78zMT4slzOC6oHND6yncWAODIUBAC\nhavyll98YH//H1Zh0Lh18/MXnxNXIgAAAIhqxKVfzzqzu27jnGsvjSEMAMDeyvIdAKA9XfoP\n3L3h3fZndq1bM+sL1WP/OCWeSAAAABDFqKuv3zzv5a2L5rc/tnP1snjyHBEWEQJAcbCCECho\n4x6flirrlHWs4d11S27/aQx5AAAAILoxd08OSrL8+1vTlq1ujQMAxExBCBS6SdNezfrYhiAI\n3n588otfuTCGPAAAABBd9XOLss7sWrdm9aMPxhAGACBDQQh0ANUzFkfpCHeueHNq1egY8gAA\nAEB0XfoPzDqz9I5bN897OYYwAACBghDoKCJ2hGE6/fov/jmGPAAAABDRuMenRRmbd8OVuU4C\nAJChIAQ6jOoZi6OMrXnisRcvvSDXYQAAACC66prarDNhOj1t0qkxhAEAUBACHUmUH1RBENSv\nXp7rJAAAAHBQep+UvfxraWqc+ZkJMYQBABJOQQh0MJEuugyDKeMrPbwBAACAwjHm7slRftLu\nrtv4/MXnxJAHAEgyBSHQ8URcRzj3uivmXHtprsMAAABAdFHWEe5at2bBD74TQxgAILEUhECH\nFLEj3LJgrnWEAAAAFI4xd0+O0hFuqJmy5PafxpAHAEgmBSHQUVXX1AapVNaxudddsfrRB2PI\nAwAAAFGMuXtylN+zm16eGUMYACCZFIRAB1Y9Y3GUseW/uyPXSQAAACC6KL9n699eXXPR+BjC\nAAAJpCAEOrYo9xpN1++ccf7ZMYQBAACAiLr0H5h1pnFTnRuNAgC5oCAEOrwoHWHTtq3PX3xO\nDGEAAAAginGPTwtKsv/T3NuPT9487+UY8gAAiaIgBIpBlAe871q3ZuZFE2IIAwAAAFFUP7co\nysMIF/7TDTGEAQASRUEIFIMxd0+Ocm+W3Zs2vnLNJTHkAQAAgCiiPIywactmN8UBAI4sBSFQ\nJMY9Pq20e3nWsa2L58+/6Rsx5AEAAIAoojw4Y9e6NVPGV65+9MEY8gAASaAgBIpH1TOzgyAI\n2r87SxjUvVyz6uH7YkkEAAAA2UV5cEYQBGuf+mOukwAACaEgBIpKdU3tabfdn2UoDJb99rZY\n4gAAAEB2Y+6eHGUdYaqsNIYwAEASKAiBYtN3zFlZZ8IwmDK+cvO8l2PIAwAAAFF06T+w/YEd\nS5c8d+7H4gkDABQ3BSFQhKJcdxkEwdzrrnj9F/+c6zAAAAAQxbjHp2Wdaa7fueT2n8YQBgAo\nbgpCoDhF7AjXPPGYZ7wDAABQIKL8mN26cF4MSQCA4qYgBIpWxI5w6R23utcoAAAAHcX2pa9Z\nRAgAHCYFIVDMInaE8757RY6DAAAAQCSl3cuzzlhECAAcJgUhUOSidIRhGEybeHIMYQAAAKB9\nVc/MzjqzfelrnpcBABwOBSFQ/KJ0hC3NzVPGV7rXKAAAAHlX2r08SKWCVBCk2h7o3Peo8hGj\n4g0FABQVBSGQCFHvNXrDlblOAgAAAO2remZ29YzFXQfboqiIAAAgAElEQVQOCcK2Bxo3b5p/\n49dd5AoAHDIFIZAUke41mk4/f/E5MYQBAACA9jW8s7b9gbnXXTHzMxPiCQMAFBkFIZAgXfoP\nzDqza92aOddeGkMYAAAAaEeqrFPWmd11G60jBAAOgYIQSJBxj08r7V6edWzLgrmv/+KfY8gD\nAAAABzJp2qtRrnP1AxYAOAQKQiBZqp6ZHZRk/6tvzZOPvXDJeTHkAQAAgAMZ9/i07EMRfuQC\nAOzD/4EAEqf6uUXZh8Kg/q2VS27/ae7jAAAAwAF16T+w/QqwftUKdxkFAA6WghBIouqa2ihj\nbz8+2a8sAAAA8mjc49OClpb2Zxb/9OZ4wgAARUNBCCRUxI5w3nevyHEQAAAAOCy717874/yz\n850CAOhIFIRAckXpCMMwmDK+0jpCAAAA8qVL/4FZZ5q2bV396IMxhAEAioOCEEi0EZddHWVs\n7nVXzPj0mbkOAwAAAPsb9/i0KL9e3/rjQzGEAQCKg4IQSLRRV18f8V6jTTu35zoMAAAAtGnU\n1ddnnWlYt+a1n/3TxlnTN86anvtEAEDHpiAEiPY8wjCYOqEy91kAAACgDVFuNLpj2ZIYkgAA\nRSAVhmG+M+RNRUVFXV1dc3NzaWlpvrMA+TdlfIT+LxUc943/MfzLV+U+DuSc8yAASeY8CHRE\n2X+3pvb8b0lpp4nTXs11Hjou50EArCAE2OOYz3wx+1AYvPnb21645LzcxwEAAIAPKkllGQj3\nvFrSzbEEAgA6KgUhwB4n3PhPvU86NftcGNS/tXLGp8/MfSIAAAB434gvfz3qaHJvGQYARKIg\nBHjfmLsnR3oeYRA07dw+/6Zv5DoPAAAAtBp19fXVNbWl3csjzIYbZ03PdR4AoONSEALsK1JH\nGAabXq7JfRYAAAD4gKpnZkcZW/D9b+U6CQDQcSkIAdrw8f94OutMGAZTxldOqzo5hjwAAADQ\nKsoDMjI/WjOvly6/KIZUAEAHoiAEaEO3Y4addvv9USZb0s2eRwgAAECcxtw9ueeHKqPPd+k/\nMHdhAICOSEEI0La+Y86K+jzCHdtnfaE613kAAACg1Rm/+2N1Te3Rn/58+2P9zhx39EUXf+i7\nP4gnFQDQUSgIAdoTsSNseHfd9L85PddhAAAAYG8Vn6guHzisnYG6l2au+fOjG1+YEVskAKBD\nUBACZBGxI0w37Fr18H25DgMAAACtKsZWnfCPP8o6tvSOW+dce2nu4wAAHYaCECC7SB1hGLz5\nm9umfuIjuY8DAAAAe/Qdc1bfUz6Wdaxzv/4xhAEAOgoFIUAk1TW1/U45K+tY2BJOnaAjBAAA\nID6n3flA1pn1056Zcf7ZMYQBADoEBSFAVKfeeX+QSmUdC8Nw6oTKGPIAAABARmn38iAIglQQ\ntPmzNRUEqaB8xKh4QwEAhUtBCHAQqmcsjjIWhsGUCZUeSQgAAEA8qp6ZnSotDcIgCNt6OwyC\nMNiyYO78f7hm46zpmVfMCQGAgqIgBDg4kZ5HGARBGCy75/YcZwEAAIA9en14dNaZuheee/vP\nj8QQBgAocApCgINWXVMblGT/+zNsca9RAAAAYjLm7sl7noux/11GU3teqdKS7kcPiz0aAFBw\nFIQAh6L6uUVRxjL3Gn3hkvNynQcAAAD2PBdj/7uMhnteYbpl/cwpsecCAAqOghDgEEW/12j9\nWystJQQAACAOJSV71gvu471FhOXHDM8c8BhCAEgyBSHAoauuqS3p1DnKZGYp4aqH78t1JAAA\nAJKs64BBe9YL7uO9RYR9TzszD7EAgAKjIAQ4LBOnzj/mgr+PNBoGb/72tvk3fSPHiQAAAEiu\nQedccPRFFx990cVdjuq/9/FUKuh35rh+Z47r1KtP60GLCAEgsVJhuP8FRUlRUVFRV1fX3Nxc\nWlqa7yxAxzb365dsfn1+pNFU0LlX3/FPzcpxIsjOeRCAJHMeBIrY1KrRYTrdzsCQC77Qf9yk\nzHbF2Ko4MlFgnAcBsIIQ4Ag47d7Jnfv2izQaBo3bNk8/5/QcJwIAACChen14dPmIkeUjRpaU\nle19vOuAQXteAwe3Htw4a7p1hACQQApCgCNj/BM13Y8eHmk0DNINu6ZMqHzhkvNyHAoAAIDE\n2bpo/s6Vy3euXN7S3Lz38Yb172Reb/3fyfnKBgAUCAUhwBFz9n/+pbqmNup0GNS/vXLqJ0bn\nMhEAAADJk0oFqWDP6wPH97y6Dxman2AAQMFQEAIcYdU1td2HRF1KGLakp008JceJAAAASJDq\nGYuDMNjz2tt7B7cumr/gh9/NTzgAoDCUZR8B4CCd+L1/nnvD1yIOtzQ3TT/n9Kq/zslpJAAA\nAJKldflguN+RIOhyVMXes/s/hrBibFVOUgEAhcEKQoAjr++Ys067/f7o8+mGXfNv+kbu8gAA\nAJAo1TW11TNqq2fU9j3xo3sOhUEqSJV26Z55NW3fvvx3dzTv3J7XmABA3igIAXKi75izqmtq\nB5w5MeJ83Us1UyZUut0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GhdZyQTaCIG4I93g8NfDBRP8A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\n9Paoj8/I3/yFe6NOizoIAN2Qlnij1dxpd1mP4TcOmP8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-      "text/plain": [
-       "plot without title"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 1500,
-       "width": 1200
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plots_km"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Competing Events"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "competing_endpoints"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fit5 <- survfit(Surv(CompetingEvents_event_time, CompetingEvents_event, type = \"mstate\") ~ 1, data = data)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "options(repr.plot.width=10, repr.plot.height=10)\n",
-    "ggcompetingrisks(fit5, palette = \"jco\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ggsurvplot(fit5,data, conf.int = TRUE, ylim = c(0.70,1), cumevents=TRUE, cumevents.y.text = FALSE)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "R [conda env:python]",
-   "language": "R",
-   "name": "conda-env-python-r"
-  },
-  "language_info": {
-   "codemirror_mode": "r",
-   "file_extension": ".r",
-   "mimetype": "text/x-r-source",
-   "name": "R",
-   "pygments_lexer": "r",
-   "version": "4.0.3"
-  },
-  "toc-autonumbering": true,
-  "toc-showcode": false,
-  "toc-showmarkdowntxt": true
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/neuralcvd/preprocessing/ukbb_tabular/5_dataset_visualization_lifetime.ipynb b/neuralcvd/preprocessing/ukbb_tabular/5_dataset_visualization_lifetime.ipynb
deleted file mode 100644
index 6a80897..0000000
--- a/neuralcvd/preprocessing/ukbb_tabular/5_dataset_visualization_lifetime.ipynb
+++ /dev/null
@@ -1,3837 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 2. Exploration"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T14:16:53.068218Z",
-     "start_time": "2020-11-04T14:16:47.983Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "-- \u001b[1mAttaching packages\u001b[22m --------------------------------------- tidyverse 1.3.0 --\n",
-      "\n",
-      "\u001b[32mv\u001b[39m \u001b[34mggplot2\u001b[39m 3.3.2     \u001b[32mv\u001b[39m \u001b[34mpurrr  \u001b[39m 0.3.4\n",
-      "\u001b[32mv\u001b[39m \u001b[34mtibble \u001b[39m 3.0.3     \u001b[32mv\u001b[39m \u001b[34mdplyr  \u001b[39m 1.0.2\n",
-      "\u001b[32mv\u001b[39m \u001b[34mtidyr  \u001b[39m 1.1.2     \u001b[32mv\u001b[39m \u001b[34mstringr\u001b[39m 1.4.0\n",
-      "\u001b[32mv\u001b[39m \u001b[34mreadr  \u001b[39m 1.3.1     \u001b[32mv\u001b[39m \u001b[34mforcats\u001b[39m 0.5.0\n",
-      "\n",
-      "-- \u001b[1mConflicts\u001b[22m ------------------------------------------ tidyverse_conflicts() --\n",
-      "\u001b[31mx\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mfilter()\u001b[39m masks \u001b[34mstats\u001b[39m::filter()\n",
-      "\u001b[31mx\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mlag()\u001b[39m    masks \u001b[34mstats\u001b[39m::lag()\n",
-      "\n",
-      "\n",
-      "Attaching package: 'lubridate'\n",
-      "\n",
-      "\n",
-      "The following objects are masked from 'package:base':\n",
-      "\n",
-      "    date, intersect, setdiff, union\n",
-      "\n",
-      "\n",
-      "\n",
-      "Attaching package: 'glue'\n",
-      "\n",
-      "\n",
-      "The following object is masked from 'package:dplyr':\n",
-      "\n",
-      "    collapse\n",
-      "\n",
-      "\n",
-      "\n",
-      "Attaching package: 'cowplot'\n",
-      "\n",
-      "\n",
-      "The following object is masked from 'package:lubridate':\n",
-      "\n",
-      "    stamp\n",
-      "\n",
-      "\n",
-      "Loading required package: ggpubr\n",
-      "\n",
-      "\n",
-      "Attaching package: 'ggpubr'\n",
-      "\n",
-      "\n",
-      "The following object is masked from 'package:cowplot':\n",
-      "\n",
-      "    get_legend\n",
-      "\n",
-      "\n",
-      "\n",
-      "Attaching package: 'arsenal'\n",
-      "\n",
-      "\n",
-      "The following object is masked from 'package:lubridate':\n",
-      "\n",
-      "    is.Date\n",
-      "\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "try(library(tidyverse), silent=TRUE)\n",
-    "library(lubridate)\n",
-    "library(glue)\n",
-    "library(cowplot)\n",
-    "library(survminer)\n",
-    "library(survival)\n",
-    "library(ggsci)\n",
-    "library(arsenal)\n",
-    "library(yaml)\n",
-    "\n",
-    "#setwd(\"/\")\n",
-    "#path = \"/home/steinfej/projects/uk_biobank/\"\n",
-    "#dataset_path = \"data/datasets/cvd_big_excl\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T14:16:53.090072Z",
-     "start_time": "2020-11-04T14:16:48.270Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "dataset_name = \"cvd_massive_from_birth\"\n",
-    "path = \"/data/analysis/ag-reils/steinfej/code/umbrella/pre/ukbb\"\n",
-    "data_path = \"/data/analysis/ag-reils/ag-reils-shared/cardioRS/data\"\n",
-    "dataset_path = glue(\"{data_path}/2_datasets_pre/{dataset_name}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T14:16:57.477187Z",
-     "start_time": "2020-11-04T14:16:49.046Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "data = arrow::read_feather(glue(\"{dataset_path}/baseline.feather\")) \n",
-    "data_description = arrow::read_feather(glue(\"{dataset_path}/baseline_description.feather\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T14:16:57.520779Z",
-     "start_time": "2020-11-04T14:16:50.668Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "phenotypes = names(read_yaml(glue(\"{dataset_path}/phenotype_list.yaml\")))\n",
-    "family_history = names(read_yaml(glue(\"{dataset_path}/fh_list.yaml\")))\n",
-    "medications = names(read_yaml(glue(\"{dataset_path}/medication_list.yaml\")))\n",
-    "endpoints_ph = names(read_yaml(glue(\"{dataset_path}/endpoint_list.yaml\")))\n",
-    "endpoints_death = names(read_yaml(glue(\"{dataset_path}/death_list.yaml\")))\n",
-    "endpoints_scores = names(read_yaml(glue(\"{dataset_path}/scores_list.yaml\")))\n",
-    "endpoints = c(endpoints_ph, endpoints_death, endpoints_scores)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T14:16:58.275310Z",
-     "start_time": "2020-11-04T14:16:57.351Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "covariates = (data_description %>% filter(isTarget==FALSE) %>% filter(based_on!=\"PGS\"))$covariate[-1]\n",
-    "targets = (data_description %>% filter(isTarget==TRUE))$covariate[-1]\n",
-    "pgs = (data_description %>% filter(isTarget==FALSE) %>% filter(based_on==\"PGS\") %>% filter(!dtype==\"Date\"))$covariate"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T14:16:58.756284Z",
-     "start_time": "2020-11-04T14:16:57.831Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style>\n",
-       ".list-inline {list-style: none; margin:0; padding: 0}\n",
-       ".list-inline>li {display: inline-block}\n",
-       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
-       "</style>\n",
-       "<ol class=list-inline><li>'age_at_recruitment'</li><li>'sex'</li><li>'ethnic_background'</li><li>'townsend_deprivation_index_at_recruitment'</li><li>'overall_health_rating'</li><li>'smoking_status'</li><li>'alcohol_intake_frequency'</li><li>'body_mass_index_bmi'</li><li>'weight'</li><li>'pulse_wave_arterial_stiffness_index'</li><li>'pulse_wave_reflection_index'</li><li>'waist_circumference'</li><li>'hip_circumference'</li><li>'standing_height'</li><li>'trunk_fat_percentage'</li><li>'body_fat_percentage'</li><li>'basal_metabolic_rate'</li><li>'forced_vital_capacity_fvc_best_measure'</li><li>'forced_expiratory_volume_in_1second_fev1_best_measure'</li><li>'fev1_fvc_ratio_zscore'</li><li>'peak_expiratory_flow_pef_f3064_0_2'</li><li>'peak_expiratory_flow_pef_f3064_0_1'</li><li>'peak_expiratory_flow_pef'</li><li>'systolic_blood_pressure'</li><li>'diastolic_blood_pressure'</li><li>'pulse_rate'</li><li>'basophill_count'</li><li>'basophill_percentage'</li><li>'eosinophill_count'</li><li>'eosinophill_percentage'</li><li>'haematocrit_percentage'</li><li>'haemoglobin_concentration'</li><li>'high_light_scatter_reticulocyte_count'</li><li>'high_light_scatter_reticulocyte_percentage'</li><li>'immature_reticulocyte_fraction'</li><li>'lymphocyte_count'</li><li>'lymphocyte_percentage'</li><li>'mean_corpuscular_haemoglobin'</li><li>'mean_corpuscular_haemoglobin_concentration'</li><li>'mean_corpuscular_volume'</li><li>'mean_platelet_thrombocyte_volume'</li><li>'mean_reticulocyte_volume'</li><li>'mean_sphered_cell_volume'</li><li>'monocyte_count'</li><li>'monocyte_percentage'</li><li>'neutrophill_count'</li><li>'neutrophill_percentage'</li><li>'nucleated_red_blood_cell_count'</li><li>'nucleated_red_blood_cell_percentage'</li><li>'platelet_count'</li><li>'platelet_crit'</li><li>'platelet_distribution_width'</li><li>'red_blood_cell_erythrocyte_count'</li><li>'red_blood_cell_erythrocyte_distribution_width'</li><li>'reticulocyte_count'</li><li>'reticulocyte_percentage'</li><li>'white_blood_cell_leukocyte_count'</li><li>'alanine_aminotransferase'</li><li>'albumin'</li><li>'alkaline_phosphatase'</li><li>'apolipoprotein_a'</li><li>'apolipoprotein_b'</li><li>'aspartate_aminotransferase'</li><li>'creactive_protein'</li><li>'calcium'</li><li>'cholesterol'</li><li>'creatinine'</li><li>'cystatin_c'</li><li>'direct_bilirubin'</li><li>'gamma_glutamyltransferase'</li><li>'glucose'</li><li>'glycated_haemoglobin_hba1c'</li><li>'hdl_cholesterol'</li><li>'igf1'</li><li>'ldl_direct'</li><li>'lipoprotein_a'</li><li>'oestradiol'</li><li>'phosphate'</li><li>'rheumatoid_factor'</li><li>'shbg'</li><li>'testosterone'</li><li>'total_bilirubin'</li><li>'total_protein'</li><li>'triglycerides'</li><li>'urate'</li><li>'urea'</li><li>'vitamin_d'</li><li>'fh_alzheimer\\'s_disease/dementia'</li><li>'fh_bowel_cancer'</li><li>'fh_breast_cancer'</li><li>'fh_chronic_bronchitis/emphysema'</li><li>'fh_diabetes'</li><li>'fh_heart_disease'</li><li>'fh_high_blood_pressure'</li><li>'fh_lung_cancer'</li><li>'fh_parkinson\\'s_disease'</li><li>'fh_severe_depression'</li><li>'fh_stroke'</li><li>'coronary_heart_disease'</li><li>'myocardial_infarction'</li><li>'stroke'</li><li>'diabetes1'</li><li>'diabetes2'</li><li>'chronic_kidney_disease'</li><li>'atrial_fibrillation'</li><li>'migraine'</li><li>'rheumatoid_arthritis'</li><li>'systemic_lupus_erythematosus'</li><li>'severe_mental_illness'</li><li>'erectile_dysfunction'</li><li>'hypertensive_disorder_systemic_arterial'</li><li>'hyperlipidemia'</li><li>'depressive_disorder'</li><li>'gastroesophageal_reflux_disease'</li><li>'diabetes_mellitus_type_2'</li><li>'essential_hypertension'</li><li>'obesity'</li><li>'diabetes_mellitus'</li><li>'asthma'</li><li>'coronary_arteriosclerosis'</li><li>'allergic_rhinitis'</li><li>'hypothyroidism'</li><li>'upper_respiratory_infection'</li><li>'hypercholesterolemia'</li><li>'backache'</li><li>'abdominal_pain'</li><li>'osteoarthritis'</li><li>'low_back_pain'</li><li>'anemia'</li><li>'anxiety'</li><li>'urinary_tract_infectious_disease'</li><li>'chronic_obstructive_lung_disease'</li><li>'pneumonia'</li><li>'chest_pain'</li><li>'congestive_heart_failure'</li><li>'headache'</li><li>'pregnant'</li><li>'knee_pain'</li><li>'osteoporosis'</li><li>'polyp_of_colon'</li><li>'otitis_media'</li><li>'sinusitis'</li><li>'cough'</li><li>'sleep_apnea'</li><li>'insomnia'</li><li>'inflammatory_disorder_due_to_increased_blood_urate_level'</li><li>'tobacco_dependence_syndrome'</li><li>'malignant_tumor_of_prostate'</li><li>'constipation'</li><li>'hearing_loss'</li><li>'fatigue'</li><li>'obstructive_sleep_apnea_syndrome'</li><li>'malignant_neoplasm_of_breast'</li><li>'delivery_normal'</li><li>'irritable_bowel_syndrome'</li><li>'tobacco_user'</li><li>'neck_pain'</li><li>'cerebrovascular_accident'</li><li>'asthenia'</li><li>'shoulder_pain'</li><li>'acne_vulgaris'</li><li>'benign_prostatic_hyperplasia'</li><li>'dyspnea'</li><li>'carpal_tunnel_syndrome'</li><li>'bronchitis'</li><li>'pharyngitis'</li><li>'arthritis'</li><li>'diarrhea'</li><li>'dizziness'</li><li>'alcohol_abuse'</li><li>'dementia'</li><li>'eczema'</li><li>'syncope'</li><li>'acute_sinusitis'</li><li>'iron_deficiency_anemia'</li><li>'allergic_rhinitis_caused_by_pollen'</li><li>'gastritis'</li><li>'cataract'</li><li>'hematuria_syndrome'</li><li>'disorder_of_the_peripheral_nervous_system'</li><li>'viral_hepatitis_type_c'</li><li>'palpitations'</li><li>'eruption_of_skin'</li><li>'diabetes_mellitus_type_1'</li><li>'renal_failure_syndrome'</li><li>'peripheral_vascular_disease'</li><li>'hyperglycemia'</li><li>'seizure_disorder'</li><li>'fever'</li><li>'osteoarthritis_of_knee'</li><li>'actinic_keratosis'</li><li>'urinary_incontinence'</li><li>'hemorrhoids'</li><li>'seizure'</li><li>'laceration_-_injury'</li><li>'glaucoma'</li><li>'body_mass_index_30+_-_obesity'</li><li>'breast_lump'</li><li>'viral_disease'</li><li>'abnormal_cervical_smear'</li><li>⋯</li><li>'viral_exanthem'</li><li>'talipes_planus'</li><li>'idiopathic_peripheral_neuropathy'</li><li>'foreign_body_in_pharynx'</li><li>'jaw_pain'</li><li>'renal_impairment'</li><li>'ataxia'</li><li>'age-related_macular_degeneration'</li><li>'uterine_prolapse'</li><li>'renal_mass'</li><li>'pneumonitis'</li><li>'coordination_problem'</li><li>'blindness_-_both_eyes'</li><li>'primary_hyperparathyroidism'</li><li>'musculoskeletal_pain'</li><li>'mycosis'</li><li>'primigravida'</li><li>'urethral_stricture'</li><li>'leukocytosis'</li><li>'ventricular_premature_complex'</li><li>'ulcer_of_foot_due_to_diabetes_mellitus'</li><li>'chronic_headache_disorder'</li><li>'hemangioma'</li><li>'lymphedema'</li><li>'postmenopausal_state'</li><li>'chronic_ulcer_of_skin'</li><li>'left_heart_failure'</li><li>'excessive_and_frequent_menstruation'</li><li>'thrombocytosis'</li><li>'disorder_of_liver'</li><li>'disorder_of_carotid_artery'</li><li>'altered_bowel_function'</li><li>'abscess_of_foot'</li><li>'malignant_tumor_of_head_and/or_neck'</li><li>'streptococcus_group_b_infection_of_the_infant'</li><li>'concussion_injury_of_brain'</li><li>'feeding_problems_in_newborn'</li><li>'bipolar_i_disorder'</li><li>'viral_pharyngitis'</li><li>'lower_respiratory_tract_infection'</li><li>'hydronephrosis'</li><li>'borderline_personality_disorder'</li><li>'esophageal_varices'</li><li>'hypersomnia'</li><li>'sensorineural_hearing_loss_bilateral'</li><li>'varicocele'</li><li>'subarachnoid_intracranial_hemorrhage'</li><li>'incisional_hernia'</li><li>'varicella'</li><li>'pain_in_testicle'</li><li>'transplant_follow-up'</li><li>'tinea_cruris'</li><li>'laryngitis'</li><li>'hypertrophy_of_nail'</li><li>'amblyopia'</li><li>'polyp_of_cervix'</li><li>'cyst_of_kidney'</li><li>'hepatic_encephalopathy'</li><li>'blood_glucose_abnormal'</li><li>'postherpetic_neuralgia'</li><li>'frank_hematuria'</li><li>'cramp'</li><li>'interstitial_lung_disease'</li><li>'complete_atrioventricular_block'</li><li>'malignant_tumor_of_kidney'</li><li>'otitis'</li><li>'septic_shock'</li><li>'disorder_of_thyroid_gland'</li><li>'hypertrophic_cardiomyopathy'</li><li>'respiratory_distress_syndrome_in_the_newborn'</li><li>'infectious_gastroenteritis'</li><li>'subdural_intracranial_hemorrhage'</li><li>'hepatitis_b_carrier'</li><li>'manic_bipolar_i_disorder'</li><li>'secondary_pulmonary_hypertension'</li><li>'gonorrhea'</li><li>'derangement_of_knee'</li><li>'appendicitis'</li><li>'polyneuropathy_due_to_diabetes_mellitus'</li><li>'neonatal_hypoglycemia'</li><li>'prolonged_rupture_of_membranes'</li><li>'vasomotor_rhinitis'</li><li>'renal_disorder_due_to_type_1_diabetes_mellitus'</li><li>'tuberculosis'</li><li>'feeding_problem'</li><li>'chronic_tonsillitis'</li><li>'acute_duodenal_ulcer_with_hemorrhage'</li><li>'hammer_toe'</li><li>'malignant_tumor_of_cervix'</li><li>'prolapsed_lumbar_intervertebral_disc'</li><li>'hematemesis'</li><li>'perianal_abscess'</li><li>'nonvenomous_insect_bite'</li><li>'spondylolisthesis'</li><li>'malignant_tumor_of_esophagus'</li><li>'aphthous_ulcer_of_mouth'</li><li>'ventricular_septal_defect'</li><li>'oropharyngeal_dysphagia'</li><li>'injury_of_knee'</li><li>'traumatic_brain_injury'</li><li>'osteoarthritis_of_glenohumeral_joint'</li><li>'fetal_or_neonatal_effect_of_maternal_medical_problem'</li><li>'stomatological_preparations'</li><li>'drugs_for_acid_related_disorders'</li><li>'drugs_for_functional_gastrointestinal_disorders'</li><li>'antiemetics_and_antinauseants'</li><li>'bile_and_liver_therapy'</li><li>'drugs_for_constipation'</li><li>'antidiarrheals,_intestinal_antiinflammatory/antiinfective_agents'</li><li>'antiobesity_preparations,_excl._diet_products'</li><li>'digestives,_incl._enzymes'</li><li>'drugs_used_in_diabetes'</li><li>'vitamins'</li><li>'mineral_supplements'</li><li>'tonics'</li><li>'anabolic_agents_for_systemic_use'</li><li>'appetite_stimulants'</li><li>'other_alimentary_tract_and_metabolism_products'</li><li>'antithrombotic_agents'</li><li>'antihemorrhagics'</li><li>'antianemic_preparations'</li><li>'blood_substitutes_and_perfusion_solutions'</li><li>'other_hematological_agents'</li><li>'cardiac_therapy'</li><li>'antihypertensives'</li><li>'diuretics'</li><li>'peripheral_vasodilators'</li><li>'vasoprotectives'</li><li>'beta_blocking_agents'</li><li>'calcium_channel_blockers'</li><li>'agents_acting_on_the_renin-angiotensin_system'</li><li>'lipid_modifying_agents'</li><li>'antifungals_for_dermatological_use'</li><li>'emollients_and_protectives'</li><li>'preparations_for_treatment_of_wounds_and_ulcers'</li><li>'antipruritics,_incl._antihistamines,_anesthetics,_etc.'</li><li>'antipsoriatics'</li><li>'antibiotics_and_chemotherapeutics_for_dermatological_use'</li><li>'corticosteroids,_dermatological_preparations'</li><li>'antiseptics_and_disinfectants'</li><li>'medicated_dressings'</li><li>'anti-acne_preparations'</li><li>'other_dermatological_preparations'</li><li>'gynecological_antiinfectives_and_antiseptics'</li><li>'other_gynecologicals'</li><li>'sex_hormones_and_modulators_of_the_genital_system'</li><li>'urologicals'</li><li>'pituitary_and_hypothalamic_hormones_and_analogues'</li><li>'corticosteroids_for_systemic_use'</li><li>'thyroid_therapy'</li><li>'pancreatic_hormones'</li><li>'calcium_homeostasis'</li><li>'antibacterials_for_systemic_use'</li><li>'antimycotics_for_systemic_use'</li><li>'antimycobacterials'</li><li>'antivirals_for_systemic_use'</li><li>'immune_sera_and_immunoglobulins'</li><li>'vaccines'</li><li>'antineoplastic_agents'</li><li>'endocrine_therapy'</li><li>'immunostimulants'</li><li>'immunosuppressants'</li><li>'antiinflammatory_and_antirheumatic_products'</li><li>'topical_products_for_joint_and_muscular_pain'</li><li>'muscle_relaxants'</li><li>'antigout_preparations'</li><li>'drugs_for_treatment_of_bone_diseases'</li><li>'other_drugs_for_disorders_of_the_musculo-skeletal_system'</li><li>'anesthetics'</li><li>'analgesics'</li><li>'antiepileptics'</li><li>'anti-parkinson_drugs'</li><li>'psycholeptics'</li><li>'psychoanaleptics'</li><li>'other_nervous_system_drugs'</li><li>'antiprotozoals'</li><li>'anthelmintics'</li><li>'ectoparasiticides,_incl._scabicides,_insecticides_and_repellents'</li><li>'nasal_preparations'</li><li>'throat_preparations'</li><li>'drugs_for_obstructive_airway_diseases'</li><li>'cough_and_cold_preparations'</li><li>'antihistamines_for_systemic_use'</li><li>'other_respiratory_system_products'</li><li>'ophthalmologicals'</li><li>'otologicals'</li><li>'ophthalmological_and_otological_preparations'</li><li>'allergens'</li><li>'all_other_therapeutic_products'</li><li>'diagnostic_agents'</li><li>'general_nutrients'</li><li>'all_other_non-therapeutic_products'</li><li>'contrast_media'</li><li>'diagnostic_radiopharmaceuticals'</li><li>'therapeutic_radiopharmaceuticals'</li><li>'surgical_dressings'</li><li>'statins'</li><li>'ass'</li><li>'atypical_antipsychotics'</li><li>'glucocorticoids'</li></ol>\n"
-      ],
-      "text/latex": [
-       "\\begin{enumerate*}\n",
-       "\\item 'age\\_at\\_recruitment'\n",
-       "\\item 'sex'\n",
-       "\\item 'ethnic\\_background'\n",
-       "\\item 'townsend\\_deprivation\\_index\\_at\\_recruitment'\n",
-       "\\item 'overall\\_health\\_rating'\n",
-       "\\item 'smoking\\_status'\n",
-       "\\item 'alcohol\\_intake\\_frequency'\n",
-       "\\item 'body\\_mass\\_index\\_bmi'\n",
-       "\\item 'weight'\n",
-       "\\item 'pulse\\_wave\\_arterial\\_stiffness\\_index'\n",
-       "\\item 'pulse\\_wave\\_reflection\\_index'\n",
-       "\\item 'waist\\_circumference'\n",
-       "\\item 'hip\\_circumference'\n",
-       "\\item 'standing\\_height'\n",
-       "\\item 'trunk\\_fat\\_percentage'\n",
-       "\\item 'body\\_fat\\_percentage'\n",
-       "\\item 'basal\\_metabolic\\_rate'\n",
-       "\\item 'forced\\_vital\\_capacity\\_fvc\\_best\\_measure'\n",
-       "\\item 'forced\\_expiratory\\_volume\\_in\\_1second\\_fev1\\_best\\_measure'\n",
-       "\\item 'fev1\\_fvc\\_ratio\\_zscore'\n",
-       "\\item 'peak\\_expiratory\\_flow\\_pef\\_f3064\\_0\\_2'\n",
-       "\\item 'peak\\_expiratory\\_flow\\_pef\\_f3064\\_0\\_1'\n",
-       "\\item 'peak\\_expiratory\\_flow\\_pef'\n",
-       "\\item 'systolic\\_blood\\_pressure'\n",
-       "\\item 'diastolic\\_blood\\_pressure'\n",
-       "\\item 'pulse\\_rate'\n",
-       "\\item 'basophill\\_count'\n",
-       "\\item 'basophill\\_percentage'\n",
-       "\\item 'eosinophill\\_count'\n",
-       "\\item 'eosinophill\\_percentage'\n",
-       "\\item 'haematocrit\\_percentage'\n",
-       "\\item 'haemoglobin\\_concentration'\n",
-       "\\item 'high\\_light\\_scatter\\_reticulocyte\\_count'\n",
-       "\\item 'high\\_light\\_scatter\\_reticulocyte\\_percentage'\n",
-       "\\item 'immature\\_reticulocyte\\_fraction'\n",
-       "\\item 'lymphocyte\\_count'\n",
-       "\\item 'lymphocyte\\_percentage'\n",
-       "\\item 'mean\\_corpuscular\\_haemoglobin'\n",
-       "\\item 'mean\\_corpuscular\\_haemoglobin\\_concentration'\n",
-       "\\item 'mean\\_corpuscular\\_volume'\n",
-       "\\item 'mean\\_platelet\\_thrombocyte\\_volume'\n",
-       "\\item 'mean\\_reticulocyte\\_volume'\n",
-       "\\item 'mean\\_sphered\\_cell\\_volume'\n",
-       "\\item 'monocyte\\_count'\n",
-       "\\item 'monocyte\\_percentage'\n",
-       "\\item 'neutrophill\\_count'\n",
-       "\\item 'neutrophill\\_percentage'\n",
-       "\\item 'nucleated\\_red\\_blood\\_cell\\_count'\n",
-       "\\item 'nucleated\\_red\\_blood\\_cell\\_percentage'\n",
-       "\\item 'platelet\\_count'\n",
-       "\\item 'platelet\\_crit'\n",
-       "\\item 'platelet\\_distribution\\_width'\n",
-       "\\item 'red\\_blood\\_cell\\_erythrocyte\\_count'\n",
-       "\\item 'red\\_blood\\_cell\\_erythrocyte\\_distribution\\_width'\n",
-       "\\item 'reticulocyte\\_count'\n",
-       "\\item 'reticulocyte\\_percentage'\n",
-       "\\item 'white\\_blood\\_cell\\_leukocyte\\_count'\n",
-       "\\item 'alanine\\_aminotransferase'\n",
-       "\\item 'albumin'\n",
-       "\\item 'alkaline\\_phosphatase'\n",
-       "\\item 'apolipoprotein\\_a'\n",
-       "\\item 'apolipoprotein\\_b'\n",
-       "\\item 'aspartate\\_aminotransferase'\n",
-       "\\item 'creactive\\_protein'\n",
-       "\\item 'calcium'\n",
-       "\\item 'cholesterol'\n",
-       "\\item 'creatinine'\n",
-       "\\item 'cystatin\\_c'\n",
-       "\\item 'direct\\_bilirubin'\n",
-       "\\item 'gamma\\_glutamyltransferase'\n",
-       "\\item 'glucose'\n",
-       "\\item 'glycated\\_haemoglobin\\_hba1c'\n",
-       "\\item 'hdl\\_cholesterol'\n",
-       "\\item 'igf1'\n",
-       "\\item 'ldl\\_direct'\n",
-       "\\item 'lipoprotein\\_a'\n",
-       "\\item 'oestradiol'\n",
-       "\\item 'phosphate'\n",
-       "\\item 'rheumatoid\\_factor'\n",
-       "\\item 'shbg'\n",
-       "\\item 'testosterone'\n",
-       "\\item 'total\\_bilirubin'\n",
-       "\\item 'total\\_protein'\n",
-       "\\item 'triglycerides'\n",
-       "\\item 'urate'\n",
-       "\\item 'urea'\n",
-       "\\item 'vitamin\\_d'\n",
-       "\\item 'fh\\_alzheimer\\textbackslash{}'s\\_disease/dementia'\n",
-       "\\item 'fh\\_bowel\\_cancer'\n",
-       "\\item 'fh\\_breast\\_cancer'\n",
-       "\\item 'fh\\_chronic\\_bronchitis/emphysema'\n",
-       "\\item 'fh\\_diabetes'\n",
-       "\\item 'fh\\_heart\\_disease'\n",
-       "\\item 'fh\\_high\\_blood\\_pressure'\n",
-       "\\item 'fh\\_lung\\_cancer'\n",
-       "\\item 'fh\\_parkinson\\textbackslash{}'s\\_disease'\n",
-       "\\item 'fh\\_severe\\_depression'\n",
-       "\\item 'fh\\_stroke'\n",
-       "\\item 'coronary\\_heart\\_disease'\n",
-       "\\item 'myocardial\\_infarction'\n",
-       "\\item 'stroke'\n",
-       "\\item 'diabetes1'\n",
-       "\\item 'diabetes2'\n",
-       "\\item 'chronic\\_kidney\\_disease'\n",
-       "\\item 'atrial\\_fibrillation'\n",
-       "\\item 'migraine'\n",
-       "\\item 'rheumatoid\\_arthritis'\n",
-       "\\item 'systemic\\_lupus\\_erythematosus'\n",
-       "\\item 'severe\\_mental\\_illness'\n",
-       "\\item 'erectile\\_dysfunction'\n",
-       "\\item 'hypertensive\\_disorder\\_systemic\\_arterial'\n",
-       "\\item 'hyperlipidemia'\n",
-       "\\item 'depressive\\_disorder'\n",
-       "\\item 'gastroesophageal\\_reflux\\_disease'\n",
-       "\\item 'diabetes\\_mellitus\\_type\\_2'\n",
-       "\\item 'essential\\_hypertension'\n",
-       "\\item 'obesity'\n",
-       "\\item 'diabetes\\_mellitus'\n",
-       "\\item 'asthma'\n",
-       "\\item 'coronary\\_arteriosclerosis'\n",
-       "\\item 'allergic\\_rhinitis'\n",
-       "\\item 'hypothyroidism'\n",
-       "\\item 'upper\\_respiratory\\_infection'\n",
-       "\\item 'hypercholesterolemia'\n",
-       "\\item 'backache'\n",
-       "\\item 'abdominal\\_pain'\n",
-       "\\item 'osteoarthritis'\n",
-       "\\item 'low\\_back\\_pain'\n",
-       "\\item 'anemia'\n",
-       "\\item 'anxiety'\n",
-       "\\item 'urinary\\_tract\\_infectious\\_disease'\n",
-       "\\item 'chronic\\_obstructive\\_lung\\_disease'\n",
-       "\\item 'pneumonia'\n",
-       "\\item 'chest\\_pain'\n",
-       "\\item 'congestive\\_heart\\_failure'\n",
-       "\\item 'headache'\n",
-       "\\item 'pregnant'\n",
-       "\\item 'knee\\_pain'\n",
-       "\\item 'osteoporosis'\n",
-       "\\item 'polyp\\_of\\_colon'\n",
-       "\\item 'otitis\\_media'\n",
-       "\\item 'sinusitis'\n",
-       "\\item 'cough'\n",
-       "\\item 'sleep\\_apnea'\n",
-       "\\item 'insomnia'\n",
-       "\\item 'inflammatory\\_disorder\\_due\\_to\\_increased\\_blood\\_urate\\_level'\n",
-       "\\item 'tobacco\\_dependence\\_syndrome'\n",
-       "\\item 'malignant\\_tumor\\_of\\_prostate'\n",
-       "\\item 'constipation'\n",
-       "\\item 'hearing\\_loss'\n",
-       "\\item 'fatigue'\n",
-       "\\item 'obstructive\\_sleep\\_apnea\\_syndrome'\n",
-       "\\item 'malignant\\_neoplasm\\_of\\_breast'\n",
-       "\\item 'delivery\\_normal'\n",
-       "\\item 'irritable\\_bowel\\_syndrome'\n",
-       "\\item 'tobacco\\_user'\n",
-       "\\item 'neck\\_pain'\n",
-       "\\item 'cerebrovascular\\_accident'\n",
-       "\\item 'asthenia'\n",
-       "\\item 'shoulder\\_pain'\n",
-       "\\item 'acne\\_vulgaris'\n",
-       "\\item 'benign\\_prostatic\\_hyperplasia'\n",
-       "\\item 'dyspnea'\n",
-       "\\item 'carpal\\_tunnel\\_syndrome'\n",
-       "\\item 'bronchitis'\n",
-       "\\item 'pharyngitis'\n",
-       "\\item 'arthritis'\n",
-       "\\item 'diarrhea'\n",
-       "\\item 'dizziness'\n",
-       "\\item 'alcohol\\_abuse'\n",
-       "\\item 'dementia'\n",
-       "\\item 'eczema'\n",
-       "\\item 'syncope'\n",
-       "\\item 'acute\\_sinusitis'\n",
-       "\\item 'iron\\_deficiency\\_anemia'\n",
-       "\\item 'allergic\\_rhinitis\\_caused\\_by\\_pollen'\n",
-       "\\item 'gastritis'\n",
-       "\\item 'cataract'\n",
-       "\\item 'hematuria\\_syndrome'\n",
-       "\\item 'disorder\\_of\\_the\\_peripheral\\_nervous\\_system'\n",
-       "\\item 'viral\\_hepatitis\\_type\\_c'\n",
-       "\\item 'palpitations'\n",
-       "\\item 'eruption\\_of\\_skin'\n",
-       "\\item 'diabetes\\_mellitus\\_type\\_1'\n",
-       "\\item 'renal\\_failure\\_syndrome'\n",
-       "\\item 'peripheral\\_vascular\\_disease'\n",
-       "\\item 'hyperglycemia'\n",
-       "\\item 'seizure\\_disorder'\n",
-       "\\item 'fever'\n",
-       "\\item 'osteoarthritis\\_of\\_knee'\n",
-       "\\item 'actinic\\_keratosis'\n",
-       "\\item 'urinary\\_incontinence'\n",
-       "\\item 'hemorrhoids'\n",
-       "\\item 'seizure'\n",
-       "\\item 'laceration\\_-\\_injury'\n",
-       "\\item 'glaucoma'\n",
-       "\\item 'body\\_mass\\_index\\_30+\\_-\\_obesity'\n",
-       "\\item 'breast\\_lump'\n",
-       "\\item 'viral\\_disease'\n",
-       "\\item 'abnormal\\_cervical\\_smear'\n",
-       "\\item ⋯\n",
-       "\\item 'viral\\_exanthem'\n",
-       "\\item 'talipes\\_planus'\n",
-       "\\item 'idiopathic\\_peripheral\\_neuropathy'\n",
-       "\\item 'foreign\\_body\\_in\\_pharynx'\n",
-       "\\item 'jaw\\_pain'\n",
-       "\\item 'renal\\_impairment'\n",
-       "\\item 'ataxia'\n",
-       "\\item 'age-related\\_macular\\_degeneration'\n",
-       "\\item 'uterine\\_prolapse'\n",
-       "\\item 'renal\\_mass'\n",
-       "\\item 'pneumonitis'\n",
-       "\\item 'coordination\\_problem'\n",
-       "\\item 'blindness\\_-\\_both\\_eyes'\n",
-       "\\item 'primary\\_hyperparathyroidism'\n",
-       "\\item 'musculoskeletal\\_pain'\n",
-       "\\item 'mycosis'\n",
-       "\\item 'primigravida'\n",
-       "\\item 'urethral\\_stricture'\n",
-       "\\item 'leukocytosis'\n",
-       "\\item 'ventricular\\_premature\\_complex'\n",
-       "\\item 'ulcer\\_of\\_foot\\_due\\_to\\_diabetes\\_mellitus'\n",
-       "\\item 'chronic\\_headache\\_disorder'\n",
-       "\\item 'hemangioma'\n",
-       "\\item 'lymphedema'\n",
-       "\\item 'postmenopausal\\_state'\n",
-       "\\item 'chronic\\_ulcer\\_of\\_skin'\n",
-       "\\item 'left\\_heart\\_failure'\n",
-       "\\item 'excessive\\_and\\_frequent\\_menstruation'\n",
-       "\\item 'thrombocytosis'\n",
-       "\\item 'disorder\\_of\\_liver'\n",
-       "\\item 'disorder\\_of\\_carotid\\_artery'\n",
-       "\\item 'altered\\_bowel\\_function'\n",
-       "\\item 'abscess\\_of\\_foot'\n",
-       "\\item 'malignant\\_tumor\\_of\\_head\\_and/or\\_neck'\n",
-       "\\item 'streptococcus\\_group\\_b\\_infection\\_of\\_the\\_infant'\n",
-       "\\item 'concussion\\_injury\\_of\\_brain'\n",
-       "\\item 'feeding\\_problems\\_in\\_newborn'\n",
-       "\\item 'bipolar\\_i\\_disorder'\n",
-       "\\item 'viral\\_pharyngitis'\n",
-       "\\item 'lower\\_respiratory\\_tract\\_infection'\n",
-       "\\item 'hydronephrosis'\n",
-       "\\item 'borderline\\_personality\\_disorder'\n",
-       "\\item 'esophageal\\_varices'\n",
-       "\\item 'hypersomnia'\n",
-       "\\item 'sensorineural\\_hearing\\_loss\\_bilateral'\n",
-       "\\item 'varicocele'\n",
-       "\\item 'subarachnoid\\_intracranial\\_hemorrhage'\n",
-       "\\item 'incisional\\_hernia'\n",
-       "\\item 'varicella'\n",
-       "\\item 'pain\\_in\\_testicle'\n",
-       "\\item 'transplant\\_follow-up'\n",
-       "\\item 'tinea\\_cruris'\n",
-       "\\item 'laryngitis'\n",
-       "\\item 'hypertrophy\\_of\\_nail'\n",
-       "\\item 'amblyopia'\n",
-       "\\item 'polyp\\_of\\_cervix'\n",
-       "\\item 'cyst\\_of\\_kidney'\n",
-       "\\item 'hepatic\\_encephalopathy'\n",
-       "\\item 'blood\\_glucose\\_abnormal'\n",
-       "\\item 'postherpetic\\_neuralgia'\n",
-       "\\item 'frank\\_hematuria'\n",
-       "\\item 'cramp'\n",
-       "\\item 'interstitial\\_lung\\_disease'\n",
-       "\\item 'complete\\_atrioventricular\\_block'\n",
-       "\\item 'malignant\\_tumor\\_of\\_kidney'\n",
-       "\\item 'otitis'\n",
-       "\\item 'septic\\_shock'\n",
-       "\\item 'disorder\\_of\\_thyroid\\_gland'\n",
-       "\\item 'hypertrophic\\_cardiomyopathy'\n",
-       "\\item 'respiratory\\_distress\\_syndrome\\_in\\_the\\_newborn'\n",
-       "\\item 'infectious\\_gastroenteritis'\n",
-       "\\item 'subdural\\_intracranial\\_hemorrhage'\n",
-       "\\item 'hepatitis\\_b\\_carrier'\n",
-       "\\item 'manic\\_bipolar\\_i\\_disorder'\n",
-       "\\item 'secondary\\_pulmonary\\_hypertension'\n",
-       "\\item 'gonorrhea'\n",
-       "\\item 'derangement\\_of\\_knee'\n",
-       "\\item 'appendicitis'\n",
-       "\\item 'polyneuropathy\\_due\\_to\\_diabetes\\_mellitus'\n",
-       "\\item 'neonatal\\_hypoglycemia'\n",
-       "\\item 'prolonged\\_rupture\\_of\\_membranes'\n",
-       "\\item 'vasomotor\\_rhinitis'\n",
-       "\\item 'renal\\_disorder\\_due\\_to\\_type\\_1\\_diabetes\\_mellitus'\n",
-       "\\item 'tuberculosis'\n",
-       "\\item 'feeding\\_problem'\n",
-       "\\item 'chronic\\_tonsillitis'\n",
-       "\\item 'acute\\_duodenal\\_ulcer\\_with\\_hemorrhage'\n",
-       "\\item 'hammer\\_toe'\n",
-       "\\item 'malignant\\_tumor\\_of\\_cervix'\n",
-       "\\item 'prolapsed\\_lumbar\\_intervertebral\\_disc'\n",
-       "\\item 'hematemesis'\n",
-       "\\item 'perianal\\_abscess'\n",
-       "\\item 'nonvenomous\\_insect\\_bite'\n",
-       "\\item 'spondylolisthesis'\n",
-       "\\item 'malignant\\_tumor\\_of\\_esophagus'\n",
-       "\\item 'aphthous\\_ulcer\\_of\\_mouth'\n",
-       "\\item 'ventricular\\_septal\\_defect'\n",
-       "\\item 'oropharyngeal\\_dysphagia'\n",
-       "\\item 'injury\\_of\\_knee'\n",
-       "\\item 'traumatic\\_brain\\_injury'\n",
-       "\\item 'osteoarthritis\\_of\\_glenohumeral\\_joint'\n",
-       "\\item 'fetal\\_or\\_neonatal\\_effect\\_of\\_maternal\\_medical\\_problem'\n",
-       "\\item 'stomatological\\_preparations'\n",
-       "\\item 'drugs\\_for\\_acid\\_related\\_disorders'\n",
-       "\\item 'drugs\\_for\\_functional\\_gastrointestinal\\_disorders'\n",
-       "\\item 'antiemetics\\_and\\_antinauseants'\n",
-       "\\item 'bile\\_and\\_liver\\_therapy'\n",
-       "\\item 'drugs\\_for\\_constipation'\n",
-       "\\item 'antidiarrheals,\\_intestinal\\_antiinflammatory/antiinfective\\_agents'\n",
-       "\\item 'antiobesity\\_preparations,\\_excl.\\_diet\\_products'\n",
-       "\\item 'digestives,\\_incl.\\_enzymes'\n",
-       "\\item 'drugs\\_used\\_in\\_diabetes'\n",
-       "\\item 'vitamins'\n",
-       "\\item 'mineral\\_supplements'\n",
-       "\\item 'tonics'\n",
-       "\\item 'anabolic\\_agents\\_for\\_systemic\\_use'\n",
-       "\\item 'appetite\\_stimulants'\n",
-       "\\item 'other\\_alimentary\\_tract\\_and\\_metabolism\\_products'\n",
-       "\\item 'antithrombotic\\_agents'\n",
-       "\\item 'antihemorrhagics'\n",
-       "\\item 'antianemic\\_preparations'\n",
-       "\\item 'blood\\_substitutes\\_and\\_perfusion\\_solutions'\n",
-       "\\item 'other\\_hematological\\_agents'\n",
-       "\\item 'cardiac\\_therapy'\n",
-       "\\item 'antihypertensives'\n",
-       "\\item 'diuretics'\n",
-       "\\item 'peripheral\\_vasodilators'\n",
-       "\\item 'vasoprotectives'\n",
-       "\\item 'beta\\_blocking\\_agents'\n",
-       "\\item 'calcium\\_channel\\_blockers'\n",
-       "\\item 'agents\\_acting\\_on\\_the\\_renin-angiotensin\\_system'\n",
-       "\\item 'lipid\\_modifying\\_agents'\n",
-       "\\item 'antifungals\\_for\\_dermatological\\_use'\n",
-       "\\item 'emollients\\_and\\_protectives'\n",
-       "\\item 'preparations\\_for\\_treatment\\_of\\_wounds\\_and\\_ulcers'\n",
-       "\\item 'antipruritics,\\_incl.\\_antihistamines,\\_anesthetics,\\_etc.'\n",
-       "\\item 'antipsoriatics'\n",
-       "\\item 'antibiotics\\_and\\_chemotherapeutics\\_for\\_dermatological\\_use'\n",
-       "\\item 'corticosteroids,\\_dermatological\\_preparations'\n",
-       "\\item 'antiseptics\\_and\\_disinfectants'\n",
-       "\\item 'medicated\\_dressings'\n",
-       "\\item 'anti-acne\\_preparations'\n",
-       "\\item 'other\\_dermatological\\_preparations'\n",
-       "\\item 'gynecological\\_antiinfectives\\_and\\_antiseptics'\n",
-       "\\item 'other\\_gynecologicals'\n",
-       "\\item 'sex\\_hormones\\_and\\_modulators\\_of\\_the\\_genital\\_system'\n",
-       "\\item 'urologicals'\n",
-       "\\item 'pituitary\\_and\\_hypothalamic\\_hormones\\_and\\_analogues'\n",
-       "\\item 'corticosteroids\\_for\\_systemic\\_use'\n",
-       "\\item 'thyroid\\_therapy'\n",
-       "\\item 'pancreatic\\_hormones'\n",
-       "\\item 'calcium\\_homeostasis'\n",
-       "\\item 'antibacterials\\_for\\_systemic\\_use'\n",
-       "\\item 'antimycotics\\_for\\_systemic\\_use'\n",
-       "\\item 'antimycobacterials'\n",
-       "\\item 'antivirals\\_for\\_systemic\\_use'\n",
-       "\\item 'immune\\_sera\\_and\\_immunoglobulins'\n",
-       "\\item 'vaccines'\n",
-       "\\item 'antineoplastic\\_agents'\n",
-       "\\item 'endocrine\\_therapy'\n",
-       "\\item 'immunostimulants'\n",
-       "\\item 'immunosuppressants'\n",
-       "\\item 'antiinflammatory\\_and\\_antirheumatic\\_products'\n",
-       "\\item 'topical\\_products\\_for\\_joint\\_and\\_muscular\\_pain'\n",
-       "\\item 'muscle\\_relaxants'\n",
-       "\\item 'antigout\\_preparations'\n",
-       "\\item 'drugs\\_for\\_treatment\\_of\\_bone\\_diseases'\n",
-       "\\item 'other\\_drugs\\_for\\_disorders\\_of\\_the\\_musculo-skeletal\\_system'\n",
-       "\\item 'anesthetics'\n",
-       "\\item 'analgesics'\n",
-       "\\item 'antiepileptics'\n",
-       "\\item 'anti-parkinson\\_drugs'\n",
-       "\\item 'psycholeptics'\n",
-       "\\item 'psychoanaleptics'\n",
-       "\\item 'other\\_nervous\\_system\\_drugs'\n",
-       "\\item 'antiprotozoals'\n",
-       "\\item 'anthelmintics'\n",
-       "\\item 'ectoparasiticides,\\_incl.\\_scabicides,\\_insecticides\\_and\\_repellents'\n",
-       "\\item 'nasal\\_preparations'\n",
-       "\\item 'throat\\_preparations'\n",
-       "\\item 'drugs\\_for\\_obstructive\\_airway\\_diseases'\n",
-       "\\item 'cough\\_and\\_cold\\_preparations'\n",
-       "\\item 'antihistamines\\_for\\_systemic\\_use'\n",
-       "\\item 'other\\_respiratory\\_system\\_products'\n",
-       "\\item 'ophthalmologicals'\n",
-       "\\item 'otologicals'\n",
-       "\\item 'ophthalmological\\_and\\_otological\\_preparations'\n",
-       "\\item 'allergens'\n",
-       "\\item 'all\\_other\\_therapeutic\\_products'\n",
-       "\\item 'diagnostic\\_agents'\n",
-       "\\item 'general\\_nutrients'\n",
-       "\\item 'all\\_other\\_non-therapeutic\\_products'\n",
-       "\\item 'contrast\\_media'\n",
-       "\\item 'diagnostic\\_radiopharmaceuticals'\n",
-       "\\item 'therapeutic\\_radiopharmaceuticals'\n",
-       "\\item 'surgical\\_dressings'\n",
-       "\\item 'statins'\n",
-       "\\item 'ass'\n",
-       "\\item 'atypical\\_antipsychotics'\n",
-       "\\item 'glucocorticoids'\n",
-       "\\end{enumerate*}\n"
-      ],
-      "text/markdown": [
-       "1. 'age_at_recruitment'\n",
-       "2. 'sex'\n",
-       "3. 'ethnic_background'\n",
-       "4. 'townsend_deprivation_index_at_recruitment'\n",
-       "5. 'overall_health_rating'\n",
-       "6. 'smoking_status'\n",
-       "7. 'alcohol_intake_frequency'\n",
-       "8. 'body_mass_index_bmi'\n",
-       "9. 'weight'\n",
-       "10. 'pulse_wave_arterial_stiffness_index'\n",
-       "11. 'pulse_wave_reflection_index'\n",
-       "12. 'waist_circumference'\n",
-       "13. 'hip_circumference'\n",
-       "14. 'standing_height'\n",
-       "15. 'trunk_fat_percentage'\n",
-       "16. 'body_fat_percentage'\n",
-       "17. 'basal_metabolic_rate'\n",
-       "18. 'forced_vital_capacity_fvc_best_measure'\n",
-       "19. 'forced_expiratory_volume_in_1second_fev1_best_measure'\n",
-       "20. 'fev1_fvc_ratio_zscore'\n",
-       "21. 'peak_expiratory_flow_pef_f3064_0_2'\n",
-       "22. 'peak_expiratory_flow_pef_f3064_0_1'\n",
-       "23. 'peak_expiratory_flow_pef'\n",
-       "24. 'systolic_blood_pressure'\n",
-       "25. 'diastolic_blood_pressure'\n",
-       "26. 'pulse_rate'\n",
-       "27. 'basophill_count'\n",
-       "28. 'basophill_percentage'\n",
-       "29. 'eosinophill_count'\n",
-       "30. 'eosinophill_percentage'\n",
-       "31. 'haematocrit_percentage'\n",
-       "32. 'haemoglobin_concentration'\n",
-       "33. 'high_light_scatter_reticulocyte_count'\n",
-       "34. 'high_light_scatter_reticulocyte_percentage'\n",
-       "35. 'immature_reticulocyte_fraction'\n",
-       "36. 'lymphocyte_count'\n",
-       "37. 'lymphocyte_percentage'\n",
-       "38. 'mean_corpuscular_haemoglobin'\n",
-       "39. 'mean_corpuscular_haemoglobin_concentration'\n",
-       "40. 'mean_corpuscular_volume'\n",
-       "41. 'mean_platelet_thrombocyte_volume'\n",
-       "42. 'mean_reticulocyte_volume'\n",
-       "43. 'mean_sphered_cell_volume'\n",
-       "44. 'monocyte_count'\n",
-       "45. 'monocyte_percentage'\n",
-       "46. 'neutrophill_count'\n",
-       "47. 'neutrophill_percentage'\n",
-       "48. 'nucleated_red_blood_cell_count'\n",
-       "49. 'nucleated_red_blood_cell_percentage'\n",
-       "50. 'platelet_count'\n",
-       "51. 'platelet_crit'\n",
-       "52. 'platelet_distribution_width'\n",
-       "53. 'red_blood_cell_erythrocyte_count'\n",
-       "54. 'red_blood_cell_erythrocyte_distribution_width'\n",
-       "55. 'reticulocyte_count'\n",
-       "56. 'reticulocyte_percentage'\n",
-       "57. 'white_blood_cell_leukocyte_count'\n",
-       "58. 'alanine_aminotransferase'\n",
-       "59. 'albumin'\n",
-       "60. 'alkaline_phosphatase'\n",
-       "61. 'apolipoprotein_a'\n",
-       "62. 'apolipoprotein_b'\n",
-       "63. 'aspartate_aminotransferase'\n",
-       "64. 'creactive_protein'\n",
-       "65. 'calcium'\n",
-       "66. 'cholesterol'\n",
-       "67. 'creatinine'\n",
-       "68. 'cystatin_c'\n",
-       "69. 'direct_bilirubin'\n",
-       "70. 'gamma_glutamyltransferase'\n",
-       "71. 'glucose'\n",
-       "72. 'glycated_haemoglobin_hba1c'\n",
-       "73. 'hdl_cholesterol'\n",
-       "74. 'igf1'\n",
-       "75. 'ldl_direct'\n",
-       "76. 'lipoprotein_a'\n",
-       "77. 'oestradiol'\n",
-       "78. 'phosphate'\n",
-       "79. 'rheumatoid_factor'\n",
-       "80. 'shbg'\n",
-       "81. 'testosterone'\n",
-       "82. 'total_bilirubin'\n",
-       "83. 'total_protein'\n",
-       "84. 'triglycerides'\n",
-       "85. 'urate'\n",
-       "86. 'urea'\n",
-       "87. 'vitamin_d'\n",
-       "88. 'fh_alzheimer\\'s_disease/dementia'\n",
-       "89. 'fh_bowel_cancer'\n",
-       "90. 'fh_breast_cancer'\n",
-       "91. 'fh_chronic_bronchitis/emphysema'\n",
-       "92. 'fh_diabetes'\n",
-       "93. 'fh_heart_disease'\n",
-       "94. 'fh_high_blood_pressure'\n",
-       "95. 'fh_lung_cancer'\n",
-       "96. 'fh_parkinson\\'s_disease'\n",
-       "97. 'fh_severe_depression'\n",
-       "98. 'fh_stroke'\n",
-       "99. 'coronary_heart_disease'\n",
-       "100. 'myocardial_infarction'\n",
-       "101. 'stroke'\n",
-       "102. 'diabetes1'\n",
-       "103. 'diabetes2'\n",
-       "104. 'chronic_kidney_disease'\n",
-       "105. 'atrial_fibrillation'\n",
-       "106. 'migraine'\n",
-       "107. 'rheumatoid_arthritis'\n",
-       "108. 'systemic_lupus_erythematosus'\n",
-       "109. 'severe_mental_illness'\n",
-       "110. 'erectile_dysfunction'\n",
-       "111. 'hypertensive_disorder_systemic_arterial'\n",
-       "112. 'hyperlipidemia'\n",
-       "113. 'depressive_disorder'\n",
-       "114. 'gastroesophageal_reflux_disease'\n",
-       "115. 'diabetes_mellitus_type_2'\n",
-       "116. 'essential_hypertension'\n",
-       "117. 'obesity'\n",
-       "118. 'diabetes_mellitus'\n",
-       "119. 'asthma'\n",
-       "120. 'coronary_arteriosclerosis'\n",
-       "121. 'allergic_rhinitis'\n",
-       "122. 'hypothyroidism'\n",
-       "123. 'upper_respiratory_infection'\n",
-       "124. 'hypercholesterolemia'\n",
-       "125. 'backache'\n",
-       "126. 'abdominal_pain'\n",
-       "127. 'osteoarthritis'\n",
-       "128. 'low_back_pain'\n",
-       "129. 'anemia'\n",
-       "130. 'anxiety'\n",
-       "131. 'urinary_tract_infectious_disease'\n",
-       "132. 'chronic_obstructive_lung_disease'\n",
-       "133. 'pneumonia'\n",
-       "134. 'chest_pain'\n",
-       "135. 'congestive_heart_failure'\n",
-       "136. 'headache'\n",
-       "137. 'pregnant'\n",
-       "138. 'knee_pain'\n",
-       "139. 'osteoporosis'\n",
-       "140. 'polyp_of_colon'\n",
-       "141. 'otitis_media'\n",
-       "142. 'sinusitis'\n",
-       "143. 'cough'\n",
-       "144. 'sleep_apnea'\n",
-       "145. 'insomnia'\n",
-       "146. 'inflammatory_disorder_due_to_increased_blood_urate_level'\n",
-       "147. 'tobacco_dependence_syndrome'\n",
-       "148. 'malignant_tumor_of_prostate'\n",
-       "149. 'constipation'\n",
-       "150. 'hearing_loss'\n",
-       "151. 'fatigue'\n",
-       "152. 'obstructive_sleep_apnea_syndrome'\n",
-       "153. 'malignant_neoplasm_of_breast'\n",
-       "154. 'delivery_normal'\n",
-       "155. 'irritable_bowel_syndrome'\n",
-       "156. 'tobacco_user'\n",
-       "157. 'neck_pain'\n",
-       "158. 'cerebrovascular_accident'\n",
-       "159. 'asthenia'\n",
-       "160. 'shoulder_pain'\n",
-       "161. 'acne_vulgaris'\n",
-       "162. 'benign_prostatic_hyperplasia'\n",
-       "163. 'dyspnea'\n",
-       "164. 'carpal_tunnel_syndrome'\n",
-       "165. 'bronchitis'\n",
-       "166. 'pharyngitis'\n",
-       "167. 'arthritis'\n",
-       "168. 'diarrhea'\n",
-       "169. 'dizziness'\n",
-       "170. 'alcohol_abuse'\n",
-       "171. 'dementia'\n",
-       "172. 'eczema'\n",
-       "173. 'syncope'\n",
-       "174. 'acute_sinusitis'\n",
-       "175. 'iron_deficiency_anemia'\n",
-       "176. 'allergic_rhinitis_caused_by_pollen'\n",
-       "177. 'gastritis'\n",
-       "178. 'cataract'\n",
-       "179. 'hematuria_syndrome'\n",
-       "180. 'disorder_of_the_peripheral_nervous_system'\n",
-       "181. 'viral_hepatitis_type_c'\n",
-       "182. 'palpitations'\n",
-       "183. 'eruption_of_skin'\n",
-       "184. 'diabetes_mellitus_type_1'\n",
-       "185. 'renal_failure_syndrome'\n",
-       "186. 'peripheral_vascular_disease'\n",
-       "187. 'hyperglycemia'\n",
-       "188. 'seizure_disorder'\n",
-       "189. 'fever'\n",
-       "190. 'osteoarthritis_of_knee'\n",
-       "191. 'actinic_keratosis'\n",
-       "192. 'urinary_incontinence'\n",
-       "193. 'hemorrhoids'\n",
-       "194. 'seizure'\n",
-       "195. 'laceration_-_injury'\n",
-       "196. 'glaucoma'\n",
-       "197. 'body_mass_index_30+_-_obesity'\n",
-       "198. 'breast_lump'\n",
-       "199. 'viral_disease'\n",
-       "200. 'abnormal_cervical_smear'\n",
-       "201. ⋯\n",
-       "202. 'viral_exanthem'\n",
-       "203. 'talipes_planus'\n",
-       "204. 'idiopathic_peripheral_neuropathy'\n",
-       "205. 'foreign_body_in_pharynx'\n",
-       "206. 'jaw_pain'\n",
-       "207. 'renal_impairment'\n",
-       "208. 'ataxia'\n",
-       "209. 'age-related_macular_degeneration'\n",
-       "210. 'uterine_prolapse'\n",
-       "211. 'renal_mass'\n",
-       "212. 'pneumonitis'\n",
-       "213. 'coordination_problem'\n",
-       "214. 'blindness_-_both_eyes'\n",
-       "215. 'primary_hyperparathyroidism'\n",
-       "216. 'musculoskeletal_pain'\n",
-       "217. 'mycosis'\n",
-       "218. 'primigravida'\n",
-       "219. 'urethral_stricture'\n",
-       "220. 'leukocytosis'\n",
-       "221. 'ventricular_premature_complex'\n",
-       "222. 'ulcer_of_foot_due_to_diabetes_mellitus'\n",
-       "223. 'chronic_headache_disorder'\n",
-       "224. 'hemangioma'\n",
-       "225. 'lymphedema'\n",
-       "226. 'postmenopausal_state'\n",
-       "227. 'chronic_ulcer_of_skin'\n",
-       "228. 'left_heart_failure'\n",
-       "229. 'excessive_and_frequent_menstruation'\n",
-       "230. 'thrombocytosis'\n",
-       "231. 'disorder_of_liver'\n",
-       "232. 'disorder_of_carotid_artery'\n",
-       "233. 'altered_bowel_function'\n",
-       "234. 'abscess_of_foot'\n",
-       "235. 'malignant_tumor_of_head_and/or_neck'\n",
-       "236. 'streptococcus_group_b_infection_of_the_infant'\n",
-       "237. 'concussion_injury_of_brain'\n",
-       "238. 'feeding_problems_in_newborn'\n",
-       "239. 'bipolar_i_disorder'\n",
-       "240. 'viral_pharyngitis'\n",
-       "241. 'lower_respiratory_tract_infection'\n",
-       "242. 'hydronephrosis'\n",
-       "243. 'borderline_personality_disorder'\n",
-       "244. 'esophageal_varices'\n",
-       "245. 'hypersomnia'\n",
-       "246. 'sensorineural_hearing_loss_bilateral'\n",
-       "247. 'varicocele'\n",
-       "248. 'subarachnoid_intracranial_hemorrhage'\n",
-       "249. 'incisional_hernia'\n",
-       "250. 'varicella'\n",
-       "251. 'pain_in_testicle'\n",
-       "252. 'transplant_follow-up'\n",
-       "253. 'tinea_cruris'\n",
-       "254. 'laryngitis'\n",
-       "255. 'hypertrophy_of_nail'\n",
-       "256. 'amblyopia'\n",
-       "257. 'polyp_of_cervix'\n",
-       "258. 'cyst_of_kidney'\n",
-       "259. 'hepatic_encephalopathy'\n",
-       "260. 'blood_glucose_abnormal'\n",
-       "261. 'postherpetic_neuralgia'\n",
-       "262. 'frank_hematuria'\n",
-       "263. 'cramp'\n",
-       "264. 'interstitial_lung_disease'\n",
-       "265. 'complete_atrioventricular_block'\n",
-       "266. 'malignant_tumor_of_kidney'\n",
-       "267. 'otitis'\n",
-       "268. 'septic_shock'\n",
-       "269. 'disorder_of_thyroid_gland'\n",
-       "270. 'hypertrophic_cardiomyopathy'\n",
-       "271. 'respiratory_distress_syndrome_in_the_newborn'\n",
-       "272. 'infectious_gastroenteritis'\n",
-       "273. 'subdural_intracranial_hemorrhage'\n",
-       "274. 'hepatitis_b_carrier'\n",
-       "275. 'manic_bipolar_i_disorder'\n",
-       "276. 'secondary_pulmonary_hypertension'\n",
-       "277. 'gonorrhea'\n",
-       "278. 'derangement_of_knee'\n",
-       "279. 'appendicitis'\n",
-       "280. 'polyneuropathy_due_to_diabetes_mellitus'\n",
-       "281. 'neonatal_hypoglycemia'\n",
-       "282. 'prolonged_rupture_of_membranes'\n",
-       "283. 'vasomotor_rhinitis'\n",
-       "284. 'renal_disorder_due_to_type_1_diabetes_mellitus'\n",
-       "285. 'tuberculosis'\n",
-       "286. 'feeding_problem'\n",
-       "287. 'chronic_tonsillitis'\n",
-       "288. 'acute_duodenal_ulcer_with_hemorrhage'\n",
-       "289. 'hammer_toe'\n",
-       "290. 'malignant_tumor_of_cervix'\n",
-       "291. 'prolapsed_lumbar_intervertebral_disc'\n",
-       "292. 'hematemesis'\n",
-       "293. 'perianal_abscess'\n",
-       "294. 'nonvenomous_insect_bite'\n",
-       "295. 'spondylolisthesis'\n",
-       "296. 'malignant_tumor_of_esophagus'\n",
-       "297. 'aphthous_ulcer_of_mouth'\n",
-       "298. 'ventricular_septal_defect'\n",
-       "299. 'oropharyngeal_dysphagia'\n",
-       "300. 'injury_of_knee'\n",
-       "301. 'traumatic_brain_injury'\n",
-       "302. 'osteoarthritis_of_glenohumeral_joint'\n",
-       "303. 'fetal_or_neonatal_effect_of_maternal_medical_problem'\n",
-       "304. 'stomatological_preparations'\n",
-       "305. 'drugs_for_acid_related_disorders'\n",
-       "306. 'drugs_for_functional_gastrointestinal_disorders'\n",
-       "307. 'antiemetics_and_antinauseants'\n",
-       "308. 'bile_and_liver_therapy'\n",
-       "309. 'drugs_for_constipation'\n",
-       "310. 'antidiarrheals,_intestinal_antiinflammatory/antiinfective_agents'\n",
-       "311. 'antiobesity_preparations,_excl._diet_products'\n",
-       "312. 'digestives,_incl._enzymes'\n",
-       "313. 'drugs_used_in_diabetes'\n",
-       "314. 'vitamins'\n",
-       "315. 'mineral_supplements'\n",
-       "316. 'tonics'\n",
-       "317. 'anabolic_agents_for_systemic_use'\n",
-       "318. 'appetite_stimulants'\n",
-       "319. 'other_alimentary_tract_and_metabolism_products'\n",
-       "320. 'antithrombotic_agents'\n",
-       "321. 'antihemorrhagics'\n",
-       "322. 'antianemic_preparations'\n",
-       "323. 'blood_substitutes_and_perfusion_solutions'\n",
-       "324. 'other_hematological_agents'\n",
-       "325. 'cardiac_therapy'\n",
-       "326. 'antihypertensives'\n",
-       "327. 'diuretics'\n",
-       "328. 'peripheral_vasodilators'\n",
-       "329. 'vasoprotectives'\n",
-       "330. 'beta_blocking_agents'\n",
-       "331. 'calcium_channel_blockers'\n",
-       "332. 'agents_acting_on_the_renin-angiotensin_system'\n",
-       "333. 'lipid_modifying_agents'\n",
-       "334. 'antifungals_for_dermatological_use'\n",
-       "335. 'emollients_and_protectives'\n",
-       "336. 'preparations_for_treatment_of_wounds_and_ulcers'\n",
-       "337. 'antipruritics,_incl._antihistamines,_anesthetics,_etc.'\n",
-       "338. 'antipsoriatics'\n",
-       "339. 'antibiotics_and_chemotherapeutics_for_dermatological_use'\n",
-       "340. 'corticosteroids,_dermatological_preparations'\n",
-       "341. 'antiseptics_and_disinfectants'\n",
-       "342. 'medicated_dressings'\n",
-       "343. 'anti-acne_preparations'\n",
-       "344. 'other_dermatological_preparations'\n",
-       "345. 'gynecological_antiinfectives_and_antiseptics'\n",
-       "346. 'other_gynecologicals'\n",
-       "347. 'sex_hormones_and_modulators_of_the_genital_system'\n",
-       "348. 'urologicals'\n",
-       "349. 'pituitary_and_hypothalamic_hormones_and_analogues'\n",
-       "350. 'corticosteroids_for_systemic_use'\n",
-       "351. 'thyroid_therapy'\n",
-       "352. 'pancreatic_hormones'\n",
-       "353. 'calcium_homeostasis'\n",
-       "354. 'antibacterials_for_systemic_use'\n",
-       "355. 'antimycotics_for_systemic_use'\n",
-       "356. 'antimycobacterials'\n",
-       "357. 'antivirals_for_systemic_use'\n",
-       "358. 'immune_sera_and_immunoglobulins'\n",
-       "359. 'vaccines'\n",
-       "360. 'antineoplastic_agents'\n",
-       "361. 'endocrine_therapy'\n",
-       "362. 'immunostimulants'\n",
-       "363. 'immunosuppressants'\n",
-       "364. 'antiinflammatory_and_antirheumatic_products'\n",
-       "365. 'topical_products_for_joint_and_muscular_pain'\n",
-       "366. 'muscle_relaxants'\n",
-       "367. 'antigout_preparations'\n",
-       "368. 'drugs_for_treatment_of_bone_diseases'\n",
-       "369. 'other_drugs_for_disorders_of_the_musculo-skeletal_system'\n",
-       "370. 'anesthetics'\n",
-       "371. 'analgesics'\n",
-       "372. 'antiepileptics'\n",
-       "373. 'anti-parkinson_drugs'\n",
-       "374. 'psycholeptics'\n",
-       "375. 'psychoanaleptics'\n",
-       "376. 'other_nervous_system_drugs'\n",
-       "377. 'antiprotozoals'\n",
-       "378. 'anthelmintics'\n",
-       "379. 'ectoparasiticides,_incl._scabicides,_insecticides_and_repellents'\n",
-       "380. 'nasal_preparations'\n",
-       "381. 'throat_preparations'\n",
-       "382. 'drugs_for_obstructive_airway_diseases'\n",
-       "383. 'cough_and_cold_preparations'\n",
-       "384. 'antihistamines_for_systemic_use'\n",
-       "385. 'other_respiratory_system_products'\n",
-       "386. 'ophthalmologicals'\n",
-       "387. 'otologicals'\n",
-       "388. 'ophthalmological_and_otological_preparations'\n",
-       "389. 'allergens'\n",
-       "390. 'all_other_therapeutic_products'\n",
-       "391. 'diagnostic_agents'\n",
-       "392. 'general_nutrients'\n",
-       "393. 'all_other_non-therapeutic_products'\n",
-       "394. 'contrast_media'\n",
-       "395. 'diagnostic_radiopharmaceuticals'\n",
-       "396. 'therapeutic_radiopharmaceuticals'\n",
-       "397. 'surgical_dressings'\n",
-       "398. 'statins'\n",
-       "399. 'ass'\n",
-       "400. 'atypical_antipsychotics'\n",
-       "401. 'glucocorticoids'\n",
-       "\n",
-       "\n"
-      ],
-      "text/plain": [
-       "  [1] \"age_at_recruitment\"                                                     \n",
-       "  [2] \"sex\"                                                                    \n",
-       "  [3] \"ethnic_background\"                                                      \n",
-       "  [4] \"townsend_deprivation_index_at_recruitment\"                              \n",
-       "  [5] \"overall_health_rating\"                                                  \n",
-       "  [6] \"smoking_status\"                                                         \n",
-       "  [7] \"alcohol_intake_frequency\"                                               \n",
-       "  [8] \"body_mass_index_bmi\"                                                    \n",
-       "  [9] \"weight\"                                                                 \n",
-       " [10] \"pulse_wave_arterial_stiffness_index\"                                    \n",
-       " [11] \"pulse_wave_reflection_index\"                                            \n",
-       " [12] \"waist_circumference\"                                                    \n",
-       " [13] \"hip_circumference\"                                                      \n",
-       " [14] \"standing_height\"                                                        \n",
-       " [15] \"trunk_fat_percentage\"                                                   \n",
-       " [16] \"body_fat_percentage\"                                                    \n",
-       " [17] \"basal_metabolic_rate\"                                                   \n",
-       " [18] \"forced_vital_capacity_fvc_best_measure\"                                 \n",
-       " [19] \"forced_expiratory_volume_in_1second_fev1_best_measure\"                  \n",
-       " [20] \"fev1_fvc_ratio_zscore\"                                                  \n",
-       " [21] \"peak_expiratory_flow_pef_f3064_0_2\"                                     \n",
-       " [22] \"peak_expiratory_flow_pef_f3064_0_1\"                                     \n",
-       " [23] \"peak_expiratory_flow_pef\"                                               \n",
-       " [24] \"systolic_blood_pressure\"                                                \n",
-       " [25] \"diastolic_blood_pressure\"                                               \n",
-       " [26] \"pulse_rate\"                                                             \n",
-       " [27] \"basophill_count\"                                                        \n",
-       " [28] \"basophill_percentage\"                                                   \n",
-       " [29] \"eosinophill_count\"                                                      \n",
-       " [30] \"eosinophill_percentage\"                                                 \n",
-       " [31] \"haematocrit_percentage\"                                                 \n",
-       " [32] \"haemoglobin_concentration\"                                              \n",
-       " [33] \"high_light_scatter_reticulocyte_count\"                                  \n",
-       " [34] \"high_light_scatter_reticulocyte_percentage\"                             \n",
-       " [35] \"immature_reticulocyte_fraction\"                                         \n",
-       " [36] \"lymphocyte_count\"                                                       \n",
-       " [37] \"lymphocyte_percentage\"                                                  \n",
-       " [38] \"mean_corpuscular_haemoglobin\"                                           \n",
-       " [39] \"mean_corpuscular_haemoglobin_concentration\"                             \n",
-       " [40] \"mean_corpuscular_volume\"                                                \n",
-       " [41] \"mean_platelet_thrombocyte_volume\"                                       \n",
-       " [42] \"mean_reticulocyte_volume\"                                               \n",
-       " [43] \"mean_sphered_cell_volume\"                                               \n",
-       " [44] \"monocyte_count\"                                                         \n",
-       " [45] \"monocyte_percentage\"                                                    \n",
-       " [46] \"neutrophill_count\"                                                      \n",
-       " [47] \"neutrophill_percentage\"                                                 \n",
-       " [48] \"nucleated_red_blood_cell_count\"                                         \n",
-       " [49] \"nucleated_red_blood_cell_percentage\"                                    \n",
-       " [50] \"platelet_count\"                                                         \n",
-       " [51] \"platelet_crit\"                                                          \n",
-       " [52] \"platelet_distribution_width\"                                            \n",
-       " [53] \"red_blood_cell_erythrocyte_count\"                                       \n",
-       " [54] \"red_blood_cell_erythrocyte_distribution_width\"                          \n",
-       " [55] \"reticulocyte_count\"                                                     \n",
-       " [56] \"reticulocyte_percentage\"                                                \n",
-       " [57] \"white_blood_cell_leukocyte_count\"                                       \n",
-       " [58] \"alanine_aminotransferase\"                                               \n",
-       " [59] \"albumin\"                                                                \n",
-       " [60] \"alkaline_phosphatase\"                                                   \n",
-       " [61] \"apolipoprotein_a\"                                                       \n",
-       " [62] \"apolipoprotein_b\"                                                       \n",
-       " [63] \"aspartate_aminotransferase\"                                             \n",
-       " [64] \"creactive_protein\"                                                      \n",
-       " [65] \"calcium\"                                                                \n",
-       " [66] \"cholesterol\"                                                            \n",
-       " [67] \"creatinine\"                                                             \n",
-       " [68] \"cystatin_c\"                                                             \n",
-       " [69] \"direct_bilirubin\"                                                       \n",
-       " [70] \"gamma_glutamyltransferase\"                                              \n",
-       " [71] \"glucose\"                                                                \n",
-       " [72] \"glycated_haemoglobin_hba1c\"                                             \n",
-       " [73] \"hdl_cholesterol\"                                                        \n",
-       " [74] \"igf1\"                                                                   \n",
-       " [75] \"ldl_direct\"                                                             \n",
-       " [76] \"lipoprotein_a\"                                                          \n",
-       " [77] \"oestradiol\"                                                             \n",
-       " [78] \"phosphate\"                                                              \n",
-       " [79] \"rheumatoid_factor\"                                                      \n",
-       " [80] \"shbg\"                                                                   \n",
-       " [81] \"testosterone\"                                                           \n",
-       " [82] \"total_bilirubin\"                                                        \n",
-       " [83] \"total_protein\"                                                          \n",
-       " [84] \"triglycerides\"                                                          \n",
-       " [85] \"urate\"                                                                  \n",
-       " [86] \"urea\"                                                                   \n",
-       " [87] \"vitamin_d\"                                                              \n",
-       " [88] \"fh_alzheimer's_disease/dementia\"                                        \n",
-       " [89] \"fh_bowel_cancer\"                                                        \n",
-       " [90] \"fh_breast_cancer\"                                                       \n",
-       " [91] \"fh_chronic_bronchitis/emphysema\"                                        \n",
-       " [92] \"fh_diabetes\"                                                            \n",
-       " [93] \"fh_heart_disease\"                                                       \n",
-       " [94] \"fh_high_blood_pressure\"                                                 \n",
-       " [95] \"fh_lung_cancer\"                                                         \n",
-       " [96] \"fh_parkinson's_disease\"                                                 \n",
-       " [97] \"fh_severe_depression\"                                                   \n",
-       " [98] \"fh_stroke\"                                                              \n",
-       " [99] \"coronary_heart_disease\"                                                 \n",
-       "[100] \"myocardial_infarction\"                                                  \n",
-       "[101] \"stroke\"                                                                 \n",
-       "[102] \"diabetes1\"                                                              \n",
-       "[103] \"diabetes2\"                                                              \n",
-       "[104] \"chronic_kidney_disease\"                                                 \n",
-       "[105] \"atrial_fibrillation\"                                                    \n",
-       "[106] \"migraine\"                                                               \n",
-       "[107] \"rheumatoid_arthritis\"                                                   \n",
-       "[108] \"systemic_lupus_erythematosus\"                                           \n",
-       "[109] \"severe_mental_illness\"                                                  \n",
-       "[110] \"erectile_dysfunction\"                                                   \n",
-       "[111] \"hypertensive_disorder_systemic_arterial\"                                \n",
-       "[112] \"hyperlipidemia\"                                                         \n",
-       "[113] \"depressive_disorder\"                                                    \n",
-       "[114] \"gastroesophageal_reflux_disease\"                                        \n",
-       "[115] \"diabetes_mellitus_type_2\"                                               \n",
-       "[116] \"essential_hypertension\"                                                 \n",
-       "[117] \"obesity\"                                                                \n",
-       "[118] \"diabetes_mellitus\"                                                      \n",
-       "[119] \"asthma\"                                                                 \n",
-       "[120] \"coronary_arteriosclerosis\"                                              \n",
-       "[121] \"allergic_rhinitis\"                                                      \n",
-       "[122] \"hypothyroidism\"                                                         \n",
-       "[123] \"upper_respiratory_infection\"                                            \n",
-       "[124] \"hypercholesterolemia\"                                                   \n",
-       "[125] \"backache\"                                                               \n",
-       "[126] \"abdominal_pain\"                                                         \n",
-       "[127] \"osteoarthritis\"                                                         \n",
-       "[128] \"low_back_pain\"                                                          \n",
-       "[129] \"anemia\"                                                                 \n",
-       "[130] \"anxiety\"                                                                \n",
-       "[131] \"urinary_tract_infectious_disease\"                                       \n",
-       "[132] \"chronic_obstructive_lung_disease\"                                       \n",
-       "[133] \"pneumonia\"                                                              \n",
-       "[134] \"chest_pain\"                                                             \n",
-       "[135] \"congestive_heart_failure\"                                               \n",
-       "[136] \"headache\"                                                               \n",
-       "[137] \"pregnant\"                                                               \n",
-       "[138] \"knee_pain\"                                                              \n",
-       "[139] \"osteoporosis\"                                                           \n",
-       "[140] \"polyp_of_colon\"                                                         \n",
-       "[141] \"otitis_media\"                                                           \n",
-       "[142] \"sinusitis\"                                                              \n",
-       "[143] \"cough\"                                                                  \n",
-       "[144] \"sleep_apnea\"                                                            \n",
-       "[145] \"insomnia\"                                                               \n",
-       "[146] \"inflammatory_disorder_due_to_increased_blood_urate_level\"               \n",
-       "[147] \"tobacco_dependence_syndrome\"                                            \n",
-       "[148] \"malignant_tumor_of_prostate\"                                            \n",
-       "[149] \"constipation\"                                                           \n",
-       "[150] \"hearing_loss\"                                                           \n",
-       "[151] \"fatigue\"                                                                \n",
-       "[152] \"obstructive_sleep_apnea_syndrome\"                                       \n",
-       "[153] \"malignant_neoplasm_of_breast\"                                           \n",
-       "[154] \"delivery_normal\"                                                        \n",
-       "[155] \"irritable_bowel_syndrome\"                                               \n",
-       "[156] \"tobacco_user\"                                                           \n",
-       "[157] \"neck_pain\"                                                              \n",
-       "[158] \"cerebrovascular_accident\"                                               \n",
-       "[159] \"asthenia\"                                                               \n",
-       "[160] \"shoulder_pain\"                                                          \n",
-       "[161] \"acne_vulgaris\"                                                          \n",
-       "[162] \"benign_prostatic_hyperplasia\"                                           \n",
-       "[163] \"dyspnea\"                                                                \n",
-       "[164] \"carpal_tunnel_syndrome\"                                                 \n",
-       "[165] \"bronchitis\"                                                             \n",
-       "[166] \"pharyngitis\"                                                            \n",
-       "[167] \"arthritis\"                                                              \n",
-       "[168] \"diarrhea\"                                                               \n",
-       "[169] \"dizziness\"                                                              \n",
-       "[170] \"alcohol_abuse\"                                                          \n",
-       "[171] \"dementia\"                                                               \n",
-       "[172] \"eczema\"                                                                 \n",
-       "[173] \"syncope\"                                                                \n",
-       "[174] \"acute_sinusitis\"                                                        \n",
-       "[175] \"iron_deficiency_anemia\"                                                 \n",
-       "[176] \"allergic_rhinitis_caused_by_pollen\"                                     \n",
-       "[177] \"gastritis\"                                                              \n",
-       "[178] \"cataract\"                                                               \n",
-       "[179] \"hematuria_syndrome\"                                                     \n",
-       "[180] \"disorder_of_the_peripheral_nervous_system\"                              \n",
-       "[181] \"viral_hepatitis_type_c\"                                                 \n",
-       "[182] \"palpitations\"                                                           \n",
-       "[183] \"eruption_of_skin\"                                                       \n",
-       "[184] \"diabetes_mellitus_type_1\"                                               \n",
-       "[185] \"renal_failure_syndrome\"                                                 \n",
-       "[186] \"peripheral_vascular_disease\"                                            \n",
-       "[187] \"hyperglycemia\"                                                          \n",
-       "[188] \"seizure_disorder\"                                                       \n",
-       "[189] \"fever\"                                                                  \n",
-       "[190] \"osteoarthritis_of_knee\"                                                 \n",
-       "[191] \"actinic_keratosis\"                                                      \n",
-       "[192] \"urinary_incontinence\"                                                   \n",
-       "[193] \"hemorrhoids\"                                                            \n",
-       "[194] \"seizure\"                                                                \n",
-       "[195] \"laceration_-_injury\"                                                    \n",
-       "[196] \"glaucoma\"                                                               \n",
-       "[197] \"body_mass_index_30+_-_obesity\"                                          \n",
-       "[198] \"breast_lump\"                                                            \n",
-       "[199] \"viral_disease\"                                                          \n",
-       "[200] \"abnormal_cervical_smear\"                                                \n",
-       "[201] \"cellulitis\"                                                             \n",
-       "[202] \"senile_hyperkeratosis\"                                                  \n",
-       "[203] \"anxiety_disorder\"                                                       \n",
-       "[204] \"vertigo\"                                                                \n",
-       "[205] \"dysphagia\"                                                              \n",
-       "[206] \"edema\"                                                                  \n",
-       "[207] \"malignant_neoplasm_of_colon\"                                            \n",
-       "[208] \"hip_pain\"                                                               \n",
-       "[209] \"posttraumatic_stress_disorder\"                                          \n",
-       "[210] \"inflammatory_dermatosis\"                                                \n",
-       "[211] \"psoriasis\"                                                              \n",
-       "[212] \"myopia\"                                                                 \n",
-       "[213] \"senile_cataract\"                                                        \n",
-       "[214] \"heart_murmur\"                                                           \n",
-       "[215] \"liver_function_tests_abnormal\"                                          \n",
-       "[216] \"angina\"                                                                 \n",
-       "[217] \"impaired_fasting_glycemia\"                                              \n",
-       "[218] \"chronic_ischemic_heart_disease\"                                         \n",
-       "[219] \"chronic_sinusitis\"                                                      \n",
-       "[220] \"menopause_present\"                                                      \n",
-       "[221] \"basal_cell_carcinoma_of_skin\"                                           \n",
-       "[222] \"raised_prostate_specific_antigen\"                                       \n",
-       "[223] \"impaired_glucose_tolerance\"                                             \n",
-       "[224] \"smoker\"                                                                 \n",
-       "[225] \"hypertriglyceridemia\"                                                   \n",
-       "[226] \"irregular_periods\"                                                      \n",
-       "[227] \"herpes_zoster\"                                                          \n",
-       "[228] \"sensorineural_hearing_loss\"                                             \n",
-       "[229] \"rectal_hemorrhage\"                                                      \n",
-       "[230] \"peptic_ulcer\"                                                           \n",
-       "[231] \"tinnitus\"                                                               \n",
-       "[232] \"bipolar_disorder\"                                                       \n",
-       "[233] \"vitamin_d_deficiency\"                                                   \n",
-       "[234] \"transient_ischemic_attack\"                                              \n",
-       "[235] \"streptococcal_sore_throat\"                                              \n",
-       "[236] \"onychomycosis\"                                                          \n",
-       "[237] \"deep_venous_thrombosis\"                                                 \n",
-       "[238] \"presbyopia\"                                                             \n",
-       "[239] \"neonatal_jaundice\"                                                      \n",
-       "[240] \"bacterial_vaginosis\"                                                    \n",
-       "[241] \"impacted_cerumen\"                                                       \n",
-       "[242] \"foot_pain\"                                                              \n",
-       "[243] \"sciatica\"                                                               \n",
-       "[244] \"vomiting\"                                                               \n",
-       "[245] \"benign_prostatic_hypertrophy_with_outflow_obstruction\"                  \n",
-       "[246] \"type_ii_diabetes_mellitus_without_complication\"                         \n",
-       "[247] \"calculus_in_biliary_tract\"                                              \n",
-       "[248] \"epigastric_pain\"                                                        \n",
-       "[249] \"late_effects_of_cerebrovascular_disease\"                                \n",
-       "[250] \"gastroenteritis\"                                                        \n",
-       "[251] \"pulmonary_embolism\"                                                     \n",
-       "[252] \"inguinal_hernia\"                                                        \n",
-       "[253] \"verruca_vulgaris\"                                                       \n",
-       "[254] \"sepsis\"                                                                 \n",
-       "[255] \"disorder_of_kidney_due_to_diabetes_mellitus\"                            \n",
-       "[256] \"nausea_and_vomiting\"                                                    \n",
-       "[257] \"hyperthyroidism\"                                                        \n",
-       "[258] \"abscess\"                                                                \n",
-       "[259] \"dental_caries\"                                                          \n",
-       "[260] \"gastrointestinal_hemorrhage\"                                            \n",
-       "[261] \"rosacea\"                                                                \n",
-       "[262] \"parkinson's_disease\"                                                    \n",
-       "[263] \"menorrhagia\"                                                            \n",
-       "[264] \"malignant_tumor_of_lung\"                                                \n",
-       "[265] \"joint_pain\"                                                             \n",
-       "[266] \"morbid_obesity\"                                                         \n",
-       "[267] \"hiatal_hernia\"                                                          \n",
-       "[268] \"arthralgia_of_the_ankle_and/or_foot\"                                    \n",
-       "[269] \"restless_legs\"                                                          \n",
-       "[270] \"thrombocytopenic_disorder\"                                              \n",
-       "[271] \"old_myocardial_infarction\"                                              \n",
-       "[272] \"neuropathy\"                                                             \n",
-       "[273] \"cardiomyopathy\"                                                         \n",
-       "[274] \"atopic_dermatitis\"                                                      \n",
-       "[275] \"pain_in_pelvis\"                                                         \n",
-       "[276] \"contact_dermatitis\"                                                     \n",
-       "[277] \"indigestion\"                                                            \n",
-       "[278] \"nicotine_dependence\"                                                    \n",
-       "[279] \"sprain_of_ankle\"                                                        \n",
-       "[280] \"degenerative_disorder_of_macula\"                                        \n",
-       "[281] \"exacerbation_of_asthma\"                                                 \n",
-       "[282] \"alcohol_dependence\"                                                     \n",
-       "[283] \"hypokalemia\"                                                            \n",
-       "[284] \"mitral_valve_regurgitation\"                                             \n",
-       "[285] \"hyponatremia\"                                                           \n",
-       "[286] \"abdominal_aortic_aneurysm\"                                              \n",
-       "[287] \"cyst_of_ovary\"                                                          \n",
-       "[288] \"otitis_externa\"                                                         \n",
-       "[289] \"threatened_abortion\"                                                    \n",
-       "[290] \"scoliosis_deformity_of_spine\"                                           \n",
-       "[291] \"seborrheic_dermatitis\"                                                  \n",
-       "[292] \"spinal_stenosis\"                                                        \n",
-       "[293] \"dysmenorrhea\"                                                           \n",
-       "[294] \"acute_otitis_media\"                                                     \n",
-       "[295] \"alzheimer's_disease\"                                                    \n",
-       "[296] \"neuropathy_due_to_diabetes_mellitus\"                                    \n",
-       "[297] \"acute_pharyngitis\"                                                      \n",
-       "[298] \"degeneration_of_intervertebral_disc\"                                    \n",
-       "[299] \"attention_deficit_hyperactivity_disorder_predominantly_inattentive_type\"\n",
-       "[300] \"unplanned_pregnancy\"                                                    \n",
-       "[301] \"secondary_erectile_dysfunction\"                                         \n",
-       "[302] \"spinal_stenosis_of_lumbar_region\"                                       \n",
-       "[303] \"proteinuria\"                                                            \n",
-       "[304] \"urticaria\"                                                              \n",
-       "[305] \"genital_herpes_simplex\"                                                 \n",
-       "[306] \"malignant_neoplasm_of_female_breast\"                                    \n",
-       "[307] \"nausea\"                                                                 \n",
-       "[308] \"chronic_rhinitis\"                                                       \n",
-       "[309] \"multiple_sclerosis\"                                                     \n",
-       "[310] \"chronic_kidney_disease_stage_3\"                                         \n",
-       "[311] \"panic_disorder\"                                                         \n",
-       "[312] \"attention_deficit_hyperactivity_disorder\"                               \n",
-       "[313] \"amnesia\"                                                                \n",
-       "[314] \"otalgia\"                                                                \n",
-       "[315] \"tremor\"                                                                 \n",
-       "[316] \"retention_of_urine\"                                                     \n",
-       "[317] \"non-hodgkin's_lymphoma\"                                                 \n",
-       "[318] \"alcoholism\"                                                             \n",
-       "[319] \"dysuria\"                                                                \n",
-       "[320] \"generalized_anxiety_disorder\"                                           \n",
-       "[321] \"paroxysmal_atrial_fibrillation\"                                         \n",
-       "[322] \"peripheral_venous_insufficiency\"                                        \n",
-       "[323] \"nonproliferative_retinopathy_due_to_diabetes_mellitus\"                  \n",
-       "[324] \"shoulder_joint_pain\"                                                    \n",
-       "[325] \"moderate_recurrent_major_depression\"                                    \n",
-       "[326] \"diverticulitis\"                                                         \n",
-       "[327] \"solitary_nodule_of_lung\"                                                \n",
-       "[328] \"hyperkalemia\"                                                           \n",
-       "[329] \"recurrent_major_depressive_episodes\"                                    \n",
-       "[330] \"multiple_myeloma\"                                                       \n",
-       "[331] \"regular_astigmatism\"                                                    \n",
-       "[332] \"secondary_malignant_neoplasm_of_liver\"                                  \n",
-       "[333] \"ulcerative_colitis\"                                                     \n",
-       "[334] \"vaginitis\"                                                              \n",
-       "[335] \"acute_renal_failure_syndrome\"                                           \n",
-       "[336] \"amenorrhea\"                                                             \n",
-       "[337] \"tendinitis\"                                                             \n",
-       "[338] \"rhinitis\"                                                               \n",
-       "[339] \"bleeding_from_nose\"                                                     \n",
-       "[340] \"crohn's_disease\"                                                        \n",
-       "[341] \"nuclear_senile_cataract\"                                                \n",
-       "[342] \"muscle_pain\"                                                            \n",
-       "[343] \"epidermoid_cyst\"                                                        \n",
-       "[344] \"impaired_cognition\"                                                     \n",
-       "[345] \"acute_exacerbation_of_chronic_obstructive_airways_disease\"              \n",
-       "[346] \"eustachian_tube_disorder\"                                               \n",
-       "[347] \"internal_hemorrhoids\"                                                   \n",
-       "[348] \"substance_abuse\"                                                        \n",
-       "[349] \"melanocytic_nevus\"                                                      \n",
-       "[350] \"pain\"                                                                   \n",
-       "[351] \"barrett's_esophagus\"                                                    \n",
-       "[352] \"cerebrovascular_disease\"                                                \n",
-       "[353] \"malignant_melanoma\"                                                     \n",
-       "[354] \"folliculitis\"                                                           \n",
-       "[355] \"mitral_valve_prolapse\"                                                  \n",
-       "[356] \"chronic_hepatitis_c\"                                                    \n",
-       "[357] \"hypermetropia\"                                                          \n",
-       "[358] \"endometriosis\"                                                          \n",
-       "[359] \"gestational_diabetes_mellitus\"                                          \n",
-       "[360] \"cirrhosis_of_liver\"                                                     \n",
-       "[361] \"injury_of_head\"                                                         \n",
-       "[362] \"dehydration\"                                                            \n",
-       "[363] \"herpes_simplex\"                                                         \n",
-       "[364] \"fracture_of_bone\"                                                       \n",
-       "[365] \"overweight\"                                                             \n",
-       "[366] \"right_inguinal_hernia\"                                                  \n",
-       "[367] \"adjustment_disorder\"                                                    \n",
-       "[368] \"tinea_pedis\"                                                            \n",
-       "[369] \"aortic_valve_stenosis\"                                                  \n",
-       "[370] \"viral_hepatitis_type_b\"                                                 \n",
-       "[371] \"umbilical_hernia\"                                                       \n",
-       "[372] \"ingrowing_nail\"                                                         \n",
-       "[373] \"postmenopausal_bleeding\"                                                \n",
-       "[374] \"goiter\"                                                                 \n",
-       "[375] \"secondary_malignant_neoplasm_of_bone\"                                   \n",
-       "[376] \"pulmonary_emphysema\"                                                    \n",
-       "[377] \"left_inguinal_hernia\"                                                   \n",
-       "[378] \"snoring\"                                                                \n",
-       "[379] \"polycystic_ovary_syndrome\"                                              \n",
-       "[380] \"polycystic_ovary\"                                                       \n",
-       "[381] \"microscopic_hematuria\"                                                  \n",
-       "[382] \"pain_in_wrist\"                                                          \n",
-       "[383] \"pleural_effusion\"                                                       \n",
-       "[384] \"false_labor\"                                                            \n",
-       "[385] \"bleeding_from_vagina\"                                                   \n",
-       "[386] \"acquired_hypothyroidism\"                                                \n",
-       "[387] \"premature_rupture_of_membranes\"                                         \n",
-       "[388] \"human_immunodeficiency_virus_infection\"                                 \n",
-       "[389] \"contusion\"                                                              \n",
-       "[390] \"secondary_malignant_neoplasm_of_lung\"                                   \n",
-       "[391] \"problem_situation_relating_to_social_and_personal_history\"              \n",
-       "[392] \"hypercalcemia\"                                                          \n",
-       "[393] \"occlusion_of_carotid_artery\"                                            \n",
-       "[394] \"prolonged_second_stage_of_labor\"                                        \n",
-       "[395] \"lipoma\"                                                                 \n",
-       "[396] \"poisoning_caused_by_drug_and/or_medicinal_substance\"                    \n",
-       "[397] \"paranoid_schizophrenia\"                                                 \n",
-       "[398] \"hypersensitivity_reaction\"                                              \n",
-       "[399] \"dysthymia\"                                                              \n",
-       "[400] \"fetal_or_neonatal_effect_of_maternal_premature_rupture_of_membrane\"     \n",
-       "[401] \"noncompliance_with_treatment\"                                           \n",
-       "[402] \"falls\"                                                                  \n",
-       "[403] \"muscle_strain\"                                                          \n",
-       "[404] \"schizophrenia\"                                                          \n",
-       "[405] \"left_bundle_branch_block\"                                               \n",
-       "[406] \"disorder_of_nervous_system_due_to_type_2_diabetes_mellitus\"             \n",
-       "[407] \"preinfarction_syndrome\"                                                 \n",
-       "[408] \"conduction_disorder_of_the_heart\"                                       \n",
-       "[409] \"lateral_epicondylitis\"                                                  \n",
-       "[410] \"burn\"                                                                   \n",
-       "[411] \"pyelonephritis\"                                                         \n",
-       "[412] \"intermittent_claudication\"                                              \n",
-       "[413] \"varicose_veins_of_lower_extremity\"                                      \n",
-       "[414] \"hemiplegia\"                                                             \n",
-       "[415] \"chronic_pain\"                                                           \n",
-       "[416] \"bronchiolitis\"                                                          \n",
-       "[417] \"mild_persistent_asthma\"                                                 \n",
-       "[418] \"secondary_malignant_neoplasm_of_lymph_node\"                             \n",
-       "[419] \"mixed_hyperlipidemia\"                                                   \n",
-       "[420] \"female_urinary_stress_incontinence\"                                     \n",
-       "[421] \"localized_primary_osteoarthritis_of_the_ankle_and/or_foot\"              \n",
-       "[422] \"hypesthesia\"                                                            \n",
-       "[423] \"hand_pain\"                                                              \n",
-       "[424] \"psychotic_disorder\"                                                     \n",
-       "[425] \"menopausal_syndrome\"                                                    \n",
-       "[426] \"acute_myocardial_infarction_of_anterior_wall\"                           \n",
-       "[427] \"bronchiectasis\"                                                         \n",
-       "[428] \"lumbar_radiculopathy\"                                                   \n",
-       "[429] \"osteoarthritis_of_hip\"                                                  \n",
-       "[430] \"deviated_nasal_septum\"                                                  \n",
-       "[431] \"paresthesia\"                                                            \n",
-       "[432] \"hypoglycemia\"                                                           \n",
-       "[433] \"deep_venous_thrombosis_of_lower_extremity\"                              \n",
-       "[434] \"complication_of_surgical_procedure\"                                     \n",
-       "[435] \"mild_intermittent_asthma\"                                               \n",
-       "[436] \"generalized_ischemic_myocardial_dysfunction\"                            \n",
-       "[437] \"lumbar_spondylosis\"                                                     \n",
-       "[438] \"acute_tonsillitis\"                                                      \n",
-       "[439] \"esophagitis\"                                                            \n",
-       "[440] \"aortic_valve_regurgitation\"                                             \n",
-       "[441] \"malignant_neoplasm_of_skin\"                                             \n",
-       "[442] \"altered_mental_status\"                                                  \n",
-       "[443] \"atrial_flutter\"                                                         \n",
-       "[444] \"alopecia\"                                                               \n",
-       "[445] \"high_risk_pregnancy\"                                                    \n",
-       "[446] \"missed_abortion\"                                                        \n",
-       "[447] \"malignant_tumor_of_urinary_bladder\"                                     \n",
-       "[448] \"pulmonary_hypertension\"                                                 \n",
-       "[449] \"nasal_congestion\"                                                       \n",
-       "[450] \"hemoptysis\"                                                             \n",
-       "[451] \"viral_gastroenteritis\"                                                  \n",
-       "[452] \"right_upper_quadrant_pain\"                                              \n",
-       "[453] \"primary_open_angle_glaucoma\"                                            \n",
-       "[454] \"malignant_tumor_of_ovary\"                                               \n",
-       "[455] \"incomplete_emptying_of_bladder\"                                         \n",
-       "[456] \"pruritic_disorder\"                                                      \n",
-       "[457] \"fibrocystic_breast_changes\"                                             \n",
-       "[458] \"tinea_corporis\"                                                         \n",
-       "[459] \"acute_pancreatitis\"                                                     \n",
-       "[460] \"acute_myocardial_infarction\"                                            \n",
-       "[461] \"cerebral_hemorrhage\"                                                    \n",
-       "[462] \"urge_incontinence_of_urine\"                                             \n",
-       "[463] \"megaloblastic_anemia_due_to_vitamin_b>12<_deficiency\"                   \n",
-       "[464] \"chronic_lymphoid_leukemia_disease\"                                      \n",
-       "[465] \"disorder_of_lymphatic_system\"                                           \n",
-       "[466] \"diverticular_disease\"                                                   \n",
-       "[467] \"tonsillitis\"                                                            \n",
-       "[468] \"tear_film_insufficiency\"                                                \n",
-       "[469] \"abnormal_gait\"                                                          \n",
-       "[470] \"orthostatic_hypotension\"                                                \n",
-       "[471] \"pancreatitis\"                                                           \n",
-       "[472] \"closed_fracture_of_distal_end_of_radius\"                                \n",
-       "[473] \"first_degree_perineal_laceration\"                                       \n",
-       "[474] \"not_for_resuscitation\"                                                  \n",
-       "[475] \"gastroesophageal_reflux_disease_with_esophagitis\"                       \n",
-       "[476] \"intracranial_injury\"                                                    \n",
-       "[477] \"bell's_palsy\"                                                           \n",
-       "[478] \"bradycardia\"                                                            \n",
-       "[479] \"polymyalgia_rheumatica\"                                                 \n",
-       "[480] \"dysplasia_of_cervix\"                                                    \n",
-       "[481] \"serous_otitis_media\"                                                    \n",
-       "[482] \"angioedema\"                                                             \n",
-       "[483] \"venous_varices\"                                                         \n",
-       "[484] \"spasm\"                                                                  \n",
-       "[485] \"sleep_disorder\"                                                         \n",
-       "[486] \"pain_of_breast\"                                                         \n",
-       "[487] \"malabsorption_syndrome_due_to_intolerance_to_lactose\"                   \n",
-       "[488] \"tachycardia\"                                                            \n",
-       "[489] \"acute_conjunctivitis\"                                                   \n",
-       "[490] \"malignant_lymphoma\"                                                     \n",
-       "[491] \"supraventricular_tachycardia\"                                           \n",
-       "[492] \"hyperparathyroidism\"                                                    \n",
-       "[493] \"periodontitis\"                                                          \n",
-       "[494] \"renal_disorder_due_to_type_2_diabetes_mellitus\"                         \n",
-       "[495] \"nerve_root_disorder\"                                                    \n",
-       "[496] \"nonexudative_age-related_macular_degeneration\"                          \n",
-       "[497] \"tension-type_headache\"                                                  \n",
-       "[498] \"chronic_pain_syndrome\"                                                  \n",
-       "[499] \"abnormal_weight_loss\"                                                   \n",
-       "[500] \"pure_hypercholesterolemia\"                                              \n",
-       "[501] \"acute_upper_respiratory_infection\"                                      \n",
-       "[502] \"inflammatory_disease_of_liver\"                                          \n",
-       "[503] \"colitis\"                                                                \n",
-       "[504] \"prolonged_pregnancy\"                                                    \n",
-       "[505] \"major_depression_single_episode\"                                        \n",
-       "[506] \"croup\"                                                                  \n",
-       "[507] \"skin_tag\"                                                               \n",
-       "[508] \"metabolic_syndrome_x\"                                                   \n",
-       "[509] \"adverse_reaction_caused_by_drug\"                                        \n",
-       "[510] \"retinopathy_due_to_diabetes_mellitus\"                                   \n",
-       "[511] \"female_infertility\"                                                     \n",
-       "[512] \"open-angle_glaucoma\"                                                    \n",
-       "[513] \"tietze's_disease\"                                                       \n",
-       "[514] \"gallbladder_calculus\"                                                   \n",
-       "[515] \"sarcoidosis\"                                                            \n",
-       "[516] \"neurogenic_bladder\"                                                     \n",
-       "[517] \"tobacco_dependence_in_remission\"                                        \n",
-       "[518] \"wheezing\"                                                               \n",
-       "[519] \"elevated_levels_of_transaminase_&_lactic_acid_dehydrogenase\"            \n",
-       "[520] \"ascites\"                                                                \n",
-       "[521] \"low_blood_pressure\"                                                     \n",
-       "[522] \"bursitis\"                                                               \n",
-       "[523] \"miscarriage\"                                                            \n",
-       "[524] \"methicillin_resistant_staphylococcus_aureus_carrier\"                    \n",
-       "[525] \"urethritis\"                                                             \n",
-       "[526] \"noninfectious_gastroenteritis\"                                          \n",
-       "[527] \"malignant_tumor_of_thyroid_gland\"                                       \n",
-       "[528] \"pressure_ulcer\"                                                         \n",
-       "[529] \"verruca_plantaris\"                                                      \n",
-       "[530] \"anemia_of_chronic_disorder\"                                             \n",
-       "[531] \"hernia_of_anterior_abdominal_wall\"                                      \n",
-       "[532] \"degeneration_of_lumbar_intervertebral_disc\"                             \n",
-       "[533] \"right_lower_quadrant_pain\"                                              \n",
-       "[534] \"infertile\"                                                              \n",
-       "[535] \"hernia_of_abdominal_wall\"                                               \n",
-       "[536] \"benign_paroxysmal_positional_vertigo\"                                   \n",
-       "[537] \"pityriasis_versicolor\"                                                  \n",
-       "[538] \"injury_of_lower_leg\"                                                    \n",
-       "[539] \"dry_skin\"                                                               \n",
-       "[540] \"impacted_tooth\"                                                         \n",
-       "[541] \"acquired_trigger_finger\"                                                \n",
-       "[542] \"chronic_osteoarthritis\"                                                 \n",
-       "[543] \"acute_stress_disorder\"                                                  \n",
-       "[544] \"peripheral_circulatory_disorder_due_to_type_2_diabetes_mellitus\"        \n",
-       "[545] \"respiratory_failure\"                                                    \n",
-       "[546] \"allergic_disposition\"                                                   \n",
-       "[547] \"stress\"                                                                 \n",
-       "[548] \"atrophic_vaginitis\"                                                     \n",
-       "[549] \"degeneration_of_lumbosacral_intervertebral_disc\"                        \n",
-       "[550] \"astigmatism\"                                                            \n",
-       "[551] \"sick_sinus_syndrome\"                                                    \n",
-       "[552] \"cocaine_abuse\"                                                          \n",
-       "[553] \"intermittent_asthma\"                                                    \n",
-       "[554] \"cervical_radiculopathy\"                                                 \n",
-       "[555] \"atherosclerosis_of_coronary_artery\"                                     \n",
-       "[556] \"methicillin_resistant_staphylococcus_aureus_infection\"                  \n",
-       "[557] \"female_pelvic_inflammatory_disease\"                                     \n",
-       "[558] \"pain_in_eye\"                                                            \n",
-       "[559] \"cervical_spondylosis\"                                                   \n",
-       "[560] \"prematurity_of_infant\"                                                  \n",
-       "[561] \"postmature_infancy\"                                                     \n",
-       "[562] \"temporomandibular_joint_disorder\"                                       \n",
-       "[563] \"nocturia\"                                                               \n",
-       "[564] \"adjustment_disorder_with_depressed_mood\"                                \n",
-       "[565] \"visual_impairment\"                                                      \n",
-       "[566] \"male_hypogonadism\"                                                      \n",
-       "[567] \"closed_intertrochanteric_fracture\"                                      \n",
-       "[568] \"pain_in_limb\"                                                           \n",
-       "[569] \"complication_occurring_during_pregnancy\"                                \n",
-       "[570] \"sprain_of_knee\"                                                         \n",
-       "[571] \"fracture_of_ankle\"                                                      \n",
-       "[572] \"injury_of_hand\"                                                         \n",
-       "[573] \"postoperative_wound_infection\"                                          \n",
-       "[574] \"congestive_cardiomyopathy\"                                              \n",
-       "[575] \"nocturnal_enuresis\"                                                     \n",
-       "[576] \"proliferative_retinopathy_due_to_diabetes_mellitus\"                     \n",
-       "[577] \"major_depressive_disorder\"                                              \n",
-       "[578] \"chronic_bronchitis\"                                                     \n",
-       "[579] \"advanced_maternal_age_gravida\"                                          \n",
-       "[580] \"recurrent_major_depression_in_full_remission\"                           \n",
-       "[581] \"pregnancy-induced_hypertension\"                                         \n",
-       "[582] \"epididymitis\"                                                           \n",
-       "[583] \"impetigo\"                                                               \n",
-       "[584] \"schizoaffective_disorder\"                                               \n",
-       "[585] \"candidiasis_of_mouth\"                                                   \n",
-       "[586] \"mild_recurrent_major_depression\"                                        \n",
-       "[587] \"cerebral_palsy\"                                                         \n",
-       "[588] \"squamous_cell_carcinoma_of_skin\"                                        \n",
-       "[589] \"hypogonadism\"                                                           \n",
-       "[590] \"greater_trochanteric_pain_syndrome\"                                     \n",
-       "[591] \"graves'_disease\"                                                        \n",
-       "[592] \"malignant_neoplastic_disease\"                                           \n",
-       "[593] \"moderate_persistent_asthma\"                                             \n",
-       "[594] \"steatosis_of_liver\"                                                     \n",
-       "[595] \"obsessive-compulsive_disorder\"                                          \n",
-       "[596] \"mood_disorder\"                                                          \n",
-       "[597] \"degeneration_of_cervical_intervertebral_disc\"                           \n",
-       "[598] \"corneal_abrasion\"                                                       \n",
-       "[599] \"anal_fissure\"                                                           \n",
-       "[600] \"heart_failure\"                                                          \n",
-       "[601] \"adjustment_disorder_with_mixed_emotional_features\"                      \n",
-       "[602] \"febrile_convulsion\"                                                     \n",
-       "[603] \"degenerative_joint_disease_of_hand\"                                     \n",
-       "[604] \"chronic_type_b_viral_hepatitis\"                                         \n",
-       "[605] \"blepharitis\"                                                            \n",
-       "[606] \"cyst\"                                                                   \n",
-       "[607] \"heartburn\"                                                              \n",
-       "[608] \"intellectual_disability\"                                                \n",
-       "[609] \"exudative_age-related_macular_degeneration\"                             \n",
-       "[610] \"hypoxia\"                                                                \n",
-       "[611] \"influenza\"                                                              \n",
-       "[612] \"intervertebral_disc_prolapse\"                                           \n",
-       "[613] \"swelling_of_first_metatarsophalangeal_joint_of_hallux\"                  \n",
-       "[614] \"genuine_stress_incontinence\"                                            \n",
-       "[615] \"opioid_dependence\"                                                      \n",
-       "[616] \"antepartum_hemorrhage\"                                                  \n",
-       "[617] \"pneumothorax\"                                                           \n",
-       "[618] \"kidney_disease\"                                                         \n",
-       "[619] \"atopic_conjunctivitis\"                                                  \n",
-       "[620] \"dermatophytosis\"                                                        \n",
-       "[621] \"influenza_caused_by_influenza_a_virus\"                                  \n",
-       "[622] \"cystitis\"                                                               \n",
-       "[623] \"moderate_major_depression_single_episode\"                               \n",
-       "[624] \"furuncle\"                                                               \n",
-       "[625] \"cannabis_abuse\"                                                         \n",
-       "[626] \"conductive_hearing_loss\"                                                \n",
-       "[627] \"neutropenic_disorder\"                                                   \n",
-       "[628] \"injury_of_finger\"                                                       \n",
-       "[629] \"intestinal_obstruction\"                                                 \n",
-       "[630] \"lymphadenopathy\"                                                        \n",
-       "[631] \"mass_of_pelvic_structure\"                                               \n",
-       "[632] \"myelodysplastic_syndrome\"                                               \n",
-       "[633] \"jaundice\"                                                               \n",
-       "[634] \"severe_recurrent_major_depression_without_psychotic_features\"           \n",
-       "[635] \"inflammation_of_cervix\"                                                 \n",
-       "[636] \"sickle_cell_trait\"                                                      \n",
-       "[637] \"squamous_cell_carcinoma\"                                                \n",
-       "[638] \"small_bowel_obstruction\"                                                \n",
-       "[639] \"compression_fracture\"                                                   \n",
-       "[640] \"premenstrual_tension_syndrome\"                                          \n",
-       "[641] \"fracture_of_proximal_end_of_femur\"                                      \n",
-       "[642] \"developmental_delay\"                                                    \n",
-       "[643] \"drug_abuse\"                                                             \n",
-       "[644] \"left_lower_quadrant_pain\"                                               \n",
-       "[645] \"hordeolum\"                                                              \n",
-       "[646] \"disorder_of_lung\"                                                       \n",
-       "[647] \"migraine_without_aura\"                                                  \n",
-       "[648] \"open-angle_glaucoma_-_borderline\"                                       \n",
-       "[649] \"disorder_of_refraction\"                                                 \n",
-       "[650] \"acute_appendicitis\"                                                     \n",
-       "[651] \"amyloidosis\"                                                            \n",
-       "[652] \"hodgkin's_disease\"                                                      \n",
-       "[653] \"hydrocele_of_testis\"                                                    \n",
-       "[654] \"cellulitis_of_foot\"                                                     \n",
-       "[655] \"pancytopenia\"                                                           \n",
-       "[656] \"pain_in_elbow\"                                                          \n",
-       "[657] \"peripheral_nerve_disease\"                                               \n",
-       "[658] \"eating_disorder\"                                                        \n",
-       "[659] \"hernia_of_abdominal_cavity\"                                             \n",
-       "[660] \"secondary_malignant_neoplasm_of_brain\"                                  \n",
-       "[661] \"late_effects_of_respiratory_tuberculosis\"                               \n",
-       "[662] \"alcohol_intoxication\"                                                   \n",
-       "[663] \"abrasion\"                                                               \n",
-       "[664] \"breech_presentation\"                                                    \n",
-       "[665] \"premature_labor\"                                                        \n",
-       "[666] \"raynaud's_phenomenon\"                                                   \n",
-       "[667] \"posterior_rhinorrhea\"                                                   \n",
-       "[668] \"acute_gastritis\"                                                        \n",
-       "[669] \"polyp_of_nasal_cavity\"                                                  \n",
-       "[670] \"essential_tremor\"                                                       \n",
-       "[671] \"candidiasis\"                                                            \n",
-       "[672] \"ocular_hypertension\"                                                    \n",
-       "[673] \"aortic_valve_disorder\"                                                  \n",
-       "[674] \"diaper_rash\"                                                            \n",
-       "[675] \"cholangitis\"                                                            \n",
-       "[676] \"primary_malignant_neoplasm_of_lung\"                                     \n",
-       "[677] \"impingement_syndrome_of_shoulder_region\"                                \n",
-       "[678] \"idiopathic_urticaria\"                                                   \n",
-       "[679] \"arthritis_of_knee\"                                                      \n",
-       "[680] \"ulcer_of_duodenum\"                                                      \n",
-       "[681] \"peripheral_neuropathy_due_to_diabetes_mellitus\"                         \n",
-       "[682] \"hypervolemia\"                                                           \n",
-       "[683] \"cholecystitis\"                                                          \n",
-       "[684] \"deficiency_of_glucose-6-phosphate_dehydrogenase\"                        \n",
-       "[685] \"acute_pyelonephritis\"                                                   \n",
-       "[686] \"musculoskeletal_chest_pain\"                                             \n",
-       "[687] \"melena\"                                                                 \n",
-       "[688] \"cigarette_smoker\"                                                       \n",
-       "[689] \"infectious_mononucleosis\"                                               \n",
-       "[690] \"hypertensive_renal_failure\"                                             \n",
-       "[691] \"cocaine_dependence\"                                                     \n",
-       "[692] \"abdominal_aortic_aneurysm_without_rupture\"                              \n",
-       "[693] \"right_bundle_branch_block\"                                              \n",
-       "[694] \"alcoholic_cirrhosis\"                                                    \n",
-       "[695] \"fibrosis_of_lung\"                                                       \n",
-       "[696] \"neoplasm_of_uncertain_behavior_of_skin\"                                 \n",
-       "[697] \"malignant_melanoma_of_skin\"                                             \n",
-       "[698] \"urgent_desire_to_urinate\"                                               \n",
-       "[699] \"bursitis_of_hip\"                                                        \n",
-       "[700] \"chronic_alcoholism_in_remission\"                                        \n",
-       "[701] \"chronic_pancreatitis\"                                                   \n",
-       "[702] \"gastroparesis\"                                                          \n",
-       "[703] \"ectopic_pregnancy\"                                                      \n",
-       "[704] \"muscle_weakness\"                                                        \n",
-       "[705] \"recurrent_major_depression\"                                             \n",
-       "[706] \"pilonidal_cyst\"                                                         \n",
-       "[707] \"pain_in_toe\"                                                            \n",
-       "[708] \"pulmonary_tuberculosis\"                                                 \n",
-       "[709] \"celiac_disease\"                                                         \n",
-       "[710] \"cramp_in_lower_leg\"                                                     \n",
-       "[711] \"secondary_malignant_neoplasm_of_pleura\"                                 \n",
-       "[712] \"fracture_of_hand\"                                                       \n",
-       "[713] \"cyst_of_breast\"                                                         \n",
-       "[714] \"nephrotic_syndrome\"                                                     \n",
-       "[715] \"polyp_of_nasal_sinus\"                                                   \n",
-       "[716] \"chondromalacia_of_patella\"                                              \n",
-       "[717] \"spinal_stenosis_in_cervical_region\"                                     \n",
-       "[718] \"disorder_of_artery\"                                                     \n",
-       "[719] \"vitiligo\"                                                               \n",
-       "[720] \"female_cystocele\"                                                       \n",
-       "[721] \"dysphasia\"                                                              \n",
-       "[722] \"retinal_disorder\"                                                       \n",
-       "[723] \"epiretinal_membrane\"                                                    \n",
-       "[724] \"recurrent_major_depression_in_partial_remission\"                        \n",
-       "[725] \"infection_caused_by_trichomonas\"                                        \n",
-       "[726] \"osteomyelitis\"                                                          \n",
-       "[727] \"polyp_of_nasal_cavity_and/or_nasal_sinus\"                               \n",
-       "[728] \"mass_of_neck\"                                                           \n",
-       "[729] \"idiopathic_thrombocytopenic_purpura\"                                    \n",
-       "[730] \"complete_miscarriage\"                                                   \n",
-       "[731] \"gastric_ulcer\"                                                          \n",
-       "[732] \"papilloma_of_skin\"                                                      \n",
-       "[733] \"fetal_or_neonatal_effect_of_breech_delivery_and_extraction\"             \n",
-       "[734] \"secondary_malignant_neoplastic_disease\"                                 \n",
-       "[735] \"hypoxemia\"                                                              \n",
-       "[736] \"paraplegia\"                                                             \n",
-       "[737] \"perforation_of_tympanic_membrane\"                                       \n",
-       "[738] \"ventricular_tachycardia\"                                                \n",
-       "[739] \"mixed_incontinence\"                                                     \n",
-       "[740] \"disorder_of_eye_due_to_type_2_diabetes_mellitus\"                        \n",
-       "[741] \"trigeminal_neuralgia\"                                                   \n",
-       "[742] \"retinal_detachment\"                                                     \n",
-       "[743] \"leukopenia\"                                                             \n",
-       "[744] \"vitreous_hemorrhage\"                                                    \n",
-       "[745] \"ischemic_ulcer\"                                                         \n",
-       "[746] \"intramural_leiomyoma_of_uterus\"                                         \n",
-       "[747] \"viral_hepatitis_type_a\"                                                 \n",
-       "[748] \"m<U+00E9>ni<U+00E8>re's_disease\"                                        \n",
-       "[749] \"fracture_of_phalanx_of_hand\"                                            \n",
-       "[750] \"muscle_atrophy\"                                                         \n",
-       "[751] \"incontinence_of_feces\"                                                  \n",
-       "[752] \"mitral_valve_disorder\"                                                  \n",
-       "[753] \"atherosclerosis_of_arteries_of_the_extremities\"                         \n",
-       "[754] \"spondylosis\"                                                            \n",
-       "[755] \"pterygium\"                                                              \n",
-       "[756] \"ulnar_neuropathy\"                                                       \n",
-       "[757] \"lung_mass\"                                                              \n",
-       "[758] \"foreign_body_in_respiratory_tract\"                                      \n",
-       "[759] \"chronic_kidney_disease_stage_4\"                                         \n",
-       "[760] \"myocardial_ischemia\"                                                    \n",
-       "[761] \"non-toxic_multinodular_goiter\"                                          \n",
-       "[762] \"pain_in_finger\"                                                         \n",
-       "[763] \"cervical_spondylosis_without_myelopathy\"                                \n",
-       "[764] \"body_mass_index_25-29_-_overweight\"                                     \n",
-       "[765] \"clouded_consciousness\"                                                  \n",
-       "[766] \"mixed_conductive_and_sensorineural_hearing_loss\"                        \n",
-       "[767] \"tooth_eruption_disorder\"                                                \n",
-       "[768] \"hyperuricemia\"                                                          \n",
-       "[769] \"closed_fracture_of_neck_of_femur\"                                       \n",
-       "[770] \"bipolar_ii_disorder\"                                                    \n",
-       "[771] \"disturbance_in_sleep_behavior\"                                          \n",
-       "[772] \"relationship_problems\"                                                  \n",
-       "[773] \"sprain_of_wrist\"                                                        \n",
-       "[774] \"personality_disorder\"                                                   \n",
-       "[775] \"external_hemorrhoids\"                                                   \n",
-       "[776] \"abnormal_vision\"                                                        \n",
-       "[777] \"hyperprolactinemia\"                                                     \n",
-       "[778] \"hemochromatosis\"                                                        \n",
-       "[779] \"lumbosacral_radiculopathy\"                                              \n",
-       "[780] \"heart_valve_disorder\"                                                   \n",
-       "[781] \"cardiac_arrest\"                                                         \n",
-       "[782] \"infection_caused_by_molluscum_contagiosum\"                              \n",
-       "[783] \"chronic_kidney_disease_stage_2\"                                         \n",
-       "[784] \"secondary_malignant_neoplasm_of_peritoneum\"                             \n",
-       "[785] \"thoracic_back_pain\"                                                     \n",
-       "[786] \"blood_in_urine\"                                                         \n",
-       "[787] \"adhesive_capsulitis_of_shoulder\"                                        \n",
-       "[788] \"diplopia\"                                                               \n",
-       "[789] \"sj<U+00F6>gren's_syndrome\"                                              \n",
-       "[790] \"ureteric_stone\"                                                         \n",
-       "[791] \"bronchospasm\"                                                           \n",
-       "[792] \"chronic_fatigue_syndrome\"                                               \n",
-       "[793] \"cannabis_dependence\"                                                    \n",
-       "[794] \"neck_sprain\"                                                            \n",
-       "[795] \"multinodular_goiter\"                                                    \n",
-       "[796] \"ptosis_of_eyelid\"                                                       \n",
-       "[797] \"failure_to_thrive\"                                                      \n",
-       "[798] \"torticollis\"                                                            \n",
-       "[799] \"acute_bronchiolitis\"                                                    \n",
-       "[800] \"viral_exanthem\"                                                         \n",
-       "[801] \"talipes_planus\"                                                         \n",
-       "[802] \"idiopathic_peripheral_neuropathy\"                                       \n",
-       "[803] \"foreign_body_in_pharynx\"                                                \n",
-       "[804] \"jaw_pain\"                                                               \n",
-       "[805] \"renal_impairment\"                                                       \n",
-       "[806] \"ataxia\"                                                                 \n",
-       "[807] \"age-related_macular_degeneration\"                                       \n",
-       "[808] \"uterine_prolapse\"                                                       \n",
-       "[809] \"renal_mass\"                                                             \n",
-       "[810] \"pneumonitis\"                                                            \n",
-       "[811] \"coordination_problem\"                                                   \n",
-       "[812] \"blindness_-_both_eyes\"                                                  \n",
-       "[813] \"primary_hyperparathyroidism\"                                            \n",
-       "[814] \"musculoskeletal_pain\"                                                   \n",
-       "[815] \"mycosis\"                                                                \n",
-       "[816] \"primigravida\"                                                           \n",
-       "[817] \"urethral_stricture\"                                                     \n",
-       "[818] \"leukocytosis\"                                                           \n",
-       "[819] \"ventricular_premature_complex\"                                          \n",
-       "[820] \"ulcer_of_foot_due_to_diabetes_mellitus\"                                 \n",
-       "[821] \"chronic_headache_disorder\"                                              \n",
-       "[822] \"hemangioma\"                                                             \n",
-       "[823] \"lymphedema\"                                                             \n",
-       "[824] \"postmenopausal_state\"                                                   \n",
-       "[825] \"chronic_ulcer_of_skin\"                                                  \n",
-       "[826] \"left_heart_failure\"                                                     \n",
-       "[827] \"excessive_and_frequent_menstruation\"                                    \n",
-       "[828] \"thrombocytosis\"                                                         \n",
-       "[829] \"disorder_of_liver\"                                                      \n",
-       "[830] \"disorder_of_carotid_artery\"                                             \n",
-       "[831] \"altered_bowel_function\"                                                 \n",
-       "[832] \"abscess_of_foot\"                                                        \n",
-       "[833] \"malignant_tumor_of_head_and/or_neck\"                                    \n",
-       "[834] \"streptococcus_group_b_infection_of_the_infant\"                          \n",
-       "[835] \"concussion_injury_of_brain\"                                             \n",
-       "[836] \"feeding_problems_in_newborn\"                                            \n",
-       "[837] \"bipolar_i_disorder\"                                                     \n",
-       "[838] \"viral_pharyngitis\"                                                      \n",
-       "[839] \"lower_respiratory_tract_infection\"                                      \n",
-       "[840] \"hydronephrosis\"                                                         \n",
-       "[841] \"borderline_personality_disorder\"                                        \n",
-       "[842] \"esophageal_varices\"                                                     \n",
-       "[843] \"hypersomnia\"                                                            \n",
-       "[844] \"sensorineural_hearing_loss_bilateral\"                                   \n",
-       "[845] \"varicocele\"                                                             \n",
-       "[846] \"subarachnoid_intracranial_hemorrhage\"                                   \n",
-       "[847] \"incisional_hernia\"                                                      \n",
-       "[848] \"varicella\"                                                              \n",
-       "[849] \"pain_in_testicle\"                                                       \n",
-       "[850] \"transplant_follow-up\"                                                   \n",
-       "[851] \"tinea_cruris\"                                                           \n",
-       "[852] \"laryngitis\"                                                             \n",
-       "[853] \"hypertrophy_of_nail\"                                                    \n",
-       "[854] \"amblyopia\"                                                              \n",
-       "[855] \"polyp_of_cervix\"                                                        \n",
-       "[856] \"cyst_of_kidney\"                                                         \n",
-       "[857] \"hepatic_encephalopathy\"                                                 \n",
-       "[858] \"blood_glucose_abnormal\"                                                 \n",
-       "[859] \"postherpetic_neuralgia\"                                                 \n",
-       "[860] \"frank_hematuria\"                                                        \n",
-       "[861] \"cramp\"                                                                  \n",
-       "[862] \"interstitial_lung_disease\"                                              \n",
-       "[863] \"complete_atrioventricular_block\"                                        \n",
-       "[864] \"malignant_tumor_of_kidney\"                                              \n",
-       "[865] \"otitis\"                                                                 \n",
-       "[866] \"septic_shock\"                                                           \n",
-       "[867] \"disorder_of_thyroid_gland\"                                              \n",
-       "[868] \"hypertrophic_cardiomyopathy\"                                            \n",
-       "[869] \"respiratory_distress_syndrome_in_the_newborn\"                           \n",
-       "[870] \"infectious_gastroenteritis\"                                             \n",
-       "[871] \"subdural_intracranial_hemorrhage\"                                       \n",
-       "[872] \"hepatitis_b_carrier\"                                                    \n",
-       "[873] \"manic_bipolar_i_disorder\"                                               \n",
-       "[874] \"secondary_pulmonary_hypertension\"                                       \n",
-       "[875] \"gonorrhea\"                                                              \n",
-       "[876] \"derangement_of_knee\"                                                    \n",
-       "[877] \"appendicitis\"                                                           \n",
-       "[878] \"polyneuropathy_due_to_diabetes_mellitus\"                                \n",
-       "[879] \"neonatal_hypoglycemia\"                                                  \n",
-       "[880] \"prolonged_rupture_of_membranes\"                                         \n",
-       "[881] \"vasomotor_rhinitis\"                                                     \n",
-       "[882] \"renal_disorder_due_to_type_1_diabetes_mellitus\"                         \n",
-       "[883] \"tuberculosis\"                                                           \n",
-       "[884] \"feeding_problem\"                                                        \n",
-       "[885] \"chronic_tonsillitis\"                                                    \n",
-       "[886] \"acute_duodenal_ulcer_with_hemorrhage\"                                   \n",
-       "[887] \"hammer_toe\"                                                             \n",
-       "[888] \"malignant_tumor_of_cervix\"                                              \n",
-       "[889] \"prolapsed_lumbar_intervertebral_disc\"                                   \n",
-       "[890] \"hematemesis\"                                                            \n",
-       "[891] \"perianal_abscess\"                                                       \n",
-       "[892] \"nonvenomous_insect_bite\"                                                \n",
-       "[893] \"spondylolisthesis\"                                                      \n",
-       "[894] \"malignant_tumor_of_esophagus\"                                           \n",
-       "[895] \"aphthous_ulcer_of_mouth\"                                                \n",
-       "[896] \"ventricular_septal_defect\"                                              \n",
-       "[897] \"oropharyngeal_dysphagia\"                                                \n",
-       "[898] \"injury_of_knee\"                                                         \n",
-       "[899] \"traumatic_brain_injury\"                                                 \n",
-       "[900] \"osteoarthritis_of_glenohumeral_joint\"                                   \n",
-       "[901] \"fetal_or_neonatal_effect_of_maternal_medical_problem\"                   \n",
-       "[902] \"stomatological_preparations\"                                            \n",
-       "[903] \"drugs_for_acid_related_disorders\"                                       \n",
-       "[904] \"drugs_for_functional_gastrointestinal_disorders\"                        \n",
-       "[905] \"antiemetics_and_antinauseants\"                                          \n",
-       "[906] \"bile_and_liver_therapy\"                                                 \n",
-       "[907] \"drugs_for_constipation\"                                                 \n",
-       "[908] \"antidiarrheals,_intestinal_antiinflammatory/antiinfective_agents\"       \n",
-       "[909] \"antiobesity_preparations,_excl._diet_products\"                          \n",
-       "[910] \"digestives,_incl._enzymes\"                                              \n",
-       "[911] \"drugs_used_in_diabetes\"                                                 \n",
-       "[912] \"vitamins\"                                                               \n",
-       "[913] \"mineral_supplements\"                                                    \n",
-       "[914] \"tonics\"                                                                 \n",
-       "[915] \"anabolic_agents_for_systemic_use\"                                       \n",
-       "[916] \"appetite_stimulants\"                                                    \n",
-       "[917] \"other_alimentary_tract_and_metabolism_products\"                         \n",
-       "[918] \"antithrombotic_agents\"                                                  \n",
-       "[919] \"antihemorrhagics\"                                                       \n",
-       "[920] \"antianemic_preparations\"                                                \n",
-       "[921] \"blood_substitutes_and_perfusion_solutions\"                              \n",
-       "[922] \"other_hematological_agents\"                                             \n",
-       "[923] \"cardiac_therapy\"                                                        \n",
-       "[924] \"antihypertensives\"                                                      \n",
-       "[925] \"diuretics\"                                                              \n",
-       "[926] \"peripheral_vasodilators\"                                                \n",
-       "[927] \"vasoprotectives\"                                                        \n",
-       "[928] \"beta_blocking_agents\"                                                   \n",
-       "[929] \"calcium_channel_blockers\"                                               \n",
-       "[930] \"agents_acting_on_the_renin-angiotensin_system\"                          \n",
-       "[931] \"lipid_modifying_agents\"                                                 \n",
-       "[932] \"antifungals_for_dermatological_use\"                                     \n",
-       "[933] \"emollients_and_protectives\"                                             \n",
-       "[934] \"preparations_for_treatment_of_wounds_and_ulcers\"                        \n",
-       "[935] \"antipruritics,_incl._antihistamines,_anesthetics,_etc.\"                 \n",
-       "[936] \"antipsoriatics\"                                                         \n",
-       "[937] \"antibiotics_and_chemotherapeutics_for_dermatological_use\"               \n",
-       "[938] \"corticosteroids,_dermatological_preparations\"                           \n",
-       "[939] \"antiseptics_and_disinfectants\"                                          \n",
-       "[940] \"medicated_dressings\"                                                    \n",
-       "[941] \"anti-acne_preparations\"                                                 \n",
-       "[942] \"other_dermatological_preparations\"                                      \n",
-       "[943] \"gynecological_antiinfectives_and_antiseptics\"                           \n",
-       "[944] \"other_gynecologicals\"                                                   \n",
-       "[945] \"sex_hormones_and_modulators_of_the_genital_system\"                      \n",
-       "[946] \"urologicals\"                                                            \n",
-       "[947] \"pituitary_and_hypothalamic_hormones_and_analogues\"                      \n",
-       "[948] \"corticosteroids_for_systemic_use\"                                       \n",
-       "[949] \"thyroid_therapy\"                                                        \n",
-       "[950] \"pancreatic_hormones\"                                                    \n",
-       "[951] \"calcium_homeostasis\"                                                    \n",
-       "[952] \"antibacterials_for_systemic_use\"                                        \n",
-       "[953] \"antimycotics_for_systemic_use\"                                          \n",
-       "[954] \"antimycobacterials\"                                                     \n",
-       "[955] \"antivirals_for_systemic_use\"                                            \n",
-       "[956] \"immune_sera_and_immunoglobulins\"                                        \n",
-       "[957] \"vaccines\"                                                               \n",
-       "[958] \"antineoplastic_agents\"                                                  \n",
-       "[959] \"endocrine_therapy\"                                                      \n",
-       "[960] \"immunostimulants\"                                                       \n",
-       "[961] \"immunosuppressants\"                                                     \n",
-       "[962] \"antiinflammatory_and_antirheumatic_products\"                            \n",
-       "[963] \"topical_products_for_joint_and_muscular_pain\"                           \n",
-       "[964] \"muscle_relaxants\"                                                       \n",
-       "[965] \"antigout_preparations\"                                                  \n",
-       "[966] \"drugs_for_treatment_of_bone_diseases\"                                   \n",
-       "[967] \"other_drugs_for_disorders_of_the_musculo-skeletal_system\"               \n",
-       "[968] \"anesthetics\"                                                            \n",
-       "[969] \"analgesics\"                                                             \n",
-       "[970] \"antiepileptics\"                                                         \n",
-       "[971] \"anti-parkinson_drugs\"                                                   \n",
-       "[972] \"psycholeptics\"                                                          \n",
-       "[973] \"psychoanaleptics\"                                                       \n",
-       "[974] \"other_nervous_system_drugs\"                                             \n",
-       "[975] \"antiprotozoals\"                                                         \n",
-       "[976] \"anthelmintics\"                                                          \n",
-       "[977] \"ectoparasiticides,_incl._scabicides,_insecticides_and_repellents\"       \n",
-       "[978] \"nasal_preparations\"                                                     \n",
-       "[979] \"throat_preparations\"                                                    \n",
-       "[980] \"drugs_for_obstructive_airway_diseases\"                                  \n",
-       "[981] \"cough_and_cold_preparations\"                                            \n",
-       "[982] \"antihistamines_for_systemic_use\"                                        \n",
-       "[983] \"other_respiratory_system_products\"                                      \n",
-       "[984] \"ophthalmologicals\"                                                      \n",
-       "[985] \"otologicals\"                                                            \n",
-       "[986] \"ophthalmological_and_otological_preparations\"                           \n",
-       "[987] \"allergens\"                                                              \n",
-       "[988] \"all_other_therapeutic_products\"                                         \n",
-       "[989] \"diagnostic_agents\"                                                      \n",
-       "[990] \"general_nutrients\"                                                      \n",
-       "[991] \"all_other_non-therapeutic_products\"                                     \n",
-       "[992] \"contrast_media\"                                                         \n",
-       "[993] \"diagnostic_radiopharmaceuticals\"                                        \n",
-       "[994] \"therapeutic_radiopharmaceuticals\"                                       \n",
-       "[995] \"surgical_dressings\"                                                     \n",
-       "[996] \"statins\"                                                                \n",
-       "[997] \"ass\"                                                                    \n",
-       "[998] \"atypical_antipsychotics\"                                                \n",
-       "[999] \"glucocorticoids\"                                                        "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "covariates"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T14:16:59.684813Z",
-     "start_time": "2020-11-04T14:16:58.773Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style>\n",
-       ".list-inline {list-style: none; margin:0; padding: 0}\n",
-       ".list-inline>li {display: inline-block}\n",
-       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
-       "</style>\n",
-       "<ol class=list-inline><li>'chronic_alcoholism_in_remission'</li><li>'chronic_pancreatitis'</li><li>'gastroparesis'</li><li>'ectopic_pregnancy'</li><li>'muscle_weakness'</li><li>'recurrent_major_depression'</li><li>'pilonidal_cyst'</li><li>'pain_in_toe'</li><li>'pulmonary_tuberculosis'</li><li>'celiac_disease'</li><li>'cramp_in_lower_leg'</li><li>'secondary_malignant_neoplasm_of_pleura'</li><li>'fracture_of_hand'</li><li>'cyst_of_breast'</li><li>'nephrotic_syndrome'</li><li>'polyp_of_nasal_sinus'</li><li>'chondromalacia_of_patella'</li><li>'spinal_stenosis_in_cervical_region'</li><li>'disorder_of_artery'</li><li>'vitiligo'</li><li>'female_cystocele'</li><li>'dysphasia'</li><li>'retinal_disorder'</li><li>'epiretinal_membrane'</li><li>'recurrent_major_depression_in_partial_remission'</li><li>'infection_caused_by_trichomonas'</li><li>'osteomyelitis'</li><li>'polyp_of_nasal_cavity_and/or_nasal_sinus'</li><li>'mass_of_neck'</li><li>'idiopathic_thrombocytopenic_purpura'</li><li>'complete_miscarriage'</li><li>'gastric_ulcer'</li><li>'papilloma_of_skin'</li><li>'fetal_or_neonatal_effect_of_breech_delivery_and_extraction'</li><li>'secondary_malignant_neoplastic_disease'</li><li>'hypoxemia'</li><li>'paraplegia'</li><li>'perforation_of_tympanic_membrane'</li><li>'ventricular_tachycardia'</li><li>'mixed_incontinence'</li><li>'disorder_of_eye_due_to_type_2_diabetes_mellitus'</li><li>'trigeminal_neuralgia'</li><li>'retinal_detachment'</li><li>'leukopenia'</li><li>'vitreous_hemorrhage'</li><li>'ischemic_ulcer'</li><li>'intramural_leiomyoma_of_uterus'</li><li>'viral_hepatitis_type_a'</li><li>'m\\u00e9ni\\u00e8re\\'s_disease'</li><li>'fracture_of_phalanx_of_hand'</li><li>'muscle_atrophy'</li><li>'incontinence_of_feces'</li><li>'mitral_valve_disorder'</li><li>'atherosclerosis_of_arteries_of_the_extremities'</li><li>'spondylosis'</li><li>'pterygium'</li><li>'ulnar_neuropathy'</li><li>'lung_mass'</li><li>'foreign_body_in_respiratory_tract'</li><li>'chronic_kidney_disease_stage_4'</li><li>'myocardial_ischemia'</li><li>'non-toxic_multinodular_goiter'</li><li>'pain_in_finger'</li><li>'cervical_spondylosis_without_myelopathy'</li><li>'body_mass_index_25-29_-_overweight'</li><li>'clouded_consciousness'</li><li>'mixed_conductive_and_sensorineural_hearing_loss'</li><li>'tooth_eruption_disorder'</li><li>'hyperuricemia'</li><li>'closed_fracture_of_neck_of_femur'</li><li>'bipolar_ii_disorder'</li><li>'disturbance_in_sleep_behavior'</li><li>'relationship_problems'</li><li>'sprain_of_wrist'</li><li>'personality_disorder'</li><li>'external_hemorrhoids'</li><li>'abnormal_vision'</li><li>'hyperprolactinemia'</li><li>'hemochromatosis'</li><li>'lumbosacral_radiculopathy'</li><li>'heart_valve_disorder'</li><li>'cardiac_arrest'</li><li>'infection_caused_by_molluscum_contagiosum'</li><li>'chronic_kidney_disease_stage_2'</li><li>'secondary_malignant_neoplasm_of_peritoneum'</li><li>'thoracic_back_pain'</li><li>'blood_in_urine'</li><li>'adhesive_capsulitis_of_shoulder'</li><li>'diplopia'</li><li>'sj\\u00f6gren\\'s_syndrome'</li><li>'ureteric_stone'</li><li>'bronchospasm'</li><li>'chronic_fatigue_syndrome'</li><li>'cannabis_dependence'</li><li>'neck_sprain'</li><li>'multinodular_goiter'</li><li>'ptosis_of_eyelid'</li><li>'failure_to_thrive'</li><li>'torticollis'</li><li>'acute_bronchiolitis'</li><li>'viral_exanthem'</li><li>'talipes_planus'</li><li>'idiopathic_peripheral_neuropathy'</li><li>'foreign_body_in_pharynx'</li><li>'jaw_pain'</li><li>'renal_impairment'</li><li>'ataxia'</li><li>'age-related_macular_degeneration'</li><li>'uterine_prolapse'</li><li>'renal_mass'</li><li>'pneumonitis'</li><li>'coordination_problem'</li><li>'blindness_-_both_eyes'</li><li>'primary_hyperparathyroidism'</li><li>'musculoskeletal_pain'</li><li>'mycosis'</li><li>'primigravida'</li><li>'urethral_stricture'</li><li>'leukocytosis'</li><li>'ventricular_premature_complex'</li><li>'ulcer_of_foot_due_to_diabetes_mellitus'</li><li>'chronic_headache_disorder'</li><li>'hemangioma'</li><li>'lymphedema'</li><li>'postmenopausal_state'</li><li>'chronic_ulcer_of_skin'</li><li>'left_heart_failure'</li><li>'excessive_and_frequent_menstruation'</li><li>'thrombocytosis'</li><li>'disorder_of_liver'</li><li>'disorder_of_carotid_artery'</li><li>'altered_bowel_function'</li><li>'abscess_of_foot'</li><li>'malignant_tumor_of_head_and/or_neck'</li><li>'streptococcus_group_b_infection_of_the_infant'</li><li>'concussion_injury_of_brain'</li><li>'feeding_problems_in_newborn'</li><li>'bipolar_i_disorder'</li><li>'viral_pharyngitis'</li><li>'lower_respiratory_tract_infection'</li><li>'hydronephrosis'</li><li>'borderline_personality_disorder'</li><li>'esophageal_varices'</li><li>'hypersomnia'</li><li>'sensorineural_hearing_loss_bilateral'</li><li>'varicocele'</li><li>'subarachnoid_intracranial_hemorrhage'</li><li>'incisional_hernia'</li><li>'varicella'</li><li>'pain_in_testicle'</li><li>'transplant_follow-up'</li><li>'tinea_cruris'</li><li>'laryngitis'</li><li>'hypertrophy_of_nail'</li><li>'amblyopia'</li><li>'polyp_of_cervix'</li><li>'cyst_of_kidney'</li><li>'hepatic_encephalopathy'</li><li>'blood_glucose_abnormal'</li><li>'postherpetic_neuralgia'</li><li>'frank_hematuria'</li><li>'cramp'</li><li>'interstitial_lung_disease'</li><li>'complete_atrioventricular_block'</li><li>'malignant_tumor_of_kidney'</li><li>'otitis'</li><li>'septic_shock'</li><li>'disorder_of_thyroid_gland'</li><li>'hypertrophic_cardiomyopathy'</li><li>'respiratory_distress_syndrome_in_the_newborn'</li><li>'infectious_gastroenteritis'</li><li>'subdural_intracranial_hemorrhage'</li><li>'hepatitis_b_carrier'</li><li>'manic_bipolar_i_disorder'</li><li>'secondary_pulmonary_hypertension'</li><li>'gonorrhea'</li><li>'derangement_of_knee'</li><li>'appendicitis'</li><li>'polyneuropathy_due_to_diabetes_mellitus'</li><li>'neonatal_hypoglycemia'</li><li>'prolonged_rupture_of_membranes'</li><li>'vasomotor_rhinitis'</li><li>'renal_disorder_due_to_type_1_diabetes_mellitus'</li><li>'tuberculosis'</li><li>'feeding_problem'</li><li>'chronic_tonsillitis'</li><li>'acute_duodenal_ulcer_with_hemorrhage'</li><li>'hammer_toe'</li><li>'malignant_tumor_of_cervix'</li><li>'prolapsed_lumbar_intervertebral_disc'</li><li>'hematemesis'</li><li>'perianal_abscess'</li><li>'nonvenomous_insect_bite'</li><li>'spondylolisthesis'</li><li>'malignant_tumor_of_esophagus'</li><li>'aphthous_ulcer_of_mouth'</li><li>'ventricular_septal_defect'</li><li>'oropharyngeal_dysphagia'</li><li>'injury_of_knee'</li><li>'traumatic_brain_injury'</li><li>'osteoarthritis_of_glenohumeral_joint'</li></ol>\n"
-      ],
-      "text/latex": [
-       "\\begin{enumerate*}\n",
-       "\\item 'chronic\\_alcoholism\\_in\\_remission'\n",
-       "\\item 'chronic\\_pancreatitis'\n",
-       "\\item 'gastroparesis'\n",
-       "\\item 'ectopic\\_pregnancy'\n",
-       "\\item 'muscle\\_weakness'\n",
-       "\\item 'recurrent\\_major\\_depression'\n",
-       "\\item 'pilonidal\\_cyst'\n",
-       "\\item 'pain\\_in\\_toe'\n",
-       "\\item 'pulmonary\\_tuberculosis'\n",
-       "\\item 'celiac\\_disease'\n",
-       "\\item 'cramp\\_in\\_lower\\_leg'\n",
-       "\\item 'secondary\\_malignant\\_neoplasm\\_of\\_pleura'\n",
-       "\\item 'fracture\\_of\\_hand'\n",
-       "\\item 'cyst\\_of\\_breast'\n",
-       "\\item 'nephrotic\\_syndrome'\n",
-       "\\item 'polyp\\_of\\_nasal\\_sinus'\n",
-       "\\item 'chondromalacia\\_of\\_patella'\n",
-       "\\item 'spinal\\_stenosis\\_in\\_cervical\\_region'\n",
-       "\\item 'disorder\\_of\\_artery'\n",
-       "\\item 'vitiligo'\n",
-       "\\item 'female\\_cystocele'\n",
-       "\\item 'dysphasia'\n",
-       "\\item 'retinal\\_disorder'\n",
-       "\\item 'epiretinal\\_membrane'\n",
-       "\\item 'recurrent\\_major\\_depression\\_in\\_partial\\_remission'\n",
-       "\\item 'infection\\_caused\\_by\\_trichomonas'\n",
-       "\\item 'osteomyelitis'\n",
-       "\\item 'polyp\\_of\\_nasal\\_cavity\\_and/or\\_nasal\\_sinus'\n",
-       "\\item 'mass\\_of\\_neck'\n",
-       "\\item 'idiopathic\\_thrombocytopenic\\_purpura'\n",
-       "\\item 'complete\\_miscarriage'\n",
-       "\\item 'gastric\\_ulcer'\n",
-       "\\item 'papilloma\\_of\\_skin'\n",
-       "\\item 'fetal\\_or\\_neonatal\\_effect\\_of\\_breech\\_delivery\\_and\\_extraction'\n",
-       "\\item 'secondary\\_malignant\\_neoplastic\\_disease'\n",
-       "\\item 'hypoxemia'\n",
-       "\\item 'paraplegia'\n",
-       "\\item 'perforation\\_of\\_tympanic\\_membrane'\n",
-       "\\item 'ventricular\\_tachycardia'\n",
-       "\\item 'mixed\\_incontinence'\n",
-       "\\item 'disorder\\_of\\_eye\\_due\\_to\\_type\\_2\\_diabetes\\_mellitus'\n",
-       "\\item 'trigeminal\\_neuralgia'\n",
-       "\\item 'retinal\\_detachment'\n",
-       "\\item 'leukopenia'\n",
-       "\\item 'vitreous\\_hemorrhage'\n",
-       "\\item 'ischemic\\_ulcer'\n",
-       "\\item 'intramural\\_leiomyoma\\_of\\_uterus'\n",
-       "\\item 'viral\\_hepatitis\\_type\\_a'\n",
-       "\\item 'm\\textbackslash{}u00e9ni\\textbackslash{}u00e8re\\textbackslash{}'s\\_disease'\n",
-       "\\item 'fracture\\_of\\_phalanx\\_of\\_hand'\n",
-       "\\item 'muscle\\_atrophy'\n",
-       "\\item 'incontinence\\_of\\_feces'\n",
-       "\\item 'mitral\\_valve\\_disorder'\n",
-       "\\item 'atherosclerosis\\_of\\_arteries\\_of\\_the\\_extremities'\n",
-       "\\item 'spondylosis'\n",
-       "\\item 'pterygium'\n",
-       "\\item 'ulnar\\_neuropathy'\n",
-       "\\item 'lung\\_mass'\n",
-       "\\item 'foreign\\_body\\_in\\_respiratory\\_tract'\n",
-       "\\item 'chronic\\_kidney\\_disease\\_stage\\_4'\n",
-       "\\item 'myocardial\\_ischemia'\n",
-       "\\item 'non-toxic\\_multinodular\\_goiter'\n",
-       "\\item 'pain\\_in\\_finger'\n",
-       "\\item 'cervical\\_spondylosis\\_without\\_myelopathy'\n",
-       "\\item 'body\\_mass\\_index\\_25-29\\_-\\_overweight'\n",
-       "\\item 'clouded\\_consciousness'\n",
-       "\\item 'mixed\\_conductive\\_and\\_sensorineural\\_hearing\\_loss'\n",
-       "\\item 'tooth\\_eruption\\_disorder'\n",
-       "\\item 'hyperuricemia'\n",
-       "\\item 'closed\\_fracture\\_of\\_neck\\_of\\_femur'\n",
-       "\\item 'bipolar\\_ii\\_disorder'\n",
-       "\\item 'disturbance\\_in\\_sleep\\_behavior'\n",
-       "\\item 'relationship\\_problems'\n",
-       "\\item 'sprain\\_of\\_wrist'\n",
-       "\\item 'personality\\_disorder'\n",
-       "\\item 'external\\_hemorrhoids'\n",
-       "\\item 'abnormal\\_vision'\n",
-       "\\item 'hyperprolactinemia'\n",
-       "\\item 'hemochromatosis'\n",
-       "\\item 'lumbosacral\\_radiculopathy'\n",
-       "\\item 'heart\\_valve\\_disorder'\n",
-       "\\item 'cardiac\\_arrest'\n",
-       "\\item 'infection\\_caused\\_by\\_molluscum\\_contagiosum'\n",
-       "\\item 'chronic\\_kidney\\_disease\\_stage\\_2'\n",
-       "\\item 'secondary\\_malignant\\_neoplasm\\_of\\_peritoneum'\n",
-       "\\item 'thoracic\\_back\\_pain'\n",
-       "\\item 'blood\\_in\\_urine'\n",
-       "\\item 'adhesive\\_capsulitis\\_of\\_shoulder'\n",
-       "\\item 'diplopia'\n",
-       "\\item 'sj\\textbackslash{}u00f6gren\\textbackslash{}'s\\_syndrome'\n",
-       "\\item 'ureteric\\_stone'\n",
-       "\\item 'bronchospasm'\n",
-       "\\item 'chronic\\_fatigue\\_syndrome'\n",
-       "\\item 'cannabis\\_dependence'\n",
-       "\\item 'neck\\_sprain'\n",
-       "\\item 'multinodular\\_goiter'\n",
-       "\\item 'ptosis\\_of\\_eyelid'\n",
-       "\\item 'failure\\_to\\_thrive'\n",
-       "\\item 'torticollis'\n",
-       "\\item 'acute\\_bronchiolitis'\n",
-       "\\item 'viral\\_exanthem'\n",
-       "\\item 'talipes\\_planus'\n",
-       "\\item 'idiopathic\\_peripheral\\_neuropathy'\n",
-       "\\item 'foreign\\_body\\_in\\_pharynx'\n",
-       "\\item 'jaw\\_pain'\n",
-       "\\item 'renal\\_impairment'\n",
-       "\\item 'ataxia'\n",
-       "\\item 'age-related\\_macular\\_degeneration'\n",
-       "\\item 'uterine\\_prolapse'\n",
-       "\\item 'renal\\_mass'\n",
-       "\\item 'pneumonitis'\n",
-       "\\item 'coordination\\_problem'\n",
-       "\\item 'blindness\\_-\\_both\\_eyes'\n",
-       "\\item 'primary\\_hyperparathyroidism'\n",
-       "\\item 'musculoskeletal\\_pain'\n",
-       "\\item 'mycosis'\n",
-       "\\item 'primigravida'\n",
-       "\\item 'urethral\\_stricture'\n",
-       "\\item 'leukocytosis'\n",
-       "\\item 'ventricular\\_premature\\_complex'\n",
-       "\\item 'ulcer\\_of\\_foot\\_due\\_to\\_diabetes\\_mellitus'\n",
-       "\\item 'chronic\\_headache\\_disorder'\n",
-       "\\item 'hemangioma'\n",
-       "\\item 'lymphedema'\n",
-       "\\item 'postmenopausal\\_state'\n",
-       "\\item 'chronic\\_ulcer\\_of\\_skin'\n",
-       "\\item 'left\\_heart\\_failure'\n",
-       "\\item 'excessive\\_and\\_frequent\\_menstruation'\n",
-       "\\item 'thrombocytosis'\n",
-       "\\item 'disorder\\_of\\_liver'\n",
-       "\\item 'disorder\\_of\\_carotid\\_artery'\n",
-       "\\item 'altered\\_bowel\\_function'\n",
-       "\\item 'abscess\\_of\\_foot'\n",
-       "\\item 'malignant\\_tumor\\_of\\_head\\_and/or\\_neck'\n",
-       "\\item 'streptococcus\\_group\\_b\\_infection\\_of\\_the\\_infant'\n",
-       "\\item 'concussion\\_injury\\_of\\_brain'\n",
-       "\\item 'feeding\\_problems\\_in\\_newborn'\n",
-       "\\item 'bipolar\\_i\\_disorder'\n",
-       "\\item 'viral\\_pharyngitis'\n",
-       "\\item 'lower\\_respiratory\\_tract\\_infection'\n",
-       "\\item 'hydronephrosis'\n",
-       "\\item 'borderline\\_personality\\_disorder'\n",
-       "\\item 'esophageal\\_varices'\n",
-       "\\item 'hypersomnia'\n",
-       "\\item 'sensorineural\\_hearing\\_loss\\_bilateral'\n",
-       "\\item 'varicocele'\n",
-       "\\item 'subarachnoid\\_intracranial\\_hemorrhage'\n",
-       "\\item 'incisional\\_hernia'\n",
-       "\\item 'varicella'\n",
-       "\\item 'pain\\_in\\_testicle'\n",
-       "\\item 'transplant\\_follow-up'\n",
-       "\\item 'tinea\\_cruris'\n",
-       "\\item 'laryngitis'\n",
-       "\\item 'hypertrophy\\_of\\_nail'\n",
-       "\\item 'amblyopia'\n",
-       "\\item 'polyp\\_of\\_cervix'\n",
-       "\\item 'cyst\\_of\\_kidney'\n",
-       "\\item 'hepatic\\_encephalopathy'\n",
-       "\\item 'blood\\_glucose\\_abnormal'\n",
-       "\\item 'postherpetic\\_neuralgia'\n",
-       "\\item 'frank\\_hematuria'\n",
-       "\\item 'cramp'\n",
-       "\\item 'interstitial\\_lung\\_disease'\n",
-       "\\item 'complete\\_atrioventricular\\_block'\n",
-       "\\item 'malignant\\_tumor\\_of\\_kidney'\n",
-       "\\item 'otitis'\n",
-       "\\item 'septic\\_shock'\n",
-       "\\item 'disorder\\_of\\_thyroid\\_gland'\n",
-       "\\item 'hypertrophic\\_cardiomyopathy'\n",
-       "\\item 'respiratory\\_distress\\_syndrome\\_in\\_the\\_newborn'\n",
-       "\\item 'infectious\\_gastroenteritis'\n",
-       "\\item 'subdural\\_intracranial\\_hemorrhage'\n",
-       "\\item 'hepatitis\\_b\\_carrier'\n",
-       "\\item 'manic\\_bipolar\\_i\\_disorder'\n",
-       "\\item 'secondary\\_pulmonary\\_hypertension'\n",
-       "\\item 'gonorrhea'\n",
-       "\\item 'derangement\\_of\\_knee'\n",
-       "\\item 'appendicitis'\n",
-       "\\item 'polyneuropathy\\_due\\_to\\_diabetes\\_mellitus'\n",
-       "\\item 'neonatal\\_hypoglycemia'\n",
-       "\\item 'prolonged\\_rupture\\_of\\_membranes'\n",
-       "\\item 'vasomotor\\_rhinitis'\n",
-       "\\item 'renal\\_disorder\\_due\\_to\\_type\\_1\\_diabetes\\_mellitus'\n",
-       "\\item 'tuberculosis'\n",
-       "\\item 'feeding\\_problem'\n",
-       "\\item 'chronic\\_tonsillitis'\n",
-       "\\item 'acute\\_duodenal\\_ulcer\\_with\\_hemorrhage'\n",
-       "\\item 'hammer\\_toe'\n",
-       "\\item 'malignant\\_tumor\\_of\\_cervix'\n",
-       "\\item 'prolapsed\\_lumbar\\_intervertebral\\_disc'\n",
-       "\\item 'hematemesis'\n",
-       "\\item 'perianal\\_abscess'\n",
-       "\\item 'nonvenomous\\_insect\\_bite'\n",
-       "\\item 'spondylolisthesis'\n",
-       "\\item 'malignant\\_tumor\\_of\\_esophagus'\n",
-       "\\item 'aphthous\\_ulcer\\_of\\_mouth'\n",
-       "\\item 'ventricular\\_septal\\_defect'\n",
-       "\\item 'oropharyngeal\\_dysphagia'\n",
-       "\\item 'injury\\_of\\_knee'\n",
-       "\\item 'traumatic\\_brain\\_injury'\n",
-       "\\item 'osteoarthritis\\_of\\_glenohumeral\\_joint'\n",
-       "\\end{enumerate*}\n"
-      ],
-      "text/markdown": [
-       "1. 'chronic_alcoholism_in_remission'\n",
-       "2. 'chronic_pancreatitis'\n",
-       "3. 'gastroparesis'\n",
-       "4. 'ectopic_pregnancy'\n",
-       "5. 'muscle_weakness'\n",
-       "6. 'recurrent_major_depression'\n",
-       "7. 'pilonidal_cyst'\n",
-       "8. 'pain_in_toe'\n",
-       "9. 'pulmonary_tuberculosis'\n",
-       "10. 'celiac_disease'\n",
-       "11. 'cramp_in_lower_leg'\n",
-       "12. 'secondary_malignant_neoplasm_of_pleura'\n",
-       "13. 'fracture_of_hand'\n",
-       "14. 'cyst_of_breast'\n",
-       "15. 'nephrotic_syndrome'\n",
-       "16. 'polyp_of_nasal_sinus'\n",
-       "17. 'chondromalacia_of_patella'\n",
-       "18. 'spinal_stenosis_in_cervical_region'\n",
-       "19. 'disorder_of_artery'\n",
-       "20. 'vitiligo'\n",
-       "21. 'female_cystocele'\n",
-       "22. 'dysphasia'\n",
-       "23. 'retinal_disorder'\n",
-       "24. 'epiretinal_membrane'\n",
-       "25. 'recurrent_major_depression_in_partial_remission'\n",
-       "26. 'infection_caused_by_trichomonas'\n",
-       "27. 'osteomyelitis'\n",
-       "28. 'polyp_of_nasal_cavity_and/or_nasal_sinus'\n",
-       "29. 'mass_of_neck'\n",
-       "30. 'idiopathic_thrombocytopenic_purpura'\n",
-       "31. 'complete_miscarriage'\n",
-       "32. 'gastric_ulcer'\n",
-       "33. 'papilloma_of_skin'\n",
-       "34. 'fetal_or_neonatal_effect_of_breech_delivery_and_extraction'\n",
-       "35. 'secondary_malignant_neoplastic_disease'\n",
-       "36. 'hypoxemia'\n",
-       "37. 'paraplegia'\n",
-       "38. 'perforation_of_tympanic_membrane'\n",
-       "39. 'ventricular_tachycardia'\n",
-       "40. 'mixed_incontinence'\n",
-       "41. 'disorder_of_eye_due_to_type_2_diabetes_mellitus'\n",
-       "42. 'trigeminal_neuralgia'\n",
-       "43. 'retinal_detachment'\n",
-       "44. 'leukopenia'\n",
-       "45. 'vitreous_hemorrhage'\n",
-       "46. 'ischemic_ulcer'\n",
-       "47. 'intramural_leiomyoma_of_uterus'\n",
-       "48. 'viral_hepatitis_type_a'\n",
-       "49. 'm\\u00e9ni\\u00e8re\\'s_disease'\n",
-       "50. 'fracture_of_phalanx_of_hand'\n",
-       "51. 'muscle_atrophy'\n",
-       "52. 'incontinence_of_feces'\n",
-       "53. 'mitral_valve_disorder'\n",
-       "54. 'atherosclerosis_of_arteries_of_the_extremities'\n",
-       "55. 'spondylosis'\n",
-       "56. 'pterygium'\n",
-       "57. 'ulnar_neuropathy'\n",
-       "58. 'lung_mass'\n",
-       "59. 'foreign_body_in_respiratory_tract'\n",
-       "60. 'chronic_kidney_disease_stage_4'\n",
-       "61. 'myocardial_ischemia'\n",
-       "62. 'non-toxic_multinodular_goiter'\n",
-       "63. 'pain_in_finger'\n",
-       "64. 'cervical_spondylosis_without_myelopathy'\n",
-       "65. 'body_mass_index_25-29_-_overweight'\n",
-       "66. 'clouded_consciousness'\n",
-       "67. 'mixed_conductive_and_sensorineural_hearing_loss'\n",
-       "68. 'tooth_eruption_disorder'\n",
-       "69. 'hyperuricemia'\n",
-       "70. 'closed_fracture_of_neck_of_femur'\n",
-       "71. 'bipolar_ii_disorder'\n",
-       "72. 'disturbance_in_sleep_behavior'\n",
-       "73. 'relationship_problems'\n",
-       "74. 'sprain_of_wrist'\n",
-       "75. 'personality_disorder'\n",
-       "76. 'external_hemorrhoids'\n",
-       "77. 'abnormal_vision'\n",
-       "78. 'hyperprolactinemia'\n",
-       "79. 'hemochromatosis'\n",
-       "80. 'lumbosacral_radiculopathy'\n",
-       "81. 'heart_valve_disorder'\n",
-       "82. 'cardiac_arrest'\n",
-       "83. 'infection_caused_by_molluscum_contagiosum'\n",
-       "84. 'chronic_kidney_disease_stage_2'\n",
-       "85. 'secondary_malignant_neoplasm_of_peritoneum'\n",
-       "86. 'thoracic_back_pain'\n",
-       "87. 'blood_in_urine'\n",
-       "88. 'adhesive_capsulitis_of_shoulder'\n",
-       "89. 'diplopia'\n",
-       "90. 'sj\\u00f6gren\\'s_syndrome'\n",
-       "91. 'ureteric_stone'\n",
-       "92. 'bronchospasm'\n",
-       "93. 'chronic_fatigue_syndrome'\n",
-       "94. 'cannabis_dependence'\n",
-       "95. 'neck_sprain'\n",
-       "96. 'multinodular_goiter'\n",
-       "97. 'ptosis_of_eyelid'\n",
-       "98. 'failure_to_thrive'\n",
-       "99. 'torticollis'\n",
-       "100. 'acute_bronchiolitis'\n",
-       "101. 'viral_exanthem'\n",
-       "102. 'talipes_planus'\n",
-       "103. 'idiopathic_peripheral_neuropathy'\n",
-       "104. 'foreign_body_in_pharynx'\n",
-       "105. 'jaw_pain'\n",
-       "106. 'renal_impairment'\n",
-       "107. 'ataxia'\n",
-       "108. 'age-related_macular_degeneration'\n",
-       "109. 'uterine_prolapse'\n",
-       "110. 'renal_mass'\n",
-       "111. 'pneumonitis'\n",
-       "112. 'coordination_problem'\n",
-       "113. 'blindness_-_both_eyes'\n",
-       "114. 'primary_hyperparathyroidism'\n",
-       "115. 'musculoskeletal_pain'\n",
-       "116. 'mycosis'\n",
-       "117. 'primigravida'\n",
-       "118. 'urethral_stricture'\n",
-       "119. 'leukocytosis'\n",
-       "120. 'ventricular_premature_complex'\n",
-       "121. 'ulcer_of_foot_due_to_diabetes_mellitus'\n",
-       "122. 'chronic_headache_disorder'\n",
-       "123. 'hemangioma'\n",
-       "124. 'lymphedema'\n",
-       "125. 'postmenopausal_state'\n",
-       "126. 'chronic_ulcer_of_skin'\n",
-       "127. 'left_heart_failure'\n",
-       "128. 'excessive_and_frequent_menstruation'\n",
-       "129. 'thrombocytosis'\n",
-       "130. 'disorder_of_liver'\n",
-       "131. 'disorder_of_carotid_artery'\n",
-       "132. 'altered_bowel_function'\n",
-       "133. 'abscess_of_foot'\n",
-       "134. 'malignant_tumor_of_head_and/or_neck'\n",
-       "135. 'streptococcus_group_b_infection_of_the_infant'\n",
-       "136. 'concussion_injury_of_brain'\n",
-       "137. 'feeding_problems_in_newborn'\n",
-       "138. 'bipolar_i_disorder'\n",
-       "139. 'viral_pharyngitis'\n",
-       "140. 'lower_respiratory_tract_infection'\n",
-       "141. 'hydronephrosis'\n",
-       "142. 'borderline_personality_disorder'\n",
-       "143. 'esophageal_varices'\n",
-       "144. 'hypersomnia'\n",
-       "145. 'sensorineural_hearing_loss_bilateral'\n",
-       "146. 'varicocele'\n",
-       "147. 'subarachnoid_intracranial_hemorrhage'\n",
-       "148. 'incisional_hernia'\n",
-       "149. 'varicella'\n",
-       "150. 'pain_in_testicle'\n",
-       "151. 'transplant_follow-up'\n",
-       "152. 'tinea_cruris'\n",
-       "153. 'laryngitis'\n",
-       "154. 'hypertrophy_of_nail'\n",
-       "155. 'amblyopia'\n",
-       "156. 'polyp_of_cervix'\n",
-       "157. 'cyst_of_kidney'\n",
-       "158. 'hepatic_encephalopathy'\n",
-       "159. 'blood_glucose_abnormal'\n",
-       "160. 'postherpetic_neuralgia'\n",
-       "161. 'frank_hematuria'\n",
-       "162. 'cramp'\n",
-       "163. 'interstitial_lung_disease'\n",
-       "164. 'complete_atrioventricular_block'\n",
-       "165. 'malignant_tumor_of_kidney'\n",
-       "166. 'otitis'\n",
-       "167. 'septic_shock'\n",
-       "168. 'disorder_of_thyroid_gland'\n",
-       "169. 'hypertrophic_cardiomyopathy'\n",
-       "170. 'respiratory_distress_syndrome_in_the_newborn'\n",
-       "171. 'infectious_gastroenteritis'\n",
-       "172. 'subdural_intracranial_hemorrhage'\n",
-       "173. 'hepatitis_b_carrier'\n",
-       "174. 'manic_bipolar_i_disorder'\n",
-       "175. 'secondary_pulmonary_hypertension'\n",
-       "176. 'gonorrhea'\n",
-       "177. 'derangement_of_knee'\n",
-       "178. 'appendicitis'\n",
-       "179. 'polyneuropathy_due_to_diabetes_mellitus'\n",
-       "180. 'neonatal_hypoglycemia'\n",
-       "181. 'prolonged_rupture_of_membranes'\n",
-       "182. 'vasomotor_rhinitis'\n",
-       "183. 'renal_disorder_due_to_type_1_diabetes_mellitus'\n",
-       "184. 'tuberculosis'\n",
-       "185. 'feeding_problem'\n",
-       "186. 'chronic_tonsillitis'\n",
-       "187. 'acute_duodenal_ulcer_with_hemorrhage'\n",
-       "188. 'hammer_toe'\n",
-       "189. 'malignant_tumor_of_cervix'\n",
-       "190. 'prolapsed_lumbar_intervertebral_disc'\n",
-       "191. 'hematemesis'\n",
-       "192. 'perianal_abscess'\n",
-       "193. 'nonvenomous_insect_bite'\n",
-       "194. 'spondylolisthesis'\n",
-       "195. 'malignant_tumor_of_esophagus'\n",
-       "196. 'aphthous_ulcer_of_mouth'\n",
-       "197. 'ventricular_septal_defect'\n",
-       "198. 'oropharyngeal_dysphagia'\n",
-       "199. 'injury_of_knee'\n",
-       "200. 'traumatic_brain_injury'\n",
-       "201. 'osteoarthritis_of_glenohumeral_joint'\n",
-       "\n",
-       "\n"
-      ],
-      "text/plain": [
-       "  [1] \"chronic_alcoholism_in_remission\"                           \n",
-       "  [2] \"chronic_pancreatitis\"                                      \n",
-       "  [3] \"gastroparesis\"                                             \n",
-       "  [4] \"ectopic_pregnancy\"                                         \n",
-       "  [5] \"muscle_weakness\"                                           \n",
-       "  [6] \"recurrent_major_depression\"                                \n",
-       "  [7] \"pilonidal_cyst\"                                            \n",
-       "  [8] \"pain_in_toe\"                                               \n",
-       "  [9] \"pulmonary_tuberculosis\"                                    \n",
-       " [10] \"celiac_disease\"                                            \n",
-       " [11] \"cramp_in_lower_leg\"                                        \n",
-       " [12] \"secondary_malignant_neoplasm_of_pleura\"                    \n",
-       " [13] \"fracture_of_hand\"                                          \n",
-       " [14] \"cyst_of_breast\"                                            \n",
-       " [15] \"nephrotic_syndrome\"                                        \n",
-       " [16] \"polyp_of_nasal_sinus\"                                      \n",
-       " [17] \"chondromalacia_of_patella\"                                 \n",
-       " [18] \"spinal_stenosis_in_cervical_region\"                        \n",
-       " [19] \"disorder_of_artery\"                                        \n",
-       " [20] \"vitiligo\"                                                  \n",
-       " [21] \"female_cystocele\"                                          \n",
-       " [22] \"dysphasia\"                                                 \n",
-       " [23] \"retinal_disorder\"                                          \n",
-       " [24] \"epiretinal_membrane\"                                       \n",
-       " [25] \"recurrent_major_depression_in_partial_remission\"           \n",
-       " [26] \"infection_caused_by_trichomonas\"                           \n",
-       " [27] \"osteomyelitis\"                                             \n",
-       " [28] \"polyp_of_nasal_cavity_and/or_nasal_sinus\"                  \n",
-       " [29] \"mass_of_neck\"                                              \n",
-       " [30] \"idiopathic_thrombocytopenic_purpura\"                       \n",
-       " [31] \"complete_miscarriage\"                                      \n",
-       " [32] \"gastric_ulcer\"                                             \n",
-       " [33] \"papilloma_of_skin\"                                         \n",
-       " [34] \"fetal_or_neonatal_effect_of_breech_delivery_and_extraction\"\n",
-       " [35] \"secondary_malignant_neoplastic_disease\"                    \n",
-       " [36] \"hypoxemia\"                                                 \n",
-       " [37] \"paraplegia\"                                                \n",
-       " [38] \"perforation_of_tympanic_membrane\"                          \n",
-       " [39] \"ventricular_tachycardia\"                                   \n",
-       " [40] \"mixed_incontinence\"                                        \n",
-       " [41] \"disorder_of_eye_due_to_type_2_diabetes_mellitus\"           \n",
-       " [42] \"trigeminal_neuralgia\"                                      \n",
-       " [43] \"retinal_detachment\"                                        \n",
-       " [44] \"leukopenia\"                                                \n",
-       " [45] \"vitreous_hemorrhage\"                                       \n",
-       " [46] \"ischemic_ulcer\"                                            \n",
-       " [47] \"intramural_leiomyoma_of_uterus\"                            \n",
-       " [48] \"viral_hepatitis_type_a\"                                    \n",
-       " [49] \"m<U+00E9>ni<U+00E8>re's_disease\"                           \n",
-       " [50] \"fracture_of_phalanx_of_hand\"                               \n",
-       " [51] \"muscle_atrophy\"                                            \n",
-       " [52] \"incontinence_of_feces\"                                     \n",
-       " [53] \"mitral_valve_disorder\"                                     \n",
-       " [54] \"atherosclerosis_of_arteries_of_the_extremities\"            \n",
-       " [55] \"spondylosis\"                                               \n",
-       " [56] \"pterygium\"                                                 \n",
-       " [57] \"ulnar_neuropathy\"                                          \n",
-       " [58] \"lung_mass\"                                                 \n",
-       " [59] \"foreign_body_in_respiratory_tract\"                         \n",
-       " [60] \"chronic_kidney_disease_stage_4\"                            \n",
-       " [61] \"myocardial_ischemia\"                                       \n",
-       " [62] \"non-toxic_multinodular_goiter\"                             \n",
-       " [63] \"pain_in_finger\"                                            \n",
-       " [64] \"cervical_spondylosis_without_myelopathy\"                   \n",
-       " [65] \"body_mass_index_25-29_-_overweight\"                        \n",
-       " [66] \"clouded_consciousness\"                                     \n",
-       " [67] \"mixed_conductive_and_sensorineural_hearing_loss\"           \n",
-       " [68] \"tooth_eruption_disorder\"                                   \n",
-       " [69] \"hyperuricemia\"                                             \n",
-       " [70] \"closed_fracture_of_neck_of_femur\"                          \n",
-       " [71] \"bipolar_ii_disorder\"                                       \n",
-       " [72] \"disturbance_in_sleep_behavior\"                             \n",
-       " [73] \"relationship_problems\"                                     \n",
-       " [74] \"sprain_of_wrist\"                                           \n",
-       " [75] \"personality_disorder\"                                      \n",
-       " [76] \"external_hemorrhoids\"                                      \n",
-       " [77] \"abnormal_vision\"                                           \n",
-       " [78] \"hyperprolactinemia\"                                        \n",
-       " [79] \"hemochromatosis\"                                           \n",
-       " [80] \"lumbosacral_radiculopathy\"                                 \n",
-       " [81] \"heart_valve_disorder\"                                      \n",
-       " [82] \"cardiac_arrest\"                                            \n",
-       " [83] \"infection_caused_by_molluscum_contagiosum\"                 \n",
-       " [84] \"chronic_kidney_disease_stage_2\"                            \n",
-       " [85] \"secondary_malignant_neoplasm_of_peritoneum\"                \n",
-       " [86] \"thoracic_back_pain\"                                        \n",
-       " [87] \"blood_in_urine\"                                            \n",
-       " [88] \"adhesive_capsulitis_of_shoulder\"                           \n",
-       " [89] \"diplopia\"                                                  \n",
-       " [90] \"sj<U+00F6>gren's_syndrome\"                                 \n",
-       " [91] \"ureteric_stone\"                                            \n",
-       " [92] \"bronchospasm\"                                              \n",
-       " [93] \"chronic_fatigue_syndrome\"                                  \n",
-       " [94] \"cannabis_dependence\"                                       \n",
-       " [95] \"neck_sprain\"                                               \n",
-       " [96] \"multinodular_goiter\"                                       \n",
-       " [97] \"ptosis_of_eyelid\"                                          \n",
-       " [98] \"failure_to_thrive\"                                         \n",
-       " [99] \"torticollis\"                                               \n",
-       "[100] \"acute_bronchiolitis\"                                       \n",
-       "[101] \"viral_exanthem\"                                            \n",
-       "[102] \"talipes_planus\"                                            \n",
-       "[103] \"idiopathic_peripheral_neuropathy\"                          \n",
-       "[104] \"foreign_body_in_pharynx\"                                   \n",
-       "[105] \"jaw_pain\"                                                  \n",
-       "[106] \"renal_impairment\"                                          \n",
-       "[107] \"ataxia\"                                                    \n",
-       "[108] \"age-related_macular_degeneration\"                          \n",
-       "[109] \"uterine_prolapse\"                                          \n",
-       "[110] \"renal_mass\"                                                \n",
-       "[111] \"pneumonitis\"                                               \n",
-       "[112] \"coordination_problem\"                                      \n",
-       "[113] \"blindness_-_both_eyes\"                                     \n",
-       "[114] \"primary_hyperparathyroidism\"                               \n",
-       "[115] \"musculoskeletal_pain\"                                      \n",
-       "[116] \"mycosis\"                                                   \n",
-       "[117] \"primigravida\"                                              \n",
-       "[118] \"urethral_stricture\"                                        \n",
-       "[119] \"leukocytosis\"                                              \n",
-       "[120] \"ventricular_premature_complex\"                             \n",
-       "[121] \"ulcer_of_foot_due_to_diabetes_mellitus\"                    \n",
-       "[122] \"chronic_headache_disorder\"                                 \n",
-       "[123] \"hemangioma\"                                                \n",
-       "[124] \"lymphedema\"                                                \n",
-       "[125] \"postmenopausal_state\"                                      \n",
-       "[126] \"chronic_ulcer_of_skin\"                                     \n",
-       "[127] \"left_heart_failure\"                                        \n",
-       "[128] \"excessive_and_frequent_menstruation\"                       \n",
-       "[129] \"thrombocytosis\"                                            \n",
-       "[130] \"disorder_of_liver\"                                         \n",
-       "[131] \"disorder_of_carotid_artery\"                                \n",
-       "[132] \"altered_bowel_function\"                                    \n",
-       "[133] \"abscess_of_foot\"                                           \n",
-       "[134] \"malignant_tumor_of_head_and/or_neck\"                       \n",
-       "[135] \"streptococcus_group_b_infection_of_the_infant\"             \n",
-       "[136] \"concussion_injury_of_brain\"                                \n",
-       "[137] \"feeding_problems_in_newborn\"                               \n",
-       "[138] \"bipolar_i_disorder\"                                        \n",
-       "[139] \"viral_pharyngitis\"                                         \n",
-       "[140] \"lower_respiratory_tract_infection\"                         \n",
-       "[141] \"hydronephrosis\"                                            \n",
-       "[142] \"borderline_personality_disorder\"                           \n",
-       "[143] \"esophageal_varices\"                                        \n",
-       "[144] \"hypersomnia\"                                               \n",
-       "[145] \"sensorineural_hearing_loss_bilateral\"                      \n",
-       "[146] \"varicocele\"                                                \n",
-       "[147] \"subarachnoid_intracranial_hemorrhage\"                      \n",
-       "[148] \"incisional_hernia\"                                         \n",
-       "[149] \"varicella\"                                                 \n",
-       "[150] \"pain_in_testicle\"                                          \n",
-       "[151] \"transplant_follow-up\"                                      \n",
-       "[152] \"tinea_cruris\"                                              \n",
-       "[153] \"laryngitis\"                                                \n",
-       "[154] \"hypertrophy_of_nail\"                                       \n",
-       "[155] \"amblyopia\"                                                 \n",
-       "[156] \"polyp_of_cervix\"                                           \n",
-       "[157] \"cyst_of_kidney\"                                            \n",
-       "[158] \"hepatic_encephalopathy\"                                    \n",
-       "[159] \"blood_glucose_abnormal\"                                    \n",
-       "[160] \"postherpetic_neuralgia\"                                    \n",
-       "[161] \"frank_hematuria\"                                           \n",
-       "[162] \"cramp\"                                                     \n",
-       "[163] \"interstitial_lung_disease\"                                 \n",
-       "[164] \"complete_atrioventricular_block\"                           \n",
-       "[165] \"malignant_tumor_of_kidney\"                                 \n",
-       "[166] \"otitis\"                                                    \n",
-       "[167] \"septic_shock\"                                              \n",
-       "[168] \"disorder_of_thyroid_gland\"                                 \n",
-       "[169] \"hypertrophic_cardiomyopathy\"                               \n",
-       "[170] \"respiratory_distress_syndrome_in_the_newborn\"              \n",
-       "[171] \"infectious_gastroenteritis\"                                \n",
-       "[172] \"subdural_intracranial_hemorrhage\"                          \n",
-       "[173] \"hepatitis_b_carrier\"                                       \n",
-       "[174] \"manic_bipolar_i_disorder\"                                  \n",
-       "[175] \"secondary_pulmonary_hypertension\"                          \n",
-       "[176] \"gonorrhea\"                                                 \n",
-       "[177] \"derangement_of_knee\"                                       \n",
-       "[178] \"appendicitis\"                                              \n",
-       "[179] \"polyneuropathy_due_to_diabetes_mellitus\"                   \n",
-       "[180] \"neonatal_hypoglycemia\"                                     \n",
-       "[181] \"prolonged_rupture_of_membranes\"                            \n",
-       "[182] \"vasomotor_rhinitis\"                                        \n",
-       "[183] \"renal_disorder_due_to_type_1_diabetes_mellitus\"            \n",
-       "[184] \"tuberculosis\"                                              \n",
-       "[185] \"feeding_problem\"                                           \n",
-       "[186] \"chronic_tonsillitis\"                                       \n",
-       "[187] \"acute_duodenal_ulcer_with_hemorrhage\"                      \n",
-       "[188] \"hammer_toe\"                                                \n",
-       "[189] \"malignant_tumor_of_cervix\"                                 \n",
-       "[190] \"prolapsed_lumbar_intervertebral_disc\"                      \n",
-       "[191] \"hematemesis\"                                               \n",
-       "[192] \"perianal_abscess\"                                          \n",
-       "[193] \"nonvenomous_insect_bite\"                                   \n",
-       "[194] \"spondylolisthesis\"                                         \n",
-       "[195] \"malignant_tumor_of_esophagus\"                              \n",
-       "[196] \"aphthous_ulcer_of_mouth\"                                   \n",
-       "[197] \"ventricular_septal_defect\"                                 \n",
-       "[198] \"oropharyngeal_dysphagia\"                                   \n",
-       "[199] \"injury_of_knee\"                                            \n",
-       "[200] \"traumatic_brain_injury\"                                    \n",
-       "[201] \"osteoarthritis_of_glenohumeral_joint\"                      "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "covariates[700:900]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T14:17:01.632846Z",
-     "start_time": "2020-11-04T14:17:00.584Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "data = data %>% mutate_at(c(\"sex\", \"overall_health_rating\", \"smoking_status\", \"ethnic_background\"), as.factor)\n",
-    "data = data %>% mutate(sex=fct_relevel(sex, c(\"Male\", \"Female\")),\n",
-    "                       overall_health_rating=fct_relevel(overall_health_rating, c(\"Excellent\", \"Good\", \"Fair\", \"Poor\")),\n",
-    "                       smoking_status=fct_relevel(smoking_status, c(\"Current\", \"Previous\", \"Never\")))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Missing values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#na_count <-data.frame(sapply(data %>% as.data.frame(), function(y) sum(length(which(is.na(y))))))\n",
-    "#na_count %>% filter(sapply(data %>% as.data.frame(), function(y) sum(length(which(is.na(y)))))>0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Covariates BIG"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Table 1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mycontrols  <- tableby.control(test=FALSE, total=TRUE,\n",
-    "                               numeric.test=\"kwt\", cat.test=\"chisq\",\n",
-    "                               numeric.stats=c(\"meansd\"), numeric.simplify=TRUE,\n",
-    "                               cat.stats=c(\"countpct\"), digits = 2L,cat.simplify = TRUE,\n",
-    "                               stats.labels=list(N='N', meansd='Median', Nmiss=\"Missing\"))"
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {},
-   "source": [
-    "library(arsenal)\n",
-    "table_one = tableby(sex~., control=mycontrols, data=data %>% select(all_of(c(covariates, targets))))\n",
-    "write2html(table_one, glue(\"{dataset_path}/table1_big.html\"));\n",
-    "#summary(table_one, text=TRUE)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# BASELINE"
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {},
-   "source": [
-    "base_size = 25\n",
-    "title_size = 30\n",
-    "facet_size = 22\n",
-    "geom_text_size=7"
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {},
-   "source": [
-    "library(ggplot2); \n",
-    "theme_set(theme_classic(base_size = base_size) + \n",
-    "          theme(strip.background = element_blank(), plot.title=element_text(size=title_size, hjust=0.5), \n",
-    "                axis.title.y = element_blank(), axis.text.y = element_blank(), axis.ticks.y = element_blank(), axis.line.y = element_blank(), \n",
-    "                strip.text.x = element_text(size = facet_size)))"
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {},
-   "source": [
-    "temp = data %>% select(all_of(c(pgs))) %>% select_if(is.numeric) %>% pivot_longer(everything(), names_to=\"parameter\", values_to=\"value\")\n",
-    "plot_pgs = ggplot(temp, aes(x=value)) + ggtitle(\"Polygenic Scores\") + \n",
-    "    geom_density(adjust=1.5, fill=\"gray70\") +\n",
-    "    scale_y_continuous(expand=c(0,0))+ xlab(\"\") + \n",
-    "    facet_wrap(~parameter, ncol=6, scales = \"free\")"
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {},
-   "source": [
-    "temp = data %>% select(all_of(c(covariates))) %>% select_if(is.numeric) %>% pivot_longer(everything(), names_to=\"parameter\", values_to=\"value\")\n",
-    "plot_cont = ggplot(temp, aes(x=value)) + ggtitle(\"Measurements\") + \n",
-    "    geom_density(adjust=1.5, fill=\"gray70\") +\n",
-    "    scale_y_continuous(expand=c(0,0))+ xlab(\"\") + \n",
-    "    facet_wrap(~parameter, ncol=4, scales = \"free\")"
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {},
-   "source": [
-    "temp = data %>% select(all_of(c(covariates))) %>% select_if(is.factor) %>% pivot_longer(everything(), names_to=\"parameter\", values_to=\"value\",values_ptypes=list(value=\"character\"))%>% \n",
-    "    group_by(parameter, value, .drop = TRUE) %>% summarise(count=n(), ratio=round((100*n()/502504), 1)) %>% ungroup()\n",
-    "plot_cat = ggplot(temp, aes(x=value, y=ratio)) + ggtitle(\"Basic Information\") + \n",
-    "    geom_bar(stat=\"identity\", position=position_dodge(width = 0.8), fill=\"gray70\", width=0.5) +\n",
-    "    geom_text(aes(label=paste0(ratio, \" %\")), vjust=-1, size = geom_text_size) + \n",
-    "    scale_y_continuous(expand=c(0,0), limits=c(0, max(temp$ratio)*1.2))+ xlab(\"\") + \n",
-    "    facet_grid(~parameter, scales = \"free\", space=\"free\")"
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {},
-   "source": [
-    "library(forcats)\n",
-    "library(ggrepel)"
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {},
-   "source": [
-    "options(repr.plot.width=40, repr.plot.height=8)\n",
-    "\n",
-    "plot_cat = ggplot(temp, aes(x=parameter, y=ratio, fill=factor(parameter))) + ggtitle(\"Basic Information\") + \n",
-    "    geom_bar(stat=\"identity\", position=\"fill\", fill=\"gray70\", aes(alpha=ratio)) + coord_flip()+# #, fill=\"gray70\", width=0.5) #+\n",
-    "    geom_text_repel(aes(label=ifelse(is.na(value), \"\", paste0(ratio*100, \" %\")), vjust=1), position = position_stack(vjust = .5), size = geom_text_size) +\n",
-    "    geom_text_repel(aes(label=value, vjust=-0.5), position = position_stack(vjust = .5), size = geom_text_size) +\n",
-    "    scale_y_continuous(limits=c(-0.05, 1.05))+ xlab(\"\") +\n",
-    "    theme_void(base_size = base_size) + theme(strip.background = element_blank(), plot.title=element_text(size=title_size, hjust=0.5), legend.position = \"None\")\n",
-    "\n",
-    "plot_cat "
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {},
-   "source": [
-    "options(repr.plot.width=20, repr.plot.height=8)\n",
-    "\n",
-    "plot_cat = ggplot(temp, aes(x=parameter, y=ratio, fill=fct_rev(value))) + ggtitle(\"Basic Information\") + \n",
-    "    geom_bar(stat=\"identity\", position=\"fill\", alpha=0.5) + coord_flip()+# #, fill=\"gray70\", width=0.5) #+\n",
-    "    geom_text(aes(label=paste0(ratio*100, \" %\"), vjust=1), position = position_stack(vjust = .5), size = geom_text_size) +\n",
-    "    geom_text(aes(label=value, vjust=-0.5), position = position_stack(vjust = .5), size = geom_text_size) +\n",
-    "    scale_y_continuous(limits=c(0, max(temp$ratio)*1.2))+ xlab(\"\") + facet_grid(~parameter, cols=1)+\n",
-    "    theme_classic(base_size = base_size) + theme(strip.background = element_blank(), plot.title=element_text(size=title_size, hjust=0.5), legend.position = \"None\")+\n",
-    "    scale_fill_d3() + theme(axis.line=element_blank(),\n",
-    "      axis.text.x=element_blank(),\n",
-    "      axis.text.y=element_blank(),\n",
-    "      axis.ticks=element_blank(),\n",
-    "      legend.position=\"none\",\n",
-    "      panel.background=element_blank(),\n",
-    "      panel.border=element_blank(),\n",
-    "      panel.grid.major=element_blank(),\n",
-    "      panel.grid.minor=element_blank(),\n",
-    "      plot.background=element_blank())\n",
-    "plot_cat "
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {},
-   "source": [
-    "temp = data %>% select(c(any_of(phenotypes))) %>% \n",
-    "    pivot_longer(everything(),names_to=\"category\", values_to=\"Value\") %>% \n",
-    "    group_by(category) %>% summarise(ratio = sum(Value==TRUE)/n(), .groups = 'drop') %>% mutate(category = fct_reorder(category, ratio)) %>% mutate(ratio=ratio*100)\n",
-    "plot_conditions = ggplot(temp, aes(x=category, y=ratio)) + ggtitle(\"Conditions\") + \n",
-    "    geom_bar(stat=\"identity\", position=position_dodge(width = 0.8), fill=\"gray70\", width=0.5) +\n",
-    "    coord_flip()+xlab(\"\") +\n",
-    "    ylab(\"Prevalence in [%]\") +\n",
-    "    scale_y_continuous(expand=c(0,0), limits=c(0, max(temp$ratio)*1.3))+\n",
-    "    geom_text(aes(x=category, y=ratio, label=glue(\"{round(ratio,1)}%\")), position = position_dodge(0.8), hjust=-0.1, size = geom_text_size)+ #nudge_y=+3000, \n",
-    "    scale_x_discrete()+\n",
-    "    theme_classic(base_size = base_size) + theme(strip.background = element_blank(), plot.title=element_text(size=title_size, hjust=0.5))#, strip.text.x = element_text(size = 16))"
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {},
-   "source": [
-    "temp = data %>% select(c(any_of(medications))) %>% \n",
-    "    pivot_longer(everything(), names_to=\"category\", values_to=\"Value\") %>% \n",
-    "    group_by(category) %>% summarise(ratio = sum(Value==TRUE)/n(), .groups = 'drop') %>% mutate(category = fct_reorder(category, ratio)) %>% mutate(ratio=ratio*100)\n",
-    "plot_medications = ggplot(temp, aes(x=category, y=ratio)) + ggtitle(\"Medications\") + \n",
-    "    geom_bar(stat=\"identity\", position=position_dodge(width = 0.8), fill=\"gray70\", width=0.5) +\n",
-    "    coord_flip()+xlab(\"\") +\n",
-    "    ylab(\"Prevalence in [%]\") +\n",
-    "    scale_y_continuous(expand=c(0,0), limits=c(0, max(temp$ratio)*1.3))+\n",
-    "    geom_text(aes(x=category, y=ratio, label=glue(\"{round(ratio,1)}%\")), position = position_dodge(0.8), hjust=-0.1, size = geom_text_size)+ #nudge_y=+3000, \n",
-    "    scale_x_discrete()"
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {
-    "jupyter": {
-     "outputs_hidden": true
-    }
-   },
-   "source": [
-    "options(repr.plot.width=30, repr.plot.height=100)\n",
-    "plot_1 = plot_grid(plot_pgs, plot_cat, plot_cont, ncol=1, rel_heights=c(1,0.7,10), align=\"v\")\n",
-    "plot_2 = plot_grid(plot_conditions, plot_medications, ncol=2, rel_widths=c(3,2.5), align=\"h\")\n",
-    "plot_grid(plot_1, plot_2, ncol=1, rel_heights=c(5,1.5), align=\"v\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Covariates Union"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T14:17:04.842258Z",
-     "start_time": "2020-11-04T14:17:03.935Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "mycontrols  <- tableby.control(test=FALSE, total=TRUE,\n",
-    "                               numeric.test=\"kwt\", cat.test=\"chisq\",\n",
-    "                               numeric.stats=c(\"meansd\"), numeric.simplify=TRUE,\n",
-    "                               cat.stats=c(\"countpct\"), digits = 2L,cat.simplify = TRUE,\n",
-    "                               stats.labels=list(N='N', meansd='Median', Nmiss=\"Missing\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T14:17:05.316730Z",
-     "start_time": "2020-11-04T14:17:04.385Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "f = list()\n",
-    "f$pgs = c('PGS000011', 'PGS000057', 'PGS000058', 'PGS000059')\n",
-    "f$basics = c('age_at_recruitment','sex', 'ethnic_background',\"townsend_deprivation_index_at_recruitment\")\n",
-    "f$questionnaire = c('overall_health_rating','smoking_status')\n",
-    "f$measurements = c('body_mass_index_bmi','weight',\"standing_height\",'systolic_blood_pressure','diastolic_blood_pressure')\n",
-    "f$labs = c(\"cholesterol\", \"hdl_cholesterol\", \"ldl_direct\",\"triglycerides\")\n",
-    "f$family_history = c('fh_heart_disease')\n",
-    "f$diagnoses = c(\"myocardial_infarction\", \"coronary_heart_disease\", \"stroke\", \"diabetes1\", \"diabetes2\", \"chronic_kidney_disease\", \"atrial_fibrillation\", \"migraine\", \n",
-    "                      \"rheumatoid_arthritis\", \"systemic_lupus_erythematosus\", \"severe_mental_illness\", \"erectile_dysfunction\")\n",
-    "f$medications = c(\"statins\", \"antihypertensives\", \"ass\", \"atypical_antipsychotics\", \"glucocorticoids\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T14:17:10.758624Z",
-     "start_time": "2020-11-04T14:17:04.775Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "processing file: table1_union.html.Rmd\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "  |......................................................................| 100%\n",
-      "  ordinary text without R code\n",
-      "\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "output file: table1_union.html.knit.md\n",
-      "\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "/home/steinfej/miniconda3/envs/python/bin/pandoc +RTS -K512m -RTS table1_union.html.utf8.md --to html4 --from markdown+autolink_bare_uris+tex_math_single_backslash --output /data/analysis/ag-reils/ag-reils-shared/cardioRS/data/2_datasets_pre/cvd_massive_from_birth/table1_union.html --lua-filter /home/steinfej/miniconda3/envs/python/lib/R/library/rmarkdown/rmarkdown/lua/pagebreak.lua --lua-filter /home/steinfej/miniconda3/envs/python/lib/R/library/rmarkdown/rmarkdown/lua/latex-div.lua --email-obfuscation none --self-contained --standalone --section-divs --template /home/steinfej/miniconda3/envs/python/lib/R/library/rmarkdown/rmd/h/default.html --no-highlight --variable highlightjs=1 --variable 'theme:bootstrap' --include-in-header /tmp/RtmpTrIitW/rmarkdown-str5f0b16f2aa34.html --mathjax --variable 'mathjax-url:https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML' \n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Output created: /data/analysis/ag-reils/ag-reils-shared/cardioRS/data/2_datasets_pre/cvd_massive_from_birth/table1_union.html\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "library(arsenal)\n",
-    "table_one = tableby(sex~., control=mycontrols, data=data %>% select(all_of(c(f$pgs, f$basics, f$questionnaire, f$measurements, f$labs, f$family_history, f$medications, f$diagnoses))))\n",
-    "write2html(table_one, glue(\"{dataset_path}/table1_union.html\"));\n",
-    "#summary(table_one, text=TRUE)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# BASELINE"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T14:17:10.778888Z",
-     "start_time": "2020-11-04T14:17:08.887Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "options(repr.plot.width=20, repr.plot.height=8)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T14:17:10.814098Z",
-     "start_time": "2020-11-04T14:17:09.367Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "base_size = 25\n",
-    "title_size = 35\n",
-    "facet_size = 25\n",
-    "geom_text_size=7\n",
-    "library(ggplot2); \n",
-    "theme_set(theme_classic(base_size = base_size) + \n",
-    "          theme(strip.background = element_blank(), plot.title=element_text(size=title_size, hjust=0.5), \n",
-    "                axis.title.y = element_blank(), axis.text.y = element_blank(), axis.ticks.y = element_blank(), axis.line.y = element_blank(), \n",
-    "                strip.text.x = element_text(size = facet_size))) "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T14:17:12.517888Z",
-     "start_time": "2020-11-04T14:17:11.602Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "library(ggplot2); \n",
-    "theme_set(theme_classic(base_size = base_size) + \n",
-    "          theme(strip.background = element_blank(), plot.title=element_text(size=title_size, hjust=0.5), \n",
-    "                axis.title.y = element_blank(), axis.text.y = element_blank(), axis.ticks.y = element_blank(), axis.line.y = element_blank(), \n",
-    "                strip.text.x = element_text(size = facet_size))) "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T14:17:12.887887Z",
-     "start_time": "2020-11-04T14:17:11.969Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style>\n",
-       ".list-inline {list-style: none; margin:0; padding: 0}\n",
-       ".list-inline>li {display: inline-block}\n",
-       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
-       "</style>\n",
-       "<ol class=list-inline><li>White</li><li>Black</li><li>&lt;NA&gt;</li><li>Asian</li><li>Mixed</li><li>Chinese</li></ol>\n",
-       "\n",
-       "<details>\n",
-       "\t<summary style=display:list-item;cursor:pointer>\n",
-       "\t\t<strong>Levels</strong>:\n",
-       "\t</summary>\n",
-       "\t<style>\n",
-       "\t.list-inline {list-style: none; margin:0; padding: 0}\n",
-       "\t.list-inline>li {display: inline-block}\n",
-       "\t.list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
-       "\t</style>\n",
-       "\t<ol class=list-inline><li>'Asian'</li><li>'Black'</li><li>'Chinese'</li><li>'Mixed'</li><li>'White'</li></ol>\n",
-       "</details>"
-      ],
-      "text/latex": [
-       "\\begin{enumerate*}\n",
-       "\\item White\n",
-       "\\item Black\n",
-       "\\item <NA>\n",
-       "\\item Asian\n",
-       "\\item Mixed\n",
-       "\\item Chinese\n",
-       "\\end{enumerate*}\n",
-       "\n",
-       "\\emph{Levels}: \\begin{enumerate*}\n",
-       "\\item 'Asian'\n",
-       "\\item 'Black'\n",
-       "\\item 'Chinese'\n",
-       "\\item 'Mixed'\n",
-       "\\item 'White'\n",
-       "\\end{enumerate*}\n"
-      ],
-      "text/markdown": [
-       "1. White\n",
-       "2. Black\n",
-       "3. &lt;NA&gt;\n",
-       "4. Asian\n",
-       "5. Mixed\n",
-       "6. Chinese\n",
-       "\n",
-       "\n",
-       "\n",
-       "**Levels**: 1. 'Asian'\n",
-       "2. 'Black'\n",
-       "3. 'Chinese'\n",
-       "4. 'Mixed'\n",
-       "5. 'White'\n",
-       "\n",
-       "\n"
-      ],
-      "text/plain": [
-       "[1] White   Black   <NA>    Asian   Mixed   Chinese\n",
-       "Levels: Asian Black Chinese Mixed White"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "unique(data$ethnic_background)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Basic Information"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T14:17:13.873348Z",
-     "start_time": "2020-11-04T14:17:12.784Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "temp = data %>% select(all_of(c(f$basics, f$questionnaire))) %>% select_if(is.numeric) %>% pivot_longer(everything(), names_to=\"parameter\", values_to=\"value\") %>% mutate_at(vars(parameter), list(~ factor(., levels=unique(.))))\n",
-    "labels = c(age_at_recruitment = \"age\", townsend_deprivation_index_at_recruitment=\"townsend\")\n",
-    "plot_age_te = ggplot(temp, aes(x=value))  + \n",
-    "    geom_density(adjust=1.5, fill=\"gray70\")+  facet_wrap(~parameter, ncol=1, scales = \"free\", labeller=labeller(parameter=labels))+\n",
-    "    scale_y_continuous(expand=c(0,0))+ xlab(\"\") "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T14:17:14.547997Z",
-     "start_time": "2020-11-04T14:17:13.377Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "`summarise()` regrouping output by 'parameter' (override with `.groups` argument)\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "temp = data %>% select(all_of(c(f$basics, f$questionnaire))) %>% select_if(is.factor) %>% select_if(is.factor) %>% drop_na()  %>% pivot_longer(everything(), names_to=\"parameter\", values_to=\"value\",values_ptypes=list(value=\"character\")) %>% \n",
-    "    group_by(parameter, value, .drop = TRUE) %>% summarise(count=n(), ratio=round((100*n()/502504), 1)) %>% ungroup() %>% mutate_at(vars(parameter), list(~ factor(., levels=unique(.))))\n",
-    "plot_seth = ggplot(temp, aes(x=value, y=ratio, fill=parameter))+ \n",
-    "    geom_bar(stat=\"identity\", position=position_dodge(width = 0.8), fill=\"gray70\", width=0.5) +\n",
-    "    geom_text(aes(label=paste0(ratio, \" %\")), vjust=-1, size = geom_text_size) + \n",
-    "    scale_y_continuous(expand=c(0,0), limits=c(0, max(temp$ratio)*1.2))+ xlab(\"\") + \n",
-    "    #theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) + \n",
-    "    facet_grid(~parameter, scales = \"free\", space=\"free\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T14:17:16.659027Z",
-     "start_time": "2020-11-04T14:17:14.329Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Warning message:\n",
-      "\"Removed 623 rows containing non-finite values (stat_density).\"\n"
-     ]
-    }
-   ],
-   "source": [
-    "options(repr.plot.width=20, repr.plot.height=8)\n",
-    "plot_title = ggplot(temp, aes(x=value))  + ggtitle(\"Basic Information\")\n",
-    "title <- ggdraw(get_title(plot_title))\n",
-    "plot_basics_raw = plot_grid(plot_age_te, plot_seth, ncol=2, rel_widths=c(1, 4), align=\"h\", axis=\"lr\")\n",
-    "plot_basics = plot_grid(title, plot_basics_raw , ncol=1, rel_heights=c(0.1, 0.9), align=\"v\", axis=\"lr\") "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T14:17:18.391317Z",
-     "start_time": "2020-11-04T14:17:17.305Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "options(repr.plot.width=20, repr.plot.height=8)\n",
-    "temp = data %>% select(all_of(f$pgs)) %>% pivot_longer(everything(), names_to=\"parameter\", values_to=\"value\") %>% mutate_at(vars(parameter), list(~ factor(., levels=unique(.))))\n",
-    "plot_pgs = ggplot(temp, aes(x=value)) + ggtitle(\"Polygenic Scores\") + \n",
-    "    geom_density(adjust=1.5, fill=\"gray70\") +\n",
-    "    scale_y_continuous(expand=c(0,0))+ xlab(\"\") + \n",
-    "    facet_wrap(~parameter, ncol=4, scales = \"free\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T14:17:19.147957Z",
-     "start_time": "2020-11-04T14:17:17.792Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "options(repr.plot.width=20, repr.plot.height=8)\n",
-    "temp = data %>% select(all_of(c(f$measurements, f$labs))) %>% pivot_longer(everything(), names_to=\"parameter\", values_to=\"value\")  %>% mutate_at(vars(parameter), list(~ factor(., levels=unique(.))))\n",
-    "plot_meas = ggplot(temp, aes(x=value)) + ggtitle(\"Measurements\") + \n",
-    "    geom_density(adjust=1.5, fill=\"gray70\") +\n",
-    "    scale_y_continuous(expand=c(0,0))+ xlab(\"\") + \n",
-    "    facet_wrap(~parameter, ncol=5, scales = \"free\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T14:17:19.829906Z",
-     "start_time": "2020-11-04T14:17:18.264Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "temp = data %>% select(c(any_of(f$diagnoses))) %>% \n",
-    "    pivot_longer(everything(),names_to=\"category\", values_to=\"Value\") %>% \n",
-    "    group_by(category) %>% summarise(ratio = sum(Value==TRUE)/n(), .groups = 'drop') %>% mutate(category = fct_reorder(category, ratio)) %>% mutate(ratio=ratio*100)\n",
-    "plot_conditions = ggplot(temp, aes(x=category, y=ratio)) + ggtitle(\"Conditions\") + \n",
-    "    geom_bar(stat=\"identity\", position=position_dodge(width = 0.8), fill=\"gray70\", width=0.5) +\n",
-    "    coord_flip()+xlab(\"\") +\n",
-    "    ylab(\"Prevalence in [%]\") +\n",
-    "    scale_y_continuous(expand=c(0,0), limits=c(0, max(temp$ratio)*1.3))+\n",
-    "    geom_text(aes(x=category, y=ratio, label=glue(\"{round(ratio,1)}%\")), position = position_dodge(0.8), hjust=-0.1, size = geom_text_size)+ #nudge_y=+3000, \n",
-    "    scale_x_discrete()+\n",
-    "    theme_classic(base_size = base_size)+ theme(strip.background = element_blank(), plot.title=element_text(size=title_size, hjust=0.35))#, strip.text.x = element_text(size = 16))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T14:17:20.135005Z",
-     "start_time": "2020-11-04T14:17:18.560Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "temp = data %>% select(c(any_of(f$medications))) %>% \n",
-    "    pivot_longer(everything(), names_to=\"category\", values_to=\"Value\") %>% \n",
-    "    group_by(category) %>% summarise(ratio = sum(Value==TRUE)/n(), .groups = 'drop') %>% mutate(category = fct_reorder(category, ratio)) %>% mutate(ratio=ratio*100)\n",
-    "plot_medications = ggplot(temp, aes(x=category, y=ratio)) + ggtitle(\"Medications\") + \n",
-    "    geom_bar(stat=\"identity\", position=position_dodge(width = 0.8), fill=\"gray70\", width=0.5) +\n",
-    "    coord_flip()+xlab(\"\") +\n",
-    "    ylab(\"Prevalence in [%]\") +\n",
-    "    scale_y_continuous(expand=c(0,0), limits=c(0, max(temp$ratio)*1.3))+\n",
-    "    geom_text(aes(x=category, y=ratio, label=glue(\"{round(ratio,1)}%\")), position = position_dodge(0.8), hjust=-0.1, size = geom_text_size)+ #nudge_y=+3000, \n",
-    "    theme_classic(base_size = base_size) + theme(strip.background = element_blank(), plot.title=element_text(size=title_size, hjust=0.35)) + \n",
-    "    scale_x_discrete()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T14:17:28.906077Z",
-     "start_time": "2020-11-04T14:17:19.409Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Warning message:\n",
-      "\"Removed 60836 rows containing non-finite values (stat_density).\"\n",
-      "Warning message:\n",
-      "\"Removed 241285 rows containing non-finite values (stat_density).\"\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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ffoA9fESp8u74Blv8PWbRkK7RkrXN4p2lgO/cvaAg7tnFfAsumhDuvOtrYc+n6m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osOIgKDQcZv2eEkGhkSwjogRWSeW6Sbu/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\nDm8c2Lx6SuWmw7ary06pHxrO8n7BoRnNPXGi0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/K0VMkVjoetNyn3eD75Ql8lbR0VXSyIgjwdZX/M8bDB1PcP//qiktcblh7kMq3ECVh2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\ndplHN/gcoY4FE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rTDfBZ/OUyqCO25G2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HvYz0xvuqpPR/tBqcj0NMubNLTagPWWanst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\nAQsAACBhBCwAAICE/X810Aq+gpFVmwAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "plot without title"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 1800,
-       "width": 1200
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_width=20; plot_height=30; plot_dpi=300\n",
-    "options(repr.plot.width=plot_width, repr.plot.height=plot_height)\n",
-    "plot_1 = plot_grid(plot_basics, plot_pgs, plot_meas, ncol=1, rel_heights=c(2,1.3,2), align=\"v\", axis=\"lr\")\n",
-    "plot_2 = plot_grid(plot_conditions, plot_medications, ncol=1, rel_heights=c(3,1.8), align=\"v\", axis=\"lr\")\n",
-    "plot_desc = plot_grid(plot_1, plot_2, ncol=1, rel_heights=c(5,3), align=\"v\", axis=\"lr\")\n",
-    "plot_desc\n",
-    "\n",
-    "plot_name = \"1_dataset_characterization\"\n",
-    "ggsave(filename=glue(\"{dataset_path}/{plot_name}.pdf\"), plot=plot_desc, width=plot_width, height=plot_height, dpi=plot_dpi, device=\"pdf\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# OBSERVATION TIME"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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FjH23VksGpeeTzrEur8pP00AOZHYAGAZbxWXYYq95XHM1Amaz8PgPkRWABgFS+l\nukZr55XH83CN+H9qPxOA6RFYAGARj1aIm6RdV15TpLv2UwGYHoEFANawIyHhLu228smQvdpP\nBmB2BBYAWML97qQ52mXlty6h3s/aTwdgcgQWAFjBbKm8TDuscl0hd2o/H4DJEVgAYH4nbpK0\nB7SzKs/etIS3tJ8SwNwILAAwvSODpP4m7aoKNlU6czx3oCgEFgCrW9y2bTvtZYiuny+WJju0\nmypUB9mo/awApkZgAbC6W0TitJchqr5oK+c8rF1U+TyYlPqV9vMCmBmBBcDq7B5Y/06XCzO1\ng+oUI+Ua7ScGMDMCC4DV2TywXq4hfXRP71ygg+nytPZTA5gYgQXA6uwdWI9UdI3RjqkCLXY1\nPaz95ADmRWABsDpbB9bG+IQp2ilViN6c9BkoHIEFwOpsHFjZM6TifO2QKszeGnEvaT9BgGkR\nWACszr6BdWyMVLtPu6MKN8/V/DftpwgwKwILgNXZNrB+vkQamurwovn1lOnazxFgVgQWAKuz\na2B9fKacvVe7oYq0OzXhDe1nCTApAguA1dk0sN6oKz3Nd/irUNPlrKPazxNgTgQWAKuzZ2A9\nkewapt1PxesmU7SfKMCcCCwAVmfLwFoXn3Cbdj2FYXcN97PaTxVgSgQWAKuzYWCdmCyVTHt4\nhhAL3fW+1X62ADMisABY3fPr1q3XXobI+m2I1FqjnU5hGiL9tZ8uwIwILAAwmc/aSfPt2uEU\nroPN5UHtJwwwIQILAMzljfpy/gHtbgrfhgqV3tF+ygDzIbAAwFQOVnJdlaVdTSVxmzT7UftJ\nA0yHwAIAM1nhTpiknUwl1Ef6nNB+2gCzIbAAwDyOXCupS7WDqaQyW8pc7ScOMBsCCwBM4/OO\nkm7qsw8WbGuq+zHtpw4wGQILAMzi1XrSaZ92LZXG4viq/9Z+8gBzIbAAwCR2lncNtdTu7XnG\ny+lfaj99gKkQWABgCscnu5Lu0g6lUusv5/ys/QwCZkJgAYAZfNNTaq3SzqTSyzpfeh3Vfg4B\nEyGwAMAE3mgkrS1z9PaCZJ4lQ7O1n0XAPAgsANC3rYJrwCHtRiqbPelyK4UFBBBYAKDtyI2S\ndKd2IJXZljpyI4UF+BFYAKzu+XXr1msvQ5l8fJ7UXa2dRxGwraEMPab9ZAImQWABsLpbROK0\nl6EsnkyTLpY8+tUptjeSqykswEBgAbA6awfW8Wnu+NHaZRQpOxtLH078DHgRWACsztKB9dkF\nkrZIu4siZ3crackx3YGTBBYA67NyYD1VU9rt1K6iSMrsLVUf135WARMgsABYnXUD69hUd9wI\ni54cp1A3xcfNO679zALqCCwAVmfZwPrwPKluo82DAYuqyjn/KPGT8dPfMzfMmzTm6oEDB90w\ndfHGJ95nb3lYG4EFwOqsGlj7qsh5tto8GLCtsyRMPxL28/DL8/df37Gm5Bff+LJZj3wRxecf\niCoCC4DVWTOwfhktieO0UyhapqVK850nin8Sst958LqW7pyaclVv02v4hGmLHtiQ4755U28a\n3LVJsjezGoza+WX0/xhA5BFYAKzOkoH1WjM5bZV2B0XP7ovd0mxrkVv5jr20pG9qTkIlNut9\n07L9Bd3JprsHnV0hJ74yZv4zVn8WIGIILABWZ8HAOrEo0XVpgVFhG2svjJP06W8V/Ph/fGLG\nhRVz4qpal9FLMou8m4OLh7aKF2lyZyH3BJgVgQXA6qwXWB9fKCkzYhQ6ejZclCByxqxnvg95\n7N88t3x4y7icuKrbc+KG8O5o18QOiSLtVn+r9OcCSoPAAmB1lgusPVWl7dboxo057J54TnxO\nSqVfOWHm8s0PLp81acg5Vb07VpVrcfnd20t0T3tvy3BJuaEvav/pgLARWACszmKB9cNwSRxj\nt4NfFWrn5P5tKgZ9NbB2274TVx8szT1tGl5bpP228L+dCKgisABY3Zz09CbayxC+PzeQ9DWR\nzhiTe3Dp7DvGj58ye/H6UqVVQNasti6ps/gn7T8hEA4CCwBi5/c73K4ri96tG0VY26ecpM74\nRvvPCBSPwAKAmHkzQ2rM144Ua9s+uKIkT/u++Oca0EVgAUCMnFiaJN12axeK5e0ZUVmqzvtZ\n+68JFI3AAoDY+KibJE/RrhNb2Du0otRYw8kKYWoEFgDExNYUabtFO03sYtegctLyUe0/KVAE\nAgsAYuDrKyVpnGMOzhADmy50Sc+3tf+sQKEILACIvkdrSbN12k1iMytaS8LtHLMBZkVgAUC0\n/TTGFT+sTIeAQkHurC51diHecCkAACAASURBVGRr/3WBAhFYABBlf24k9Vdox4gtPTwoQbr9\nn/bfFygIgQUAUfXbbW5Xv/3aKWJX686WcrM4fQ5MiMACgGh6pSXHFo2qyVWl2Z+0/8rAKQgs\nAIie36fFy0V7tRvE3nb3crnG/KD9lwbyIbAAIGreOFOqzdIOEPtbVF/qZGr/rYFQBBYARMnR\nWYlyIafGiYEDQ+Jl4Jfaf28gGIEFwOrurFo1TXsZCvJaG0mdpp0eTrGqqaTt0P6LA0EILABW\nd4tInPYynOq3qfFy4S7t7nCOQyMTpe+n2n91IBeBBcDqTBlYf2omaTO1o8NZ1raQKpu1/+5A\nAIEFwOpMGFjfXedyXbJHuzicJmt0Oen9ifbfHvAhsABYnekCK3tHTam/SDs3nGh9K6nykPaf\nHzAQWACszmyB9dYFkjA0U7s1nClrTBIfYsEcCCwAVmeuwPrp9gQ5e512aDjXhtZSZZP2iwAg\nsABYn5kC6/iG2lJ9qnZkOFrWDUnS+2PtFwJAYAGwOhMF1hOtJXHgPu3EcDrvh1jsiQVtBBYA\nqzNNYL18kbjOf1A7L2DsiXUJH2JBF4EFwOpMElhv9HXJGcu02wKGDa0lZWO29ksCjkZgAbA6\nUwTW6wNd0pjzOpuGd0+siz/SflXAyQgsAFZngsD6Yy+XNJqWpV0VCLKxjVRey4dYUENgAbA6\n7cD6fVcHkRbkldlkja8gF36g+cqAoxFYAKzu9b179+n99s9m1RbXOQu0awIFeOhsqbTqhN5r\nA45GYAFAqR1/fGCiJPVeo10SKFjWhIrS9T3tVwmcicACgFL697R6InVH79bOCBRuczupsIwP\nsaCAwAKA0vhk2TkiST0WahcEijEpWTr+S/vVAgcisACgxD5a0dUt7jYT9mrXA4q39VxJWnhc\n+yUDxyGwAKBk3prfziWuZqO3aJcDwjS5spzLh1iIMQILAML325M3NxJxtxr9kHY0oAS2d5Sk\nxXyIhZgisAAgTP9e1buCSNJ5E7ZrBwNKanKydPy39gsIjkJgAUAYvtk7upGI1L5s1n7tVkBp\nbO0gFVZzYHfEDoEFAMX4+bHJbd0iSe3HrtfOBJTerRWl58faryU4B4EFAEX46fE7O8aLxDUf\nsiBTuxBQNg+dKVV2ar+g4BgEFgAU4ssDt7bLiSvX6f1ncDwGO8gakyhDf9B+WcEhCCwAONWJ\ntzaMbC4i7saXT9+l3QWImDXp0vAv2i8uOAOBBQChPjt4V4+UnLgq13rIHD65spnMK1xxMzhg\nA2KAwAJgdU8vWLAwQnf1+WNz+tXLaSup0XXMsoPaMYBomJcmXdnXHdFHYAGwultE4sp8J8ff\n3TP1ktretqp89pAZHOjKxna2lzRPBF53QJEILABWV9bA+vqZFdefU97bVlXaDpz6oPb4j2jL\nuj7edevRSL38gIIRWACsrvSB9esrD03qaXxsFVe/64hZnFzQKZbXlo5sJkR0EVgArK40gXXi\n/f2zrmwS522r1LP6T1xxQHvIR0ztPleqPxWNFyMQQGABsLoSBtZ3f1o1ukNFb1qVb97rhnt3\nao/10JA1Ki5uDmfOQRQRWACsLuzAOvb27jsvre9NK3e9zsOnbczSHuWhaEGq9Psxyi9NOBmB\nBcDqwgmsDx9dOCwj0fiS4Jn9blnOCZvh2dpSWrwbixconInAAmB1RQbWsf87uODa9snetIpP\n73btbHZkR0Bmb0nJiuELFc5CYAGwuoID66d/Hlo54ZImCcYWwbodh0xZw4FDkc+EBPe97IiF\n6CCwAFhdcGAd/+zvj2ycc8NlbaqJoWLjrkOnrOI7gijY0lQZ+pvmixf2RWABsLqcwHI/smn+\nrcN6tKrp8nWVJNZu03PopMUckR1F29xYOnym/QqGLRFYAKzqi1cPrZp6Tc9W5SUgsUazDr2u\nHj99JWGFMO3vIvX+rv1Shh0RWACs5rtX984fc3HzpEBVVRRxXT3u7kVr92oP1rCgrKGuSuzq\njsgjsABYxpF/7Jt37Xlpvq4q36Bd7+G3zluz2zMuI+Ms7VEaFnZHYtxK7dc27IfAAmABXz+3\nflKvdLc3rFw1Mnpde+dKDsCOiFmUIjcd136Nw24ILABmduKDx5aO7uL7SmClZj1G3HU/3whE\nxG2oJ31+0X6tw2YILADm9MtrO2cMOjPJ96HV2f3G38uO64iaXa2l3ZfaL3nYC4EFwGw+emr1\n+B6nGR9aJTToNOj25fu0h1/Y3oGukv5/2q982AqBBcA0fn9z79yrzq5opFVKq4tHzVh/SHvc\nhVNkDZBqz2uvAbATAguACRx5fcfUy5vGG2cMrHfegFuW7NIeb+E449zlM7VXBNgIgQVAVfb7\nB+YMaGakVfkmF464a22m9jgLp5pWLm6N9voA+yCwAGj55YW14zome9MqqWnP6+Y8pD2+wukW\nJ8vdnPsZEUJgAVDw5ZMLBjeP835BsE7noXdvyNIeWQGvtTXk2mPaKwdsgsACEFPZ7++7+9K6\nxsdWzS4Zv4QvCMJMtqRLbw6IhYggsADEyuFXNtzUpbLxFcGMKyc/wDcEYT57zpQOX2uvKbAF\nAgtA9GV/kDVvkG+TYO2Ow2Zs0h5FgcJkdpVmH2qvMLADAgtAVH321PLrO/j2ZG/Wa9yivdrj\nJ1C0rH5S+w3t1QY2QGABiJLPn145pnOqN63cdTtdPXU9e7LDGq51pTyrvfbA+ggsABH3zR/v\nH9u1mu8sgu0GTFq5P7rjYd+chIvub4CzTIovt0d7JYLlEVgAIujnlzbc0r2mP62unLgsJl8S\nJLAQYbOS3Pdpr0uwOgILQESceO/hmVec7va2VdpZ/SfEJq18CCxE2rIUuZNDjqJMCCwAZfXD\nc6vHnFvJm1YVW14ybkHMTyNIYCHi1tWSEUe11yxYGoEFoPSOvrn7rj6NfPuxdx4+7UGdsZDA\nQuRtPV0u4ZCjKAMCC0Bp/PzarhkDWiR426pS6z43L4vyfuxFIrAQBXszpP1X2usZLIzAAlAS\nRz/667Y5155vnOtGyp3ebeSszdoDIYGFqMg8X5q8r73CwboILADF+vXjN57ZvWrG6D4d6hh7\nsYtUbd1r1AyznKOZwEJUZPWXmq9or3ywLAILwCm+/+Dvzx7YtGLObaMH9uzQvGaiBLirNe/U\nb/S01eY6RTOBhSi53lXpce21EVZFYAEwfPf2s9uXTx3V57xm1d0SrFLN09t07DXo+ltnrdpq\nzvMzE1iIlikJCZu0V01YFIEFONrxT57ft2LSVV0al88LqtpN23a9ZOCI8XfMWrR6027tES4M\nBBai5t6KrtnaaymsicACnOjYJ8/vXT5xYMe6cf6qqtywbfcBoyfPW23SD6mKRGAhelanyXXH\ntFdYWBGBBTjIt2/9Ycu8m/q1r+PvKldqs459R01euFHzIAtlR2AhijY3lEt+1l51YUEEFmB3\nxz977ZGNc8Zf3rFhkv/jqrhqzb1dteihTO2xKzIILETTngw561Pt1RjWQ2ABNvXF649smDXm\nsrNrB7YCSpWG5/QYMm7asi0W3ApYpE3Ll6/QXgbYWGY3Oe1N7RUalkNgAbZy5IO/7Fo6cVCn\nBoFDK8RXa9rhkqvHT1+22SYfVwExlzVEUp7WXrdhNQQWYAO/f/zioQdmXn9pmxr+rHJVTW93\nydAJM1dtM8mxQAFLmxCfuEV7NYfFEFiANf3wv3/++eDmpXeNuaJL86qBjYCJtVp2vfz6KbbZ\nuQowizkVXDOytdd6WAqBBVjAL5++88JTex9cOW/KuGuu7NG+Zf2U4COBVqjbsmOfYRNnrt6h\nPQgBtrWqulx9RPudAFZCYAHm9Mt//nZww7xbr+l9XrMa8RKqUlr9Zmd1vvjKETdOmbdqs7UP\nsQBYxJbG0vlr7fcFWAiBBZjJz+88u23xxMFdmlbMy6mKtRpndOzZb8io8XfMuHf5+q3mOg8g\n4BQPnyuN39V+i4B1EFiAvsPvPbd7+e3DLmiem1UV67fu2nf4hGmL1u5gL3XAHLL6S1W+TIhw\nEViAlsPv/2X3ituHdWuRkpdVGRcOHDN10YNs9QPM6Kb4hLXabxywCgILiLHf33tm8+zre7eu\nFsiqpHqtu/W/fvKC9WQVYHLzKsnE49rvIbAGAguIlaPvHlx4XbcG/gOrJ9ZpdUFOVs1fyy5V\ngHWsrSO9vtd+L4ElEFhADPznwNxBLROMsEpp1nXAuBmrdmqPEwBKY2eGNGNXd4SBwAKi6987\nbutWxVtW5RqfP2zKyr3awwOAMjnYR6o8rv2+AgsgsICo+fGJmb1SvW1Vq+Owu9fzbUDAHibE\nxy3kqO4oDoEFRMWH28a2due0VVrHEfN2a48Hdjd9wICB2ssAJ1lURQb9ov0eA7MjsIBIy35r\nzVX1ctoqoUX/u7ZojwSO0FfErb0McJTNzaT1e9rvNDA5AguIpGMvLe3nPf5CxXYjFh3QHgQc\ng8BCrB24SKo+qv12A3MjsIBI+fHJmd29h2JP7Tp2FTtcxRKBhdgbH++exhGxUAQCC4iE/9t6\n45neA1zV7TlxvfYbv/MQWFCwpLp0/1L7nQcmRmABZfRZ1vRLvFsF45tdPnWb9nu+MxFY0LCz\nrdT5i/b7D8yLwAJK78MD0y+r6z0OQ1qnUYs40Y0aAgsqsoa64+eymRCFILCA0vjpxfU3nW8c\nP7Ry2yHT+K6gLgILSualSrdPtN+NYFIEFlAyP7606Y5LG7py0spV89yrp23SfocHgQU928+R\ntCztNyWYE4EFhOvjZ+6/qWd944SCya1637hoj/Z7O/wILKjJui7edQMHHUUBCCygWN++uG36\n4LMq+c7V3OqSMXPZJGguBBYULa8vjV/QfpOCCRFYQKFOfPznTdOvam+cTlDi65175YRFO7Xf\nzFEAAgua9vdxxU/7XfvtCqZDYAGn+uyF3QvG9mpWziiruNpnXTpm1vqD2u/iKBSBBV1z06TV\ny9pvWzAbAgvIdfjdpzfNGtmjaZIRVlK+4bmXj521PlP7zRvFIbCgbFd3ibv9sPY7GMyFwAJO\nfvbi3iUT+p1d3ddVUrFh+z6j7lzO1kDLmNi5cxftZYDDza4hTZ7WfiuDqRBYcLDvX9mz4IaL\nAx9Yxdds1W3QuBmr+XIggBLbe6lLrv5c+00NJkJgwYl+eW33nKH+ndelQsP2l42asngL52cG\nUAZL0iVlFQd2RwCBBWf55s9rJ1zUwHuYUHHXzOg1cipbAgFExsHrK0jGn7Tf5GAWBBac4tvn\n1t50YQ3jM6vKZ1w8atpadl4HEFlburrkyg+03+xgDgQW7O+b59bd0qO2kVZpGX3HL+QzKwBR\nsqiJJE35XvtND2ZAYMHGTnzw+PKx3XyfWqVm9Ltp8W7tN18ANpc1MVVSF/+m/e4HfQQW7OjI\nu4+vub1fy3L+T636jF+4S/tdF4BD7BteQU576Jj22yC0EViwkaP/+9v++24f3LGOsRO7JDXq\nNGjSMo66ACC2dvSLlybbT2i/I0IXgQWr+/rd5w89OG/isO5nBA4U6kptccHgCfdu1n6TBeBU\nD3aPk5Z7SSxHI7BgLd9/85+3X30qc9ua+VNuGNKrfZM0twSUq92ic9+Rk+Zv5OuBALStvcAl\nrfeQWA5GYEHX0Y9fenTrsrtvHjmwR+e2rdPT01Or+tVMD9WwatVkyceVXLvZORf2G37ztEVr\n92m/nwJAkPu7uuSMXSSWYxFY0PHZ8zsX3HhZm5qu4F6qWKlKrSA1KwXxzklv3DLj3M69+gwc\nMW7StPkrN/KlQAAmtuZ8l7TYwcHdHYrAQmwd+dcj991yWcvyvqJKrNmiU5+rx06eu3ztZr7n\nB8BuHujmlmZbSSxHIrAQI9++smf+9d1O8+0ylXhauz7XT122TfvND/awc8OGDdrLABRs7YVx\n0nQ7ieVABBai68SnL+xdNqFvmxTfR1YpzS4YcuuirdpvebCXviJu7WUACrO+e5w038m+WI5D\nYCEaDn/0YtaGOTf1P69egi+sEuqedcnIqSs5JhWigcCCua3r5pZWmdnab8yILQILEXLsi3f+\nkrlxwW0jLu3QqGJgr3V3apMOfUbdsWhLlvYbHOyMwILZre3qkg5Pa79NI6YILJTa4U/f/qtn\ny4oZN1/dq116StCxE1JOa9XlsqE3T1+65ZD2uxocgcCC+a1qL9Ljde23bcQQgYWw/PbdB/96\n9dnH9j5434IpN424ske7JjXKBR1fwZ1Sr0X77v1HTLh7wZrtfFqFGCOwYAWLW4l75Cfab+aI\nGQILBfn5vy8/tnPNvMljBl/atW16nar5D/ApruRap7fp2LP/8HGTZy9bz/GooIrAgjVMrycV\nZvyq/f6OGCGwkOfoB89tX3BT/44Nk4JbKrFSrdMaZ7TrfPFlA4ZfN/6O6fcsX7eFo6bDTAgs\nWMTBcSnSYL/2Wz1ig8BCjl9e33fvqG4N4/1JldKgTdfLrhozafqCVRt2HdR+RwKKQ2DBMnb3\ni5OL3tV+z0csEFjOlv3hEyvGXljX11WVm3TuP/qupZs5VzIshsCChaxuLeVm/6795o/oI7Cc\n6sg/984ecpbvjDVVW1907dRVe7XfdoBSIrBgKZOrSqsXtccARB2B5Tw/vLRpSt8mcd6yij+t\n46BJy9hFHRZHYMFadvV0xd3Czu52R2A5yadPr76xex3jQ6sKTbqPmLaO/atgCwQWrOae2tL0\nJe0hAdFFYDnCif94Fo7s4DsWaNU2vcfM3aT97gJEEIEFy3n4Ulf8zGPaYwOiicCyu08eXzj8\n7ArGwUBrt79iwhK2B8J+CCxY0OxUaf9v7RECUURg2dfxt7ZN6pZq7GpVv9OQO+47oP12AkTJ\nzg0bNmgvA1BSOztL5T3aAwWih8Cypw92TepSydtWNdoPvON+jrsAAOYzIVHGH9EeLhAtBJbt\nHHtp2ZW1vSezqXv+qHt2ar9/AAAKs6qetP2P9qCBKCGwbOX4SwsvSfYeMrT9NXPZ2QoATG7f\n+VLtGe2RA9FBYNnHf9cP8O5xVbP7LWu13zMAAGEZGx+/Wnv0QFQQWPZw/C9TWuXEVeqFtz6k\n/W4BAAjfvGS54aj2GIIoILBs4LesEdVEEjJGrdJ+owAAlNCGBnLBd9rjCCKPwLK6I/sHJ4uk\n9JjKqQQBwIr2tpOWH2mPJYg4AsvSTjw7qopIWp/5h7TfIAAApXSol9R5XXs8QaQRWBb24Yz6\nIlX6LMnSfnMAAJTFcFflP2gPKYgwAsuqju7p6ZakC2fz2RUAWN6k+MR92sMKIovAsqav59UV\naTp+j/Z7AgAgEuYkxW3WHlkQUQSWFb17XZIk9Vql/X4AAIiURRVdHBDLVggs63ljkFtqjNyl\n/WYAAIigFZVlvvb4gggisKzm1ctc0nAye14BeSZ27txFexmAMluTKnO1hxhEDoFlLf8e7JJm\n0/jaIBCsr4hbexmAslufJvdqjzKIGALLSj4fmyCNZmm/BQBmQ2DBJtZVk4XaAw0ihcCyjqNL\nK0ut2/j0CsiPwIJdrE2VJdpjDSKEwLKMZ8+Qitdnaq/9gAkRWLCNB6oK3yW0CQLLIr4ZKq4L\nt2qv+oApEViwjzWV3Tu0BxxEBIFlDftrSqNF2us9YFIEFmxkWfmER7SHHEQCgWUF342R+AFs\nHQQKQWDBTu5NLP9n7VEHEUBgWcAfaknjVdqrPGBeBBZs5e64lDe0xx2UHYFlesfudMcNO6i9\nwgMmRmDBXia66nykPfSgzAgss/uok1Rn7yugKAQWbOYaafmd9uCDsiKwTO7xqtJxt/a6Dpgb\ngQW76SUX/q49/KCMCCxzWxQXP057RQfMjsCC3RxsJ8OztQcglA2BZWaHr5aqbB4EikNgwXb2\nNZbp2kMQyobAMrFPzpbGm7TXcsD8pg8YMFB7GYDI2lrDtVN7EEKZEFjm9dZp0m2/9joOANCw\nKqn8y9rDEMqCwDKtv1WTAdorOABAyTRX7Y+1ByKUAYFlVvuT3OO1V28AgJrhcvav2kMRSo/A\nMqlV7qRZ2is3AEBP1gUyhK8SWheBZU4LJWWZ9roNANC0v6ks1h6NUGoEliktkCqrtNdsAICu\nLalxT2qPRygtAsuMpkv1ddrrNQBA27y4ah9qj0goJQLLhG6R2g9qr9UAAH2jpP0R7TEJpUNg\nmc+dUnez9joNADCBrM4yRntQQukQWKYzR2px+HYAgNe+02SL9rCEUiGwzGaFpG3QXqEBACax\nrkLFt7UHJpQGgWUya1xV12qvzgAA07hNzuB4o1ZEYJnLXnfl1dorMwDARC6Wa7XHJpQCgWUq\nfyxXbrH2qgxYzqbly1doLwMQNfvTZZP26ISSI7DM5PWUeM6PA5RYXxG39jIA0bO2fAV2w7Ie\nAstEPqjtmqS9HgMWRGDB5m6XMzkaluUQWObxTVMZqb0WA1ZEYMHuusst2kMUSorAMo0jXaWf\n9joMWBKBBbvbW9v1mPYghRIisMwie7icc0h7HQYsicCC7S2Lr/659jCFkiGwzGK2pO/TXoMB\nayKwYH/D5eJs7XEKJUJgmcROV9oW7fUXsCgCC/aX1Ubu0x6oUCIEljm8kFR+pfbqC1gVgQUH\n2FSpwrvaQxVKgsAyhU9qu2Zor7yAZRFYcILJ0v6Y9mCFEiCwzOC39jJCe9UFrIvAgiN0kVna\noxVKgMAyg1HSKUt7zQWsi8CCI+xOi39Je7hC+AgsE1gk6Q9rr7iAhRFYcIa5ruaHtQcshI3A\n0vdEXJVN2qstYGUEFhziEpmoPWIhbASWug+rxS/SXmkBSyOw4BD7arv/oj1mIVwElrbf2so4\n7XUWAGAFi1yNftYetRAmAkvbKDlfe40FAFhDHzYSWgaBpewBacgO7gCAsDxcx/1n7XEL4SGw\ndL1UrtJ67fUVAGAV812Nf9UeuRAWAkvVtw04gjsAIHx95BbtoQthIbA0ZV8mg7TXVQCAhTxc\n2/289uCFcBBYmhZIq0Pa6yoAwErudTX7TXv0QhgILEUvJKRs0V5TAQDWcrFM0x6+EAYCS89X\ndV1ztddTAIDF7EmL/7v2AIbiEVhqTlwkQ7VXUwCA5UyTc45pD2EoFoGlZoFkZGmvpQAA6+ks\ni7SHMBSLwNLyQkLKVu11FABgQduSK7yvPYihOASWku8bumZpr6IAAEuaKD21RzEUh8BScpVc\nob2CAgAsKkO2aQ9jKAaBpWOdNM3UXj8BuxiXkXGW9jIAMbU+sfo32gMZikZgqXinQsUN2qsn\nYBt9RdzaywDE1jUyQnskQ9EILA1H28nt2isnYB8EFpznYLo8pT2WoUgElobbpZv2ugnYCIEF\nB1rianJYezBDUQgsBU+5a+3RXjUBGyGw4ESXcsYccyOwYu+7+nGLtVdMwE4ILDjRntTEd7TH\nMxSBwIq9yzlFDhBRBBYcabJckK09oKFwBFbMbZAWh7RXS8BWCCw401myRXtEQ+EIrFj7T3LS\neu2VErAXAgvOtD6x2tfaYxoKRWDF2LHzZKL2OgnYDIEFhxoqY7QHNRSKwIqxuXKe9hoJ2A2B\nBYc6UNf9vPaohsIQWLH1SkLVHdprJGA3BBacap5kHNMe11AIAiumfm3umqm9PgK2Q2DBsc6X\nFdoDGwpBYMXUBOmtvTYC9kNgwbG2VKj8mfbIhoIRWLH0jKvWPu21EbCfe0aMuFZ7GQAdo+Vq\n7aENBSOwYujHBq4F2usiAMBGDqXLM9qDGwpEYMXQSBmgvSoCAGxlmavpEe3RDQUhsGInSxoe\n0F4TAQD20lMWaA9vKAiBFTNf14pfqb0eAgBsZkdypY+1BzgUgMCKmcEyTHs1BADYzo0ySHuA\nQwEIrFjJlPRM7bUQAGA7WU3lCe0hDqcisGLk6xoJq7VXQgCADS1ztTyqPcjhFARWjAyQEdqr\nIADAlnrIEu1BDqcgsGJjhzQ7pL0GAgBsaWdy8ifawxzyI7Bi4vNqiQ9or4AAAJsay/HczYfA\niol+Mkp79QMA2NWhRq7ntAc65ENgxcIOac4GQgBAtCxwnXlce6hDKAIrBr6qnrBGe+UDANhY\nV7lfe6xDKAIrBq5gAyEQTSunTLmzBDdf2LNOuUoNeywxrsyQEM2is4RAtG1Oqvq19mCHEARW\n9PENQiC6+oq4w77xgYsCMWWcfJ3Agk2MkLHaox1CEFhR91WNhPu1VzzA1koSWFkdRSpcNOaa\nDJfI6Jzra6/K00ukcxQXE4imzLrul7XHOwQjsKLuSrlWe70D7K0kgTVOpOkW74WpbknaGzqv\nkyRujOySAbEzUzplaw94CEJgRdseNhACUVaCwHo4RZK3+y72b9v/wZB500WuieyCAbHUTrZp\nj3gIQmBF2ddsIASirQSBdYfI8EJm7UuT+sFnZM8QmZh75WyRO4wLK3s3qJBQrfV1u3N/bGzb\ntMT4lFbX7vJdbybxnt29kuOvz7l8cNJ5NZPcFdP7rgr7sQCltj6h1o/aYx7yEFhRNoj/JAai\nrQSB1VmksK2AfcS1IPh6Tou1ClzeGScV93uC95BPmeWbsyQ1MCV1qTGhtciBFjlXr/J4NjcK\nzHMNLuljAkruSpmqPeYhD4EVXYckPbP4lQJAWZQgsNIkLef/d65cdEpmrXBJ95AJB5LFtd5/\nebzIJd5/O4qkjZiz4u5uLkmY752wNVmkyQ0zZ41tkNNcxmdY3g++JLHVmeM8npzOajLunvl3\nXpIocktpHxwQtr2pif+nPeohF4EVVd/UjF+lvcYBthd+YO0RyfDMa+MSkeojD4TMaiOJm0Nv\n3EdkcO5M8X4+NTGnmHzbBie7pK73P54Gi7Qx/iPqUFuREd4LOf82a27s5rUq59a+37EyQeqU\n5oEBJTNR+moPe8hFYEXVUBmqvb4B9ndKYK2dt2x3gbe8T6TrGJd/u13z4NvMEBmU78Y5gZSW\nZVza5pb6Of9k1RF34DOtjiIzcv4Z0Tbdv61wrrfdPN4djSXR+JqidxvjEP+tbxo2iS+7IPqy\nmsmT2uMeAgisaGIDI2vx9AAAIABJREFUIRALoYGVNSHNu9dT+vCHjKv7RwbdcoFII3eNiesP\nPHh9JZEOQXOaSNKu/PfbRGSuceEGMY62cr/ImYF5d4tcHHLjTSKnef/NCayuvinTRc4ry8MC\nSmypq8VR7ZEPfgRWFH1XO36l9toGOEBoYA3L3bO81bWzZ16dVi3oljNzJtfbaVy8P0nk3twZ\nc0UuP+V+xwdaqaW4t/iu9wvMy+mpprk3zNy9e/cGkdreyzmBNdY3dWeiyAW8ByCmesgK7aEP\nfgRWFA33fo8IQLSFBtag+rdsOvDgXd2TA1sCg27pDSz/Jj3PVSI9cmecJa5Np9zvnnKS6N2M\nuNkl5/h/IlgV40b39Kgf+E2BwJrq//kJ3o2RdXpP3RmxRwoUY3vFKl9pj33wIbCi5xFpyAZC\nIAZCA2uVf73LvPv86vFJja8P3pd9vkhiYGeo1ZK35/lDLt8OVPl0F7kx55/rRaZ4r/YJDazE\nnEl7OwRNCATW/MDPz21mTHdn3JUVkQcKFOtaTkloFgRW1PxQL2659poGOMKASpUqh3fLVSLV\nApczRZICl4eKTCrg5gt82wGbSSWj0vqJ9Lw3j7ejzhWpOHzl9oMez468wFqSdw9LBqUb+9Rn\nFLzTPRBpmXXj3tAe/mAgsKJmRO4XiACYxH6XVMy94paEwMXTxHXKLu5edUXWeTaK9DauXSVy\nZej8FTmRtsZ3cVuBgZVjxx2d4kTOLfvCA+GYJp05JaEpEFjR8igbCAHzySmmwL5WO/I+zXoo\neI/1YNeKDPMMF1lmXLv1lK8FjhDp5b+4pLDAyrEiWWRVGZccCFOGPKw9AMKLwIqS7+uygRAw\nn3653/HL+Q99aee/OP6Uj6b8tsVJg5woO8137QGR5IMh8/uKXO+/OKCIwPJcWfAmSCAK1sQ1\n+k17CMRJAitqRgjnHgPMZ7lIjYd9FzNExvmnXlxo/5ybM0dklP9ag7zzP99dv/9aI5z8uwJs\nLC9S3XshN7CyBmZ0C9zN1XlfLQSirY/coz0E4iSBFS18gxAwp/NE2u3L+TdriEjyPv/EZoGN\ngKeYkXMrcW/1X5soUmmVcemBahK33uPJia+GxrcSH6zXLEkSvbvC532CdYa4Jvsubatd0FEg\ngOjYVbnC/7QHQRBYUfJ9vbhC3q4BqNqcJpI2cPywdBHXXYGJySLbCr75oVTJ25Jo5Fliv5mL\n7uxdTmRgzvW9FUUyZt1/z+Xlktbm3GP/1VuDAmuBW1xtx02fN3VgNZGLovmggBBj5RrtURAE\nVpQM4xCjgEmtbeQ/alX5O3KnuUUeLuTmgyR4615mz8CJDOMGGYe2mhrvu1p5iXeHd+/GxKB9\nsCYn5R4iq9uBgu4ciIpDDV0vag+DILCi4pA0YgMhYFKZE86qFp/cZMj23Cl7RVyF3Xq9SHLw\n6rz80tMqxlVqcuXawPUuqXFJ6SN25txvv9TE+jNDdnLfNrxNaoK74umXnrrbOxBFc+U8DtWg\njsCKgq9rcQ5CwCZWiPTVXgagpDrIdu2REARWFAyS4dorF4DI6C6uNdrLAJTU+oS6v2gPhY5H\nYEXeTml6sPiXPwALWOUO2sUdsIz+MkN7LHQ8AiviPk1NvF971QIQEVvqiYsN/rCgvanl/6s9\nGjodgRVxvWW09poFIALmzhhaSaS/9mIApXGzXKU9GjodgRVp66V1lvaKBSACkr3HV+jEBn9Y\n0qF011+1x0OHI7Ai7L1KFTZqr1eAwwytVatOFO62jpRrMoH/XoJFzZd2J7RHRGcjsCLr2Hm5\npyoDECN9RdzaywCYTSfZrD0kOhuBFVmz5VztdQpwHAILONXGxJo/ao+JjkZgRdTLCak7tNcp\nwHEILKAAA+Uu7UHR0QisSPq1mWuW9hoFOA+BBRRgX2rSh9rDopMRWJE0Tnprr1CAAxFYQEEm\nygDtYdHJCKwIOij1H9ZenwAHIrCAgmQ1lj9pD4wORmBFzidpCRzyGVBAYAEFWuzKOK49NDoX\ngRUxJ7rLGO2VCXAkAgsoWFfZoD02OheBFTHzpC2HJAQ0EFhAwTYl1fhBe3B0LAIrUl5ISN2u\nvSoBzkRgAYUYIpO1R0fHIrAi5LtGrrnaKxLgUAQWUIh9aYn/1h4fnYrAiozsfnKl9noEOBWB\nBRTmNumjPUA6FYEVGUulWab2agQ4FYEFFCbrDHlce4R0KAIrIl5KTH5Qey0CHGvR+PE3aS8D\nYFIrXC2Oao+RzkRgRcJ3DTlFDgDAjLrLSu1B0pkIrAg4cakM0l6DAAAowJbyVb/RHiYdicCK\ngFnS+pD2GgQAQEFGyI3aw6QjEVhl53GnbdNefwAAKNCB2nH/0B4onYjAKrN/V0lYqr36AABQ\niGnSOVt7qHQgAqusfj5DbtZeeQAAKFRb2ac9VjoQgVVG2VdKL+1VBwCAwq2Jb3BYe7R0HgKr\njGZKC44wCgAws74yW3u0dB4Cq2x2utK2aq84AAAUZXdKhf9pj5eOQ2CVySsVku7TXm8AACja\neBmsPWA6DoFVFh/WdE3XXmsAACjGoXTXn7SHTKchsMrgx1ZynfZKAwBAsRa62hzTHjQdhsAq\nvd+7y8XaqwwAAGG4QFZpj5oOQ2CVWvY1ciZfIARMYNH48TdpLwNgclsrVP1Ke9x0FgKr1CZJ\n433aKwwAj/cr6OLWXgbA7K6V0drjprMQWKW1RGpzBkLAFAgsoHiZdd0vaY+cjkJgldJ2d5X1\n2msLAAOBBYRhtpzLKQljiMAqncz48su11xUAPgQWEI4O8pD24OkkBFapPFmu3HztNQWAH4EF\nhGNjYo3vtIdPByGwSuP5SvEztFcUAAEEFhCWoXKT9vjpIARWKbxcOe4u7dUEQC4CCwjL/tpx\nf9ceQZ2DwCq5l6u4JmmvJQDyEFhAeGbKeeznHisEVom9WMU1QXsdARCEwALCxH7usUNgldSr\nVV0TtdcQAMEILCBMDyVV+0Z7GHUKAquEnq/sZvsgYC4EFhCuoTJOexx1CgKrZJ6u5L5Ne/UA\nEIrAAsK1vw7Hc48RAqtEDibF36G9dgDIh8ACwjZXzj6uPZY6A4FVEjsSEu7WXjcA5DeqceMm\n2ssAWEUXuU97MHUGAqsEVrsrcPx2AICVbalY+VPt4dQRCKywZU+Xypx/EABgbWNksPaA6ggE\nVriOjZLqa7RXCwAAyuZQY3lSe0h1AgIrTL/0loZbtNcKAADKapn79MPag6oDEFjh+bK9tNmt\nvU4AAFB2fWSK9qjqAARWWP6VLl0OaK8RAABEwN60hDe0x1X7I7DC8dc06ZOlvUIAABARM6Ud\nB8OKNgIrDLuT4sZprw0AAERKR1mpPbTaHoFVrOxprgpztNcFAAAiZkvF5P9pj652R2AV59eB\nUmOV9qoAAEAE3Sh9tIdXuyOwivHpOdJ8m/aKAABAJGW1lB3aA6zNEVhFe7WudNuvvR4AABBZ\naxPTvtQeYu2NwCrSngqu4Xx9EABgOyNloPYYa28EVhGyZ7iSpmqvAgAARN6hpvKw9jBrawRW\n4X67WqpxdmfA/AZUqlRZexkAy3kgsfpX2gOtnRFYhfrfWdKC3dsBC+gr4tZeBsB6hskw7ZHW\nzgiswvytlnTn7DiAFRBYQGlkpssB7bHWxgisQuws77pK+7UPICwEFlAq9yemfaE92toXgVWg\n47dLhZnar3wA4SGwgNK5Ri7XHm/ti8AqyI+XSu012q97AGEisIDSOdRcNmuPuLZFYBXgP2dI\ny+3aL3sA4SKwgFLakJTykfaYa1cE1qn+nCa9MrVf9ADCRmABpTVWemRrj7o2RWCd4qHEuDHa\nr3gAJUBgAaWVlSHLtIddmyKw8jlxh1Sco/2CB1ASBBZQapuTy72uPfLaE4EV6pf+UpPd2wFr\nIbCA0rtbWvyqPfbaEoEV4pOz2b0dsBwCCyiDXjJWe/C1JQIr2Ov1pBtHbweshsACyuDh+nJQ\ne/i1IwIryCOVXEOztF/pAEqKwALKYmVC2ifaA7ANEVh5VsUl3K79MgdQcmvnzr1HexkAC7tO\nuh7THoLth8AKOHGrVF6o/SIHACDWstrLndqDsP0QWH6Hr5S667Vf4wAAxN7OGu5HtYdh2yGw\nfL7tIs34+iAAwJGWxVf9UHsgthsCy/B+E+m4X/v1DQCAjtFy3lHtodhmCCyvl2tIP74+CABw\nqqyOMlF7LLYZAivHIxVdo7Vf2wAA6NlTR7Zqj8b2QmCdPLkpIWGy9isbAABNaysmvaw9HtsK\ngXVyplRaoP26BgBA11RXg6+0R2Q7cXxgHR8t1Tm7MwDA8QZLN443GjlOD6zDfaXhFu3XNAAA\n6rLayc3ao7KNODywvu0orXZrv6QBADCB3XVltfa4bB/ODqyPWkinA9ovaAAATGF9cvxj2iOz\nbTg6sN6sK5dy+CsAAHwWJFT+h/bYbBdODqwXqskA7dcygLK7Z8SIa7WXAbCHO1x1P9EenW3C\nwYF1IClugvYrGUAE9BVxay8DYBND5JyftMdne3BuYK2NS5yu/ToGEAkEFhAxWRdI9yPaI7Qt\nODawZkryIu2XMYCIILCAyMlsKwOPa4/RduDQwDo+VlJXab+IAUQGgQVE0P6WcoP2KG0Hzgys\nw/2k4WbtlzCACCGwgEjaeZpM1x6nbcCRgfUNhxcF7ITAAiJqUw25V3uktj4nBtYHzaTTfu2X\nL4CIIbCAyFpXTRZqj9WW58DA+kdd6cPhRQEbIbCACFuXKvO1R2urc15gPV7JNUr7lQsgkggs\nINIeqCrLtcdri3NcYK2NT5is/boFEFEEFhBxa6q42A+rTBwWWCcmS/J87VctgMgisIDIW5Mq\nt2drj9pW5qzAOnKV1HpA+zULIMIILCAKHqwjw49pj9sW5qjA+qyDNN+u/YoFEGkEFhANWxvK\nlZw1p9ScFFiv1ZPOHJ4BsJ9xGRlnaS8DYEO7Wkjnr7THbstyUGA9XMF1NYdnAAAgTA+fJ43e\n0h69rcoxgZU921VuivZLFQAAC8kaICmPaw/gFuWUwPqxn1Rbrv1CBQDAWibGxy/jy4Sl4ZDA\neruZtNyq/SoFAMBq5qdI/++1R3ErckZg7UuWPpnar1EAAKxnU0s5/TXtcdyCnBBYRya4Eidp\nv0ABALCkg1e4yt3HZsKSckBgvddW6qzUfnkCAGBV0ytJ94+0R3OrsX9g7aos5+/Vfm0CAGBd\nm86Syg9pj+cWY/fA+uEaKTdB+4UJAIClZd2YJL0/0B7TLcXmgfX0adLofu2XJQAAVrehlZSf\ny5lzwmfrwPp1gss9gG8PAgBQZlm3pEiTJ7RHduuwc2A9ni61F2m/IAEAsIedvVxy0d+1B3er\nsG9gfXGVuPvs0341AgBgG8vOEPdV/9Ee4K3BroF17P6qkr5M+5UIIBYe3rVrl/YyAA4xo4Ek\njnpXe5C3ApsG1uMtJem6g9ovQwAx0VfErb0MgFNkTawt7gEc2r1YtgysNy8WV/fN2q9BADFC\nYAGxdOj2hiLn7/5de7A3ORsG1luD3dJqufbrD0DMEFhAbGXNaCVSa9qH2gO+qdkusN4a4paG\nd2u/9gDEEIEFxNz9l1YQV+e132mP+uZlr8DKfuwil/x/e/ceH1V553H8CeF+CVguYkRALlWQ\nblnUZbdg9WW1/OH+kgCJcomReGMBC0iogloksliXy9qNMQoWs6JSFGgB0Soo1q0il6IWKEIB\nFauwyEXBQEiCOfucM2dmcqMzmT3kmcN83n8wv+d3Zng9efF7Md/M5Zzu01ebnjsADYmABRiw\nbELfJNVMFn1l+rk/Tp1PAetY4WVKXUa8AhINAQsw49fZXZRK/vHcjypNJ4A4dN4ErIpXspqr\nxj+eZ3raADQ4AhZgTFFO7ySlLhz17G7TOSDenB8Bq/TVuzspddHoYtODBsAAAhZg0n9PvCZF\nKdUpfc76b0zngThyHgSsvc9ktlaq9ZA5vDcIJCYCFmDY6l/d8S8X6JCV1OvmR1fu+850MIgL\n/g5YZ7Yvyu1mx2aZzVlFgYRFwALiwcK89H4t9XOyajVgxIwXNx4ynREM823A+nbzorxrW+t/\nx5b/dOcTpocKgEkELCBerF4wffTgSxorJ2f9QCb88vl3dpWYTgxm+C5gle7745LZt1/XNcn+\nx0u99q75q0xPEwDDCFhAfFm5cOadN111SVMV0OqywUPHzSh44feb9x5NnLcPIwWstCvN6t//\nB/36Xt6756VdL+7coV3r5o3df63kFm07XnxpLwDo1U7/n2B6DwBq695FP3W3adG0kQpLbtqi\nVcoF7Tt2Tr24a7cePXr37tOnb79+P+zff4DhwPH/UOeVGSMFrB4KAAAAZ/MHAlZtl/fp06dR\n5LsBNTTTk9PT9CbgR2316HQxvQn4USc9Oh1NbwJ1ImDVwX5FMtn0JuBDLfTk9DO9CfjR9/To\nkM0Rg4v16KSa3gTqFFPA2v6n89tVemDfM70J+NBqPTlDTG8CfvSkHp07TW8CfvSAHp2ZpjeB\nOp2IJWCd767WA3vS9CbgQ3v15KSZ3gT86DU9OlNNbwJ+VKBHZ4HpTSB6BCwCFmJBwEKMCFiI\nEQHLZwhYBCzEgoCFGBGwECMCls8QsAhYiAUBCzEiYCFGBCyfIWARsBALAhZiRMBCjAhYPkPA\nImAhFgQsxIiAhRgRsHyGgEXAQiwIWIgRAQsxImD5TKIHrMKCgoIy05uADx3Vk/Oc6U3Aj3br\n0XnV9CbgR+/p0dloehOIXqIHLAAAAM8RsAAAADxGwAIAAPAYAQsAAMBjBCwAAACPEbAAAAA8\nRsACAADwGAELAADAYwkXsD6UKu4Ndr9YOHHk0Jz8N86Y3Bri367CsZkj73l8R7jD6CCSzVLN\nXW6b0UFklVvm3501NHva0mPhHpPjFwkXsN6tK2Aty3Ab4/7X6OYQ3yqK0txBKap0W4wOIqo7\nYDE6iOzwfcGxGb462GNyfCPhAtbrIvlLgl4P9FbqUf3FsjXP3i6Se8Ls9hDHKueJZP3X6mX5\nOmYtCbQYHUT2xZKwhSIPOk1GB5GdvFvknjXbPt5QqEPVmkCPyfGPhAtYK0TeqtE6OFwyNtnF\n6VkiBQb2BH9YJzLpsF1sHS5DnRfsGR3U0xOS8Zl9y+ggCs+JPBx4I3BrmmQ5cYrJ8ZGEC1iL\nRWpeLPPp0OsRpdmSfqzWQwBb2W1yy9FA+ZsZz3xu3zI6qJ9tafKCUzA6iMJdIrvd8n6RP9i3\nTI6PJFzAKhLZXr1zZrQM/datXxD5bYNvCf6wQeTF6h1GB/VT9m9yV5ldMDqIRrpIqVs+KbLU\nYnL8JeEC1lyRT6p3PhaZFqz/IvJAQ+8IPqFH54vqHUYH9bNYZKtTMDqIxs0iJ91SB6zfWUyO\nvyRcwJopcqh6Z43Is8G6LE1uaegdwSfukBz957f7dh4Mdhgd1MvnGTI7UDE6iEa+yJ/dclrg\n3UImx08SLmD9XOTE24/kZIyY+Kz7PLko9O0M7VZ92NDOEN9K0/Rvizsesk/UkLv0tNNidFAv\n+ZLxZaBidBCNnSL3Bl7C2pwmD9m3TI6fJFzAGicy3j2JSMZS52RG80XeDR3+mcjnxvaGePap\nyGOvpbuzM+lru8XooD62iSxwS0YHUfmtyO3LP9jx7uPpMuGI3WBy/CThAlaOfnYcMX/Zqqdz\ndfG83Zktsjl0OE/kr8b2hnj2F5GfZeSuO1B+eE22yHQ7nDM6qI/7ZVjwS1+MDqKz5YHA73S5\ni0ucNZPjJwkXsIaLPOW85FqxUA/tHl08IvJB6PA0kY9NbQ1x7U/2Obi/ccoDI0Q2WIwO6kVH\n9MJgzeggKieLcwIBKy3vf5wGk+MnCRewTpYEv5RhzRKZY9X4jWAKvxGgblskfAq134nMshgd\n1Iv+D+dvwZrRQTSOjBV5fOepisNvjRcpsjtMjp8kXMCq4q8it1Ra1n9WfU/7nlpfxQccO0Qy\nghdXPSwy0mJ0UB/H0mVqaMHoIBoPhD7SfnqqiP0aFpPjJ4kcsCqHiRy3rGKRV0K9USIlBreE\n+PWZyK2hRaZIOaOD+lgm8lpoweggCrtFJgbrbSJ5FpPjL4kcsKyRIoedyz//Otg5KTLa5I4Q\nv8rTJSu0GOWcYJnRQfQmiRwNLRgdRGGFSHGwPiWSdobJ8ZdEDlhlaSJllrVXwq/cbxXJN7kl\nxLHx4XPU6rA1zGJ0UA9HRMaHV4wOovBc4PI4ju/SnZNeMTl+kmgBa2Phw+uDtZ7OCfqm8vbw\nFTOLRN4wszHEvWKRVW65PfBqPaODqK0TeTq8YnQQhRUiTwTrQyLplUyOvyRawForMq4sUFZO\nE1lsF4tFFgVaRzIl8+TZHooEt09kzKlAOVvkN/Yto4NoFVX9CBajg2hsE7mtwq3Xuy9dMTk+\nkmgB63S2yKPOtcjLCkRudk5r9M0ISXvHLk783H3eBOrwmMgM5/+z5SJZzqncGR1E6z6R7VWW\njA4iOzNWpMi54Ih1aIz7chWT4yOJFrCsTekiI4tWrnoqRyRtQ6C3Pk3kwZdWP6XD15SKv/9w\nJLBjd+jfJ4tffzlPRN4MtBgdRCm7xmXmGR1Eti1D5N5Xtu3aXDxCj0vgNDFMjn8kXMCy3h/l\nXk5OsrcEe2uHu60H+c4rzu7AZHdOMtcGW4wOojO05nV5GR1E9tFtwecrmed+QIHJ8Y/EC1hW\nyaoZOcOG5+a/ejrc+6p40ohhuY+9b25X8IMzb83MHTpy8uLw1+0ZHUSlTD8f1ni5gdFBZGVr\nH70jK2PU5AX7wj0mxy8SMGABAACcWwQsAAAAjxGwAAAAPEbAAgAA8BgBCwAAwGMELAAAAI8R\nsAAAADxGwAIAAPAYAQsAAMBjBCwAAACPEbAAAAA8RsACAADwGAELAADAYwQsAAAAjxGwAAAA\nPEbAAgAA8BgBCwAAwGMELADmVF6nlHq4Zvcm3cwzsBsA8AwBC4BBe1oo1XRn9d5LOl/1OmVm\nPwDgDQIWAJPm6DR1TWXVzjcXKZX0tqHtAIA3CFgATDpzlU5YC6p2xurGWFPbAQBvELAAGPXn\nJkq1Oxhev5ekVJfj5vYDAF4gYAEw6yGlVFZoVX6FXq4xuB0A8AIBC4BZZX10pHoluPp3vcgO\nLt6eNKBTk/Z9Mp8/UfUBX8z51+4pySk9MxeWBFsb9KPesY5O7N604y6nsXfWkK6tk9v0lIKv\nGuBHAICaCFgADNvQSKmu3wbqPc2V6nQkUO/8ZxXUeWno3uX3NQm1O6x0mx/aL3sdt5Oa+lAv\nS8c1Ct2n1X9U+wg9ADQIAhYA0ybqHDQ5UP5Ely8FyrVt7HzUZUDvZPv2Mfe+leLEppSu7eyb\npGWB7i77YVOVG7Aqf2oXjS/p2dbp3NvgPxAAELAAmFbSXankLXb1nM5DGYHmPp2gGk38VFfH\nn7TD1PJA+0lddnr6qK722F83bHsscGddFrVRfe6fO32/ZRXr1dXrynX/QIH90A0N/hMBSHgE\nLADGrdUpqH+FZR3poFS7A4HejUolveAe35miVLdSp+yhY9cHbnuCfthcp9qvq+tVnvtm4E+U\nush9y9Ha016pkQ3yQwBAFQQsAOaN0QFpjmXdpm8WBTpbdTkmdLxQrxbbhf1S1XXB7t/0Ykio\nUtcGP2zVucrn5K15V2bOPtfbB4CaCFgAzDumM1HLT9brlHSD27E/lxW+hM6plsG3Dk9/tnF7\nqH2JUt93CidgvRFsX6BUegNsGgDOjoAFIA4st7PV95Vq9anb6K9UjyrHhyj1vdqPGqBUR6ew\nA1brimD7H5Vquvnc7RUAIiNgAYgHwwInVShwl6caKXVjlcN5+tjBWg8aqFR7p7AD1jWh9jy9\najF93zncLQBEQMACEA8OXmDnqx995y7txNSyW5h99P3AodMv3znwwhbB01yFA9atob+rfJBz\n6PLxy4818E8BAC4CFoC4YJ9bodnHwdU2VVvgM1YvplZrhgPWPeG/qyTbPZo86AkyFgATCFgA\n4sLXOg/9MLR6v46AtcI+MMspuw9KG611qBqw8qr+bZtuTXEf1faRMw35YwCAg4AFIC5UD1g7\nqr3nF/Zmkj4wYb+7GnjWgGVZ5W9O6ReIWDeVnpsdA8DZEbAAxIXqAetzVfepFm7U/cdDq6v+\nTsCyHXxmsJ2wZnm9VwCIhIAFIC5UD1jlzZS6ovadShopdWn44s2pEQKWtqK5Uq1OertVAIiI\ngAUgLlQPWNbVSjU5XutO9kWdc0Or3SpywLLy9aF3PN0pAERGwAIQF2oErCnBi+ME7AqErU26\nOynUnHy2gLV/f7i2r3O4+pzsGADOjoAFIC7UCFj2eRr6hL7/V9qlyfX2lwh3V/1o1gdN7ZNl\nOWW1gDWlQ5WTjlpL9KGN53LjAFAHAhaAuFAjYFk36PXd7setyrP04mVdnGmjVIp7Rvcdqa1+\npNtH7LpawJqrF4XBRYW+TwpfIwTQ0AhYAOJCzYD1SWvduP6POmKVvnylLq9zujm6GmRfBOfL\n/BaqcLpe/dJuVwtYJy7Uq5wN5bosec3OYPc15M8BADYCFoC4UDNgWevshKVa9epkn/pK9T3k\nNPfYzeTeg3s3UmpM5Rr7yBUDd9f4DNb6Zs5Z3FO7tXHOgzXoVMP+JABAwAIQJ2oFLOujwaGT\nuCflfu023wieoT35F5ZV8Q9Oub3mh9w39Q2f/73xZPIVgIZHwAIQF2oHLMtaP3lA56YtU2+Y\n+Um4d/DBK9smtx0wdbe9+PKW9k1Sb/6q1mkaKn8/fuCFzZNTeqTN//Lcbx0AaiFgAQAAeIyA\nBQAA4DECFgAAgMcIWAAAAB4jYAEAAHiMgAUAAOAxAhYAAIDHCFgAAAAeI2ABAAB4jIAFAADg\nMQIWAACAxwhYAAAAHiNgAQAAeIyABQAA4DECFgAAgMcIWAAAAB4jYAEAAHiMgAUAAOAxAhYA\nAIDHCFgAAAAeI2ABAAB4jIAFAADgMQIWAACAxwhYAAAAHiNgAQAAeIyABQAA4DECFgAAgMcI\nWAAAAB4jYAF9B4q8AAAACklEQVQAAHjs/wAlgStTjLnhZAAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "plot without title"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 360,
-       "width": 1200
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_width=20; plot_height=6; plot_dpi=300\n",
-    "options(repr.plot.width=plot_width, repr.plot.height=plot_height)\n",
-    "temp = data %>% filter(QRISK3_event==0)  %>% select(c(eid, QRISK3_event_time))\n",
-    "mean = round((temp %>% summarise(mean=mean(QRISK3_event_time)))$mean, 1)\n",
-    "obs_time = ggplot(temp, aes(x=QRISK3_event_time)) + ggtitle(\"Observation Time\") + \n",
-    "    geom_density(fill=\"gray70\") +\n",
-    "    xlab(\"Years\") +\n",
-    "    geom_vline(aes(xintercept=mean(QRISK3_event_time)),color=\"black\", linetype=\"dashed\", size=1)+\n",
-    "    #geom_text(x=mean, label=mean, y=0.15, hjust=-0.5)+\n",
-    "    #ylab(\"Prevalence in [%]\") +\n",
-    "    scale_y_continuous(expand=c(0,0))+\n",
-    "   #theme_classic(base_size = 25) + theme(strip.background = element_blank(), plot.title=element_text(size=24, hjust=0.5))+\n",
-    "    annotate(\"text\", x=mean+1.5, y=0.015, label=paste0(\"~ \", mean, \" years\"), size = geom_text_size)\n",
-    "obs_time\n",
-    "plot_name = \"2_observation_time\"\n",
-    "ggsave(filename=glue(\"{dataset_path}/{plot_name}.pdf\"), plot=last_plot(), width=plot_width, height=plot_height, dpi=plot_dpi, device=\"pdf\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# ENDPOINTS"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Frequencies"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plot_width=20; plot_height=6; plot_dpi=300\n",
-    "options(repr.plot.width=plot_width, repr.plot.height=plot_height)\n",
-    "\n",
-    "temp = data %>% select(c(any_of(targets))) %>% select(!contains(\"_time\")) %>% select(!contains(\"cancer_breast\")) %>% \n",
-    "    pivot_longer(everything(), names_to=\"category\", values_to=\"Value\") %>% \n",
-    "    group_by(category) %>% summarise(ratio = sum(Value!=0)/n(), count=sum(Value!=0), .groups = 'drop')  %>% mutate(ratio=ratio*100) %>% arrange(desc(count)) %>% mutate(category = fct_reorder(category, count))\n",
-    "\n",
-    "plot_endpoints = ggplot(temp, aes(x=category, y=count)) + ggtitle(\"Endpoints\") + \n",
-    "    geom_bar(stat=\"identity\", position=position_dodge(width = 0.8), fill=\"gray70\", width=0.5) +\n",
-    "    coord_flip()+xlab(\"\") +\n",
-    "    ylab(\"Number of Events (%)\") +\n",
-    "    scale_y_continuous(expand=c(0,0), limits=c(0, max(temp$count)*1.3))+\n",
-    "    geom_text(aes(x=category, y=count, label=glue(\"{count} ({round(ratio,1)}%)\")), position = position_dodge(0.8), hjust=-0.1, size = geom_text_size)+\n",
-    "    theme_classic(base_size = base_size) + theme(strip.background = element_blank(), plot.title=element_text(size=title_size, hjust=0.35)) + \n",
-    "    scale_x_discrete()\n",
-    "\n",
-    "plot_name = \"3_endpoints\"\n",
-    "ggsave(filename=glue(\"{dataset_path}/{plot_name}.pdf\"), plot=last_plot(), width=plot_width, height=plot_height, dpi=plot_dpi, device=\"pdf\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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3rqAUtNVFtmjxj41vS1Oa5r2Ltg2rDB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tM4+eRtC8VsloYxOwDx5oVP/WKdafPe8D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\nVyW6tawB3WVWq19ZLtBKtoB1GlGuteGzV7Z4K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-      "text/plain": [
-       "plot without title"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 360,
-       "width": 1200
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_endpoints"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Event densities"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T14:29:06.050717Z",
-     "start_time": "2020-11-04T14:28:59.615Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "plot_width=20; plot_height=10; plot_dpi=300\n",
-    "options(repr.plot.width=plot_width, repr.plot.height=plot_height)\n",
-    "\n",
-    "plots = list()\n",
-    "for (e in endpoints[-2]){\n",
-    "    temp = data %>% filter(!!sym(paste0(e, \"_event\"))!=0)\n",
-    "    \n",
-    "    plots[[e]] = ggplot(temp, aes(x=!!sym(paste0(e, \"_event_time\")))) + \n",
-    "        geom_density(adjust=1.5, fill=\"gray70\") + \n",
-    "        scale_y_continuous(expand=c(0,0)) +\n",
-    "        scale_x_continuous(expand=c(0,0)) +\n",
-    "    xlab(e)\n",
-    "    #+ \n",
-    "   # theme_classic(base_size = base_size) \n",
-    "    #print(paste0(nrow(temp), \" events in \", nrow(data), \" people in observation time\"))\n",
-    "    #print(paste0(round((nrow(temp)/nrow(data))*100, 1), \" %\"))\n",
-    "}\n",
-    "title <- ggdraw(get_title(ggplot()  + ggtitle(\"Endpoint Densities\")))\n",
-    "plots_density_raw = plot_grid(plotlist = plots, ncol=4)\n",
-    "plots_density = plot_grid(title, plots_density_raw , ncol=1, rel_heights=c(0.1, 0.9), align=\"v\", axis=\"lr\") \n",
-    "\n",
-    "plot_name = \"4_endpoint_densities\"\n",
-    "ggsave(filename=glue(\"{dataset_path}/{plot_name}.pdf\"), plot=last_plot(), width=plot_width, height=plot_height, dpi=plot_dpi, device=\"pdf\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T14:29:06.952215Z",
-     "start_time": "2020-11-04T14:29:00.872Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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49xF58LApaxPEYDfW7LiTSOu2zQgRn0oK+ttYi6cVcNxjCZpnpotrtCL3IXCQJ8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CxWQaEFjkPHWlidwdAUt41l5U5gaEMg4V\nj8Jdny3tkwSH3xfmy7YiIupl7uaD4fxdjaYHmKi0nUlkrKs1oMAyg8x+1M6XiyR7cbic7Vnu\nroAFfF3M4etFj6bScO7WgoZ+r2ibGvAKaZGjyHvcHQCDSe9CvQNOVNrWorZ93D3whALLDB6g\nWgFcXrSgtfaKOCQGgnWuLt3ta+ROVAj7mLu9oJnLTWlQECukmbak77m7AMYynxqdCCJSj0RF\nvsbdBQ8osEzgJXtxX+8Z4EVfupu7M2B2GT2pu++Ru496cTcYtJLZn9oGtWt9FNXGbVLBQ5qt\n1L5gEpW22F7iW+5O5EKBZXx/JNlXBRW5XEeS7O9wdwdMbiHVO+Z75FKr2wx6H1YI2mKqHeSu\n9Tuo3TXuXoBx/FAsfG1wiUqbSHWMc184FFiGl9mZRgQZuVxLqLnBzmMFkzlkK+nX/QQWYxeW\nVR2xJQZwuaI8TjSlUdzdAMO43pwmBpmotLTbqWs6d0fcUGAZ3lpqqMIB7m63UAp3h8DMPoiN\nXO9f5KrbPuJuNGjhk7iIdUGvkA5VNth5X8BoNrUKOlFpxxsa564lKLCM7tOoeJUOwHJ5IqLs\nRe4ugXn9WdE2x8/ILaBB3K0GDZypTrNUWCNtT7A/x90VMIZ/25L8vkOOjH1JNqPchwkFlsHd\nSCZ/N2iF60tLuPsEpnXlFv/PGkutaP+Gu92guozbqacqa6SVjhI4lRBEf5Z2BHbN2vw2RMZ9\nxt2ZLCiwDG4ZtVElcjn2x8ef4u4UmFTmYGrl/+/VM2gCd8NBdUuofuD3ns9jIt18mbs3wC+z\nOw1XJ1Fps6i2MS5IhALL2D6NKBrcSasFjaJp3L0Ck1pM1Q/7n7hjidGo6a3m2bASu9VaJd2G\nA93B6dxAN6l2tPHtdCd3d1xQYBlaRguaq1bk3I4kRv3K3S8wpSdtiQEdDzgKP0tbzU+JPl/M\n37vDlelJ7g4Bt8+j43y7/5YvjtWkzdwdkqDAMrR11FK1yOWYRJO4+wVm9HxE7IaAEncguvRV\n7saDmq61oHEqrpK2RMca5JgZ4HI9mWarGKntcVEfcHfJiQLL2H6Ki1XzDMJsx0pFYhcW+O29\nIo77A4ycgD0U1jKNWqu6TrqHbsJhWKFtIbVVNVLzbTUvcPcJBZax3UGTVc1ctkk0lbtnYDpf\nl7bdE2jittqacDcfVHSIyh1Qc42UltYJJ0KEtrccJZ5SN1ICjeDuFAosQztMdVW8xGiuoyWi\nT3L3DUzml8o0JvDIJdNb3B0A1XybEPGoeqsjl0MV6Dh3t4DP5Vq2pSpH6mhV2svdLRRYBnau\nnGOjypnLNpru4+4cmMvJ2jQgiMQtomHcPQC1XE2mu1VbF7mtD0/8nbtjwGaaPzeQ99HmqCLs\nF1hDgWVcU4LapBXmUHxRY1wlBEzirwZ0RzCJSy0d/Td3H0AlU6idWmsiD6OoG26TGqpeCStz\nSP1ITaaWN5g7hgLLsN6xlwnyTvXKBtFa7u6BifzViDoH92v1MFrH3QlQx2Eqp8HGMC21gTFO\nrAf9XahiW6VBpNJuoaXMPUOBZVTpyaT2j9K59kRU4i7twTxONqCOQR4NuNNRj7sXoIrvi0Zs\nUGUllN+O2LhvuTsHLCZQL00itbe4g/nYTxRYRrVO7Xvk5NGF9nN3EMzi19rUKeizLVrQm9z9\nABVca0aT1FgDyZhGt2Zwdw8YvGgrr9GPNctsNS6ydg0FlkH9HB+7S5vMuWyyNefuIZjEN5Xp\njuDPZl1EY7k7AiqYqfIVsDw1xe/Ioeh8pTB17vEsQ6DxrH1DgWVQvWiiVplzSaY3uLsIpvBu\naVVOtjhevMgl7q5A0NJsZfarkAZ5O+NivuHuIOhuHPXVLFJHKtie4ewbCixjOka1NLkEVo6l\nNJC7j2AG/4qzBXH9Kw99aTd3XyBYvyQ61qqSBnnTqR3OJAw1z9sqaHY2V1raI44yfzF2DgWW\nIZ0v79igXeYkqeXDcb8c8OoxR/i96iRuE3Xk7gwE6catNFadNChIpse5+wj6OldRux8IJYNp\nAGPvUGAZ0hTqp2XmJBNpAXcvwehuTKZ41c6frhGGkt7k5lNzbXes74hO+I27k6CrsRpv645X\n5zyhCwWWEb0Zpt0lsNwOxZa6yt1PMLZT7aj8VtUSN44e4u4QBOWFsMR9qsVBKSS9uXsJenrO\nVuGotpHaFFGC7x4BKLAM6NpNtvu1zZykB+3i7igY2v8qUlMVj2jebW/I3SMIxh9J9tXqxUFe\nai06wd1P0M+5CvaHtc7UaBLY+ocCy4CWUQetMyfagis1QGG2RNoGqvqDUDJ9yt0nCFxGBxqu\nZhzkbXBUvMDdU9DNGO0PhklLrUcpXP1DgWU8n0cW1XpHvMvN9A53V8GwLgyh2AXqBm4GDvsz\ns2XUWNsDsLL0pZncPQW9PGurqPEPhJKtUQm/MHUQBZbhZLSkOdpnTrSARnH3FYzqo9pUTb3D\nr7IciKjG3S0I2Kv24ntUDoSsQ6UcH3H3FfRxtoJdy6t+5JhAXZgu/4ECy3DWU3M9MpeWdqJk\nzBnuzoIxPRZFt6v/1bIl9pma1qmyYQ+oHghZC6gVLoYVGkZRf10ildqAtvH0EAWW0XwfG7tT\nl9ClpQ2jR7h7C0b0d2+KnadB4ObQvdxdg8BkdKUhGiRCVjO+Q2ZAT/+iSjr8QCh5IqrITyxd\nRIFlMJmdaao+mUtL2+Woha+KUMD/VaQ627UI3KHIKgicOT1AjfQ4AMvliYhS/3D3F7R3ppz9\nEb0yNZE6s6x6UGAZzBM6rsjSWtNL3P0Fo0lfarcNOK5N4FrhN0Jzes1RfLc2kZAzhO7m7jBo\nbygN0i1SXD8SosAylt+KRmmy70DeCta7CIAR/daOiq/QKnCzaR53/yAAJ8uFaZYJGUeS7DjO\n3fKOUdVj+mXqiaiEnxk6iQLLWHrQOP0yl5ZaLuJP7h6DoTxbkprs1SxwByNqcncQ/JfRWb8D\nsFzmUzvuPoPGTpXS+n67eU2grgw/EqLAMpT9VEe/HwjTpGvcruLuMhhI+vwwxygtE9gU1xo1\noeV6Hrfg0ogOcXcatNWXhukaqdT6tEP/XqLAMpK/SoVv1jV0+yKqZXB3Ggzj5G1UUtM726dN\no/u5Own+esmu5wFYLpvsla9wdxu0tIdqanSgp5JtUUX1v5E4CiwjGUFD9c1cWjt6jrvTYBRv\nl6dkje8hsDesCXcvwU+/l7av1DYVMgRawd1v0NCvxSL03ZeQJt1IXP97EqLAMpAXbJV1POrP\nZRX15e41GERKpG2w5r8E3WTjumkFBOZGWxqhdSoK2hcf/wd3z0EzmV11Pdg4S2pd2qt3R1Fg\nGcflajZtf5+Ry1z5cKzHQJQ+g2IXah+4MbSRu6fglznUTOcDsFzG0RjunoNmNlMDhlBtiShx\nUueOosAyjnkk6J+50bSSu99gAOe7U1k99tlvpa7cXQV/nLCV0uXW8/kdK2f/kLvvoJGvY2N3\ncIRqpO7XJUKBZRifhZc4oH/k9oZXx8W14ZcG1ECf7WiFyAvcnQXffV8sXJfb8Ra0gDpxdx60\ncaMFzWDJ1PHqdEzfrqLAMorMtjSXI3NtcDV3+KQ8ddLp8L++dJS7t+CzK8k0SZ9cFFSfnuHu\nPmhiGbVkytQGR5kzunYVBZZR7KJklsjdT4O4uw7M/lPUptuFJFfRaO7ugs/GUju9glHAI7YG\n6dz9Bw28E15cu2sZezGIRunaVxRYBnG2dMRWlsSllon8i7vzwCo12j5Nt7wdjy+D36TNYgdV\nOqRbMgpoQ09yDwCo71It2xK2TB2taNP1FxsUWAYxXccbX+Y1nNZxdx447XZELNIxb23pXe4e\ng2/ej47ZomMy8nvCURFXG7WeiXQ7Y6jWhFW7pGNnUWAZw2fhpQ4zJW6n/Sbu3gOjLWExul5H\nchYt5e4y+ORMVds8PZNRQA96iHsMQG1P2ypwbepcetI9OvYWBZYxdOI5wt2lOb3N3X1g84gt\nXt/TxPbabuHuM/gi43bqo2syCtgTXeIs9yiAuv4s5VjHGqpDpezv6NddFFiGcIIa8CVuIY3j\n7j9weYgSNuict1p2HPRnBkupgc53iytgMC3gHgVQVWZ3jvsC5LGEbr6hW39RYBnBtZphG/gC\nd7x4kYvcIwA8VlPxTXrn7U7ax91t8O5fYYl79I5GfgcT4vS+9DZoagPdxHFfgDza0mrd+osC\nywjWUlfOwPXF2Toh6mEqrv9RzGtoBHe/watvizl0v3FXQWNoGvdAgIo+jY5L4c5U2p74mO/0\n6jAKLAP4u3j0bs7AbaY23EMAHDZQUd1vaZ+WdgIXajC+Sw34rjDq4Uhi5M/cQwGquVKf5nBH\nSjSNuujVYxRYBjCThvEGro7tW+4xAP1tsyVs5IjbrYS7zBndYOrIEY0CJtNY7qEA1UwxRqpS\n6+t2lAIKLH7fRyYe4Q3cVFrIPQiguz1hcY8yxU2/YyAgIGupBvM6KduxpHB8+bOKNFtZxuvW\netgSnvSPPl1GgcVvEE1nztuBqIoZ3KMAOjvuiHmYJ24p1JG781Colx0JO3iyUcB0uot7NEAd\nv5d0MN04vIDBNEGfPqPAYveurTL7eRXt6UXuYQB9vRQVoev1RT1ViLrM3X0oxI8l7Su4spHf\niXKOb7jHA9SQ0YFGcafJ7Wi5sLd06TQKLHYdiO/OTG4raBj3MICu3ol36Hl/nLwEep67/6Ds\ncmMaw5aNAmbRcO4BATWspMbsexJy3E8363IncRRY3J7jvMaoW2rJ2PPcAwE6+rKk7R6+uC2k\n2dwDAMoGU3u+bBRwojx2YVnBm+FFWc+Vz6ctrdej1yiwmGU0shnhd+kBlMI9EqCfXyvROMa0\nHXQ05h4BULSaqhvjAPdsM3EUlgX8U8W2lDtJnnbGJPyhQ7dRYDHbTa25oyZ53HYb90iAbs7W\npwGscasThrvlGNWz9qL814L0dKKc43vuQYFg9aO+3EHKawwN1aHbKLB4Xavq0P9S2nJqhf3E\nPRagk6vtqDPv0RCD6BD3IIC8r4o5HmTNRkHTcS0s09tMtY5x5yiv45Vtr2nfbxRYvDZQN+6g\nZZlAK7nHAvSRMZCaneBN20oazz0KIOtsbbqbNxsFHU+KwOXcze3DqNjt3DHKb5WtgfbHuaPA\nYnWhdORO7pxl2euoxz0YoI/ZVOswc9qORdXgHgWQk96N7mDOhozJdDf3wEAwLtSiedwhKqgd\nbdC85yiwWC2l/twpc2tGH3CPBuhhI5XZwx22tMaEfRJGNIsaHOfORkFHE6P/5B4ZCMJQI5bt\naTujip3WuucosDidio/fz50yt9l0D/dwgA7+ZY9/nDtraWl30U7ugYCCUqjMPu5oyBlDc7iH\nBgK3jaod5Y6QnBHaX88dBRanKca5tG3a4eiyulx5DVh9GB9uhGOY19II7pGAAv4vMnYTdzJk\nHSpSRKd7x4H6Po6O2cqdIFlHy9g/0rjvKLAY8d/l2VN7epl7QEBrv1fgvMBorhOxFbiHAvL7\nvmQY/00l5A2hFdyjAwE6X4vmcudHwQLS+upEKLAYDWG/y7OnJTgZ2vIuN6PB3DnL0oxweW6D\nOVfPSHfIyeup6FK4faVJDSKBOz6KGtFRbTuPAovPe2GVmc+Wz+N4QvFr3EMCmsocRG0Mcjuw\nMbSNezQgjxtdqSt3KpT1pk3cAwQB2Ug1DXYFLA8bwqpru9FDgcWnI/HdbldOd0rjHhLQ1P1U\n0yi/Sa+nIdyjAXlMpkbG3RCmpTiq4RBRM3o7Mt5wV8Dy0JXWatp9FFhs/k31udOV14PY5lnb\nibASBrnqWlpaanw57uEAT+upgmHOaJbTgQ5yDxH4769KNmPtRshnd3Txv7XsPwosLga5y7OH\n1FLxOM7Bwj4vEvEwd8ZyNaevuQcEcqXZE7ZxR6JQG21NuccI/JbRzThXepQ3jGZqOQAosLhs\npzbc2cVq6FoAACAASURBVMqvNx3mHhXQzNmaNIM7YR5wEJaRvB8XsZo7EV40pVe5Rwn8tYQa\nGuk4YxlHEiO1vJU4CiwmF8uGP8Gdrfwepn7cwwJayRCMdTIPDsIykJ/L2u7lDoQ3D9Ad3MME\nfnomLJH/rhFeTKc7NRwBFFhMllAf7mQVkFo65gL3uIBGltJNhjqGOTWuPPeQQLZz9Wk4dx68\nq277nHugwC/fF3es4U6NV6mVbe9oNwQosHj8GptgwENK+9JT3AMD2hC/S+7mjldezelb7kEB\nl+udqDN3GnwwGxfqM5dLjWgid2h8sIQ6aDcGKLB4DDdk9B6hPtwDA5r4wXjfJUfRDu5RAUnm\nSEo24B2eCzheMuoU91iBH4ZQB+7M+KQ+PafZGKDAYvG2raIhV2llovEboRVdbUrjuLOV31q6\ni3tYQLKEqh7kDoNPRtFS7rEC362l6ka56l7h1tiSM7UaBBRYHDJvoWXcqZLVjw5wjw1o4G7j\nnbKadjymKvewgFM6nTnRMFdHK9z+6KSr3KMFvnrRkbCDOzE+aqndNdZQYHHYTc25MyVvLfXn\nHhtQ30EqZ8B9FMn0C/fAgPPf4bGPcSfBVz0ohXu4wEfflXA8wJ0XX20Kq63VbQJQYDE4XzZ8\nK3em5KWWjr3EPTqgtm8TIjZwJ0vGcNrLPTLwbly4abaDaVttDbnHC3xzob7xDkpQdhs9qdE4\noMBicC/1406Ukt50hHt0QGVXk2kqd67kPEjjuYcm5H1X2vgXwPLQnF7hHjHwRUZv6sQdFj88\n4ahyXZuBQIGlvy8iEg9xJ0rJGhrMPTygsinUjjtWso5G1OUemlB3qiaN5o6BP1ZQb+4hA18s\noLqGuuqeN91oszYDgQJLfx1oDneeFKWWKILjSK3luK2sAQ/AktS34bR7VhebU0/uEPinsl3L\n+5qASvbaShn+Cu55PBlR/oomI4ECS3f7qSF3nApxBz3NPUCgpl9KhK/nDpWCQXSUe3RC2o3b\nqXUqdwj8M5VmcY8aePVmVNQG7qT4SaBHNRkKFFh6O1fWsZk7TYVYQaO4RwhUlN6WxnJnSsly\nmsE9PKEsczQ1OMqdAT8dKVLsIve4gRc/lLYt4A6Kv3ZGlr2sxVigwNLbNBrAHabCHC9SUqsz\nVoHBcmpq2J0Uh+xNuIcnlM2nKga8XZcX/bU6WAbUcrYejeKOif960TotBgMFls7es5c+zJ2l\nQnXCiToW8oajuIEPhqjpOM89QKFrE5UyyQVGPaXY62l21W1Qw/WO1JU7JQHYHVlGi11YKLD0\nld6UlnBHqXCLaCr3IIFazlaxLecOVCF6a3gTMCjcEXu8kQ9VUNSKXuIeOiiEWe5sWYA2u7BQ\nYOlrHbXmDpIXR6Ir4iuiVQym3tx5Ksx8Wsg9QqHqP1GRRrv7t29WUi/usYNCLDbLnS3z2xVZ\nVoMTCVFg6ern+FjD75ZvTe9yDxOoYxdVM/TVaPba2nEPUYj6qKh9EffsB6iy/Ufu0QNF20xz\nZ8sCetJj6o8HCixdCTSJO0ZezaYF3MMEqvi+SNQW7jQVrkLMNe5BCkk/lLUZ8uL+vphMc7iH\nD5SkOmI3cgckUDsjKqi/NkKBpadDVMewp3Tl2O+4iXucQA03bqEp3GHyoiu9wT1KoehUTRrO\nPfUBOxSXqM01ISFor0dHrOTOR+C60+OqjwgKLB39U8axiTtEPkimb7hHClSwmFpyR8mbmbSa\ne5RC0IVmJHDPfBB6anZnXgjOx8XC5nOnIwg7HFVvqD0kKLB0NJYGcWfIF5NoDfdIQfBed5TY\nxx0lb7ZTD+5hCj3Xu1Ab4+9IV7bV1pR7CEHOdyb+4dmlM+1Se0xQYOnnVVsFU1w4eaftVu6h\ngqCdrWxbwZ0k7xKL45RVnWUMoUaGPvXBq2R6m3sQoaBfq9JI7mgEZ2tYnQyVBwUFlm6u1LI9\nyJ0g39S04ya8pjeY+nLnyAdt6FPugQo106jGIe5pD84iGsE9iFDAqTrUnzsZwWpLR1QeFRRY\nullA3bjz46NhtIN7sCBIu6i6GXZTTKBN3CMVYlZQOQNf298nqaWj/uIeRsjn74bUnTsYQXvM\npvbNu1Bg6eWTiOJmufPXRurJPVoQnO8Mf4WGLI/SEO6hCi1bbSW2c0960O7CuRFG808T6mjm\nA/uyNadn1R0XFFg6yWhJ87jT47MyMZe4xwuCcb2F4a/QkCU1tiL3WIWUg/Z4016nKNfeiKpq\nHysDQTnbnNqd4I6FCtZQW3UHBgWWTjZTc+7w+K4XneAeLwjGfca/QkO2ZPqJe7BCyHMRUea8\nQU4+7elf3EMJHsT66lYr1FdpaQ1UvjIfCix9/FE0OoU7O75bSaO5BwyC8Iq95FPcGfLRMNrL\nPVqh481YxzLuCVfFGrqdeywh1z/N6FZT3uC5oOUqXzgGBZY+BtEY7uj44Xh8aeyCN6+/yoet\n4o6Qr1bSRO7hChkfFQ+bwz3fKqkW9j33aILbX42pjUXqq7S06jZVz2tGgaWL56mqqRLYHrcw\nMa/MHnQnd4B8diQcN2bSyddJNnMcmOeDqbghoWH8cRPdZo3fByVzaLiag4MCSw9Xa9ge5g6O\nX+bQXO4xg0A9SvVMtL6rE/Y394CFhp8q0ijuyVbN4bjEq9wDCi4/1qCuFjh/0O1E2XA1jwpF\ngaWH5dSVOzf+ORhej3vMIEDvR8ab6HC/tH6Uxj1iIeH36ua4UZePetJu7hEFyeflqZeF6qu0\ntLtpqorDgwJLBz/GJBj+rnD5NKZvuUcNAnK+pm0Bd3r8sQg/9ujhVF3qzT3Vatpia8k9pCB6\nqwQN4c6Cuo4Ui1XxMrYosHTQm0x3D8wJ9Aj3qEFAhpDAHR6/7Le14h6yECBdZ9tS+xnSGtEH\n3IMKzqdjwyZxJ0FtI2ipegOEAkt7z1Et063bdtjacw8bBOIJqmaKO4rnqhJ5mXvQLO+fZOpk\nunVQ4ebROO5RhW2OCPNcPttX+2NKqnedbRRYmrtex7aWOzP+qxp+hnvgwH+fxMRs5Y6On26n\nV7lHzer+aUbtLVZfpR0vEXeWe1xDXOZ9FLeSOwca6E0bVRsjFFiaW0cduRMTgEG0j3vgwG8X\n69C93Mnx1710P/ewWdw/TamNic4r9dEQepR7YEPb5YFUahN3CrTwpKNqulqDhAJLa6eLRe/i\nTkwA1tJg7pEDvw2jbtzB8duT1IV72KztTBMr1ldpOx11M7mHNpT90Zxq7eYOgTY60gG1RgkF\nltYm0wjuvAQitXixG9xDB37aRtWOcAfHf0lFVPu+CAWdvpnaW7C+SktrRa9wj20Ie7cCtTbh\nysYnm2zJag0TCiyNfeFIMmcMO2P1ZTYfRMWa7QAsSQd6l3vkLOz3etTBasdfZVlB/bkHN3Tt\nibYNsWasJM3oJZXGCQWWxu6gudxpCcwCmsU9duCXs9Vt93GnJhBTaR330FnXjzWom0U3hKkV\nwn/nHt4QdX0aRVnv9MFcq6irSiOFAktbL1Adk67eDkfU5B488Edmb+rFHZqAbKE+3GNnWV9U\nsNh1tj2NU/OCReC731pT2ce4Z19TtWwfqTNUKLA0ldHQtoY7K4FqSl9yDx/4YQ3VOcadmcAU\nK4WjlbXxv0SrXWfb0/6ocjhOlMFLpan5fu7J19Y8GqbOWKHA0tST1IY7KgGbTGu4hw9895oj\n4UnuyASoNX3OPXrW9GycbQL35GqpKx3mHuLQk77Ebr/LsntFs6WWC/9ZldFCgaWly+XDn+CO\nSsBSbO24xw989keZsPu5ExOocfQ49/BZ0s5wh+muiuaXDXQb9xiHnF/aUolV3BOvvck0U5Xh\nQoGlpQdMelRMlqoOXMzdLG60pWHceQnYozSEe/ys6H5brGlrbh/VtWHfp74OF6eme7mnXQdH\nEuL/UWO8UGBp6FSRuH3cQQkCLuZuHrOoqXn32qfGVeAeP+u5NpJKbOCeWa3Npinc4xxSzo2k\niHHmXdH4Yyg9oMaIocDS0N00ijsmwcDF3E3jmK2UmUv5pvQ99whazem2VDWFe141d6xokfPc\nIx1CXq1ClTdwz7lO9kUlXVVhyFBgaeer8FJHuWMSjNTixXGSjil8ER+xnjstwRhFT3IPocV8\nUpWaHeKeVh0MpE3cQx0yLk8Ps/U29RbNLz1oqwqDhgJLO33oHu6QBKczvco9huCDC3VpOndW\ngvIwjeIeQ2s5Gk99QuKXnBR7Pe6xDhX/V4OSQuDo9hzbHbUygh81FFiaeZ2qm3wdt4Du4R5E\n8C5zgAlv8ZzH8ahq3INoJenzbBGzuOdUJy1xRy9dXJgaZrs9FPaJ5mpHR4MfNxRYWslsQSu4\nIxKkQxG1uUcRvHuYapn0CqM5GtMv3KNoHSc7UKl13DOqlxXUl3u8Q8GzlanMA9xzrbNHbbcE\nP3AosLSyn5pxJyRoyfQN9zCCN686Ekx/NPNw2s09jJbxSllqbOYzHvyTWsGhzhUhQdnp4RTW\n+zD3VOuuMb0W9NChwNLI1SqOLdwBCdpEeoR7HMGLX0uHmX1PaVraahrDPY4Wkb7IHjbE5Icm\n+GUi3cc95la3uyRVXss9zwxW0B1Bjx0KLI2sIoE7H8HbQR24xxEKd60FjeSOSfCORdXgHkhr\n+L4VJa7knk1dHYotpcbp9KDku84UMdzsxyAEpobtk2BHDwWWNv6Ij7PC9W6rhJ/lHkko1ARq\nZYX9FTfjICw1bI+nW0Ln58EsPWkn97Bb2PVVMdRgK/ccM5lHw4MdPxRY2hhFY7jToYYBdIB7\nJKEwKVThIHdI1DCcdnEPpfn92p2ip3DPpO622ppwD7x1vd2Aiky3wje4gKSWC/8pyAFEgaWJ\n/4WVt8RO1TU0lHsooRDvRsWY/0g/yUO4ElawMrcVpZu2cU8kg6b0X+6xt6jzU8Ko/R7u+WV0\nN00NcghRYGkhowUt5c6GKlKLlkjnHkxQdLqibQF3RNRxLKoq92Ca3JdtKSpEbhOXz1IaxD34\n1nSiPJVZzj27rI4Wjzkd3BiiwNLCdmrBHQ2VdFThTFXQyI0ONIA7IGppQj9yD6eZXV4QScnb\nuSeRR2r58F+5x9+C/uxP9n5HuCeX2UhaGNwoosDSwF+JkVZZ191Hs7lHE5TMpGTL7LIYRTu4\nh9PEjlSm4rO5p5DNBFypQX1PFqfqpr7BqSoOxhUL7m7iKLA0MIaGcQdDLbiYu3HtpTJPcedD\nNetwtF/APu5A9p4HuGeQz6HYxMvcc2AxP3WhyNEnuCfWAAbS6qAGEgWW+v5jq2CJI9xdcDF3\no3o/Juox7nSoJzW+HPeAmtRvY+zU0EJJCEAv2sY9C5aSuTk+dK/NkNeeyDJXghlKFFiqu1Lb\nZqGbjk+ktdwDCnJOVrLN4w6HmlrS59xDakbnFsZSOYuc6RCw7WE3ZXJPhIX82IFiJlvm4IMg\n9aDNwYwlCizVzaVu3KFQUYqtPfeAgoxrt9Ig7myoaiI9yj2m5nP1kURKGGed/eWBaknPc0+F\nZWRuiaebd3DPqGGkhFe+HsRoosBS21uOkpY6HKJq+BnuIYWCxlJza33H3EI9ucfUbK5vrUBR\ngyy1tgnQaurGPRlW8WsXisbuKw9d6ckghhMFlsou1bJZ69Ihg2gv95hCAeupkiWu4O6hZMIN\n7lE1lfQnq1G4EMqXgfRQ04YfmFWxtxg1sMop8OrYZq8VxLUgUWCpbBJ1506EutbSQO4xhfye\ndSQ8wR0MtXXEBbn9kL6nJjm64JecbLNpDPeMWMHfAyliLHZf5dWe9gc+oiiw1HXCVv4wdyDU\nlVoi4Rr3qEJenyaEW+g8imyzaQn3uJpGxt7aZO8QivfFUXC8VNRJ7kkxv+fKUY3N3FNpOJvD\n6mcEPKQosFT1c4nwddx5UFs3HEBqMH9UpuncqVDfHlsr7oE1iXSxvAq77XHuCTOU0bSYe1rM\n7vIUm30QTpgoqA0dDnhQUWCp6XorGsudBtUtobu5xxU8XWxinTvkeKrmOMc9tGbgKq/aW+Me\n3+o5EFsSFxsNyvt1qOwa7mk0pI22hgFfBQQFlpqm0y3W+wH7aFRF7nEFDzdup3bWS5moHx3l\nHlvjy0B5Ja8PbeKeGzNLXxlBXQ9xT6JBtQp8zYQCS0X7qMx+7ixooBV9wD2ykCNzJDWw5n78\nlTSee3CNLn0PyisFKY7qQZzsFep+uJUSFnJPoWFtCHwXFgos9bxnqXuX5JqJwxsMZC5VsWIV\nLzoWXZl7cI0tq7zCsVfyOtAh7gkyrZQi1AwX/FDWOuCjsFBgqeaPCra53EHQxFOORtxjC26r\nKWkndyC00py+4B5eA0vfXQt7rwqx0daUe4pM6nQfirqbe/oMbaPtpgBPJESBpZZLzSx275Jc\nDel77tGFLBttxa17E9ZJ9DD3+BrW9ZQaZL8N5VUhmtFL3LNkSqlJVNu6KxV1tKV9gQ0uCiyV\nZPSmNpY89Fg0Hjd8NohttiKW/BU6yw7qyD3ABnXt8Srk6ICtYKEeRHwCcPYucgw7zj13RrfF\nXjOw+0ygwFLJFKp7hDsFWkmx3co9vCDZGha3njsMWqoYeYF7iI3owtpy5OiCy4p6U5fe5p4q\n03m2AlW29DpFJR1pe0DjiwJLHSuo/D7uDGinph2XSTaAx2zxj3BHQVN96Bj3GBvPyYXFKUJI\n4Z4bE1hMvbgny2TO3EX2AdY8J1ll2x2VrgYywiiwVLGZSlj5DpnDaSv3CIPzAUqw+HfNlTSK\ne5CN5vNx0RQ7AGd4+SK1qu1T7vkylYNJVHkt96yZxB30SCBDjAJLDXvD4jdyB0BLW6gr9xCH\nvIwZVGITdxA0djy+dOB3/bKgjH91tVHJ0Qe558Us5tJg7ikzkR/uIMdg7L7y0e6okoHcaAIF\nlgoOO6If5p5/bVWM+Id7kEPclQFUzso7SbO0pze5B9o4Tj9UnajGbGwCfZZawf4V96yZxdUV\nMVTX6t/Y1DSIFgYwzCiwgnc0PGoV9+xrbBDt4h7l0HayJdXay50C7c2hudwjbRAZL94ZSeHt\ncXM4v9xDI7gnziTSqlORqVY97V0TBxNif/d/nFFgBe1geOQK7snX2gbqwT3MIe39StTSsiep\nejgYUZt7qA3hm8WVicqMwKFXfjpRzvEN99yZwQcdKaybhc/K0sR4GuP/SKPAClaKPcry9VVa\nWrmoQH6ABnXsjLYNDI1vm01xMXfnqY0tbRTRbkVozLi6ZmIXlnffDgmjBo9yT5XpHCtn/9jv\nsUaBFaSHbLGruWdeBwNoD/dIh6zLYyl6HncAdDKFHuAebl5/P9HZQbabplj0fpNaO1HO8TX3\nFBrcd6PDqdIi7okyo/nU2e/RRoEVlPQpVMzip85neRS/EXL5tD5VDJmDUffYk7nHm9FfT3QL\nJ6o6Ygf3NJjXLBrKPYuG9uEQB5WZiZ2jAalPT/s73iiwgnG2O5UPkQssl4s8yz3aISlzfTR1\nOsw9+/ppELL3vfxtY0cHUaUhuN9gME5UsONaWErST3QkKj8DN8YJ0PqwWtf9HHIUWEH4pCY1\nCJUjBQfRk9zDHYq+b09xc7jnXk8TaTX3mHP49qGWYURVh4XMrkrNzKPe3JNpUD8uqUhU5z7s\nvQpcZ1rj56CjwArcEzHUI2QuUrMJ1xrVX/q6WEp+knvqdbU7LPR+I/xgcQMiW+1RIbIzXFup\n1W24I2FBf21rF0aRHa19qy3N7Ykt4uelGlBgBepkH4qezT3hOqriwP0IdfZBM4oNuWvVNKKQ\nOko5/T8zqhA5Gk3cyT3wVrGMbuOeVKP5Zt1tDqJak3DmRLDG+XuvABRYgcnclUi1t3JPt55G\n0AbuQQ8tZ6c5qGXobXan0DLukdfNlbTRJYmiWs7Elk9FDegZ7ok1kN/3j69ORNWGhdTWSivH\nq9he8Wv4UWAF5NP2FDHiBPds62qHrTn3qIeSjJQkKhWKJ1M/FR4i1xo9s6tfLFGRjgtC4Qqy\nelprq5/OPbmGcPH1dUOricVVVJMJ1r/Llk5W22pf82cOUGAF4NQkB938OPdU660B4T5funm9\nKUUMCs0tbwt6h3v0tffj+g4OolI9Hwitb2n6aEtPcM8vs2ufHVrSv6ZdLK6iGw1ZGTIHCuuh\nKy31ZyZQYPnt4ooilBQqF370MI0WcA99qPhugI1aPsE94Uzm0TTu8dfYewsbiZu+qoNxMW1t\nbI8oc4F7jrn8+vLjs4SaYvFOFFW7+9QNKOBV9lTRSH9uNoECy0/XN5WhuFFHuaeZwcGoihnc\nox8S/poRQdUe4J5uNkfiSvq1E95crj0/qSKRo+E4XExUO/1oPvc86+7y+08tHnhznFRZUUz1\n9iMWbA2102N0ci+18mM7iALLLxn7qlNEn6e455jHbfQ89/iHgIv3J1DijFBeOd5OR7gnQSNn\nnxpYRNz8tb4nRNcgejlYLPoH7rnWT+Y3hxb2rh4mVVaO8s373P1A6J0Zo6vmtM73yUGB5Y8X\nG5O9Swr3BHNZQYO4J8Dyrq5Pori7QvPgK7e1dDv3NGjh542dIogSuy8Nxd3fOptGvbinWx8/\nH5jVLkEqrWJrdRq1aCuu0a6DnXExvh+NjALLd591I1urEL6RRWqZyNPcc2Bt17dVpMh+Ib97\no6r9J+6ZUNvHy5vYiCoPeiSUd03qJ7WW9S/VkPHRowPKiaWVrUzrYYtwlqCOZlHzG77OEgos\nX/092UF113DPLasR9DD3LFjZ9e1VyHHHLu5Z5jfZWqdTpP/fPdWIwuqPCdXTFhisC6t2mXve\ntfTdlv6JYnEV33TYclxDTXctfT+TEAWWbzIeT6TSc7knltkuR61M7omwrOtPVCVHVxz6LDoY\nnWSZw9wvp0rXEo1sMT1UblpqEHdY9zj3q89OqSEWV8XaTN6E/aEs9hZ3/NfHyUKB5ZMPW1DU\n0NA+MkbShl7gngmLuvJYJXJ0xo7+LALt5p4QVZxO6S1dS7TD/MPcIxpy9pcI/5h7+rXw966+\ncWLB3mTMRu4RDmXLbZXP+DZfKLB8cGm2g27BvoW0tFWhcuyozs6tSqLw7kiY2xZbE+4pCd4X\nD7a2EyX1XIlLEXG4j5pa7nruJ7d0CicqdccSfNdn1pd6+/ZjDgos716pRiUXcs+oMVSxf8s9\nG9bz+5wEiuqFc6s9NKVXuGclKNdenF6dyFZj2AbukQxdrWkldwxU9dfWDmLBXgWXpzWCY3Vo\njU+zhgLLm/MTbDbhIPeEGsR0mso9H1bzyahIih+MI3TyWEndueclcL890SeeKLLZZNTMnPYk\nRH7CHQXVXNp7RzhRtRG4Y7NBPFnU8ZIvE4cCy4sXKlH51dyzaRhHi8X9xT0jVpL5bBcbJY07\nxD2vhlPT9j733ATk+qtzG9mISnZfjF9xuM2hRle586CK9OeGxRFVHIbqykBWOhK/82HuUGAV\n6tx4W1gfrCpzjaAl3HNiHRc31yGqNQfH6BS0gPpwz47/fny8TxEiR/27NnAPH0ja00zuSKjg\no1lliBL7beAeTchrPNU76332UGAV5rmKVCG0L32V3/7YEiF7H1WVfT2jKNlbP8Q9o8aUWtVk\nu7CuPDdTrJapZJd5B7jHDrIdKGN7mjsXQfrj4QZEMZ1W4HoMxtONOl33OoEosJSdGYndVwUM\nsNiho0zSj3UJoyL9Q/a2S14tpG7cc+S7L9d1iyGKuHn0Ju5hA09rHcV/4M5GEC4/1c1OYcmz\nsQ0ypOONaajXUwlRYCk6kESVHuaeRMPZF5N4jntmTO+XpeWJak7HirMQdegj7mnyyYUTE6sQ\nUTlhMS52ZTjjqbFZL+ie8eqoBKIqo3FnB8M6WM37OV8osBR8250cdx7jnkIDutO6l0jWx/Xj\nd9gpqtNa7ok0uIds/+GeKe8+Wn1bBFFU84m4C44xtac7TXnziY/nViQq3guXZDC0PeXoXi8T\niQJL1oX5UVQP+/vlHCwa8zP39JjYp/eUFr+XTsANxLxqZ/AC6/T+UWWJqHKfFfgeZlhHapjw\nrJxvV9wkVu3tl+DsF6NLKeOtwkKBJePG1jJUbAaOK5Q3mQZxT5BZnXq0CVFsN+y88sWxK9zT\npezqy/OahBHFtZ76JPcwQaF2Jtp2cKfFL9+uSiZyNLkHl24xgx1l6O6MwqYTBVYBN3bVpIi+\nOBdIyYmqtue558iMLuy5PZxsjWbhyCsfcU+YgvS3V3WOIQqrfedD2MVgfBtiHce4I+OrzPcW\nNRCT1WAyrjtsFikVaGBhV1tDgZXPhceqkr0TbgtXiDVhVS9yT5PZXNjfN5qo0gicNug77jmT\nceOt1bcXkQ5p734ffuQ1iZWRkancufHF+ePjyxM5Gk3ewz1i4Ie9tajVSeVZRYGVx4dTEsjR\nGVfMLVwPmsA9UaZyakcPsbpK6r+Be+LMhXva8jn//OIOsWJxVarDDJTJZrIsIuIQd3i8uP76\nsrbhRLGtZ6FuN5vDLaniO4oziwIr19cPNCBKGIA7iHlzuLzNNHvduWX8b3mLMKKy/XDglb+4\np87Dt7snNbKLxVXZTtO3cw8L+Gt5lH0Ld4KUnXt+iVS426r2xdkSppQ6yBa5QelcVRRYWS7+\ne2ZtInvyHGTcB+siin7NPWNm8Pmm/iXEVWetYRu5Z8yMuGcvyz/PLbujpFhbOWr1nIdrEpnT\n6nialc4dJBnnXnt0RN0w6SfnLrN3cw8SBGxhHHX7VX6KUWA5nT8fntkinCgiGb9++2oK1fqb\ne9qM7fwrD/aUtsvF2s9CqgLDPYXOS/9dP6yWTZzE4reMfBDnJpjYljLUsZADZfR37n+75grS\nBWopok6veSiuTG5HAyq6WfZswtAusDK/PTz/9iQx5WFVe+JCzP4QqAXuSSgv4/unVw6qJX0v\nLdZ6PHZdBY5zEv9+ee3w+g5xDqPq9p6DU15Mb18ylTbEoe5/vLlv+cg20jaHKO4mYeqjx7mH\nBlSQOj6aXpOb75AtsP548dFxt8RLMS/WdPAyXJTBTydaU8t/uOfQYK5///IT9w1oFCOFKqpO\nMJddegAAIABJREFUj1k4VyI4PLN4+vVts7qUd+1bqNl96gZcicEaUkc46M7feSIluvrdq7uW\nj+taJ9pVWVFig25jl+FUCStJaf1fuYkPvQLr+tdPrxnTqrgUc1vZVkMX4UqBATnWmmrjOCzJ\n9V/eOLJ+9uDWFeyudaejYstBczZjuxw8fafx7KfPbJk3qGkx1yQmNOo1YwP2LVjK+qoUt1zn\n68tc/e61Paun9m6aZMsqrKIqNRVGz9+An5utSDYBIVRgnfvo+Nop3WqEu0qrUsl9pj+CHwWD\ncOJ2it9uyvt8qeKvz/9zYP38u7o3dK87qVjN1n0nLX8CpZVatJ/ECz998MJTjy2e3Ld1jZis\nSbSXSRYmrtjL3XXQwIlx8VRyqQ6HYqX//l7a1iVj72hUKnvVYC9Zp02fcQvW4/qhViabBcsX\nWJl/fvxMyrIJd9QvmpX12Opt7py9DqWVCqZFUZu3uedXBxlnzvz93Xcfv/vyvw8+sW7ZzJG9\n2tRLcmSvOim8VJ1WwsiZK7ce5Z4Oy9FgKtNPfvF62s51i6YMvf2W2qXDKUd8xcYd75y6fCtO\nIray/QNjKGKAwvleQbr007vP7Hp49vBuDZLC3OuGpHpteo+dt+ZJfOkKBbKxsGaBdeXXj17Y\nu+6+kbcnl3OvRCPKNew09J41+G6qom3JRJ2OXuOebT9dPfPLdx+8+8LzBw88/viaVQ/MmTN7\n3Lhxw/v379+ro6hFsqhWVVHZYqI4khNVsnqTDr1Hz1y+EXnSjkrzfear/57YvnLmiNtb1Cjq\nOYmxpas1at19wJiZSx5JQXUcIvaPLkcq3UT87M+fvfHMU5tX3ju2b/sG5aNzchWeWKt59yFT\nF2/A2cOhRTYl3gqsfx00jAMpKds2bHhk1aqVCxcuXDBDMnFclhFDRL17Ch3b3dKwVoUSkTlh\nD4tLqtawVZd+I6fMAQ0MLE8U83IAaydtcpXiisiG1atWrRAzMmPG9HHjRknB6Nax/S3N6tep\nWr50fKxsxZSHLUpSVFIySVS5cuVates1Sm7R5rYuPfsPHXP3PdzDHhpOBZir/Ts2PLho1oSh\nvTo0q1OhqD13XqOLl6tev2mbzj0Gjhg/jbt3wGTIJwHkaseqpQtn3D1uSP+eHW9tWr9auRIx\neVYZjviSlWo3btWp5+AxCFaokl1feSuwkrxvjyDErQ9ghVXK+5+FEPdqALmK8f5nIcQFkqtw\n738WQpxsrrwVWEW4Wx1WrFixBOY2hIttiGduQ5TYBu6tR6zYhsiCi18KYIXFnCu7AVIlijTA\npIrixGZEcDeCqKjYDJvHc5MVWPFi8xm3w1KWfNg9qxWb9IM638e71k0+ZjiQXDGMrMLaVkcO\n/u1eEbEJdu8v05DvG96ACqyqGjffq8jk5OQGzG1IENtQk7kNSWIbKjC3obLYhpIFFweywmLO\nVbTYk5t4myApJTajEncjiKqJzSjO3Qiim8VmODyemyxXtcTmM35vSBQ/vgrfx4dJxy7yfTxV\nFT++hG8vNUmuqog9StT/Yz3Eiy2ozdoCqic2gfcraGmxBRV9emVABdawjsxuE/vXhLkNbcU2\ntGBuQ2uxDS2Z23CL2IY2BRd/FMAKizlXUqqa8jZBcqsBJrVj1oH9bbkb0bFjE7EZt3k8N1mu\nmEeRO0tSgcX48S3k101yTJIrP3qkkXZiC5qxtqBjU7EJ7VlbIP3/6hafXimbK8OfRfib2L9O\nzG14TWzDBOY2pIhteIi5DQvFNhxhboM6vhN7InA3wul8SmzGcu5GOJ0zxGY8x90Ip1P6DnGG\nuxGBGys2X/Zizvo4In78Qr6PvyoVWIyXxZstfvy/+D5eA/PEHp1gbcG7YguGs7bA2Udswpes\nLdgrtmBF4G9HgeUDFFhZUGCpDAWWJxRYwUCBhQJLZSiwnCiw9IACKwsKLJWhwPKEAisYKLBQ\nYKkMBZYTBZYeUGBlQYGlMhRYnlBgBQMFFgoslaHAcqLA0gMKrCwosFSGAssTCqxgoMBCgaUy\nFFhOFFh6QIGVBQWWylBgeUKBFQwUWCiwVIYCy4kCSw8osLKgwFIZCixPKLCCgQILBZbKUGA5\nUWDpAQVWFhRYKkOB5QkFVjBQYKHAUhkKLKf1C6yMc+fOXWBuww2xDZeY23BNbMMV5jZcFttw\njbkN6jBCqpzGmFTRJbEZ17kb4XSeF5vBuIkOljSKN/g+XsrSZb6PzxQ//hzfx7vWTQbIsIr4\n17bp/Nu9i2ITMlhbEOQ62vAFFgAAAIDZoMACAAAAUBkKLAAAAACVocACAAAAUBkKLAAAAACV\nocACAAAAUBkKLAAAAACVGbPA+nbT5IG9Bs/e/Wfuol+3Tr2z9/Clz6Xr2pCTAwThNcY2fPnY\n+H533v3Ip7lLdG5D5jsPj+vfe+jc/R5XgOSZiuAZJlVO/mAZIFoSU8frA8HDDPdSfZvPN4v/\nE/IYq/PHO00eHhlGyJOTecXAnyqJiskyYoF1bYN7fHsfcy871Ct70cQ/C3uryjIXCB7bQd3b\ncGNTj+xP3OS+AKPObTh9r3sq+qa6l/FMRdCMkyone7CMEC2JueP1utwGUdfmc86i/KZQx+6b\nOzwy+PPkZF8xsKdKomayDFhgZS4VuzE35chjw8V/s+/gcVx8uPDQ0ztGCcLI8/o15RlpRN3b\nQd3bkLlGEPqvTz20VIz8PpY2XBonCHc//fEXbzwmxutpliaoxUCpcnIHywjRkpg8Xs8KwtJ9\nbs9mLdO1+ayz+Ou+XFsFYb7OH2/28Mhgz5OTf8XAnSqJqskyYIEl5qzvu9KDK+sFYbDrZgF/\n9BV6vS09uLpcEB7VrSUn+wt35WwH9W/DC4Iw7bT04L2+Qu8zHG3YKQiLs3aJvtdD6H+eowlq\nMU6qnOzBMkK0JCaP1xFBeCnfIn2bb4xZFG0Qev2o98ebPDwy2PPkNFCknDypkqiaLAMWWBMF\n4ZmsR+livejaKG7JKaevDBV66nVD2Mz5wtBDOdtB3dtwbYQw8O+sh08t2vYzRxvGCsJX2Q/n\nCMKrHE1Qi2FS5WQPliGiJTF5vHYJwlv5FunafIPMotP5cQ9hj1Pvjzd5eGRw58lpoEg5mVIl\nUTVZxiuwzvYQ+rhvrviYIEj3E08fIvR235p3jyAc1akl/xK/UTzt3g7q34Y3BGFv3iW6t6Gn\nILinYqMg7OdogkqMkyone7AMES2JyeO1SRA+ybtE3+YbZBad1yYIY6/p/vEmD48M7jw5jRMp\nJ1eqJKomy3gFljP99M/uh9sF4bD4zxeCMNe96DNBuE+fdvzZX1jszNkO6t+GhwTh17xLdG/D\nAEFw301djNoxjiaoxSipcvIHyxDRkpg8XuIofp93ib7NN8gsSnte3tP/400eHhnceXIaJ1JO\nrlRJVE2WAQssDw8Iwn/Ff8SN0Q73oms9hIG6fHbmfcLA07nbQf3bMFoYLv7vhe8+/8O9RPc2\nLBWEj7Ifzs3ab8oyFSrjTJXTAMEyRLQkJo/XEkE4mXeJvs03yCz+3EtYwfDxJg+PDO48OQ0T\nKSdbqiSqJsvQBdb5vsIAqZbcnnMwv2iYIOhygsjTrpPNcraDurfhSg+xUv50gXTS7Mj9V3na\n8LkgzMgq5v/XQ1jA0gT1sabKyR8sY0RLYvJ4zRbb98qy4b0GTd2RvT3StflGmcWlQq/fnPp/\nvMnDI4M5T07jRMrJliqJqskydIG1JvvQsocF4fWchVME4WfFd6jnz/7CQqfHdlD3NvwgCKue\n6Zl98Y1p/7C0wXlUEEYdfv/T1x/pKUz+i6cJquNMldMAwTJItCTmjtdEQZiUPYi99rsuGqRr\n8w0yix8LwuPZD/X9eHOHRwZznpyGiZSTMVUSNZNl5AJrvyDcc0N6sEIQ/pezdJYgfK39Z2fe\nJwyQ9tfmbAd1b8NngjCl18gXfr9++umhgjAvk6MNTuc792X9v23krouu5wxNUBlnqpxGCJZR\noiUxdbykK6oNevjQiS0jxQe7pSW6Nt8gszhH6OM+q0rnjzd1eGQw58lpmEg5OVMlUTFZBi6w\ndgvChHOuR8sE4f2cxXMF4QvtPzwt+6z+nO2g7m14V5zgsWddD38fJAhvcLTBeSlleFbUesxi\nGga1sabKaYRgGSRaEnPHq68gbHb9lHBjq9iFb5w6N98Ysyhukx9zP9b3480dHhnMeXIaJVJO\nzlRJ1EyWYQusq6sEYdLprMd5CsiZetSwf/QX7nPtpJXf0aBHG94Rci+LckwQlnO04a/xgvDI\n55dvnH5pkiBscnI0QV28qXIaIljGiJbE5PG6dNF9spFzuSCsdurcfGPMotjzX9yPdf14k4dH\nBnOenEaJlJMxVRJVk2XUAuvUNEGY4770xFrPn0DvLnAmqfoy5wr9s245lLMd1L0NnwpCL/eN\nJU8Lwp0cbbgv5+C+q/dkDYTuTVAVb6qcxgiWMaIlsU68vhaEgZk6N98Qs3imp3BPzhNdP946\n4ZHBkSenQSLl5EyVRNVkGbTA+myoIKy77n6WIghpOf9psCBc1PrjU3Mu+52zHdS9DT8KwrCc\nJ/0E4br+bfhKEKa6H38sCLOcDMOgJuZUOY0RLENES2KheGX2EYRzOjffELN4KCfQTn0/3kLh\nkcGRJ6dBIuVkTJVE3WQZs8B6s7fQ41ju02cF4Qn340uCMETrjz/dTxj3epZHBWHb669/r38b\nnNd7Cv1zngx2XVxW7zYcEYQU9+PLgtAjnWEYVMScKqdBgmWIaEmsFK87BeG0zs03xCxOE4S/\nc57o+fFWCo8Mhjw5DRIpJ2OqJOomy5AF1pu9hH6ed2X6VsjdY/ieICzV+vM/E/LZqn8bnM5J\nudedE4Pfx6l/G3Zm3SjAJaOn6/If+g+DarhT5TRKsIwQLYmF4nWthyBc07v5BpjFvwRhUu4z\nPT/eQuGRwZInpyEi5eRMlUTdZBmxwPqyr9D/c88FmaNyb7C4yXWZRm3JbQf1boNrv+SJ7Ief\nZO2p1LsNYi2/wf34pCD0zOQYBrWwp8pplGAZIVoSk8frrccWv+x+LK51Jzv1br4BZvEFQdiS\n+0zPjzd5eGSw58lpiEg5OVMlUTdZBiywLo0Wen+Ud9EuQdie9eivfkK/SwXfo5mcQ2X0b8N3\ngnDX5ayHKwThKYY2fCwII25kP345u4jnm4rgGCpVTtZgGSFaEpPH63lBmHgt62HmXEHYJT3Q\ntfkGmMVNngfL6PrxJg+PDPY8OQ0RKSdnqiTqJsuABdamgrerPjtI6PEf6cH52dnTrpfc7aD+\nbVglCItcc3lYEPr/w9CG9PGCsMl1UQHnybuyC3e+qQiOoVLl5A2WAaIlMXm8rg4VhAdcp6Re\ne1QQBrguH6Rv8/ln8V5B+MTjqY4fb/LwyODPk9MIkXJypkqibrKMV2Cd7CX02LUvR6pr4cs9\nBGH+gdTNYgZn3vDyB1SVux3Uvw1nRou1dMqzB2cJgvAiSxs+7iUIM9I+/vJ/KYPEz03naIJK\njJUqJ2+wjBAticnj9XZPQbhz0/ETm4cLQo83spbp2nz+WRya7/7EOn68ycMjgz1PTiNEysma\nKomqyTJegfV63qNUxmYtfb5v9vP5+p5867Ed1L8Nv0/P/sB+zzO14cMROROx5jJPE9RhrFQ5\nmYNlhGhJTB6vNwe7Wz/0HfcyXZvPPou989/4VsePN3l4ZLDnyWmASDl5UyVRM1lmKbCcp1Km\nDeozctWbOrfGczuofxvSX1oysved03flnrOqdxuuPf/A6P69Bk9//Du2JqjCWKlycgfLCNGS\nmDxeF08sGt6n78il/7qau0zX5jPP4jXx/0r5vs/r+PEmD48M9jw52SPl5E6VqwXqJct4BRYA\nAACAyaHAAgAAAFAZCiwAAAAAlaHAAgAAAFAZCiwAAAAAlaHAAgAAAFAZCiwAAAAAlaHAAgAA\nAFAZCiwAAAAAlaHAAgAAAFAZCiwAAAAAlaHAAgAAAFAZCiwAAAAAlaHAAgAAAFAZCiwAAAAA\nlaHAAgAAAFAZCiyXNCJKkR68KT54qJAXfiD+95WF/Hdv7xf9PKlGTHiphX63MfBPBPPxFjQw\nk5z1S+B+Ef/ErGDbkZMqxCvUGCWCXqnQUANBgeWiY4H1YTGSDPG/kYF+IpgQtoBWYpStGwqs\nkGWUCHqFAsuCdCywWkjllT0cBRYUBltAKzHK1g0FVsgySgS9QoFlQTmT+sPkyZNfLOSF3lZM\n3t7v/FP8A3GH0p2XAmilhy5Uy9dPBEPInTGfYAtoJQFvNHJTgwILgmGUCHqFAsuCfJ7UoFdM\n/xP/wJRg/oBLZjH/NtfAzd8ZwxbQSgLdaHikBgUWBMMoEfQKBZYF6VdgSZ+0LZg/4PIVocAy\nF39nDFtAKwl0o+GRGhRYEAyjRNArFFgWpF+BdUz8A08F8wdcdqHAMhl/ZwxbQCsJdKOxCwUW\nqMMoEfQKBZYFma3AmowCy2T8nTFsAa0k0I3GZBRYoA6jRNArFFjBeEccvZedzs9GVoqIrjH2\nU2lRxqHOiY5izZafdb3gdvEF73m84Vnx+bSsh+lHx91UKrxY9Z4bTnr+yWv7BtxUPCKpw+q/\nPRb+uvqOykXsRar123rRvegN8S/9x/n31MoRJb90Lcg80q9KdLF6I15WOotQ5o/4fBZhdkf/\nfqBpgqN441nfu/7rZsoxxPd25u9hSs4fKZ2/xTJDJNsQ0NS3y7tUjLPHVxMePeV6nnfGCk6w\nzLTlCVpqGFHl37OfvDKtcanwEnX67T6vW38gEHLrFxf5GSywKsibGmnrNlv8Z16LchGJN9/7\no/fPL2z1lSdevq5B8x7eIDXv39mP8we+0I6CbrgjmC9ZPcU/8I7Hf94jPl9UeEPNTu8C6zNx\n9NKcS21Zs+bY43T+3jB7Cst/Jb3giPhoqscbRuUUXM/Xzpnt2EXpOS94oXLO0g3uZdfvDc95\nbeLx7IXSOuXpc3WkZR9Iz/9o735J57NyBZbiH/GpwMrq6MGY7D8QvkNamL/A8qmdBXqoXGDJ\nDZFsQ0BDVyaG5U7Dg5nO/DNWYILlps0zaO/GivH4Kuvx5y1yXpu0X++egR9k1y9OpRmUWRUU\n3LrNc+6Mzl4UtdPLxxe++vKMl89rUKUCq2DgC+ko6Ic5ggWSdUB8NNfjv/cQn39VWEPNT+8C\n6xtx9A4+TGQrFikNZ8SXZ6qJdVZxu/SkobRtuZ4ozu71nNffKE5Uz/Voh+s1FRrXipD+7eN+\nyS7XYnuUa3amZy3LFFzPilQsKv1jO5S19Evx8YF7XP9F2q6ddxV2xRrWEUuPlsepQIEl/0d8\nLrBcHX1KXPFEFHdIfyDs/8SFaR06NBAf39ShQ4eVvrazYA+f69BB3OLGiH+kf54Wyw+RbENA\nO5mdpWF2VKiW4JqxGc78M5Z/gmWnzSNoPyaJK6i3sx4/Hy+9pnzjGq73rGLoHvhGfv2iNINy\nq4K8qZG2bsv2iN9MI4q5ypmw/xT68V5WXx7x8n0NqlBgyQReuaOgH+YIFkzW5TiiGh7tE2uA\nJoU11AL0LrB+FEdvaXTRjeecGa/XFx+PH0eNn093XtojzXmq9Ipp4oPjOa//t/jsQenBW+Jk\n/T979x3YRN3/AfxzaZuWbkoZZU8BGSIICCLKUBA4ypa995IhArJkCSJDZAoiG9mjde+9J4r6\nuH8ORNnKHs3vLqtJm0sz7u5zSd6vP57nci3td7z95tPLDdPE36Wt8xuLSztn2b7+oVR0xz70\n43XLX0sT5fct685V0laxtfJRyR+GSZspp6x7f5I2VydR9cmPTv0/6eV4+U3tGammu7S7LN1F\n+Qoszz/E5wJL7uiCFGHwFxbL5VfkqqqZ7Rtcz8HyqZ0ee1gj94Px3BZ7HiKlhoBG5Dee+i/L\nZdLRx+W16j3rXpcZyzPBnqctN2hnpH8abf8s5ifpB5rG/iJtnV0l/+y9+nUL/ON5fVGaQc9L\ngWtq5He38fFC/89yLJdfqCm9uM3rry9g+cqNlx8rqEKB5TnwiCo75gh6SFYv6f8PO79hq/Tq\nMS8NDQd6F1i/SaMXn/iFbVsqbROFxhesL+TBHiJvfCltdHR+/wApC39K/59Tw2XYv02Wiuhf\nrJt1pTef1217X5Wq6rLWD1gqSv/oM/v3jnLWH/8nbTWnifYj2Eel6jn5e9v276Uof4Hl+Yf4\nXGDJHU0Qttn2/lNY+pPAdnaCa4HlSzs999BTgaUwREoNAY20IMr4z779QxGiHtYtlxlzn2CF\naXMG7Upzacq22L8uLT6OqbR8I31vuYta9gQCp7C+KM2g56Ug77tbIWGr7cVxKVfCX95+fwHL\nV+465scKqlBgeQ48osqNO4IekvVM7qERiUgUdcxLQ8OB3gWWPEe2Q1IW20ewpm9t21ekIreB\ndUual5gT9u+4IhUEd8sbr0rf2sb5Ux6Rj4PJG6+Ty2075dO1nrXYjhDc6foLW+X+6jscdcsK\n6cU0xzc9nb/AUvghPhdY1t82zLF7pPTiJeuWS4HlUzs99tBjgaUwREoNAY2UIOrtfLG4Xpf5\n1o0861TuBCtMmzNo/aSNRfavfipt93d+70rp1RYLGJLC+qIwgwpLQb7UDHd8zzjpxQtefn1B\ny5dzw58VVKHA8hh4RJUdcwQ9JetKGlFNx76zsfZfotDQsMBRYJntBx8tM8jlA6v6RCWtG/Jw\nO062fE7athbbAxylhdU/UUQ3yhvDpd1HHHtfKF2n5Q5549KvH3zl/N4yRDc4fzW96NjdSnrx\nrePFtSL5j2B5/iF+FViC85K9jc6lyfUIli/t9NxDTwWWwhApNQQ0Iv1NkJl/b951yjnBCtPm\nCNpDlHtWi2Ws9OIb5/deiCfqoHbrQR0K64vSDHpeCvKmxuS8cEu+/uoJb7+/gOXLueHPCqpQ\nYHkMPKLKjjmCHpM1lOyntVusd9iy1XYKDQ0LHAXWrY4X8qe+cxwvRKIk68Ypx6lvkv7STutT\n+6oRxeae+m65RZpp+UrSKrbLG7ypS1TU+asTrzp2S392Fcv9pnvzF1ief4hfBZazVre8LL1a\nat1SvA+WQjs999BTgaUwREoNAY3cLP0J8VG+vXnWqdwJVpg2e9DkRaiH87qsOkQVXX6mtDCl\nqd16UIfC+uLTDDqXgrypudn5LfJxzyU+Nyb/8uXc8GcFVSiwPAYeUWXHHEGPyZIPaz1s35be\n8eP/89LQsMBRYA10vNggvdjteNFVeqOxbXVzlr7yJ4TWo5nnTEQ3ufyYvtK3vC8V3dLuJgX8\nxoZERZy/+nbH3tPSi8a53zSngALL+UP8KrB6OXe/6/xnigWW53Yq9NBDgaUwRIoNAY0sloa4\n0NSf8uzNs045J1hp2mxBe91M1NJZfl2Qvvcul++dKH2P17MggIvC+uLbDDqXgryp6ev8Fv/+\nO86/fDk2/FpBFQosT4FHVNkxR9Bzsq6XJKpn25Q/IezhpaHhgaPAmuB4If8n6jwh6F5ngSVf\nOfiAdetZaet1eUO+14DrIWb5w8UDtt3dPP2aS7sHNyzuuF+HS+HSx/EN30ovuuZ+/0ZPBZan\nH+JXgTXGbbenAqvAdir00EOBpTBEig0BjVy5zTqZ1UbuPeWyN8865TbBnqbNGrRvUonq5N4K\nUP6H8eVyFbYXY2A4CuuLlxn0tBTkTc19zh/ow3/HXpcvx4ZfK6hCgeUp8IgqO+YIKiRLvmDw\nF+vWZrKfG6H0XhwWOAqsyY4XGx31kyy3wLpemqik9WKWfkTlrJ+PyCfm9Xb5MQul15stls/I\n9Xy9XNtLkqvcwmW04zvk+5v3y/0Huz0UWB5/iF8F1kS33R4KrILbqdBDDwWWwhApNgS0cq63\nfT6jblvhfMvJs045J1hp2uSgjSsv/U+5M86vHab8XrSAASmsL8oz6HEpyJsaf/479r58OTb8\nWkGVbjTqIfCIKjvmCCok6yNyfLDYjqjoVS8NDQ9GLLAsD5Lt+oQrqY6rC94m+00c7B6TXq+x\nPXTEdbfdXGtAyt/Wvpck3bVwccbjLfd/6eFGo55/iKoFlg/tVOihhwJLYYhQYDH4sE+yfZVK\nmWO/M7vSOqU0bXLQbPfH7uH82vse1sZ92vcG/KewvijOoOelIIgCq4Dly7Hh1wqq/KicfIFH\nVNkxR1AhWZbK9s8Dz5gdf2YqvReHBUMWWPLRxZ4W2yeEtksO5HK4l8uPeVh6vdVi+YJcPxR2\neEV+EM+o/7O/auixcPmQ3Krm7ZS3wFL4IWoWWL6003MPPRVYCkOEAovFlVcm1LQtXm1td5lR\nWqeUpu1z6z8uUpVsB7SsviaXzxbByBTWF6UZVFgKAi+wClq+HBt+raDKBVa+wCOq7JgjqPDO\nZZlOJMh3tpQ/IXzfW0PDgyELLMvtRAkXLJY+zisOfyb3S4GnkfW+779I/9c+3++QbwW7zPnq\nFo+Fi/yEPpfPfVfnK7AUfoiaBZYv7fTcQ08FlsIQocBi89f6JvIbzlzrC6V1SmnarAXWDT98\nHUeU5Dh/+DfyeAcIMB6F9UVpBhWWgsALrIKWL8eGXyuoe4G11q3AkrkEHlFlxxxBhXcua7NW\nSv/flqiS14aGB2MWWE9JX9hjuZhEtNq240IUUW2XH9ODrI+AvhJDVCfvr5Cvy6rgvLLdUtJj\n4XKM3K5cGJ+3wFL6ISoWWD6102MPPRZYCkOEAovTPqk+SrDeZ0RpnVKaNjlot560WB6X/r+h\n/Z4OV2IdD+YEg1NYXxRmUGkpCPjdrcDly7Hh1woqF1i5dz56OF+BZckNPKLKjjmCCu9cFktt\noua2Twhnem1oeDBmgfVfAlFn+Ww380n7nlrS9uXcH1PT/lKaffMF597vvv32d9uzdAc49/2P\nPBYullS3e280z1tgKf0QFQss39rpqYee74OlMEQosDjNlsbb+khUxXVKYdqcQbuHcm9zXJ8o\n5qwezYZgKawvnmdQaSkI+N2twOXLueHPCvoi2Z8dZ3WvpwLLGXhElR1vBBXeuSyWBUQoF9qw\nAAAgAElEQVTRpy2bKPeOowoNDQvGLLDkG4zGn+9I1MmxY7j9kxObX6VXjeQN+ZlJhxx75YOf\nw20f6eZeTDpOoXCRLy123j32bEzeAkvph6hYYPnWTk899FxgKQwRCiy9/d//5W6/5JgUxXVK\nYdqcQTtWNPex9RPI7Ykj3+EdzLAU1hfPM6i0FAT87lbg8uXc8GcFle98dL9j98UizgLLU+AR\nVXa8EVR457J9drjLkklUv4CGhgWDFljyhQVPxhIddOz4gFxvjzZZerXW8X1NHXsfJeuTweXq\n2/kh82dm6VW881fnxmOOa0PmUd4CS+mHqFhg+dZOTz20Hucok/c3KgwRCix9TUjPvYuoxbJD\nGu8P5A2XGXOfEYVpyw2a/MFMmdPWTfkS6+rXHN97sXRMc1yZZVAK64vnGVRaCpRTU8B/xwUu\nX84Nf1dQ5/fKl7taCyzPgUdU2fFGUOGdS9KIqN+FQkTLC2hoWDBogSVfzJlGlJ77DBFpVpwn\nWL4vzX8R6/0Xc+Td82x7j6QQlbhssVxLIkq235v265IJjaXvOOH41bnx+EYgSvjatv1BQr4C\nS+mHqFhg+dZOTz20noMY81+e36gwRCiw9CUvJSsdL65Ks5psvYzQZcbcZ0Rh2lyCNoKcJ4G2\nlDaH2s+UuNKVXB6EAMaisL54nkGlpUA5NQX8d1zg8uXc8GcFtaQTmT617X87PsFeYCkEHlHl\nxhtBhXcui/W00pLPEUUdK6ih4cCoBZa1jnW5A7nlSJz0H/cD8pyceSwptxz+WD6g2P2DCzm/\nPCLficV66FN+2sht8pVXf84uRCunOnKQ531NfiBP+lPSm9kvcxNoQN4CS+mHqHkVoW/t9NRD\nSyf5v5F/rv1yyrXA8jxEKLD09W9xaYj7vif/aXDuOXmdsj2UwGXG8kyw52lzCdqF6tL2U9bN\nnxOlzeZvS6vjxd31pM07dewY+MXz+qIwgwpLgXJqCvrvuKDlKzdefqyg1o+zM/ack3oxPc60\nXHrxjEUx8IgqO94Ien7nkhyLIrqLqHWBDQ0HRi2wfrfeZPFjl3+5Rz5sKVSsV8n6lXmO3bts\nd2O0/a/tQ+Qf5ABFVWlSRdrZP+cZ+Qs1Gv4v7/vaH6Wt/yRVenujFvL9tDfIe53lisIPUbPA\n8q2dnnpovURa9rLbvec9DhEKLJ29FiuPflTJcknWKbrNdqKny4zlnWCP0+YaNPmIfeL31s2X\n5cxQQuVi8l1r6Ma/9esW+Mfz+qIwgwpLgXJqCvrvuKDlyyVevq+gll+tbTcVjpf+d6b8UZP1\nFA7PgUdU2fFG0PM7l6yldcfWghsaBoxaYMkP+abqbv/0rRrkUNblcPNrlR17kxwHql903FY4\naobFcrW2dfOrfO9r39Z2/MM2/8p3I5Lvn+1Srnj+Iareyd23dnrq4cWaztS7Pj3R0xChwNLb\nhzc6Z4Gix9nfblxmLN8Ee5o2t6Atkl7cYvu4/Ismzu8VBpzWp0MQCI/ri0VhBj0vBcqpKfC/\n4wKWL9d4+byCWiwvpTj2L7Dewc2WVo+BR1T58UbQY7JkG+Q98ed8aGjoM2yBtTP/9F3fN+jG\nItFp1fpuv+y6++KubtVSzSWaLzrp3PXXtHopUSl177deB/rnvUViSnb7J//72tVNYtm41Bv7\nv2axnCH7XdZcyhWPP0TdZxH61k5PPTw+vFRUXIUuP7oXWJ6GCAWW7nKeH9mweFxUcsX2S/50\n7sydsfwT7GHa3IKW09zlP5vXxtUtYY4v2fKhnzXuBgTH0/pi5WkGPS4Fyqkp+L9j78uXW7x8\nXUEl/8xsVCQ6vvq4X6RNcn7s4zHwCh0FHfFG0GOyJKflQ549fWtoqNO7wPLZU9IfQ0e5GwEA\nAAAQAMMWWPWJOnC3AQAAACAQRi2wXiei17gbAQAAABAIgxZYV+oQ1eNuBAAAAEBAjFlg5Qwk\nohe5WwEAAAAQEEMWWJ/dJdVXHblb4cXiVp4t4m4YAEQMrEPADBH0zngFVv/i1vvVVTxZ8Ley\n6Uee9eJuGABEDKxDwAwR9M54BVYv6/zU+o27Hd4gVQDADesQMEMEvTNegTXBTCkNl18p+BsB\nAAAAjMl4BRYAAABAiEOBBQAAAKAyFFgAAAAAKkOBBQAAAKAyFFgAAAAAKkOBBQAAAKAyFFgA\nAAAAKkOBBQAAAKAyFFgAAAAAKkOBBQAAAKAyFFgAAAAAKkOBBQAAAKAyFFgAAAAAKkOBBQAA\nAKAyFFgAAAAAKkOBBQAAAKAyFFgAAAAAKkOBBQAAAKAyFFgAAAAAKkOBBQAAAKAyFFgAAAAA\nKkOBBQAAAKAyFFgAAAAAKkOBBQAAAKAyFFgAAAAAKkOBBQAAAKAyFFgAAAAAKkOBBQAAAKAy\nFFgAAAAAKkOBBQAAAKAyFFgAAAAAKkOBBQAAAKAyFFgAAAAAKkOBBQAAAKAyFFgAAAAAKkOB\nBQAAAKAyFFgAAAAAKkOBBQAAAKAyFFgAAAAAKkOBBQAAAKAyFFgAAAAAKkOBFSKuv/Oy3cc5\n3G2B8HXy45fze+cid7MgtFx53R6dV89zNwXCwJnXXNejV45yt8dnKLBCw2s1iNJuadKwfBRR\nuek/cTcHwtDX64Y2SiMbc3rF2g2bNGnSqFYZs/SyULtN/3I3D0LH/nJEGQ2a1ClMlDzuV+7W\nQGi7urettAgJxWo2btKkYVUpUyTUmfUNd6t8gwIrFFydKAh3LsmW7ZraNI6EZlvxhyGoKOf1\nIRnSwmUqUfeefhMWrNmdnStr/WSxJFHC4C+5Gwmh4eJAim6zxhqeFV1SKGbQL9wtgtB16fGy\nROW6zdvlWJB2zu9dO5qo/pMXuJvmAxRYIeDcPZTxaO473u6x1YiSeu//j7tdECauPiUnqsmw\nJfuyFazqlk5C24+4Gwoh4ORtVH6lMzn7R2eQefjv3I2CELW3PJlbLc+7Hj09ro5ARWef4m5d\ngVBgGd+5O6n2Tvd8re6UTmRuOm3/T9e5Wwch7/UbKer2OQeViiubQ1OqktD5B+62gtEdv4ka\n73VNzsH7ilHs6N+42wUh6Gg7im63xeOC9GTHeEqdd467hQVAgWV4l1tRg/354pW1qFN5gYhi\nq7UaNGv9s1+HwuFSMKJLowWh+ZPeqyubhyqTeQo+mwZvztaju7LyBOfAyHQy9/uCu2kQarKK\nUPXViuvRzt6JVHoHdxu9Q4FleP2pzgHPAds+s1eT8nH205LL3P3A7j+42woh589bqNQiX8or\nuaqfUITKv8jdYjCwK3dTs7z1lVxijc4gun0LqnPw3fVpQsxgD2HK9XTHaGr5I3c7vUGBZXSL\nqeIer+96T6+cNbZHi5opcpVVZcRzl7gbDKHkm9LU1Hu+3OzONAlDcfIfKBlOdT3/OXhoai2B\nkvu/cIW7hRAiLnSmYo8VtCCtrUPxjxv4xkUosAzu1ejUjb69922b0+vmWKKUAW8YOG9gLF8X\npV5e/0bMZ2kZuuFz7maDQa2lsrsUo7OmYxGitAHPXOZuJYSA07dT9W0+LEjjEqjVMe7GKkKB\nZWxHi0ct8OPdb//cdmlENzyGmxaBL37KEIb5ES+rfe2EuHXcDQdD+iA2cZ237GTNvyeVKHXA\nq7g2B7w7UZcaKV7U7GZjbcp4k7u5SlBgGdr1ZtTfz/e/Q7ObRFPqtBPcTQfjO1mVBvgZL9m0\nBBqCoxCQz4mywqwC16cFbQsTlZtr3IMOYACn6lCzQz6uR1l9TNGPcTdYAQosQ5tH9fz7AMdq\nS/ckSnrI6BewArerLUn0P12SdeXozpPcrQejyWlL3XyJz6G5zcwUOwTPowAl526lFn68881P\noUHGPLkPBZaRfRBT2JdPofPb0y+JSu3ibj4Y2xS62dc/EvPmqwFV+4W7+WAwi6mWr3naOagY\nxQzDUSzw6EprauLX0rShPLU8w91qT1BgGdi5KsJDAbz72VawTtHUBrdtAGXPCsV2BBqvQ22p\nVIg8DQx08mFMymbfE3RwXHFKXnyVu9FgRIPoJoVbEynZXY/q/MXdbA9QYBnY0AA/wbFZU5NS\ncRALlPxVLHpJEPHqQ0XxcELIdbqCn38OHhicQDd9wt1sMJ5FVF75WlQFB1tQ5V+4G54fCizj\nyqIyvl1GoSBrqJmG47ZY4Fk76hdMurKHCkW/4u4DGEcX6uxvhLY1o+hZOIgF7p41FX7K//Uo\nqxOVNd6TvFBgGdaxYtH5nnHpp1Vl6Naj3P0AQ3qKagZw/YSrEUIJ4y1owGQFVfXzUx3ZrDRq\n9Ct308FQfkiNeTSgBaknlTbcXd1RYBlVTtsgjzDI9jShsl9z9wQM6I/UuPXBpmsgVTDiaQ/A\n4KPYpA2BZGhHYyqcxd14MJALtWlUgAtSbyprtGodBZZRrQr6CIMsq7tQ+D3uroDxZNLQ4NPV\nherhZiAgOVlemBlgiIbHCDNw31FwGEQtAl6QelAlg31igwLLoL4qlBjA59AejDYlGvY2t8Bl\nH1VXo3xvRh3xXCawXL+HugScoqVFSTzL3QMwiO1Ubm/gK1InqnWauwduUGAZ0/kaNDnwmLmZ\nFJX4Dnd3wFj+LR29Uo1s7a9Os7j7Avym000B3lFNtq0m1fiZuwtgCD8mx60OYkHKakVNLnL3\nwRUKLGMaTK2CiJm7yaaUL7j7A4YyPogDDm62ppue5e4McNsjFNseTIoOtKZiOJEBLJart9LY\noBakQ7dSZyN94IwCy5C2BXWcNK9xQsYv3D0CA/kyupha8VoSnfYbd3eA12cJscFe7zzYVGgf\ndzeA32xqHGSS9lWjidy9cIECy4i+TgjqOGk+/ehGnOQADjlNaLpq2RpKt1/j7hBw+rO0EPzp\nDNNjTau4OwLcPolJC/jhEg7bM+gJ7n7kQoFlQGer0v1Br1hu7qE2RjpuCqy20i3qRSurIc3j\n7hAw+q8u9VIhR4uTaSZ3V4DXxRsDfzZcrrWJMa9y98QJBZbxXM8M6hE5nhyoTQ9ydwsM4mxG\nzDoVs7UtNeYz7i4Bm2vtqLkKF6RK74tFaTQuSY1oD6hz5vH86CKGueEoCizjmUU1Argnsnfb\niwnZ3P0CY5hI3VTN1gyqc4W7T8BlBNVSabXaWJoG4NPmCPZhVFG/H0Ho0Qiq+R93Z+xQYBnO\nfiF9qyoxc7M0Bicjg+ybmKJ71M3WHbSQu1PA5BEqs1OtHG2rSL1QYUWsSzWEOSolqTV1NcjB\nUBRYRnM40bxMpZi5GUp3YPECi6WlandYc9ieFI/bGEWmXaa0gJ6Q49nTlak3FqlINZPuUitI\nB6rRIu7u2KDAMpjjFYRJasXMTVZ9eoS7c8BvD9VWPVtjqT13t4DDe3Fxqv41uLMKDcDVOJHp\na3OaasdCszelRr/G3SErFFjGcuVO6qpaytxtTY79irt7wO1c2WhV7wBilVWNnufuGOjv/4qb\n1Lvfh9XTFWkMd6+Aw/XGNEXFIC2IKv4nd5dkKLCMZTTVV+WSHE+mUAMcf490k6mjBtFaJlS/\nyt0z0Nv5OjRQ7SRtK0OzufsFDFZTQ1WD1J9uN8KShALLUDZRafUOk+bTmJZydxB4fWsusluL\naLWkldxdA731oBbqJ2ljOj3J3THQ3dGUQhtVzVFWA5rK3SkLCixj+bxQ/BpVU+ZuS0Li/3F3\nEVg1V/0Md5vNccXwrIAIs4Kq7NMgSisTYl7i7hrorSsNVTlHO4qZXuDuFQosQzlTSZiqcsrc\njaIO3H0ETlupjkbRupdmcHcOdPWpOUnFCwhdzI9O/Za7c6Cv56jyIbVztDi62FHufqHAMpKu\n1EHtkLnLqkrPcXcS+JwsZn5Co2jtSk76h7t7oKNzNwgzNMrSWLrhNHf3QE8XKpqWqp+jAdSc\n/ZJUFFjG8SRVVf0O7nksE6pe5u4msBlAvTWL1kBDPcQetDaM2mmWpfbUlv2dEXQ0XfWHw8my\n6tFc7p6hwDKMHxPj12uQMnetaDF3P4HLq0I57Sr4fWnxx7g7CLp5QSijxQlYNgdr8b8zgn6+\ni1XxFlguthWOfo+5ayiwjOJ6ExqnRcjyRC4+9QR3T4HHuYrCoxpGazBN4O4h6OVs2SgNPtNx\n2poW9Tp3F0E3LUmbm2tnzxUqnOHtGgoso3hM5fuAKOhH93H3FHjcR+21TNa+tITj3F0EnYyi\nLlpmKXuBqSTCFCl2avBwCbuO1Iu3byiwDOLnhMTNWqXM1b6iZjw3LiK9bSqh8kOe8xhI07n7\nCPr4wJSh3QeEVr1wwXOk+LdktGY3JzpQibaxdg4FlkHcQ2O1Cpm7cdw1PbA4X0WYr22y9iSl\n/svdS9DDtZtpnrZZyj5UnTZwdxN0MUHLo6Fr45JZDyigwDKG3VRTs0fkuMsqazrM3VvQ32hN\nLtRxcy+uoIgMK+kOrbOUvS4uGXdFjgRfRRfV8sj6KLqN8wFxKLAM4b/SGjyCV8GDOPgegV4S\nSu7VOlnbzKWvcPcTtHc8rZAOpzOMpNbcHQXt5TSlaZrmqCHrFakosAxhKnXSNGSusioLn3H3\nF3R2urRJyysI7e5hPuEBdDGc+mufpeys2rSZu6eguS1UT9scbS8c8xFf91BgGcGPsWnann/s\nZiZlcncYdNaTuumQrLVCXe6Ogua+isrYr0OYsteZ03ElYbg7U9y8TuMcPSRUPc/WPxRYRtCJ\nxmscMjdVhM+5ewy6epoqaf2QAKsG9CZ3V0Frd9ODemQpO7svDeTuK2hsDHXXPEdtaQRb/1Bg\nGcBbVEWnM9xtZlBn7i6Dnn4vbNbnFL951Im7r6CxF6imLlnKzj5QRniXu7egqc+jimt8vw/J\n3tLCs1wdRIHFL6cBLdQ8ZK6yKpqOcHca9JNzFw3VKVnlonDpV3i7VkvQ8h7ubh6mungmYTjL\naUQzdcjRsugSXE+iR4HFbyfdqkPIXE2mPtydBv08TnX0OkI6mqZw9xY0tYHu1ClLkttpHXd/\nQUNP6vP4kuw+1JGphyiw2F2uFLVWl5TlyioV/Qt3t0Ev3xZK3KhXsvYmpl/g7i9o6Hwp8wa9\nwpSd/ZS52FnuHoNmTqbH6hOmQ9XpKZ4uosBit4Lu0SVkrsbSGO5ug06uNqCJ+iWrI23i7jBo\naB511C9M2dndaTJ3j0EzQ6m3Tjlax3VDdxRY3P4tFqfLQwjdHCgSjyugI8QCuk3HZK0TGnB3\nGLTzd1LS0zqmKXtPWhxO6gtXH5hK6XK/D9loaspyOh8KLG4z6V69QuZiAM3m7jjo4khsyjY9\nk1WXPuHuMmhmJA3WM0zZ2WOoL3efQRvXbqa5+gWpPi3i6CQKLGZ/Jabs0i9lTjvji+FcmUhw\nrSFN1jVZ02gwd59BK99El9DtmIPNoTJRX3H3GjTxGDXVMUhbUmK/ZOgkCixmI/S6gj6Pjrg+\nJyIspcb6ButgesIZ7k6DRtrpXK1LpuK5E+Hpz+QEXc+NmUq1L+nfSxRYvP4XU0KXW2znsyGq\neg5350FzvyQkbtE5WT1pFXevQRuvUHVd74gsy6oifMjdb9BANxqmb5Ja0AP69xIFFq/ONEnf\nlDk1oee4Ow+aa0Nj9A7WRtNN3L0GTVyrLSzWO03Z2XPobu6Og/qep8qH9A3SzmJRb+veTRRY\nrN4VKuv+J6HdYmrF3XvQ2h6qoX++GtIH3P0GLazS8x6juW4kPDAn7JyvaHpM7yA9LFT6T+9+\nosDilHMrPax3ypyqCnheTpj7r3S0Ps8gdDOTBnF3HDRwvEjcJv3TJD/gEoewws4Uaq9/kjrQ\nEL37iQKL0w6dnhTg0STGZ4yDLiZRF4ZgHSqS+C93z0F9g6gfQ5qy5UNYOCQaZg7HpO/WP0j7\nytAzOncUBRajC2Wj9X5IjosDRRJOc48AaOk7c/oejmTdiytUw9A7ptI81+Nkz6U23J0HVV2/\nlaZxJGlZdMYJfXuKAovRLMrkSJlDb1rCPQKgpXuYrqB4UmjI3XVQ2+UaAtvpDFWFz7i7D2pa\nQY14ktSLuunbUxRYfH4qlLqTJ2Y2W2Mqszw9APTxDNVkuoKiDuHmkOFmFrXkCVO2fFZfF+7u\ng4p+T05gOZsvO/tgFXpa166iwOJzD43jSZlDM3qWewxAM1eqCcuZgjWJxnH3HtT1RUzaDqY0\nZWdnVTB9yz0AoJ72NIIrSmvMaUf17CoKLDY72Q4wOCymttyDAJpZQXdxBWtfYtHL3N0HNV2q\nTdO50pQtV+wDuUcAVLOL4X61ToP1fdNDgcXlZPEYhkvo3VUy/cw9DKCR0+lxuj6Jwk1b2svd\nf1DTBMYPCCWHSpj/4B4CUAnvO19WTXpSx86iwOLSg3rxpcxuLMfDA0AXk6gnX7CW4dhoWHlO\nyGC4qt7FCLqfewxAJX153/meLJT8q36dRYHFZBdVYrro2cXehKIMz78EHfwal8Zyiwa7CtF/\nco8AqOa39OgljGGS7EtJxiPEw8PzVJ73nW8UtdDvMbwosHj8Xti8kjVlNu1pG/dIgCb66P8Q\nQldDaCH3CIBaLjWkwZxhkvWiR7mHAdRwtkzUUt4oZdWh1bp1FwUWi6tNaChvymxWC024hwK0\n8IWprM6PUnW3Pboa9xCAWgbS7cyX40iBii19hXscQAVDWJ4u4WZjQqJu5x6jwGJxP93KvmRZ\n1cQdi8JSa5rBG6zG9D73GIA6llF5zk+b7drQVu6BgOC9LJTZxx2l7LHUTK8PCVFgcdgllHia\nO2Q2k2gM92CA+l6jGszBmkHDuAcBVPFMVMoG5jDJ1gp1uUcCgvZvOdNi7iRlZ2fVpTU6dRgF\nFoNP42NXcGfM7kBK6gXu4QC15dSnR5mDdTAVwQoLXyTFLGLOkk0DeoN7LCBYQ6kjd45kT8Un\n6XQlIQos/f1eUpjCnTCnzrSJezxAbbvpVu5cZXekHdzDAMH7s7RwP3eUbOZTB+7BgCC9YIQP\nCGWjqJU+HxKiwNLdmVrUhztfuZ4QGnMPCKjs6g0m9nvYZq+ku7nHAYJ2vp4BbtdnVzHqR+7h\ngKCcLh3FfLsPh6za9JQuXUaBpbdLzagVd7xc1cZp7uFmDd9DclxUjvqdeyAgSDmdqZkxrsaR\njMMTLkNcH+rGHSKH9XGF/9KjyyiwdHa9G9U/yJ0uV5NoLPeYgKrOZ5g3cqdKMpQWcI8EBGkW\nVTfGRzqy/Skp/3IPCARhH1Xkv7m2w2DqrEefUWDp7D6qupc7W272J6Vd5B4UUNN86sQdKtkO\n3Aor1B0S0rdyx8hFd1rBPSIQuL+KxqzgjlCuQzfQAR06jQJLX0up1HbuaOWRSdu5RwVUdDwl\n0Rj3AGlMH3KPBQTj59QY5ptuu9scXVW/Z5yAynLa0QDuBLlaEV3qrPa9RoGlqwOm1PXcwcpr\nFTXnHhZQ0X1GWcem00jusYAgXGlAI7kz5K4pPc89KBCotVTTMKfzWXWl0dr3GgWWnr5IMBvk\nKgpX1YSfuAcGVPOTuahBTps5kFIEjxIPYdOpCXeE8lhEbbgHBQL0v4QEI9yv1sW+DJP2h9hR\nYOnoZAXhAe5UeTCWpnOPDKimO43nDpSDSPu4RwMC9lF0+k7uBOVVyYQ7NYSmK7fQBO705DWX\n6lzVut8osPST0446c2fKk91xpa9xjw2o5EOhomEOxC+j9tzDAYG6UpvmcAcon/toAve4QECm\nUlPu8OR3By3Rut8osPTzGNU01A0anFrSs9xjAyppQnO545SrbMw/3OMBAVpELbjjk9++pMLn\nuQcGAvC6qajhDodmZ29JSNT6Xn0osHRzJC5pE3eiPFtIXbgHB9Sxj+pxp8lFf1rOPSAQmD8S\nk7Zxx8eDzrSee2TAfydLmxZwR8eTEdRV456jwNLLtVtpMneeFGSVMp/gHh5Qw+VKUau40+Ri\nk3AL94hAYPrSCO70eLLBdDP3yID/OlF37uR4dKgyvaxtz1Fg6eVxaswdJ0X9cKQhPDxKbbiz\n5KYOHeEeEgjEZ6YyxjydoQG9xz024K9VVN2YacpeIlS7rGnXUWDp5K+U+M3caVK0CX8XhoW/\nUxKM9bnOBJrCPSYQiNY0kzs7nj1EvbnHBvx0OM5od2jIdTc9qmnfUWDpZDAN5s6SF/XoC+4B\nguANoYHcSXK3J67Mde5BAf+9RTdyR0dBVkYsLpwILeeq0xTu2Cjalph8VMvOo8DSx5dRpY3z\nnMv8JuNB9WHgU1Mpo4WsGb3CPSrgv+ZkyHOSZQNpIffogF/6G+y8BXdDqa+WnUeBpY82NIM7\nSd7sSyx6hXuIIEg5jWkWd5Dymkv9uYcF/PYO1eQOjqId5go4KBpKNlN5gzxawqODZQQt7+eO\nAksXb1N17iB514YOco8RBGkTNeCOUT6HiiThxkUhp42RbqaWV3N6hnt8wHdHEuLWckfGq7nU\nSMNHiKPA0kUL4x5yt1lCHbnHCIJzurh5HXeM8utEO7gHBvx0WKjCHRsvHqV23AMEPjtXg+7n\nTkwBGtJ27fqPAksP71At7hQVpIz5OPcoQVDGUA/uEHmwAs/nDTn9DHxSsqRi1K/cIwS+6kOt\nufNSkCeiy13QrP8osPTQhuZzp6gg/WgF9yhBMD6JyjDkqQ4Voo9xDw345a/YjEPcqfFmJB5O\nHzLWUiVDrkpuMmm+ZgOAAksHh4UbuDNUoE2mBtzDBEG4Vp8e4s6QRwPpMe6xAb/MoKHcofFq\nd6EMXJETGj6OTVjPHZeC7UhM/lurEUCBpYM+9CB3hgp2E33DPU4QuJVGfVDAJhMelxNSLhVP\n2M0dGu/uob3cgwS+OFFOMPTF8w6DaKRWQ4ACS3t/mktmcUeoYONpKvdAQcCOphTayJ0gBXXo\nW+7RAT9sJZE7MgVYQXdxDxL44For6sqdFZ/sLx7zP43GAAWW9h6kYdwJ8sGeuLK4v0zI6kpD\nuAOkZBxOmQkpjYU13JEpSFXhB+5RgoI9SHUMfTJfrknUWaMxQIGluYtFE/ZwB8gXzeg17qGC\nAD1DlQ27lO2OrajhfWZAZV/STdyJKdA4eoB7mKBAB4Ri27mT4qOsysIH2gwCCl7ILw4AACAA\nSURBVCzNbaZM7vz4ZA4N5B4qCMy5clGPccdH2e30LvcAgc9G0mTuwBRob0Kxy9zjBAU4kmQ2\n8KKUxzy6U5tRQIGluYaCse9k63AoLUW724GAliZSB+70eDFTu1NIQW3nUlKN9jxLD9rRTu6B\nAu9OV6EJ3DHxQx16QZNhQIGltc+oDnd4fNSBdnEPFgTi8+iiRv4Q+kByOi6rDxVPURfuvPhg\nNTXnHijw6to9IfLBjd1Soa4mJzKgwNLaMJrKHR4fLaf23IMFAbhWn2ZyZ8erNpTNPUbgo8bC\nE9xx8UV14XvukQJvJtFNB7lD4pfGtFuLcUCBpbH/ktJC4JC7TRnzCe7hAv89TrdxJ8e7RXQv\n9xiBb44Y/6FeVuNpEvdQgRdbKGMHd0b8s9pU7ZoGA4ECS2PrqRt3dHzWl1ZzDxf47Why/Cbu\n5HiXVSz+X+5RAp9MoIncafHJvsT0S9xjBYrei4tfxR0RfzWnTRqMBAosjTUQQuBZAXYbhCbc\nwwV+627wJ5tIutFm7lECX1wummj8R8dZiTjN3bh+LS4Y+6QFT9ZHV9TgVFEUWNr6MmROcZfV\nEH7hHjDw00tUybC3wHJYRa25hwl8sZfacGfFRyuoBfdggYJ/a9NA7nwEoDWtU38sUGBpayw9\nwJ0bP4zU8LHioInLVYXF3LEpWIVozZ6mCipqQ8u4o+KrqsKP3KMFHl1rQ3dxpyMQT8WUVf/u\naiiwNHUpLWk/d278sCO6BveIgX8WUCvu1PigH63kHigo2J9RFbiT4rOxNIV7uMCjUVQ7ZK7r\nctNWg3OQUWBpamdo3Qwkuz59wT1k4I8/EpNC4Wqdp4TbuEcKCvaw8U/nc9oTXwJ3VzOipVTm\nae5wBGaTuYzqV06gwNLU3bSCOzV+uZ8mcw8Z+KMnjeDOjE9uxNl9xpdTJSYUqnW71rSfe8Ag\nv32mlNC5rCsPUf1DWCiwtPSrqQp3ZvyzJ64cHswbQt4Vyhv+DHerEbSAe6ygIG/Q7dw58cNS\nasM9YJDPe4Vil3AnI2CbzKqfhYUCS0uzaBR3ZvzUlN7hHjTwWU4Dms+dGN9si6rNPVhQkD40\nlzsn/qho+j/uEYM8vitims6diyC0pbUqDwgKLA1dLxe7kzsyfppBo7hHDXy2jRpxB8ZX9egI\n92iBd2fii2Vxx8Qfw2kO95CBuz/Lh8gpCwo2xpRX+cQ+FFgaepGacyfGXweSil3lHjbw0cWy\n0eu4A+OrcTSDe7jAu1XUizslftlpLn+de8zA1enadC93KoJzD21Qd0hQYGmoKy3gDozfWtEL\n3MMGPlpI7bnj4rNd5ircwwXe3WzayJ0S/zSjF7nHDFxcaEp3hdQx0Pw2RFdW9wADCizt/GMu\nFXpxm0/9uccNfHMyNSGELvpqTJ9wDxh48wndwp0RPy2gbtyDBrmutqeGB7kzEay7aKuqg4IC\nSzuLqT93XPx3KC0VT1ENDROpH3da/DCZ7uceMPBmCE3jzoifskrFHuceNXDI6Us1Q+RBll6s\nM1VX9XNnFFjaqR61mTsuAWhPB7gHDnzxe1yRUFrP9hUqgzuAGNjZxCIhd/ihHy3jHjZwGE2V\nQu2SLk/uoL1qjgoKLM28SY25wxKIxXQv98iBL4aE2D1A7qS3uYcMlK2i7twJ8duWqJrcwwZ2\nU6jMNu48qGGlUFfNPwRRYGmmJ83hDksgsoonnOMeOijYD9EZofXErxk0mnvMQFnNqBA7xV3W\nkD7iHjewmkMlNnGnQR230rMqjgsKLK0cj80IvVPcZV1oB/fYQcF60QTupPjnQGLxa9yDBkpe\nD8nj7dNpKPfAgewRSn+SOwwqWUpNVBwYFFhaWUADuKMSmOWUyT12UKAjUWVD4yE5uVrSK9yj\nBko6hsozAdwcTEs+zz1yID/gOe0J7iyopg69pd7IoMDSyLVy5hC6ht5N6dgz3KMHBelGk7lz\n4q/ZNIR71EDBL1HlQvJ4e2fawj10YFkmpK7mToJ65tM96g0NCiyNHKCW3EEJVA/axD16UIDD\npvIh94Z4MLmIys+hALWMo7Hc8QjIWuEO7qGDpULKKu4gqKmq8JlqY4MCSyN30nLunARqtZoV\nPGiiMz3IHRP/tabnuccNPDqVmBpKt/xwUV34gXvwIt0iSg2r+ip7uoo3sEWBpY3PqCZ3TAJX\nPuYE9/iBV18KFUPuABaeEmBc86kPdzgCNJamcQ9ehJtDaWH0+aAsq1zU92qNDgosbfSm6dwx\nCVwfWsc9fuBV55C767bsUGrqZe6RAw/OFyv0NHc4ArQ7rjSuTeU0mdLXcodAbRPUO1sUBZYm\nfosJwccQOq0XWnAPIHhz2BSKB7Cys9tSNvfQgQePUWfuaASsBT53ZnR9BJXYwB0B1R0sFntU\npQFCgaWJsTSaOyTBqBx1jHsEwYuQPAMrW346b1/uoYP8LmSYt3BHI2AL8cRnPpe7U5lQfB5c\nQYbRAyqNEAosLRwrlLafOyPB6E+ruIcQlB02VQjJA1jZWWkpeJK48SyhDtzJCFxWRuxJ7gGM\nVP+1ohtC9WZEXu1NST6tzhChwNLCJBrMHZGgPCk05R5CUNaFpnInJEAiZXEPHuR1Nj0ulJ8i\n14dWcI9ghPq7PtXZwz392uhF89UZIxRYGvgrvvBe7oQE5wbTn9yDCEq+CsF7YNktpN7cowd5\nTQvBxzy72Giqyz2Ckem7SnRHaD0O1Xc7ChW/qMogocDSwBgayh2QIA2i5dyDCEq6huwBrOys\nIsnqrFugml8Lpe7mzkVQ6tGX3GMYid4sQp1C9Q+9gnWkNaqMEgos9f1gLhrSZ2BJNgq3cY8i\nKAjhA1jyZ4SHuMcP3HWlMdypCM4DNJ57DCPQ5tioEdwzr6GN0ZVUuf0HCiz1daXx3PEIWnXh\nN+5hBM86h+4BrOzsR/AZocE8T5VDt1632pdYDI9g0tn1Byj+Ie6J11RL2qnGQKHAUt0bVCnE\nVyzJEFrCPY7g0RcheRN3h6wiybiO0EjOVTAt5Q5FsNrQQe5hjDCn21DxldzTrq3Vws1qjBQK\nLLVdqSUs4g5H8DYLDbkHEjxqTzO4wxEMfEZoLKMpkzsSQVtCmdzDGFkOV6Ha27lnXWuN6EUV\nhgoFltoWUHPuaKihpvAL90iCBx8KVbmjEZRHqA/3EEKul4SMEL/gWVYm5m/ugYwk2xIo8yD3\nnGtuMTVXYaxQYKns20LJYVHbD6dF3EMJHrSkudzRCAruNWokx0tGhcHh9uwBtIx7JCPHhWEU\nN4l7xvVQiz4MfrRQYKnr6q0UHuHbYrqFeywhv1eoNncygtQOzyM0jJw21JM7D2rYHHUT91BG\njCO1qOxq7gnXxUPUOfjhQoGlrlnUmDsXKqlFP3EPJuSVU09YzB2MIOF5hMYxn2od4s6DKm6h\nz7nHMjLkrC5ELcPgQ2VfZFUwfRf0gKHAUtUbUUXC5dlMI2gB92hCXjtCv37PKpx6mXsYweqF\nqLTQfcizm8l0H/dgRoSjbSkhPD6h8cX9NCjoEUOBpaZjJU3zuVOhlm0mVS5TBRVdrBC9ljsX\nQWtDz3KPI8i+LxwdDidgyfYnFcWtsLS3owjV3MA91/o5WML8R7BDhgJLRVfvpF7coVDPTfQ9\n94CCu0eoLXcqgvcwDeAeR5CcrkajubOgmjZ0gHs8w97RDmQeGML34PPfCJoY7KChwFLRWKof\nRvkbRfO4BxTcHEtOCIMrVA+lpuFoA78rLUnkjoJ6llAH7gENczlPFqZqa7jnWV/7UpNOBjls\nKLDU8ySV2skdCRVtj6rNPaLgZiAN5g6FGu5R5Q5+EJyhVC+c7mVUxvwP94iGtW/uoLjB4XFF\nhB/60pwgxw0FlmreNCeE/gkyrm6mb7nHFFy8bypzgDsTaphHQ7iHEpZQ2V3cQVBTf1rOPaRh\n7PyDZqr3JPcc629nfPq54EYOBZZafkyPmsOdB3WNDbp8BxVdrUvzuCOhioPJ6Ve5BzPSHTKl\nhNfZyptNdbnHNHwdLE9pD3DPMIsuwd7CFgWWSk5Xo2HcaVDZjuga3KMKuZZQU+5EqKQVvcw9\nmBHui0RzqN9PLa96dJh7VMPU/1pTVGZYHe703VZz6eBuKoMCSx1X7wqHC7zyqEdfc48rOPyU\nkLiVOxAqmUPDuEczsv1dTgi7uxlNCv6KL/Dg3wfMVHMF9+yyaUtPBjV8KLDUMZpuDqdTRm3G\n0UzucQW7nGZ0H3ce1HIgqfg17vGMZJdvo+7cGVDdvoQS+OBZdTlbSlL6pDC6Nt5fG6KrBLVW\nocBSxVoqHU4XENrtjKnGPbBgt5zqccdBPS3pde7xjGSDqHEYvmW2xkMuVfdxI4rptod7Zlm1\noKeDGUEUWGp4KybpCe4gaKEBfck9tGB1pFDiRu40qGcWjeIe0Ai2ksqH43vmIurKPbJh5u/B\nJmq4jntema0RauUEMYYosFTwWzHTXO4caGICPcg9tiC7eBOF00kzBxIyrnMPacR6IyYpLC+4\nzyoVG+xdIcHF1eWpVDrMLowPRBM6GMQoosAK3sX6NJA7BdrYba7CPbggG0XNubOgqub0FveQ\nRqrfikWF51+D2X1oFffghpG3alOhgWFx370gLRfqBzGMKLCCNyhsrp/PpzF9yj26YLHsolLh\n9aHOTBrDPaYR6uIt4frXYPZTQgPu0Q0bx/oIQrPN3DNqDA2CefIECqygrQ/PUxqsJtMD3MML\nliOJsSu4k6Cu/Qkl8Rkhi37h+9dgdh06wj284eH6msJUfiH3dBrFYmoS+FCiwArWx3EJ4Xse\n4N64csGc4QdqOF2FJnIHQW3N6G3uYY1Ij1HFvdxzr5kJ+HNQFV82oriB4XfXoYDVpdcCHksU\nWEE6UU6Yzh0ADTWl97hHONJdvZsyuWOguuk0lntcI9HL0WH2hBw3e+NL4v5qQbswJYYahtEl\ny8FbRHcEPJoosIJzvRV14Z5/LeGNkN0Iqht+T7HfF18KnxHq7rvC0Qu4Z15Ld9Fz3EMc8l6v\nQunTuCfSYG6iNwIdThRYwZlON4Xfu5+L/bigntmjVCYM72GLzwgZnKhMo7nnXVOPUDfuMQ5x\nZ4cJQtsIfe6gsgXULNABRYEVlINC0W3cs6+tFkF8/gzB2yEUDssPdabTfdxDG2kuNKYO3NOu\nrayMuFPcoxzSni9DpR/hnkUDqh3wISwUWME4kmxexj33GptNQ7lHOZK9ZI4Lz4ThM0K9Xc0M\nyyfkuMGtsIJxZgBFddnHPYdGtDDgs7BQYAXhRCUaxz31WjuYXOQK9zhHrvcTo8P1XsrNcK9R\nXV3vSzXC/s1zo3AL9ziHrudLU7ml3DNoUHXo1cAGFQVW4C7fQR25J157begZ7oGOWF8WNk3m\nnn+tzKDR3MMbSa4PokrheC5fHnXoMPdIh6izg4Wobrhzu4JFdFtgw4oCK2A5PalBWJ/gbrOA\nenGPdKT6ppgwhnv6NbM/IQPX1OvmSi+qsIN7ynUwicZzD3VoerkslcXhK2X16PmAxhUFVsDG\nU+WwvYO7i6z0xPPcQx2Z/pdBQ7hnX0O4fEI/Z+6mypFQX2XvSyiGMxr8999wwdRlP/fkGdky\noV5At9xGgRWomVRyK/es66ITPc091hHpu5LUn3vutfQQDece4kjxXXW6ORL+GMyWz2jYzz3a\noef1ClRqMffMGVwj2hPI0KLACtB0KhqWl8/nt5wyuQc7En1dgvpxT72mDiQVvco9yJFhaxK1\njZQnnywlkXu4Q81/owShQ9hf/xCsVabqgZzSgAIrINdGUrH13FOulzLmk9zjHXk+TqeB3BOv\nsVb0AvcoR4Lj91Jc2F/snKt89FHuEQ8tL5enUou4Zy0ENKcNAYwuCqxAnGlDZSLnaU296Qnu\nAY84ryQLw7nnXWvzqT/3MIe/nM1FqfIa7qnW0RBawD3moeTUQMHUEYevfLAhpsxF/8cXBVYA\nPq9CtZ/mnm/9rBeaco94pNkWGz2Re9o1dygtJYAVC/zxSWMy94uUjwetdsRUCehs5Mi0qwSV\nxdlXvmlPi/wfYBRYfrv2SKyQGVFrVnXhV+5Bjyg5DwmFwvX+oq7a017uoQ5vxwaZqP467mnW\nWZPAn8wbaX5uQzE9cPGgj7bHF/b/QUwosPz1WX1Kmc491/oaQfO4Rz2S/NeF0h/nnnM9LKHO\n3GMdzq4uS6HSD3FPsu7mUB/ukQ8NF+fGU43V3NMVQvrQBL8HGQWWf46PiKImkXF7hlw7oqtx\nj3sEOXIjVdvCPeX6yIg7wz3a4euTmylhcATemjurWKHT3GMfCg5WopT7wv3hlKralx77k7+j\njALLH5cXp1LGLO551t+t9BH30EeM9fHUJlKO2ncP6MIc8MHVGdF0R4TU6Xn0ohXco298n7cg\nU5sIOpFYFeOpi7/jjALLD1mVKWFApLz5uZqG58bp5O8OVOh+7unWzVpqzj3gYeq3RlQkAv8S\ntNpoqsU9/Eb3c28T3bSCe6JCjlQA+PuEehRYPvu1HZlab+OeYxYHkotc5h7+iLAtnapHzP3V\nJDeYfuce8rD0djFqHBGPxvGoAb3PPQGG9ttwM5WZyT1LoWihUPe6f2ONAstHOWuSqPpy7gnm\n0o72cU9ABPjhbjIPiIDnh+caipsWaWFHrGlQBJ9dM4v6cc+Agf0wxEzFx0XUMqOeJrTev9FG\ngeWbkyLFj4rcNWsZteWegbB38aE4qr2We6b1tS26Ovewh6HHhUKRd/Ggi0PFCvl/PX2EeKdL\nFJUYHYHXPqjjqdii/l1BgQLLJ5+Xp5oR8uhBz/AACq0drEgp4yOuhG+IyydU9wilLOOeV159\naBn3JBjSuSfrEZUfj/IqcL38PB0ZBZYvshKErpF9THUwLeSehLD2XSuKEndyz7L+HqSR3EMf\nbh6ltEi/udHW6Kq4m3teOe8OSSbhlrkR90ecqvaViPrMn1FHgeWDDVHmB7gnltn2mBuwZGnm\nv8lmqrmCe445HEgpjMflqGoNFY6wz5k9aEovcc+DwXz+YEWiwl0i7bb+6ptJDf05zx0FVsEe\nFxIe4Z5Wdk3oTe55CFt7SlP6JO4JZpJJO7iHP6zsNCWt4p5TfgupPfdEGMiV1yZI1ZW5ycyI\nesCbVhrTKj/GHgVWgZYLKRHx4BLv5lIv7okIU7+0oejOe7jnl8tK3ApLTa+a45ZwT6kRVIj6\nmXsqDOK7VR2SiWIb3b+be07CxKb45D98H34UWAVZJ6TgT8Ls7KyMuBPcUxGOri9PiOwHglUV\nfuCeg/DxVUr0bO4JNYQxNJF7Lvhd+3JVj5JEVLT19L3cExJGhlOm73OAAqsAu6KSVnDPqCH0\no8XccxGGfridEkZH9GmnY+kB7kkIG3+WEcZxz6cx7EtK/Y97NlgdPfRgi2SpuEq6ddga7skI\nM1nV6Wmf5wEFlnevmOMWc0+oMWyLqeznTWyhQGsTqP5m7pnltSeh6CXuaQgT/9ahntzTaRRd\naSX3dHA59uzczNJSbUUZzUasjOg/3jSyxlz0b18nAwWWV58n45C7w530Avd0hJnj7SkeRxxE\n2sY9EeHhSitqifdTu03RVSLw78HfD85sJ38oSMn1us+IzMe66aE/dfJ1RlBgefNrSWEC92Qa\nxiLczV1db5ai6hF991qb1UJj7pkICzkDqA5uIenUjA5wz4iufj8wvXUxubZKrddtKpYVTR2q\nRpt8nBYUWF6cupH6cs+lgVQ2fc89I2Hk+twoU4/IvnutXR36lHsywsE0qrCLeyoN5PHIqdv/\nfWVe+xJybZXWoMf0jdwDHwnWxSX96NvcoMBSdukOuod7Jo1kPI3hnpLw8U8rSnuYe0aNYTr1\n5Z6NMPA4FYvws/nyqEdvcc+JDn7dOrx2lFRbpdTvMRPzr5sx1PCKT/ODAkvR9a7UEEcYXOwv\nnHCSe1LCxXulqQ7OkbA5lGH+k3s+Qt4mUwpu4O5mPt3DPSka+2F97zJSbRVdLfMBfCaos9t8\nvPgZBZaiUVQVdw9x04ce5p6UMPGYWcDHg07DaCr3hIS67VEJy7mn0WiqhfNHz8e2DSgrFVcJ\n9fs9so97oCPRzuLCM77MEwosJTOpzA7uWTSYHXHFL3BPSzg424WScHFqrj1Jqf9yz0lo2xgV\nj7vJ5DWTOnDPizauvD65jiAVVw0GL8dVo1yWRBf5xYe5QoGl4BEqhtMF8+oQuTeXUdFnlanq\nU9xTaSjdaRH3pIS0xULCo9xzaDxZlYUwPIT198YuyUTRNXsvxiFwVsPp5vMFTxcKLM8WURoe\nPJ7Pppiyl7lnJtTlLI8VMnE5vZvtcSV8WKvAs6sjKRWfD3owk9pxz43Kvn74VhNReutpeLAg\nvxbUNafAGUOB5dEsSsMDBjxoS6u5pybE/dWGkqZzT6PhdKRl3BMTso7eQWWe5J5AY6pGb3PP\njnquvzOpMpFQre8K7mEFq31VaUaBs4YCy4Orwygdl+R4sslcCmdhBWNnEaqFj57z2Rpb/Bz3\n1ISoQ8Wo/k7u+TOoBdS44GMMIeHKi8NKEJkbjsWlx8axpZiwsaCJQ4GV38m7qSzeBD3LpEe4\npyeE/Z5J5oE4L9WDLjSPe3JC0l89KXoAEqWkPu3iniEVXDzYtzBRYrOpuKrdWFYmxDxbwNyh\nwMrnwwp0M/4kVLA9vvAJ7gkKVZcWJlL11dwzaExPJ6b4/PxUcDg3P5kq4vQrZWuiy4f6Eff/\ndt2bRJTaeg7O2zSeh83xBdzNFgVWHpdnxQhdcH2Gon40mnuKQtP1bRUoaRQONigYRMO4ZyjU\nnJhXlBKGHOSeOUPL9OE0GQM7tTkzjqho+4VYN4zpwaik97zOIAosdy9XpzTcosiLfcWjv+Se\npBB0dXsNim67nXv2jOtAyaiPuScppLw3sBDFd8Wt+rzbWTj2O+6ZCtSfq++KIcrovATVlXHd\nb0r2eiEFCixX77ci4S6sWV5Np8bXuecp1Jx4tDyZ7niCe+oM7SG65Rr3PIWMH+ZWIyraD2cy\nFGgSNQ3J5eqr+Q0EovI9VnAPIHh3vynhRS/ziALL6cqepkQ1cEPkgjTE3Ub9cvXZe2PJ3AqX\npRagCT3KPVWh4Zv5dYmiG83EiQy+qB96dwC5+OKYCkSmGgNxK8YQMDXavE15LlFg2X0yvhhR\n7XncsxUCNiUk/sg9WyHjQvbgIkQZ/XB1dYG2JhX6mnu6DO/a2w9Uld57bxqN4+w+2pxU6Cvu\nWfPH96vEBKK4RrghQ6iYW0iYoXg3EBRYkuvvPlCJKKHNCu6pCg330a1XuKcsFOR8/VibQkTJ\n9+AUVZ9MptoXuefM0P7e1lOq1s31x+JcPj9Mphqhco+1YzuHVCCiEm1n4wHOIeTxdMo8ozCl\nKLBO7+5fTFq1Gk9Gpn11G03gnjWju/rJ8i5SrKhk5nx8kuOrFjSIe94M6+yz998sEKW0nLqH\ne5pCTWvqHgK3G/112/Dq0oIRV38oTiYINVtvpEoKl+hEdoF17cO5TaKJkptj1fLHzgxhC/fU\nGVfO9zsn3p4grZUpTUat556qkLK3PC3nnj0DuvbN5pE3RxFF39hrKY6F+m/fDTSbew69OvPa\nI51KSguGuXavR3C3q1B0oIMQM8fjxzqRW2DlfPV4p8JEQuVuj+IYg59Wxptf5p4/Izr30frR\nTZOlpVIo3WIMbinqtydTovZxz6Gh/PXqqmGN5GI9umrHWfgjMECb04W13DOp4JfseV0qCdIE\nJ9fv+8h+7oGCgM0sTG96muDILLBOvji3rVRcUVrziTiVMBBzohPe4J5EQzn/6dapmZVMcm2V\n0aTfXFw/H5hH42KzuafSEE59tGN2rwYpJAeqVNNBC3H+QjBWJpmMVmFd+mrfgr71E+UJLlQj\n837cwiXUbW/+rqd5jrQC6/h7G6e0KyvHOr3pyDXckxK6pkYVOsQ9l8Zw9PUnJrSuIJdWlFC9\n9YhHdnFPTUibY46J7E+fL3yxe17fW4vIcaKojPodxy7FgavgLUsSZhvkPKx/Pty5YEjzstYF\nI6pk4+5T1uFj3/DgcbojpMC68P0bWxeMEmsnWReupNqdJuNpzsGZbo5aYJAli8fl719c+0CX\nOom2RFW/e9AcREoF8+OFiZF5jerxN58Y37q8/HERmYrXaTN4xlqcj6OalenU5Szn7OYcfX/X\nolHtaiRY1wtKrd6iz5SVmOBw4nHew7zA+vuDnYtGizel21JN5lL1xGFzN3PPRFh4NJXu/pV7\nfhmcPvzM6ik9bytl/ROUYso07DhqAT5nVs/KElTvC+5J1tXfH+6c379xEXulflf/aatxMo7q\nNlelci8wTO6JL7PXTu97ZyWz/R2ozC1tBk5dvpt7OEB9Huc/TAusM18eeuy+9jXtfy7EZNRs\n1nnotKVbuKcgrGyuQ/EzlW7/EWZO/fzx81uWTOzdrLo9UkJa9abdxszfgMP7qtvZlKKG/cY9\n45o6f+KnL995fsfK2WPuvb1KnC1QxepmolLX0IEuJmqv02NUr/11+MUtj47vcXvlOPuf9kkV\nG4oDJy/Zyj0KoB2PUQifAuv8qZ++eOvQxsWT+t5dI9nx50L9tgOnLEFdpY2sMcmUPOZT7okP\n3sVTx3766ctPPnnz5Zef3717xxNPLF+4cOrkMUP7d+3Y8vZ6N5ROIaeEMnXv6jlu/hM4xqCh\nGRkU0+PlEH4y4el/fvrmk7dezt69+4knVi5cOHPy5OFD+3Xt2rJl/bqVyhVOJFdJ5eu3GTht\nJQKluaVVSWj59HktJvzssZ8+eT1r66p5Ewa0b1K9qHNyk8vfcnfP++au3svdd9Cex2QUVGA9\nu5vTro0bV65YtHDOjGnjx48ZOnRQr169OmZmii1btryzUaNGN9eqVatixYqlihdPSohxXbNi\nipSvfXu73qMmg7bG3x5PVNTr08SNkqttG59cseKRhbNm3D9+xNDePTPbtrytQa0bKhQv4h6d\n/KLiUtJLVaxe97aWmT2HTOAe8cgwqXVhqfJ4Pahc7di48YkVCjZstNvqDh7DxAAAH3VJREFU\nttZ42JvXTsf3yD/m8YULF86YMXn8WGll6pJ5V7NGdWpULFnUa6Bi4pJSi2eUr1K9ToPbW7br\n0mfERO6xjiSdSxHF3qxwS0ivdq9asWTh/BkzJo4fPXRwr16dM9u1bNGo0U21qlTMKJ4Q6z7H\n5sKlKtdu3LJDr+GY3Ejyj6fgFFRgpXl/9wEI6OaQ6QX/WIhwgeQqmbvRYHiB5KqAv8IAyOON\niwoqsOK5W20qXLhwSsHfpqkYqQ1JzG2Ik9rAPRkJUhti8+9+KYAFi7sryJWdgXP1KnIVGOTK\nRiFXz4RGrhRar6No/hwlS02IYm2B70EOqMCqqHHzCxRbr1692sxtSJHacANzG0pIbSjD3Iby\nUhuK5t8dyC1HkStCrhyQK3UhVzahnasKUut5D/QnSS2oxtoCqiE1gbdOLy61oKxP3xlQgdWn\nJbPmUv9uYW7DHVIbbmVuQxOpDY2Z29BIakPT/LsDuTQHuWqJXDkgV+pCrmxCO1e3em69ju6U\nWtCAtQUt60tNaMbagtulFjTy6Ts95srwVxH+KfXvLuY2vCW1YThzGzZKbXiUuQ0zpDaEy7Pi\nkCsb5EpdyJUNchWsqVLreZ+W8YnUgr6sLbB0kprwHWsLtkstmB/4P0eB5QMsWDahvWC5Q65s\nkCt1IVc2yFWwUGBZUGBpDwuWDRYsdSFXNsiVupArG+QqWCiwLCiwtIcFywYLlrqQKxvkSl3I\nlQ1yFSwUWBYUWNrDgmWDBUtdyJUNcqUu5MoGuQoWCiwLCiztYcGywYKlLuTKBrlSF3Jlg1wF\nCwWWBQWW9rBg2WDBUhdyZYNcqQu5skGugoUCy4ICS3tYsGywYKkLubJBrtSFXNkgV8FCgWVB\ngaW962fPnv2PuQ1XpTZo8hB2P1yW2nCRuQ0XpDZcZm6DWpArG+RKXciVDXIVLP7WX+PP0Tmp\nCddZWxBkkA1fYAEAAACEGhRYAAAAACpDgQUAAACgMhRYAAAAACpDgQUAAACgMhRYAAAAACpD\ngQUAAACgMmMWWD+uHnVvh56Tth7L3fXHurE9Ovad/eI1XRvydzdRfIuxDd+tHNalx+hlX+fu\n0bkNOR8vGdq1Y+8pO0+xNUFFyJUdcqUq5MoOuQrC56KL8Y69ureedQ4/Et0M0b8FMhVTZMQC\n6/IKx/h2PODYt6eDfdeIY97+qcpyposuC5bubbi6ur39N67O4WnD8QccU9E5y7GPZypUgFzZ\nIVeqQq7skKugvONaWjgKLL1bzzyHngusEE6RAQusnNlSN6Zs3Leyr/T/L9r2HZQ2Z+x55qmB\nojjgX/2a8pzosmDp3oacxaLYdXnWntlS5HewtOH8UFEc/czhb99bKcXrGZYmqAe5skOuVIVc\n2SFXwXlBFGfvcHjBtk/v1nPP4R87cq0TxWn6t0DlFBmwwJJy1vkTeePiclHsaX1YwF+dxQ4f\nyhuX5ori47q15O+uYn/ngqV/G14WxfuOyxufdhY7nuJow2ZRnGU7JPppe7HrvxxNUA9yZYdc\nqQq5skOugrNPFF/Ns0v31vPPYa4VYodfOVqgaooMWGCNEMXnbFvXpHrRunStdZbTF3uLmaeU\n/qXKcqaJvfc4Fyzd23C5n3jvSdvm0zPX/8bRhiGi+D/75mRRfIOjCepBrmyQK3UhVzbIVZC2\niOIHeXbp3XoDzKHT4fbiNgtHC1RNkfEKrDPtxU6OhyuuFEX5eeLXeokdHQ9Q3SaK+3VqybPS\nXxTPOBYs/dvwnihud9+jexsyRdExFatEcSdHE1SDXNkhV6pCruyQqyCtFsWv3Pfo3noDzKHD\n5eHikMssLVA1RcYrsCzXjv/m2Nwginul//tWFKc4dh0RxQf1acexruIsi3PB0r8Nj4riH+57\ndG9DN1F0PE1ditoBjiaoB7myQa7UhVzZIFdBkgbwZ/c9urfeAHPosEUUP+VpgaopMmCB5eJh\nUXxX+j9p1XjKsetye/FeXX53zoPivcdzFyz92zBI7Cv9738/ffOXY4/ubZgtil/aN6fYjpuy\nTIXqkCvkSgvIFXIVuIdE8W/3Pbq33gBzaPdbB3E+UwtUTZGhC6x/O4vd5Fpyg/NkfkkfUdTl\nYpBnrJcEORcs3dtwsb1UKX89Xb5odsDOSzxt+EYUx9uK+Y/ai9NZmqAF5Aq50gJyhVwFYZLU\nvNfn9O3QfexT9vpG79YbYQ7tZosd/rTwtEDVFBm6wFpsP7VsiSi+49w5RhR/U/wX6jnWVZxh\ncVmwdG/DL6K48LlM+8037jvN0gbLflEcuPezr99ZlimOOsHTBA0gV8iVFpAr5CoII0RxpH38\nOuy03oRK79YbYg6tDoviE/bN0E6RkQusnaJ4/1V5Y74ofuTcO1EUv9f+d+c8KHaTj9c6Fyzd\n23BEFMd0GPDy0SvHn+ktilNzONpgsXz8oO2/tgFbzllfMzRBdcgVcqUF5Aq5CoZ8H7XuS/Yc\nWjtA2tgq79G79caYQ9lksZPjWr3QTpGBC6ytojj8rHVrjih+5tw9RRS/1f6XZ9uvvXYuWLq3\n4RP5RrZnrJtHu4viexxtsJzf2NcWtfYTmYZBfcgVcqUF5Aq5CkpnUVxj/Wjq6jqpBz9Y9G+9\nIeZQJlV6Kx3boZ0iwxZYlxaK4sjjtm23AnKCHjXsX13FB60HaT3/RahHGz4Wc2+LckAU53K0\n4cQwUVz2zYWrx18dKYqrLRxNUBtyhVxpAblCroJ0/pzj4jXLXFFcZNG/9UaYQyup/787tkM7\nRUYtsP65TxQnO249sdT1I9DR+a4kVV/OFLGr7ZFDzgVL9zZ8LYodHA+WPC6KPTja8KDz5L5L\n99sGQvcmqAy5Qq60gFwhVyr6XhTvzdG/9UaYQ9mpTPF+54vQTpFBC6wjvUXxsSuOVxtFMdv5\npZ6ieE7rX5/lvDmzc8HSvQ2/imIf54suonhF/zb8TxTHOrYPi+JEC8MwqAu5Qq60gFwhV2rK\n6SSKZ/VvvQHm0GqPM8+WUE+RMQus9zuK7Q/kvnxBFJ90bJ8XxV5a//rjXcSh79g8Lorr33nn\nZ/3bYLmSKXZ1vuhpvbms3m3YJ4obHdsXRLH9NYZhUBVyhVxpAblCrtTVQxSP6996A8yh1X2i\neNL5IrRTZMgC6/0OYhfXpzL9KOYeMfxUFGdr/fuPiHms078NFsvI3PvOScHvZNG/DZttDwqw\nup5pvf2H/sOgIuRKhlypDbmSIVfqudxeFC8ztJ5/DmUnRHFk7qvQTpERC6zvOotdv3HdkTMw\n9wGLq63309OWpwVL7zZYj0sesm9+ZTtSqXcbpFp+hWP7b1HMzOEYBvUgV1bIlcqQKyvkKjgf\nrJz1mmNbehcfZWFoPf8cyl4WxbW5r0I7RQYssM4PEjt+6b5riyhusG2d6CJ2OZ//32jGeU6D\n/m34SRT7X7BtzhfFpxnacFgU+121b79mL+L5piJYyJUNcqUu5MoGuQrOS6I44rJtM2eKKG6R\nN/RuPf8cyla7noIV4ikyYIG1Ov/jqs90F9u/KW/8O8k+7XrJXbD0b8NCUZxpncu9otj1NEMb\nrg0TxdXWq78tf/e3F+58UxEs5MoOuVIVcmWHXAXlUm9RfNh6Ierlx0Wxm/V2VLq3nn0OZQ+I\n4lcuL0M6RcYrsP7uILbfssMpy7rztfaiOG1X1hopgxOuFvADVJW7YOnfhlODpFp64wu7J4qi\n+ApLGw53EMXx2Ye/+2hjd+n3XuNogmqQKwfkSk3IlQNyFZwPM0Wxx+qDh9b0FcX279n26d16\n/jmU9M7z1OtQTpHxCqx33E8nGGLb+1Jn++tp+l5o67Jg6d+Go+Psv7DLS0xt+KKfcyIWX+Bp\nglqQKyfkSkXIlRNyFZz3ezoa3/tjxz69W88/hxZLx7yPUw7hFIVKgWX5Z+N93TsNWPi+zq1x\nXbD0b8O1Vx8a0LHHuC2516zq3YbLLz08qGuHnuOe+ImtCSpBrnIhV+pBrnIhV8E5d2hm306d\nB8x+9lLuPr1bb4A5lP4zynOUKHRTZLwCCwAAACDEocACAAAAUBkKLAAAAACVocACAAAAUBkK\nLAAAAACVocACAAAAUBkKLAAAAACVocACAAAAUBkKLAAAAACVocACAAAAUBkKLAAAAACVocAC\nAAAAUBkKLAAAAACVocACAAAAUBkKLAAAAACVocACAAAAUBkKLAAAAACVocACAAAAUFlEFlgr\nSPJE/v05H8/peGOR2OiU0k2H7T6b78u/r+xxU5rZXKRqx/mHHfsGSj9pUf6f1FHavVb6/1Hk\nEF+yRtvZL1xQsxtgQNYZb513b04FeXe2+06lGFo8R83iGidXX6nZfjCUP1b3ujk9NiqpcqsZ\n7+a4fsE9CoUybhmx/0ruV7+V9rVw+W6llU3+KQdcf+w2k/TDXnO8+m9z96op0amVOj12XOV+\nAasA3ulyOTN597S3rrt+wXsmI3PxisgCq6Y8s/Xy7T5Y23XmE6e6V0NHugouX61nX5Y+krar\n5vtJ/8RI//5fS/5QpY79VosOgWFYZzzqzzx73yIPBZZCDJWiZonUNSpyfe0WhBu2ubydeYhC\n8Q3Or+YpsBRXtrwF1v5ootgX7S9ylhd2/pu4KZc16iPoL5B3OofDnV2/WmnTtdwvec9kZC5e\nkVhgvUtUJo7oM/e9OWPzzv3Nf+d+9dp4U56vdj5l/cLN0ubbeX/DYmnnUHkjf6iiZ17VsGvA\nzTbjC/PsHWzd615geY6hl6hF6hoVqa6MFPLMdI3cP888RqGnowJzK7C8rGx5CqwXzEQxjpRe\nvdft39x5Sdvegl4CfKezujw0byarfu38ovdMRubiFYkFVh+i8e2JhrvvnSNP9y1L3/vn0pWT\nH69oIL9q6vyL8Wwraxqq37/+0Is7Zza1hqzOCfkra6Wtfnl/w43Szk/lDTlUSz+XvZ21um95\n6w+55aSmvQNW0owXkoLivvNiCsVS3gLLcwy9RM0Wp7mv53VOu94AnxN3WINQ8/51B1/Y+kgr\nOUCU8rzjq7kri+SzNzcPTZO/PtH+VbcCy8vK5l5gvSUlN3qf49UD0tdMPQ7++Nd3u9vK/2ac\n1h0GfQT4Tic71ih/JpOecXzVeyYjc/GKwALrVBzRu1uIkt3m9vco6Y+3p3Jfb46R8rDJ/iKn\njTWFHzm++GNX+XUzOZX/JRHF5/kU+z3pi/WtW+4LWM6LzeV/d+t5dTsEBiLNeMNkog/ddu4k\napK3wPIcQ29R83DKDIStq7fJE3+P8xyYM1OkwFDsJ/aX+aJwpre0J/qI7YVrgeVtZXP7KR9J\nS5lph+PV9yYpm2/YX8h/RppxHlZYCPSdTnLZWo21y83kg/FyMByLnfdMRubiFYEF1jKiMjln\npOJ7g+veedL0T3fd8Zhcqdu3H5ZzNdP1q2vl0n6ZvDWMbOezuxgk7XrSupU3VDkrzNKebqr0\nA4xImvEGXYhGuO2UVq0H8xZYnmPoNWoRukZFpvHyAaRVrnu+zJB2VThte5E/Ctb3RvvhAtcC\ny9vK5vpTDqcRCZuc3zXB5W3XdtXODguEgYDf6SyWEdJm1HrXr35dWtpV1uUAu3ImI3PxisAC\nqzrRJItFqswbuu7tLk3/j647rpUVyrc/Y938U66KZrn/lPnSrqLywYfPnIerHM4lOo9L5A/V\ndjmv+U7agnAhzXjtrUSFXc9YORZNwqt5CyzPMfQatQhdoyLSV4LzrzSnH1OlfRNs2x6i8FHu\nG6VrgeVtZXP5Kd8Xl+orl2tabyFKzT1d9GnpG+cE1SEwiIDf6SyfytXXVvev/ip/DDjWtu09\nk5G5eEVegfWGNM9fWCzPSP/3pcvuZtLr/9y+8ed/HVuTpK81vOb2Rcv1OoX7HrJeWdMgz0+y\nbJB2jLJtegiV/JdhQwuEKWnGbzgVTbTbZd9SqQT///bOPEiq4o7jv93ZZZdDjkVcRNAVQTCW\nhkOjaIVweGCgjAdJCkTFSrRiFeW5ImiiURJF0dUSr1gF4oFSBWhpxQOVQ6PiFUAtohGMrAqs\noiAKwsLuvvTvnf3e69ezOzPWzkx/P3+w/brfNPPqfevX3+lzXcRgJchQLzUzY5SR8ODKudHM\nhSKzizPlWCWFCqJuTko2WLrIFtRS308k75Fu+nzN8hXB1aui9PrMngTkF5m3dDxcOCla3WKR\n2cnpwtJr0szgZZ7BmkQ0VPxpOphompR9Frnz0uM0dhVlr0Rzt3o/7+YFFt7hJJHhDlMrRPVt\nN5H3boZfHuQ74o33t04j+rWUN4Tozn9HDJZahmmkZmaMMpEG4dFLNsayhwkBzLFTKin0Iip1\nUrLB0kS2oJatA/yalXAPlnrLNlBgZNzSbS4V+vos9iHuYLjFTuk1aWbwMs5gbatwf6hNJ+ou\n7f/BI9Pjm5Qf+ZcoGpxc427hmKqkESEObiPctEpUl+PHYBEj3niN9RhRaquf9SFvjPVe2GAl\nyDCN1MyMUSbChub0ePZ8kX2KnVJIYa8wZT2dZHQOVkJk82v55miRmKX5PmeIdvJ/bXkAkK9k\n3NI9KkonxLNFsKNRdkqvSTODl3EG63aiDnaH5sehSZzWJl5LcVr8N6PlKPIqTZXTRPmTwWWt\nuHzETatE9ZwvSFB8iDd+mPVjV3l//1re2v3dsMFKkGEaqZkZo0yEl87MjWdv4/EYe3dshRRe\nEFljnKRssDSRzatl5/DoxOcwTTNF+UVtfQaQl2Tc0vGpJffHs7eXEFXaHQx6TZoZvEwzWC0D\niSY6yRFEJ0slLC5KTZhfH/vMOaJgsabOD0neNnl/NVEPr09CJapdIq93Jt8dFAC2wbIuITra\ny2nqQ7QoYrCSZJhGambGKBPhscD3FPmDRP5aTsSlsI/HauqcdGgfrOTI5taym/cQqU34Jk0N\na+fymQOjf0i4ARQYmbZ0QzztRTiapF0fkzVpZvAyzWC9LN6yuzHaQyK5XiqqS5FNv0n3rQsd\nsTTSmY+czElEJX7/+dPi7su9C6WoDhCZ+6KZoDhwDNZqaZ7di0Td90QMVpIM00hNtRly55/g\nIUB7w1POVY6GJ9DYR9nEIsv2M0VOT3fT7fBROYmRzall76nq8UjmWldl1XNwAEXRkGFLd4go\nVe0Lylt4vMgJvSbNDF6mGayJRAe74887O0lOiHnrdP/Fdz334WC/dXboil9/AY/KHewTxMV/\nvAulweKDf3dk/AQgr3EMFm/C4C4ktSbbu7WHDVaSDNNIzcwYZSJCFGWq/IvI3Y+KpXDvRx7v\nLJnGa2dKFrm3Rc4iTIpsdi2Lf8P5ZbG5zTaOwTropu9y81ggL8ispaskKlfl87aPCzmh16SZ\nwcswg9VQTjTdu5gSnpwuWDtjkP/uO13qDVPXiCvtLsZ7qoj6uj8FNotfByP9EqXBOipdfaBw\ncQ3WbUJbzvG434u28q2IwUqUYRqpmRmjDKSJ/NXtYXiFjL2tsUoK5Q94t0UMVlJks2vp7+R2\n/1j1/3k9WF3+ED2/HBQyGbR0Wk3aK0z1mjQzeBlmsHijWr97ifd+fDx6x5eLLhte5rz9CneW\n6c9FeoO22ivEHc8F/8NCv0BpsA4SmTgtp0hxDdZWoaEldsZ8Z2FOyGAlyjCN1FhOt60O807y\n7aBgqSRKtSjyeaKx34MVYVQghZjBspSRza2l/J6rxb8DvrHifLpy+eL7Lq7mtnWFohgULm1u\n6boQlao0OZXcbf/0mjQzeJllsJoPlzf5bKmRO5skdr08fbAtD2fjPd6ZbbW2Xo5n5zjJAUQH\nBt1iSoNVboJzNxXXYPFIsbOkeRTRbCtssJJlmEZqZs4TNZE+4k2rhuV4vot94HOsMbvpI+k2\nlcFiIpHNqaXvaqt5nPg7sjHp2zTeKlriLv/N4nlAXtKmlu5QUbpdkc9DzMs5odekmcHLLIP1\nQtxiKzvGBcuPEYUd7AHp80TqgYTbXH4lfgZ+xYlVFFqPoxLVepKWmIEiwzNYS4nKGsTf+hJK\n8eiKbLCSZZhGambGKBMZltDU/YzcTYxDUrhdXEyRb0syWIwU2exaRn8tEjuOFKmpyd+HN+A6\nu22PAAqDVrd0J4rSVxX5vEet3R+v16SZwcssg3VWvGW7Oune3awne0PQe0Risr7iJ8jdB/l8\nopJPgnyVqO4VeRdk+AAg3/EM1r4Die607DXR4/haNljJMkwjNTNjlInwPlh3xbN/KCXqYq+O\nCElh/7Hkz1Gw0RksKbJJtXzME5JnJ3+hoUQpzHQvSlrb0l2hVsh2kV1lDx3qNWlm8DLKYG0u\ni7dsByb2i/O2tvbQDR/n3O37WLm8wLWxlzPT5ruO/r5qNipRjaVgI1JQbHgGi6d+HmPZGxfZ\ny2gkg6WRYRqpmRmjTGQBdy3Fs5f4xikshdUlRH13BvdpDVYQ2eRanhfereSpxM9cKe7ELKzi\npJUt3VJRelz80w+TNz9Gr0kzg5dRBusm8YqXfiFxN7nNn4q9QiCDONHcV9x2d7T4i8NukAak\n+ZTMNy3rHxQ+5lchKj6UriN2aShWfIP1PvGeMm/bm2BZIYOlkWEaqZkZo0xkC5vw9bHsM/wx\nnIgUuMfrkuBSb7D8yBaqZY5IdwoOqfth4+vyiv2/s2jb/BigEGhlS7ejE6nGrU8WufPslF6T\nZgYvkwxWcz+iQ0PLIHZ6HU77F0w78fjI7bvJOY/XsmZzJ2h0mbKIdQes8q82CI1eac8SPEje\nQzQuquYxIuuK7B4E5C++weJpNDfwD/8/2VeBwdLIMJ3UzIxRRjKRFAe/vVEqlOD0MESksKOa\nqCToYQoMlj6yhWo5X1wc4mmP90u+RvoMerCKg2xaOlbLidFTDFkofZxlXXpNmhm8TDJYz1Ls\nlOVJQgL2ulTeDGZluOwl8uZ1bue910eHt8zi33t9pcOix4rLloYU0Qz5rrio/swdWFstUKQE\nBmsu0XDeWeYt+yowWDoZppGamTHKSHjYhh4K5+3iFV8znXRUCgvFdX9/8xepB0sb2UK17OFz\nTYa7ddSL9EDpZ8Dx4vrTrJ4I5AVZtHQbuVs1cmTll71F3h1OWq9JM4OXSQZrvHjDkaXGvJ7r\nWk7cKBJHfC0X7eVwM99J84wIGiXP8eSdjEqelzIWi4zXHpDPzGGiomqezjXVZf0oIF8JDNa3\nFVT6mn86fWCwdDJMIzUzY5SZ8I5Xqflyzo4RbHr2OBcxKZwq94xLBksb2cK1bD6Yp9O4rmqY\nP/LDrOK2MutnAu1PNi3drVw6Q+5931jDvVruoI1ek2YGL4MMVn0p0YhIXpMIKdUsj+/ZiNe8\nHJR88kuR0c87DozXr1Lved55XO+fwtd/kWvaV000bUz0TK+IqF7l8ep0KxJBIRMYLOu34seg\nv+7GN1haGVp6qZkZo8xkJ5+oVXLBFj9jCbdlXd52r2JS2FBJVPqmeyEZLG1ki9TydiX5XfCP\niWTlM27BGq7klhw9GWhPsmnpWuyrE97wPvrj3zqL656fu5d6TZoZvAwyWDw492A0s5bc88Nf\nr2DtDLnx6XUbN61/6a5xohmkDsu82xqncCn1OK/uiWcfm3mCfXFVuKaZRL1SROF1OCyqurU2\nKxbVHmt/7uwfLVC0SAbL3u4q5c5o8A2WXoZ6qbGcZq2M8dM+EWgfNh3Br77TOQveqW/4YNkM\nPmCLOq70SuPN1c0i5yh3dEee5K6LbNFa+FRVWmAnW3i1M53xyLrP1i+9oFwkByBuFQXZtHS7\nJtgZR9bOf+6VJ+vO5FnvdOgHXqlek2YGL3MM1v4+RBWx1Xsfird+mp1a2ZsiVC2TbpxVES7s\nvCBS02csVOoTPnQ+fnhA6mbVaQOgWJAMVjMfPz/OvfAMVjoZWjqpqY7zEvyUzwPajc0joy96\nyPt+Ybwxa+QZWtc56dAqQk1ki9XCZ+Z0eM1O7vhF6DN9NlqgKMimpWu6pjzy2TFf+YV6TZoZ\nvIr9+QKeEi/zd/Hsof60qW21neUX3+2yb0M3bpqSCgorL94Sq4lPmwgPG8ZFVTZVf6ghKHQk\ng8Wdmv4uIJ7BSitDSyM1M2OUsbQ8XC2/5v510gJlxYDLKg4wa+xkeJuG5MgWq8U+M6en46Ua\nrws+VjpJdVIhKEiyauk2/L5U+uxx8jxkvSbNDF7F/nwBp4uX+c94Nu9B5Fpsa/czl59S0728\nQ8/Dx1791N7YrVvvn3Rsj/IOvY6ZOk+1pzGvVy2tD+dJoqroO+zCx79SfA4UE7LB+sTbBMsK\nDFYrZGglSs3MGGUwjUv+OKwHr91KjZ65TN7ZWDmj5UKRN8TuQo/ug5UU2eK12GfmDHa7WL+5\nd+LAqrKuNeNv2ZSzRwJ5QFYt3Zb7Jw+pKk8dMHD8rDWhAr0mzQxexf58AABQyDRXi19uX7b3\ntwAAtBkYLAAAyGMuFT/0/9reXwIA0GZgsAAAII/h07V6NLT3twAAtBUYLAAAyGdGC4c1cnf6\n+wAAeQUMFgAA5DNreZ774Cca9m/7IP3NAIB8AQYLAADymrnemqtB7f1NAACtBwYLAADymwcr\nYbAAKDhgsAAAIM/5/NqhVR17H399e38PAEDrgcECAAAAAMgxMFgAAAAAADkGBgsAAAAAIMfA\nYAEAAAAA5BgYLAAAAACAHAODBQAAAACQY2CwAAAAAAByDAwWAAAAAECOgcECAAAAAMgxMFgA\nAAAAADkGBgsAAAAAIMfAYAEAAAAA5BgYLAAAAACAHAODBQAAAACQY2CwAAAAAAByDAwWAAAA\nAECOgcECAAAAAMgxMFgAAAAAADkGBgsAAAAAIMfAYAEAAAAA5BgYLAAAAACAHAODBQAAAACQ\nY/4PRK+4IHWLHjQAAAAASUVORK5CYII=",
-      "text/plain": [
-       "plot without title"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 600,
-       "width": 1200
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plots_density"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Kaplan Meyer estimates"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "options(warn=0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Warning message:\n",
-      "\"Vectorized input to `element_text()` is not officially supported.\n",
-      "Results may be unexpected or may change in future versions of ggplot2.\"\n",
-      "Scale for 'x' is already present. Adding another scale for 'x', which will\n",
-      "replace the existing scale.\n",
-      "\n",
-      "Scale for 'x' is already present. Adding another scale for 'x', which will\n",
-      "replace the existing scale.\n",
-      "\n",
-      "Warning message:\n",
-      "\"Vectorized input to `element_text()` is not officially supported.\n",
-      "Results may be unexpected or may change in future versions of ggplot2.\"\n",
-      "Scale for 'x' is already present. Adding another scale for 'x', which will\n",
-      "replace the existing scale.\n",
-      "\n",
-      "Scale for 'x' is already present. Adding another scale for 'x', which will\n",
-      "replace the existing scale.\n",
-      "\n",
-      "Warning message:\n",
-      "\"Vectorized input to `element_text()` is not officially supported.\n",
-      "Results may be unexpected or may change in future versions of ggplot2.\"\n",
-      "Scale for 'x' is already present. Adding another scale for 'x', which will\n",
-      "replace the existing scale.\n",
-      "\n",
-      "Scale for 'x' is already present. Adding another scale for 'x', which will\n",
-      "replace the existing scale.\n",
-      "\n",
-      "Warning message:\n",
-      "\"Vectorized input to `element_text()` is not officially supported.\n",
-      "Results may be unexpected or may change in future versions of ggplot2.\"\n",
-      "Scale for 'x' is already present. Adding another scale for 'x', which will\n",
-      "replace the existing scale.\n",
-      "\n",
-      "Scale for 'x' is already present. Adding another scale for 'x', which will\n",
-      "replace the existing scale.\n",
-      "\n",
-      "Warning message:\n",
-      "\"Vectorized input to `element_text()` is not officially supported.\n",
-      "Results may be unexpected or may change in future versions of ggplot2.\"\n",
-      "Scale for 'x' is already present. Adding another scale for 'x', which will\n",
-      "replace the existing scale.\n",
-      "\n",
-      "Scale for 'x' is already present. Adding another scale for 'x', which will\n",
-      "replace the existing scale.\n",
-      "\n",
-      "Warning message:\n",
-      "\"Removed 4 row(s) containing missing values (geom_path).\"\n",
-      "Warning message:\n",
-      "\"Removed 3 rows containing missing values (geom_point).\"\n",
-      "Warning message:\n",
-      "\"Removed 4 row(s) containing missing values (geom_path).\"\n",
-      "Warning message:\n",
-      "\"Removed 3 rows containing missing values (geom_point).\"\n",
-      "Warning message:\n",
-      "\"Vectorized input to `element_text()` is not officially supported.\n",
-      "Results may be unexpected or may change in future versions of ggplot2.\"\n",
-      "Scale for 'x' is already present. Adding another scale for 'x', which will\n",
-      "replace the existing scale.\n",
-      "\n",
-      "Scale for 'x' is already present. Adding another scale for 'x', which will\n",
-      "replace the existing scale.\n",
-      "\n",
-      "Warning message:\n",
-      "\"Vectorized input to `element_text()` is not officially supported.\n",
-      "Results may be unexpected or may change in future versions of ggplot2.\"\n",
-      "Scale for 'x' is already present. Adding another scale for 'x', which will\n",
-      "replace the existing scale.\n",
-      "\n",
-      "Scale for 'x' is already present. Adding another scale for 'x', which will\n",
-      "replace the existing scale.\n",
-      "\n",
-      "Warning message:\n",
-      "\"Vectorized input to `element_text()` is not officially supported.\n",
-      "Results may be unexpected or may change in future versions of ggplot2.\"\n",
-      "Scale for 'x' is already present. Adding another scale for 'x', which will\n",
-      "replace the existing scale.\n",
-      "\n",
-      "Scale for 'x' is already present. Adding another scale for 'x', which will\n",
-      "replace the existing scale.\n",
-      "\n",
-      "Warning message:\n",
-      "\"Vectorized input to `element_text()` is not officially supported.\n",
-      "Results may be unexpected or may change in future versions of ggplot2.\"\n",
-      "Scale for 'x' is already present. Adding another scale for 'x', which will\n",
-      "replace the existing scale.\n",
-      "\n",
-      "Scale for 'x' is already present. Adding another scale for 'x', which will\n",
-      "replace the existing scale.\n",
-      "\n",
-      "Warning message:\n",
-      "\"Removed 6 row(s) containing missing values (geom_path).\"\n",
-      "Warning message:\n",
-      "\"Removed 5 rows containing missing values (geom_point).\"\n",
-      "Warning message:\n",
-      "\"Removed 6 row(s) containing missing values (geom_path).\"\n",
-      "Warning message:\n",
-      "\"Removed 5 rows containing missing values (geom_point).\"\n",
-      "Warning message:\n",
-      "\"Vectorized input to `element_text()` is not officially supported.\n",
-      "Results may be unexpected or may change in future versions of ggplot2.\"\n",
-      "Scale for 'x' is already present. Adding another scale for 'x', which will\n",
-      "replace the existing scale.\n",
-      "\n",
-      "Scale for 'x' is already present. Adding another scale for 'x', which will\n",
-      "replace the existing scale.\n",
-      "\n",
-      "Warning message:\n",
-      "\"Removed 4 row(s) containing missing values (geom_path).\"\n",
-      "Warning message:\n",
-      "\"Removed 3 rows containing missing values (geom_point).\"\n",
-      "Warning message:\n",
-      "\"Removed 4 row(s) containing missing values (geom_path).\"\n",
-      "Warning message:\n",
-      "\"Removed 3 rows containing missing values (geom_point).\"\n",
-      "Warning message:\n",
-      "\"Vectorized input to `element_text()` is not officially supported.\n",
-      "Results may be unexpected or may change in future versions of ggplot2.\"\n",
-      "Scale for 'x' is already present. Adding another scale for 'x', which will\n",
-      "replace the existing scale.\n",
-      "\n",
-      "Scale for 'x' is already present. Adding another scale for 'x', which will\n",
-      "replace the existing scale.\n",
-      "\n",
-      "Warning message:\n",
-      "\"Removed 4 row(s) containing missing values (geom_path).\"\n",
-      "Warning message:\n",
-      "\"Removed 3 rows containing missing values (geom_point).\"\n",
-      "Warning message:\n",
-      "\"Removed 4 row(s) containing missing values (geom_path).\"\n",
-      "Warning message:\n",
-      "\"Removed 3 rows containing missing values (geom_point).\"\n",
-      "Warning message:\n",
-      "\"Vectorized input to `element_text()` is not officially supported.\n",
-      "Results may be unexpected or may change in future versions of ggplot2.\"\n",
-      "Scale for 'x' is already present. Adding another scale for 'x', which will\n",
-      "replace the existing scale.\n",
-      "\n",
-      "Scale for 'x' is already present. Adding another scale for 'x', which will\n",
-      "replace the existing scale.\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "stratum=\"sex\"\n",
-    "plots = list()\n",
-    "tables = list()\n",
-    "plots_tables = list()\n",
-    "expand = expansion(mult = c(0.05, .1))\n",
-    "for (e in endpoints[-2]){\n",
-    "    #print(e)\n",
-    "    #fit = survfit(Surv(death_cens_time, death_cens)~sex, data = data)\n",
-    "    fit = survfit(as.formula(glue(\"Surv({e}_event_time, {e}_event)~{stratum}\")), data=data %>% select(all_of(c(\"sex\", targets))))\n",
-    "    plot = ggsurvplot(fit,data %>% select(all_of(c(\"sex\", targets))), conf.int = TRUE, ylim = c(0.6,1), cumevents=TRUE, cumevents.y.text = FALSE) #, risk.table =\"percentage\") + scale_color_\n",
-    "    plots[[e]] = plot$plot  + ylab(\"\") + theme(legend.position=\"none\") + \n",
-    "        ggtitle(e) + theme(plot.title = element_text(size=facet_size, hjust=0.5)) + \n",
-    "        scale_color_nejm() + scale_fill_nejm() + scale_x_continuous(expand=expand)\n",
-    "\n",
-    "    tables[[e]] = plot$cumevents + ylab(\"\") + scale_color_nejm() + scale_fill_nejm() + scale_x_continuous(expand=expand)#+ scale_x_continuous(expand=c(0,0))\n",
-    "    plots_tables[[e]] = plot_grid(plots[[e]], tables[[e]], align=\"v\", nrow=2, rel_heights=c(4,1))\n",
-    "    legend <- get_legend(plots[[e]] + guides(color = guide_legend(nrow = 1)) + theme(legend.position = \"bottom\", legend.title=element_text(size=base_size), legend.text=element_text(size=base_size)))\n",
-    "}          "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plot_width=20; plot_height=25; plot_dpi=300\n",
-    "options(repr.plot.width=plot_width, repr.plot.height=plot_height)\n",
-    "\n",
-    "plots_f = plot_grid(plotlist = plots_tables, ncol=round(length(plots_tables)/3, 0))\n",
-    "plots_km = plot_grid(legend, plots_f, ncol=1, rel_heights=c(0.05, 1))\n",
-    "\n",
-    "plot_name = \"5_endpoint_km\"\n",
-    "ggsave(filename=glue(\"{dataset_path}/{plot_name}.pdf\"), plot=last_plot(), width=plot_width, height=plot_height, dpi=plot_dpi, device=\"pdf\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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76vM9oZtfd3x97siB7qjR/oju3tiOzvjrf1xcdeiegY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\nAKhw6v5LBr7mJA4P/lWVE1sZ2PKTcwPJr/89b0qhggQAFJXSr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ybaEBQAAD7TumyuLC2JRnIycjepRebOeOLKS5aavExIKVbOmkh2BgAIIRLL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\nnkNpYWjhAAAgAElEQVSBEAA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D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\nr127NoRw7LHHjv7nN998c3d390c/+tFW5QOAV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-      "text/plain": [
-       "plot without title"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 1500,
-       "width": 1200
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plots_km"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Competing Events"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "competing_endpoints"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fit5 <- survfit(Surv(CompetingEvents_event_time, CompetingEvents_event, type = \"mstate\") ~ 1, data = data)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "options(repr.plot.width=10, repr.plot.height=10)\n",
-    "ggcompetingrisks(fit5, palette = \"jco\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ggsurvplot(fit5,data, conf.int = TRUE, ylim = c(0.70,1), cumevents=TRUE, cumevents.y.text = FALSE)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "R [conda env:python]",
-   "language": "R",
-   "name": "conda-env-python-r"
-  },
-  "language_info": {
-   "codemirror_mode": "r",
-   "file_extension": ".r",
-   "mimetype": "text/x-r-source",
-   "name": "R",
-   "pygments_lexer": "r",
-   "version": "4.0.2"
-  },
-  "toc-autonumbering": true,
-  "toc-showcode": false,
-  "toc-showmarkdowntxt": true
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/neuralcvd/preprocessing/ukbb_tabular/6_imputation.ipynb b/neuralcvd/preprocessing/ukbb_tabular/6_imputation.ipynb
deleted file mode 100644
index 0d48dad..0000000
--- a/neuralcvd/preprocessing/ukbb_tabular/6_imputation.ipynb
+++ /dev/null
@@ -1,2051 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 3. Imputation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The autoreload extension is already loaded. To reload it, use:\n",
-      "  %reload_ext autoreload\n"
-     ]
-    }
-   ],
-   "source": [
-    "%load_ext autoreload\n",
-    "%autoreload 2\n",
-    "\n",
-    "import os\n",
-    "from tqdm.auto import tqdm\n",
-    "import matplotlib.pyplot as pl\n",
-    "import pathlib\n",
-    "\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import lifelines\n",
-    "from lifelines import CoxPHFitter\n",
-    "from sklearn.model_selection import StratifiedKFold\n",
-    "\n",
-    "\n",
-    "from joblib import Parallel, delayed\n",
-    "from tqdm.notebook import tqdm\n",
-    "import neptune\n",
-    "import warnings\n",
-    "warnings.filterwarnings(\"ignore\")\n",
-    "import shutil\n",
-    "\n",
-    "dataset_name = \"cvd_massive_excl_emb\"\n",
-    "path = \"/data/analysis/ag-reils/steinfej/code/umbrella/pre/ukbb\"\n",
-    "data_path = \"/data/analysis/ag-reils/ag-reils-shared/cardioRS/data\"\n",
-    "dataset_path = f\"{data_path}/2_datasets_pre/{dataset_name}\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Read data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data = pd.read_feather(f\"{dataset_path}/baseline.feather\")\n",
-    "data_description = pd.read_feather(f\"{dataset_path}/baseline_clinical_description.feather\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>covariate</th>\n",
-       "      <th>dtype</th>\n",
-       "      <th>isTarget</th>\n",
-       "      <th>based_on</th>\n",
-       "      <th>aggr_fn</th>\n",
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-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
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-       "      <th>3</th>\n",
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-       "      <td>category</td>\n",
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-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5</td>\n",
-       "      <td>townsend_deprivation_index_at_recruitment</td>\n",
-       "      <td>numeric</td>\n",
-       "      <td>False</td>\n",
-       "      <td>basics</td>\n",
-       "      <td>NaN</td>\n",
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-       "    <tr>\n",
-       "      <th>3741</th>\n",
-       "      <td>3742</td>\n",
-       "      <td>ASCVD_event_time</td>\n",
-       "      <td>numeric</td>\n",
-       "      <td>True</td>\n",
-       "      <td>score_ASCVD</td>\n",
-       "      <td>NaN</td>\n",
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-       "    <tr>\n",
-       "      <th>3742</th>\n",
-       "      <td>3743</td>\n",
-       "      <td>QRISK3_event</td>\n",
-       "      <td>integer</td>\n",
-       "      <td>True</td>\n",
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-       "    <tr>\n",
-       "      <th>3743</th>\n",
-       "      <td>3744</td>\n",
-       "      <td>QRISK3_event_time</td>\n",
-       "      <td>numeric</td>\n",
-       "      <td>True</td>\n",
-       "      <td>score_QRISK3</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3744</th>\n",
-       "      <td>3745</td>\n",
-       "      <td>MACE_event</td>\n",
-       "      <td>integer</td>\n",
-       "      <td>True</td>\n",
-       "      <td>score_MACE</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3745</th>\n",
-       "      <td>3746</td>\n",
-       "      <td>MACE_event_time</td>\n",
-       "      <td>numeric</td>\n",
-       "      <td>True</td>\n",
-       "      <td>score_MACE</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>3746 rows × 6 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        id                                  covariate     dtype  isTarget  \\\n",
-       "0        1                                        eid   integer     False   \n",
-       "1        2                         age_at_recruitment   numeric     False   \n",
-       "2        3                                        sex  category     False   \n",
-       "3        4                          ethnic_background  category     False   \n",
-       "4        5  townsend_deprivation_index_at_recruitment   numeric     False   \n",
-       "...    ...                                        ...       ...       ...   \n",
-       "3741  3742                           ASCVD_event_time   numeric      True   \n",
-       "3742  3743                               QRISK3_event   integer      True   \n",
-       "3743  3744                          QRISK3_event_time   numeric      True   \n",
-       "3744  3745                                 MACE_event   integer      True   \n",
-       "3745  3746                            MACE_event_time   numeric      True   \n",
-       "\n",
-       "          based_on  aggr_fn  \n",
-       "0              eid      NaN  \n",
-       "1           basics      NaN  \n",
-       "2           basics      NaN  \n",
-       "3           basics      NaN  \n",
-       "4           basics      NaN  \n",
-       "...            ...      ...  \n",
-       "3741   score_ASCVD      NaN  \n",
-       "3742  score_QRISK3      NaN  \n",
-       "3743  score_QRISK3      NaN  \n",
-       "3744    score_MACE      NaN  \n",
-       "3745    score_MACE      NaN  \n",
-       "\n",
-       "[3746 rows x 6 columns]"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "covariates = data_description[data_description.isTarget==False].covariate.to_list()[1:]\n",
-    "targets = data_description[data_description.isTarget==True].covariate.to_list()\n",
-    "data_description"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
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-       "      <td>5.541451</td>\n",
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-       "      <td>14.325340</td>\n",
-       "      <td>25.449361</td>\n",
-       "      <td>-7.429906</td>\n",
-       "      <td>0.208367</td>\n",
-       "      <td>3.740000</td>\n",
-       "      <td>8.743372</td>\n",
-       "      <td>4.110000</td>\n",
-       "      <td>2.428418</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>10.420260</td>\n",
-       "      <td>0</td>\n",
-       "      <td>10.420260</td>\n",
-       "      <td>0</td>\n",
-       "      <td>10.130048</td>\n",
-       "      <td>0</td>\n",
-       "      <td>10.130048</td>\n",
-       "      <td>0</td>\n",
-       "      <td>10.130048</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>502504 rows × 3758 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            eid  PGS000011  PGS000013  PGS000016  PGS000018  PGS000039  \\\n",
-       "0       1000018   3.170770  14.182161  25.478597  -7.910004   0.196099   \n",
-       "1       1000020   3.935565  14.203609  25.508855  -7.338207   0.382054   \n",
-       "2       1000037   4.223654  14.248176  25.549722  -7.533061   0.189988   \n",
-       "3       1000043   3.529575  14.176502  25.394145  -7.768648   0.289388   \n",
-       "4       1000051        NaN        NaN        NaN        NaN        NaN   \n",
-       "...         ...        ...        ...        ...        ...        ...   \n",
-       "502499  6025150   4.181800  14.053589  25.402026  -7.499164   0.272956   \n",
-       "502500  6025165   4.315743  14.131477  25.417482  -7.736421   0.275103   \n",
-       "502501  6025173   3.096671  14.186225  25.488755  -8.079815   0.352918   \n",
-       "502502  6025182   3.699646  14.134693  25.439013  -7.962928   0.343860   \n",
-       "502503  6025198   3.663789  14.325340  25.449361  -7.429906   0.208367   \n",
-       "\n",
-       "        PGS000057  PGS000058  PGS000059  PGS000116  ...  death_cvd_event  \\\n",
-       "0        3.690275   8.513230   4.060235   2.543097  ...                0   \n",
-       "1        4.170000   8.665529   3.640000   2.575014  ...                0   \n",
-       "2        4.288627   8.540908   5.541451   2.441255  ...                0   \n",
-       "3        4.130000   8.515447   4.261098   2.740589  ...                0   \n",
-       "4             NaN        NaN        NaN        NaN  ...                0   \n",
-       "...           ...        ...        ...        ...  ...              ...   \n",
-       "502499   4.470000   8.425411   4.660235   2.419166  ...                0   \n",
-       "502500   3.900941   8.046961   4.000196   2.721753  ...                0   \n",
-       "502501   3.507804   7.994697   3.690706   2.687853  ...                0   \n",
-       "502502   4.020000   8.151277   3.960000   2.692526  ...                0   \n",
-       "502503   3.740000   8.743372   4.110000   2.428418  ...                0   \n",
-       "\n",
-       "        death_cvd_event_time  SCORE_event  SCORE_event_time ASCVD_event  \\\n",
-       "0                  10.625599            0         10.625599           0   \n",
-       "1                  12.355921            0         12.355921           0   \n",
-       "2                  11.627652            0         11.627652           1   \n",
-       "3                  11.069131            0         11.069131           1   \n",
-       "4                  14.050650            0         14.050650           0   \n",
-       "...                      ...          ...               ...         ...   \n",
-       "502499             12.996578            0         12.996578           0   \n",
-       "502500             11.819302            0         11.819302           0   \n",
-       "502501             11.778234            0         11.778234           0   \n",
-       "502502              9.993155            0          9.993155           0   \n",
-       "502503             10.420260            0         10.420260           0   \n",
-       "\n",
-       "       ASCVD_event_time  QRISK3_event QRISK3_event_time MACE_event  \\\n",
-       "0             10.335387             0         10.335387          0   \n",
-       "1             12.065708             0         12.065708          0   \n",
-       "2              7.969884             1          7.969884          0   \n",
-       "3              5.122519             1          5.122519          1   \n",
-       "4             13.760438             0         13.760438          0   \n",
-       "...                 ...           ...               ...        ...   \n",
-       "502499        12.706366             0         12.706366          0   \n",
-       "502500        11.529090             0         11.529090          0   \n",
-       "502501        11.488022             0         11.488022          0   \n",
-       "502502         9.702943             0          9.702943          0   \n",
-       "502503        10.130048             0         10.130048          0   \n",
-       "\n",
-       "       MACE_event_time  \n",
-       "0            10.335387  \n",
-       "1            12.065708  \n",
-       "2            11.337440  \n",
-       "3             5.122519  \n",
-       "4            13.760438  \n",
-       "...                ...  \n",
-       "502499       12.706366  \n",
-       "502500       11.529090  \n",
-       "502501       11.488022  \n",
-       "502502        9.702943  \n",
-       "502503       10.130048  \n",
-       "\n",
-       "[502504 rows x 3758 columns]"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Visualize missing values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import matplotlib as mpl\n",
-    "from matplotlib import gridspec\n",
-    "import matplotlib.pyplot as plt\n",
-    "from scipy.cluster import hierarchy\n",
-    "import seaborn as sns\n",
-    "import pandas as pd\n",
-    "import warnings\n",
-    "\n",
-    "def nullity_sort(df, sort=None, axis='columns'):\n",
-    "    \"\"\"\n",
-    "    Sorts a DataFrame according to its nullity, in either ascending or descending order.\n",
-    "    :param df: The DataFrame object being sorted.\n",
-    "    :param sort: The sorting method: either \"ascending\", \"descending\", or None (default).\n",
-    "    :return: The nullity-sorted DataFrame.\n",
-    "    \"\"\"\n",
-    "    if sort is None:\n",
-    "        return df\n",
-    "    elif sort not in ['ascending', 'descending']:\n",
-    "        raise ValueError('The \"sort\" parameter must be set to \"ascending\" or \"descending\".')\n",
-    "\n",
-    "    if axis not in ['rows', 'columns']:\n",
-    "        raise ValueError('The \"axis\" parameter must be set to \"rows\" or \"columns\".')\n",
-    "\n",
-    "    if axis == 'columns':\n",
-    "        if sort == 'ascending':\n",
-    "            return df.iloc[np.argsort(df.count(axis='columns').values), :]\n",
-    "        elif sort == 'descending':\n",
-    "            return df.iloc[np.flipud(np.argsort(df.count(axis='columns').values)), :]\n",
-    "    elif axis == 'rows':\n",
-    "        if sort == 'ascending':\n",
-    "            return df.iloc[:, np.argsort(df.count(axis='rows').values)]\n",
-    "        elif sort == 'descending':\n",
-    "            return df.iloc[:, np.flipud(np.argsort(df.count(axis='rows').values))]\n",
-    "\n",
-    "\n",
-    "def nullity_filter(df, filter=None, p=0, n=0):\n",
-    "    \"\"\"\n",
-    "    Filters a DataFrame according to its nullity, using some combination of 'top' and 'bottom' numerical and\n",
-    "    percentage values. Percentages and numerical thresholds can be specified simultaneously: for example,\n",
-    "    to get a DataFrame with columns of at least 75% completeness but with no more than 5 columns, use\n",
-    "    `nullity_filter(df, filter='top', p=.75, n=5)`.\n",
-    "    :param df: The DataFrame whose columns are being filtered.\n",
-    "    :param filter: The orientation of the filter being applied to the DataFrame. One of, \"top\", \"bottom\",\n",
-    "    or None (default). The filter will simply return the DataFrame if you leave the filter argument unspecified or\n",
-    "    as None.\n",
-    "    :param p: A completeness ratio cut-off. If non-zero the filter will limit the DataFrame to columns with at least p\n",
-    "    completeness. Input should be in the range [0, 1].\n",
-    "    :param n: A numerical cut-off. If non-zero no more than this number of columns will be returned.\n",
-    "    :return: The nullity-filtered `DataFrame`.\n",
-    "    \"\"\"\n",
-    "    if filter == 'top':\n",
-    "        if p:\n",
-    "            df = df.iloc[:, [c >= p for c in df.count(axis='rows').values / len(df)]]\n",
-    "        if n:\n",
-    "            df = df.iloc[:, np.sort(np.argsort(df.count(axis='rows').values)[-n:])]\n",
-    "    elif filter == 'bottom':\n",
-    "        if p:\n",
-    "            df = df.iloc[:, [c <= p for c in df.count(axis='rows').values / len(df)]]\n",
-    "        if n:\n",
-    "            df = df.iloc[:, np.sort(np.argsort(df.count(axis='rows').values)[:n])]\n",
-    "    return df\n",
-    "\n",
-    "def matrix(df,\n",
-    "           filter=None, n=0, p=0, sort=None,\n",
-    "           figsize=(25, 10), width_ratios=(15, 1), color=(0.25, 0.25, 0.25),\n",
-    "           fontsize=16, labels=None, sparkline=True, inline=False,\n",
-    "           freq=None, ax=None):\n",
-    "    \"\"\"\n",
-    "    A matrix visualization of the nullity of the given DataFrame.\n",
-    "    :param df: The `DataFrame` being mapped.\n",
-    "    :param filter: The filter to apply to the heatmap. Should be one of \"top\", \"bottom\", or None (default).\n",
-    "    :param n: The max number of columns to include in the filtered DataFrame.\n",
-    "    :param p: The max percentage fill of the columns in the filtered DataFrame.\n",
-    "    :param sort: The row sort order to apply. Can be \"ascending\", \"descending\", or None.\n",
-    "    :param figsize: The size of the figure to display.\n",
-    "    :param fontsize: The figure's font size. Default to 16.\n",
-    "    :param labels: Whether or not to display the column names. Defaults to the underlying data labels when there are\n",
-    "    50 columns or less, and no labels when there are more than 50 columns.\n",
-    "    :param sparkline: Whether or not to display the sparkline. Defaults to True.\n",
-    "    :param width_ratios: The ratio of the width of the matrix to the width of the sparkline. Defaults to `(15, 1)`.\n",
-    "    Does nothing if `sparkline=False`.\n",
-    "    :param color: The color of the filled columns. Default is `(0.25, 0.25, 0.25)`.\n",
-    "    :return: If `inline` is False, the underlying `matplotlib.figure` object. Else, nothing.\n",
-    "    \"\"\"\n",
-    "    df = nullity_filter(df, filter=filter, n=n, p=p)\n",
-    "    df = nullity_sort(df, sort=sort, axis='columns')\n",
-    "\n",
-    "    height = df.shape[0]\n",
-    "    width = df.shape[1]\n",
-    "\n",
-    "    # z is the color-mask array, g is a NxNx3 matrix. Apply the z color-mask to set the RGB of each pixel.\n",
-    "    z = df.notnull().values\n",
-    "    g = np.zeros((height, width, 3))\n",
-    "\n",
-    "    g[z < 0.5] = [1, 1, 1]\n",
-    "    g[z > 0.5] = color\n",
-    "\n",
-    "    # Set up the matplotlib grid layout. A unary subplot if no sparkline, a left-right splot if yes sparkline.\n",
-    "    if ax is None:\n",
-    "        plt.figure(figsize=figsize)\n",
-    "        if sparkline:\n",
-    "            gs = gridspec.GridSpec(1, 2, width_ratios=width_ratios)\n",
-    "            gs.update(wspace=0.08)\n",
-    "            ax1 = plt.subplot(gs[1])\n",
-    "        else:\n",
-    "            gs = gridspec.GridSpec(1, 1)\n",
-    "        ax0 = plt.subplot(gs[0])\n",
-    "    else:\n",
-    "        if sparkline is not False:\n",
-    "            warnings.warn(\n",
-    "                \"Plotting a sparkline on an existing axis is not currently supported. \"\n",
-    "                \"To remove this warning, set sparkline=False.\"\n",
-    "            )\n",
-    "            sparkline = False\n",
-    "        ax0 = ax\n",
-    "\n",
-    "    # Create the nullity plot.\n",
-    "    ax0.imshow(g, interpolation='none')\n",
-    "\n",
-    "    # Remove extraneous default visual elements.\n",
-    "    ax0.set_aspect('auto')\n",
-    "    ax0.grid(b=False)\n",
-    "    ax0.xaxis.tick_top()\n",
-    "    ax0.xaxis.set_ticks_position('none')\n",
-    "    ax0.yaxis.set_ticks_position('none')\n",
-    "    ax0.spines['top'].set_visible(False)\n",
-    "    ax0.spines['right'].set_visible(False)\n",
-    "    ax0.spines['bottom'].set_visible(False)\n",
-    "    ax0.spines['left'].set_visible(False)\n",
-    "\n",
-    "    # Set up and rotate the column ticks. The labels argument is set to None by default. If the user specifies it in\n",
-    "    # the argument, respect that specification. Otherwise display for <= 50 columns and do not display for > 50.\n",
-    "    if labels or (labels is None and len(df.columns) <= 50):\n",
-    "        ha = 'left'\n",
-    "        ax0.set_xticks(list(range(0, width)))\n",
-    "        ax0.set_xticklabels(list(df.columns), rotation=45, ha=ha, fontsize=fontsize)\n",
-    "    else:\n",
-    "        ax0.set_xticks([])\n",
-    "\n",
-    "    # Adds Timestamps ticks if freq is not None, else set up the two top-bottom row ticks.\n",
-    "    if freq:\n",
-    "        ts_list = []\n",
-    "\n",
-    "        if type(df.index) == pd.PeriodIndex:\n",
-    "            ts_array = pd.date_range(df.index.to_timestamp().date[0],\n",
-    "                                     df.index.to_timestamp().date[-1],\n",
-    "                                     freq=freq).values\n",
-    "\n",
-    "            ts_ticks = pd.date_range(df.index.to_timestamp().date[0],\n",
-    "                                     df.index.to_timestamp().date[-1],\n",
-    "                                     freq=freq).map(lambda t:\n",
-    "                                                    t.strftime('%Y-%m-%d'))\n",
-    "\n",
-    "        elif type(df.index) == pd.DatetimeIndex:\n",
-    "            ts_array = pd.date_range(df.index[0], df.index[-1],\n",
-    "                                     freq=freq).values\n",
-    "\n",
-    "            ts_ticks = pd.date_range(df.index[0], df.index[-1],\n",
-    "                                     freq=freq).map(lambda t:\n",
-    "                                                    t.strftime('%Y-%m-%d'))\n",
-    "        else:\n",
-    "            raise KeyError('Dataframe index must be PeriodIndex or DatetimeIndex.')\n",
-    "        try:\n",
-    "            for value in ts_array:\n",
-    "                ts_list.append(df.index.get_loc(value))\n",
-    "        except KeyError:\n",
-    "            raise KeyError('Could not divide time index into desired frequency.')\n",
-    "\n",
-    "        ax0.set_yticks(ts_list)\n",
-    "        ax0.set_yticklabels(ts_ticks, fontsize=int(fontsize / 16 * 20), rotation=0)\n",
-    "    else:\n",
-    "        ax0.set_yticks([0, df.shape[0] - 1])\n",
-    "        ax0.set_yticklabels([1, df.shape[0]], fontsize=int(fontsize / 16 * 20), rotation=0)\n",
-    "\n",
-    "    if sparkline:\n",
-    "        # Calculate row-wise completeness for the sparkline.\n",
-    "        completeness_srs = df.notnull().astype(bool).sum(axis=1)\n",
-    "        x_domain = list(range(0, height))\n",
-    "        y_range = list(reversed(completeness_srs.values))\n",
-    "        min_completeness = min(y_range)\n",
-    "        max_completeness = max(y_range)\n",
-    "        min_completeness_index = y_range.index(min_completeness)\n",
-    "        max_completeness_index = y_range.index(max_completeness)\n",
-    "\n",
-    "        # Set up the sparkline, remove the border element.\n",
-    "        ax1.grid(b=False)\n",
-    "        ax1.set_aspect('auto')\n",
-    "        # GH 25\n",
-    "        if int(mpl.__version__[0]) <= 1:\n",
-    "            ax1.set_axis_bgcolor((1, 1, 1))\n",
-    "        else:\n",
-    "            ax1.set_facecolor((1, 1, 1))\n",
-    "        ax1.spines['top'].set_visible(False)\n",
-    "        ax1.spines['right'].set_visible(False)\n",
-    "        ax1.spines['bottom'].set_visible(False)\n",
-    "        ax1.spines['left'].set_visible(False)\n",
-    "        ax1.set_ymargin(0)\n",
-    "\n",
-    "        # Plot sparkline---plot is sideways so the x and y axis are reversed.\n",
-    "        ax1.plot(y_range, x_domain, color=color)\n",
-    "\n",
-    "        if labels:\n",
-    "            # Figure out what case to display the label in: mixed, upper, lower.\n",
-    "            label = 'Data Completeness'\n",
-    "            if str(df.columns[0]).islower():\n",
-    "                label = label.lower()\n",
-    "            if str(df.columns[0]).isupper():\n",
-    "                label = label.upper()\n",
-    "\n",
-    "            # Set up and rotate the sparkline label.\n",
-    "            ha = 'left'\n",
-    "            ax1.set_xticks([min_completeness + (max_completeness - min_completeness) / 2])\n",
-    "            ax1.set_xticklabels([label], rotation=45, ha=ha, fontsize=fontsize)\n",
-    "            ax1.xaxis.tick_top()\n",
-    "            ax1.set_yticks([])\n",
-    "        else:\n",
-    "            ax1.set_xticks([])\n",
-    "            ax1.set_yticks([])\n",
-    "\n",
-    "        # Add maximum and minimum labels, circles.\n",
-    "        ax1.annotate(max_completeness,\n",
-    "                     xy=(max_completeness, max_completeness_index),\n",
-    "                     xytext=(max_completeness + 2, max_completeness_index),\n",
-    "                     fontsize=int(fontsize / 16 * 14),\n",
-    "                     va='center',\n",
-    "                     ha='left')\n",
-    "        ax1.annotate(min_completeness,\n",
-    "                     xy=(min_completeness, min_completeness_index),\n",
-    "                     xytext=(min_completeness - 2, min_completeness_index),\n",
-    "                     fontsize=int(fontsize / 16 * 14),\n",
-    "                     va='center',\n",
-    "                     ha='right')\n",
-    "\n",
-    "        ax1.set_xlim([min_completeness - 2, max_completeness + 2])  # Otherwise the circles are cut off.\n",
-    "        ax1.plot([min_completeness], [min_completeness_index], '.', color=color, markersize=10.0)\n",
-    "        ax1.plot([max_completeness], [max_completeness_index], '.', color=color, markersize=10.0)\n",
-    "\n",
-    "        # Remove tick mark (only works after plotting).\n",
-    "        ax1.xaxis.set_ticks_position('none')\n",
-    "\n",
-    "    if inline:\n",
-    "        warnings.warn(\n",
-    "            \"The 'inline' argument has been deprecated, and will be removed in a future version \"\n",
-    "            \"of missingno.\"\n",
-    "        )\n",
-    "        plt.show()\n",
-    "    else:\n",
-    "        return ax0\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 3600x1080 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
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-      "text/plain": [
-       "<Figure size 3600x1080 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import missingno as msno\n",
-    "%matplotlib inline\n",
-    "fig = matrix(data[covariates].sample(1000), sparkline=True, figsize=(50, 15))\n",
-    "\n",
-    "missing = data.columns[data.isnull().any()]\n",
-    "fig = matrix(data[missing].sample(1000), sparkline=True, labels=True, figsize=(50, 15))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Preprocessing"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cat_features = data.select_dtypes(['O', \"category\"]).columns.to_list()\n",
-    "cat_idx = data.columns.get_indexer(cat_features)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['Female', 'Male']\n",
-      "Categories (2, object): ['Female', 'Male']\n",
-      "['White', 'Black', NaN, 'Asian', 'Mixed', 'Chinese']\n",
-      "Categories (5, object): ['White', 'Black', 'Asian', 'Mixed', 'Chinese']\n",
-      "[datetime.date(2009, 11, 12) datetime.date(2008, 2, 19)\n",
-      " datetime.date(2008, 11, 11) ... datetime.date(2006, 3, 13)\n",
-      " datetime.date(2010, 9, 3) datetime.date(2010, 8, 16)]\n",
-      "[datetime.date(1960, 11, 12) datetime.date(1949, 2, 19)\n",
-      " datetime.date(1949, 11, 11) ... datetime.date(1938, 3, 22)\n",
-      " datetime.date(1970, 8, 17) datetime.date(1936, 6, 12)]\n",
-      "['Fair', 'Good', 'Poor', 'Excellent', NaN]\n",
-      "Categories (4, object): ['Poor' < 'Fair' < 'Good' < 'Excellent']\n",
-      "['Current', 'Previous', 'Never', NaN]\n",
-      "Categories (3, object): ['Current' < 'Previous' < 'Never']\n",
-      "['Once or twice a week', 'Three or four times a week', 'One to three times a month', 'Daily or almost daily', 'Special occasions only', 'Never', NaN]\n",
-      "Categories (6, object): ['Daily or almost daily' < 'Three or four times a week' < 'Once or twice a week' < 'One to three times a month' < 'Special occasions only' < 'Never']\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Show Categories\n",
-    "df = data\n",
-    "for col in cat_features:\n",
-    "    print(df[col].unique())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Encode Categories\n",
-    "from category_encoders import *\n",
-    "enc = OrdinalEncoder(cols=cat_features, handle_missing=\"return_nan\", handle_unknown=\"return_nan\")\n",
-    "enc.fit(data)\n",
-    "data_tf = enc.transform(data)\n",
-    "\n",
-    "df = data_tf\n",
-    "for col in cat_features: print(df[col].unique())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Imputation with miceforest"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df = data.sample(10000)\n",
-    "missing = df.columns[df.isnull().any()].to_list()\n",
-    "complete = df.columns[~df.isnull().any()].to_list()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "variable_schema = {}\n",
-    "for m in missing: \n",
-    "    variable_schema[m] = [x for x in missing if x != m]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1  | PGS000011 | PGS000013 | PGS000016 | PGS000018 | PGS000039 | PGS000057 | PGS000058 | PGS000059 | PGS000116 | PGS000117 | PGS000192 | PGS000296 | ethnic_background | townsend_deprivation_index_at_recruitment | overall_health_rating | smoking_status | alcohol_intake_frequency | body_mass_index_bmi | weight | pulse_wave_arterial_stiffness_index | pulse_wave_reflection_index | waist_circumference | hip_circumference | standing_height | trunk_fat_percentage | body_fat_percentage | basal_metabolic_rate | forced_vital_capacity_fvc_best_measure | forced_expiratory_volume_in_1second_fev1_best_measure | fev1_fvc_ratio_zscore | peak_expiratory_flow_pef_f3064_0_2 | peak_expiratory_flow_pef_f3064_0_1 | peak_expiratory_flow_pef | systolic_blood_pressure | diastolic_blood_pressure | pulse_rate | basophill_count | basophill_percentage | eosinophill_count | eosinophill_percentage | haematocrit_percentage | haemoglobin_concentration | high_light_scatter_reticulocyte_count | high_light_scatter_reticulocyte_percentage | immature_reticulocyte_fraction | lymphocyte_count | lymphocyte_percentage | mean_corpuscular_haemoglobin | mean_corpuscular_haemoglobin_concentration | mean_corpuscular_volume | mean_platelet_thrombocyte_volume | mean_reticulocyte_volume | mean_sphered_cell_volume | monocyte_count | monocyte_percentage | neutrophill_count | neutrophill_percentage | nucleated_red_blood_cell_count | nucleated_red_blood_cell_percentage | platelet_count | platelet_crit | platelet_distribution_width | red_blood_cell_erythrocyte_count | red_blood_cell_erythrocyte_distribution_width | reticulocyte_count | reticulocyte_percentage | white_blood_cell_leukocyte_count | alanine_aminotransferase | albumin | alkaline_phosphatase | apolipoprotein_a | apolipoprotein_b | aspartate_aminotransferase | creactive_protein | calcium | cholesterol | creatinine | cystatin_c | direct_bilirubin | gamma_glutamyltransferase | glucose | glycated_haemoglobin_hba1c | hdl_cholesterol | igf1 | ldl_direct | lipoprotein_a | oestradiol | phosphate | rheumatoid_factor | shbg | testosterone | total_bilirubin | total_protein | triglycerides | urate | urea | vitamin_d\n",
-      "2  | PGS000011 | PGS000013 | PGS000016 | PGS000018 | PGS000039 | PGS000057 | PGS000058 | PGS000059 | PGS000116 | PGS000117 | PGS000192 | PGS000296 | ethnic_background | townsend_deprivation_index_at_recruitment | overall_health_rating | smoking_status | alcohol_intake_frequency | body_mass_index_bmi | weight | pulse_wave_arterial_stiffness_index | pulse_wave_reflection_index | waist_circumference | hip_circumference | standing_height | trunk_fat_percentage | body_fat_percentage | basal_metabolic_rate | forced_vital_capacity_fvc_best_measure | forced_expiratory_volume_in_1second_fev1_best_measure | fev1_fvc_ratio_zscore | peak_expiratory_flow_pef_f3064_0_2 | peak_expiratory_flow_pef_f3064_0_1 | peak_expiratory_flow_pef | systolic_blood_pressure | diastolic_blood_pressure | pulse_rate | basophill_count | basophill_percentage | eosinophill_count | eosinophill_percentage | haematocrit_percentage | haemoglobin_concentration | high_light_scatter_reticulocyte_count | high_light_scatter_reticulocyte_percentage | immature_reticulocyte_fraction | lymphocyte_count | lymphocyte_percentage | mean_corpuscular_haemoglobin | mean_corpuscular_haemoglobin_concentration | mean_corpuscular_volume | mean_platelet_thrombocyte_volume | mean_reticulocyte_volume | mean_sphered_cell_volume | monocyte_count | monocyte_percentage | neutrophill_count | neutrophill_percentage | nucleated_red_blood_cell_count | nucleated_red_blood_cell_percentage | platelet_count | platelet_crit | platelet_distribution_width | red_blood_cell_erythrocyte_count | red_blood_cell_erythrocyte_distribution_width | reticulocyte_count | reticulocyte_percentage | white_blood_cell_leukocyte_count | alanine_aminotransferase | albumin | alkaline_phosphatase | apolipoprotein_a | apolipoprotein_b | aspartate_aminotransferase | creactive_protein | calcium | cholesterol | creatinine | cystatin_c | direct_bilirubin | gamma_glutamyltransferase | glucose | glycated_haemoglobin_hba1c | hdl_cholesterol | igf1 | ldl_direct | lipoprotein_a | oestradiol | phosphate | rheumatoid_factor | shbg | testosterone | total_bilirubin | total_protein | triglycerides | urate | urea | vitamin_d\n",
-      "3  | PGS000011 | PGS000013 | PGS000016 | PGS000018 | PGS000039 | PGS000057 | PGS000058 | PGS000059 | PGS000116 | PGS000117 | PGS000192 | PGS000296 | ethnic_background | townsend_deprivation_index_at_recruitment | overall_health_rating | smoking_status | alcohol_intake_frequency | body_mass_index_bmi | weight | pulse_wave_arterial_stiffness_index | pulse_wave_reflection_index | waist_circumference | hip_circumference | standing_height | trunk_fat_percentage | body_fat_percentage | basal_metabolic_rate | forced_vital_capacity_fvc_best_measure | forced_expiratory_volume_in_1second_fev1_best_measure | fev1_fvc_ratio_zscore | peak_expiratory_flow_pef_f3064_0_2 | peak_expiratory_flow_pef_f3064_0_1 | peak_expiratory_flow_pef | systolic_blood_pressure | diastolic_blood_pressure | pulse_rate | basophill_count | basophill_percentage | eosinophill_count | eosinophill_percentage | haematocrit_percentage | haemoglobin_concentration | high_light_scatter_reticulocyte_count | high_light_scatter_reticulocyte_percentage | immature_reticulocyte_fraction | lymphocyte_count | lymphocyte_percentage | mean_corpuscular_haemoglobin | mean_corpuscular_haemoglobin_concentration | mean_corpuscular_volume | mean_platelet_thrombocyte_volume | mean_reticulocyte_volume | mean_sphered_cell_volume | monocyte_count | monocyte_percentage | neutrophill_count | neutrophill_percentage | nucleated_red_blood_cell_count | nucleated_red_blood_cell_percentage | platelet_count | platelet_crit | platelet_distribution_width | red_blood_cell_erythrocyte_count | red_blood_cell_erythrocyte_distribution_width | reticulocyte_count | reticulocyte_percentage | white_blood_cell_leukocyte_count | alanine_aminotransferase | albumin | alkaline_phosphatase | apolipoprotein_a | apolipoprotein_b | aspartate_aminotransferase | creactive_protein | calcium | cholesterol | creatinine | cystatin_c | direct_bilirubin | gamma_glutamyltransferase | glucose | glycated_haemoglobin_hba1c | hdl_cholesterol | igf1 | ldl_direct | lipoprotein_a | oestradiol | phosphate | rheumatoid_factor | shbg | testosterone | total_bilirubin | total_protein | triglycerides | urate | urea | vitamin_d\n"
-     ]
-    }
-   ],
-   "source": [
-    "import miceforest as mf\n",
-    "kernel = mf.KernelDataSet(df, variable_schema=variable_schema, save_all_iterations=True, random_state=1991)\n",
-    "\n",
-    "# Run the MICE algorithm for 3 iterations\n",
-    "kernel.mice(3, n_jobs=20, n_estimators=20, max_features=\"sqrt\", bootstrap=True, max_depth=10, verbose=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 2160x1440 with 100 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import seaborn as sns\n",
-    "\n",
-    "sns.set(rc={'figure.figsize':(30,20)})\n",
-    "kernel.plot_imputed_distributions(wspace=1, hspace=1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "import miceforest as mf\n",
-    "kds = mf.KernelDataSet(df, mean_match_candidates = 10, save_all_iterations=True, random_state=1991)\n",
-    "\n",
-    "Run the MICE algorithm for 3 iterations\n",
-    "kds.mice(3, n_jobs=10, n_estimators=10, max_features=\"sqrt\", bootstrap=True, max_depth=10, verbose=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "import seaborn as sns\n",
-    "\n",
-    "sns.set(rc={'figure.figsize':(30,20)})\n",
-    "kds.plot_imputed_distributions(wspace=1,hspace=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#kernel_all = kernel.impute_new_data(data, verbose=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# get back imputed data\n",
-    "#data_imp = kernel_all.complete_data()\n",
-    "data_imp = kernel.complete_data()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# for validation set\n",
-    "# data_imp_pre = kds.impute_new_data(data, verbose=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>PGS000011</th>\n",
-       "      <th>PGS000013</th>\n",
-       "      <th>PGS000016</th>\n",
-       "      <th>PGS000018</th>\n",
-       "      <th>PGS000039</th>\n",
-       "      <th>PGS000057</th>\n",
-       "      <th>PGS000058</th>\n",
-       "      <th>PGS000059</th>\n",
-       "      <th>PGS000116</th>\n",
-       "      <th>...</th>\n",
-       "      <th>death_cvd_event</th>\n",
-       "      <th>death_cvd_event_time</th>\n",
-       "      <th>SCORE_event</th>\n",
-       "      <th>SCORE_event_time</th>\n",
-       "      <th>ASCVD_event</th>\n",
-       "      <th>ASCVD_event_time</th>\n",
-       "      <th>QRISK3_event</th>\n",
-       "      <th>QRISK3_event_time</th>\n",
-       "      <th>MACE_event</th>\n",
-       "      <th>MACE_event_time</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>3.170770</td>\n",
-       "      <td>14.182161</td>\n",
-       "      <td>25.478597</td>\n",
-       "      <td>-7.910004</td>\n",
-       "      <td>0.196099</td>\n",
-       "      <td>3.690275</td>\n",
-       "      <td>8.513230</td>\n",
-       "      <td>4.060235</td>\n",
-       "      <td>2.543097</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>10.625599</td>\n",
-       "      <td>0</td>\n",
-       "      <td>10.625599</td>\n",
-       "      <td>0</td>\n",
-       "      <td>10.335387</td>\n",
-       "      <td>0</td>\n",
-       "      <td>10.335387</td>\n",
-       "      <td>0</td>\n",
-       "      <td>10.335387</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1000020</td>\n",
-       "      <td>3.935565</td>\n",
-       "      <td>14.203609</td>\n",
-       "      <td>25.508855</td>\n",
-       "      <td>-7.338207</td>\n",
-       "      <td>0.382054</td>\n",
-       "      <td>4.170000</td>\n",
-       "      <td>8.665529</td>\n",
-       "      <td>3.640000</td>\n",
-       "      <td>2.575014</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>12.355921</td>\n",
-       "      <td>0</td>\n",
-       "      <td>12.355921</td>\n",
-       "      <td>0</td>\n",
-       "      <td>12.065708</td>\n",
-       "      <td>0</td>\n",
-       "      <td>12.065708</td>\n",
-       "      <td>0</td>\n",
-       "      <td>12.065708</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1000037</td>\n",
-       "      <td>4.223654</td>\n",
-       "      <td>14.248176</td>\n",
-       "      <td>25.549722</td>\n",
-       "      <td>-7.533061</td>\n",
-       "      <td>0.189988</td>\n",
-       "      <td>4.288627</td>\n",
-       "      <td>8.540908</td>\n",
-       "      <td>5.541451</td>\n",
-       "      <td>2.441255</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>11.627652</td>\n",
-       "      <td>0</td>\n",
-       "      <td>11.627652</td>\n",
-       "      <td>1</td>\n",
-       "      <td>7.969884</td>\n",
-       "      <td>1</td>\n",
-       "      <td>7.969884</td>\n",
-       "      <td>0</td>\n",
-       "      <td>11.337440</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1000043</td>\n",
-       "      <td>3.529575</td>\n",
-       "      <td>14.176502</td>\n",
-       "      <td>25.394145</td>\n",
-       "      <td>-7.768648</td>\n",
-       "      <td>0.289388</td>\n",
-       "      <td>4.130000</td>\n",
-       "      <td>8.515447</td>\n",
-       "      <td>4.261098</td>\n",
-       "      <td>2.740589</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>11.069131</td>\n",
-       "      <td>0</td>\n",
-       "      <td>11.069131</td>\n",
-       "      <td>1</td>\n",
-       "      <td>5.122519</td>\n",
-       "      <td>1</td>\n",
-       "      <td>5.122519</td>\n",
-       "      <td>1</td>\n",
-       "      <td>5.122519</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1000051</td>\n",
-       "      <td>3.892568</td>\n",
-       "      <td>14.194866</td>\n",
-       "      <td>25.445731</td>\n",
-       "      <td>-7.851320</td>\n",
-       "      <td>0.291973</td>\n",
-       "      <td>3.960000</td>\n",
-       "      <td>7.834208</td>\n",
-       "      <td>3.920980</td>\n",
-       "      <td>2.634049</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>14.050650</td>\n",
-       "      <td>0</td>\n",
-       "      <td>14.050650</td>\n",
-       "      <td>0</td>\n",
-       "      <td>13.760438</td>\n",
-       "      <td>0</td>\n",
-       "      <td>13.760438</td>\n",
-       "      <td>0</td>\n",
-       "      <td>13.760438</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>502499</th>\n",
-       "      <td>6025150</td>\n",
-       "      <td>4.181800</td>\n",
-       "      <td>14.053589</td>\n",
-       "      <td>25.402026</td>\n",
-       "      <td>-7.499164</td>\n",
-       "      <td>0.272956</td>\n",
-       "      <td>4.470000</td>\n",
-       "      <td>8.425411</td>\n",
-       "      <td>4.660235</td>\n",
-       "      <td>2.419166</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>12.996578</td>\n",
-       "      <td>0</td>\n",
-       "      <td>12.996578</td>\n",
-       "      <td>0</td>\n",
-       "      <td>12.706366</td>\n",
-       "      <td>0</td>\n",
-       "      <td>12.706366</td>\n",
-       "      <td>0</td>\n",
-       "      <td>12.706366</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>502500</th>\n",
-       "      <td>6025165</td>\n",
-       "      <td>4.315743</td>\n",
-       "      <td>14.131477</td>\n",
-       "      <td>25.417482</td>\n",
-       "      <td>-7.736421</td>\n",
-       "      <td>0.275103</td>\n",
-       "      <td>3.900941</td>\n",
-       "      <td>8.046961</td>\n",
-       "      <td>4.000196</td>\n",
-       "      <td>2.721753</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>11.819302</td>\n",
-       "      <td>0</td>\n",
-       "      <td>11.819302</td>\n",
-       "      <td>0</td>\n",
-       "      <td>11.529090</td>\n",
-       "      <td>0</td>\n",
-       "      <td>11.529090</td>\n",
-       "      <td>0</td>\n",
-       "      <td>11.529090</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>502501</th>\n",
-       "      <td>6025173</td>\n",
-       "      <td>3.096671</td>\n",
-       "      <td>14.186225</td>\n",
-       "      <td>25.488755</td>\n",
-       "      <td>-8.079815</td>\n",
-       "      <td>0.352918</td>\n",
-       "      <td>3.507804</td>\n",
-       "      <td>7.994697</td>\n",
-       "      <td>3.690706</td>\n",
-       "      <td>2.687853</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>11.778234</td>\n",
-       "      <td>0</td>\n",
-       "      <td>11.778234</td>\n",
-       "      <td>0</td>\n",
-       "      <td>11.488022</td>\n",
-       "      <td>0</td>\n",
-       "      <td>11.488022</td>\n",
-       "      <td>0</td>\n",
-       "      <td>11.488022</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>502502</th>\n",
-       "      <td>6025182</td>\n",
-       "      <td>3.699646</td>\n",
-       "      <td>14.134693</td>\n",
-       "      <td>25.439013</td>\n",
-       "      <td>-7.962928</td>\n",
-       "      <td>0.343860</td>\n",
-       "      <td>4.020000</td>\n",
-       "      <td>8.151277</td>\n",
-       "      <td>3.960000</td>\n",
-       "      <td>2.692526</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>9.993155</td>\n",
-       "      <td>0</td>\n",
-       "      <td>9.993155</td>\n",
-       "      <td>0</td>\n",
-       "      <td>9.702943</td>\n",
-       "      <td>0</td>\n",
-       "      <td>9.702943</td>\n",
-       "      <td>0</td>\n",
-       "      <td>9.702943</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>502503</th>\n",
-       "      <td>6025198</td>\n",
-       "      <td>3.663789</td>\n",
-       "      <td>14.325340</td>\n",
-       "      <td>25.449361</td>\n",
-       "      <td>-7.429906</td>\n",
-       "      <td>0.208367</td>\n",
-       "      <td>3.740000</td>\n",
-       "      <td>8.743372</td>\n",
-       "      <td>4.110000</td>\n",
-       "      <td>2.428418</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>10.420260</td>\n",
-       "      <td>0</td>\n",
-       "      <td>10.420260</td>\n",
-       "      <td>0</td>\n",
-       "      <td>10.130048</td>\n",
-       "      <td>0</td>\n",
-       "      <td>10.130048</td>\n",
-       "      <td>0</td>\n",
-       "      <td>10.130048</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>502504 rows × 3758 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            eid  PGS000011  PGS000013  PGS000016  PGS000018  PGS000039  \\\n",
-       "0       1000018   3.170770  14.182161  25.478597  -7.910004   0.196099   \n",
-       "1       1000020   3.935565  14.203609  25.508855  -7.338207   0.382054   \n",
-       "2       1000037   4.223654  14.248176  25.549722  -7.533061   0.189988   \n",
-       "3       1000043   3.529575  14.176502  25.394145  -7.768648   0.289388   \n",
-       "4       1000051   3.892568  14.194866  25.445731  -7.851320   0.291973   \n",
-       "...         ...        ...        ...        ...        ...        ...   \n",
-       "502499  6025150   4.181800  14.053589  25.402026  -7.499164   0.272956   \n",
-       "502500  6025165   4.315743  14.131477  25.417482  -7.736421   0.275103   \n",
-       "502501  6025173   3.096671  14.186225  25.488755  -8.079815   0.352918   \n",
-       "502502  6025182   3.699646  14.134693  25.439013  -7.962928   0.343860   \n",
-       "502503  6025198   3.663789  14.325340  25.449361  -7.429906   0.208367   \n",
-       "\n",
-       "        PGS000057  PGS000058  PGS000059  PGS000116  ...  death_cvd_event  \\\n",
-       "0        3.690275   8.513230   4.060235   2.543097  ...                0   \n",
-       "1        4.170000   8.665529   3.640000   2.575014  ...                0   \n",
-       "2        4.288627   8.540908   5.541451   2.441255  ...                0   \n",
-       "3        4.130000   8.515447   4.261098   2.740589  ...                0   \n",
-       "4        3.960000   7.834208   3.920980   2.634049  ...                0   \n",
-       "...           ...        ...        ...        ...  ...              ...   \n",
-       "502499   4.470000   8.425411   4.660235   2.419166  ...                0   \n",
-       "502500   3.900941   8.046961   4.000196   2.721753  ...                0   \n",
-       "502501   3.507804   7.994697   3.690706   2.687853  ...                0   \n",
-       "502502   4.020000   8.151277   3.960000   2.692526  ...                0   \n",
-       "502503   3.740000   8.743372   4.110000   2.428418  ...                0   \n",
-       "\n",
-       "        death_cvd_event_time  SCORE_event  SCORE_event_time ASCVD_event  \\\n",
-       "0                  10.625599            0         10.625599           0   \n",
-       "1                  12.355921            0         12.355921           0   \n",
-       "2                  11.627652            0         11.627652           1   \n",
-       "3                  11.069131            0         11.069131           1   \n",
-       "4                  14.050650            0         14.050650           0   \n",
-       "...                      ...          ...               ...         ...   \n",
-       "502499             12.996578            0         12.996578           0   \n",
-       "502500             11.819302            0         11.819302           0   \n",
-       "502501             11.778234            0         11.778234           0   \n",
-       "502502              9.993155            0          9.993155           0   \n",
-       "502503             10.420260            0         10.420260           0   \n",
-       "\n",
-       "       ASCVD_event_time  QRISK3_event QRISK3_event_time MACE_event  \\\n",
-       "0             10.335387             0         10.335387          0   \n",
-       "1             12.065708             0         12.065708          0   \n",
-       "2              7.969884             1          7.969884          0   \n",
-       "3              5.122519             1          5.122519          1   \n",
-       "4             13.760438             0         13.760438          0   \n",
-       "...                 ...           ...               ...        ...   \n",
-       "502499        12.706366             0         12.706366          0   \n",
-       "502500        11.529090             0         11.529090          0   \n",
-       "502501        11.488022             0         11.488022          0   \n",
-       "502502         9.702943             0          9.702943          0   \n",
-       "502503        10.130048             0         10.130048          0   \n",
-       "\n",
-       "       MACE_event_time  \n",
-       "0            10.335387  \n",
-       "1            12.065708  \n",
-       "2            11.337440  \n",
-       "3             5.122519  \n",
-       "4            13.760438  \n",
-       "...                ...  \n",
-       "502499       12.706366  \n",
-       "502500       11.529090  \n",
-       "502501       11.488022  \n",
-       "502502        9.702943  \n",
-       "502503       10.130048  \n",
-       "\n",
-       "[502504 rows x 3758 columns]"
-      ]
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data_imp"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['Female', 'Male']\n",
-      "Categories (2, object): ['Female', 'Male']\n",
-      "['White', 'Black', 'Asian', 'Mixed', 'Chinese']\n",
-      "Categories (5, object): ['White', 'Black', 'Asian', 'Mixed', 'Chinese']\n",
-      "[datetime.date(2009, 11, 12) datetime.date(2008, 2, 19)\n",
-      " datetime.date(2008, 11, 11) ... datetime.date(2006, 3, 13)\n",
-      " datetime.date(2010, 9, 3) datetime.date(2010, 8, 16)]\n",
-      "[datetime.date(1960, 11, 12) datetime.date(1949, 2, 19)\n",
-      " datetime.date(1949, 11, 11) ... datetime.date(1938, 3, 22)\n",
-      " datetime.date(1970, 8, 17) datetime.date(1936, 6, 12)]\n",
-      "['Fair', 'Good', 'Poor', 'Excellent']\n",
-      "Categories (4, object): ['Poor' < 'Fair' < 'Good' < 'Excellent']\n",
-      "['Current', 'Previous', 'Never']\n",
-      "Categories (3, object): ['Current' < 'Previous' < 'Never']\n",
-      "['Once or twice a week', 'Three or four times a week', 'One to three times a month', 'Daily or almost daily', 'Special occasions only', 'Never']\n",
-      "Categories (6, object): ['Daily or almost daily' < 'Three or four times a week' < 'Once or twice a week' < 'One to three times a month' < 'Special occasions only' < 'Never']\n"
-     ]
-    }
-   ],
-   "source": [
-    "df = data_imp\n",
-    "for col in cat_features:\n",
-    "    print(df[col].unique())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Visualize Post Imputation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "4"
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "2+2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 2880x720 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
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-      "text/plain": [
-       "<Figure size 2880x720 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import missingno as msno\n",
-    "%matplotlib inline\n",
-    "fig = matrix(data_imp[covariates].sample(1000), sparkline=True, figsize=(40, 10))\n",
-    "\n",
-    "missing = data.columns[data.isnull().any()]\n",
-    "fig = matrix(data_imp[missing].sample(1000), sparkline=True, labels=True, figsize=(40, 10))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Write to disk"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_imp.to_feather(f\"{dataset_path}/baseline_complete_imputed.feather\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Plus 1 year "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>myocardial_infarction_event_time</th>\n",
-       "      <th>stroke_event_time</th>\n",
-       "      <th>cancer_breast_event_time</th>\n",
-       "      <th>diabetes_event_time</th>\n",
-       "      <th>atrial_fibrillation_event_time</th>\n",
-       "      <th>copd_event_time</th>\n",
-       "      <th>dementia_event_time</th>\n",
-       "      <th>death_allcause_event_time</th>\n",
-       "      <th>death_cvd_event_time</th>\n",
-       "      <th>SCORE_event_time</th>\n",
-       "      <th>ASCVD_event_time</th>\n",
-       "      <th>QRISK3_event_time</th>\n",
-       "      <th>MACE_event_time</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>10.335387</td>\n",
-       "      <td>10.335387</td>\n",
-       "      <td>10.335387</td>\n",
-       "      <td>10.335387</td>\n",
-       "      <td>10.335387</td>\n",
-       "      <td>10.335387</td>\n",
-       "      <td>10.335387</td>\n",
-       "      <td>10.625599</td>\n",
-       "      <td>10.625599</td>\n",
-       "      <td>10.625599</td>\n",
-       "      <td>10.335387</td>\n",
-       "      <td>10.335387</td>\n",
-       "      <td>10.335387</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>12.065708</td>\n",
-       "      <td>12.065708</td>\n",
-       "      <td>12.065708</td>\n",
-       "      <td>12.065708</td>\n",
-       "      <td>12.065708</td>\n",
-       "      <td>12.065708</td>\n",
-       "      <td>12.065708</td>\n",
-       "      <td>12.355921</td>\n",
-       "      <td>12.355921</td>\n",
-       "      <td>12.355921</td>\n",
-       "      <td>12.065708</td>\n",
-       "      <td>12.065708</td>\n",
-       "      <td>12.065708</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>11.337440</td>\n",
-       "      <td>11.337440</td>\n",
-       "      <td>11.337440</td>\n",
-       "      <td>11.337440</td>\n",
-       "      <td>11.337440</td>\n",
-       "      <td>11.337440</td>\n",
-       "      <td>11.337440</td>\n",
-       "      <td>11.627652</td>\n",
-       "      <td>11.627652</td>\n",
-       "      <td>11.627652</td>\n",
-       "      <td>7.969884</td>\n",
-       "      <td>7.969884</td>\n",
-       "      <td>11.337440</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>5.122519</td>\n",
-       "      <td>10.778919</td>\n",
-       "      <td>10.778919</td>\n",
-       "      <td>10.778919</td>\n",
-       "      <td>10.778919</td>\n",
-       "      <td>0.292950</td>\n",
-       "      <td>10.778919</td>\n",
-       "      <td>11.069131</td>\n",
-       "      <td>11.069131</td>\n",
-       "      <td>11.069131</td>\n",
-       "      <td>5.122519</td>\n",
-       "      <td>5.122519</td>\n",
-       "      <td>5.122519</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>13.760438</td>\n",
-       "      <td>13.760438</td>\n",
-       "      <td>13.760438</td>\n",
-       "      <td>4.722793</td>\n",
-       "      <td>13.760438</td>\n",
-       "      <td>4.840520</td>\n",
-       "      <td>13.760438</td>\n",
-       "      <td>14.050650</td>\n",
-       "      <td>14.050650</td>\n",
-       "      <td>14.050650</td>\n",
-       "      <td>13.760438</td>\n",
-       "      <td>13.760438</td>\n",
-       "      <td>13.760438</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   myocardial_infarction_event_time  stroke_event_time  \\\n",
-       "0                         10.335387          10.335387   \n",
-       "1                         12.065708          12.065708   \n",
-       "2                         11.337440          11.337440   \n",
-       "3                          5.122519          10.778919   \n",
-       "4                         13.760438          13.760438   \n",
-       "\n",
-       "   cancer_breast_event_time  diabetes_event_time  \\\n",
-       "0                 10.335387            10.335387   \n",
-       "1                 12.065708            12.065708   \n",
-       "2                 11.337440            11.337440   \n",
-       "3                 10.778919            10.778919   \n",
-       "4                 13.760438             4.722793   \n",
-       "\n",
-       "   atrial_fibrillation_event_time  copd_event_time  dementia_event_time  \\\n",
-       "0                       10.335387        10.335387            10.335387   \n",
-       "1                       12.065708        12.065708            12.065708   \n",
-       "2                       11.337440        11.337440            11.337440   \n",
-       "3                       10.778919         0.292950            10.778919   \n",
-       "4                       13.760438         4.840520            13.760438   \n",
-       "\n",
-       "   death_allcause_event_time  death_cvd_event_time  SCORE_event_time  \\\n",
-       "0                  10.625599             10.625599         10.625599   \n",
-       "1                  12.355921             12.355921         12.355921   \n",
-       "2                  11.627652             11.627652         11.627652   \n",
-       "3                  11.069131             11.069131         11.069131   \n",
-       "4                  14.050650             14.050650         14.050650   \n",
-       "\n",
-       "   ASCVD_event_time  QRISK3_event_time  MACE_event_time  \n",
-       "0         10.335387          10.335387        10.335387  \n",
-       "1         12.065708          12.065708        12.065708  \n",
-       "2          7.969884           7.969884        11.337440  \n",
-       "3          5.122519           5.122519         5.122519  \n",
-       "4         13.760438          13.760438        13.760438  "
-      ]
-     },
-     "execution_count": 45,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data_imp_yearplus1 = data_imp.copy()\n",
-    "event_time_cols = [s for s in data.columns.to_list() if \"_event_time\" in s] \n",
-    "data_imp[event_time_cols].head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>myocardial_infarction_event_time</th>\n",
-       "      <th>stroke_event_time</th>\n",
-       "      <th>cancer_breast_event_time</th>\n",
-       "      <th>diabetes_event_time</th>\n",
-       "      <th>atrial_fibrillation_event_time</th>\n",
-       "      <th>copd_event_time</th>\n",
-       "      <th>dementia_event_time</th>\n",
-       "      <th>death_allcause_event_time</th>\n",
-       "      <th>death_cvd_event_time</th>\n",
-       "      <th>SCORE_event_time</th>\n",
-       "      <th>ASCVD_event_time</th>\n",
-       "      <th>QRISK3_event_time</th>\n",
-       "      <th>MACE_event_time</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>11.335387</td>\n",
-       "      <td>11.335387</td>\n",
-       "      <td>11.335387</td>\n",
-       "      <td>11.335387</td>\n",
-       "      <td>11.335387</td>\n",
-       "      <td>11.335387</td>\n",
-       "      <td>11.335387</td>\n",
-       "      <td>11.625599</td>\n",
-       "      <td>11.625599</td>\n",
-       "      <td>11.625599</td>\n",
-       "      <td>11.335387</td>\n",
-       "      <td>11.335387</td>\n",
-       "      <td>11.335387</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>13.065708</td>\n",
-       "      <td>13.065708</td>\n",
-       "      <td>13.065708</td>\n",
-       "      <td>13.065708</td>\n",
-       "      <td>13.065708</td>\n",
-       "      <td>13.065708</td>\n",
-       "      <td>13.065708</td>\n",
-       "      <td>13.355921</td>\n",
-       "      <td>13.355921</td>\n",
-       "      <td>13.355921</td>\n",
-       "      <td>13.065708</td>\n",
-       "      <td>13.065708</td>\n",
-       "      <td>13.065708</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>12.337440</td>\n",
-       "      <td>12.337440</td>\n",
-       "      <td>12.337440</td>\n",
-       "      <td>12.337440</td>\n",
-       "      <td>12.337440</td>\n",
-       "      <td>12.337440</td>\n",
-       "      <td>12.337440</td>\n",
-       "      <td>12.627652</td>\n",
-       "      <td>12.627652</td>\n",
-       "      <td>12.627652</td>\n",
-       "      <td>8.969884</td>\n",
-       "      <td>8.969884</td>\n",
-       "      <td>12.337440</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>6.122519</td>\n",
-       "      <td>11.778919</td>\n",
-       "      <td>11.778919</td>\n",
-       "      <td>11.778919</td>\n",
-       "      <td>11.778919</td>\n",
-       "      <td>1.292950</td>\n",
-       "      <td>11.778919</td>\n",
-       "      <td>12.069131</td>\n",
-       "      <td>12.069131</td>\n",
-       "      <td>12.069131</td>\n",
-       "      <td>6.122519</td>\n",
-       "      <td>6.122519</td>\n",
-       "      <td>6.122519</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>14.760438</td>\n",
-       "      <td>14.760438</td>\n",
-       "      <td>14.760438</td>\n",
-       "      <td>5.722793</td>\n",
-       "      <td>14.760438</td>\n",
-       "      <td>5.840520</td>\n",
-       "      <td>14.760438</td>\n",
-       "      <td>15.050650</td>\n",
-       "      <td>15.050650</td>\n",
-       "      <td>15.050650</td>\n",
-       "      <td>14.760438</td>\n",
-       "      <td>14.760438</td>\n",
-       "      <td>14.760438</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   myocardial_infarction_event_time  stroke_event_time  \\\n",
-       "0                         11.335387          11.335387   \n",
-       "1                         13.065708          13.065708   \n",
-       "2                         12.337440          12.337440   \n",
-       "3                          6.122519          11.778919   \n",
-       "4                         14.760438          14.760438   \n",
-       "\n",
-       "   cancer_breast_event_time  diabetes_event_time  \\\n",
-       "0                 11.335387            11.335387   \n",
-       "1                 13.065708            13.065708   \n",
-       "2                 12.337440            12.337440   \n",
-       "3                 11.778919            11.778919   \n",
-       "4                 14.760438             5.722793   \n",
-       "\n",
-       "   atrial_fibrillation_event_time  copd_event_time  dementia_event_time  \\\n",
-       "0                       11.335387        11.335387            11.335387   \n",
-       "1                       13.065708        13.065708            13.065708   \n",
-       "2                       12.337440        12.337440            12.337440   \n",
-       "3                       11.778919         1.292950            11.778919   \n",
-       "4                       14.760438         5.840520            14.760438   \n",
-       "\n",
-       "   death_allcause_event_time  death_cvd_event_time  SCORE_event_time  \\\n",
-       "0                  11.625599             11.625599         11.625599   \n",
-       "1                  13.355921             13.355921         13.355921   \n",
-       "2                  12.627652             12.627652         12.627652   \n",
-       "3                  12.069131             12.069131         12.069131   \n",
-       "4                  15.050650             15.050650         15.050650   \n",
-       "\n",
-       "   ASCVD_event_time  QRISK3_event_time  MACE_event_time  \n",
-       "0         11.335387          11.335387        11.335387  \n",
-       "1         13.065708          13.065708        13.065708  \n",
-       "2          8.969884           8.969884        12.337440  \n",
-       "3          6.122519           6.122519         6.122519  \n",
-       "4         14.760438          14.760438        14.760438  "
-      ]
-     },
-     "execution_count": 46,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "for col in event_time_cols: data_imp_yearplus1[col] = data_imp_yearplus1[col]+1\n",
-    "data_imp_yearplus1[event_time_cols].head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_imp.to_feather(f\"{dataset_path}/baseline_complete_imputed_years+1.feather\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python [conda env:python]",
-   "language": "python",
-   "name": "conda-env-python-py"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/neuralcvd/preprocessing/ukbb_tabular/7_calculate_scores.ipynb b/neuralcvd/preprocessing/ukbb_tabular/7_calculate_scores.ipynb
deleted file mode 100644
index 3bff70a..0000000
--- a/neuralcvd/preprocessing/ukbb_tabular/7_calculate_scores.ipynb
+++ /dev/null
@@ -1,1719 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 4. Scores"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T09:37:16.635459Z",
-     "start_time": "2020-11-04T09:37:08.687Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "try(library(tidyverse), silent=TRUE)\n",
-    "library(lubridate)\n",
-    "library(data.table)\n",
-    "library(glue)\n",
-    "library(jsonlite)\n",
-    "dataset_name = \"210223_cvd_gp\"\n",
-    "path = \"/data/analysis/ag-reils/steinfej/code/umbrella/pre/ukbb\"\n",
-    "data_path = \"/data/analysis/ag-reils/ag-reils-shared/cardioRS/data\"\n",
-    "dataset_path = glue(\"{data_path}/3_datasets_post/{dataset_name}\")\n",
-    "dataset_path_pre = glue(\"{data_path}/2_datasets_pre/{dataset_name}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T09:37:19.066784Z",
-     "start_time": "2020-11-04T09:37:16.560Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "#data = arrow::read_feather(glue(\"{dataset_path}/data_merged.feather\"))\n",
-    "description = arrow::read_feather(glue(\"{dataset_path}/description.feather\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "files = c()\n",
-    "for (i in 0:9){\n",
-    "    files = c(files, glue(\"{dataset_path}/partition_{i}/test/data_imputed.feather\"))\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data = map(files, arrow::read_feather) %>% bind_rows()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "data = data %>% mutate_at(c(\"sex\", \"overall_health_rating\", \"smoking_status\", \"ethnic_background\"), as.factor)\n",
-    "data = data %>% mutate(sex=fct_relevel(sex, c(\"Male\", \"Female\")),\n",
-    "                       overall_health_rating=fct_relevel(overall_health_rating, c(\"Excellent\", \"Good\", \"Fair\", \"Poor\")),\n",
-    "                       smoking_status=fct_relevel(smoking_status, c(\"Current\", \"Previous\", \"Never\")))\n",
-    "\n",
-    "covariates = (data_description %>% filter(isTarget==FALSE) %>% filter(!dtype==\"Date\"))$covariate[-1]\n",
-    "targets = (data_description %>% filter(isTarget==TRUE))$covariate\n",
-    "\n",
-    "load(file = glue(\"{path}data/phenotypes.rda\"))\n",
-    "load(file = glue(\"{path}data/medications_list.rda\"))\n",
-    "load(file = glue(\"{path}data/endpoints.rda\"))\n",
-    "load(file = glue(\"{path}data/scores.rda\"))\n",
-    "death_endpoints = c(death = c(\"death\"), death_cvd = c(\"death_cvd\"))\n",
-    "competing_endpoints = c(CE_QRISK3death=c(\"CE_QRISK3death\"))\n",
-    "endpoints = append(endpoints, death_endpoints)\n",
-    "endpoints = append(endpoints, scores)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Missing values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "ExecuteTime": {
-     "start_time": "2020-11-04T09:36:46.941Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<table>\n",
-       "<caption>A data.frame: 0 × 1</caption>\n",
-       "<thead>\n",
-       "\t<tr><th scope=col>sapply.data..function.y..sum.length.which.is.na.y.....</th></tr>\n",
-       "\t<tr><th scope=col>&lt;int&gt;</th></tr>\n",
-       "</thead>\n",
-       "<tbody>\n",
-       "</tbody>\n",
-       "</table>\n"
-      ],
-      "text/latex": [
-       "A data.frame: 0 × 1\n",
-       "\\begin{tabular}{l}\n",
-       " sapply.data..function.y..sum.length.which.is.na.y.....\\\\\n",
-       " <int>\\\\\n",
-       "\\hline\n",
-       "\\end{tabular}\n"
-      ],
-      "text/markdown": [
-       "\n",
-       "A data.frame: 0 × 1\n",
-       "\n",
-       "| sapply.data..function.y..sum.length.which.is.na.y..... &lt;int&gt; |\n",
-       "|---|\n",
-       "\n"
-      ],
-      "text/plain": [
-       "     sapply.data..function.y..sum.length.which.is.na.y....."
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "na_count <-data.frame(sapply(data, function(y) sum(length(which(is.na(y))))))\n",
-    "na_count %>% filter(sapply(data, function(y) sum(length(which(is.na(y)))))>0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "map_smoking = jsonlite::fromJSON(str_replace_all(str_replace_all((description %>% filter(covariate == \"smoking_status\"))$mapping, \"'\", '\"'), \", nan: -2\", \"\"))\n",
-    "map_smoking = setNames(names(map_smoking), map_smoking)\n",
-    "map_gender = jsonlite::fromJSON(str_replace_all(str_replace_all((description %>% filter(covariate == \"sex\"))$mapping, \"'\", '\"'), \", nan: -2\", \"\"))\n",
-    "map_gender = setNames(names(map_gender), map_gender)\n",
-    "map_ethnicity = jsonlite::fromJSON(str_replace_all(str_replace_all((description %>% filter(covariate == \"ethnic_background\"))$mapping, \"'\", '\"'), \", nan: -2\", \"\"))\n",
-    "map_ethnicity = setNames(names(map_ethnicity), map_ethnicity)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data$smoking_status = recode(data$smoking_status, !!!map_smoking)\n",
-    "data$sex = recode(data$sex, !!!map_gender)\n",
-    "data$ethnic_background = recode(data$ethnic_background, !!!map_ethnicity)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Scores"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## FRAMINGHAM RISK SCORE (Anderson 1991, Wilson 1998)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "women_bmi <- function(age, bmi, sbp, smoking, diabetes, treated_bp){\n",
-    "    b_age=2.72107\n",
-    "    b_bmi=0.51125\n",
-    "    b_smoking=0.61868\n",
-    "    b_diabetes=0.77763\n",
-    "    if (treated_bp==FALSE){b_sbp=2.81291}\n",
-    "    if (treated_bp==TRUE){b_sbp=2.88267}\n",
-    "    E = b_age*age + b_bmi*bmi + b_smoking*smoking + b_diabetes*diabetes + b_sbp*sbp\n",
-    "    return (1-0.94833^(E-26.0145))\n",
-    "}\n",
-    "\n",
-    "women_chol <- function(age, chol, hdl_chol, sbp, smoking, diabetes, bp_treated){\n",
-    "    b_age=2.32888\n",
-    "    b_chol = 1.20904\n",
-    "    b_hdl_chol = -0.70833\n",
-    "    b_smoking=0.52873\n",
-    "    b_diabetes=0.69154\n",
-    "    if (bp_treated==FALSE){b_sbp=2.76157}\n",
-    "    if (bp_treated==TRUE){b_sbp=2.82263}\n",
-    "    E = b_age*age + b_chol*chol + b_hdl_chol*hdl_chol + b_smoking*smoking + b_diabetes*diabetes + b_sbp*sbp\n",
-    "    return (1-0.95012^(E-26.1931))\n",
-    "}\n",
-    "\n",
-    "men_bmi <- function(age, bmi, sbp, smoking, diabetes, treated_bp){\n",
-    "    b_age=3.11296\n",
-    "    b_bmi=0.79277\n",
-    "    b_smoking=0.70953\n",
-    "    b_diabetes=0.53160\n",
-    "    if (treated_bp==FALSE){b_sbp=1.85508}\n",
-    "    if (treated_bp==TRUE){b_sbp=1.92672}\n",
-    "    E = b_age*age + b_bmi*bmi + b_smoking*smoking + b_diabetes*diabetes + b_sbp*sbp\n",
-    "    return (1-0.88431^(E-23.9388))\n",
-    "}\n",
-    "\n",
-    "men_chol <- function(age, chol, hdl_chol, sbp, smoking, diabetes, bp_treated){\n",
-    "    b_age=3.06117\n",
-    "    b_chol = 1.12370\n",
-    "    b_hdl_chol = -0.93263\n",
-    "    b_smoking=0.65451\n",
-    "    b_diabetes=0.57367\n",
-    "    if (bp_treated==FALSE){b_sbp=1.93303}\n",
-    "    if (bp_treated==TRUE){b_sbp=1.99881}\n",
-    "    E = b_age*age + b_chol*chol + b_hdl_chol*hdl_chol + b_smoking*smoking + b_diabetes*diabetes + b_sbp*sbp\n",
-    "    return (1-0.88936^(E-23.9802))\n",
-    "}\n",
-    "\n",
-    "calculateFRS <- function(sex, age, bmi, chol, hdl_chol, sbp, smoking, diabetes, bp_treated){\n",
-    "    print(bp_treated)\n",
-    "    if (sex==\"M\"){\n",
-    "        frs_bmi = men_bmi(age, bmi, sbp, smoking, diabetes, bp_treated)\n",
-    "        frs_chol = men_chol(age, chol, hdl_chol, smoking, diabetes, bp_treated)\n",
-    "    }\n",
-    "    if (sex==\"F\"){\n",
-    "        frs_bmi = women_bmi(age, bmi, sbp, smoking, diabetes, bp_treated)\n",
-    "        frs_chol = women_chol(age, chol, hdl_chol, smoking, diabetes, bp_treated)\n",
-    "    }  \n",
-    "    return (list(frs_bmi, frs_chol))\n",
-    "}\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[1] FALSE\n"
-     ]
-    },
-    {
-     "ename": "ERROR",
-     "evalue": "Error in men_chol(age, chol, hdl_chol, smoking, diabetes, bp_treated): argument \"bp_treated\" is missing, with no default\n",
-     "output_type": "error",
-     "traceback": [
-      "Error in men_chol(age, chol, hdl_chol, smoking, diabetes, bp_treated): argument \"bp_treated\" is missing, with no default\nTraceback:\n",
-      "1. calculateFRS(sex = \"M\", age = 30, bmi = 22.5, chol = 180, hdl_chol = 45, \n .     sbp = 125, smoking = 0, diabetes = 0, bp_treated = FALSE)",
-      "2. men_chol(age, chol, hdl_chol, smoking, diabetes, bp_treated)   # at line 51 of file <text>"
-     ]
-    }
-   ],
-   "source": [
-    "frs = calculateFRS(sex=\"M\", age=30, bmi=22.5, chol=180, hdl_chol=45, sbp=125, smoking=0, diabetes=0, bp_treated=FALSE)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true
-    }
-   },
-   "outputs": [
-    {
-     "ename": "ERROR",
-     "evalue": "Error in eval(expr, envir, enclos): object 'frs' not found\n",
-     "output_type": "error",
-     "traceback": [
-      "Error in eval(expr, envir, enclos): object 'frs' not found\nTraceback:\n"
-     ]
-    }
-   ],
-   "source": [
-    "frs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<table>\n",
-       "<caption>A tibble: 6 × 330</caption>\n",
-       "<thead>\n",
-       "\t<tr><th scope=col>eid</th><th scope=col>PGS000011</th><th scope=col>PGS000013</th><th scope=col>PGS000016</th><th scope=col>PGS000018</th><th scope=col>PGS000039</th><th scope=col>PGS000057</th><th scope=col>PGS000058</th><th scope=col>PGS000059</th><th scope=col>PGS000116</th><th scope=col>⋯</th><th scope=col>death_cvd_event</th><th scope=col>death_cvd_event_time</th><th scope=col>SCORE_event</th><th scope=col>SCORE_event_time</th><th scope=col>ASCVD_event</th><th scope=col>ASCVD_event_time</th><th scope=col>QRISK3_event</th><th scope=col>QRISK3_event_time</th><th scope=col>MACE_event</th><th scope=col>MACE_event_time</th></tr>\n",
-       "\t<tr><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>⋯</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
-       "</thead>\n",
-       "<tbody>\n",
-       "\t<tr><td>1000018</td><td>3.170770</td><td>14.18216</td><td>25.47860</td><td>-7.910004</td><td>0.1960993</td><td>3.690275</td><td>8.513230</td><td>4.060235</td><td>2.543097</td><td>⋯</td><td>0</td><td>10.62560</td><td>0</td><td>10.62560</td><td>0</td><td>10.335387</td><td>0</td><td>10.335387</td><td>0</td><td>10.335387</td></tr>\n",
-       "\t<tr><td>1000020</td><td>3.935565</td><td>14.20361</td><td>25.50886</td><td>-7.338207</td><td>0.3820538</td><td>4.170000</td><td>8.665529</td><td>3.640000</td><td>2.575014</td><td>⋯</td><td>0</td><td>12.35592</td><td>0</td><td>12.35592</td><td>0</td><td>12.065708</td><td>0</td><td>12.065708</td><td>0</td><td>12.065708</td></tr>\n",
-       "\t<tr><td>1000037</td><td>4.223654</td><td>14.24818</td><td>25.54972</td><td>-7.533061</td><td>0.1899884</td><td>4.288627</td><td>8.540908</td><td>5.541451</td><td>2.441255</td><td>⋯</td><td>0</td><td>11.62765</td><td>0</td><td>11.62765</td><td>1</td><td> 7.969884</td><td>1</td><td> 7.969884</td><td>0</td><td>11.337440</td></tr>\n",
-       "\t<tr><td>1000043</td><td>3.529575</td><td>14.17650</td><td>25.39414</td><td>-7.768648</td><td>0.2893883</td><td>4.130000</td><td>8.515447</td><td>4.261098</td><td>2.740589</td><td>⋯</td><td>0</td><td>11.06913</td><td>0</td><td>11.06913</td><td>1</td><td> 5.122519</td><td>1</td><td> 5.122519</td><td>1</td><td> 5.122519</td></tr>\n",
-       "\t<tr><td>1000051</td><td>3.618199</td><td>14.21691</td><td>25.39648</td><td>-7.486226</td><td>0.1776932</td><td>3.870000</td><td>8.043932</td><td>4.207255</td><td>2.460508</td><td>⋯</td><td>0</td><td>14.05065</td><td>0</td><td>14.05065</td><td>0</td><td>13.760438</td><td>0</td><td>13.760438</td><td>0</td><td>13.760438</td></tr>\n",
-       "\t<tr><td>1000066</td><td>3.555040</td><td>14.19133</td><td>25.47087</td><td>-7.501505</td><td>0.4534260</td><td>4.100000</td><td>8.345422</td><td>3.521569</td><td>2.746012</td><td>⋯</td><td>0</td><td>10.53525</td><td>0</td><td>10.53525</td><td>0</td><td>10.245038</td><td>0</td><td>10.245038</td><td>0</td><td>10.245038</td></tr>\n",
-       "</tbody>\n",
-       "</table>\n"
-      ],
-      "text/latex": [
-       "A tibble: 6 × 330\n",
-       "\\begin{tabular}{lllllllllllllllllllll}\n",
-       " eid & PGS000011 & PGS000013 & PGS000016 & PGS000018 & PGS000039 & PGS000057 & PGS000058 & PGS000059 & PGS000116 & ⋯ & death\\_cvd\\_event & death\\_cvd\\_event\\_time & SCORE\\_event & SCORE\\_event\\_time & ASCVD\\_event & ASCVD\\_event\\_time & QRISK3\\_event & QRISK3\\_event\\_time & MACE\\_event & MACE\\_event\\_time\\\\\n",
-       " <int> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & ⋯ & <int> & <dbl> & <int> & <dbl> & <int> & <dbl> & <int> & <dbl> & <int> & <dbl>\\\\\n",
-       "\\hline\n",
-       "\t 1000018 & 3.170770 & 14.18216 & 25.47860 & -7.910004 & 0.1960993 & 3.690275 & 8.513230 & 4.060235 & 2.543097 & ⋯ & 0 & 10.62560 & 0 & 10.62560 & 0 & 10.335387 & 0 & 10.335387 & 0 & 10.335387\\\\\n",
-       "\t 1000020 & 3.935565 & 14.20361 & 25.50886 & -7.338207 & 0.3820538 & 4.170000 & 8.665529 & 3.640000 & 2.575014 & ⋯ & 0 & 12.35592 & 0 & 12.35592 & 0 & 12.065708 & 0 & 12.065708 & 0 & 12.065708\\\\\n",
-       "\t 1000037 & 4.223654 & 14.24818 & 25.54972 & -7.533061 & 0.1899884 & 4.288627 & 8.540908 & 5.541451 & 2.441255 & ⋯ & 0 & 11.62765 & 0 & 11.62765 & 1 &  7.969884 & 1 &  7.969884 & 0 & 11.337440\\\\\n",
-       "\t 1000043 & 3.529575 & 14.17650 & 25.39414 & -7.768648 & 0.2893883 & 4.130000 & 8.515447 & 4.261098 & 2.740589 & ⋯ & 0 & 11.06913 & 0 & 11.06913 & 1 &  5.122519 & 1 &  5.122519 & 1 &  5.122519\\\\\n",
-       "\t 1000051 & 3.618199 & 14.21691 & 25.39648 & -7.486226 & 0.1776932 & 3.870000 & 8.043932 & 4.207255 & 2.460508 & ⋯ & 0 & 14.05065 & 0 & 14.05065 & 0 & 13.760438 & 0 & 13.760438 & 0 & 13.760438\\\\\n",
-       "\t 1000066 & 3.555040 & 14.19133 & 25.47087 & -7.501505 & 0.4534260 & 4.100000 & 8.345422 & 3.521569 & 2.746012 & ⋯ & 0 & 10.53525 & 0 & 10.53525 & 0 & 10.245038 & 0 & 10.245038 & 0 & 10.245038\\\\\n",
-       "\\end{tabular}\n"
-      ],
-      "text/markdown": [
-       "\n",
-       "A tibble: 6 × 330\n",
-       "\n",
-       "| eid &lt;int&gt; | PGS000011 &lt;dbl&gt; | PGS000013 &lt;dbl&gt; | PGS000016 &lt;dbl&gt; | PGS000018 &lt;dbl&gt; | PGS000039 &lt;dbl&gt; | PGS000057 &lt;dbl&gt; | PGS000058 &lt;dbl&gt; | PGS000059 &lt;dbl&gt; | PGS000116 &lt;dbl&gt; | ⋯ ⋯ | death_cvd_event &lt;int&gt; | death_cvd_event_time &lt;dbl&gt; | SCORE_event &lt;int&gt; | SCORE_event_time &lt;dbl&gt; | ASCVD_event &lt;int&gt; | ASCVD_event_time &lt;dbl&gt; | QRISK3_event &lt;int&gt; | QRISK3_event_time &lt;dbl&gt; | MACE_event &lt;int&gt; | MACE_event_time &lt;dbl&gt; |\n",
-       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
-       "| 1000018 | 3.170770 | 14.18216 | 25.47860 | -7.910004 | 0.1960993 | 3.690275 | 8.513230 | 4.060235 | 2.543097 | ⋯ | 0 | 10.62560 | 0 | 10.62560 | 0 | 10.335387 | 0 | 10.335387 | 0 | 10.335387 |\n",
-       "| 1000020 | 3.935565 | 14.20361 | 25.50886 | -7.338207 | 0.3820538 | 4.170000 | 8.665529 | 3.640000 | 2.575014 | ⋯ | 0 | 12.35592 | 0 | 12.35592 | 0 | 12.065708 | 0 | 12.065708 | 0 | 12.065708 |\n",
-       "| 1000037 | 4.223654 | 14.24818 | 25.54972 | -7.533061 | 0.1899884 | 4.288627 | 8.540908 | 5.541451 | 2.441255 | ⋯ | 0 | 11.62765 | 0 | 11.62765 | 1 |  7.969884 | 1 |  7.969884 | 0 | 11.337440 |\n",
-       "| 1000043 | 3.529575 | 14.17650 | 25.39414 | -7.768648 | 0.2893883 | 4.130000 | 8.515447 | 4.261098 | 2.740589 | ⋯ | 0 | 11.06913 | 0 | 11.06913 | 1 |  5.122519 | 1 |  5.122519 | 1 |  5.122519 |\n",
-       "| 1000051 | 3.618199 | 14.21691 | 25.39648 | -7.486226 | 0.1776932 | 3.870000 | 8.043932 | 4.207255 | 2.460508 | ⋯ | 0 | 14.05065 | 0 | 14.05065 | 0 | 13.760438 | 0 | 13.760438 | 0 | 13.760438 |\n",
-       "| 1000066 | 3.555040 | 14.19133 | 25.47087 | -7.501505 | 0.4534260 | 4.100000 | 8.345422 | 3.521569 | 2.746012 | ⋯ | 0 | 10.53525 | 0 | 10.53525 | 0 | 10.245038 | 0 | 10.245038 | 0 | 10.245038 |\n",
-       "\n"
-      ],
-      "text/plain": [
-       "  eid     PGS000011 PGS000013 PGS000016 PGS000018 PGS000039 PGS000057 PGS000058\n",
-       "1 1000018 3.170770  14.18216  25.47860  -7.910004 0.1960993 3.690275  8.513230 \n",
-       "2 1000020 3.935565  14.20361  25.50886  -7.338207 0.3820538 4.170000  8.665529 \n",
-       "3 1000037 4.223654  14.24818  25.54972  -7.533061 0.1899884 4.288627  8.540908 \n",
-       "4 1000043 3.529575  14.17650  25.39414  -7.768648 0.2893883 4.130000  8.515447 \n",
-       "5 1000051 3.618199  14.21691  25.39648  -7.486226 0.1776932 3.870000  8.043932 \n",
-       "6 1000066 3.555040  14.19133  25.47087  -7.501505 0.4534260 4.100000  8.345422 \n",
-       "  PGS000059 PGS000116 <U+22EF>   death_cvd_event death_cvd_event_time SCORE_event\n",
-       "1 4.060235  2.543097  <U+22EF> 0               10.62560             0          \n",
-       "2 3.640000  2.575014  <U+22EF> 0               12.35592             0          \n",
-       "3 5.541451  2.441255  <U+22EF> 0               11.62765             0          \n",
-       "4 4.261098  2.740589  <U+22EF> 0               11.06913             0          \n",
-       "5 4.207255  2.460508  <U+22EF> 0               14.05065             0          \n",
-       "6 3.521569  2.746012  <U+22EF> 0               10.53525             0          \n",
-       "  SCORE_event_time ASCVD_event ASCVD_event_time QRISK3_event QRISK3_event_time\n",
-       "1 10.62560         0           10.335387        0            10.335387        \n",
-       "2 12.35592         0           12.065708        0            12.065708        \n",
-       "3 11.62765         1            7.969884        1             7.969884        \n",
-       "4 11.06913         1            5.122519        1             5.122519        \n",
-       "5 14.05065         0           13.760438        0            13.760438        \n",
-       "6 10.53525         0           10.245038        0            10.245038        \n",
-       "  MACE_event MACE_event_time\n",
-       "1 0          10.335387      \n",
-       "2 0          12.065708      \n",
-       "3 0          11.337440      \n",
-       "4 1           5.122519      \n",
-       "5 0          13.760438      \n",
-       "6 0          10.245038      "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "head(data)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## ESC SCORE (Conroy 2003)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "[Conroy 2003](http://eurheartj.oxfordjournals.org/content/24/11/987.full.pdf)"
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {},
-   "source": [
-    "Endpoints: Composite for fatal cardiovascular disease\n",
-    "defined in ICD9: 401 - 414 + 426 - 443 + 798.1 + 798.2, except: 426.7, 429.0, 430.0, 432.1, 437.3, 437.4, 437.5 + death\n",
-    "ICD10 (Keil, 2005): I10-15, I20-25, I44-51, I61-69, I70-73 + death"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T09:37:25.761469Z",
-     "start_time": "2020-11-04T09:37:25.008Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "calculateRisk <- function(age, cholesterol, SBP, currentSmoker, betaSmoker, betaSBP, betaChol, coefs) {\n",
-    "  # step 1 risks\n",
-    "  Sage0 = exp(-exp(coefs[\"alpha\"])*(age - 20)^coefs[\"p\"])\n",
-    "  Sage10 = exp(-exp(coefs[\"alpha\"])*(age - 10)^coefs[\"p\"])\n",
-    "  # step 2 weights\n",
-    "  w = betaChol*(cholesterol - 6) + betaSBP*(SBP - 120) + betaSmoker*currentSmoker\n",
-    "  # step 3 weighted risks\n",
-    "  Sage   = (Sage0)^exp(w) \n",
-    "  Sage1 = (Sage10)^exp(w) \n",
-    "  # step 4 - 10 years survival\n",
-    "  S10 = Sage1/Sage\n",
-    "  # step 5 - endpoint\n",
-    "  Risk10 = 1 - S10\n",
-    "  Risk10\n",
-    "}\n",
-    "\n",
-    "calculateScoreEur <- function(age, cholesterol, SBP, currentSmoker, gender = \"Men\", risk = \"Low risk\") {\n",
-    "  betaSmoker = c(0.71, 0.63)\n",
-    "  betaSBP    = c(0.018, 0.022)\n",
-    "  betaChol   = c(0.24, 0.02)\n",
-    "  \n",
-    "  coeffs <- array(c(-22.1, 4.71, -26.7, 5.64, -29.8, 6.36, -31.0, 6.62, -21.0, 4.62, -25.7, 5.47, -28.7, 6.23, -30.0, 6.42), \n",
-    "                  c(2,2,2,2),\n",
-    "                  dimnames = list(c(\"alpha\", \"p\"), c(\"CHD\", \"non CHD\"), c(\"Male\", \"Female\"), c(\"Low risk\", \"High risk\")))\n",
-    "  \n",
-    "  # step 6 - score\n",
-    "  CVDrisk = calculateRisk(age, cholesterol, SBP, currentSmoker,\n",
-    "                          betaSmoker[1], betaSBP[1], betaChol[1], coeffs[,\"CHD\",gender,risk])\n",
-    "  NonCVDrisk = calculateRisk(age, cholesterol, SBP, currentSmoker,\n",
-    "                          betaSmoker[2], betaSBP[2], betaChol[2], coeffs[,\"non CHD\",gender,risk])\n",
-    " \n",
-    "  CVDrisk + NonCVDrisk\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T09:37:27.777535Z",
-     "start_time": "2020-11-04T09:37:27.089Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "sex=\"Male\"\n",
-    "age_at_recruitment = 64\n",
-    "cholesterol = 6.8\n",
-    "systolic_blood_pressure = 140\n",
-    "current_smoker = 0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T09:37:35.468832Z",
-     "start_time": "2020-11-04T09:37:34.753Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<strong>alpha:</strong> 0.0544567488850661"
-      ],
-      "text/latex": [
-       "\\textbf{alpha:} 0.0544567488850661"
-      ],
-      "text/markdown": [
-       "**alpha:** 0.0544567488850661"
-      ],
-      "text/plain": [
-       "     alpha \n",
-       "0.05445675 "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "calculateScoreEur(age_at_recruitment, cholesterol, systolic_blood_pressure, current_smoker, sex,risk=\"Low risk\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T09:37:47.821364Z",
-     "start_time": "2020-11-04T09:37:35.426Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<table>\n",
-       "<caption>A data.table: 395738 × 2</caption>\n",
-       "<thead>\n",
-       "\t<tr><th scope=col>eid</th><th scope=col>score_SCORE</th></tr>\n",
-       "\t<tr><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
-       "</thead>\n",
-       "<tbody>\n",
-       "\t<tr><td>1000285</td><td>0.0343943723</td></tr>\n",
-       "\t<tr><td>1000686</td><td>0.0401695746</td></tr>\n",
-       "\t<tr><td>1000974</td><td>0.0709517856</td></tr>\n",
-       "\t<tr><td>1001073</td><td>0.0319272214</td></tr>\n",
-       "\t<tr><td>1001098</td><td>0.0022416772</td></tr>\n",
-       "\t<tr><td>1001119</td><td>0.0157146322</td></tr>\n",
-       "\t<tr><td>1001458</td><td>0.0653505615</td></tr>\n",
-       "\t<tr><td>1001517</td><td>0.0630663630</td></tr>\n",
-       "\t<tr><td>1001591</td><td>0.0071638047</td></tr>\n",
-       "\t<tr><td>1001741</td><td>0.0025406444</td></tr>\n",
-       "\t<tr><td>1001759</td><td>0.0008309934</td></tr>\n",
-       "\t<tr><td>1002001</td><td>0.0205967683</td></tr>\n",
-       "\t<tr><td>1002182</td><td>0.0030319019</td></tr>\n",
-       "\t<tr><td>1002213</td><td>0.0206450410</td></tr>\n",
-       "\t<tr><td>1002710</td><td>0.0003370405</td></tr>\n",
-       "\t<tr><td>1002811</td><td>0.0158786970</td></tr>\n",
-       "\t<tr><td>1002977</td><td>0.0104503691</td></tr>\n",
-       "\t<tr><td>1003288</td><td>0.0773918236</td></tr>\n",
-       "\t<tr><td>1003535</td><td>0.0179705427</td></tr>\n",
-       "\t<tr><td>1003631</td><td>0.0179181051</td></tr>\n",
-       "\t<tr><td>1003650</td><td>0.0559637654</td></tr>\n",
-       "\t<tr><td>1003884</td><td>0.0031048374</td></tr>\n",
-       "\t<tr><td>1004007</td><td>0.0175649353</td></tr>\n",
-       "\t<tr><td>1004083</td><td>0.0314311047</td></tr>\n",
-       "\t<tr><td>1004105</td><td>0.0124366189</td></tr>\n",
-       "\t<tr><td>1004333</td><td>0.0041574702</td></tr>\n",
-       "\t<tr><td>1004767</td><td>0.0115299993</td></tr>\n",
-       "\t<tr><td>1004846</td><td>0.0129171473</td></tr>\n",
-       "\t<tr><td>1005011</td><td>0.0495698810</td></tr>\n",
-       "\t<tr><td>1005089</td><td>0.0179615686</td></tr>\n",
-       "\t<tr><td>⋮</td><td>⋮</td></tr>\n",
-       "\t<tr><td>6022598</td><td>0.045923338</td></tr>\n",
-       "\t<tr><td>6022635</td><td>0.031011270</td></tr>\n",
-       "\t<tr><td>6022700</td><td>0.105076824</td></tr>\n",
-       "\t<tr><td>6022819</td><td>0.026161350</td></tr>\n",
-       "\t<tr><td>6022826</td><td>0.012035669</td></tr>\n",
-       "\t<tr><td>6022906</td><td>0.015613808</td></tr>\n",
-       "\t<tr><td>6022985</td><td>0.021142888</td></tr>\n",
-       "\t<tr><td>6023018</td><td>0.055558255</td></tr>\n",
-       "\t<tr><td>6023051</td><td>0.001246383</td></tr>\n",
-       "\t<tr><td>6023092</td><td>0.016823842</td></tr>\n",
-       "\t<tr><td>6023206</td><td>0.026466977</td></tr>\n",
-       "\t<tr><td>6023285</td><td>0.035044543</td></tr>\n",
-       "\t<tr><td>6023334</td><td>0.031188639</td></tr>\n",
-       "\t<tr><td>6023815</td><td>0.040879787</td></tr>\n",
-       "\t<tr><td>6023859</td><td>0.006178257</td></tr>\n",
-       "\t<tr><td>6023888</td><td>0.025984983</td></tr>\n",
-       "\t<tr><td>6023974</td><td>0.042899815</td></tr>\n",
-       "\t<tr><td>6024246</td><td>0.022738162</td></tr>\n",
-       "\t<tr><td>6024470</td><td>0.008010350</td></tr>\n",
-       "\t<tr><td>6024505</td><td>0.032987159</td></tr>\n",
-       "\t<tr><td>6024518</td><td>0.016344226</td></tr>\n",
-       "\t<tr><td>6024543</td><td>0.031282738</td></tr>\n",
-       "\t<tr><td>6024584</td><td>0.004796794</td></tr>\n",
-       "\t<tr><td>6024810</td><td>0.047738609</td></tr>\n",
-       "\t<tr><td>6024835</td><td>0.001795017</td></tr>\n",
-       "\t<tr><td>6024861</td><td>0.001027473</td></tr>\n",
-       "\t<tr><td>6024883</td><td>0.009021477</td></tr>\n",
-       "\t<tr><td>6025060</td><td>0.005033320</td></tr>\n",
-       "\t<tr><td>6025116</td><td>0.011599217</td></tr>\n",
-       "\t<tr><td>6025147</td><td>0.002766033</td></tr>\n",
-       "</tbody>\n",
-       "</table>\n"
-      ],
-      "text/latex": [
-       "A data.table: 395738 × 2\n",
-       "\\begin{tabular}{ll}\n",
-       " eid & score\\_SCORE\\\\\n",
-       " <int> & <dbl>\\\\\n",
-       "\\hline\n",
-       "\t 1000285 & 0.0343943723\\\\\n",
-       "\t 1000686 & 0.0401695746\\\\\n",
-       "\t 1000974 & 0.0709517856\\\\\n",
-       "\t 1001073 & 0.0319272214\\\\\n",
-       "\t 1001098 & 0.0022416772\\\\\n",
-       "\t 1001119 & 0.0157146322\\\\\n",
-       "\t 1001458 & 0.0653505615\\\\\n",
-       "\t 1001517 & 0.0630663630\\\\\n",
-       "\t 1001591 & 0.0071638047\\\\\n",
-       "\t 1001741 & 0.0025406444\\\\\n",
-       "\t 1001759 & 0.0008309934\\\\\n",
-       "\t 1002001 & 0.0205967683\\\\\n",
-       "\t 1002182 & 0.0030319019\\\\\n",
-       "\t 1002213 & 0.0206450410\\\\\n",
-       "\t 1002710 & 0.0003370405\\\\\n",
-       "\t 1002811 & 0.0158786970\\\\\n",
-       "\t 1002977 & 0.0104503691\\\\\n",
-       "\t 1003288 & 0.0773918236\\\\\n",
-       "\t 1003535 & 0.0179705427\\\\\n",
-       "\t 1003631 & 0.0179181051\\\\\n",
-       "\t 1003650 & 0.0559637654\\\\\n",
-       "\t 1003884 & 0.0031048374\\\\\n",
-       "\t 1004007 & 0.0175649353\\\\\n",
-       "\t 1004083 & 0.0314311047\\\\\n",
-       "\t 1004105 & 0.0124366189\\\\\n",
-       "\t 1004333 & 0.0041574702\\\\\n",
-       "\t 1004767 & 0.0115299993\\\\\n",
-       "\t 1004846 & 0.0129171473\\\\\n",
-       "\t 1005011 & 0.0495698810\\\\\n",
-       "\t 1005089 & 0.0179615686\\\\\n",
-       "\t ⋮ & ⋮\\\\\n",
-       "\t 6022598 & 0.045923338\\\\\n",
-       "\t 6022635 & 0.031011270\\\\\n",
-       "\t 6022700 & 0.105076824\\\\\n",
-       "\t 6022819 & 0.026161350\\\\\n",
-       "\t 6022826 & 0.012035669\\\\\n",
-       "\t 6022906 & 0.015613808\\\\\n",
-       "\t 6022985 & 0.021142888\\\\\n",
-       "\t 6023018 & 0.055558255\\\\\n",
-       "\t 6023051 & 0.001246383\\\\\n",
-       "\t 6023092 & 0.016823842\\\\\n",
-       "\t 6023206 & 0.026466977\\\\\n",
-       "\t 6023285 & 0.035044543\\\\\n",
-       "\t 6023334 & 0.031188639\\\\\n",
-       "\t 6023815 & 0.040879787\\\\\n",
-       "\t 6023859 & 0.006178257\\\\\n",
-       "\t 6023888 & 0.025984983\\\\\n",
-       "\t 6023974 & 0.042899815\\\\\n",
-       "\t 6024246 & 0.022738162\\\\\n",
-       "\t 6024470 & 0.008010350\\\\\n",
-       "\t 6024505 & 0.032987159\\\\\n",
-       "\t 6024518 & 0.016344226\\\\\n",
-       "\t 6024543 & 0.031282738\\\\\n",
-       "\t 6024584 & 0.004796794\\\\\n",
-       "\t 6024810 & 0.047738609\\\\\n",
-       "\t 6024835 & 0.001795017\\\\\n",
-       "\t 6024861 & 0.001027473\\\\\n",
-       "\t 6024883 & 0.009021477\\\\\n",
-       "\t 6025060 & 0.005033320\\\\\n",
-       "\t 6025116 & 0.011599217\\\\\n",
-       "\t 6025147 & 0.002766033\\\\\n",
-       "\\end{tabular}\n"
-      ],
-      "text/markdown": [
-       "\n",
-       "A data.table: 395738 × 2\n",
-       "\n",
-       "| eid &lt;int&gt; | score_SCORE &lt;dbl&gt; |\n",
-       "|---|---|\n",
-       "| 1000285 | 0.0343943723 |\n",
-       "| 1000686 | 0.0401695746 |\n",
-       "| 1000974 | 0.0709517856 |\n",
-       "| 1001073 | 0.0319272214 |\n",
-       "| 1001098 | 0.0022416772 |\n",
-       "| 1001119 | 0.0157146322 |\n",
-       "| 1001458 | 0.0653505615 |\n",
-       "| 1001517 | 0.0630663630 |\n",
-       "| 1001591 | 0.0071638047 |\n",
-       "| 1001741 | 0.0025406444 |\n",
-       "| 1001759 | 0.0008309934 |\n",
-       "| 1002001 | 0.0205967683 |\n",
-       "| 1002182 | 0.0030319019 |\n",
-       "| 1002213 | 0.0206450410 |\n",
-       "| 1002710 | 0.0003370405 |\n",
-       "| 1002811 | 0.0158786970 |\n",
-       "| 1002977 | 0.0104503691 |\n",
-       "| 1003288 | 0.0773918236 |\n",
-       "| 1003535 | 0.0179705427 |\n",
-       "| 1003631 | 0.0179181051 |\n",
-       "| 1003650 | 0.0559637654 |\n",
-       "| 1003884 | 0.0031048374 |\n",
-       "| 1004007 | 0.0175649353 |\n",
-       "| 1004083 | 0.0314311047 |\n",
-       "| 1004105 | 0.0124366189 |\n",
-       "| 1004333 | 0.0041574702 |\n",
-       "| 1004767 | 0.0115299993 |\n",
-       "| 1004846 | 0.0129171473 |\n",
-       "| 1005011 | 0.0495698810 |\n",
-       "| 1005089 | 0.0179615686 |\n",
-       "| ⋮ | ⋮ |\n",
-       "| 6022598 | 0.045923338 |\n",
-       "| 6022635 | 0.031011270 |\n",
-       "| 6022700 | 0.105076824 |\n",
-       "| 6022819 | 0.026161350 |\n",
-       "| 6022826 | 0.012035669 |\n",
-       "| 6022906 | 0.015613808 |\n",
-       "| 6022985 | 0.021142888 |\n",
-       "| 6023018 | 0.055558255 |\n",
-       "| 6023051 | 0.001246383 |\n",
-       "| 6023092 | 0.016823842 |\n",
-       "| 6023206 | 0.026466977 |\n",
-       "| 6023285 | 0.035044543 |\n",
-       "| 6023334 | 0.031188639 |\n",
-       "| 6023815 | 0.040879787 |\n",
-       "| 6023859 | 0.006178257 |\n",
-       "| 6023888 | 0.025984983 |\n",
-       "| 6023974 | 0.042899815 |\n",
-       "| 6024246 | 0.022738162 |\n",
-       "| 6024470 | 0.008010350 |\n",
-       "| 6024505 | 0.032987159 |\n",
-       "| 6024518 | 0.016344226 |\n",
-       "| 6024543 | 0.031282738 |\n",
-       "| 6024584 | 0.004796794 |\n",
-       "| 6024810 | 0.047738609 |\n",
-       "| 6024835 | 0.001795017 |\n",
-       "| 6024861 | 0.001027473 |\n",
-       "| 6024883 | 0.009021477 |\n",
-       "| 6025060 | 0.005033320 |\n",
-       "| 6025116 | 0.011599217 |\n",
-       "| 6025147 | 0.002766033 |\n",
-       "\n"
-      ],
-      "text/plain": [
-       "       eid     score_SCORE \n",
-       "1      1000285 0.0343943723\n",
-       "2      1000686 0.0401695746\n",
-       "3      1000974 0.0709517856\n",
-       "4      1001073 0.0319272214\n",
-       "5      1001098 0.0022416772\n",
-       "6      1001119 0.0157146322\n",
-       "7      1001458 0.0653505615\n",
-       "8      1001517 0.0630663630\n",
-       "9      1001591 0.0071638047\n",
-       "10     1001741 0.0025406444\n",
-       "11     1001759 0.0008309934\n",
-       "12     1002001 0.0205967683\n",
-       "13     1002182 0.0030319019\n",
-       "14     1002213 0.0206450410\n",
-       "15     1002710 0.0003370405\n",
-       "16     1002811 0.0158786970\n",
-       "17     1002977 0.0104503691\n",
-       "18     1003288 0.0773918236\n",
-       "19     1003535 0.0179705427\n",
-       "20     1003631 0.0179181051\n",
-       "21     1003650 0.0559637654\n",
-       "22     1003884 0.0031048374\n",
-       "23     1004007 0.0175649353\n",
-       "24     1004083 0.0314311047\n",
-       "25     1004105 0.0124366189\n",
-       "26     1004333 0.0041574702\n",
-       "27     1004767 0.0115299993\n",
-       "28     1004846 0.0129171473\n",
-       "29     1005011 0.0495698810\n",
-       "30     1005089 0.0179615686\n",
-       "<U+22EE> <U+22EE> <U+22EE>    \n",
-       "395709 6022598 0.045923338 \n",
-       "395710 6022635 0.031011270 \n",
-       "395711 6022700 0.105076824 \n",
-       "395712 6022819 0.026161350 \n",
-       "395713 6022826 0.012035669 \n",
-       "395714 6022906 0.015613808 \n",
-       "395715 6022985 0.021142888 \n",
-       "395716 6023018 0.055558255 \n",
-       "395717 6023051 0.001246383 \n",
-       "395718 6023092 0.016823842 \n",
-       "395719 6023206 0.026466977 \n",
-       "395720 6023285 0.035044543 \n",
-       "395721 6023334 0.031188639 \n",
-       "395722 6023815 0.040879787 \n",
-       "395723 6023859 0.006178257 \n",
-       "395724 6023888 0.025984983 \n",
-       "395725 6023974 0.042899815 \n",
-       "395726 6024246 0.022738162 \n",
-       "395727 6024470 0.008010350 \n",
-       "395728 6024505 0.032987159 \n",
-       "395729 6024518 0.016344226 \n",
-       "395730 6024543 0.031282738 \n",
-       "395731 6024584 0.004796794 \n",
-       "395732 6024810 0.047738609 \n",
-       "395733 6024835 0.001795017 \n",
-       "395734 6024861 0.001027473 \n",
-       "395735 6024883 0.009021477 \n",
-       "395736 6025060 0.005033320 \n",
-       "395737 6025116 0.011599217 \n",
-       "395738 6025147 0.002766033 "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "temp = data.table(data %>% select(eid, age_at_recruitment, cholesterol, systolic_blood_pressure, smoking_status, sex) %>% mutate(current_smoker = case_when(smoking_status==\"Current\" ~ 1, TRUE ~ 0)))\n",
-    "SCORE_df = temp[, score_SCORE:=calculateScoreEur(age_at_recruitment, cholesterol, systolic_blood_pressure, current_smoker, sex,risk=\"Low risk\"), by=\"eid\"] %>% select(c(eid, score_SCORE))\n",
-    "SCORE_df"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## ACC/AHA ASCVD (Goff 2014)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "[Goof 2014](https://www.ahajournals.org/doi/pdf/10.1161/01.cir.0000437741.48606.98)"
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {},
-   "source": [
-    "endpoints: composite of first occurrence of\n",
-    "- nonfatal myocardial infarction I20-25 - hospital admission record\n",
-    "- coronary heart disease death I20-I25 - mortality record\n",
-    "- nonfatal or fatal stroke I63 hospital admission + mortality record\n",
-    "exclusion: free from ASCVD event"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T09:38:02.240851Z",
-     "start_time": "2020-11-04T09:38:01.564Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "coefs_string = '\"ln_age\" \"ln_age_squared\" \"ln_total_cholest\" \"ln_age_totcholest\" \"ln_hdlC\" \"ln_age_hdlC\" \"ln_treated_BP\" \"ln_age_BP\" \"ln_untreated_BP\" \"ln_age_ln_untreated_BP\" \"smoker\" \"nonsmoker\" \"ln_age_smoker\" \"diabetes\" \"nondiabetes\" \"meancoef\" \"baseline\"\n",
-    "                \"white_female\" -29.799 4.884 13.54 -3.114 -13.578 3.149 2.019 0 1.957 0 7.574 0 -1.665 0.661 0 -29.18 0.9665\n",
-    "                \"afroamer_female\" 17.114 0 0.94 0 -18.92 4.475 29.291 -6.432 27.82 -6.087 0.691 0 0 0.874 0 86.61 0.9533\n",
-    "                \"white_male\" 12.344 0 11.853 -2.664 -7.99 1.769 1.797 0 1.764 0 7.837 0 -1.795 0.658 0 61.18 0.9144\n",
-    "                \"afroamer_male\" 2.469 0 0.302 0 -0.307 0 1.916 0 1.809 0 0.549 0 0 0.645 0 19.54 0.8954'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T09:38:03.013264Z",
-     "start_time": "2020-11-04T09:38:02.333Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# from Appendix 7\n",
-    "coefs <- read.table(text=coefs_string, row.names=1)\n",
-    "#coefs\n",
-    "\n",
-    "calculateASCVD <- function(coefs=coefs, sex=\"Male\", ethnicity=\"White\", age=53, cholesterol=2, hdl_cholesterol=1.1, systolic_blood_pressure=120, antihypertensives=1, diabetes=0, smoking=1) {\n",
-    "    \n",
-    "    if (!is.na(ethnicity)) {\n",
-    "            \n",
-    "        if ((sex==\"Female\")&(ethnicity != \"Black\")){const=coefs[1,]} \n",
-    "        if ((sex==\"Female\")&(ethnicity==\"Black\")){const=coefs[2,]} \n",
-    "        if ((sex==\"Male\")&(ethnicity != \"Black\")){const=coefs[3,]} \n",
-    "        if ((sex==\"Male\")&(ethnicity==\"Black\")){const=coefs[4,]} \n",
-    "\n",
-    "        if (smoking==TRUE){smokc=const$smoker} else {smokc=const$nonsmoker}\n",
-    "        if (smoking==TRUE){smokcov=1} else {smokcov=0}\n",
-    "\n",
-    "        if (antihypertensives==TRUE){BPc=const$ln_treated_BP} else {BPc=const$ln_untreated_BP}\n",
-    "        if (antihypertensives==TRUE){BPcov=const$ln_age_BP} else {BPcov=const$ln_age_ln_untreated_BP}\n",
-    "\n",
-    "        if (diabetes==TRUE){diab=const$diabetes} else {diab=const$nondiabetes}\n",
-    "\n",
-    "       # meancoef = const$meancoef\n",
-    "\n",
-    "        calc = log(age)*const$ln_age+log(age)*log(age)*const$ln_age_squared+\n",
-    "            log(cholesterol*38.67)*const$ln_total_cholest+\n",
-    "            log(age)*log(cholesterol*38.67)*const$ln_age_totcholest+\n",
-    "            log(hdl_cholesterol*38.67)*const$ln_hdlC+\n",
-    "            log(age)*log(hdl_cholesterol*38.67)*const$ln_age_hdlC+\n",
-    "            smokc+smokcov*log(age)*const$ln_age_smoker+\n",
-    "            log(systolic_blood_pressure)*BPc+\n",
-    "            log(age)*log(systolic_blood_pressure)*BPcov+diab\n",
-    "        \n",
-    "        ASCVD<-(1-(const$baseline^exp(calc-const$meancoef)))\n",
-    "    } else {ASCVD=NA}\n",
-    "    return (ASCVD)\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T09:39:29.590535Z",
-     "start_time": "2020-11-04T09:38:03.549Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<table>\n",
-       "<caption>A data.table: 6 × 2</caption>\n",
-       "<thead>\n",
-       "\t<tr><th scope=col>eid</th><th scope=col>score_ASCVD</th></tr>\n",
-       "\t<tr><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
-       "</thead>\n",
-       "<tbody>\n",
-       "\t<tr><td>1000285</td><td>0.14320501</td></tr>\n",
-       "\t<tr><td>1000686</td><td>0.13781988</td></tr>\n",
-       "\t<tr><td>1000974</td><td>0.38666508</td></tr>\n",
-       "\t<tr><td>1001073</td><td>0.07376216</td></tr>\n",
-       "\t<tr><td>1001098</td><td>0.01139464</td></tr>\n",
-       "\t<tr><td>1001119</td><td>0.04382689</td></tr>\n",
-       "</tbody>\n",
-       "</table>\n"
-      ],
-      "text/latex": [
-       "A data.table: 6 × 2\n",
-       "\\begin{tabular}{ll}\n",
-       " eid & score\\_ASCVD\\\\\n",
-       " <int> & <dbl>\\\\\n",
-       "\\hline\n",
-       "\t 1000285 & 0.14320501\\\\\n",
-       "\t 1000686 & 0.13781988\\\\\n",
-       "\t 1000974 & 0.38666508\\\\\n",
-       "\t 1001073 & 0.07376216\\\\\n",
-       "\t 1001098 & 0.01139464\\\\\n",
-       "\t 1001119 & 0.04382689\\\\\n",
-       "\\end{tabular}\n"
-      ],
-      "text/markdown": [
-       "\n",
-       "A data.table: 6 × 2\n",
-       "\n",
-       "| eid &lt;int&gt; | score_ASCVD &lt;dbl&gt; |\n",
-       "|---|---|\n",
-       "| 1000285 | 0.14320501 |\n",
-       "| 1000686 | 0.13781988 |\n",
-       "| 1000974 | 0.38666508 |\n",
-       "| 1001073 | 0.07376216 |\n",
-       "| 1001098 | 0.01139464 |\n",
-       "| 1001119 | 0.04382689 |\n",
-       "\n"
-      ],
-      "text/plain": [
-       "  eid     score_ASCVD\n",
-       "1 1000285 0.14320501 \n",
-       "2 1000686 0.13781988 \n",
-       "3 1000974 0.38666508 \n",
-       "4 1001073 0.07376216 \n",
-       "5 1001098 0.01139464 \n",
-       "6 1001119 0.04382689 "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "temp = data.table(data %>% select(eid, age_at_recruitment, ethnic_background, sex, cholesterol, hdl_cholesterol, systolic_blood_pressure, antihypertensives, diabetes2, smoking_status) %>%\n",
-    "                  mutate(current_smoker = case_when(smoking_status==\"Current\" ~ TRUE, TRUE ~ FALSE)))\n",
-    "ASCVD_df = temp[, score_ASCVD:=calculateASCVD(coefs, sex, ethnic_background, age_at_recruitment, cholesterol, hdl_cholesterol, systolic_blood_pressure, antihypertensives, diabetes2, current_smoker), by=eid] %>% select(c(eid, score_ASCVD))\n",
-    "head(ASCVD_df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<table>\n",
-       "<caption>A data.table: 395738 × 12</caption>\n",
-       "<thead>\n",
-       "\t<tr><th scope=col>eid</th><th scope=col>age_at_recruitment</th><th scope=col>ethnic_background</th><th scope=col>sex</th><th scope=col>cholesterol</th><th scope=col>hdl_cholesterol</th><th scope=col>systolic_blood_pressure</th><th scope=col>antihypertensives</th><th scope=col>diabetes2</th><th scope=col>smoking_status</th><th scope=col>current_smoker</th><th scope=col>score_ASCVD</th></tr>\n",
-       "\t<tr><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;lgl&gt;</th><th scope=col>&lt;lgl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;lgl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
-       "</thead>\n",
-       "<tbody>\n",
-       "\t<tr><td>1000285</td><td>64</td><td>White</td><td>Male  </td><td>4.484</td><td>0.918</td><td>135.5</td><td>FALSE</td><td>FALSE</td><td>Never   </td><td>FALSE</td><td>0.143205008</td></tr>\n",
-       "\t<tr><td>1000686</td><td>58</td><td>White</td><td>Male  </td><td>7.139</td><td>1.159</td><td>147.5</td><td>FALSE</td><td>FALSE</td><td>Never   </td><td>FALSE</td><td>0.137819883</td></tr>\n",
-       "\t<tr><td>1000974</td><td>69</td><td>White</td><td>Male  </td><td>4.302</td><td>1.075</td><td>154.0</td><td>FALSE</td><td> TRUE</td><td>Previous</td><td>FALSE</td><td>0.386665084</td></tr>\n",
-       "\t<tr><td>1001073</td><td>64</td><td>White</td><td>Female</td><td>6.944</td><td>1.608</td><td>148.0</td><td>FALSE</td><td>FALSE</td><td>Never   </td><td>FALSE</td><td>0.073762156</td></tr>\n",
-       "\t<tr><td>1001098</td><td>47</td><td>White</td><td>Female</td><td>5.389</td><td>1.477</td><td>139.5</td><td>FALSE</td><td>FALSE</td><td>Never   </td><td>FALSE</td><td>0.011394640</td></tr>\n",
-       "\t<tr><td>1001119</td><td>62</td><td>White</td><td>Female</td><td>4.833</td><td>1.461</td><td>137.5</td><td>FALSE</td><td>FALSE</td><td>Previous</td><td>FALSE</td><td>0.043826887</td></tr>\n",
-       "\t<tr><td>1001458</td><td>59</td><td>White</td><td>Male  </td><td>4.879</td><td>1.097</td><td>153.0</td><td>FALSE</td><td>FALSE</td><td>Current </td><td> TRUE</td><td>0.184407293</td></tr>\n",
-       "\t<tr><td>1001517</td><td>66</td><td>White</td><td>Male  </td><td>6.353</td><td>1.879</td><td>143.5</td><td>FALSE</td><td>FALSE</td><td>Previous</td><td>FALSE</td><td>0.154071008</td></tr>\n",
-       "\t<tr><td>1001591</td><td>50</td><td>White</td><td>Male  </td><td>5.675</td><td>1.418</td><td>112.5</td><td>FALSE</td><td>FALSE</td><td>Never   </td><td>FALSE</td><td>0.028630793</td></tr>\n",
-       "\t<tr><td>1001741</td><td>47</td><td>White</td><td>Female</td><td>4.880</td><td>1.157</td><td>149.0</td><td>FALSE</td><td>FALSE</td><td>Previous</td><td>FALSE</td><td>0.015815727</td></tr>\n",
-       "\t<tr><td>1001759</td><td>44</td><td>White</td><td>Female</td><td>6.177</td><td>1.560</td><td>110.0</td><td>FALSE</td><td>FALSE</td><td>Never   </td><td>FALSE</td><td>0.006567331</td></tr>\n",
-       "\t<tr><td>1002001</td><td>59</td><td>White</td><td>Male  </td><td>6.449</td><td>1.884</td><td>114.0</td><td>FALSE</td><td>FALSE</td><td>Previous</td><td>FALSE</td><td>0.060675171</td></tr>\n",
-       "\t<tr><td>1002182</td><td>48</td><td>White</td><td>Female</td><td>5.381</td><td>1.642</td><td>146.5</td><td>FALSE</td><td>FALSE</td><td>Never   </td><td>FALSE</td><td>0.011755503</td></tr>\n",
-       "\t<tr><td>1002213</td><td>59</td><td>White</td><td>Female</td><td>7.881</td><td>1.497</td><td>148.0</td><td>FALSE</td><td>FALSE</td><td>Previous</td><td>FALSE</td><td>0.053596257</td></tr>\n",
-       "\t<tr><td>1002710</td><td>40</td><td>White</td><td>Female</td><td>4.482</td><td>1.512</td><td>115.5</td><td>FALSE</td><td>FALSE</td><td>Never   </td><td>FALSE</td><td>0.003121729</td></tr>\n",
-       "\t<tr><td>1002811</td><td>59</td><td>White</td><td>Female</td><td>6.842</td><td>2.215</td><td>142.5</td><td>FALSE</td><td>FALSE</td><td>Never   </td><td>FALSE</td><td>0.033436135</td></tr>\n",
-       "\t<tr><td>1002977</td><td>57</td><td>White</td><td>Female</td><td>7.024</td><td>2.850</td><td>132.5</td><td>FALSE</td><td>FALSE</td><td>Previous</td><td>FALSE</td><td>0.019330514</td></tr>\n",
-       "\t<tr><td>1003288</td><td>70</td><td>White</td><td>Female</td><td>7.540</td><td>1.470</td><td>157.5</td><td>FALSE</td><td>FALSE</td><td>Never   </td><td>FALSE</td><td>0.153816413</td></tr>\n",
-       "\t<tr><td>1003535</td><td>51</td><td>White</td><td>Male  </td><td>6.688</td><td>0.876</td><td>145.5</td><td>FALSE</td><td>FALSE</td><td>Never   </td><td>FALSE</td><td>0.099090730</td></tr>\n",
-       "\t<tr><td>1003631</td><td>66</td><td>White</td><td>Female</td><td>6.136</td><td>1.159</td><td>113.5</td><td>FALSE</td><td>FALSE</td><td>Previous</td><td>FALSE</td><td>0.057791311</td></tr>\n",
-       "\t<tr><td>1003650</td><td>65</td><td>White</td><td>Male  </td><td>5.621</td><td>1.536</td><td>147.5</td><td>FALSE</td><td>FALSE</td><td>Never   </td><td>FALSE</td><td>0.154145834</td></tr>\n",
-       "\t<tr><td>1003884</td><td>40</td><td>White</td><td>Male  </td><td>6.472</td><td>1.076</td><td>128.0</td><td>FALSE</td><td>FALSE</td><td>Previous</td><td>FALSE</td><td>0.022764260</td></tr>\n",
-       "\t<tr><td>1004007</td><td>64</td><td>White</td><td>Female</td><td>4.113</td><td>0.705</td><td>136.0</td><td>FALSE</td><td>FALSE</td><td>Previous</td><td>FALSE</td><td>0.068493283</td></tr>\n",
-       "\t<tr><td>1004083</td><td>60</td><td>White</td><td>Female</td><td>8.257</td><td>1.266</td><td>160.5</td><td>FALSE</td><td>FALSE</td><td>Never   </td><td>FALSE</td><td>0.078568847</td></tr>\n",
-       "\t<tr><td>1004105</td><td>45</td><td>Black</td><td>Male  </td><td>4.359</td><td>1.375</td><td>149.0</td><td>FALSE</td><td>FALSE</td><td>Current </td><td> TRUE</td><td>0.085530257</td></tr>\n",
-       "\t<tr><td>1004333</td><td>50</td><td>White</td><td>Female</td><td>5.167</td><td>1.327</td><td>148.0</td><td>FALSE</td><td>FALSE</td><td>Previous</td><td>FALSE</td><td>0.017895970</td></tr>\n",
-       "\t<tr><td>1004767</td><td>47</td><td>White</td><td>Male  </td><td>6.171</td><td>1.050</td><td>151.0</td><td>FALSE</td><td>FALSE</td><td>Never   </td><td>FALSE</td><td>0.056475791</td></tr>\n",
-       "\t<tr><td>1004846</td><td>47</td><td>White</td><td>Male  </td><td>6.321</td><td>1.487</td><td>119.0</td><td>FALSE</td><td>FALSE</td><td>Current </td><td> TRUE</td><td>0.064303517</td></tr>\n",
-       "\t<tr><td>1005011</td><td>56</td><td>White</td><td>Male  </td><td>6.424</td><td>1.648</td><td>175.0</td><td>FALSE</td><td>FALSE</td><td>Never   </td><td>FALSE</td><td>0.107297652</td></tr>\n",
-       "\t<tr><td>1005089</td><td>66</td><td>White</td><td>Female</td><td>5.108</td><td>1.447</td><td>120.5</td><td>FALSE</td><td>FALSE</td><td>Never   </td><td>FALSE</td><td>0.054585602</td></tr>\n",
-       "\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
-       "\t<tr><td>6022598</td><td>66</td><td>White</td><td>Female</td><td>6.309</td><td>1.759</td><td>160.0</td><td>FALSE</td><td>FALSE</td><td>Previous</td><td>FALSE</td><td>0.09574848</td></tr>\n",
-       "\t<tr><td>6022635</td><td>63</td><td>White</td><td>Female</td><td>5.859</td><td>1.699</td><td>159.5</td><td>FALSE</td><td>FALSE</td><td>Previous</td><td>FALSE</td><td>0.06756539</td></tr>\n",
-       "\t<tr><td>6022700</td><td>70</td><td>White</td><td>Male  </td><td>5.335</td><td>1.198</td><td>163.0</td><td>FALSE</td><td>FALSE</td><td>Never   </td><td>FALSE</td><td>0.27527480</td></tr>\n",
-       "\t<tr><td>6022819</td><td>62</td><td>White</td><td>Female</td><td>6.593</td><td>1.560</td><td>117.5</td><td>FALSE</td><td>FALSE</td><td>Current </td><td> TRUE</td><td>0.07620732</td></tr>\n",
-       "\t<tr><td>6022826</td><td>60</td><td>White</td><td>Female</td><td>5.140</td><td>1.425</td><td>134.0</td><td>FALSE</td><td>FALSE</td><td>Never   </td><td>FALSE</td><td>0.03579095</td></tr>\n",
-       "\t<tr><td>6022906</td><td>60</td><td>White</td><td>Female</td><td>7.436</td><td>1.952</td><td>131.0</td><td>FALSE</td><td>FALSE</td><td>Never   </td><td>FALSE</td><td>0.03693190</td></tr>\n",
-       "\t<tr><td>6022985</td><td>62</td><td>White</td><td>Female</td><td>7.059</td><td>2.340</td><td>137.5</td><td>FALSE</td><td>FALSE</td><td>Previous</td><td>FALSE</td><td>0.04326065</td></tr>\n",
-       "\t<tr><td>6023018</td><td>64</td><td>White</td><td>Male  </td><td>6.302</td><td>0.916</td><td>145.5</td><td>FALSE</td><td>FALSE</td><td>Previous</td><td>FALSE</td><td>0.20415579</td></tr>\n",
-       "\t<tr><td>6023051</td><td>45</td><td>White</td><td>Female</td><td>6.580</td><td>1.404</td><td>118.5</td><td>FALSE</td><td>FALSE</td><td>Never   </td><td>FALSE</td><td>0.01070025</td></tr>\n",
-       "\t<tr><td>6023092</td><td>61</td><td>White</td><td>Female</td><td>6.474</td><td>1.504</td><td>136.0</td><td>FALSE</td><td>FALSE</td><td>Never   </td><td>FALSE</td><td>0.04668794</td></tr>\n",
-       "\t<tr><td>6023206</td><td>61</td><td>White</td><td>Female</td><td>7.172</td><td>2.400</td><td>154.0</td><td>FALSE</td><td>FALSE</td><td>Previous</td><td>FALSE</td><td>0.04775392</td></tr>\n",
-       "\t<tr><td>6023285</td><td>63</td><td>White</td><td>Male  </td><td>4.315</td><td>0.588</td><td>142.0</td><td>FALSE</td><td>FALSE</td><td>Previous</td><td>FALSE</td><td>0.18297574</td></tr>\n",
-       "\t<tr><td>6023334</td><td>60</td><td>White</td><td>Male  </td><td>6.038</td><td>1.060</td><td>135.0</td><td>FALSE</td><td>FALSE</td><td>Never   </td><td>FALSE</td><td>0.12451617</td></tr>\n",
-       "\t<tr><td>6023815</td><td>64</td><td>White</td><td>Male  </td><td>6.563</td><td>1.398</td><td>127.0</td><td>FALSE</td><td>FALSE</td><td>Never   </td><td>FALSE</td><td>0.13225327</td></tr>\n",
-       "\t<tr><td>6023859</td><td>46</td><td>Black</td><td>Male  </td><td>4.360</td><td>1.082</td><td>141.5</td><td>FALSE</td><td>FALSE</td><td>Never   </td><td>FALSE</td><td>0.05204528</td></tr>\n",
-       "\t<tr><td>6023888</td><td>61</td><td>White</td><td>Female</td><td>5.472</td><td>2.153</td><td>164.5</td><td>FALSE</td><td>FALSE</td><td>Previous</td><td>FALSE</td><td>0.04765866</td></tr>\n",
-       "\t<tr><td>6023974</td><td>62</td><td>White</td><td>Male  </td><td>6.520</td><td>0.942</td><td>138.5</td><td>FALSE</td><td>FALSE</td><td>Previous</td><td>FALSE</td><td>0.16982618</td></tr>\n",
-       "\t<tr><td>6024246</td><td>64</td><td>White</td><td>Female</td><td>6.833</td><td>1.472</td><td>131.5</td><td>FALSE</td><td>FALSE</td><td>Previous</td><td>FALSE</td><td>0.06091005</td></tr>\n",
-       "\t<tr><td>6024470</td><td>49</td><td>White</td><td>Female</td><td>6.200</td><td>2.116</td><td>148.0</td><td>FALSE</td><td>FALSE</td><td>Current </td><td> TRUE</td><td>0.03402195</td></tr>\n",
-       "\t<tr><td>6024505</td><td>58</td><td>White</td><td>Male  </td><td>6.068</td><td>1.048</td><td>147.0</td><td>FALSE</td><td>FALSE</td><td>Never   </td><td>FALSE</td><td>0.12637519</td></tr>\n",
-       "\t<tr><td>6024518</td><td>49</td><td>White</td><td>Male  </td><td>6.338</td><td>1.301</td><td>155.5</td><td>FALSE</td><td>FALSE</td><td>Never   </td><td>FALSE</td><td>0.05838098</td></tr>\n",
-       "\t<tr><td>6024543</td><td>64</td><td>White</td><td>Female</td><td>7.986</td><td>1.502</td><td>139.0</td><td>FALSE</td><td>FALSE</td><td>Never   </td><td>FALSE</td><td>0.07322825</td></tr>\n",
-       "\t<tr><td>6024584</td><td>47</td><td>White</td><td>Female</td><td>7.493</td><td>1.945</td><td>128.0</td><td>FALSE</td><td>FALSE</td><td>Current </td><td> TRUE</td><td>0.03404419</td></tr>\n",
-       "\t<tr><td>6024810</td><td>61</td><td>White</td><td>Female</td><td>7.792</td><td>1.694</td><td>145.0</td><td>FALSE</td><td>FALSE</td><td>Current </td><td> TRUE</td><td>0.11267643</td></tr>\n",
-       "\t<tr><td>6024835</td><td>45</td><td>White</td><td>Female</td><td>6.853</td><td>1.539</td><td>135.0</td><td>FALSE</td><td>FALSE</td><td>Never   </td><td>FALSE</td><td>0.01276662</td></tr>\n",
-       "\t<tr><td>6024861</td><td>44</td><td>White</td><td>Female</td><td>5.156</td><td>0.977</td><td>128.0</td><td>FALSE</td><td>FALSE</td><td>Never   </td><td>FALSE</td><td>0.01394681</td></tr>\n",
-       "\t<tr><td>6024883</td><td>49</td><td>White</td><td>Male  </td><td>6.042</td><td>1.241</td><td>127.0</td><td>FALSE</td><td>FALSE</td><td>Never   </td><td>FALSE</td><td>0.04046171</td></tr>\n",
-       "\t<tr><td>6025060</td><td>45</td><td>White</td><td>Male  </td><td>4.907</td><td>1.137</td><td>132.5</td><td>FALSE</td><td>FALSE</td><td>Never   </td><td>FALSE</td><td>0.02311942</td></tr>\n",
-       "\t<tr><td>6025116</td><td>52</td><td>White</td><td>Female</td><td>7.653</td><td>1.604</td><td>167.5</td><td>FALSE</td><td>FALSE</td><td>Never   </td><td>FALSE</td><td>0.03533682</td></tr>\n",
-       "\t<tr><td>6025147</td><td>43</td><td>Asian</td><td>Male  </td><td>4.907</td><td>1.411</td><td>115.0</td><td>FALSE</td><td>FALSE</td><td>Never   </td><td>FALSE</td><td>0.01078727</td></tr>\n",
-       "</tbody>\n",
-       "</table>\n"
-      ],
-      "text/latex": [
-       "A data.table: 395738 × 12\n",
-       "\\begin{tabular}{llllllllllll}\n",
-       " eid & age\\_at\\_recruitment & ethnic\\_background & sex & cholesterol & hdl\\_cholesterol & systolic\\_blood\\_pressure & antihypertensives & diabetes2 & smoking\\_status & current\\_smoker & score\\_ASCVD\\\\\n",
-       " <int> & <dbl> & <chr> & <chr> & <dbl> & <dbl> & <dbl> & <lgl> & <lgl> & <chr> & <lgl> & <dbl>\\\\\n",
-       "\\hline\n",
-       "\t 1000285 & 64 & White & Male   & 4.484 & 0.918 & 135.5 & FALSE & FALSE & Never    & FALSE & 0.143205008\\\\\n",
-       "\t 1000686 & 58 & White & Male   & 7.139 & 1.159 & 147.5 & FALSE & FALSE & Never    & FALSE & 0.137819883\\\\\n",
-       "\t 1000974 & 69 & White & Male   & 4.302 & 1.075 & 154.0 & FALSE &  TRUE & Previous & FALSE & 0.386665084\\\\\n",
-       "\t 1001073 & 64 & White & Female & 6.944 & 1.608 & 148.0 & FALSE & FALSE & Never    & FALSE & 0.073762156\\\\\n",
-       "\t 1001098 & 47 & White & Female & 5.389 & 1.477 & 139.5 & FALSE & FALSE & Never    & FALSE & 0.011394640\\\\\n",
-       "\t 1001119 & 62 & White & Female & 4.833 & 1.461 & 137.5 & FALSE & FALSE & Previous & FALSE & 0.043826887\\\\\n",
-       "\t 1001458 & 59 & White & Male   & 4.879 & 1.097 & 153.0 & FALSE & FALSE & Current  &  TRUE & 0.184407293\\\\\n",
-       "\t 1001517 & 66 & White & Male   & 6.353 & 1.879 & 143.5 & FALSE & FALSE & Previous & FALSE & 0.154071008\\\\\n",
-       "\t 1001591 & 50 & White & Male   & 5.675 & 1.418 & 112.5 & FALSE & FALSE & Never    & FALSE & 0.028630793\\\\\n",
-       "\t 1001741 & 47 & White & Female & 4.880 & 1.157 & 149.0 & FALSE & FALSE & Previous & FALSE & 0.015815727\\\\\n",
-       "\t 1001759 & 44 & White & Female & 6.177 & 1.560 & 110.0 & FALSE & FALSE & Never    & FALSE & 0.006567331\\\\\n",
-       "\t 1002001 & 59 & White & Male   & 6.449 & 1.884 & 114.0 & FALSE & FALSE & Previous & FALSE & 0.060675171\\\\\n",
-       "\t 1002182 & 48 & White & Female & 5.381 & 1.642 & 146.5 & FALSE & FALSE & Never    & FALSE & 0.011755503\\\\\n",
-       "\t 1002213 & 59 & White & Female & 7.881 & 1.497 & 148.0 & FALSE & FALSE & Previous & FALSE & 0.053596257\\\\\n",
-       "\t 1002710 & 40 & White & Female & 4.482 & 1.512 & 115.5 & FALSE & FALSE & Never    & FALSE & 0.003121729\\\\\n",
-       "\t 1002811 & 59 & White & Female & 6.842 & 2.215 & 142.5 & FALSE & FALSE & Never    & FALSE & 0.033436135\\\\\n",
-       "\t 1002977 & 57 & White & Female & 7.024 & 2.850 & 132.5 & FALSE & FALSE & Previous & FALSE & 0.019330514\\\\\n",
-       "\t 1003288 & 70 & White & Female & 7.540 & 1.470 & 157.5 & FALSE & FALSE & Never    & FALSE & 0.153816413\\\\\n",
-       "\t 1003535 & 51 & White & Male   & 6.688 & 0.876 & 145.5 & FALSE & FALSE & Never    & FALSE & 0.099090730\\\\\n",
-       "\t 1003631 & 66 & White & Female & 6.136 & 1.159 & 113.5 & FALSE & FALSE & Previous & FALSE & 0.057791311\\\\\n",
-       "\t 1003650 & 65 & White & Male   & 5.621 & 1.536 & 147.5 & FALSE & FALSE & Never    & FALSE & 0.154145834\\\\\n",
-       "\t 1003884 & 40 & White & Male   & 6.472 & 1.076 & 128.0 & FALSE & FALSE & Previous & FALSE & 0.022764260\\\\\n",
-       "\t 1004007 & 64 & White & Female & 4.113 & 0.705 & 136.0 & FALSE & FALSE & Previous & FALSE & 0.068493283\\\\\n",
-       "\t 1004083 & 60 & White & Female & 8.257 & 1.266 & 160.5 & FALSE & FALSE & Never    & FALSE & 0.078568847\\\\\n",
-       "\t 1004105 & 45 & Black & Male   & 4.359 & 1.375 & 149.0 & FALSE & FALSE & Current  &  TRUE & 0.085530257\\\\\n",
-       "\t 1004333 & 50 & White & Female & 5.167 & 1.327 & 148.0 & FALSE & FALSE & Previous & FALSE & 0.017895970\\\\\n",
-       "\t 1004767 & 47 & White & Male   & 6.171 & 1.050 & 151.0 & FALSE & FALSE & Never    & FALSE & 0.056475791\\\\\n",
-       "\t 1004846 & 47 & White & Male   & 6.321 & 1.487 & 119.0 & FALSE & FALSE & Current  &  TRUE & 0.064303517\\\\\n",
-       "\t 1005011 & 56 & White & Male   & 6.424 & 1.648 & 175.0 & FALSE & FALSE & Never    & FALSE & 0.107297652\\\\\n",
-       "\t 1005089 & 66 & White & Female & 5.108 & 1.447 & 120.5 & FALSE & FALSE & Never    & FALSE & 0.054585602\\\\\n",
-       "\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
-       "\t 6022598 & 66 & White & Female & 6.309 & 1.759 & 160.0 & FALSE & FALSE & Previous & FALSE & 0.09574848\\\\\n",
-       "\t 6022635 & 63 & White & Female & 5.859 & 1.699 & 159.5 & FALSE & FALSE & Previous & FALSE & 0.06756539\\\\\n",
-       "\t 6022700 & 70 & White & Male   & 5.335 & 1.198 & 163.0 & FALSE & FALSE & Never    & FALSE & 0.27527480\\\\\n",
-       "\t 6022819 & 62 & White & Female & 6.593 & 1.560 & 117.5 & FALSE & FALSE & Current  &  TRUE & 0.07620732\\\\\n",
-       "\t 6022826 & 60 & White & Female & 5.140 & 1.425 & 134.0 & FALSE & FALSE & Never    & FALSE & 0.03579095\\\\\n",
-       "\t 6022906 & 60 & White & Female & 7.436 & 1.952 & 131.0 & FALSE & FALSE & Never    & FALSE & 0.03693190\\\\\n",
-       "\t 6022985 & 62 & White & Female & 7.059 & 2.340 & 137.5 & FALSE & FALSE & Previous & FALSE & 0.04326065\\\\\n",
-       "\t 6023018 & 64 & White & Male   & 6.302 & 0.916 & 145.5 & FALSE & FALSE & Previous & FALSE & 0.20415579\\\\\n",
-       "\t 6023051 & 45 & White & Female & 6.580 & 1.404 & 118.5 & FALSE & FALSE & Never    & FALSE & 0.01070025\\\\\n",
-       "\t 6023092 & 61 & White & Female & 6.474 & 1.504 & 136.0 & FALSE & FALSE & Never    & FALSE & 0.04668794\\\\\n",
-       "\t 6023206 & 61 & White & Female & 7.172 & 2.400 & 154.0 & FALSE & FALSE & Previous & FALSE & 0.04775392\\\\\n",
-       "\t 6023285 & 63 & White & Male   & 4.315 & 0.588 & 142.0 & FALSE & FALSE & Previous & FALSE & 0.18297574\\\\\n",
-       "\t 6023334 & 60 & White & Male   & 6.038 & 1.060 & 135.0 & FALSE & FALSE & Never    & FALSE & 0.12451617\\\\\n",
-       "\t 6023815 & 64 & White & Male   & 6.563 & 1.398 & 127.0 & FALSE & FALSE & Never    & FALSE & 0.13225327\\\\\n",
-       "\t 6023859 & 46 & Black & Male   & 4.360 & 1.082 & 141.5 & FALSE & FALSE & Never    & FALSE & 0.05204528\\\\\n",
-       "\t 6023888 & 61 & White & Female & 5.472 & 2.153 & 164.5 & FALSE & FALSE & Previous & FALSE & 0.04765866\\\\\n",
-       "\t 6023974 & 62 & White & Male   & 6.520 & 0.942 & 138.5 & FALSE & FALSE & Previous & FALSE & 0.16982618\\\\\n",
-       "\t 6024246 & 64 & White & Female & 6.833 & 1.472 & 131.5 & FALSE & FALSE & Previous & FALSE & 0.06091005\\\\\n",
-       "\t 6024470 & 49 & White & Female & 6.200 & 2.116 & 148.0 & FALSE & FALSE & Current  &  TRUE & 0.03402195\\\\\n",
-       "\t 6024505 & 58 & White & Male   & 6.068 & 1.048 & 147.0 & FALSE & FALSE & Never    & FALSE & 0.12637519\\\\\n",
-       "\t 6024518 & 49 & White & Male   & 6.338 & 1.301 & 155.5 & FALSE & FALSE & Never    & FALSE & 0.05838098\\\\\n",
-       "\t 6024543 & 64 & White & Female & 7.986 & 1.502 & 139.0 & FALSE & FALSE & Never    & FALSE & 0.07322825\\\\\n",
-       "\t 6024584 & 47 & White & Female & 7.493 & 1.945 & 128.0 & FALSE & FALSE & Current  &  TRUE & 0.03404419\\\\\n",
-       "\t 6024810 & 61 & White & Female & 7.792 & 1.694 & 145.0 & FALSE & FALSE & Current  &  TRUE & 0.11267643\\\\\n",
-       "\t 6024835 & 45 & White & Female & 6.853 & 1.539 & 135.0 & FALSE & FALSE & Never    & FALSE & 0.01276662\\\\\n",
-       "\t 6024861 & 44 & White & Female & 5.156 & 0.977 & 128.0 & FALSE & FALSE & Never    & FALSE & 0.01394681\\\\\n",
-       "\t 6024883 & 49 & White & Male   & 6.042 & 1.241 & 127.0 & FALSE & FALSE & Never    & FALSE & 0.04046171\\\\\n",
-       "\t 6025060 & 45 & White & Male   & 4.907 & 1.137 & 132.5 & FALSE & FALSE & Never    & FALSE & 0.02311942\\\\\n",
-       "\t 6025116 & 52 & White & Female & 7.653 & 1.604 & 167.5 & FALSE & FALSE & Never    & FALSE & 0.03533682\\\\\n",
-       "\t 6025147 & 43 & Asian & Male   & 4.907 & 1.411 & 115.0 & FALSE & FALSE & Never    & FALSE & 0.01078727\\\\\n",
-       "\\end{tabular}\n"
-      ],
-      "text/markdown": [
-       "\n",
-       "A data.table: 395738 × 12\n",
-       "\n",
-       "| eid &lt;int&gt; | age_at_recruitment &lt;dbl&gt; | ethnic_background &lt;chr&gt; | sex &lt;chr&gt; | cholesterol &lt;dbl&gt; | hdl_cholesterol &lt;dbl&gt; | systolic_blood_pressure &lt;dbl&gt; | antihypertensives &lt;lgl&gt; | diabetes2 &lt;lgl&gt; | smoking_status &lt;chr&gt; | current_smoker &lt;lgl&gt; | score_ASCVD &lt;dbl&gt; |\n",
-       "|---|---|---|---|---|---|---|---|---|---|---|---|\n",
-       "| 1000285 | 64 | White | Male   | 4.484 | 0.918 | 135.5 | FALSE | FALSE | Never    | FALSE | 0.143205008 |\n",
-       "| 1000686 | 58 | White | Male   | 7.139 | 1.159 | 147.5 | FALSE | FALSE | Never    | FALSE | 0.137819883 |\n",
-       "| 1000974 | 69 | White | Male   | 4.302 | 1.075 | 154.0 | FALSE |  TRUE | Previous | FALSE | 0.386665084 |\n",
-       "| 1001073 | 64 | White | Female | 6.944 | 1.608 | 148.0 | FALSE | FALSE | Never    | FALSE | 0.073762156 |\n",
-       "| 1001098 | 47 | White | Female | 5.389 | 1.477 | 139.5 | FALSE | FALSE | Never    | FALSE | 0.011394640 |\n",
-       "| 1001119 | 62 | White | Female | 4.833 | 1.461 | 137.5 | FALSE | FALSE | Previous | FALSE | 0.043826887 |\n",
-       "| 1001458 | 59 | White | Male   | 4.879 | 1.097 | 153.0 | FALSE | FALSE | Current  |  TRUE | 0.184407293 |\n",
-       "| 1001517 | 66 | White | Male   | 6.353 | 1.879 | 143.5 | FALSE | FALSE | Previous | FALSE | 0.154071008 |\n",
-       "| 1001591 | 50 | White | Male   | 5.675 | 1.418 | 112.5 | FALSE | FALSE | Never    | FALSE | 0.028630793 |\n",
-       "| 1001741 | 47 | White | Female | 4.880 | 1.157 | 149.0 | FALSE | FALSE | Previous | FALSE | 0.015815727 |\n",
-       "| 1001759 | 44 | White | Female | 6.177 | 1.560 | 110.0 | FALSE | FALSE | Never    | FALSE | 0.006567331 |\n",
-       "| 1002001 | 59 | White | Male   | 6.449 | 1.884 | 114.0 | FALSE | FALSE | Previous | FALSE | 0.060675171 |\n",
-       "| 1002182 | 48 | White | Female | 5.381 | 1.642 | 146.5 | FALSE | FALSE | Never    | FALSE | 0.011755503 |\n",
-       "| 1002213 | 59 | White | Female | 7.881 | 1.497 | 148.0 | FALSE | FALSE | Previous | FALSE | 0.053596257 |\n",
-       "| 1002710 | 40 | White | Female | 4.482 | 1.512 | 115.5 | FALSE | FALSE | Never    | FALSE | 0.003121729 |\n",
-       "| 1002811 | 59 | White | Female | 6.842 | 2.215 | 142.5 | FALSE | FALSE | Never    | FALSE | 0.033436135 |\n",
-       "| 1002977 | 57 | White | Female | 7.024 | 2.850 | 132.5 | FALSE | FALSE | Previous | FALSE | 0.019330514 |\n",
-       "| 1003288 | 70 | White | Female | 7.540 | 1.470 | 157.5 | FALSE | FALSE | Never    | FALSE | 0.153816413 |\n",
-       "| 1003535 | 51 | White | Male   | 6.688 | 0.876 | 145.5 | FALSE | FALSE | Never    | FALSE | 0.099090730 |\n",
-       "| 1003631 | 66 | White | Female | 6.136 | 1.159 | 113.5 | FALSE | FALSE | Previous | FALSE | 0.057791311 |\n",
-       "| 1003650 | 65 | White | Male   | 5.621 | 1.536 | 147.5 | FALSE | FALSE | Never    | FALSE | 0.154145834 |\n",
-       "| 1003884 | 40 | White | Male   | 6.472 | 1.076 | 128.0 | FALSE | FALSE | Previous | FALSE | 0.022764260 |\n",
-       "| 1004007 | 64 | White | Female | 4.113 | 0.705 | 136.0 | FALSE | FALSE | Previous | FALSE | 0.068493283 |\n",
-       "| 1004083 | 60 | White | Female | 8.257 | 1.266 | 160.5 | FALSE | FALSE | Never    | FALSE | 0.078568847 |\n",
-       "| 1004105 | 45 | Black | Male   | 4.359 | 1.375 | 149.0 | FALSE | FALSE | Current  |  TRUE | 0.085530257 |\n",
-       "| 1004333 | 50 | White | Female | 5.167 | 1.327 | 148.0 | FALSE | FALSE | Previous | FALSE | 0.017895970 |\n",
-       "| 1004767 | 47 | White | Male   | 6.171 | 1.050 | 151.0 | FALSE | FALSE | Never    | FALSE | 0.056475791 |\n",
-       "| 1004846 | 47 | White | Male   | 6.321 | 1.487 | 119.0 | FALSE | FALSE | Current  |  TRUE | 0.064303517 |\n",
-       "| 1005011 | 56 | White | Male   | 6.424 | 1.648 | 175.0 | FALSE | FALSE | Never    | FALSE | 0.107297652 |\n",
-       "| 1005089 | 66 | White | Female | 5.108 | 1.447 | 120.5 | FALSE | FALSE | Never    | FALSE | 0.054585602 |\n",
-       "| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |\n",
-       "| 6022598 | 66 | White | Female | 6.309 | 1.759 | 160.0 | FALSE | FALSE | Previous | FALSE | 0.09574848 |\n",
-       "| 6022635 | 63 | White | Female | 5.859 | 1.699 | 159.5 | FALSE | FALSE | Previous | FALSE | 0.06756539 |\n",
-       "| 6022700 | 70 | White | Male   | 5.335 | 1.198 | 163.0 | FALSE | FALSE | Never    | FALSE | 0.27527480 |\n",
-       "| 6022819 | 62 | White | Female | 6.593 | 1.560 | 117.5 | FALSE | FALSE | Current  |  TRUE | 0.07620732 |\n",
-       "| 6022826 | 60 | White | Female | 5.140 | 1.425 | 134.0 | FALSE | FALSE | Never    | FALSE | 0.03579095 |\n",
-       "| 6022906 | 60 | White | Female | 7.436 | 1.952 | 131.0 | FALSE | FALSE | Never    | FALSE | 0.03693190 |\n",
-       "| 6022985 | 62 | White | Female | 7.059 | 2.340 | 137.5 | FALSE | FALSE | Previous | FALSE | 0.04326065 |\n",
-       "| 6023018 | 64 | White | Male   | 6.302 | 0.916 | 145.5 | FALSE | FALSE | Previous | FALSE | 0.20415579 |\n",
-       "| 6023051 | 45 | White | Female | 6.580 | 1.404 | 118.5 | FALSE | FALSE | Never    | FALSE | 0.01070025 |\n",
-       "| 6023092 | 61 | White | Female | 6.474 | 1.504 | 136.0 | FALSE | FALSE | Never    | FALSE | 0.04668794 |\n",
-       "| 6023206 | 61 | White | Female | 7.172 | 2.400 | 154.0 | FALSE | FALSE | Previous | FALSE | 0.04775392 |\n",
-       "| 6023285 | 63 | White | Male   | 4.315 | 0.588 | 142.0 | FALSE | FALSE | Previous | FALSE | 0.18297574 |\n",
-       "| 6023334 | 60 | White | Male   | 6.038 | 1.060 | 135.0 | FALSE | FALSE | Never    | FALSE | 0.12451617 |\n",
-       "| 6023815 | 64 | White | Male   | 6.563 | 1.398 | 127.0 | FALSE | FALSE | Never    | FALSE | 0.13225327 |\n",
-       "| 6023859 | 46 | Black | Male   | 4.360 | 1.082 | 141.5 | FALSE | FALSE | Never    | FALSE | 0.05204528 |\n",
-       "| 6023888 | 61 | White | Female | 5.472 | 2.153 | 164.5 | FALSE | FALSE | Previous | FALSE | 0.04765866 |\n",
-       "| 6023974 | 62 | White | Male   | 6.520 | 0.942 | 138.5 | FALSE | FALSE | Previous | FALSE | 0.16982618 |\n",
-       "| 6024246 | 64 | White | Female | 6.833 | 1.472 | 131.5 | FALSE | FALSE | Previous | FALSE | 0.06091005 |\n",
-       "| 6024470 | 49 | White | Female | 6.200 | 2.116 | 148.0 | FALSE | FALSE | Current  |  TRUE | 0.03402195 |\n",
-       "| 6024505 | 58 | White | Male   | 6.068 | 1.048 | 147.0 | FALSE | FALSE | Never    | FALSE | 0.12637519 |\n",
-       "| 6024518 | 49 | White | Male   | 6.338 | 1.301 | 155.5 | FALSE | FALSE | Never    | FALSE | 0.05838098 |\n",
-       "| 6024543 | 64 | White | Female | 7.986 | 1.502 | 139.0 | FALSE | FALSE | Never    | FALSE | 0.07322825 |\n",
-       "| 6024584 | 47 | White | Female | 7.493 | 1.945 | 128.0 | FALSE | FALSE | Current  |  TRUE | 0.03404419 |\n",
-       "| 6024810 | 61 | White | Female | 7.792 | 1.694 | 145.0 | FALSE | FALSE | Current  |  TRUE | 0.11267643 |\n",
-       "| 6024835 | 45 | White | Female | 6.853 | 1.539 | 135.0 | FALSE | FALSE | Never    | FALSE | 0.01276662 |\n",
-       "| 6024861 | 44 | White | Female | 5.156 | 0.977 | 128.0 | FALSE | FALSE | Never    | FALSE | 0.01394681 |\n",
-       "| 6024883 | 49 | White | Male   | 6.042 | 1.241 | 127.0 | FALSE | FALSE | Never    | FALSE | 0.04046171 |\n",
-       "| 6025060 | 45 | White | Male   | 4.907 | 1.137 | 132.5 | FALSE | FALSE | Never    | FALSE | 0.02311942 |\n",
-       "| 6025116 | 52 | White | Female | 7.653 | 1.604 | 167.5 | FALSE | FALSE | Never    | FALSE | 0.03533682 |\n",
-       "| 6025147 | 43 | Asian | Male   | 4.907 | 1.411 | 115.0 | FALSE | FALSE | Never    | FALSE | 0.01078727 |\n",
-       "\n"
-      ],
-      "text/plain": [
-       "       eid     age_at_recruitment ethnic_background sex    cholesterol\n",
-       "1      1000285 64                 White             Male   4.484      \n",
-       "2      1000686 58                 White             Male   7.139      \n",
-       "3      1000974 69                 White             Male   4.302      \n",
-       "4      1001073 64                 White             Female 6.944      \n",
-       "5      1001098 47                 White             Female 5.389      \n",
-       "6      1001119 62                 White             Female 4.833      \n",
-       "7      1001458 59                 White             Male   4.879      \n",
-       "8      1001517 66                 White             Male   6.353      \n",
-       "9      1001591 50                 White             Male   5.675      \n",
-       "10     1001741 47                 White             Female 4.880      \n",
-       "11     1001759 44                 White             Female 6.177      \n",
-       "12     1002001 59                 White             Male   6.449      \n",
-       "13     1002182 48                 White             Female 5.381      \n",
-       "14     1002213 59                 White             Female 7.881      \n",
-       "15     1002710 40                 White             Female 4.482      \n",
-       "16     1002811 59                 White             Female 6.842      \n",
-       "17     1002977 57                 White             Female 7.024      \n",
-       "18     1003288 70                 White             Female 7.540      \n",
-       "19     1003535 51                 White             Male   6.688      \n",
-       "20     1003631 66                 White             Female 6.136      \n",
-       "21     1003650 65                 White             Male   5.621      \n",
-       "22     1003884 40                 White             Male   6.472      \n",
-       "23     1004007 64                 White             Female 4.113      \n",
-       "24     1004083 60                 White             Female 8.257      \n",
-       "25     1004105 45                 Black             Male   4.359      \n",
-       "26     1004333 50                 White             Female 5.167      \n",
-       "27     1004767 47                 White             Male   6.171      \n",
-       "28     1004846 47                 White             Male   6.321      \n",
-       "29     1005011 56                 White             Male   6.424      \n",
-       "30     1005089 66                 White             Female 5.108      \n",
-       "<U+22EE> <U+22EE> <U+22EE>           <U+22EE>          <U+22EE> <U+22EE>   \n",
-       "395709 6022598 66                 White             Female 6.309      \n",
-       "395710 6022635 63                 White             Female 5.859      \n",
-       "395711 6022700 70                 White             Male   5.335      \n",
-       "395712 6022819 62                 White             Female 6.593      \n",
-       "395713 6022826 60                 White             Female 5.140      \n",
-       "395714 6022906 60                 White             Female 7.436      \n",
-       "395715 6022985 62                 White             Female 7.059      \n",
-       "395716 6023018 64                 White             Male   6.302      \n",
-       "395717 6023051 45                 White             Female 6.580      \n",
-       "395718 6023092 61                 White             Female 6.474      \n",
-       "395719 6023206 61                 White             Female 7.172      \n",
-       "395720 6023285 63                 White             Male   4.315      \n",
-       "395721 6023334 60                 White             Male   6.038      \n",
-       "395722 6023815 64                 White             Male   6.563      \n",
-       "395723 6023859 46                 Black             Male   4.360      \n",
-       "395724 6023888 61                 White             Female 5.472      \n",
-       "395725 6023974 62                 White             Male   6.520      \n",
-       "395726 6024246 64                 White             Female 6.833      \n",
-       "395727 6024470 49                 White             Female 6.200      \n",
-       "395728 6024505 58                 White             Male   6.068      \n",
-       "395729 6024518 49                 White             Male   6.338      \n",
-       "395730 6024543 64                 White             Female 7.986      \n",
-       "395731 6024584 47                 White             Female 7.493      \n",
-       "395732 6024810 61                 White             Female 7.792      \n",
-       "395733 6024835 45                 White             Female 6.853      \n",
-       "395734 6024861 44                 White             Female 5.156      \n",
-       "395735 6024883 49                 White             Male   6.042      \n",
-       "395736 6025060 45                 White             Male   4.907      \n",
-       "395737 6025116 52                 White             Female 7.653      \n",
-       "395738 6025147 43                 Asian             Male   4.907      \n",
-       "       hdl_cholesterol systolic_blood_pressure antihypertensives diabetes2\n",
-       "1      0.918           135.5                   FALSE             FALSE    \n",
-       "2      1.159           147.5                   FALSE             FALSE    \n",
-       "3      1.075           154.0                   FALSE              TRUE    \n",
-       "4      1.608           148.0                   FALSE             FALSE    \n",
-       "5      1.477           139.5                   FALSE             FALSE    \n",
-       "6      1.461           137.5                   FALSE             FALSE    \n",
-       "7      1.097           153.0                   FALSE             FALSE    \n",
-       "8      1.879           143.5                   FALSE             FALSE    \n",
-       "9      1.418           112.5                   FALSE             FALSE    \n",
-       "10     1.157           149.0                   FALSE             FALSE    \n",
-       "11     1.560           110.0                   FALSE             FALSE    \n",
-       "12     1.884           114.0                   FALSE             FALSE    \n",
-       "13     1.642           146.5                   FALSE             FALSE    \n",
-       "14     1.497           148.0                   FALSE             FALSE    \n",
-       "15     1.512           115.5                   FALSE             FALSE    \n",
-       "16     2.215           142.5                   FALSE             FALSE    \n",
-       "17     2.850           132.5                   FALSE             FALSE    \n",
-       "18     1.470           157.5                   FALSE             FALSE    \n",
-       "19     0.876           145.5                   FALSE             FALSE    \n",
-       "20     1.159           113.5                   FALSE             FALSE    \n",
-       "21     1.536           147.5                   FALSE             FALSE    \n",
-       "22     1.076           128.0                   FALSE             FALSE    \n",
-       "23     0.705           136.0                   FALSE             FALSE    \n",
-       "24     1.266           160.5                   FALSE             FALSE    \n",
-       "25     1.375           149.0                   FALSE             FALSE    \n",
-       "26     1.327           148.0                   FALSE             FALSE    \n",
-       "27     1.050           151.0                   FALSE             FALSE    \n",
-       "28     1.487           119.0                   FALSE             FALSE    \n",
-       "29     1.648           175.0                   FALSE             FALSE    \n",
-       "30     1.447           120.5                   FALSE             FALSE    \n",
-       "<U+22EE> <U+22EE>        <U+22EE>                <U+22EE>          <U+22EE> \n",
-       "395709 1.759           160.0                   FALSE             FALSE    \n",
-       "395710 1.699           159.5                   FALSE             FALSE    \n",
-       "395711 1.198           163.0                   FALSE             FALSE    \n",
-       "395712 1.560           117.5                   FALSE             FALSE    \n",
-       "395713 1.425           134.0                   FALSE             FALSE    \n",
-       "395714 1.952           131.0                   FALSE             FALSE    \n",
-       "395715 2.340           137.5                   FALSE             FALSE    \n",
-       "395716 0.916           145.5                   FALSE             FALSE    \n",
-       "395717 1.404           118.5                   FALSE             FALSE    \n",
-       "395718 1.504           136.0                   FALSE             FALSE    \n",
-       "395719 2.400           154.0                   FALSE             FALSE    \n",
-       "395720 0.588           142.0                   FALSE             FALSE    \n",
-       "395721 1.060           135.0                   FALSE             FALSE    \n",
-       "395722 1.398           127.0                   FALSE             FALSE    \n",
-       "395723 1.082           141.5                   FALSE             FALSE    \n",
-       "395724 2.153           164.5                   FALSE             FALSE    \n",
-       "395725 0.942           138.5                   FALSE             FALSE    \n",
-       "395726 1.472           131.5                   FALSE             FALSE    \n",
-       "395727 2.116           148.0                   FALSE             FALSE    \n",
-       "395728 1.048           147.0                   FALSE             FALSE    \n",
-       "395729 1.301           155.5                   FALSE             FALSE    \n",
-       "395730 1.502           139.0                   FALSE             FALSE    \n",
-       "395731 1.945           128.0                   FALSE             FALSE    \n",
-       "395732 1.694           145.0                   FALSE             FALSE    \n",
-       "395733 1.539           135.0                   FALSE             FALSE    \n",
-       "395734 0.977           128.0                   FALSE             FALSE    \n",
-       "395735 1.241           127.0                   FALSE             FALSE    \n",
-       "395736 1.137           132.5                   FALSE             FALSE    \n",
-       "395737 1.604           167.5                   FALSE             FALSE    \n",
-       "395738 1.411           115.0                   FALSE             FALSE    \n",
-       "       smoking_status current_smoker score_ASCVD\n",
-       "1      Never          FALSE          0.143205008\n",
-       "2      Never          FALSE          0.137819883\n",
-       "3      Previous       FALSE          0.386665084\n",
-       "4      Never          FALSE          0.073762156\n",
-       "5      Never          FALSE          0.011394640\n",
-       "6      Previous       FALSE          0.043826887\n",
-       "7      Current         TRUE          0.184407293\n",
-       "8      Previous       FALSE          0.154071008\n",
-       "9      Never          FALSE          0.028630793\n",
-       "10     Previous       FALSE          0.015815727\n",
-       "11     Never          FALSE          0.006567331\n",
-       "12     Previous       FALSE          0.060675171\n",
-       "13     Never          FALSE          0.011755503\n",
-       "14     Previous       FALSE          0.053596257\n",
-       "15     Never          FALSE          0.003121729\n",
-       "16     Never          FALSE          0.033436135\n",
-       "17     Previous       FALSE          0.019330514\n",
-       "18     Never          FALSE          0.153816413\n",
-       "19     Never          FALSE          0.099090730\n",
-       "20     Previous       FALSE          0.057791311\n",
-       "21     Never          FALSE          0.154145834\n",
-       "22     Previous       FALSE          0.022764260\n",
-       "23     Previous       FALSE          0.068493283\n",
-       "24     Never          FALSE          0.078568847\n",
-       "25     Current         TRUE          0.085530257\n",
-       "26     Previous       FALSE          0.017895970\n",
-       "27     Never          FALSE          0.056475791\n",
-       "28     Current         TRUE          0.064303517\n",
-       "29     Never          FALSE          0.107297652\n",
-       "30     Never          FALSE          0.054585602\n",
-       "<U+22EE> <U+22EE>       <U+22EE>       <U+22EE>   \n",
-       "395709 Previous       FALSE          0.09574848 \n",
-       "395710 Previous       FALSE          0.06756539 \n",
-       "395711 Never          FALSE          0.27527480 \n",
-       "395712 Current         TRUE          0.07620732 \n",
-       "395713 Never          FALSE          0.03579095 \n",
-       "395714 Never          FALSE          0.03693190 \n",
-       "395715 Previous       FALSE          0.04326065 \n",
-       "395716 Previous       FALSE          0.20415579 \n",
-       "395717 Never          FALSE          0.01070025 \n",
-       "395718 Never          FALSE          0.04668794 \n",
-       "395719 Previous       FALSE          0.04775392 \n",
-       "395720 Previous       FALSE          0.18297574 \n",
-       "395721 Never          FALSE          0.12451617 \n",
-       "395722 Never          FALSE          0.13225327 \n",
-       "395723 Never          FALSE          0.05204528 \n",
-       "395724 Previous       FALSE          0.04765866 \n",
-       "395725 Previous       FALSE          0.16982618 \n",
-       "395726 Previous       FALSE          0.06091005 \n",
-       "395727 Current         TRUE          0.03402195 \n",
-       "395728 Never          FALSE          0.12637519 \n",
-       "395729 Never          FALSE          0.05838098 \n",
-       "395730 Never          FALSE          0.07322825 \n",
-       "395731 Current         TRUE          0.03404419 \n",
-       "395732 Current         TRUE          0.11267643 \n",
-       "395733 Never          FALSE          0.01276662 \n",
-       "395734 Never          FALSE          0.01394681 \n",
-       "395735 Never          FALSE          0.04046171 \n",
-       "395736 Never          FALSE          0.02311942 \n",
-       "395737 Never          FALSE          0.03533682 \n",
-       "395738 Never          FALSE          0.01078727 "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "temp"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## UK QRISK3 (Hippisley-Cox 2017)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "[Hippisley-Cox 2017](https://www.bmj.com/content/357/bmj.j2099)"
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {},
-   "source": [
-    "Population: 25-84J\n",
-    "Exclusion: Diagnosis of coronary heart disease (Angina/Heart attack) or Stroke/TIA\n",
-    "Endpoints: Composite outcome of CHD, ischemic Stroke or TIA record in GP, Hospital or Mortality records\n",
-    "ICD10: G45, I20, I21, I22, I23, I24, I25, I63, I64"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T09:40:22.037710Z",
-     "start_time": "2020-11-04T09:40:15.383Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "options(warn=-1)\n",
-    "library(QRISK3)\n",
-    "temp = head(data)\n",
-    "temp = as.data.frame(data %>% \n",
-    "                     mutate_if(is.logical, as.integer) %>%\n",
-    "                     mutate(cholesterol_HDL_ratio=cholesterol/hdl_cholesterol, \n",
-    "                            gender=case_when(sex==\"Female\"~1, sex==\"Male\"~0),\n",
-    "                            ethnicity=case_when(ethnic_background == \"White\" ~ 1,\n",
-    "                                                ethnic_background == \"Asian\" ~ 5,\n",
-    "                                                ethnic_background == \"Black\" ~ 1,\n",
-    "                                                ethnic_background == \"Chinese\" ~ 5,\n",
-    "                                                ethnic_background == \"Mixed\" ~ 1,\n",
-    "                                                TRUE ~ 1\n",
-    "                                               ),\n",
-    "                            smoke=case_when(smoking_status == \"Never\" ~ 1,\n",
-    "                                            smoking_status == \"Previous\" ~ 2,\n",
-    "                                            smoking_status == \"Current\" ~ 4,\n",
-    "                                            ),\n",
-    "                            age = as.numeric(age_at_recruitment),\n",
-    "                            std_systolic_blood_pressure = 9.002537727355957,\n",
-    "                           ) #%>% drop_na()\n",
-    "                    )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T09:40:42.773146Z",
-     "start_time": "2020-11-04T09:40:41.164Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "This R package was based on open-sourced original QRISK3-2017 algorithm.\n",
-      "\n",
-      "<https://qrisk.org/three/src.php> Copyright 2017 ClinRisk Ltd.\n",
-      "\n",
-      "\n",
-      "The risk score calculated from this R package can only be used for research purpose.\n",
-      "\n",
-      "\n",
-      "Please refer to QRISK3 website for more information\n",
-      "\n",
-      "<https://qrisk.org/three/index.php>\n",
-      "\n",
-      "\n",
-      "Important: Please double check whether your variables are coded the same as the QRISK3 calculator\n",
-      "\n",
-      "\n",
-      "Height should have unit as (cm)\n",
-      "\n",
-      "Weight should have unit as (kg)\n",
-      "\n",
-      "\n",
-      "Ethiniciy should be coded as: \n",
-      "\n",
-      "   Ethiniciy_category Ethinicity\n",
-      "1 White or not stated          1\n",
-      "2              Indian          2\n",
-      "3           Pakistani          3\n",
-      "4         Bangladeshi          4\n",
-      "5         Other Asian          5\n",
-      "6     Black Caribbean          6\n",
-      "\n",
-      "\n",
-      "Smoke should be coded as: \n",
-      "\n",
-      "               Smoke_category Smoke\n",
-      "1                  non-smoker     1\n",
-      "2                   ex-smoker     2\n",
-      "3 light smoker (less than 10)     3\n",
-      "4  moderate smoker (10 to 19)     4\n",
-      "5   heavy smoker (20 or over)     5\n",
-      "\n",
-      "\n",
-      "The head of result in all patients is:\n",
-      "\n",
-      "\n",
-      "      eid QRISK3_2017 QRISK3_2017_1digit\n",
-      "1 1000285   20.871601               20.9\n",
-      "2 1000686   19.559240               19.6\n",
-      "3 1000974   32.595680               32.6\n",
-      "4 1001073    8.159153                8.2\n",
-      "5 1001098    2.239020                2.2\n",
-      "6 1001119    8.931211                8.9\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "options(warn=0)\n",
-    "QRISK3_df = QRISK3_2017(data = temp, \n",
-    "            patid=\"eid\", \n",
-    "            gender=\"gender\", \n",
-    "            age=\"age\", \n",
-    "            atrial_fibrillation=\"atrial_fibrillation\", \n",
-    "            atypical_antipsy=\"atypical_antipsychotics\", \n",
-    "            regular_steroid_tablets=\"glucocorticoids\",\n",
-    "            erectile_disfunction=\"erectile_dysfunction\",\n",
-    "            migraine=\"migraine\",\n",
-    "            rheumatoid_arthritis=\"rheumatoid_arthritis\",\n",
-    "            chronic_kidney_disease=\"chronic_kidney_disease\",\n",
-    "            severe_mental_illness=\"severe_mental_illness\",\n",
-    "            systemic_lupus_erythematosis=\"systemic_lupus_erythematosus\",\n",
-    "            blood_pressure_treatment=\"antihypertensives\",\n",
-    "            diabetes1=\"diabetes1\",\n",
-    "            diabetes2=\"diabetes2\",\n",
-    "            weight=\"weight\",\n",
-    "            height=\"standing_height\",\n",
-    "            ethiniciy=\"ethnicity\",\n",
-    "            heart_attack_relative=\"fh_heart_disease\",\n",
-    "            cholesterol_HDL_ratio = \"cholesterol_HDL_ratio\",\n",
-    "            systolic_blood_pressure = \"systolic_blood_pressure\",\n",
-    "            std_systolic_blood_pressure = \"std_systolic_blood_pressure\", ### MISSING!\n",
-    "            smoke = \"smoke\",\n",
-    "            townsend = \"townsend_deprivation_index_at_recruitment\")\n",
-    "QRISK3_df = QRISK3_df %>% mutate(score_QRISK3=QRISK3_2017/100) %>% select(c(eid, score_QRISK3)) "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T09:40:44.271222Z",
-     "start_time": "2020-11-04T09:40:43.588Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "395738"
-      ],
-      "text/latex": [
-       "395738"
-      ],
-      "text/markdown": [
-       "395738"
-      ],
-      "text/plain": [
-       "[1] 395738"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "nrow(QRISK3_df)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# JOIN SCORE OUTPUTS"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-11-04T09:41:08.487583Z",
-     "start_time": "2020-11-04T09:41:07.046Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "score_df = SCORE_df %>% left_join(ASCVD_df, by=\"eid\") %>% left_join(QRISK3_df, by=\"eid\") %>% arrange(eid)\n",
-    "score_df %>% write_csv(glue(\"{dataset_path}/predictions_scores.csv\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "'/data/analysis/ag-reils/ag-reils-shared/cardioRS/data/3_datasets_post/210223_cvd_gp/predictions_scores.csv'"
-      ],
-      "text/latex": [
-       "'/data/analysis/ag-reils/ag-reils-shared/cardioRS/data/3\\_datasets\\_post/210223\\_cvd\\_gp/predictions\\_scores.csv'"
-      ],
-      "text/markdown": [
-       "'/data/analysis/ag-reils/ag-reils-shared/cardioRS/data/3_datasets_post/210223_cvd_gp/predictions_scores.csv'"
-      ],
-      "text/plain": [
-       "/data/analysis/ag-reils/ag-reils-shared/cardioRS/data/3_datasets_post/210223_cvd_gp/predictions_scores.csv"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "glue(\"{dataset_path}/predictions_scores.csv\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "'/data/analysis/ag-reils/steinfej/data/2_datasets_pre/210223_cvd_gp/predictions_coxph.csv'"
-      ],
-      "text/latex": [
-       "'/data/analysis/ag-reils/steinfej/data/2\\_datasets\\_pre/210223\\_cvd\\_gp/predictions\\_coxph.csv'"
-      ],
-      "text/markdown": [
-       "'/data/analysis/ag-reils/steinfej/data/2_datasets_pre/210223_cvd_gp/predictions_coxph.csv'"
-      ],
-      "text/plain": [
-       "[1] \"/data/analysis/ag-reils/steinfej/data/2_datasets_pre/210223_cvd_gp/predictions_coxph.csv\""
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "\"/data/analysis/ag-reils/steinfej/data/2_datasets_pre/210223_cvd_gp/predictions_coxph.csv\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "'/data/analysis/ag-reils/steinfej/data/3_datasets_post/210223_cvd_gp/predictions_scores.csv'"
-      ],
-      "text/latex": [
-       "'/data/analysis/ag-reils/steinfej/data/3\\_datasets\\_post/210223\\_cvd\\_gp/predictions\\_scores.csv'"
-      ],
-      "text/markdown": [
-       "'/data/analysis/ag-reils/steinfej/data/3_datasets_post/210223_cvd_gp/predictions_scores.csv'"
-      ],
-      "text/plain": [
-       "[1] \"/data/analysis/ag-reils/steinfej/data/3_datasets_post/210223_cvd_gp/predictions_scores.csv\""
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "'/data/analysis/ag-reils/steinfej/data/3_datasets_post/210223_cvd_gp/predictions_scores.csv'"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "R [conda env:python]",
-   "language": "R",
-   "name": "conda-env-python-r"
-  },
-  "language_info": {
-   "codemirror_mode": "r",
-   "file_extension": ".r",
-   "mimetype": "text/x-r-source",
-   "name": "R",
-   "pygments_lexer": "r",
-   "version": "4.0.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/neuralcvd/preprocessing/ukbb_tabular/8_train_coxph_models_comp.ipynb b/neuralcvd/preprocessing/ukbb_tabular/8_train_coxph_models_comp.ipynb
deleted file mode 100644
index 32ec881..0000000
--- a/neuralcvd/preprocessing/ukbb_tabular/8_train_coxph_models_comp.ipynb
+++ /dev/null
@@ -1,14389 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Cox model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "-- \u001b[1mAttaching packages\u001b[22m --------------------------------------- tidyverse 1.3.0 --\n",
-      "\n",
-      "\u001b[32mv\u001b[39m \u001b[34mggplot2\u001b[39m 3.3.2     \u001b[32mv\u001b[39m \u001b[34mpurrr  \u001b[39m 0.3.4\n",
-      "\u001b[32mv\u001b[39m \u001b[34mtibble \u001b[39m 3.0.3     \u001b[32mv\u001b[39m \u001b[34mdplyr  \u001b[39m 1.0.2\n",
-      "\u001b[32mv\u001b[39m \u001b[34mtidyr  \u001b[39m 1.1.2     \u001b[32mv\u001b[39m \u001b[34mstringr\u001b[39m 1.4.0\n",
-      "\u001b[32mv\u001b[39m \u001b[34mreadr  \u001b[39m 1.3.1     \u001b[32mv\u001b[39m \u001b[34mforcats\u001b[39m 0.5.0\n",
-      "\n",
-      "-- \u001b[1mConflicts\u001b[22m ------------------------------------------ tidyverse_conflicts() --\n",
-      "\u001b[31mx\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mfilter()\u001b[39m masks \u001b[34mstats\u001b[39m::filter()\n",
-      "\u001b[31mx\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mlag()\u001b[39m    masks \u001b[34mstats\u001b[39m::lag()\n",
-      "\n",
-      "riskRegression version 2020.12.08\n",
-      "\n",
-      "\n",
-      "Attaching package: 'glue'\n",
-      "\n",
-      "\n",
-      "The following object is masked from 'package:dplyr':\n",
-      "\n",
-      "    collapse\n",
-      "\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "library(tidyverse)\n",
-    "library(riskRegression)\n",
-    "library(glue)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dataset_name = \"210226_cvd_gp_full_cluster\"\n",
-    "path = \"/data/analysis/ag-reils/steinfej/code/umbrella/pre/ukbb\"\n",
-    "data_path = \"/data/analysis/ag-reils/ag-reils-shared/cardioRS/data\"\n",
-    "dataset_path = glue(\"{data_path}/3_datasets_post/{dataset_name}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "partitions = 0:4\n",
-    "splits = c(\"train\", \"valid\", \"test\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "partition=0; split=\"train\"\n",
-    "data = arrow::read_feather(glue(\"{dataset_path}/partition_{partition}/{split}/data_imputed_normalized.feather\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<table>\n",
-       "<caption>A tibble: 6 × 2996</caption>\n",
-       "<thead>\n",
-       "\t<tr><th scope=col>eid</th><th scope=col>age_at_recruitment</th><th scope=col>sex</th><th scope=col>ethnic_background</th><th scope=col>townsend_deprivation_index_at_recruitment</th><th scope=col>date_of_attending_assessment_centre</th><th scope=col>uk_biobank_assessment_centre</th><th scope=col>birth_date</th><th scope=col>overall_health_rating</th><th scope=col>smoking_status</th><th scope=col>⋯</th><th scope=col>overall_health_rating_3.0</th><th scope=col>smoking_status_0.0</th><th scope=col>smoking_status_1.0</th><th scope=col>smoking_status_2.0</th><th scope=col>alcohol_intake_frequency_0.0</th><th scope=col>alcohol_intake_frequency_1.0</th><th scope=col>alcohol_intake_frequency_2.0</th><th scope=col>alcohol_intake_frequency_3.0</th><th scope=col>alcohol_intake_frequency_4.0</th><th scope=col>alcohol_intake_frequency_5.0</th></tr>\n",
-       "\t<tr><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;date&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;date&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>⋯</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
-       "</thead>\n",
-       "<tbody>\n",
-       "\t<tr><td>1000018</td><td>-0.7865451</td><td>0</td><td>0</td><td>-0.1630698</td><td>2009-11-12</td><td>0</td><td>1960-11-12</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>1</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
-       "\t<tr><td>1000020</td><td> 0.4480444</td><td>1</td><td>0</td><td> 0.5095038</td><td>2008-02-19</td><td>0</td><td>1949-02-19</td><td>1</td><td>0</td><td>⋯</td><td>0</td><td>1</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
-       "\t<tr><td>1000043</td><td> 0.9418802</td><td>1</td><td>0</td><td>-1.3067774</td><td>2009-06-03</td><td>0</td><td>1946-06-03</td><td>0</td><td>1</td><td>⋯</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
-       "\t<tr><td>1000079</td><td> 0.5715033</td><td>0</td><td>0</td><td>-0.4426393</td><td>2008-03-18</td><td>0</td><td>1948-03-18</td><td>0</td><td>2</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>1</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
-       "\t<tr><td>1000084</td><td>-1.5272987</td><td>1</td><td>0</td><td> 2.9163868</td><td>2007-10-18</td><td>0</td><td>1964-10-18</td><td>0</td><td>2</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>1</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
-       "\t<tr><td>1000092</td><td>-0.6630861</td><td>0</td><td>1</td><td> 2.9484531</td><td>2009-06-16</td><td>0</td><td>1959-06-16</td><td>2</td><td>0</td><td>⋯</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td></tr>\n",
-       "</tbody>\n",
-       "</table>\n"
-      ],
-      "text/latex": [
-       "A tibble: 6 × 2996\n",
-       "\\begin{tabular}{lllllllllllllllllllll}\n",
-       " eid & age\\_at\\_recruitment & sex & ethnic\\_background & townsend\\_deprivation\\_index\\_at\\_recruitment & date\\_of\\_attending\\_assessment\\_centre & uk\\_biobank\\_assessment\\_centre & birth\\_date & overall\\_health\\_rating & smoking\\_status & ⋯ & overall\\_health\\_rating\\_3.0 & smoking\\_status\\_0.0 & smoking\\_status\\_1.0 & smoking\\_status\\_2.0 & alcohol\\_intake\\_frequency\\_0.0 & alcohol\\_intake\\_frequency\\_1.0 & alcohol\\_intake\\_frequency\\_2.0 & alcohol\\_intake\\_frequency\\_3.0 & alcohol\\_intake\\_frequency\\_4.0 & alcohol\\_intake\\_frequency\\_5.0\\\\\n",
-       " <int> & <dbl> & <dbl> & <dbl> & <dbl> & <date> & <dbl> & <date> & <dbl> & <dbl> & ⋯ & <int> & <int> & <int> & <int> & <int> & <int> & <int> & <int> & <int> & <int>\\\\\n",
-       "\\hline\n",
-       "\t 1000018 & -0.7865451 & 0 & 0 & -0.1630698 & 2009-11-12 & 0 & 1960-11-12 & 0 & 0 & ⋯ & 0 & 1 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0\\\\\n",
-       "\t 1000020 &  0.4480444 & 1 & 0 &  0.5095038 & 2008-02-19 & 0 & 1949-02-19 & 1 & 0 & ⋯ & 0 & 1 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0\\\\\n",
-       "\t 1000043 &  0.9418802 & 1 & 0 & -1.3067774 & 2009-06-03 & 0 & 1946-06-03 & 0 & 1 & ⋯ & 0 & 0 & 1 & 0 & 0 & 1 & 0 & 0 & 0 & 0\\\\\n",
-       "\t 1000079 &  0.5715033 & 0 & 0 & -0.4426393 & 2008-03-18 & 0 & 1948-03-18 & 0 & 2 & ⋯ & 0 & 0 & 0 & 1 & 1 & 0 & 0 & 0 & 0 & 0\\\\\n",
-       "\t 1000084 & -1.5272987 & 1 & 0 &  2.9163868 & 2007-10-18 & 0 & 1964-10-18 & 0 & 2 & ⋯ & 0 & 0 & 0 & 1 & 1 & 0 & 0 & 0 & 0 & 0\\\\\n",
-       "\t 1000092 & -0.6630861 & 0 & 1 &  2.9484531 & 2009-06-16 & 0 & 1959-06-16 & 2 & 0 & ⋯ & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0\\\\\n",
-       "\\end{tabular}\n"
-      ],
-      "text/markdown": [
-       "\n",
-       "A tibble: 6 × 2996\n",
-       "\n",
-       "| eid &lt;int&gt; | age_at_recruitment &lt;dbl&gt; | sex &lt;dbl&gt; | ethnic_background &lt;dbl&gt; | townsend_deprivation_index_at_recruitment &lt;dbl&gt; | date_of_attending_assessment_centre &lt;date&gt; | uk_biobank_assessment_centre &lt;dbl&gt; | birth_date &lt;date&gt; | overall_health_rating &lt;dbl&gt; | smoking_status &lt;dbl&gt; | ⋯ ⋯ | overall_health_rating_3.0 &lt;int&gt; | smoking_status_0.0 &lt;int&gt; | smoking_status_1.0 &lt;int&gt; | smoking_status_2.0 &lt;int&gt; | alcohol_intake_frequency_0.0 &lt;int&gt; | alcohol_intake_frequency_1.0 &lt;int&gt; | alcohol_intake_frequency_2.0 &lt;int&gt; | alcohol_intake_frequency_3.0 &lt;int&gt; | alcohol_intake_frequency_4.0 &lt;int&gt; | alcohol_intake_frequency_5.0 &lt;int&gt; |\n",
-       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
-       "| 1000018 | -0.7865451 | 0 | 0 | -0.1630698 | 2009-11-12 | 0 | 1960-11-12 | 0 | 0 | ⋯ | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |\n",
-       "| 1000020 |  0.4480444 | 1 | 0 |  0.5095038 | 2008-02-19 | 0 | 1949-02-19 | 1 | 0 | ⋯ | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |\n",
-       "| 1000043 |  0.9418802 | 1 | 0 | -1.3067774 | 2009-06-03 | 0 | 1946-06-03 | 0 | 1 | ⋯ | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |\n",
-       "| 1000079 |  0.5715033 | 0 | 0 | -0.4426393 | 2008-03-18 | 0 | 1948-03-18 | 0 | 2 | ⋯ | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 |\n",
-       "| 1000084 | -1.5272987 | 1 | 0 |  2.9163868 | 2007-10-18 | 0 | 1964-10-18 | 0 | 2 | ⋯ | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 |\n",
-       "| 1000092 | -0.6630861 | 0 | 1 |  2.9484531 | 2009-06-16 | 0 | 1959-06-16 | 2 | 0 | ⋯ | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |\n",
-       "\n"
-      ],
-      "text/plain": [
-       "  eid     age_at_recruitment sex ethnic_background\n",
-       "1 1000018 -0.7865451         0   0                \n",
-       "2 1000020  0.4480444         1   0                \n",
-       "3 1000043  0.9418802         1   0                \n",
-       "4 1000079  0.5715033         0   0                \n",
-       "5 1000084 -1.5272987         1   0                \n",
-       "6 1000092 -0.6630861         0   1                \n",
-       "  townsend_deprivation_index_at_recruitment date_of_attending_assessment_centre\n",
-       "1 -0.1630698                                2009-11-12                         \n",
-       "2  0.5095038                                2008-02-19                         \n",
-       "3 -1.3067774                                2009-06-03                         \n",
-       "4 -0.4426393                                2008-03-18                         \n",
-       "5  2.9163868                                2007-10-18                         \n",
-       "6  2.9484531                                2009-06-16                         \n",
-       "  uk_biobank_assessment_centre birth_date overall_health_rating smoking_status\n",
-       "1 0                            1960-11-12 0                     0             \n",
-       "2 0                            1949-02-19 1                     0             \n",
-       "3 0                            1946-06-03 0                     1             \n",
-       "4 0                            1948-03-18 0                     2             \n",
-       "5 0                            1964-10-18 0                     2             \n",
-       "6 0                            1959-06-16 2                     0             \n",
-       "  <U+22EF>   overall_health_rating_3.0 smoking_status_0.0 smoking_status_1.0\n",
-       "1 <U+22EF> 0                         1                  0                 \n",
-       "2 <U+22EF> 0                         1                  0                 \n",
-       "3 <U+22EF> 0                         0                  1                 \n",
-       "4 <U+22EF> 0                         0                  0                 \n",
-       "5 <U+22EF> 0                         0                  0                 \n",
-       "6 <U+22EF> 0                         1                  0                 \n",
-       "  smoking_status_2.0 alcohol_intake_frequency_0.0 alcohol_intake_frequency_1.0\n",
-       "1 0                  1                            0                           \n",
-       "2 0                  1                            0                           \n",
-       "3 0                  0                            1                           \n",
-       "4 1                  1                            0                           \n",
-       "5 1                  1                            0                           \n",
-       "6 0                  0                            0                           \n",
-       "  alcohol_intake_frequency_2.0 alcohol_intake_frequency_3.0\n",
-       "1 0                            0                           \n",
-       "2 0                            0                           \n",
-       "3 0                            0                           \n",
-       "4 0                            0                           \n",
-       "5 0                            0                           \n",
-       "6 0                            1                           \n",
-       "  alcohol_intake_frequency_4.0 alcohol_intake_frequency_5.0\n",
-       "1 0                            0                           \n",
-       "2 0                            0                           \n",
-       "3 0                            0                           \n",
-       "4 0                            0                           \n",
-       "5 0                            0                           \n",
-       "6 0                            0                           "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "head(data)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "ERROR",
-     "evalue": "Error in parse(text = x, srcfile = src): <text>:1:49: unexpected string constant\n1: data_all = {partition: {split: pd.read_feather(f\"{dataset_path}/partition_{partition}/{split}/data_imputed_normalized.feather\"\n                                                    ^\n",
-     "output_type": "error",
-     "traceback": [
-      "Error in parse(text = x, srcfile = src): <text>:1:49: unexpected string constant\n1: data_all = {partition: {split: pd.read_feather(f\"{dataset_path}/partition_{partition}/{split}/data_imputed_normalized.feather\"\n                                                    ^\nTraceback:\n"
-     ]
-    }
-   ],
-   "source": [
-    "#data_all = {partition: {split: pd.read_feather(f\"{dataset_path}/partition_{partition}/{split}/data_imputed_normalized.feather\").set_index(\"eid\") for split in splits} for partition in tqdm(partitions)}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "basics = c(\n",
-    "'age_at_recruitment',\n",
-    "'ethnic_background_0.0',\n",
-    "'ethnic_background_1.0',\n",
-    "'ethnic_background_2.0',#na 2 -> 5\n",
-    "'ethnic_background_3.0',\n",
-    "'ethnic_background_4.0',\n",
-    "'townsend_deprivation_index_at_recruitment',\n",
-    "'sex'\n",
-    ")\n",
-    "questionnaire = c(\n",
-    "'overall_health_rating_0.0',\n",
-    "'overall_health_rating_1.0',\n",
-    "'overall_health_rating_2.0',\n",
-    "'overall_health_rating_3.0',\n",
-    "'smoking_status_0.0',\n",
-    "'smoking_status_1.0',\n",
-    "'smoking_status_2.0'\n",
-    ")\n",
-    "measurements = c(\n",
-    "'body_mass_index_bmi',\n",
-    "'weight',\n",
-    "\"standing_height\",\n",
-    "'systolic_blood_pressure',\n",
-    "'diastolic_blood_pressure'\n",
-    ")\n",
-    "\n",
-    "labs = c(\n",
-    "\"cholesterol\",\n",
-    "\"hdl_cholesterol\",\n",
-    "\"ldl_direct\",\n",
-    "\"triglycerides\"\n",
-    ")\n",
-    "\n",
-    "family_history = c(\n",
-    "'fh_heart_disease'\n",
-    ")\n",
-    "\n",
-    "diagnoses = c(\n",
-    "'diabetes1',\n",
-    "'diabetes2',\n",
-    "'chronic_kidney_disease',\n",
-    "'atrial_fibrillation',\n",
-    "'migraine',\n",
-    "'rheumatoid_arthritis',\n",
-    "'systemic_lupus_erythematosus',\n",
-    "'severe_mental_illness',\n",
-    "'erectile_dysfunction'\n",
-    ")\n",
-    "\n",
-    "medications = c(\n",
-    "\"antihypertensives\",\n",
-    "\"ass\",\n",
-    "\"atypical_antipsychotics\",\n",
-    "\"glucocorticoids\"\n",
-    ")\n",
-    "\n",
-    "pgs = c('PGS000011', 'PGS000057', 'PGS000058', 'PGS000059')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "features = c()\n",
-    "features[[\"clinical\"]] = c(basics, questionnaire, measurements, labs, family_history, diagnoses, medications)\n",
-    "features[[\"clinical_pgs\"]] = c(features[[\"clinical\"]], pgs)"
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "source": [
-    "basics = [\n",
-    "    'age_at_recruitment',\n",
-    "    'ethnic_background',\n",
-    "    'townsend_deprivation_index_at_recruitment',\n",
-    "    'sex'\n",
-    "]\n",
-    "questionnaire = [\n",
-    "    'overall_health_rating',\n",
-    "    'smoking_status',\n",
-    "\n",
-    "    'alcohol_intake_frequency'\n",
-    "]\n",
-    "measurements = [\n",
-    "    'body_mass_index_bmi',\n",
-    "    'weight',\n",
-    "    \"standing_height\",\n",
-    "    'systolic_blood_pressure',\n",
-    "    'diastolic_blood_pressure',\n",
-    "\n",
-    "    'pulse_wave_arterial_stiffness_index',\n",
-    "    'pulse_wave_reflection_index',\n",
-    "    'waist_circumference',\n",
-    "    'hip_circumference',\n",
-    "    'trunk_fat_percentage',\n",
-    "    'body_fat_percentage',\n",
-    "    'basal_metabolic_rate',\n",
-    "    'forced_vital_capacity_fvc_best_measure',\n",
-    "    'forced_expiratory_volume_in_1second_fev1_best_measure',\n",
-    "    'fev1_fvc_ratio_zscore',\n",
-    "    'peak_expiratory_flow_pef_f3064_0_2',\n",
-    "    'peak_expiratory_flow_pef_f3064_0_1',\n",
-    "    'peak_expiratory_flow_pef',\n",
-    "    'pulse_rate'\n",
-    "]\n",
-    "labs = [\n",
-    "    'cholesterol',\n",
-    "    'hdl_cholesterol',\n",
-    "    'ldl_direct',\n",
-    "    'triglycerides',\n",
-    "\n",
-    "    'basophill_count',\n",
-    "    'basophill_percentage',\n",
-    "    'eosinophill_count',\n",
-    "    'eosinophill_percentage',\n",
-    "    'haematocrit_percentage',\n",
-    "    'haemoglobin_concentration',\n",
-    "    'high_light_scatter_reticulocyte_count',\n",
-    "    'high_light_scatter_reticulocyte_percentage',\n",
-    "    'immature_reticulocyte_fraction',\n",
-    "    'lymphocyte_count',\n",
-    "    'lymphocyte_percentage',\n",
-    "    'mean_corpuscular_haemoglobin',\n",
-    "    'mean_corpuscular_haemoglobin_concentration',\n",
-    "    'mean_corpuscular_volume',\n",
-    "    'mean_platelet_thrombocyte_volume',\n",
-    "    'mean_reticulocyte_volume',\n",
-    "    'mean_sphered_cell_volume',\n",
-    "    'monocyte_count',\n",
-    "    'monocyte_percentage',\n",
-    "    'neutrophill_count',\n",
-    "    'neutrophill_percentage',\n",
-    "    'nucleated_red_blood_cell_count',\n",
-    "    'nucleated_red_blood_cell_percentage',\n",
-    "    'platelet_count',\n",
-    "    'platelet_crit',\n",
-    "    'platelet_distribution_width',\n",
-    "    'red_blood_cell_erythrocyte_count',\n",
-    "    'red_blood_cell_erythrocyte_distribution_width',\n",
-    "    'reticulocyte_count',\n",
-    "    'reticulocyte_percentage',\n",
-    "    'white_blood_cell_leukocyte_count',\n",
-    "    'alanine_aminotransferase',\n",
-    "    'albumin',\n",
-    "    'alkaline_phosphatase',\n",
-    "    'apolipoprotein_a',\n",
-    "    'apolipoprotein_b',\n",
-    "    'aspartate_aminotransferase',\n",
-    "    'creactive_protein',\n",
-    "    'calcium',\n",
-    "    'creatinine',\n",
-    "    'cystatin_c',\n",
-    "    'direct_bilirubin',\n",
-    "    'gamma_glutamyltransferase',\n",
-    "    'glucose',\n",
-    "    'glycated_haemoglobin_hba1c',\n",
-    "    'igf1',\n",
-    "    'lipoprotein_a',\n",
-    "    'oestradiol',\n",
-    "    'phosphate',\n",
-    "    'rheumatoid_factor',\n",
-    "    'shbg',\n",
-    "    'testosterone',\n",
-    "    'total_bilirubin',\n",
-    "    'total_protein',\n",
-    "    'urate',\n",
-    "    'urea',\n",
-    "    'vitamin_d'\n",
-    "]\n",
-    "family_history = [\n",
-    "    'fh_heart_disease',\n",
-    "\n",
-    "    \"fh_alzheimer's_disease/dementia\",\n",
-    "    'fh_bowel_cancer',\n",
-    "    'fh_breast_cancer',\n",
-    "    'fh_chronic_bronchitis/emphysema',\n",
-    "    'fh_diabetes',\n",
-    "    'fh_high_blood_pressure',\n",
-    "    'fh_lung_cancer',\n",
-    "    \"fh_parkinson's_disease\",\n",
-    "    'fh_severe_depression',\n",
-    "    'fh_stroke'\n",
-    "]\n",
-    "diagnoses = [\n",
-    "    'stroke',\n",
-    "    'diabetes1',\n",
-    "    'diabetes2',\n",
-    "    'chronic_kidney_disease',\n",
-    "    'atrial_fibrillation',\n",
-    "    'migraine',\n",
-    "    'rheumatoid_arthritis',\n",
-    "    'systemic_lupus_erythematosus',\n",
-    "    'severe_mental_illness',\n",
-    "    'erectile_dysfunction',\n",
-    "\n",
-    "    'intestinal_infection',\n",
-    "    'bacterial_infection',\n",
-    "    'arthropathies',\n",
-    "    'viral_infection',\n",
-    "    'infections_specific_to_the_perinatal_period',\n",
-    "    'acute_upper_respiratory_infections',\n",
-    "    'mycoses',\n",
-    "    \"kaposi's_sarcoma\",\n",
-    "    'malaise_and_fatigue',\n",
-    "    'malignant_neoplasm_of_skin',\n",
-    "    'benign_neoplasm_of_respiratory_and_intrathoracic_organs',\n",
-    "    'benign_neoplasm_of_skin',\n",
-    "    'nonmalignant_breast_conditions',\n",
-    "    'anemias',\n",
-    "    'other_endocrine_disorders',\n",
-    "    'diabetes',\n",
-    "    'disorders_of_mineral_metabolism',\n",
-    "    'obesity',\n",
-    "    'respiratory_abnormalities',\n",
-    "    'disorders_of_lipoid_metabolism',\n",
-    "    'nonspecific_findings_on_examination_of_blood',\n",
-    "    'sleep_disorders',\n",
-    "    'male_genital_disorders',\n",
-    "    'cerebrovascular_disease',\n",
-    "    'cataract',\n",
-    "    'blindness_and_low_vision',\n",
-    "    'disorders_of_external_ear',\n",
-    "    'heart_valve_disorders',\n",
-    "    'ischemic_heart_disease',\n",
-    "    'cardiac_conduction_disorders',\n",
-    "    'heart_failure',\n",
-    "    'noninfectious_disorders_of_lymphatic_channels',\n",
-    "    'other_symptoms_of_respiratory_system',\n",
-    "    'disorders_of_stomach',\n",
-    "    'abdominal_pain',\n",
-    "    'abdominal_hernia',\n",
-    "    'liver_disease',\n",
-    "    'biliary_tract_disease',\n",
-    "    'bariatric_surgery',\n",
-    "    'complications',\n",
-    "    'other_hypertrophic_and_atrophic_conditions_of_skin',\n",
-    "    'symptoms_affecting_skin',\n",
-    "    'chronic_ulcer_of_skin',\n",
-    "    'gout',\n",
-    "    'osteoarthritis',\n",
-    "    'acquired_deformities',\n",
-    "    'pain_in_joint',\n",
-    "    'back_pain',\n",
-    "    'myalgia_and_myositis_unspecified',\n",
-    "    'symptoms_involving_nervous_and_musculoskeletal_systems',\n",
-    "    'pain_in_limb',\n",
-    "    'musculoskeletal_symptoms_referable_to_limbs',\n",
-    "    'congenital_musculoskeletal_anomalies',\n",
-    "    'other_symptoms/disorders_or_the_urinary_system',\n",
-    "    'urinary_calculus',\n",
-    "    'symptoms_involving_female_genital_tract',\n",
-    "    'other_nonmalignant_breast_conditions',\n",
-    "    'complications_of_the_puerperium',\n",
-    "    'complications_of_pregnancy',\n",
-    "    'postoperative_infection',\n",
-    "    'other_complications_of_pregnancy_nec',\n",
-    "    'excessive_vomiting_in_pregnancy',\n",
-    "    'infectious_and_parasitic_complications_affecting_pregnancy',\n",
-    "    'multiple_gestation',\n",
-    "    'late_pregnancy_and_failed_induction',\n",
-    "    'normal_delivery',\n",
-    "    'anemia_during_pregnancy',\n",
-    "    'maternal_complication_of_pregnancy_affecting_fetus_or_newborn',\n",
-    "    'gangrene',\n",
-    "    'abnormal_sputum',\n",
-    "    'symptoms_involving_head_and_neck',\n",
-    "    'nonspecific_chest_pain',\n",
-    "    'nausea_and_vomiting',\n",
-    "    'nonspecific_findings_on_examination_of_urine',\n",
-    "    'fever',\n",
-    "    'pain',\n",
-    "    'syncope_and_collapse',\n",
-    "    'shock',\n",
-    "    'hypothermia/chills',\n",
-    "    'abnormal_findings_examination_of_lungs',\n",
-    "    'contusion',\n",
-    "    'open_wound',\n",
-    "    'bone_marrow_or_stem_cell_transplant']\n",
-    "\n",
-    "medications= [\n",
-    "    'ass',\n",
-    "    'atypical_antipsychotics',\n",
-    "    'glucocorticoids',\n",
-    "\n",
-    "    'stomatological_preparations',\n",
-    "    'drugs_for_acid_related_disorders',\n",
-    "    'drugs_for_functional_gastrointestinal_disorders',\n",
-    "    'antiemetics_and_antinauseants',\n",
-    "    'bile_and_liver_therapy',\n",
-    "    'drugs_for_constipation',\n",
-    "    'antidiarrheals,_intestinal_antiinflammatory/antiinfective_agents',\n",
-    "    'antiobesity_preparations,_excl._diet_products',\n",
-    "    'digestives,_incl._enzymes',\n",
-    "    'drugs_used_in_diabetes',\n",
-    "    'vitamins',\n",
-    "    'mineral_supplements',\n",
-    "    'tonics',\n",
-    "    'anabolic_agents_for_systemic_use',\n",
-    "    'appetite_stimulants',\n",
-    "    'other_alimentary_tract_and_metabolism_products',\n",
-    "    'antithrombotic_agents',\n",
-    "    'antihemorrhagics',\n",
-    "    'antianemic_preparations',\n",
-    "    'blood_substitutes_and_perfusion_solutions',\n",
-    "    'other_hematological_agents',\n",
-    "    'cardiac_therapy',\n",
-    "    'antihypertensives',\n",
-    "    'diuretics',\n",
-    "    'peripheral_vasodilators',\n",
-    "    'vasoprotectives',\n",
-    "    'beta_blocking_agents',\n",
-    "    'calcium_channel_blockers',\n",
-    "    'agents_acting_on_the_renin-angiotensin_system',\n",
-    "    'lipid_modifying_agents',\n",
-    "    'antifungals_for_dermatological_use',\n",
-    "    'emollients_and_protectives',\n",
-    "    'preparations_for_treatment_of_wounds_and_ulcers',\n",
-    "    'antipruritics,_incl._antihistamines,_anesthetics,_etc.',\n",
-    "    'antipsoriatics',\n",
-    "    'antibiotics_and_chemotherapeutics_for_dermatological_use',\n",
-    "    'corticosteroids,_dermatological_preparations',\n",
-    "    'antiseptics_and_disinfectants',\n",
-    "    'medicated_dressings',\n",
-    "    'anti-acne_preparations',\n",
-    "    'other_dermatological_preparations',\n",
-    "    'gynecological_antiinfectives_and_antiseptics',\n",
-    "    'other_gynecologicals',\n",
-    "    'sex_hormones_and_modulators_of_the_genital_system',\n",
-    "    'urologicals',\n",
-    "    'pituitary_and_hypothalamic_hormones_and_analogues',\n",
-    "    'corticosteroids_for_systemic_use',\n",
-    "    'thyroid_therapy',\n",
-    "    'pancreatic_hormones',\n",
-    "    'calcium_homeostasis',\n",
-    "    'antibacterials_for_systemic_use',\n",
-    "    'antimycotics_for_systemic_use',\n",
-    "    'antimycobacterials',\n",
-    "    'antivirals_for_systemic_use',\n",
-    "    'immune_sera_and_immunoglobulins',\n",
-    "    'vaccines',\n",
-    "    'antineoplastic_agents',\n",
-    "    'endocrine_therapy',\n",
-    "    'immunostimulants',\n",
-    "    'immunosuppressants',\n",
-    "    'antiinflammatory_and_antirheumatic_products',\n",
-    "    'topical_products_for_joint_and_muscular_pain',\n",
-    "    'muscle_relaxants',\n",
-    "    'antigout_preparations',\n",
-    "    'drugs_for_treatment_of_bone_diseases',\n",
-    "    'other_drugs_for_disorders_of_the_musculo-skeletal_system',\n",
-    "    'anesthetics',\n",
-    "    'analgesics',\n",
-    "    'antiepileptics',\n",
-    "    'anti-parkinson_drugs',\n",
-    "    'psycholeptics',\n",
-    "    'psychoanaleptics',\n",
-    "    'other_nervous_system_drugs',\n",
-    "    'antiprotozoals',\n",
-    "    'anthelmintics',\n",
-    "    'ectoparasiticides,_incl._scabicides,_insecticides_and_repellents',\n",
-    "    'nasal_preparations',\n",
-    "    'throat_preparations',\n",
-    "    'drugs_for_obstructive_airway_diseases',\n",
-    "    'cough_and_cold_preparations',\n",
-    "    'antihistamines_for_systemic_use',\n",
-    "    'other_respiratory_system_products',\n",
-    "    'ophthalmologicals',\n",
-    "    'otologicals',\n",
-    "    'ophthalmological_and_otological_preparations',\n",
-    "    'allergens',\n",
-    "    'all_other_therapeutic_products',\n",
-    "    'diagnostic_agents',\n",
-    "    'general_nutrients',\n",
-    "    'all_other_non-therapeutic_products',\n",
-    "    'contrast_media',\n",
-    "    'diagnostic_radiopharmaceuticals',\n",
-    "    'therapeutic_radiopharmaceuticals',\n",
-    "    'surgical_dressings'\n",
-    "]\n",
-    "\n",
-    "feature_dict = {\n",
-    "    \"pgs\": [],\n",
-    "    \"basics\": basics,\n",
-    "    \"questionnaire\": questionnaire,\n",
-    "    \"measurements\": measurements,\n",
-    "    \"labs\": labs,\n",
-    "    \"family_history\": family_history,\n",
-    "    \"medications\": medications,\n",
-    "    \"diagnoses\": diagnoses\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "source": [
-    "features[\"clinical_big\"] = [f for group_list in feature_dict.values() for f in group_list]\n",
-    "features[\"clinical_big_with_pgs\"] = features[\"pgs\"]+[f for group_list in feature_dict.values() for f in group_list]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Predictions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "endpoint = \"MACE\"; event=glue(\"{endpoint}_event\"); time=glue(\"{endpoint}_event_time\")\n",
-    "groups = c(\"clinical\", \"clinical_pgs\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_cox = data %>% select(all_of(c(features[[\"clinical\"]], event, time))) %>% sample_n(10000)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "library(prodlim)\n",
-    "library(survival)\n",
-    "library(cmprsk)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "library(tictoc)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tic(\"starting to fit\")\n",
-    "fg_fit = FGR(reformulate(termlabels = features[[\"clinical\"]], response = glue('Hist({time}, {event})')), \n",
-    "             data=data_cox, cause=1, variance=FALSE)\n",
-    "toc()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "times = 1:27"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "preds = predictRisk(fg_fit, data_cox, times)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<table>\n",
-       "<caption>A matrix: 10000 × 27 of type dbl</caption>\n",
-       "<tbody>\n",
-       "\t<tr><td>0.0023360608</td><td>0.005331257</td><td>0.007918320</td><td>0.011940620</td><td>0.014687471</td><td>0.018234157</td><td>0.022143071</td><td>0.025914614</td><td>0.029720789</td><td>0.033445591</td><td>⋯</td><td>0.039889763</td><td>0.039889763</td><td>0.039889763</td><td>0.039889763</td><td>0.039889763</td><td>0.039889763</td><td>0.039889763</td><td>0.039889763</td><td>0.039889763</td><td>0.039889763</td></tr>\n",
-       "\t<tr><td>0.0084408266</td><td>0.019187663</td><td>0.028402002</td><td>0.042603515</td><td>0.052215023</td><td>0.064521644</td><td>0.077950666</td><td>0.090774902</td><td>0.103585453</td><td>0.115995093</td><td>⋯</td><td>0.137170199</td><td>0.137170199</td><td>0.137170199</td><td>0.137170199</td><td>0.137170199</td><td>0.137170199</td><td>0.137170199</td><td>0.137170199</td><td>0.137170199</td><td>0.137170199</td></tr>\n",
-       "\t<tr><td>0.0021213810</td><td>0.004841993</td><td>0.007192493</td><td>0.010848114</td><td>0.013345343</td><td>0.016570670</td><td>0.020126646</td><td>0.023558894</td><td>0.027023898</td><td>0.030416034</td><td>⋯</td><td>0.036287514</td><td>0.036287514</td><td>0.036287514</td><td>0.036287514</td><td>0.036287514</td><td>0.036287514</td><td>0.036287514</td><td>0.036287514</td><td>0.036287514</td><td>0.036287514</td></tr>\n",
-       "\t<tr><td>0.0129751415</td><td>0.029408795</td><td>0.043421734</td><td>0.064878567</td><td>0.079303248</td><td>0.097657467</td><td>0.117537182</td><td>0.136376047</td><td>0.155051908</td><td>0.173006229</td><td>⋯</td><td>0.203328496</td><td>0.203328496</td><td>0.203328496</td><td>0.203328496</td><td>0.203328496</td><td>0.203328496</td><td>0.203328496</td><td>0.203328496</td><td>0.203328496</td><td>0.203328496</td></tr>\n",
-       "\t<tr><td>0.0102557255</td><td>0.023285989</td><td>0.034433608</td><td>0.051570007</td><td>0.063136747</td><td>0.077909856</td><td>0.093982446</td><td>0.109284048</td><td>0.124522823</td><td>0.139239886</td><td>⋯</td><td>0.164249035</td><td>0.164249035</td><td>0.164249035</td><td>0.164249035</td><td>0.164249035</td><td>0.164249035</td><td>0.164249035</td><td>0.164249035</td><td>0.164249035</td><td>0.164249035</td></tr>\n",
-       "\t<tr><td>0.0283559466</td><td>0.063631924</td><td>0.093149941</td><td>0.137350005</td><td>0.166387605</td><td>0.202552230</td><td>0.240736872</td><td>0.275980335</td><td>0.310018115</td><td>0.341899024</td><td>⋯</td><td>0.393877693</td><td>0.393877693</td><td>0.393877693</td><td>0.393877693</td><td>0.393877693</td><td>0.393877693</td><td>0.393877693</td><td>0.393877693</td><td>0.393877693</td><td>0.393877693</td></tr>\n",
-       "\t<tr><td>0.0033415546</td><td>0.007621018</td><td>0.011312882</td><td>0.017044658</td><td>0.020953144</td><td>0.025992768</td><td>0.031538009</td><td>0.036879324</td><td>0.042260652</td><td>0.047518127</td><td>⋯</td><td>0.056593310</td><td>0.056593310</td><td>0.056593310</td><td>0.056593310</td><td>0.056593310</td><td>0.056593310</td><td>0.056593310</td><td>0.056593310</td><td>0.056593310</td><td>0.056593310</td></tr>\n",
-       "\t<tr><td>0.0034457422</td><td>0.007858109</td><td>0.011664151</td><td>0.017572313</td><td>0.021600460</td><td>0.026793633</td><td>0.032506859</td><td>0.038009015</td><td>0.043551426</td><td>0.048965337</td><td>⋯</td><td>0.058308353</td><td>0.058308353</td><td>0.058308353</td><td>0.058308353</td><td>0.058308353</td><td>0.058308353</td><td>0.058308353</td><td>0.058308353</td><td>0.058308353</td><td>0.058308353</td></tr>\n",
-       "\t<tr><td>0.0023794401</td><td>0.005430104</td><td>0.008064938</td><td>0.012161260</td><td>0.014958482</td><td>0.018569991</td><td>0.022550068</td><td>0.026389994</td><td>0.030264898</td><td>0.034056687</td><td>⋯</td><td>0.040616109</td><td>0.040616109</td><td>0.040616109</td><td>0.040616109</td><td>0.040616109</td><td>0.040616109</td><td>0.040616109</td><td>0.040616109</td><td>0.040616109</td><td>0.040616109</td></tr>\n",
-       "\t<tr><td>0.0022151196</td><td>0.005055644</td><td>0.007509467</td><td>0.011325270</td><td>0.013931565</td><td>0.017297324</td><td>0.021007563</td><td>0.024588143</td><td>0.028202329</td><td>0.031739958</td><td>⋯</td><td>0.037861978</td><td>0.037861978</td><td>0.037861978</td><td>0.037861978</td><td>0.037861978</td><td>0.037861978</td><td>0.037861978</td><td>0.037861978</td><td>0.037861978</td><td>0.037861978</td></tr>\n",
-       "\t<tr><td>0.0043873251</td><td>0.009999351</td><td>0.014834712</td><td>0.022330598</td><td>0.027434177</td><td>0.034005329</td><td>0.041223442</td><td>0.048163863</td><td>0.055144078</td><td>0.061951773</td><td>⋯</td><td>0.073675132</td><td>0.073675132</td><td>0.073675132</td><td>0.073675132</td><td>0.073675132</td><td>0.073675132</td><td>0.073675132</td><td>0.073675132</td><td>0.073675132</td><td>0.073675132</td></tr>\n",
-       "\t<tr><td>0.0022649944</td><td>0.005169309</td><td>0.007678088</td><td>0.011579071</td><td>0.014243351</td><td>0.017683758</td><td>0.021475977</td><td>0.025135364</td><td>0.028828790</td><td>0.032443681</td><td>⋯</td><td>0.038698702</td><td>0.038698702</td><td>0.038698702</td><td>0.038698702</td><td>0.038698702</td><td>0.038698702</td><td>0.038698702</td><td>0.038698702</td><td>0.038698702</td><td>0.038698702</td></tr>\n",
-       "\t<tr><td>0.0076146494</td><td>0.017318828</td><td>0.025647511</td><td>0.038499248</td><td>0.047207853</td><td>0.058371080</td><td>0.070568910</td><td>0.082233702</td><td>0.093902198</td><td>0.105221156</td><td>⋯</td><td>0.124571392</td><td>0.124571392</td><td>0.124571392</td><td>0.124571392</td><td>0.124571392</td><td>0.124571392</td><td>0.124571392</td><td>0.124571392</td><td>0.124571392</td><td>0.124571392</td></tr>\n",
-       "\t<tr><td>0.0073917072</td><td>0.016814183</td><td>0.024903273</td><td>0.037389295</td><td>0.045852869</td><td>0.056705327</td><td>0.068567911</td><td>0.079916387</td><td>0.091272709</td><td>0.102292951</td><td>⋯</td><td>0.121142037</td><td>0.121142037</td><td>0.121142037</td><td>0.121142037</td><td>0.121142037</td><td>0.121142037</td><td>0.121142037</td><td>0.121142037</td><td>0.121142037</td><td>0.121142037</td></tr>\n",
-       "\t<tr><td>0.0005919557</td><td>0.001352452</td><td>0.002010699</td><td>0.003036676</td><td>0.003739115</td><td>0.004648258</td><td>0.005653099</td><td>0.006625474</td><td>0.007609633</td><td>0.008575547</td><td>⋯</td><td>0.010253232</td><td>0.010253232</td><td>0.010253232</td><td>0.010253232</td><td>0.010253232</td><td>0.010253232</td><td>0.010253232</td><td>0.010253232</td><td>0.010253232</td><td>0.010253232</td></tr>\n",
-       "\t<tr><td>0.0012320305</td><td>0.002813684</td><td>0.004181632</td><td>0.006311837</td><td>0.007768923</td><td>0.009653131</td><td>0.011733509</td><td>0.013744507</td><td>0.015777707</td><td>0.017771095</td><td>⋯</td><td>0.021228395</td><td>0.021228395</td><td>0.021228395</td><td>0.021228395</td><td>0.021228395</td><td>0.021228395</td><td>0.021228395</td><td>0.021228395</td><td>0.021228395</td><td>0.021228395</td></tr>\n",
-       "\t<tr><td>0.0075015963</td><td>0.017062943</td><td>0.025270161</td><td>0.037936524</td><td>0.046520949</td><td>0.057526705</td><td>0.069554693</td><td>0.081059266</td><td>0.092569673</td><td>0.103737388</td><td>⋯</td><td>0.122833959</td><td>0.122833959</td><td>0.122833959</td><td>0.122833959</td><td>0.122833959</td><td>0.122833959</td><td>0.122833959</td><td>0.122833959</td><td>0.122833959</td><td>0.122833959</td></tr>\n",
-       "\t<tr><td>0.0091341222</td><td>0.020754372</td><td>0.030709219</td><td>0.046036757</td><td>0.056399731</td><td>0.069655866</td><td>0.084104585</td><td>0.097886398</td><td>0.111637500</td><td>0.124942805</td><td>⋯</td><td>0.147610547</td><td>0.147610547</td><td>0.147610547</td><td>0.147610547</td><td>0.147610547</td><td>0.147610547</td><td>0.147610547</td><td>0.147610547</td><td>0.147610547</td><td>0.147610547</td></tr>\n",
-       "\t<tr><td>0.0039124959</td><td>0.008919873</td><td>0.013236734</td><td>0.019933376</td><td>0.024495973</td><td>0.030374408</td><td>0.036836602</td><td>0.043055162</td><td>0.049314339</td><td>0.055423649</td><td>⋯</td><td>0.065955631</td><td>0.065955631</td><td>0.065955631</td><td>0.065955631</td><td>0.065955631</td><td>0.065955631</td><td>0.065955631</td><td>0.065955631</td><td>0.065955631</td><td>0.065955631</td></tr>\n",
-       "\t<tr><td>0.0074511939</td><td>0.016948850</td><td>0.025101894</td><td>0.037685560</td><td>0.046214573</td><td>0.057150045</td><td>0.069102206</td><td>0.080535228</td><td>0.091975013</td><td>0.103075144</td><td>⋯</td><td>0.122058315</td><td>0.122058315</td><td>0.122058315</td><td>0.122058315</td><td>0.122058315</td><td>0.122058315</td><td>0.122058315</td><td>0.122058315</td><td>0.122058315</td><td>0.122058315</td></tr>\n",
-       "\t<tr><td>0.0157602179</td><td>0.035656998</td><td>0.052565503</td><td>0.078352167</td><td>0.095615996</td><td>0.117498240</td><td>0.141090754</td><td>0.163342188</td><td>0.185297675</td><td>0.206306174</td><td>⋯</td><td>0.241562071</td><td>0.241562071</td><td>0.241562071</td><td>0.241562071</td><td>0.241562071</td><td>0.241562071</td><td>0.241562071</td><td>0.241562071</td><td>0.241562071</td><td>0.241562071</td></tr>\n",
-       "\t<tr><td>0.0004549238</td><td>0.001039464</td><td>0.001545496</td><td>0.002334376</td><td>0.002874595</td><td>0.003573912</td><td>0.004347013</td><td>0.005095309</td><td>0.005852844</td><td>0.006596506</td><td>⋯</td><td>0.007888561</td><td>0.007888561</td><td>0.007888561</td><td>0.007888561</td><td>0.007888561</td><td>0.007888561</td><td>0.007888561</td><td>0.007888561</td><td>0.007888561</td><td>0.007888561</td></tr>\n",
-       "\t<tr><td>0.0026059117</td><td>0.005946066</td><td>0.008830147</td><td>0.013312514</td><td>0.016372336</td><td>0.020321663</td><td>0.024672430</td><td>0.028868393</td><td>0.033100979</td><td>0.037241217</td><td>⋯</td><td>0.044399762</td><td>0.044399762</td><td>0.044399762</td><td>0.044399762</td><td>0.044399762</td><td>0.044399762</td><td>0.044399762</td><td>0.044399762</td><td>0.044399762</td><td>0.044399762</td></tr>\n",
-       "\t<tr><td>0.0016347515</td><td>0.003732443</td><td>0.005545825</td><td>0.008368052</td><td>0.010297349</td><td>0.012790816</td><td>0.015542082</td><td>0.018199794</td><td>0.020885047</td><td>0.023515957</td><td>⋯</td><td>0.028074825</td><td>0.028074825</td><td>0.028074825</td><td>0.028074825</td><td>0.028074825</td><td>0.028074825</td><td>0.028074825</td><td>0.028074825</td><td>0.028074825</td><td>0.028074825</td></tr>\n",
-       "\t<tr><td>0.0028410944</td><td>0.006481716</td><td>0.009624347</td><td>0.014506908</td><td>0.017838764</td><td>0.022137824</td><td>0.026872059</td><td>0.031436038</td><td>0.036038048</td><td>0.040537888</td><td>⋯</td><td>0.048314062</td><td>0.048314062</td><td>0.048314062</td><td>0.048314062</td><td>0.048314062</td><td>0.048314062</td><td>0.048314062</td><td>0.048314062</td><td>0.048314062</td><td>0.048314062</td></tr>\n",
-       "\t<tr><td>0.0019297588</td><td>0.004405164</td><td>0.006544308</td><td>0.009872126</td><td>0.012146066</td><td>0.015083771</td><td>0.018323650</td><td>0.021451808</td><td>0.024610827</td><td>0.027704399</td><td>⋯</td><td>0.033061407</td><td>0.033061407</td><td>0.033061407</td><td>0.033061407</td><td>0.033061407</td><td>0.033061407</td><td>0.033061407</td><td>0.033061407</td><td>0.033061407</td><td>0.033061407</td></tr>\n",
-       "\t<tr><td>0.0027263661</td><td>0.006220432</td><td>0.009236971</td><td>0.013924397</td><td>0.017123632</td><td>0.021252220</td><td>0.025799578</td><td>0.030184247</td><td>0.034606298</td><td>0.038931001</td><td>⋯</td><td>0.046406455</td><td>0.046406455</td><td>0.046406455</td><td>0.046406455</td><td>0.046406455</td><td>0.046406455</td><td>0.046406455</td><td>0.046406455</td><td>0.046406455</td><td>0.046406455</td></tr>\n",
-       "\t<tr><td>0.0013652065</td><td>0.003117562</td><td>0.004632904</td><td>0.006992187</td><td>0.008605650</td><td>0.010691695</td><td>0.012994425</td><td>0.015219862</td><td>0.017469370</td><td>0.019674340</td><td>⋯</td><td>0.023497459</td><td>0.023497459</td><td>0.023497459</td><td>0.023497459</td><td>0.023497459</td><td>0.023497459</td><td>0.023497459</td><td>0.023497459</td><td>0.023497459</td><td>0.023497459</td></tr>\n",
-       "\t<tr><td>0.0013768164</td><td>0.003144050</td><td>0.004672237</td><td>0.007051480</td><td>0.008678566</td><td>0.010782189</td><td>0.013104280</td><td>0.015348384</td><td>0.017616716</td><td>0.019840096</td><td>⋯</td><td>0.023695034</td><td>0.023695034</td><td>0.023695034</td><td>0.023695034</td><td>0.023695034</td><td>0.023695034</td><td>0.023695034</td><td>0.023695034</td><td>0.023695034</td><td>0.023695034</td></tr>\n",
-       "\t<tr><td>0.0074538769</td><td>0.016954923</td><td>0.025110852</td><td>0.037698921</td><td>0.046230883</td><td>0.057170098</td><td>0.069126298</td><td>0.080563130</td><td>0.092006676</td><td>0.103110407</td><td>⋯</td><td>0.122099619</td><td>0.122099619</td><td>0.122099619</td><td>0.122099619</td><td>0.122099619</td><td>0.122099619</td><td>0.122099619</td><td>0.122099619</td><td>0.122099619</td><td>0.122099619</td></tr>\n",
-       "\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋱</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
-       "\t<tr><td>8.902584e-03</td><td>2.023130e-02</td><td>2.993912e-02</td><td>4.489127e-02</td><td>5.500391e-02</td><td>6.794395e-02</td><td>8.205348e-02</td><td>9.551705e-02</td><td>1.089558e-01</td><td>1.219640e-01</td><td>⋯</td><td>1.441371e-01</td><td>1.441371e-01</td><td>1.441371e-01</td><td>1.441371e-01</td><td>1.441371e-01</td><td>1.441371e-01</td><td>1.441371e-01</td><td>1.441371e-01</td><td>1.441371e-01</td><td>1.441371e-01</td></tr>\n",
-       "\t<tr><td>3.183496e-03</td><td>7.261275e-03</td><td>1.077982e-02</td><td>1.624374e-02</td><td>1.997044e-02</td><td>2.477671e-02</td><td>3.006655e-02</td><td>3.516321e-02</td><td>4.029940e-02</td><td>4.531871e-02</td><td>⋯</td><td>5.398586e-02</td><td>5.398586e-02</td><td>5.398586e-02</td><td>5.398586e-02</td><td>5.398586e-02</td><td>5.398586e-02</td><td>5.398586e-02</td><td>5.398586e-02</td><td>5.398586e-02</td><td>5.398586e-02</td></tr>\n",
-       "\t<tr><td>1.356859e-03</td><td>3.098516e-03</td><td>4.604622e-03</td><td>6.949553e-03</td><td>8.553221e-03</td><td>1.062662e-02</td><td>1.291543e-02</td><td>1.512744e-02</td><td>1.736341e-02</td><td>1.955514e-02</td><td>⋯</td><td>2.335538e-02</td><td>2.335538e-02</td><td>2.335538e-02</td><td>2.335538e-02</td><td>2.335538e-02</td><td>2.335538e-02</td><td>2.335538e-02</td><td>2.335538e-02</td><td>2.335538e-02</td><td>2.335538e-02</td></tr>\n",
-       "\t<tr><td>2.551993e-03</td><td>5.823239e-03</td><td>8.648003e-03</td><td>1.303852e-02</td><td>1.603588e-02</td><td>1.990487e-02</td><td>2.416751e-02</td><td>2.827886e-02</td><td>3.242646e-02</td><td>3.648393e-02</td><td>⋯</td><td>4.350022e-02</td><td>4.350022e-02</td><td>4.350022e-02</td><td>4.350022e-02</td><td>4.350022e-02</td><td>4.350022e-02</td><td>4.350022e-02</td><td>4.350022e-02</td><td>4.350022e-02</td><td>4.350022e-02</td></tr>\n",
-       "\t<tr><td>2.181691e-03</td><td>4.979455e-03</td><td>7.396437e-03</td><td>1.115513e-02</td><td>1.372254e-02</td><td>1.703824e-02</td><td>2.069350e-02</td><td>2.422121e-02</td><td>2.778223e-02</td><td>3.126802e-02</td><td>⋯</td><td>3.730078e-02</td><td>3.730078e-02</td><td>3.730078e-02</td><td>3.730078e-02</td><td>3.730078e-02</td><td>3.730078e-02</td><td>3.730078e-02</td><td>3.730078e-02</td><td>3.730078e-02</td><td>3.730078e-02</td></tr>\n",
-       "\t<tr><td>3.988582e-03</td><td>9.092892e-03</td><td>1.349292e-02</td><td>2.031782e-02</td><td>2.496729e-02</td><td>3.095702e-02</td><td>3.754075e-02</td><td>4.387545e-02</td><td>5.025072e-02</td><td>5.647255e-02</td><td>⋯</td><td>6.719666e-02</td><td>6.719666e-02</td><td>6.719666e-02</td><td>6.719666e-02</td><td>6.719666e-02</td><td>6.719666e-02</td><td>6.719666e-02</td><td>6.719666e-02</td><td>6.719666e-02</td><td>6.719666e-02</td></tr>\n",
-       "\t<tr><td>7.047696e-03</td><td>1.603520e-02</td><td>2.375409e-02</td><td>3.567456e-02</td><td>4.375887e-02</td><td>5.412994e-02</td><td>6.547269e-02</td><td>7.633019e-02</td><td>8.720146e-02</td><td>9.775706e-02</td><td>⋯</td><td>1.158255e-01</td><td>1.158255e-01</td><td>1.158255e-01</td><td>1.158255e-01</td><td>1.158255e-01</td><td>1.158255e-01</td><td>1.158255e-01</td><td>1.158255e-01</td><td>1.158255e-01</td><td>1.158255e-01</td></tr>\n",
-       "\t<tr><td>2.108022e-03</td><td>4.811543e-03</td><td>7.147316e-03</td><td>1.078010e-02</td><td>1.326178e-02</td><td>1.646708e-02</td><td>2.000105e-02</td><td>2.341214e-02</td><td>2.685585e-02</td><td>3.022723e-02</td><td>⋯</td><td>3.606294e-02</td><td>3.606294e-02</td><td>3.606294e-02</td><td>3.606294e-02</td><td>3.606294e-02</td><td>3.606294e-02</td><td>3.606294e-02</td><td>3.606294e-02</td><td>3.606294e-02</td><td>3.606294e-02</td></tr>\n",
-       "\t<tr><td>2.086971e-03</td><td>4.763558e-03</td><td>7.076119e-03</td><td>1.067291e-02</td><td>1.313008e-02</td><td>1.630381e-02</td><td>1.980310e-02</td><td>2.318082e-02</td><td>2.659098e-02</td><td>2.992962e-02</td><td>⋯</td><td>3.570894e-02</td><td>3.570894e-02</td><td>3.570894e-02</td><td>3.570894e-02</td><td>3.570894e-02</td><td>3.570894e-02</td><td>3.570894e-02</td><td>3.570894e-02</td><td>3.570894e-02</td><td>3.570894e-02</td></tr>\n",
-       "\t<tr><td>2.316063e-03</td><td>5.285688e-03</td><td>7.850725e-03</td><td>1.183889e-02</td><td>1.456252e-02</td><td>1.807931e-02</td><td>2.195540e-02</td><td>2.569540e-02</td><td>2.946986e-02</td><td>3.316376e-02</td><td>⋯</td><td>3.955475e-02</td><td>3.955475e-02</td><td>3.955475e-02</td><td>3.955475e-02</td><td>3.955475e-02</td><td>3.955475e-02</td><td>3.955475e-02</td><td>3.955475e-02</td><td>3.955475e-02</td><td>3.955475e-02</td></tr>\n",
-       "\t<tr><td>8.696201e-03</td><td>1.976492e-02</td><td>2.925231e-02</td><td>4.386930e-02</td><td>5.375827e-02</td><td>6.641570e-02</td><td>8.022175e-02</td><td>9.340033e-02</td><td>1.065592e-01</td><td>1.193008e-01</td><td>⋯</td><td>1.410298e-01</td><td>1.410298e-01</td><td>1.410298e-01</td><td>1.410298e-01</td><td>1.410298e-01</td><td>1.410298e-01</td><td>1.410298e-01</td><td>1.410298e-01</td><td>1.410298e-01</td><td>1.410298e-01</td></tr>\n",
-       "\t<tr><td>9.594427e-07</td><td>2.192890e-06</td><td>3.261256e-06</td><td>4.927868e-06</td><td>6.069908e-06</td><td>7.549207e-06</td><td>9.185790e-06</td><td>1.077107e-05</td><td>1.237714e-05</td><td>1.395498e-05</td><td>⋯</td><td>1.669916e-05</td><td>1.669916e-05</td><td>1.669916e-05</td><td>1.669916e-05</td><td>1.669916e-05</td><td>1.669916e-05</td><td>1.669916e-05</td><td>1.669916e-05</td><td>1.669916e-05</td><td>1.669916e-05</td></tr>\n",
-       "\t<tr><td>7.805825e-04</td><td>1.783195e-03</td><td>2.650809e-03</td><td>4.002751e-03</td><td>4.928108e-03</td><td>6.125457e-03</td><td>7.448436e-03</td><td>8.728266e-03</td><td>1.002320e-02</td><td>1.129372e-02</td><td>⋯</td><td>1.349955e-02</td><td>1.349955e-02</td><td>1.349955e-02</td><td>1.349955e-02</td><td>1.349955e-02</td><td>1.349955e-02</td><td>1.349955e-02</td><td>1.349955e-02</td><td>1.349955e-02</td><td>1.349955e-02</td></tr>\n",
-       "\t<tr><td>2.613744e-03</td><td>5.963907e-03</td><td>8.856602e-03</td><td>1.335231e-02</td><td>1.642120e-02</td><td>2.038219e-02</td><td>2.474575e-02</td><td>2.895400e-02</td><td>3.319892e-02</td><td>3.735117e-02</td><td>⋯</td><td>4.453036e-02</td><td>4.453036e-02</td><td>4.453036e-02</td><td>4.453036e-02</td><td>4.453036e-02</td><td>4.453036e-02</td><td>4.453036e-02</td><td>4.453036e-02</td><td>4.453036e-02</td><td>4.453036e-02</td></tr>\n",
-       "\t<tr><td>3.947062e-04</td><td>9.019070e-04</td><td>1.341018e-03</td><td>2.025631e-03</td><td>2.494489e-03</td><td>3.101481e-03</td><td>3.772580e-03</td><td>4.422212e-03</td><td>5.079932e-03</td><td>5.725670e-03</td><td>⋯</td><td>6.847744e-03</td><td>6.847744e-03</td><td>6.847744e-03</td><td>6.847744e-03</td><td>6.847744e-03</td><td>6.847744e-03</td><td>6.847744e-03</td><td>6.847744e-03</td><td>6.847744e-03</td><td>6.847744e-03</td></tr>\n",
-       "\t<tr><td>3.335508e-03</td><td>7.607256e-03</td><td>1.129249e-02</td><td>1.701403e-02</td><td>2.091556e-02</td><td>2.594627e-02</td><td>3.148175e-02</td><td>3.681372e-02</td><td>4.218569e-02</td><td>4.743407e-02</td><td>⋯</td><td>5.649368e-02</td><td>5.649368e-02</td><td>5.649368e-02</td><td>5.649368e-02</td><td>5.649368e-02</td><td>5.649368e-02</td><td>5.649368e-02</td><td>5.649368e-02</td><td>5.649368e-02</td><td>5.649368e-02</td></tr>\n",
-       "\t<tr><td>8.407480e-03</td><td>1.911227e-02</td><td>2.829093e-02</td><td>4.243813e-02</td><td>5.201335e-02</td><td>6.427407e-02</td><td>7.765374e-02</td><td>9.043157e-02</td><td>1.031965e-01</td><td>1.155626e-01</td><td>⋯</td><td>1.366650e-01</td><td>1.366650e-01</td><td>1.366650e-01</td><td>1.366650e-01</td><td>1.366650e-01</td><td>1.366650e-01</td><td>1.366650e-01</td><td>1.366650e-01</td><td>1.366650e-01</td><td>1.366650e-01</td></tr>\n",
-       "\t<tr><td>3.266753e-03</td><td>7.450779e-03</td><td>1.106063e-02</td><td>1.666569e-02</td><td>2.048818e-02</td><td>2.541743e-02</td><td>3.084189e-02</td><td>3.606752e-02</td><td>4.133295e-02</td><td>4.647784e-02</td><td>⋯</td><td>5.536018e-02</td><td>5.536018e-02</td><td>5.536018e-02</td><td>5.536018e-02</td><td>5.536018e-02</td><td>5.536018e-02</td><td>5.536018e-02</td><td>5.536018e-02</td><td>5.536018e-02</td><td>5.536018e-02</td></tr>\n",
-       "\t<tr><td>3.492571e-03</td><td>7.964663e-03</td><td>1.182200e-02</td><td>1.780940e-02</td><td>2.189129e-02</td><td>2.715340e-02</td><td>3.294204e-02</td><td>3.851638e-02</td><td>4.413106e-02</td><td>4.961514e-02</td><td>⋯</td><td>5.907824e-02</td><td>5.907824e-02</td><td>5.907824e-02</td><td>5.907824e-02</td><td>5.907824e-02</td><td>5.907824e-02</td><td>5.907824e-02</td><td>5.907824e-02</td><td>5.907824e-02</td><td>5.907824e-02</td></tr>\n",
-       "\t<tr><td>2.916911e-03</td><td>6.654359e-03</td><td>9.880279e-03</td><td>1.489170e-02</td><td>1.831111e-02</td><td>2.272267e-02</td><td>2.758021e-02</td><td>3.226245e-02</td><td>3.698312e-02</td><td>4.159840e-02</td><td>⋯</td><td>4.957269e-02</td><td>4.957269e-02</td><td>4.957269e-02</td><td>4.957269e-02</td><td>4.957269e-02</td><td>4.957269e-02</td><td>4.957269e-02</td><td>4.957269e-02</td><td>4.957269e-02</td><td>4.957269e-02</td></tr>\n",
-       "\t<tr><td>6.090936e-03</td><td>1.386690e-02</td><td>2.055297e-02</td><td>3.089262e-02</td><td>3.791474e-02</td><td>4.693500e-02</td><td>5.681584e-02</td><td>6.628927e-02</td><td>7.578991e-02</td><td>8.502945e-02</td><td>⋯</td><td>1.008794e-01</td><td>1.008794e-01</td><td>1.008794e-01</td><td>1.008794e-01</td><td>1.008794e-01</td><td>1.008794e-01</td><td>1.008794e-01</td><td>1.008794e-01</td><td>1.008794e-01</td><td>1.008794e-01</td></tr>\n",
-       "\t<tr><td>2.142094e-03</td><td>4.889204e-03</td><td>7.262539e-03</td><td>1.095356e-02</td><td>1.347490e-02</td><td>1.673127e-02</td><td>2.032136e-02</td><td>2.378641e-02</td><td>2.728440e-02</td><td>3.070872e-02</td><td>⋯</td><td>3.663562e-02</td><td>3.663562e-02</td><td>3.663562e-02</td><td>3.663562e-02</td><td>3.663562e-02</td><td>3.663562e-02</td><td>3.663562e-02</td><td>3.663562e-02</td><td>3.663562e-02</td><td>3.663562e-02</td></tr>\n",
-       "\t<tr><td>4.550822e-04</td><td>1.039826e-03</td><td>1.546034e-03</td><td>2.335188e-03</td><td>2.875594e-03</td><td>3.575155e-03</td><td>4.348524e-03</td><td>5.097079e-03</td><td>5.854877e-03</td><td>6.598795e-03</td><td>⋯</td><td>7.891298e-03</td><td>7.891298e-03</td><td>7.891298e-03</td><td>7.891298e-03</td><td>7.891298e-03</td><td>7.891298e-03</td><td>7.891298e-03</td><td>7.891298e-03</td><td>7.891298e-03</td><td>7.891298e-03</td></tr>\n",
-       "\t<tr><td>1.614296e-03</td><td>3.685788e-03</td><td>5.476566e-03</td><td>8.263693e-03</td><td>1.016905e-02</td><td>1.263165e-02</td><td>1.534895e-02</td><td>1.797394e-02</td><td>2.062622e-02</td><td>2.322491e-02</td><td>⋯</td><td>2.772817e-02</td><td>2.772817e-02</td><td>2.772817e-02</td><td>2.772817e-02</td><td>2.772817e-02</td><td>2.772817e-02</td><td>2.772817e-02</td><td>2.772817e-02</td><td>2.772817e-02</td><td>2.772817e-02</td></tr>\n",
-       "\t<tr><td>3.181868e-03</td><td>7.257569e-03</td><td>1.077432e-02</td><td>1.623548e-02</td><td>1.996031e-02</td><td>2.476417e-02</td><td>3.005138e-02</td><td>3.514551e-02</td><td>4.027918e-02</td><td>4.529602e-02</td><td>⋯</td><td>5.395897e-02</td><td>5.395897e-02</td><td>5.395897e-02</td><td>5.395897e-02</td><td>5.395897e-02</td><td>5.395897e-02</td><td>5.395897e-02</td><td>5.395897e-02</td><td>5.395897e-02</td><td>5.395897e-02</td></tr>\n",
-       "\t<tr><td>1.457518e-03</td><td>3.328166e-03</td><td>4.945621e-03</td><td>7.463556e-03</td><td>9.185285e-03</td><td>1.141103e-02</td><td>1.386759e-02</td><td>1.624133e-02</td><td>1.864038e-02</td><td>2.099157e-02</td><td>⋯</td><td>2.506737e-02</td><td>2.506737e-02</td><td>2.506737e-02</td><td>2.506737e-02</td><td>2.506737e-02</td><td>2.506737e-02</td><td>2.506737e-02</td><td>2.506737e-02</td><td>2.506737e-02</td><td>2.506737e-02</td></tr>\n",
-       "\t<tr><td>3.704694e-03</td><td>8.447247e-03</td><td>1.253683e-02</td><td>1.888278e-02</td><td>2.320776e-02</td><td>2.878164e-02</td><td>3.491112e-02</td><td>4.081157e-02</td><td>4.675262e-02</td><td>5.255342e-02</td><td>⋯</td><td>6.255827e-02</td><td>6.255827e-02</td><td>6.255827e-02</td><td>6.255827e-02</td><td>6.255827e-02</td><td>6.255827e-02</td><td>6.255827e-02</td><td>6.255827e-02</td><td>6.255827e-02</td><td>6.255827e-02</td></tr>\n",
-       "\t<tr><td>6.887545e-04</td><td>1.573512e-03</td><td>2.339224e-03</td><td>3.532537e-03</td><td>4.349427e-03</td><td>5.406561e-03</td><td>6.574787e-03</td><td>7.705085e-03</td><td>8.848895e-03</td><td>9.971320e-03</td><td>⋯</td><td>1.192042e-02</td><td>1.192042e-02</td><td>1.192042e-02</td><td>1.192042e-02</td><td>1.192042e-02</td><td>1.192042e-02</td><td>1.192042e-02</td><td>1.192042e-02</td><td>1.192042e-02</td><td>1.192042e-02</td></tr>\n",
-       "\t<tr><td>7.715644e-04</td><td>1.762604e-03</td><td>2.620212e-03</td><td>3.956581e-03</td><td>4.871290e-03</td><td>6.054877e-03</td><td>7.362669e-03</td><td>8.627826e-03</td><td>9.907931e-03</td><td>1.116393e-02</td><td>⋯</td><td>1.334458e-02</td><td>1.334458e-02</td><td>1.334458e-02</td><td>1.334458e-02</td><td>1.334458e-02</td><td>1.334458e-02</td><td>1.334458e-02</td><td>1.334458e-02</td><td>1.334458e-02</td><td>1.334458e-02</td></tr>\n",
-       "\t<tr><td>1.443946e-03</td><td>3.297203e-03</td><td>4.899647e-03</td><td>7.394263e-03</td><td>9.100080e-03</td><td>1.130529e-02</td><td>1.373925e-02</td><td>1.609121e-02</td><td>1.846829e-02</td><td>2.079800e-02</td><td>⋯</td><td>2.483669e-02</td><td>2.483669e-02</td><td>2.483669e-02</td><td>2.483669e-02</td><td>2.483669e-02</td><td>2.483669e-02</td><td>2.483669e-02</td><td>2.483669e-02</td><td>2.483669e-02</td><td>2.483669e-02</td></tr>\n",
-       "</tbody>\n",
-       "</table>\n"
-      ],
-      "text/latex": [
-       "A matrix: 10000 × 27 of type dbl\n",
-       "\\begin{tabular}{lllllllllllllllllllll}\n",
-       "\t 0.0023360608 & 0.005331257 & 0.007918320 & 0.011940620 & 0.014687471 & 0.018234157 & 0.022143071 & 0.025914614 & 0.029720789 & 0.033445591 & ⋯ & 0.039889763 & 0.039889763 & 0.039889763 & 0.039889763 & 0.039889763 & 0.039889763 & 0.039889763 & 0.039889763 & 0.039889763 & 0.039889763\\\\\n",
-       "\t 0.0084408266 & 0.019187663 & 0.028402002 & 0.042603515 & 0.052215023 & 0.064521644 & 0.077950666 & 0.090774902 & 0.103585453 & 0.115995093 & ⋯ & 0.137170199 & 0.137170199 & 0.137170199 & 0.137170199 & 0.137170199 & 0.137170199 & 0.137170199 & 0.137170199 & 0.137170199 & 0.137170199\\\\\n",
-       "\t 0.0021213810 & 0.004841993 & 0.007192493 & 0.010848114 & 0.013345343 & 0.016570670 & 0.020126646 & 0.023558894 & 0.027023898 & 0.030416034 & ⋯ & 0.036287514 & 0.036287514 & 0.036287514 & 0.036287514 & 0.036287514 & 0.036287514 & 0.036287514 & 0.036287514 & 0.036287514 & 0.036287514\\\\\n",
-       "\t 0.0129751415 & 0.029408795 & 0.043421734 & 0.064878567 & 0.079303248 & 0.097657467 & 0.117537182 & 0.136376047 & 0.155051908 & 0.173006229 & ⋯ & 0.203328496 & 0.203328496 & 0.203328496 & 0.203328496 & 0.203328496 & 0.203328496 & 0.203328496 & 0.203328496 & 0.203328496 & 0.203328496\\\\\n",
-       "\t 0.0102557255 & 0.023285989 & 0.034433608 & 0.051570007 & 0.063136747 & 0.077909856 & 0.093982446 & 0.109284048 & 0.124522823 & 0.139239886 & ⋯ & 0.164249035 & 0.164249035 & 0.164249035 & 0.164249035 & 0.164249035 & 0.164249035 & 0.164249035 & 0.164249035 & 0.164249035 & 0.164249035\\\\\n",
-       "\t 0.0283559466 & 0.063631924 & 0.093149941 & 0.137350005 & 0.166387605 & 0.202552230 & 0.240736872 & 0.275980335 & 0.310018115 & 0.341899024 & ⋯ & 0.393877693 & 0.393877693 & 0.393877693 & 0.393877693 & 0.393877693 & 0.393877693 & 0.393877693 & 0.393877693 & 0.393877693 & 0.393877693\\\\\n",
-       "\t 0.0033415546 & 0.007621018 & 0.011312882 & 0.017044658 & 0.020953144 & 0.025992768 & 0.031538009 & 0.036879324 & 0.042260652 & 0.047518127 & ⋯ & 0.056593310 & 0.056593310 & 0.056593310 & 0.056593310 & 0.056593310 & 0.056593310 & 0.056593310 & 0.056593310 & 0.056593310 & 0.056593310\\\\\n",
-       "\t 0.0034457422 & 0.007858109 & 0.011664151 & 0.017572313 & 0.021600460 & 0.026793633 & 0.032506859 & 0.038009015 & 0.043551426 & 0.048965337 & ⋯ & 0.058308353 & 0.058308353 & 0.058308353 & 0.058308353 & 0.058308353 & 0.058308353 & 0.058308353 & 0.058308353 & 0.058308353 & 0.058308353\\\\\n",
-       "\t 0.0023794401 & 0.005430104 & 0.008064938 & 0.012161260 & 0.014958482 & 0.018569991 & 0.022550068 & 0.026389994 & 0.030264898 & 0.034056687 & ⋯ & 0.040616109 & 0.040616109 & 0.040616109 & 0.040616109 & 0.040616109 & 0.040616109 & 0.040616109 & 0.040616109 & 0.040616109 & 0.040616109\\\\\n",
-       "\t 0.0022151196 & 0.005055644 & 0.007509467 & 0.011325270 & 0.013931565 & 0.017297324 & 0.021007563 & 0.024588143 & 0.028202329 & 0.031739958 & ⋯ & 0.037861978 & 0.037861978 & 0.037861978 & 0.037861978 & 0.037861978 & 0.037861978 & 0.037861978 & 0.037861978 & 0.037861978 & 0.037861978\\\\\n",
-       "\t 0.0043873251 & 0.009999351 & 0.014834712 & 0.022330598 & 0.027434177 & 0.034005329 & 0.041223442 & 0.048163863 & 0.055144078 & 0.061951773 & ⋯ & 0.073675132 & 0.073675132 & 0.073675132 & 0.073675132 & 0.073675132 & 0.073675132 & 0.073675132 & 0.073675132 & 0.073675132 & 0.073675132\\\\\n",
-       "\t 0.0022649944 & 0.005169309 & 0.007678088 & 0.011579071 & 0.014243351 & 0.017683758 & 0.021475977 & 0.025135364 & 0.028828790 & 0.032443681 & ⋯ & 0.038698702 & 0.038698702 & 0.038698702 & 0.038698702 & 0.038698702 & 0.038698702 & 0.038698702 & 0.038698702 & 0.038698702 & 0.038698702\\\\\n",
-       "\t 0.0076146494 & 0.017318828 & 0.025647511 & 0.038499248 & 0.047207853 & 0.058371080 & 0.070568910 & 0.082233702 & 0.093902198 & 0.105221156 & ⋯ & 0.124571392 & 0.124571392 & 0.124571392 & 0.124571392 & 0.124571392 & 0.124571392 & 0.124571392 & 0.124571392 & 0.124571392 & 0.124571392\\\\\n",
-       "\t 0.0073917072 & 0.016814183 & 0.024903273 & 0.037389295 & 0.045852869 & 0.056705327 & 0.068567911 & 0.079916387 & 0.091272709 & 0.102292951 & ⋯ & 0.121142037 & 0.121142037 & 0.121142037 & 0.121142037 & 0.121142037 & 0.121142037 & 0.121142037 & 0.121142037 & 0.121142037 & 0.121142037\\\\\n",
-       "\t 0.0005919557 & 0.001352452 & 0.002010699 & 0.003036676 & 0.003739115 & 0.004648258 & 0.005653099 & 0.006625474 & 0.007609633 & 0.008575547 & ⋯ & 0.010253232 & 0.010253232 & 0.010253232 & 0.010253232 & 0.010253232 & 0.010253232 & 0.010253232 & 0.010253232 & 0.010253232 & 0.010253232\\\\\n",
-       "\t 0.0012320305 & 0.002813684 & 0.004181632 & 0.006311837 & 0.007768923 & 0.009653131 & 0.011733509 & 0.013744507 & 0.015777707 & 0.017771095 & ⋯ & 0.021228395 & 0.021228395 & 0.021228395 & 0.021228395 & 0.021228395 & 0.021228395 & 0.021228395 & 0.021228395 & 0.021228395 & 0.021228395\\\\\n",
-       "\t 0.0075015963 & 0.017062943 & 0.025270161 & 0.037936524 & 0.046520949 & 0.057526705 & 0.069554693 & 0.081059266 & 0.092569673 & 0.103737388 & ⋯ & 0.122833959 & 0.122833959 & 0.122833959 & 0.122833959 & 0.122833959 & 0.122833959 & 0.122833959 & 0.122833959 & 0.122833959 & 0.122833959\\\\\n",
-       "\t 0.0091341222 & 0.020754372 & 0.030709219 & 0.046036757 & 0.056399731 & 0.069655866 & 0.084104585 & 0.097886398 & 0.111637500 & 0.124942805 & ⋯ & 0.147610547 & 0.147610547 & 0.147610547 & 0.147610547 & 0.147610547 & 0.147610547 & 0.147610547 & 0.147610547 & 0.147610547 & 0.147610547\\\\\n",
-       "\t 0.0039124959 & 0.008919873 & 0.013236734 & 0.019933376 & 0.024495973 & 0.030374408 & 0.036836602 & 0.043055162 & 0.049314339 & 0.055423649 & ⋯ & 0.065955631 & 0.065955631 & 0.065955631 & 0.065955631 & 0.065955631 & 0.065955631 & 0.065955631 & 0.065955631 & 0.065955631 & 0.065955631\\\\\n",
-       "\t 0.0074511939 & 0.016948850 & 0.025101894 & 0.037685560 & 0.046214573 & 0.057150045 & 0.069102206 & 0.080535228 & 0.091975013 & 0.103075144 & ⋯ & 0.122058315 & 0.122058315 & 0.122058315 & 0.122058315 & 0.122058315 & 0.122058315 & 0.122058315 & 0.122058315 & 0.122058315 & 0.122058315\\\\\n",
-       "\t 0.0157602179 & 0.035656998 & 0.052565503 & 0.078352167 & 0.095615996 & 0.117498240 & 0.141090754 & 0.163342188 & 0.185297675 & 0.206306174 & ⋯ & 0.241562071 & 0.241562071 & 0.241562071 & 0.241562071 & 0.241562071 & 0.241562071 & 0.241562071 & 0.241562071 & 0.241562071 & 0.241562071\\\\\n",
-       "\t 0.0004549238 & 0.001039464 & 0.001545496 & 0.002334376 & 0.002874595 & 0.003573912 & 0.004347013 & 0.005095309 & 0.005852844 & 0.006596506 & ⋯ & 0.007888561 & 0.007888561 & 0.007888561 & 0.007888561 & 0.007888561 & 0.007888561 & 0.007888561 & 0.007888561 & 0.007888561 & 0.007888561\\\\\n",
-       "\t 0.0026059117 & 0.005946066 & 0.008830147 & 0.013312514 & 0.016372336 & 0.020321663 & 0.024672430 & 0.028868393 & 0.033100979 & 0.037241217 & ⋯ & 0.044399762 & 0.044399762 & 0.044399762 & 0.044399762 & 0.044399762 & 0.044399762 & 0.044399762 & 0.044399762 & 0.044399762 & 0.044399762\\\\\n",
-       "\t 0.0016347515 & 0.003732443 & 0.005545825 & 0.008368052 & 0.010297349 & 0.012790816 & 0.015542082 & 0.018199794 & 0.020885047 & 0.023515957 & ⋯ & 0.028074825 & 0.028074825 & 0.028074825 & 0.028074825 & 0.028074825 & 0.028074825 & 0.028074825 & 0.028074825 & 0.028074825 & 0.028074825\\\\\n",
-       "\t 0.0028410944 & 0.006481716 & 0.009624347 & 0.014506908 & 0.017838764 & 0.022137824 & 0.026872059 & 0.031436038 & 0.036038048 & 0.040537888 & ⋯ & 0.048314062 & 0.048314062 & 0.048314062 & 0.048314062 & 0.048314062 & 0.048314062 & 0.048314062 & 0.048314062 & 0.048314062 & 0.048314062\\\\\n",
-       "\t 0.0019297588 & 0.004405164 & 0.006544308 & 0.009872126 & 0.012146066 & 0.015083771 & 0.018323650 & 0.021451808 & 0.024610827 & 0.027704399 & ⋯ & 0.033061407 & 0.033061407 & 0.033061407 & 0.033061407 & 0.033061407 & 0.033061407 & 0.033061407 & 0.033061407 & 0.033061407 & 0.033061407\\\\\n",
-       "\t 0.0027263661 & 0.006220432 & 0.009236971 & 0.013924397 & 0.017123632 & 0.021252220 & 0.025799578 & 0.030184247 & 0.034606298 & 0.038931001 & ⋯ & 0.046406455 & 0.046406455 & 0.046406455 & 0.046406455 & 0.046406455 & 0.046406455 & 0.046406455 & 0.046406455 & 0.046406455 & 0.046406455\\\\\n",
-       "\t 0.0013652065 & 0.003117562 & 0.004632904 & 0.006992187 & 0.008605650 & 0.010691695 & 0.012994425 & 0.015219862 & 0.017469370 & 0.019674340 & ⋯ & 0.023497459 & 0.023497459 & 0.023497459 & 0.023497459 & 0.023497459 & 0.023497459 & 0.023497459 & 0.023497459 & 0.023497459 & 0.023497459\\\\\n",
-       "\t 0.0013768164 & 0.003144050 & 0.004672237 & 0.007051480 & 0.008678566 & 0.010782189 & 0.013104280 & 0.015348384 & 0.017616716 & 0.019840096 & ⋯ & 0.023695034 & 0.023695034 & 0.023695034 & 0.023695034 & 0.023695034 & 0.023695034 & 0.023695034 & 0.023695034 & 0.023695034 & 0.023695034\\\\\n",
-       "\t 0.0074538769 & 0.016954923 & 0.025110852 & 0.037698921 & 0.046230883 & 0.057170098 & 0.069126298 & 0.080563130 & 0.092006676 & 0.103110407 & ⋯ & 0.122099619 & 0.122099619 & 0.122099619 & 0.122099619 & 0.122099619 & 0.122099619 & 0.122099619 & 0.122099619 & 0.122099619 & 0.122099619\\\\\n",
-       "\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋱ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
-       "\t 8.902584e-03 & 2.023130e-02 & 2.993912e-02 & 4.489127e-02 & 5.500391e-02 & 6.794395e-02 & 8.205348e-02 & 9.551705e-02 & 1.089558e-01 & 1.219640e-01 & ⋯ & 1.441371e-01 & 1.441371e-01 & 1.441371e-01 & 1.441371e-01 & 1.441371e-01 & 1.441371e-01 & 1.441371e-01 & 1.441371e-01 & 1.441371e-01 & 1.441371e-01\\\\\n",
-       "\t 3.183496e-03 & 7.261275e-03 & 1.077982e-02 & 1.624374e-02 & 1.997044e-02 & 2.477671e-02 & 3.006655e-02 & 3.516321e-02 & 4.029940e-02 & 4.531871e-02 & ⋯ & 5.398586e-02 & 5.398586e-02 & 5.398586e-02 & 5.398586e-02 & 5.398586e-02 & 5.398586e-02 & 5.398586e-02 & 5.398586e-02 & 5.398586e-02 & 5.398586e-02\\\\\n",
-       "\t 1.356859e-03 & 3.098516e-03 & 4.604622e-03 & 6.949553e-03 & 8.553221e-03 & 1.062662e-02 & 1.291543e-02 & 1.512744e-02 & 1.736341e-02 & 1.955514e-02 & ⋯ & 2.335538e-02 & 2.335538e-02 & 2.335538e-02 & 2.335538e-02 & 2.335538e-02 & 2.335538e-02 & 2.335538e-02 & 2.335538e-02 & 2.335538e-02 & 2.335538e-02\\\\\n",
-       "\t 2.551993e-03 & 5.823239e-03 & 8.648003e-03 & 1.303852e-02 & 1.603588e-02 & 1.990487e-02 & 2.416751e-02 & 2.827886e-02 & 3.242646e-02 & 3.648393e-02 & ⋯ & 4.350022e-02 & 4.350022e-02 & 4.350022e-02 & 4.350022e-02 & 4.350022e-02 & 4.350022e-02 & 4.350022e-02 & 4.350022e-02 & 4.350022e-02 & 4.350022e-02\\\\\n",
-       "\t 2.181691e-03 & 4.979455e-03 & 7.396437e-03 & 1.115513e-02 & 1.372254e-02 & 1.703824e-02 & 2.069350e-02 & 2.422121e-02 & 2.778223e-02 & 3.126802e-02 & ⋯ & 3.730078e-02 & 3.730078e-02 & 3.730078e-02 & 3.730078e-02 & 3.730078e-02 & 3.730078e-02 & 3.730078e-02 & 3.730078e-02 & 3.730078e-02 & 3.730078e-02\\\\\n",
-       "\t 3.988582e-03 & 9.092892e-03 & 1.349292e-02 & 2.031782e-02 & 2.496729e-02 & 3.095702e-02 & 3.754075e-02 & 4.387545e-02 & 5.025072e-02 & 5.647255e-02 & ⋯ & 6.719666e-02 & 6.719666e-02 & 6.719666e-02 & 6.719666e-02 & 6.719666e-02 & 6.719666e-02 & 6.719666e-02 & 6.719666e-02 & 6.719666e-02 & 6.719666e-02\\\\\n",
-       "\t 7.047696e-03 & 1.603520e-02 & 2.375409e-02 & 3.567456e-02 & 4.375887e-02 & 5.412994e-02 & 6.547269e-02 & 7.633019e-02 & 8.720146e-02 & 9.775706e-02 & ⋯ & 1.158255e-01 & 1.158255e-01 & 1.158255e-01 & 1.158255e-01 & 1.158255e-01 & 1.158255e-01 & 1.158255e-01 & 1.158255e-01 & 1.158255e-01 & 1.158255e-01\\\\\n",
-       "\t 2.108022e-03 & 4.811543e-03 & 7.147316e-03 & 1.078010e-02 & 1.326178e-02 & 1.646708e-02 & 2.000105e-02 & 2.341214e-02 & 2.685585e-02 & 3.022723e-02 & ⋯ & 3.606294e-02 & 3.606294e-02 & 3.606294e-02 & 3.606294e-02 & 3.606294e-02 & 3.606294e-02 & 3.606294e-02 & 3.606294e-02 & 3.606294e-02 & 3.606294e-02\\\\\n",
-       "\t 2.086971e-03 & 4.763558e-03 & 7.076119e-03 & 1.067291e-02 & 1.313008e-02 & 1.630381e-02 & 1.980310e-02 & 2.318082e-02 & 2.659098e-02 & 2.992962e-02 & ⋯ & 3.570894e-02 & 3.570894e-02 & 3.570894e-02 & 3.570894e-02 & 3.570894e-02 & 3.570894e-02 & 3.570894e-02 & 3.570894e-02 & 3.570894e-02 & 3.570894e-02\\\\\n",
-       "\t 2.316063e-03 & 5.285688e-03 & 7.850725e-03 & 1.183889e-02 & 1.456252e-02 & 1.807931e-02 & 2.195540e-02 & 2.569540e-02 & 2.946986e-02 & 3.316376e-02 & ⋯ & 3.955475e-02 & 3.955475e-02 & 3.955475e-02 & 3.955475e-02 & 3.955475e-02 & 3.955475e-02 & 3.955475e-02 & 3.955475e-02 & 3.955475e-02 & 3.955475e-02\\\\\n",
-       "\t 8.696201e-03 & 1.976492e-02 & 2.925231e-02 & 4.386930e-02 & 5.375827e-02 & 6.641570e-02 & 8.022175e-02 & 9.340033e-02 & 1.065592e-01 & 1.193008e-01 & ⋯ & 1.410298e-01 & 1.410298e-01 & 1.410298e-01 & 1.410298e-01 & 1.410298e-01 & 1.410298e-01 & 1.410298e-01 & 1.410298e-01 & 1.410298e-01 & 1.410298e-01\\\\\n",
-       "\t 9.594427e-07 & 2.192890e-06 & 3.261256e-06 & 4.927868e-06 & 6.069908e-06 & 7.549207e-06 & 9.185790e-06 & 1.077107e-05 & 1.237714e-05 & 1.395498e-05 & ⋯ & 1.669916e-05 & 1.669916e-05 & 1.669916e-05 & 1.669916e-05 & 1.669916e-05 & 1.669916e-05 & 1.669916e-05 & 1.669916e-05 & 1.669916e-05 & 1.669916e-05\\\\\n",
-       "\t 7.805825e-04 & 1.783195e-03 & 2.650809e-03 & 4.002751e-03 & 4.928108e-03 & 6.125457e-03 & 7.448436e-03 & 8.728266e-03 & 1.002320e-02 & 1.129372e-02 & ⋯ & 1.349955e-02 & 1.349955e-02 & 1.349955e-02 & 1.349955e-02 & 1.349955e-02 & 1.349955e-02 & 1.349955e-02 & 1.349955e-02 & 1.349955e-02 & 1.349955e-02\\\\\n",
-       "\t 2.613744e-03 & 5.963907e-03 & 8.856602e-03 & 1.335231e-02 & 1.642120e-02 & 2.038219e-02 & 2.474575e-02 & 2.895400e-02 & 3.319892e-02 & 3.735117e-02 & ⋯ & 4.453036e-02 & 4.453036e-02 & 4.453036e-02 & 4.453036e-02 & 4.453036e-02 & 4.453036e-02 & 4.453036e-02 & 4.453036e-02 & 4.453036e-02 & 4.453036e-02\\\\\n",
-       "\t 3.947062e-04 & 9.019070e-04 & 1.341018e-03 & 2.025631e-03 & 2.494489e-03 & 3.101481e-03 & 3.772580e-03 & 4.422212e-03 & 5.079932e-03 & 5.725670e-03 & ⋯ & 6.847744e-03 & 6.847744e-03 & 6.847744e-03 & 6.847744e-03 & 6.847744e-03 & 6.847744e-03 & 6.847744e-03 & 6.847744e-03 & 6.847744e-03 & 6.847744e-03\\\\\n",
-       "\t 3.335508e-03 & 7.607256e-03 & 1.129249e-02 & 1.701403e-02 & 2.091556e-02 & 2.594627e-02 & 3.148175e-02 & 3.681372e-02 & 4.218569e-02 & 4.743407e-02 & ⋯ & 5.649368e-02 & 5.649368e-02 & 5.649368e-02 & 5.649368e-02 & 5.649368e-02 & 5.649368e-02 & 5.649368e-02 & 5.649368e-02 & 5.649368e-02 & 5.649368e-02\\\\\n",
-       "\t 8.407480e-03 & 1.911227e-02 & 2.829093e-02 & 4.243813e-02 & 5.201335e-02 & 6.427407e-02 & 7.765374e-02 & 9.043157e-02 & 1.031965e-01 & 1.155626e-01 & ⋯ & 1.366650e-01 & 1.366650e-01 & 1.366650e-01 & 1.366650e-01 & 1.366650e-01 & 1.366650e-01 & 1.366650e-01 & 1.366650e-01 & 1.366650e-01 & 1.366650e-01\\\\\n",
-       "\t 3.266753e-03 & 7.450779e-03 & 1.106063e-02 & 1.666569e-02 & 2.048818e-02 & 2.541743e-02 & 3.084189e-02 & 3.606752e-02 & 4.133295e-02 & 4.647784e-02 & ⋯ & 5.536018e-02 & 5.536018e-02 & 5.536018e-02 & 5.536018e-02 & 5.536018e-02 & 5.536018e-02 & 5.536018e-02 & 5.536018e-02 & 5.536018e-02 & 5.536018e-02\\\\\n",
-       "\t 3.492571e-03 & 7.964663e-03 & 1.182200e-02 & 1.780940e-02 & 2.189129e-02 & 2.715340e-02 & 3.294204e-02 & 3.851638e-02 & 4.413106e-02 & 4.961514e-02 & ⋯ & 5.907824e-02 & 5.907824e-02 & 5.907824e-02 & 5.907824e-02 & 5.907824e-02 & 5.907824e-02 & 5.907824e-02 & 5.907824e-02 & 5.907824e-02 & 5.907824e-02\\\\\n",
-       "\t 2.916911e-03 & 6.654359e-03 & 9.880279e-03 & 1.489170e-02 & 1.831111e-02 & 2.272267e-02 & 2.758021e-02 & 3.226245e-02 & 3.698312e-02 & 4.159840e-02 & ⋯ & 4.957269e-02 & 4.957269e-02 & 4.957269e-02 & 4.957269e-02 & 4.957269e-02 & 4.957269e-02 & 4.957269e-02 & 4.957269e-02 & 4.957269e-02 & 4.957269e-02\\\\\n",
-       "\t 6.090936e-03 & 1.386690e-02 & 2.055297e-02 & 3.089262e-02 & 3.791474e-02 & 4.693500e-02 & 5.681584e-02 & 6.628927e-02 & 7.578991e-02 & 8.502945e-02 & ⋯ & 1.008794e-01 & 1.008794e-01 & 1.008794e-01 & 1.008794e-01 & 1.008794e-01 & 1.008794e-01 & 1.008794e-01 & 1.008794e-01 & 1.008794e-01 & 1.008794e-01\\\\\n",
-       "\t 2.142094e-03 & 4.889204e-03 & 7.262539e-03 & 1.095356e-02 & 1.347490e-02 & 1.673127e-02 & 2.032136e-02 & 2.378641e-02 & 2.728440e-02 & 3.070872e-02 & ⋯ & 3.663562e-02 & 3.663562e-02 & 3.663562e-02 & 3.663562e-02 & 3.663562e-02 & 3.663562e-02 & 3.663562e-02 & 3.663562e-02 & 3.663562e-02 & 3.663562e-02\\\\\n",
-       "\t 4.550822e-04 & 1.039826e-03 & 1.546034e-03 & 2.335188e-03 & 2.875594e-03 & 3.575155e-03 & 4.348524e-03 & 5.097079e-03 & 5.854877e-03 & 6.598795e-03 & ⋯ & 7.891298e-03 & 7.891298e-03 & 7.891298e-03 & 7.891298e-03 & 7.891298e-03 & 7.891298e-03 & 7.891298e-03 & 7.891298e-03 & 7.891298e-03 & 7.891298e-03\\\\\n",
-       "\t 1.614296e-03 & 3.685788e-03 & 5.476566e-03 & 8.263693e-03 & 1.016905e-02 & 1.263165e-02 & 1.534895e-02 & 1.797394e-02 & 2.062622e-02 & 2.322491e-02 & ⋯ & 2.772817e-02 & 2.772817e-02 & 2.772817e-02 & 2.772817e-02 & 2.772817e-02 & 2.772817e-02 & 2.772817e-02 & 2.772817e-02 & 2.772817e-02 & 2.772817e-02\\\\\n",
-       "\t 3.181868e-03 & 7.257569e-03 & 1.077432e-02 & 1.623548e-02 & 1.996031e-02 & 2.476417e-02 & 3.005138e-02 & 3.514551e-02 & 4.027918e-02 & 4.529602e-02 & ⋯ & 5.395897e-02 & 5.395897e-02 & 5.395897e-02 & 5.395897e-02 & 5.395897e-02 & 5.395897e-02 & 5.395897e-02 & 5.395897e-02 & 5.395897e-02 & 5.395897e-02\\\\\n",
-       "\t 1.457518e-03 & 3.328166e-03 & 4.945621e-03 & 7.463556e-03 & 9.185285e-03 & 1.141103e-02 & 1.386759e-02 & 1.624133e-02 & 1.864038e-02 & 2.099157e-02 & ⋯ & 2.506737e-02 & 2.506737e-02 & 2.506737e-02 & 2.506737e-02 & 2.506737e-02 & 2.506737e-02 & 2.506737e-02 & 2.506737e-02 & 2.506737e-02 & 2.506737e-02\\\\\n",
-       "\t 3.704694e-03 & 8.447247e-03 & 1.253683e-02 & 1.888278e-02 & 2.320776e-02 & 2.878164e-02 & 3.491112e-02 & 4.081157e-02 & 4.675262e-02 & 5.255342e-02 & ⋯ & 6.255827e-02 & 6.255827e-02 & 6.255827e-02 & 6.255827e-02 & 6.255827e-02 & 6.255827e-02 & 6.255827e-02 & 6.255827e-02 & 6.255827e-02 & 6.255827e-02\\\\\n",
-       "\t 6.887545e-04 & 1.573512e-03 & 2.339224e-03 & 3.532537e-03 & 4.349427e-03 & 5.406561e-03 & 6.574787e-03 & 7.705085e-03 & 8.848895e-03 & 9.971320e-03 & ⋯ & 1.192042e-02 & 1.192042e-02 & 1.192042e-02 & 1.192042e-02 & 1.192042e-02 & 1.192042e-02 & 1.192042e-02 & 1.192042e-02 & 1.192042e-02 & 1.192042e-02\\\\\n",
-       "\t 7.715644e-04 & 1.762604e-03 & 2.620212e-03 & 3.956581e-03 & 4.871290e-03 & 6.054877e-03 & 7.362669e-03 & 8.627826e-03 & 9.907931e-03 & 1.116393e-02 & ⋯ & 1.334458e-02 & 1.334458e-02 & 1.334458e-02 & 1.334458e-02 & 1.334458e-02 & 1.334458e-02 & 1.334458e-02 & 1.334458e-02 & 1.334458e-02 & 1.334458e-02\\\\\n",
-       "\t 1.443946e-03 & 3.297203e-03 & 4.899647e-03 & 7.394263e-03 & 9.100080e-03 & 1.130529e-02 & 1.373925e-02 & 1.609121e-02 & 1.846829e-02 & 2.079800e-02 & ⋯ & 2.483669e-02 & 2.483669e-02 & 2.483669e-02 & 2.483669e-02 & 2.483669e-02 & 2.483669e-02 & 2.483669e-02 & 2.483669e-02 & 2.483669e-02 & 2.483669e-02\\\\\n",
-       "\\end{tabular}\n"
-      ],
-      "text/markdown": [
-       "\n",
-       "A matrix: 10000 × 27 of type dbl\n",
-       "\n",
-       "| 0.0023360608 | 0.005331257 | 0.007918320 | 0.011940620 | 0.014687471 | 0.018234157 | 0.022143071 | 0.025914614 | 0.029720789 | 0.033445591 | ⋯ | 0.039889763 | 0.039889763 | 0.039889763 | 0.039889763 | 0.039889763 | 0.039889763 | 0.039889763 | 0.039889763 | 0.039889763 | 0.039889763 |\n",
-       "| 0.0084408266 | 0.019187663 | 0.028402002 | 0.042603515 | 0.052215023 | 0.064521644 | 0.077950666 | 0.090774902 | 0.103585453 | 0.115995093 | ⋯ | 0.137170199 | 0.137170199 | 0.137170199 | 0.137170199 | 0.137170199 | 0.137170199 | 0.137170199 | 0.137170199 | 0.137170199 | 0.137170199 |\n",
-       "| 0.0021213810 | 0.004841993 | 0.007192493 | 0.010848114 | 0.013345343 | 0.016570670 | 0.020126646 | 0.023558894 | 0.027023898 | 0.030416034 | ⋯ | 0.036287514 | 0.036287514 | 0.036287514 | 0.036287514 | 0.036287514 | 0.036287514 | 0.036287514 | 0.036287514 | 0.036287514 | 0.036287514 |\n",
-       "| 0.0129751415 | 0.029408795 | 0.043421734 | 0.064878567 | 0.079303248 | 0.097657467 | 0.117537182 | 0.136376047 | 0.155051908 | 0.173006229 | ⋯ | 0.203328496 | 0.203328496 | 0.203328496 | 0.203328496 | 0.203328496 | 0.203328496 | 0.203328496 | 0.203328496 | 0.203328496 | 0.203328496 |\n",
-       "| 0.0102557255 | 0.023285989 | 0.034433608 | 0.051570007 | 0.063136747 | 0.077909856 | 0.093982446 | 0.109284048 | 0.124522823 | 0.139239886 | ⋯ | 0.164249035 | 0.164249035 | 0.164249035 | 0.164249035 | 0.164249035 | 0.164249035 | 0.164249035 | 0.164249035 | 0.164249035 | 0.164249035 |\n",
-       "| 0.0283559466 | 0.063631924 | 0.093149941 | 0.137350005 | 0.166387605 | 0.202552230 | 0.240736872 | 0.275980335 | 0.310018115 | 0.341899024 | ⋯ | 0.393877693 | 0.393877693 | 0.393877693 | 0.393877693 | 0.393877693 | 0.393877693 | 0.393877693 | 0.393877693 | 0.393877693 | 0.393877693 |\n",
-       "| 0.0033415546 | 0.007621018 | 0.011312882 | 0.017044658 | 0.020953144 | 0.025992768 | 0.031538009 | 0.036879324 | 0.042260652 | 0.047518127 | ⋯ | 0.056593310 | 0.056593310 | 0.056593310 | 0.056593310 | 0.056593310 | 0.056593310 | 0.056593310 | 0.056593310 | 0.056593310 | 0.056593310 |\n",
-       "| 0.0034457422 | 0.007858109 | 0.011664151 | 0.017572313 | 0.021600460 | 0.026793633 | 0.032506859 | 0.038009015 | 0.043551426 | 0.048965337 | ⋯ | 0.058308353 | 0.058308353 | 0.058308353 | 0.058308353 | 0.058308353 | 0.058308353 | 0.058308353 | 0.058308353 | 0.058308353 | 0.058308353 |\n",
-       "| 0.0023794401 | 0.005430104 | 0.008064938 | 0.012161260 | 0.014958482 | 0.018569991 | 0.022550068 | 0.026389994 | 0.030264898 | 0.034056687 | ⋯ | 0.040616109 | 0.040616109 | 0.040616109 | 0.040616109 | 0.040616109 | 0.040616109 | 0.040616109 | 0.040616109 | 0.040616109 | 0.040616109 |\n",
-       "| 0.0022151196 | 0.005055644 | 0.007509467 | 0.011325270 | 0.013931565 | 0.017297324 | 0.021007563 | 0.024588143 | 0.028202329 | 0.031739958 | ⋯ | 0.037861978 | 0.037861978 | 0.037861978 | 0.037861978 | 0.037861978 | 0.037861978 | 0.037861978 | 0.037861978 | 0.037861978 | 0.037861978 |\n",
-       "| 0.0043873251 | 0.009999351 | 0.014834712 | 0.022330598 | 0.027434177 | 0.034005329 | 0.041223442 | 0.048163863 | 0.055144078 | 0.061951773 | ⋯ | 0.073675132 | 0.073675132 | 0.073675132 | 0.073675132 | 0.073675132 | 0.073675132 | 0.073675132 | 0.073675132 | 0.073675132 | 0.073675132 |\n",
-       "| 0.0022649944 | 0.005169309 | 0.007678088 | 0.011579071 | 0.014243351 | 0.017683758 | 0.021475977 | 0.025135364 | 0.028828790 | 0.032443681 | ⋯ | 0.038698702 | 0.038698702 | 0.038698702 | 0.038698702 | 0.038698702 | 0.038698702 | 0.038698702 | 0.038698702 | 0.038698702 | 0.038698702 |\n",
-       "| 0.0076146494 | 0.017318828 | 0.025647511 | 0.038499248 | 0.047207853 | 0.058371080 | 0.070568910 | 0.082233702 | 0.093902198 | 0.105221156 | ⋯ | 0.124571392 | 0.124571392 | 0.124571392 | 0.124571392 | 0.124571392 | 0.124571392 | 0.124571392 | 0.124571392 | 0.124571392 | 0.124571392 |\n",
-       "| 0.0073917072 | 0.016814183 | 0.024903273 | 0.037389295 | 0.045852869 | 0.056705327 | 0.068567911 | 0.079916387 | 0.091272709 | 0.102292951 | ⋯ | 0.121142037 | 0.121142037 | 0.121142037 | 0.121142037 | 0.121142037 | 0.121142037 | 0.121142037 | 0.121142037 | 0.121142037 | 0.121142037 |\n",
-       "| 0.0005919557 | 0.001352452 | 0.002010699 | 0.003036676 | 0.003739115 | 0.004648258 | 0.005653099 | 0.006625474 | 0.007609633 | 0.008575547 | ⋯ | 0.010253232 | 0.010253232 | 0.010253232 | 0.010253232 | 0.010253232 | 0.010253232 | 0.010253232 | 0.010253232 | 0.010253232 | 0.010253232 |\n",
-       "| 0.0012320305 | 0.002813684 | 0.004181632 | 0.006311837 | 0.007768923 | 0.009653131 | 0.011733509 | 0.013744507 | 0.015777707 | 0.017771095 | ⋯ | 0.021228395 | 0.021228395 | 0.021228395 | 0.021228395 | 0.021228395 | 0.021228395 | 0.021228395 | 0.021228395 | 0.021228395 | 0.021228395 |\n",
-       "| 0.0075015963 | 0.017062943 | 0.025270161 | 0.037936524 | 0.046520949 | 0.057526705 | 0.069554693 | 0.081059266 | 0.092569673 | 0.103737388 | ⋯ | 0.122833959 | 0.122833959 | 0.122833959 | 0.122833959 | 0.122833959 | 0.122833959 | 0.122833959 | 0.122833959 | 0.122833959 | 0.122833959 |\n",
-       "| 0.0091341222 | 0.020754372 | 0.030709219 | 0.046036757 | 0.056399731 | 0.069655866 | 0.084104585 | 0.097886398 | 0.111637500 | 0.124942805 | ⋯ | 0.147610547 | 0.147610547 | 0.147610547 | 0.147610547 | 0.147610547 | 0.147610547 | 0.147610547 | 0.147610547 | 0.147610547 | 0.147610547 |\n",
-       "| 0.0039124959 | 0.008919873 | 0.013236734 | 0.019933376 | 0.024495973 | 0.030374408 | 0.036836602 | 0.043055162 | 0.049314339 | 0.055423649 | ⋯ | 0.065955631 | 0.065955631 | 0.065955631 | 0.065955631 | 0.065955631 | 0.065955631 | 0.065955631 | 0.065955631 | 0.065955631 | 0.065955631 |\n",
-       "| 0.0074511939 | 0.016948850 | 0.025101894 | 0.037685560 | 0.046214573 | 0.057150045 | 0.069102206 | 0.080535228 | 0.091975013 | 0.103075144 | ⋯ | 0.122058315 | 0.122058315 | 0.122058315 | 0.122058315 | 0.122058315 | 0.122058315 | 0.122058315 | 0.122058315 | 0.122058315 | 0.122058315 |\n",
-       "| 0.0157602179 | 0.035656998 | 0.052565503 | 0.078352167 | 0.095615996 | 0.117498240 | 0.141090754 | 0.163342188 | 0.185297675 | 0.206306174 | ⋯ | 0.241562071 | 0.241562071 | 0.241562071 | 0.241562071 | 0.241562071 | 0.241562071 | 0.241562071 | 0.241562071 | 0.241562071 | 0.241562071 |\n",
-       "| 0.0004549238 | 0.001039464 | 0.001545496 | 0.002334376 | 0.002874595 | 0.003573912 | 0.004347013 | 0.005095309 | 0.005852844 | 0.006596506 | ⋯ | 0.007888561 | 0.007888561 | 0.007888561 | 0.007888561 | 0.007888561 | 0.007888561 | 0.007888561 | 0.007888561 | 0.007888561 | 0.007888561 |\n",
-       "| 0.0026059117 | 0.005946066 | 0.008830147 | 0.013312514 | 0.016372336 | 0.020321663 | 0.024672430 | 0.028868393 | 0.033100979 | 0.037241217 | ⋯ | 0.044399762 | 0.044399762 | 0.044399762 | 0.044399762 | 0.044399762 | 0.044399762 | 0.044399762 | 0.044399762 | 0.044399762 | 0.044399762 |\n",
-       "| 0.0016347515 | 0.003732443 | 0.005545825 | 0.008368052 | 0.010297349 | 0.012790816 | 0.015542082 | 0.018199794 | 0.020885047 | 0.023515957 | ⋯ | 0.028074825 | 0.028074825 | 0.028074825 | 0.028074825 | 0.028074825 | 0.028074825 | 0.028074825 | 0.028074825 | 0.028074825 | 0.028074825 |\n",
-       "| 0.0028410944 | 0.006481716 | 0.009624347 | 0.014506908 | 0.017838764 | 0.022137824 | 0.026872059 | 0.031436038 | 0.036038048 | 0.040537888 | ⋯ | 0.048314062 | 0.048314062 | 0.048314062 | 0.048314062 | 0.048314062 | 0.048314062 | 0.048314062 | 0.048314062 | 0.048314062 | 0.048314062 |\n",
-       "| 0.0019297588 | 0.004405164 | 0.006544308 | 0.009872126 | 0.012146066 | 0.015083771 | 0.018323650 | 0.021451808 | 0.024610827 | 0.027704399 | ⋯ | 0.033061407 | 0.033061407 | 0.033061407 | 0.033061407 | 0.033061407 | 0.033061407 | 0.033061407 | 0.033061407 | 0.033061407 | 0.033061407 |\n",
-       "| 0.0027263661 | 0.006220432 | 0.009236971 | 0.013924397 | 0.017123632 | 0.021252220 | 0.025799578 | 0.030184247 | 0.034606298 | 0.038931001 | ⋯ | 0.046406455 | 0.046406455 | 0.046406455 | 0.046406455 | 0.046406455 | 0.046406455 | 0.046406455 | 0.046406455 | 0.046406455 | 0.046406455 |\n",
-       "| 0.0013652065 | 0.003117562 | 0.004632904 | 0.006992187 | 0.008605650 | 0.010691695 | 0.012994425 | 0.015219862 | 0.017469370 | 0.019674340 | ⋯ | 0.023497459 | 0.023497459 | 0.023497459 | 0.023497459 | 0.023497459 | 0.023497459 | 0.023497459 | 0.023497459 | 0.023497459 | 0.023497459 |\n",
-       "| 0.0013768164 | 0.003144050 | 0.004672237 | 0.007051480 | 0.008678566 | 0.010782189 | 0.013104280 | 0.015348384 | 0.017616716 | 0.019840096 | ⋯ | 0.023695034 | 0.023695034 | 0.023695034 | 0.023695034 | 0.023695034 | 0.023695034 | 0.023695034 | 0.023695034 | 0.023695034 | 0.023695034 |\n",
-       "| 0.0074538769 | 0.016954923 | 0.025110852 | 0.037698921 | 0.046230883 | 0.057170098 | 0.069126298 | 0.080563130 | 0.092006676 | 0.103110407 | ⋯ | 0.122099619 | 0.122099619 | 0.122099619 | 0.122099619 | 0.122099619 | 0.122099619 | 0.122099619 | 0.122099619 | 0.122099619 | 0.122099619 |\n",
-       "| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋱ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |\n",
-       "| 8.902584e-03 | 2.023130e-02 | 2.993912e-02 | 4.489127e-02 | 5.500391e-02 | 6.794395e-02 | 8.205348e-02 | 9.551705e-02 | 1.089558e-01 | 1.219640e-01 | ⋯ | 1.441371e-01 | 1.441371e-01 | 1.441371e-01 | 1.441371e-01 | 1.441371e-01 | 1.441371e-01 | 1.441371e-01 | 1.441371e-01 | 1.441371e-01 | 1.441371e-01 |\n",
-       "| 3.183496e-03 | 7.261275e-03 | 1.077982e-02 | 1.624374e-02 | 1.997044e-02 | 2.477671e-02 | 3.006655e-02 | 3.516321e-02 | 4.029940e-02 | 4.531871e-02 | ⋯ | 5.398586e-02 | 5.398586e-02 | 5.398586e-02 | 5.398586e-02 | 5.398586e-02 | 5.398586e-02 | 5.398586e-02 | 5.398586e-02 | 5.398586e-02 | 5.398586e-02 |\n",
-       "| 1.356859e-03 | 3.098516e-03 | 4.604622e-03 | 6.949553e-03 | 8.553221e-03 | 1.062662e-02 | 1.291543e-02 | 1.512744e-02 | 1.736341e-02 | 1.955514e-02 | ⋯ | 2.335538e-02 | 2.335538e-02 | 2.335538e-02 | 2.335538e-02 | 2.335538e-02 | 2.335538e-02 | 2.335538e-02 | 2.335538e-02 | 2.335538e-02 | 2.335538e-02 |\n",
-       "| 2.551993e-03 | 5.823239e-03 | 8.648003e-03 | 1.303852e-02 | 1.603588e-02 | 1.990487e-02 | 2.416751e-02 | 2.827886e-02 | 3.242646e-02 | 3.648393e-02 | ⋯ | 4.350022e-02 | 4.350022e-02 | 4.350022e-02 | 4.350022e-02 | 4.350022e-02 | 4.350022e-02 | 4.350022e-02 | 4.350022e-02 | 4.350022e-02 | 4.350022e-02 |\n",
-       "| 2.181691e-03 | 4.979455e-03 | 7.396437e-03 | 1.115513e-02 | 1.372254e-02 | 1.703824e-02 | 2.069350e-02 | 2.422121e-02 | 2.778223e-02 | 3.126802e-02 | ⋯ | 3.730078e-02 | 3.730078e-02 | 3.730078e-02 | 3.730078e-02 | 3.730078e-02 | 3.730078e-02 | 3.730078e-02 | 3.730078e-02 | 3.730078e-02 | 3.730078e-02 |\n",
-       "| 3.988582e-03 | 9.092892e-03 | 1.349292e-02 | 2.031782e-02 | 2.496729e-02 | 3.095702e-02 | 3.754075e-02 | 4.387545e-02 | 5.025072e-02 | 5.647255e-02 | ⋯ | 6.719666e-02 | 6.719666e-02 | 6.719666e-02 | 6.719666e-02 | 6.719666e-02 | 6.719666e-02 | 6.719666e-02 | 6.719666e-02 | 6.719666e-02 | 6.719666e-02 |\n",
-       "| 7.047696e-03 | 1.603520e-02 | 2.375409e-02 | 3.567456e-02 | 4.375887e-02 | 5.412994e-02 | 6.547269e-02 | 7.633019e-02 | 8.720146e-02 | 9.775706e-02 | ⋯ | 1.158255e-01 | 1.158255e-01 | 1.158255e-01 | 1.158255e-01 | 1.158255e-01 | 1.158255e-01 | 1.158255e-01 | 1.158255e-01 | 1.158255e-01 | 1.158255e-01 |\n",
-       "| 2.108022e-03 | 4.811543e-03 | 7.147316e-03 | 1.078010e-02 | 1.326178e-02 | 1.646708e-02 | 2.000105e-02 | 2.341214e-02 | 2.685585e-02 | 3.022723e-02 | ⋯ | 3.606294e-02 | 3.606294e-02 | 3.606294e-02 | 3.606294e-02 | 3.606294e-02 | 3.606294e-02 | 3.606294e-02 | 3.606294e-02 | 3.606294e-02 | 3.606294e-02 |\n",
-       "| 2.086971e-03 | 4.763558e-03 | 7.076119e-03 | 1.067291e-02 | 1.313008e-02 | 1.630381e-02 | 1.980310e-02 | 2.318082e-02 | 2.659098e-02 | 2.992962e-02 | ⋯ | 3.570894e-02 | 3.570894e-02 | 3.570894e-02 | 3.570894e-02 | 3.570894e-02 | 3.570894e-02 | 3.570894e-02 | 3.570894e-02 | 3.570894e-02 | 3.570894e-02 |\n",
-       "| 2.316063e-03 | 5.285688e-03 | 7.850725e-03 | 1.183889e-02 | 1.456252e-02 | 1.807931e-02 | 2.195540e-02 | 2.569540e-02 | 2.946986e-02 | 3.316376e-02 | ⋯ | 3.955475e-02 | 3.955475e-02 | 3.955475e-02 | 3.955475e-02 | 3.955475e-02 | 3.955475e-02 | 3.955475e-02 | 3.955475e-02 | 3.955475e-02 | 3.955475e-02 |\n",
-       "| 8.696201e-03 | 1.976492e-02 | 2.925231e-02 | 4.386930e-02 | 5.375827e-02 | 6.641570e-02 | 8.022175e-02 | 9.340033e-02 | 1.065592e-01 | 1.193008e-01 | ⋯ | 1.410298e-01 | 1.410298e-01 | 1.410298e-01 | 1.410298e-01 | 1.410298e-01 | 1.410298e-01 | 1.410298e-01 | 1.410298e-01 | 1.410298e-01 | 1.410298e-01 |\n",
-       "| 9.594427e-07 | 2.192890e-06 | 3.261256e-06 | 4.927868e-06 | 6.069908e-06 | 7.549207e-06 | 9.185790e-06 | 1.077107e-05 | 1.237714e-05 | 1.395498e-05 | ⋯ | 1.669916e-05 | 1.669916e-05 | 1.669916e-05 | 1.669916e-05 | 1.669916e-05 | 1.669916e-05 | 1.669916e-05 | 1.669916e-05 | 1.669916e-05 | 1.669916e-05 |\n",
-       "| 7.805825e-04 | 1.783195e-03 | 2.650809e-03 | 4.002751e-03 | 4.928108e-03 | 6.125457e-03 | 7.448436e-03 | 8.728266e-03 | 1.002320e-02 | 1.129372e-02 | ⋯ | 1.349955e-02 | 1.349955e-02 | 1.349955e-02 | 1.349955e-02 | 1.349955e-02 | 1.349955e-02 | 1.349955e-02 | 1.349955e-02 | 1.349955e-02 | 1.349955e-02 |\n",
-       "| 2.613744e-03 | 5.963907e-03 | 8.856602e-03 | 1.335231e-02 | 1.642120e-02 | 2.038219e-02 | 2.474575e-02 | 2.895400e-02 | 3.319892e-02 | 3.735117e-02 | ⋯ | 4.453036e-02 | 4.453036e-02 | 4.453036e-02 | 4.453036e-02 | 4.453036e-02 | 4.453036e-02 | 4.453036e-02 | 4.453036e-02 | 4.453036e-02 | 4.453036e-02 |\n",
-       "| 3.947062e-04 | 9.019070e-04 | 1.341018e-03 | 2.025631e-03 | 2.494489e-03 | 3.101481e-03 | 3.772580e-03 | 4.422212e-03 | 5.079932e-03 | 5.725670e-03 | ⋯ | 6.847744e-03 | 6.847744e-03 | 6.847744e-03 | 6.847744e-03 | 6.847744e-03 | 6.847744e-03 | 6.847744e-03 | 6.847744e-03 | 6.847744e-03 | 6.847744e-03 |\n",
-       "| 3.335508e-03 | 7.607256e-03 | 1.129249e-02 | 1.701403e-02 | 2.091556e-02 | 2.594627e-02 | 3.148175e-02 | 3.681372e-02 | 4.218569e-02 | 4.743407e-02 | ⋯ | 5.649368e-02 | 5.649368e-02 | 5.649368e-02 | 5.649368e-02 | 5.649368e-02 | 5.649368e-02 | 5.649368e-02 | 5.649368e-02 | 5.649368e-02 | 5.649368e-02 |\n",
-       "| 8.407480e-03 | 1.911227e-02 | 2.829093e-02 | 4.243813e-02 | 5.201335e-02 | 6.427407e-02 | 7.765374e-02 | 9.043157e-02 | 1.031965e-01 | 1.155626e-01 | ⋯ | 1.366650e-01 | 1.366650e-01 | 1.366650e-01 | 1.366650e-01 | 1.366650e-01 | 1.366650e-01 | 1.366650e-01 | 1.366650e-01 | 1.366650e-01 | 1.366650e-01 |\n",
-       "| 3.266753e-03 | 7.450779e-03 | 1.106063e-02 | 1.666569e-02 | 2.048818e-02 | 2.541743e-02 | 3.084189e-02 | 3.606752e-02 | 4.133295e-02 | 4.647784e-02 | ⋯ | 5.536018e-02 | 5.536018e-02 | 5.536018e-02 | 5.536018e-02 | 5.536018e-02 | 5.536018e-02 | 5.536018e-02 | 5.536018e-02 | 5.536018e-02 | 5.536018e-02 |\n",
-       "| 3.492571e-03 | 7.964663e-03 | 1.182200e-02 | 1.780940e-02 | 2.189129e-02 | 2.715340e-02 | 3.294204e-02 | 3.851638e-02 | 4.413106e-02 | 4.961514e-02 | ⋯ | 5.907824e-02 | 5.907824e-02 | 5.907824e-02 | 5.907824e-02 | 5.907824e-02 | 5.907824e-02 | 5.907824e-02 | 5.907824e-02 | 5.907824e-02 | 5.907824e-02 |\n",
-       "| 2.916911e-03 | 6.654359e-03 | 9.880279e-03 | 1.489170e-02 | 1.831111e-02 | 2.272267e-02 | 2.758021e-02 | 3.226245e-02 | 3.698312e-02 | 4.159840e-02 | ⋯ | 4.957269e-02 | 4.957269e-02 | 4.957269e-02 | 4.957269e-02 | 4.957269e-02 | 4.957269e-02 | 4.957269e-02 | 4.957269e-02 | 4.957269e-02 | 4.957269e-02 |\n",
-       "| 6.090936e-03 | 1.386690e-02 | 2.055297e-02 | 3.089262e-02 | 3.791474e-02 | 4.693500e-02 | 5.681584e-02 | 6.628927e-02 | 7.578991e-02 | 8.502945e-02 | ⋯ | 1.008794e-01 | 1.008794e-01 | 1.008794e-01 | 1.008794e-01 | 1.008794e-01 | 1.008794e-01 | 1.008794e-01 | 1.008794e-01 | 1.008794e-01 | 1.008794e-01 |\n",
-       "| 2.142094e-03 | 4.889204e-03 | 7.262539e-03 | 1.095356e-02 | 1.347490e-02 | 1.673127e-02 | 2.032136e-02 | 2.378641e-02 | 2.728440e-02 | 3.070872e-02 | ⋯ | 3.663562e-02 | 3.663562e-02 | 3.663562e-02 | 3.663562e-02 | 3.663562e-02 | 3.663562e-02 | 3.663562e-02 | 3.663562e-02 | 3.663562e-02 | 3.663562e-02 |\n",
-       "| 4.550822e-04 | 1.039826e-03 | 1.546034e-03 | 2.335188e-03 | 2.875594e-03 | 3.575155e-03 | 4.348524e-03 | 5.097079e-03 | 5.854877e-03 | 6.598795e-03 | ⋯ | 7.891298e-03 | 7.891298e-03 | 7.891298e-03 | 7.891298e-03 | 7.891298e-03 | 7.891298e-03 | 7.891298e-03 | 7.891298e-03 | 7.891298e-03 | 7.891298e-03 |\n",
-       "| 1.614296e-03 | 3.685788e-03 | 5.476566e-03 | 8.263693e-03 | 1.016905e-02 | 1.263165e-02 | 1.534895e-02 | 1.797394e-02 | 2.062622e-02 | 2.322491e-02 | ⋯ | 2.772817e-02 | 2.772817e-02 | 2.772817e-02 | 2.772817e-02 | 2.772817e-02 | 2.772817e-02 | 2.772817e-02 | 2.772817e-02 | 2.772817e-02 | 2.772817e-02 |\n",
-       "| 3.181868e-03 | 7.257569e-03 | 1.077432e-02 | 1.623548e-02 | 1.996031e-02 | 2.476417e-02 | 3.005138e-02 | 3.514551e-02 | 4.027918e-02 | 4.529602e-02 | ⋯ | 5.395897e-02 | 5.395897e-02 | 5.395897e-02 | 5.395897e-02 | 5.395897e-02 | 5.395897e-02 | 5.395897e-02 | 5.395897e-02 | 5.395897e-02 | 5.395897e-02 |\n",
-       "| 1.457518e-03 | 3.328166e-03 | 4.945621e-03 | 7.463556e-03 | 9.185285e-03 | 1.141103e-02 | 1.386759e-02 | 1.624133e-02 | 1.864038e-02 | 2.099157e-02 | ⋯ | 2.506737e-02 | 2.506737e-02 | 2.506737e-02 | 2.506737e-02 | 2.506737e-02 | 2.506737e-02 | 2.506737e-02 | 2.506737e-02 | 2.506737e-02 | 2.506737e-02 |\n",
-       "| 3.704694e-03 | 8.447247e-03 | 1.253683e-02 | 1.888278e-02 | 2.320776e-02 | 2.878164e-02 | 3.491112e-02 | 4.081157e-02 | 4.675262e-02 | 5.255342e-02 | ⋯ | 6.255827e-02 | 6.255827e-02 | 6.255827e-02 | 6.255827e-02 | 6.255827e-02 | 6.255827e-02 | 6.255827e-02 | 6.255827e-02 | 6.255827e-02 | 6.255827e-02 |\n",
-       "| 6.887545e-04 | 1.573512e-03 | 2.339224e-03 | 3.532537e-03 | 4.349427e-03 | 5.406561e-03 | 6.574787e-03 | 7.705085e-03 | 8.848895e-03 | 9.971320e-03 | ⋯ | 1.192042e-02 | 1.192042e-02 | 1.192042e-02 | 1.192042e-02 | 1.192042e-02 | 1.192042e-02 | 1.192042e-02 | 1.192042e-02 | 1.192042e-02 | 1.192042e-02 |\n",
-       "| 7.715644e-04 | 1.762604e-03 | 2.620212e-03 | 3.956581e-03 | 4.871290e-03 | 6.054877e-03 | 7.362669e-03 | 8.627826e-03 | 9.907931e-03 | 1.116393e-02 | ⋯ | 1.334458e-02 | 1.334458e-02 | 1.334458e-02 | 1.334458e-02 | 1.334458e-02 | 1.334458e-02 | 1.334458e-02 | 1.334458e-02 | 1.334458e-02 | 1.334458e-02 |\n",
-       "| 1.443946e-03 | 3.297203e-03 | 4.899647e-03 | 7.394263e-03 | 9.100080e-03 | 1.130529e-02 | 1.373925e-02 | 1.609121e-02 | 1.846829e-02 | 2.079800e-02 | ⋯ | 2.483669e-02 | 2.483669e-02 | 2.483669e-02 | 2.483669e-02 | 2.483669e-02 | 2.483669e-02 | 2.483669e-02 | 2.483669e-02 | 2.483669e-02 | 2.483669e-02 |\n",
-       "\n"
-      ],
-      "text/plain": [
-       "      [,1]         [,2]         [,3]         [,4]         [,5]        \n",
-       " [1,] 0.0023360608 0.005331257  0.007918320  0.011940620  0.014687471 \n",
-       " [2,] 0.0084408266 0.019187663  0.028402002  0.042603515  0.052215023 \n",
-       " [3,] 0.0021213810 0.004841993  0.007192493  0.010848114  0.013345343 \n",
-       " [4,] 0.0129751415 0.029408795  0.043421734  0.064878567  0.079303248 \n",
-       " [5,] 0.0102557255 0.023285989  0.034433608  0.051570007  0.063136747 \n",
-       " [6,] 0.0283559466 0.063631924  0.093149941  0.137350005  0.166387605 \n",
-       " [7,] 0.0033415546 0.007621018  0.011312882  0.017044658  0.020953144 \n",
-       " [8,] 0.0034457422 0.007858109  0.011664151  0.017572313  0.021600460 \n",
-       " [9,] 0.0023794401 0.005430104  0.008064938  0.012161260  0.014958482 \n",
-       "[10,] 0.0022151196 0.005055644  0.007509467  0.011325270  0.013931565 \n",
-       "[11,] 0.0043873251 0.009999351  0.014834712  0.022330598  0.027434177 \n",
-       "[12,] 0.0022649944 0.005169309  0.007678088  0.011579071  0.014243351 \n",
-       "[13,] 0.0076146494 0.017318828  0.025647511  0.038499248  0.047207853 \n",
-       "[14,] 0.0073917072 0.016814183  0.024903273  0.037389295  0.045852869 \n",
-       "[15,] 0.0005919557 0.001352452  0.002010699  0.003036676  0.003739115 \n",
-       "[16,] 0.0012320305 0.002813684  0.004181632  0.006311837  0.007768923 \n",
-       "[17,] 0.0075015963 0.017062943  0.025270161  0.037936524  0.046520949 \n",
-       "[18,] 0.0091341222 0.020754372  0.030709219  0.046036757  0.056399731 \n",
-       "[19,] 0.0039124959 0.008919873  0.013236734  0.019933376  0.024495973 \n",
-       "[20,] 0.0074511939 0.016948850  0.025101894  0.037685560  0.046214573 \n",
-       "[21,] 0.0157602179 0.035656998  0.052565503  0.078352167  0.095615996 \n",
-       "[22,] 0.0004549238 0.001039464  0.001545496  0.002334376  0.002874595 \n",
-       "[23,] 0.0026059117 0.005946066  0.008830147  0.013312514  0.016372336 \n",
-       "[24,] 0.0016347515 0.003732443  0.005545825  0.008368052  0.010297349 \n",
-       "[25,] 0.0028410944 0.006481716  0.009624347  0.014506908  0.017838764 \n",
-       "[26,] 0.0019297588 0.004405164  0.006544308  0.009872126  0.012146066 \n",
-       "[27,] 0.0027263661 0.006220432  0.009236971  0.013924397  0.017123632 \n",
-       "[28,] 0.0013652065 0.003117562  0.004632904  0.006992187  0.008605650 \n",
-       "[29,] 0.0013768164 0.003144050  0.004672237  0.007051480  0.008678566 \n",
-       "[30,] 0.0074538769 0.016954923  0.025110852  0.037698921  0.046230883 \n",
-       "[31,] <U+22EE>     <U+22EE>     <U+22EE>     <U+22EE>     <U+22EE>    \n",
-       "[32,] 8.902584e-03 2.023130e-02 2.993912e-02 4.489127e-02 5.500391e-02\n",
-       "[33,] 3.183496e-03 7.261275e-03 1.077982e-02 1.624374e-02 1.997044e-02\n",
-       "[34,] 1.356859e-03 3.098516e-03 4.604622e-03 6.949553e-03 8.553221e-03\n",
-       "[35,] 2.551993e-03 5.823239e-03 8.648003e-03 1.303852e-02 1.603588e-02\n",
-       "[36,] 2.181691e-03 4.979455e-03 7.396437e-03 1.115513e-02 1.372254e-02\n",
-       "[37,] 3.988582e-03 9.092892e-03 1.349292e-02 2.031782e-02 2.496729e-02\n",
-       "[38,] 7.047696e-03 1.603520e-02 2.375409e-02 3.567456e-02 4.375887e-02\n",
-       "[39,] 2.108022e-03 4.811543e-03 7.147316e-03 1.078010e-02 1.326178e-02\n",
-       "[40,] 2.086971e-03 4.763558e-03 7.076119e-03 1.067291e-02 1.313008e-02\n",
-       "[41,] 2.316063e-03 5.285688e-03 7.850725e-03 1.183889e-02 1.456252e-02\n",
-       "[42,] 8.696201e-03 1.976492e-02 2.925231e-02 4.386930e-02 5.375827e-02\n",
-       "[43,] 9.594427e-07 2.192890e-06 3.261256e-06 4.927868e-06 6.069908e-06\n",
-       "[44,] 7.805825e-04 1.783195e-03 2.650809e-03 4.002751e-03 4.928108e-03\n",
-       "[45,] 2.613744e-03 5.963907e-03 8.856602e-03 1.335231e-02 1.642120e-02\n",
-       "[46,] 3.947062e-04 9.019070e-04 1.341018e-03 2.025631e-03 2.494489e-03\n",
-       "[47,] 3.335508e-03 7.607256e-03 1.129249e-02 1.701403e-02 2.091556e-02\n",
-       "[48,] 8.407480e-03 1.911227e-02 2.829093e-02 4.243813e-02 5.201335e-02\n",
-       "[49,] 3.266753e-03 7.450779e-03 1.106063e-02 1.666569e-02 2.048818e-02\n",
-       "[50,] 3.492571e-03 7.964663e-03 1.182200e-02 1.780940e-02 2.189129e-02\n",
-       "[51,] 2.916911e-03 6.654359e-03 9.880279e-03 1.489170e-02 1.831111e-02\n",
-       "[52,] 6.090936e-03 1.386690e-02 2.055297e-02 3.089262e-02 3.791474e-02\n",
-       "[53,] 2.142094e-03 4.889204e-03 7.262539e-03 1.095356e-02 1.347490e-02\n",
-       "[54,] 4.550822e-04 1.039826e-03 1.546034e-03 2.335188e-03 2.875594e-03\n",
-       "[55,] 1.614296e-03 3.685788e-03 5.476566e-03 8.263693e-03 1.016905e-02\n",
-       "[56,] 3.181868e-03 7.257569e-03 1.077432e-02 1.623548e-02 1.996031e-02\n",
-       "[57,] 1.457518e-03 3.328166e-03 4.945621e-03 7.463556e-03 9.185285e-03\n",
-       "[58,] 3.704694e-03 8.447247e-03 1.253683e-02 1.888278e-02 2.320776e-02\n",
-       "[59,] 6.887545e-04 1.573512e-03 2.339224e-03 3.532537e-03 4.349427e-03\n",
-       "[60,] 7.715644e-04 1.762604e-03 2.620212e-03 3.956581e-03 4.871290e-03\n",
-       "[61,] 1.443946e-03 3.297203e-03 4.899647e-03 7.394263e-03 9.100080e-03\n",
-       "      [,6]         [,7]         [,8]         [,9]         [,10]        [,11] \n",
-       " [1,] 0.018234157  0.022143071  0.025914614  0.029720789  0.033445591  <U+22EF>\n",
-       " [2,] 0.064521644  0.077950666  0.090774902  0.103585453  0.115995093  <U+22EF>\n",
-       " [3,] 0.016570670  0.020126646  0.023558894  0.027023898  0.030416034  <U+22EF>\n",
-       " [4,] 0.097657467  0.117537182  0.136376047  0.155051908  0.173006229  <U+22EF>\n",
-       " [5,] 0.077909856  0.093982446  0.109284048  0.124522823  0.139239886  <U+22EF>\n",
-       " [6,] 0.202552230  0.240736872  0.275980335  0.310018115  0.341899024  <U+22EF>\n",
-       " [7,] 0.025992768  0.031538009  0.036879324  0.042260652  0.047518127  <U+22EF>\n",
-       " [8,] 0.026793633  0.032506859  0.038009015  0.043551426  0.048965337  <U+22EF>\n",
-       " [9,] 0.018569991  0.022550068  0.026389994  0.030264898  0.034056687  <U+22EF>\n",
-       "[10,] 0.017297324  0.021007563  0.024588143  0.028202329  0.031739958  <U+22EF>\n",
-       "[11,] 0.034005329  0.041223442  0.048163863  0.055144078  0.061951773  <U+22EF>\n",
-       "[12,] 0.017683758  0.021475977  0.025135364  0.028828790  0.032443681  <U+22EF>\n",
-       "[13,] 0.058371080  0.070568910  0.082233702  0.093902198  0.105221156  <U+22EF>\n",
-       "[14,] 0.056705327  0.068567911  0.079916387  0.091272709  0.102292951  <U+22EF>\n",
-       "[15,] 0.004648258  0.005653099  0.006625474  0.007609633  0.008575547  <U+22EF>\n",
-       "[16,] 0.009653131  0.011733509  0.013744507  0.015777707  0.017771095  <U+22EF>\n",
-       "[17,] 0.057526705  0.069554693  0.081059266  0.092569673  0.103737388  <U+22EF>\n",
-       "[18,] 0.069655866  0.084104585  0.097886398  0.111637500  0.124942805  <U+22EF>\n",
-       "[19,] 0.030374408  0.036836602  0.043055162  0.049314339  0.055423649  <U+22EF>\n",
-       "[20,] 0.057150045  0.069102206  0.080535228  0.091975013  0.103075144  <U+22EF>\n",
-       "[21,] 0.117498240  0.141090754  0.163342188  0.185297675  0.206306174  <U+22EF>\n",
-       "[22,] 0.003573912  0.004347013  0.005095309  0.005852844  0.006596506  <U+22EF>\n",
-       "[23,] 0.020321663  0.024672430  0.028868393  0.033100979  0.037241217  <U+22EF>\n",
-       "[24,] 0.012790816  0.015542082  0.018199794  0.020885047  0.023515957  <U+22EF>\n",
-       "[25,] 0.022137824  0.026872059  0.031436038  0.036038048  0.040537888  <U+22EF>\n",
-       "[26,] 0.015083771  0.018323650  0.021451808  0.024610827  0.027704399  <U+22EF>\n",
-       "[27,] 0.021252220  0.025799578  0.030184247  0.034606298  0.038931001  <U+22EF>\n",
-       "[28,] 0.010691695  0.012994425  0.015219862  0.017469370  0.019674340  <U+22EF>\n",
-       "[29,] 0.010782189  0.013104280  0.015348384  0.017616716  0.019840096  <U+22EF>\n",
-       "[30,] 0.057170098  0.069126298  0.080563130  0.092006676  0.103110407  <U+22EF>\n",
-       "[31,] <U+22EE>     <U+22EE>     <U+22EE>     <U+22EE>     <U+22EE>     <U+22F1>\n",
-       "[32,] 6.794395e-02 8.205348e-02 9.551705e-02 1.089558e-01 1.219640e-01 <U+22EF>\n",
-       "[33,] 2.477671e-02 3.006655e-02 3.516321e-02 4.029940e-02 4.531871e-02 <U+22EF>\n",
-       "[34,] 1.062662e-02 1.291543e-02 1.512744e-02 1.736341e-02 1.955514e-02 <U+22EF>\n",
-       "[35,] 1.990487e-02 2.416751e-02 2.827886e-02 3.242646e-02 3.648393e-02 <U+22EF>\n",
-       "[36,] 1.703824e-02 2.069350e-02 2.422121e-02 2.778223e-02 3.126802e-02 <U+22EF>\n",
-       "[37,] 3.095702e-02 3.754075e-02 4.387545e-02 5.025072e-02 5.647255e-02 <U+22EF>\n",
-       "[38,] 5.412994e-02 6.547269e-02 7.633019e-02 8.720146e-02 9.775706e-02 <U+22EF>\n",
-       "[39,] 1.646708e-02 2.000105e-02 2.341214e-02 2.685585e-02 3.022723e-02 <U+22EF>\n",
-       "[40,] 1.630381e-02 1.980310e-02 2.318082e-02 2.659098e-02 2.992962e-02 <U+22EF>\n",
-       "[41,] 1.807931e-02 2.195540e-02 2.569540e-02 2.946986e-02 3.316376e-02 <U+22EF>\n",
-       "[42,] 6.641570e-02 8.022175e-02 9.340033e-02 1.065592e-01 1.193008e-01 <U+22EF>\n",
-       "[43,] 7.549207e-06 9.185790e-06 1.077107e-05 1.237714e-05 1.395498e-05 <U+22EF>\n",
-       "[44,] 6.125457e-03 7.448436e-03 8.728266e-03 1.002320e-02 1.129372e-02 <U+22EF>\n",
-       "[45,] 2.038219e-02 2.474575e-02 2.895400e-02 3.319892e-02 3.735117e-02 <U+22EF>\n",
-       "[46,] 3.101481e-03 3.772580e-03 4.422212e-03 5.079932e-03 5.725670e-03 <U+22EF>\n",
-       "[47,] 2.594627e-02 3.148175e-02 3.681372e-02 4.218569e-02 4.743407e-02 <U+22EF>\n",
-       "[48,] 6.427407e-02 7.765374e-02 9.043157e-02 1.031965e-01 1.155626e-01 <U+22EF>\n",
-       "[49,] 2.541743e-02 3.084189e-02 3.606752e-02 4.133295e-02 4.647784e-02 <U+22EF>\n",
-       "[50,] 2.715340e-02 3.294204e-02 3.851638e-02 4.413106e-02 4.961514e-02 <U+22EF>\n",
-       "[51,] 2.272267e-02 2.758021e-02 3.226245e-02 3.698312e-02 4.159840e-02 <U+22EF>\n",
-       "[52,] 4.693500e-02 5.681584e-02 6.628927e-02 7.578991e-02 8.502945e-02 <U+22EF>\n",
-       "[53,] 1.673127e-02 2.032136e-02 2.378641e-02 2.728440e-02 3.070872e-02 <U+22EF>\n",
-       "[54,] 3.575155e-03 4.348524e-03 5.097079e-03 5.854877e-03 6.598795e-03 <U+22EF>\n",
-       "[55,] 1.263165e-02 1.534895e-02 1.797394e-02 2.062622e-02 2.322491e-02 <U+22EF>\n",
-       "[56,] 2.476417e-02 3.005138e-02 3.514551e-02 4.027918e-02 4.529602e-02 <U+22EF>\n",
-       "[57,] 1.141103e-02 1.386759e-02 1.624133e-02 1.864038e-02 2.099157e-02 <U+22EF>\n",
-       "[58,] 2.878164e-02 3.491112e-02 4.081157e-02 4.675262e-02 5.255342e-02 <U+22EF>\n",
-       "[59,] 5.406561e-03 6.574787e-03 7.705085e-03 8.848895e-03 9.971320e-03 <U+22EF>\n",
-       "[60,] 6.054877e-03 7.362669e-03 8.627826e-03 9.907931e-03 1.116393e-02 <U+22EF>\n",
-       "[61,] 1.130529e-02 1.373925e-02 1.609121e-02 1.846829e-02 2.079800e-02 <U+22EF>\n",
-       "      [,12]        [,13]        [,14]        [,15]        [,16]       \n",
-       " [1,] 0.039889763  0.039889763  0.039889763  0.039889763  0.039889763 \n",
-       " [2,] 0.137170199  0.137170199  0.137170199  0.137170199  0.137170199 \n",
-       " [3,] 0.036287514  0.036287514  0.036287514  0.036287514  0.036287514 \n",
-       " [4,] 0.203328496  0.203328496  0.203328496  0.203328496  0.203328496 \n",
-       " [5,] 0.164249035  0.164249035  0.164249035  0.164249035  0.164249035 \n",
-       " [6,] 0.393877693  0.393877693  0.393877693  0.393877693  0.393877693 \n",
-       " [7,] 0.056593310  0.056593310  0.056593310  0.056593310  0.056593310 \n",
-       " [8,] 0.058308353  0.058308353  0.058308353  0.058308353  0.058308353 \n",
-       " [9,] 0.040616109  0.040616109  0.040616109  0.040616109  0.040616109 \n",
-       "[10,] 0.037861978  0.037861978  0.037861978  0.037861978  0.037861978 \n",
-       "[11,] 0.073675132  0.073675132  0.073675132  0.073675132  0.073675132 \n",
-       "[12,] 0.038698702  0.038698702  0.038698702  0.038698702  0.038698702 \n",
-       "[13,] 0.124571392  0.124571392  0.124571392  0.124571392  0.124571392 \n",
-       "[14,] 0.121142037  0.121142037  0.121142037  0.121142037  0.121142037 \n",
-       "[15,] 0.010253232  0.010253232  0.010253232  0.010253232  0.010253232 \n",
-       "[16,] 0.021228395  0.021228395  0.021228395  0.021228395  0.021228395 \n",
-       "[17,] 0.122833959  0.122833959  0.122833959  0.122833959  0.122833959 \n",
-       "[18,] 0.147610547  0.147610547  0.147610547  0.147610547  0.147610547 \n",
-       "[19,] 0.065955631  0.065955631  0.065955631  0.065955631  0.065955631 \n",
-       "[20,] 0.122058315  0.122058315  0.122058315  0.122058315  0.122058315 \n",
-       "[21,] 0.241562071  0.241562071  0.241562071  0.241562071  0.241562071 \n",
-       "[22,] 0.007888561  0.007888561  0.007888561  0.007888561  0.007888561 \n",
-       "[23,] 0.044399762  0.044399762  0.044399762  0.044399762  0.044399762 \n",
-       "[24,] 0.028074825  0.028074825  0.028074825  0.028074825  0.028074825 \n",
-       "[25,] 0.048314062  0.048314062  0.048314062  0.048314062  0.048314062 \n",
-       "[26,] 0.033061407  0.033061407  0.033061407  0.033061407  0.033061407 \n",
-       "[27,] 0.046406455  0.046406455  0.046406455  0.046406455  0.046406455 \n",
-       "[28,] 0.023497459  0.023497459  0.023497459  0.023497459  0.023497459 \n",
-       "[29,] 0.023695034  0.023695034  0.023695034  0.023695034  0.023695034 \n",
-       "[30,] 0.122099619  0.122099619  0.122099619  0.122099619  0.122099619 \n",
-       "[31,] <U+22EE>     <U+22EE>     <U+22EE>     <U+22EE>     <U+22EE>    \n",
-       "[32,] 1.441371e-01 1.441371e-01 1.441371e-01 1.441371e-01 1.441371e-01\n",
-       "[33,] 5.398586e-02 5.398586e-02 5.398586e-02 5.398586e-02 5.398586e-02\n",
-       "[34,] 2.335538e-02 2.335538e-02 2.335538e-02 2.335538e-02 2.335538e-02\n",
-       "[35,] 4.350022e-02 4.350022e-02 4.350022e-02 4.350022e-02 4.350022e-02\n",
-       "[36,] 3.730078e-02 3.730078e-02 3.730078e-02 3.730078e-02 3.730078e-02\n",
-       "[37,] 6.719666e-02 6.719666e-02 6.719666e-02 6.719666e-02 6.719666e-02\n",
-       "[38,] 1.158255e-01 1.158255e-01 1.158255e-01 1.158255e-01 1.158255e-01\n",
-       "[39,] 3.606294e-02 3.606294e-02 3.606294e-02 3.606294e-02 3.606294e-02\n",
-       "[40,] 3.570894e-02 3.570894e-02 3.570894e-02 3.570894e-02 3.570894e-02\n",
-       "[41,] 3.955475e-02 3.955475e-02 3.955475e-02 3.955475e-02 3.955475e-02\n",
-       "[42,] 1.410298e-01 1.410298e-01 1.410298e-01 1.410298e-01 1.410298e-01\n",
-       "[43,] 1.669916e-05 1.669916e-05 1.669916e-05 1.669916e-05 1.669916e-05\n",
-       "[44,] 1.349955e-02 1.349955e-02 1.349955e-02 1.349955e-02 1.349955e-02\n",
-       "[45,] 4.453036e-02 4.453036e-02 4.453036e-02 4.453036e-02 4.453036e-02\n",
-       "[46,] 6.847744e-03 6.847744e-03 6.847744e-03 6.847744e-03 6.847744e-03\n",
-       "[47,] 5.649368e-02 5.649368e-02 5.649368e-02 5.649368e-02 5.649368e-02\n",
-       "[48,] 1.366650e-01 1.366650e-01 1.366650e-01 1.366650e-01 1.366650e-01\n",
-       "[49,] 5.536018e-02 5.536018e-02 5.536018e-02 5.536018e-02 5.536018e-02\n",
-       "[50,] 5.907824e-02 5.907824e-02 5.907824e-02 5.907824e-02 5.907824e-02\n",
-       "[51,] 4.957269e-02 4.957269e-02 4.957269e-02 4.957269e-02 4.957269e-02\n",
-       "[52,] 1.008794e-01 1.008794e-01 1.008794e-01 1.008794e-01 1.008794e-01\n",
-       "[53,] 3.663562e-02 3.663562e-02 3.663562e-02 3.663562e-02 3.663562e-02\n",
-       "[54,] 7.891298e-03 7.891298e-03 7.891298e-03 7.891298e-03 7.891298e-03\n",
-       "[55,] 2.772817e-02 2.772817e-02 2.772817e-02 2.772817e-02 2.772817e-02\n",
-       "[56,] 5.395897e-02 5.395897e-02 5.395897e-02 5.395897e-02 5.395897e-02\n",
-       "[57,] 2.506737e-02 2.506737e-02 2.506737e-02 2.506737e-02 2.506737e-02\n",
-       "[58,] 6.255827e-02 6.255827e-02 6.255827e-02 6.255827e-02 6.255827e-02\n",
-       "[59,] 1.192042e-02 1.192042e-02 1.192042e-02 1.192042e-02 1.192042e-02\n",
-       "[60,] 1.334458e-02 1.334458e-02 1.334458e-02 1.334458e-02 1.334458e-02\n",
-       "[61,] 2.483669e-02 2.483669e-02 2.483669e-02 2.483669e-02 2.483669e-02\n",
-       "      [,17]        [,18]        [,19]        [,20]        [,21]       \n",
-       " [1,] 0.039889763  0.039889763  0.039889763  0.039889763  0.039889763 \n",
-       " [2,] 0.137170199  0.137170199  0.137170199  0.137170199  0.137170199 \n",
-       " [3,] 0.036287514  0.036287514  0.036287514  0.036287514  0.036287514 \n",
-       " [4,] 0.203328496  0.203328496  0.203328496  0.203328496  0.203328496 \n",
-       " [5,] 0.164249035  0.164249035  0.164249035  0.164249035  0.164249035 \n",
-       " [6,] 0.393877693  0.393877693  0.393877693  0.393877693  0.393877693 \n",
-       " [7,] 0.056593310  0.056593310  0.056593310  0.056593310  0.056593310 \n",
-       " [8,] 0.058308353  0.058308353  0.058308353  0.058308353  0.058308353 \n",
-       " [9,] 0.040616109  0.040616109  0.040616109  0.040616109  0.040616109 \n",
-       "[10,] 0.037861978  0.037861978  0.037861978  0.037861978  0.037861978 \n",
-       "[11,] 0.073675132  0.073675132  0.073675132  0.073675132  0.073675132 \n",
-       "[12,] 0.038698702  0.038698702  0.038698702  0.038698702  0.038698702 \n",
-       "[13,] 0.124571392  0.124571392  0.124571392  0.124571392  0.124571392 \n",
-       "[14,] 0.121142037  0.121142037  0.121142037  0.121142037  0.121142037 \n",
-       "[15,] 0.010253232  0.010253232  0.010253232  0.010253232  0.010253232 \n",
-       "[16,] 0.021228395  0.021228395  0.021228395  0.021228395  0.021228395 \n",
-       "[17,] 0.122833959  0.122833959  0.122833959  0.122833959  0.122833959 \n",
-       "[18,] 0.147610547  0.147610547  0.147610547  0.147610547  0.147610547 \n",
-       "[19,] 0.065955631  0.065955631  0.065955631  0.065955631  0.065955631 \n",
-       "[20,] 0.122058315  0.122058315  0.122058315  0.122058315  0.122058315 \n",
-       "[21,] 0.241562071  0.241562071  0.241562071  0.241562071  0.241562071 \n",
-       "[22,] 0.007888561  0.007888561  0.007888561  0.007888561  0.007888561 \n",
-       "[23,] 0.044399762  0.044399762  0.044399762  0.044399762  0.044399762 \n",
-       "[24,] 0.028074825  0.028074825  0.028074825  0.028074825  0.028074825 \n",
-       "[25,] 0.048314062  0.048314062  0.048314062  0.048314062  0.048314062 \n",
-       "[26,] 0.033061407  0.033061407  0.033061407  0.033061407  0.033061407 \n",
-       "[27,] 0.046406455  0.046406455  0.046406455  0.046406455  0.046406455 \n",
-       "[28,] 0.023497459  0.023497459  0.023497459  0.023497459  0.023497459 \n",
-       "[29,] 0.023695034  0.023695034  0.023695034  0.023695034  0.023695034 \n",
-       "[30,] 0.122099619  0.122099619  0.122099619  0.122099619  0.122099619 \n",
-       "[31,] <U+22EE>     <U+22EE>     <U+22EE>     <U+22EE>     <U+22EE>    \n",
-       "[32,] 1.441371e-01 1.441371e-01 1.441371e-01 1.441371e-01 1.441371e-01\n",
-       "[33,] 5.398586e-02 5.398586e-02 5.398586e-02 5.398586e-02 5.398586e-02\n",
-       "[34,] 2.335538e-02 2.335538e-02 2.335538e-02 2.335538e-02 2.335538e-02\n",
-       "[35,] 4.350022e-02 4.350022e-02 4.350022e-02 4.350022e-02 4.350022e-02\n",
-       "[36,] 3.730078e-02 3.730078e-02 3.730078e-02 3.730078e-02 3.730078e-02\n",
-       "[37,] 6.719666e-02 6.719666e-02 6.719666e-02 6.719666e-02 6.719666e-02\n",
-       "[38,] 1.158255e-01 1.158255e-01 1.158255e-01 1.158255e-01 1.158255e-01\n",
-       "[39,] 3.606294e-02 3.606294e-02 3.606294e-02 3.606294e-02 3.606294e-02\n",
-       "[40,] 3.570894e-02 3.570894e-02 3.570894e-02 3.570894e-02 3.570894e-02\n",
-       "[41,] 3.955475e-02 3.955475e-02 3.955475e-02 3.955475e-02 3.955475e-02\n",
-       "[42,] 1.410298e-01 1.410298e-01 1.410298e-01 1.410298e-01 1.410298e-01\n",
-       "[43,] 1.669916e-05 1.669916e-05 1.669916e-05 1.669916e-05 1.669916e-05\n",
-       "[44,] 1.349955e-02 1.349955e-02 1.349955e-02 1.349955e-02 1.349955e-02\n",
-       "[45,] 4.453036e-02 4.453036e-02 4.453036e-02 4.453036e-02 4.453036e-02\n",
-       "[46,] 6.847744e-03 6.847744e-03 6.847744e-03 6.847744e-03 6.847744e-03\n",
-       "[47,] 5.649368e-02 5.649368e-02 5.649368e-02 5.649368e-02 5.649368e-02\n",
-       "[48,] 1.366650e-01 1.366650e-01 1.366650e-01 1.366650e-01 1.366650e-01\n",
-       "[49,] 5.536018e-02 5.536018e-02 5.536018e-02 5.536018e-02 5.536018e-02\n",
-       "[50,] 5.907824e-02 5.907824e-02 5.907824e-02 5.907824e-02 5.907824e-02\n",
-       "[51,] 4.957269e-02 4.957269e-02 4.957269e-02 4.957269e-02 4.957269e-02\n",
-       "[52,] 1.008794e-01 1.008794e-01 1.008794e-01 1.008794e-01 1.008794e-01\n",
-       "[53,] 3.663562e-02 3.663562e-02 3.663562e-02 3.663562e-02 3.663562e-02\n",
-       "[54,] 7.891298e-03 7.891298e-03 7.891298e-03 7.891298e-03 7.891298e-03\n",
-       "[55,] 2.772817e-02 2.772817e-02 2.772817e-02 2.772817e-02 2.772817e-02\n",
-       "[56,] 5.395897e-02 5.395897e-02 5.395897e-02 5.395897e-02 5.395897e-02\n",
-       "[57,] 2.506737e-02 2.506737e-02 2.506737e-02 2.506737e-02 2.506737e-02\n",
-       "[58,] 6.255827e-02 6.255827e-02 6.255827e-02 6.255827e-02 6.255827e-02\n",
-       "[59,] 1.192042e-02 1.192042e-02 1.192042e-02 1.192042e-02 1.192042e-02\n",
-       "[60,] 1.334458e-02 1.334458e-02 1.334458e-02 1.334458e-02 1.334458e-02\n",
-       "[61,] 2.483669e-02 2.483669e-02 2.483669e-02 2.483669e-02 2.483669e-02"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "preds"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fit = crr(ftime=data_cox[[time]], fstatus=data_cox[[event]], cov1=data_cox %>% select(all_of(features[[\"clinical\"]])), failcode=1, cencode=0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Competing Risks Regression\n",
-       "\n",
-       "Call:\n",
-       "crr(ftime = data_cox[[time]], fstatus = data_cox[[event]], cov1 = data_cox %>% \n",
-       "    select(all_of(features[[\"clinical\"]])), failcode = 1, cencode = 0)\n",
-       "\n",
-       "                                               coef exp(coef) se(coef)\n",
-       "age_at_recruitment                         5.70e-01  1.767910   0.0503\n",
-       "ethnic_background_0.0                     -8.37e-02  0.919684   0.3635\n",
-       "ethnic_background_1.0                     -2.35e-01  0.790231   0.4872\n",
-       "ethnic_background_2.0                      1.64e-01  1.178012   0.4728\n",
-       "ethnic_background_3.0                     -1.19e+00  0.305197   1.0801\n",
-       "ethnic_background_4.0                     -7.68e+00  0.000460   0.4391\n",
-       "townsend_deprivation_index_at_recruitment  3.16e-02  1.032069   0.0373\n",
-       "sex                                        6.76e-01  1.966116   0.1237\n",
-       "overall_health_rating_0.0                  3.41e-01  1.406432   0.5363\n",
-       "overall_health_rating_1.0                  1.22e-01  1.130077   0.5341\n",
-       "overall_health_rating_2.0                  7.19e-01  2.051881   0.5618\n",
-       "overall_health_rating_3.0                 -2.08e-01  0.812016   0.5431\n",
-       "smoking_status_0.0                        -7.98e-02  0.923340   0.4801\n",
-       "smoking_status_1.0                        -4.23e-01  0.655178   0.4769\n",
-       "smoking_status_2.0                        -5.87e-01  0.555778   0.4756\n",
-       "body_mass_index_bmi                        2.64e-01  1.301603   0.3479\n",
-       "weight                                    -2.82e-01  0.754031   0.4085\n",
-       "standing_height                            5.23e-02  1.053702   0.2321\n",
-       "systolic_blood_pressure                    2.14e-01  1.238483   0.0573\n",
-       "diastolic_blood_pressure                   3.42e-02  1.034798   0.0548\n",
-       "cholesterol                                1.52e-01  1.164306   0.1985\n",
-       "hdl_cholesterol                           -1.78e-01  0.837290   0.0833\n",
-       "ldl_direct                                -5.76e-02  0.944052   0.1792\n",
-       "triglycerides                             -7.99e-02  0.923178   0.0550\n",
-       "fh_heart_disease                           1.57e-01  1.169898   0.0790\n",
-       "diabetes1                                 -1.05e+00  0.349879   0.6536\n",
-       "diabetes2                                  6.24e-01  1.866990   0.2369\n",
-       "chronic_kidney_disease                     3.50e-01  1.418361   0.1552\n",
-       "atrial_fibrillation                        6.25e-01  1.867433   0.2363\n",
-       "migraine                                   2.52e-01  1.287100   0.1461\n",
-       "rheumatoid_arthritis                       1.08e-01  1.114040   0.1507\n",
-       "systemic_lupus_erythematosus               5.57e-01  1.744744   0.6532\n",
-       "severe_mental_illness                     -7.11e-05  0.999929   0.1252\n",
-       "erectile_dysfunction                      -3.77e-02  0.962997   0.1991\n",
-       "antihypertensives                         -5.42e-01  0.581436   0.4148\n",
-       "ass                                        4.13e-01  1.511168   0.2779\n",
-       "atypical_antipsychotics                    9.92e-03  1.009965   0.2734\n",
-       "glucocorticoids                           -8.23e+00  0.000266   0.4911\n",
-       "                                                  z p-value\n",
-       "age_at_recruitment                         1.13e+01 0.0e+00\n",
-       "ethnic_background_0.0                     -2.30e-01 8.2e-01\n",
-       "ethnic_background_1.0                     -4.83e-01 6.3e-01\n",
-       "ethnic_background_2.0                      3.47e-01 7.3e-01\n",
-       "ethnic_background_3.0                     -1.10e+00 2.7e-01\n",
-       "ethnic_background_4.0                     -1.75e+01 0.0e+00\n",
-       "townsend_deprivation_index_at_recruitment  8.45e-01 4.0e-01\n",
-       "sex                                        5.47e+00 4.6e-08\n",
-       "overall_health_rating_0.0                  6.36e-01 5.2e-01\n",
-       "overall_health_rating_1.0                  2.29e-01 8.2e-01\n",
-       "overall_health_rating_2.0                  1.28e+00 2.0e-01\n",
-       "overall_health_rating_3.0                 -3.83e-01 7.0e-01\n",
-       "smoking_status_0.0                        -1.66e-01 8.7e-01\n",
-       "smoking_status_1.0                        -8.87e-01 3.8e-01\n",
-       "smoking_status_2.0                        -1.24e+00 2.2e-01\n",
-       "body_mass_index_bmi                        7.58e-01 4.5e-01\n",
-       "weight                                    -6.91e-01 4.9e-01\n",
-       "standing_height                            2.25e-01 8.2e-01\n",
-       "systolic_blood_pressure                    3.73e+00 1.9e-04\n",
-       "diastolic_blood_pressure                   6.24e-01 5.3e-01\n",
-       "cholesterol                                7.66e-01 4.4e-01\n",
-       "hdl_cholesterol                           -2.13e+00 3.3e-02\n",
-       "ldl_direct                                -3.21e-01 7.5e-01\n",
-       "triglycerides                             -1.45e+00 1.5e-01\n",
-       "fh_heart_disease                           1.99e+00 4.7e-02\n",
-       "diabetes1                                 -1.61e+00 1.1e-01\n",
-       "diabetes2                                  2.64e+00 8.4e-03\n",
-       "chronic_kidney_disease                     2.25e+00 2.4e-02\n",
-       "atrial_fibrillation                        2.64e+00 8.2e-03\n",
-       "migraine                                   1.73e+00 8.4e-02\n",
-       "rheumatoid_arthritis                       7.17e-01 4.7e-01\n",
-       "systemic_lupus_erythematosus               8.52e-01 3.9e-01\n",
-       "severe_mental_illness                     -5.68e-04 1.0e+00\n",
-       "erectile_dysfunction                      -1.89e-01 8.5e-01\n",
-       "antihypertensives                         -1.31e+00 1.9e-01\n",
-       "ass                                        1.49e+00 1.4e-01\n",
-       "atypical_antipsychotics                    3.63e-02 9.7e-01\n",
-       "glucocorticoids                           -1.68e+01 0.0e+00\n",
-       "\n",
-       "                                          exp(coef) exp(-coef)     2.5%\n",
-       "age_at_recruitment                         1.767910      0.566 1.602010\n",
-       "ethnic_background_0.0                      0.919684      1.087 0.451094\n",
-       "ethnic_background_1.0                      0.790231      1.265 0.304122\n",
-       "ethnic_background_2.0                      1.178012      0.849 0.466371\n",
-       "ethnic_background_3.0                      0.305197      3.277 0.036744\n",
-       "ethnic_background_4.0                      0.000460   2172.523 0.000195\n",
-       "townsend_deprivation_index_at_recruitment  1.032069      0.969 0.959233\n",
-       "sex                                        1.966116      0.509 1.542827\n",
-       "overall_health_rating_0.0                  1.406432      0.711 0.491624\n",
-       "overall_health_rating_1.0                  1.130077      0.885 0.396680\n",
-       "overall_health_rating_2.0                  2.051881      0.487 0.682204\n",
-       "overall_health_rating_3.0                  0.812016      1.232 0.280091\n",
-       "smoking_status_0.0                         0.923340      1.083 0.360337\n",
-       "smoking_status_1.0                         0.655178      1.526 0.257309\n",
-       "smoking_status_2.0                         0.555778      1.799 0.218831\n",
-       "body_mass_index_bmi                        1.301603      0.768 0.658122\n",
-       "weight                                     0.754031      1.326 0.338560\n",
-       "standing_height                            1.053702      0.949 0.668565\n",
-       "systolic_blood_pressure                    1.238483      0.807 1.106913\n",
-       "diastolic_blood_pressure                   1.034798      0.966 0.929367\n",
-       "cholesterol                                1.164306      0.859 0.789038\n",
-       "hdl_cholesterol                            0.837290      1.194 0.711107\n",
-       "ldl_direct                                 0.944052      1.059 0.664422\n",
-       "triglycerides                              0.923178      1.083 0.828836\n",
-       "fh_heart_disease                           1.169898      0.855 1.002095\n",
-       "diabetes1                                  0.349879      2.858 0.097186\n",
-       "diabetes2                                  1.866990      0.536 1.173606\n",
-       "chronic_kidney_disease                     1.418361      0.705 1.046436\n",
-       "atrial_fibrillation                        1.867433      0.535 1.175137\n",
-       "migraine                                   1.287100      0.777 0.966517\n",
-       "rheumatoid_arthritis                       1.114040      0.898 0.829209\n",
-       "systemic_lupus_erythematosus               1.744744      0.573 0.484975\n",
-       "severe_mental_illness                      0.999929      1.000 0.782381\n",
-       "erectile_dysfunction                       0.962997      1.038 0.651853\n",
-       "antihypertensives                          0.581436      1.720 0.257861\n",
-       "ass                                        1.511168      0.662 0.876445\n",
-       "atypical_antipsychotics                    1.009965      0.990 0.591033\n",
-       "glucocorticoids                            0.000266   3756.604 0.000102\n",
-       "                                             97.5%\n",
-       "age_at_recruitment                        1.950991\n",
-       "ethnic_background_0.0                     1.875039\n",
-       "ethnic_background_1.0                     2.053341\n",
-       "ethnic_background_2.0                     2.975555\n",
-       "ethnic_background_3.0                     2.535013\n",
-       "ethnic_background_4.0                     0.001088\n",
-       "townsend_deprivation_index_at_recruitment 1.110436\n",
-       "sex                                       2.505539\n",
-       "overall_health_rating_0.0                 4.023506\n",
-       "overall_health_rating_1.0                 3.219411\n",
-       "overall_health_rating_2.0                 6.171491\n",
-       "overall_health_rating_3.0                 2.354124\n",
-       "smoking_status_0.0                        2.365996\n",
-       "smoking_status_1.0                        1.668259\n",
-       "smoking_status_2.0                        1.411541\n",
-       "body_mass_index_bmi                       2.574250\n",
-       "weight                                    1.679354\n",
-       "standing_height                           1.660702\n",
-       "systolic_blood_pressure                   1.385692\n",
-       "diastolic_blood_pressure                  1.152190\n",
-       "cholesterol                               1.718051\n",
-       "hdl_cholesterol                           0.985864\n",
-       "ldl_direct                                1.341368\n",
-       "triglycerides                             1.028258\n",
-       "fh_heart_disease                          1.365801\n",
-       "diabetes1                                 1.259600\n",
-       "diabetes2                                 2.970035\n",
-       "chronic_kidney_disease                    1.922476\n",
-       "atrial_fibrillation                       2.967575\n",
-       "migraine                                  1.714016\n",
-       "rheumatoid_arthritis                      1.496710\n",
-       "systemic_lupus_erythematosus              6.276880\n",
-       "severe_mental_illness                     1.277968\n",
-       "erectile_dysfunction                      1.422657\n",
-       "antihypertensives                         1.311043\n",
-       "ass                                       2.605559\n",
-       "atypical_antipsychotics                   1.725842\n",
-       "glucocorticoids                           0.000697\n",
-       "\n",
-       "Num. cases = 10000\n",
-       "Pseudo Log-likelihood = -6190 \n",
-       "Pseudo likelihood ratio test = 524  on 38 df,"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "summary(fit)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "10000"
-      ],
-      "text/latex": [
-       "10000"
-      ],
-      "text/markdown": [
-       "10000"
-      ],
-      "text/plain": [
-       "[1] 10000"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fit$n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "library(cmprsk)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "ERROR",
-     "evalue": "Error in predict.ccr(fit, data_cox %>% select(all_of(features[[\"clinical\"]]))): could not find function \"predict.ccr\"\n",
-     "output_type": "error",
-     "traceback": [
-      "Error in predict.ccr(fit, data_cox %>% select(all_of(features[[\"clinical\"]]))): could not find function \"predict.ccr\"\nTraceback:\n"
-     ]
-    }
-   ],
-   "source": [
-    "predict.ccr(fit, data_cox %>% select(all_of(features[[\"clinical\"]])))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e8c593612a5c472690bd06b5ea2d4eb7",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/2 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "data = {}\n",
-    "for group in tqdm(groups): \n",
-    "    data[group] = {\"features\":features[group]+[time, event]}\n",
-    "    for partition in partitions: \n",
-    "        data[group][partition] = {}\n",
-    "        for split in splits: data[group][partition][split] = data_all[partition][split].loc[:, data[group][\"features\"]].copy()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from lifelines.utils import concordance_index\n",
-    "def fit_predict_coxph(data_h5ad, group, partition, time, event, pickle_path):\n",
-    "    cph = CoxPHFitter()\n",
-    "    train_data, val_data, test_data = data_h5ad[group][partition][\"train\"], data_h5ad[group][partition][\"valid\"], data_h5ad[group][partition][\"test\"]\n",
-    "    covariates_with_tte = data[group][\"features\"]\n",
-    "    cph.fit(train_data[covariates_with_tte], duration_col=time, event_col=event, show_progress=True, step_size=0.5)\n",
-    "    pickle.dump(cph, open(pickle_path, \"wb\" ) )\n",
-    "    print(concordance_index(val_data[time], -cph.predict_partial_hazard(val_data[covariates_with_tte]), val_data[event]))\n",
-    "    display(val_data[covariates_with_tte])\n",
-    "    surv_train = 1-cph.predict_survival_function(train_data[covariates_with_tte], times=[11]) # as years + 1\n",
-    "    surv_train_all = 1-cph.predict_survival_function(train_data[covariates_with_tte], times=[t for t in range(1,27)])\n",
-    "    display(surv_train)\n",
-    "    surv_val = 1-cph.predict_survival_function(val_data[covariates_with_tte], times=[11]) # as years + 1  \n",
-    "    surv_val_all = 1-cph.predict_survival_function(val_data[covariates_with_tte], times=[t for t in range(1,27)]) # as years + 1  \n",
-    "    surv_test = 1-cph.predict_survival_function(test_data[covariates_with_tte], times=[11]) # as years + 1  \n",
-    "    surv_test_all = 1-cph.predict_survival_function(test_data[covariates_with_tte], times=[t for t in range(1,27)]) # as years + 1  \n",
-    "    pred = {\"train\":train_data.reset_index()[[\"eid\"]],\n",
-    "            \"val\":val_data.reset_index()[[\"eid\"]],\n",
-    "           \"test\":test_data.reset_index()[[\"eid\"]],}\n",
-    "    pred[\"train\"][f\"score_COX_{group}\"] = surv_train.iloc[0].to_list()\n",
-    "    pred[\"val\"][f\"score_COX_{group}\"] = surv_val.iloc[0].to_list()\n",
-    "    pred[\"test\"][f\"score_COX_{group}\"] = surv_test.iloc[0].to_list()\n",
-    "    \n",
-    "    for t in range(1, 27):\n",
-    "        prefix = f\"0_{t}_Ft_COX_{group}\"\n",
-    "        pred[\"train\"][prefix] = surv_train_all.T[t].to_list()\n",
-    "        pred[\"val\"][prefix] = surv_val_all.T[t].to_list()\n",
-    "        pred[\"test\"][prefix] = surv_test_all.T[t].to_list()\n",
-    "        \n",
-    "    return pd.concat([pred[\"train\"].assign(partition=partition, split=\"train\"), pred[\"val\"].assign(partition=partition, split=\"valid\"), pred[\"test\"].assign(partition=partition, split=\"test\")])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "bb64705f77c0499599f8cc6ddad4c860",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/2 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "clinical\n",
-      "0\n",
-      "Iteration 1: norm_delta = 0.81033, step_size = 0.5000, log_lik = -303524.54541, newton_decrement = 8794.12765, seconds_since_start = 5.8\n",
-      "Iteration 2: norm_delta = 0.40673, step_size = 0.5000, log_lik = -297055.06313, newton_decrement = 2122.22326, seconds_since_start = 11.2\n",
-      "Iteration 3: norm_delta = 0.23225, step_size = 0.5000, log_lik = -295450.28202, newton_decrement = 620.42031, seconds_since_start = 16.6\n",
-      "Iteration 4: norm_delta = 0.10579, step_size = 0.6000, log_lik = -294924.48865, newton_decrement = 116.26797, seconds_since_start = 22.1\n",
-      "Iteration 5: norm_delta = 0.03280, step_size = 0.7200, log_lik = -294816.58306, newton_decrement = 10.41973, seconds_since_start = 27.5\n",
-      "Iteration 6: norm_delta = 0.00481, step_size = 0.8640, log_lik = -294806.31729, newton_decrement = 0.21500, seconds_since_start = 33.2\n",
-      "Iteration 7: norm_delta = 0.00001, step_size = 1.0000, log_lik = -294806.10209, newton_decrement = 0.00000, seconds_since_start = 38.8\n",
-      "Iteration 8: norm_delta = 0.00000, step_size = 1.0000, log_lik = -294806.10209, newton_decrement = 0.00000, seconds_since_start = 44.6\n",
-      "Convergence success after 8 iterations.\n",
-      "0.7355226854628919\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
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-       "      <th></th>\n",
-       "      <th>age_at_recruitment</th>\n",
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-       "      <th>migraine</th>\n",
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-       "      <th>severe_mental_illness</th>\n",
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-       "         age_at_recruitment  ethnic_background_0.0  ethnic_background_1.0  \\\n",
-       "eid                                                                         \n",
-       "5773759            1.683621                      1                      0   \n",
-       "5773764            0.942714                      1                      0   \n",
-       "5773777            0.325291                      1                      0   \n",
-       "5773788           -1.773947                      1                      0   \n",
-       "5773796           -0.786070                      1                      0   \n",
-       "...                     ...                    ...                    ...   \n",
-       "6025147           -1.526978                      0                      0   \n",
-       "6025150           -1.526978                      1                      0   \n",
-       "6025165           -1.280009                      1                      0   \n",
-       "6025182            0.078322                      1                      0   \n",
-       "6025198            1.436652                      1                      0   \n",
-       "\n",
-       "         ethnic_background_2.0  ethnic_background_3.0  ethnic_background_4.0  \\\n",
-       "eid                                                                            \n",
-       "5773759                      0                      0                      0   \n",
-       "5773764                      0                      0                      0   \n",
-       "5773777                      0                      0                      0   \n",
-       "5773788                      0                      0                      0   \n",
-       "5773796                      0                      0                      0   \n",
-       "...                        ...                    ...                    ...   \n",
-       "6025147                      1                      0                      0   \n",
-       "6025150                      0                      0                      0   \n",
-       "6025165                      0                      0                      0   \n",
-       "6025182                      0                      0                      0   \n",
-       "6025198                      0                      0                      0   \n",
-       "\n",
-       "         townsend_deprivation_index_at_recruitment  sex  \\\n",
-       "eid                                                       \n",
-       "5773759                                   0.301657  1.0   \n",
-       "5773764                                   0.149831  1.0   \n",
-       "5773777                                  -0.784237  0.0   \n",
-       "5773788                                   0.695582  0.0   \n",
-       "5773796                                   0.667375  0.0   \n",
-       "...                                            ...  ...   \n",
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-       "eid                                                            ...              \n",
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-       "eid                                                              \n",
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-       "eid                                                           \n",
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-       "eid                                                                     \n",
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-      "clinical\n",
-      "1\n",
-      "Iteration 1: norm_delta = 0.88708, step_size = 0.5000, log_lik = -301749.28339, newton_decrement = 8771.80829, seconds_since_start = 5.2\n",
-      "Iteration 2: norm_delta = 0.40479, step_size = 0.5000, log_lik = -295306.15540, newton_decrement = 2094.66191, seconds_since_start = 9.8\n",
-      "Iteration 3: norm_delta = 0.22763, step_size = 0.5000, log_lik = -293722.43856, newton_decrement = 610.64566, seconds_since_start = 14.8\n",
-      "Iteration 4: norm_delta = 0.10283, step_size = 0.6000, log_lik = -293205.03206, newton_decrement = 114.01681, seconds_since_start = 19.9\n",
-      "Iteration 5: norm_delta = 0.03170, step_size = 0.7200, log_lik = -293099.23778, newton_decrement = 10.17535, seconds_since_start = 25.0\n",
-      "Iteration 6: norm_delta = 0.00462, step_size = 0.8640, log_lik = -293089.21444, newton_decrement = 0.20888, seconds_since_start = 30.2\n",
-      "Iteration 7: norm_delta = 0.00001, step_size = 1.0000, log_lik = -293089.00538, newton_decrement = 0.00000, seconds_since_start = 35.6\n",
-      "Iteration 8: norm_delta = 0.00000, step_size = 1.0000, log_lik = -293089.00538, newton_decrement = 0.00000, seconds_since_start = 40.4\n",
-      "Convergence success after 8 iterations.\n",
-      "0.7369282503558089\n"
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-       "         age_at_recruitment  ethnic_background_0.0  ethnic_background_1.0  \\\n",
-       "eid                                                                         \n",
-       "5773886            1.188748                      1                      0   \n",
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-       "5773928           -1.033889                      1                      0   \n",
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-       "5773944           -0.910410                      1                      0   \n",
-       "...                     ...                    ...                    ...   \n",
-       "6025150           -1.527809                      1                      0   \n",
-       "6025165           -1.280849                      1                      0   \n",
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-       "6025182            0.077429                      1                      0   \n",
-       "6025198            1.435708                      1                      0   \n",
-       "\n",
-       "         ethnic_background_2.0  ethnic_background_3.0  ethnic_background_4.0  \\\n",
-       "eid                                                                            \n",
-       "5773886                      0                      0                      0   \n",
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-       "6025198                      0                      0                      0   \n",
-       "\n",
-       "         townsend_deprivation_index_at_recruitment  sex  \\\n",
-       "eid                                                       \n",
-       "5773886                                  -0.912450  1.0   \n",
-       "5773895                                  -0.813290  0.0   \n",
-       "5773928                                   0.716580  0.0   \n",
-       "5773935                                  -0.874358  0.0   \n",
-       "5773944                                  -0.463479  0.0   \n",
-       "...                                            ...  ...   \n",
-       "6025150                                   0.458705  0.0   \n",
-       "6025165                                  -0.245528  0.0   \n",
-       "6025173                                  -0.154035  1.0   \n",
-       "6025182                                   0.439890  1.0   \n",
-       "6025198                                  -0.187854  1.0   \n",
-       "\n",
-       "         overall_health_rating_0.0  overall_health_rating_1.0  ...  diabetes2  \\\n",
-       "eid                                                            ...              \n",
-       "5773886                          1                          0  ...       True   \n",
-       "5773895                          0                          1  ...      False   \n",
-       "5773928                          0                          1  ...      False   \n",
-       "5773935                          0                          1  ...      False   \n",
-       "5773944                          0                          0  ...      False   \n",
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-       "6025198                          0                          1  ...      False   \n",
-       "\n",
-       "         chronic_kidney_disease  atrial_fibrillation  migraine  \\\n",
-       "eid                                                              \n",
-       "5773886                   False                 True     False   \n",
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-       "...                         ...                  ...       ...   \n",
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-       "6025198                    True                False     False   \n",
-       "\n",
-       "         rheumatoid_arthritis  systemic_lupus_erythematosus  \\\n",
-       "eid                                                           \n",
-       "5773886                  True                         False   \n",
-       "5773895                 False                         False   \n",
-       "5773928                 False                         False   \n",
-       "5773935                 False                         False   \n",
-       "5773944                 False                         False   \n",
-       "...                       ...                           ...   \n",
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-       "6025173                 False                         False   \n",
-       "6025182                 False                         False   \n",
-       "6025198                 False                         False   \n",
-       "\n",
-       "         severe_mental_illness  erectile_dysfunction  MACE_event_time  \\\n",
-       "eid                                                                     \n",
-       "5773886                   True                  True        11.331964   \n",
-       "5773895                  False                 False        10.483231   \n",
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-       "5773944                   True                 False        12.366872   \n",
-       "...                        ...                   ...              ...   \n",
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-       "6025182                  False                 False        10.250513   \n",
-       "6025198                  False                 False        10.677618   \n",
-       "\n",
-       "         MACE_event  \n",
-       "eid                  \n",
-       "5773886           0  \n",
-       "5773895           0  \n",
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-       "5773935           0  \n",
-       "5773944           0  \n",
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-       "      1000018  1000020   1000043   1000079   1000084   1000092   1000110  \\\n",
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-     "text": [
-      "clinical\n",
-      "2\n",
-      "Iteration 1: norm_delta = 0.85475, step_size = 0.5000, log_lik = -302233.28004, newton_decrement = 8897.11939, seconds_since_start = 4.7\n",
-      "Iteration 2: norm_delta = 0.41055, step_size = 0.5000, log_lik = -295700.37866, newton_decrement = 2125.67835, seconds_since_start = 9.5\n",
-      "Iteration 3: norm_delta = 0.23200, step_size = 0.5000, log_lik = -294093.12182, newton_decrement = 620.37684, seconds_since_start = 14.8\n",
-      "Iteration 4: norm_delta = 0.10492, step_size = 0.6000, log_lik = -293567.42865, newton_decrement = 115.99847, seconds_since_start = 20.0\n",
-      "Iteration 5: norm_delta = 0.03235, step_size = 0.7200, log_lik = -293459.78771, newton_decrement = 10.36657, seconds_since_start = 25.1\n",
-      "Iteration 6: norm_delta = 0.00472, step_size = 0.8640, log_lik = -293449.57558, newton_decrement = 0.21305, seconds_since_start = 31.3\n",
-      "Iteration 7: norm_delta = 0.00001, step_size = 1.0000, log_lik = -293449.36234, newton_decrement = 0.00000, seconds_since_start = 36.6\n",
-      "Iteration 8: norm_delta = 0.00000, step_size = 1.0000, log_lik = -293449.36234, newton_decrement = 0.00000, seconds_since_start = 41.4\n",
-      "Convergence success after 8 iterations.\n",
-      "0.7312492235208388\n"
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-       "         age_at_recruitment  ethnic_background_0.0  ethnic_background_1.0  \\\n",
-       "eid                                                                         \n",
-       "5772660           -0.046225                      1                      0   \n",
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-       "5772717            0.941529                      1                      0   \n",
-       "...                     ...                    ...                    ...   \n",
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-       "\n",
-       "         ethnic_background_2.0  ethnic_background_3.0  ethnic_background_4.0  \\\n",
-       "eid                                                                            \n",
-       "5772660                      0                      0                      0   \n",
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-       "6025173                      0                      0                      0   \n",
-       "6025182                      0                      0                      0   \n",
-       "\n",
-       "         townsend_deprivation_index_at_recruitment  sex  \\\n",
-       "eid                                                       \n",
-       "5772660                                  -0.419361  1.0   \n",
-       "5772678                                  -0.976129  0.0   \n",
-       "5772693                                   0.084991  1.0   \n",
-       "5772709                                  -0.438517  1.0   \n",
-       "5772717                                   1.230596  1.0   \n",
-       "...                                            ...  ...   \n",
-       "6025131                                  -0.505083  1.0   \n",
-       "6025147                                   0.055598  1.0   \n",
-       "6025165                                  -0.245955  0.0   \n",
-       "6025173                                  -0.154497  1.0   \n",
-       "6025182                                   0.439208  1.0   \n",
-       "\n",
-       "         overall_health_rating_0.0  overall_health_rating_1.0  ...  diabetes2  \\\n",
-       "eid                                                            ...              \n",
-       "5772660                          0                          1  ...      False   \n",
-       "5772678                          0                          1  ...      False   \n",
-       "5772693                          0                          0  ...      False   \n",
-       "5772709                          0                          0  ...      False   \n",
-       "5772717                          0                          0  ...      False   \n",
-       "...                            ...                        ...  ...        ...   \n",
-       "6025131                          1                          0  ...      False   \n",
-       "6025147                          0                          1  ...      False   \n",
-       "6025165                          0                          1  ...      False   \n",
-       "6025173                          0                          1  ...      False   \n",
-       "6025182                          0                          0  ...      False   \n",
-       "\n",
-       "         chronic_kidney_disease  atrial_fibrillation  migraine  \\\n",
-       "eid                                                              \n",
-       "5772660                   False                False     False   \n",
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-       "...                         ...                  ...       ...   \n",
-       "6025131                   False                False     False   \n",
-       "6025147                   False                False     False   \n",
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-       "6025173                   False                False     False   \n",
-       "6025182                   False                False     False   \n",
-       "\n",
-       "         rheumatoid_arthritis  systemic_lupus_erythematosus  \\\n",
-       "eid                                                           \n",
-       "5772660                 False                         False   \n",
-       "5772678                 False                         False   \n",
-       "5772693                 False                         False   \n",
-       "5772709                 False                         False   \n",
-       "5772717                 False                         False   \n",
-       "...                       ...                           ...   \n",
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-       "6025165                 False                         False   \n",
-       "6025173                 False                         False   \n",
-       "6025182                 False                         False   \n",
-       "\n",
-       "         severe_mental_illness  erectile_dysfunction  MACE_event_time  \\\n",
-       "eid                                                                     \n",
-       "5772660                  False                 False        11.956194   \n",
-       "5772678                  False                 False        12.057495   \n",
-       "5772693                  False                 False        11.288159   \n",
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-       "5772717                  False                 False        10.431211   \n",
-       "...                        ...                   ...              ...   \n",
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-       "6025147                  False                 False        11.879535   \n",
-       "6025165                  False                 False        12.076660   \n",
-       "6025173                  False                 False        12.035592   \n",
-       "6025182                  False                 False        10.250513   \n",
-       "\n",
-       "         MACE_event  \n",
-       "eid                  \n",
-       "5772660           0  \n",
-       "5772678           0  \n",
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-       "5772709           0  \n",
-       "5772717           0  \n",
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-       "\n",
-       "[17809 rows x 40 columns]"
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-       "       1000020   1000037  1000043   1000079   1000084   1000107   1000110  \\\n",
-       "11.0  0.141977  0.055692  0.13488  0.078594  0.057122  0.018252  0.014405   \n",
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-     "text": [
-      "clinical\n",
-      "3\n",
-      "Iteration 1: norm_delta = 0.82225, step_size = 0.5000, log_lik = -303597.59644, newton_decrement = 8833.91307, seconds_since_start = 4.6\n",
-      "Iteration 2: norm_delta = 0.40702, step_size = 0.5000, log_lik = -297107.37481, newton_decrement = 2112.81535, seconds_since_start = 9.2\n",
-      "Iteration 3: norm_delta = 0.23149, step_size = 0.5000, log_lik = -295509.90103, newton_decrement = 616.22740, seconds_since_start = 14.3\n",
-      "Iteration 4: norm_delta = 0.10508, step_size = 0.6000, log_lik = -294987.73984, newton_decrement = 115.15510, seconds_since_start = 19.5\n",
-      "Iteration 5: norm_delta = 0.03244, step_size = 0.7200, log_lik = -294880.88533, newton_decrement = 10.28292, seconds_since_start = 24.1\n",
-      "Iteration 6: norm_delta = 0.00473, step_size = 0.8640, log_lik = -294870.75601, newton_decrement = 0.21104, seconds_since_start = 28.7\n",
-      "Iteration 7: norm_delta = 0.00001, step_size = 1.0000, log_lik = -294870.54479, newton_decrement = 0.00000, seconds_since_start = 33.3\n",
-      "Iteration 8: norm_delta = 0.00000, step_size = 1.0000, log_lik = -294870.54479, newton_decrement = 0.00000, seconds_since_start = 39.4\n",
-      "Convergence success after 8 iterations.\n",
-      "0.7299441336455134\n"
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-       "         age_at_recruitment  ethnic_background_0.0  ethnic_background_1.0  \\\n",
-       "eid                                                                         \n",
-       "5773031           -0.293248                      1                      0   \n",
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-       "5773082            1.065288                      1                      0   \n",
-       "...                     ...                    ...                    ...   \n",
-       "6025150           -1.528280                      1                      0   \n",
-       "6025165           -1.281274                      1                      0   \n",
-       "6025173            0.200765                      1                      0   \n",
-       "6025182            0.077262                      1                      0   \n",
-       "6025198            1.435798                      1                      0   \n",
-       "\n",
-       "         ethnic_background_2.0  ethnic_background_3.0  ethnic_background_4.0  \\\n",
-       "eid                                                                            \n",
-       "5773031                      0                      0                      0   \n",
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-       "6025182                      0                      0                      0   \n",
-       "6025198                      0                      0                      0   \n",
-       "\n",
-       "         townsend_deprivation_index_at_recruitment  sex  \\\n",
-       "eid                                                       \n",
-       "5773031                                  -0.140006  0.0   \n",
-       "5773047                                  -1.316792  1.0   \n",
-       "5773050                                  -1.499979  1.0   \n",
-       "5773065                                  -0.692253  0.0   \n",
-       "5773082                                   1.067854  1.0   \n",
-       "...                                            ...  ...   \n",
-       "6025150                                   0.458667  0.0   \n",
-       "6025165                                  -0.245111  0.0   \n",
-       "6025173                                  -0.153678  1.0   \n",
-       "6025182                                   0.439864  1.0   \n",
-       "6025198                                  -0.187474  1.0   \n",
-       "\n",
-       "         overall_health_rating_0.0  overall_health_rating_1.0  ...  diabetes2  \\\n",
-       "eid                                                            ...              \n",
-       "5773031                          0                          1  ...      False   \n",
-       "5773047                          0                          0  ...      False   \n",
-       "5773050                          0                          1  ...      False   \n",
-       "5773065                          0                          1  ...      False   \n",
-       "5773082                          0                          1  ...      False   \n",
-       "...                            ...                        ...  ...        ...   \n",
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-       "6025198                          0                          1  ...      False   \n",
-       "\n",
-       "         chronic_kidney_disease  atrial_fibrillation  migraine  \\\n",
-       "eid                                                              \n",
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-       "6025165                   False                False     False   \n",
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-       "6025182                   False                False     False   \n",
-       "6025198                    True                False     False   \n",
-       "\n",
-       "         rheumatoid_arthritis  systemic_lupus_erythematosus  \\\n",
-       "eid                                                           \n",
-       "5773031                 False                         False   \n",
-       "5773047                 False                         False   \n",
-       "5773050                 False                         False   \n",
-       "5773065                 False                         False   \n",
-       "5773082                 False                         False   \n",
-       "...                       ...                           ...   \n",
-       "6025150                 False                         False   \n",
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-       "6025173                 False                         False   \n",
-       "6025182                 False                         False   \n",
-       "6025198                 False                         False   \n",
-       "\n",
-       "         severe_mental_illness  erectile_dysfunction  MACE_event_time  \\\n",
-       "eid                                                                     \n",
-       "5773031                   True                 False        12.895277   \n",
-       "5773047                  False                 False        12.479124   \n",
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-       "5773065                  False                 False        12.136893   \n",
-       "5773082                  False                  True        10.543463   \n",
-       "...                        ...                   ...              ...   \n",
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-       "6025182                  False                 False        10.250513   \n",
-       "6025198                  False                 False        10.677618   \n",
-       "\n",
-       "         MACE_event  \n",
-       "eid                  \n",
-       "5773031           0  \n",
-       "5773047           0  \n",
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-       "5773065           0  \n",
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-       "       1000018   1000020   1000037  1000043   1000079   1000084  1000092  \\\n",
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-     "text": [
-      "clinical\n",
-      "4\n",
-      "Iteration 1: norm_delta = 0.86783, step_size = 0.5000, log_lik = -302316.54463, newton_decrement = 8830.58356, seconds_since_start = 5.7\n",
-      "Iteration 2: norm_delta = 0.40842, step_size = 0.5000, log_lik = -295830.19130, newton_decrement = 2107.72553, seconds_since_start = 11.1\n",
-      "Iteration 3: norm_delta = 0.23159, step_size = 0.5000, log_lik = -294236.60001, newton_decrement = 614.52354, seconds_since_start = 16.3\n",
-      "Iteration 4: norm_delta = 0.10517, step_size = 0.6000, log_lik = -293715.89345, newton_decrement = 114.79972, seconds_since_start = 21.3\n",
-      "Iteration 5: norm_delta = 0.03251, step_size = 0.7200, log_lik = -293609.36992, newton_decrement = 10.24957, seconds_since_start = 26.6\n",
-      "Iteration 6: norm_delta = 0.00475, step_size = 0.8640, log_lik = -293599.27345, newton_decrement = 0.21038, seconds_since_start = 31.4\n",
-      "Iteration 7: norm_delta = 0.00001, step_size = 1.0000, log_lik = -293599.06290, newton_decrement = 0.00000, seconds_since_start = 36.0\n",
-      "Iteration 8: norm_delta = 0.00000, step_size = 1.0000, log_lik = -293599.06290, newton_decrement = 0.00000, seconds_since_start = 40.8\n",
-      "Convergence success after 8 iterations.\n",
-      "0.7305989964767126\n"
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-       "         age_at_recruitment  ethnic_background_0.0  ethnic_background_1.0  \\\n",
-       "eid                                                                         \n",
-       "5773047           -0.540188                      1                      0   \n",
-       "5773050           -1.034344                      1                      0   \n",
-       "5773065            0.571662                      1                      0   \n",
-       "5773073            0.942279                      1                      0   \n",
-       "5773082            1.065818                      1                      0   \n",
-       "...                     ...                    ...                    ...   \n",
-       "6025150           -1.528500                      1                      0   \n",
-       "6025165           -1.281422                      1                      0   \n",
-       "6025173            0.201046                      1                      0   \n",
-       "6025182            0.077507                      1                      0   \n",
-       "6025198            1.436435                      1                      0   \n",
-       "\n",
-       "         ethnic_background_2.0  ethnic_background_3.0  ethnic_background_4.0  \\\n",
-       "eid                                                                            \n",
-       "5773047                      0                      0                      0   \n",
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-       "6025182                      0                      0                      0   \n",
-       "6025198                      0                      0                      0   \n",
-       "\n",
-       "         townsend_deprivation_index_at_recruitment  sex  \\\n",
-       "eid                                                       \n",
-       "5773047                                  -1.318831  1.0   \n",
-       "5773050                                  -1.502140  1.0   \n",
-       "5773065                                  -0.693879  0.0   \n",
-       "5773073                                  -0.526958  0.0   \n",
-       "5773082                                   1.067394  1.0   \n",
-       "...                                            ...  ...   \n",
-       "6025150                                   0.457803  0.0   \n",
-       "6025165                                  -0.246441  0.0   \n",
-       "6025173                                  -0.154947  1.0   \n",
-       "6025182                                   0.438988  1.0   \n",
-       "6025198                                  -0.188766  1.0   \n",
-       "\n",
-       "         overall_health_rating_0.0  overall_health_rating_1.0  ...  diabetes2  \\\n",
-       "eid                                                            ...              \n",
-       "5773047                          0                          0  ...      False   \n",
-       "5773050                          0                          1  ...      False   \n",
-       "5773065                          0                          1  ...      False   \n",
-       "5773073                          1                          0  ...      False   \n",
-       "5773082                          0                          1  ...      False   \n",
-       "...                            ...                        ...  ...        ...   \n",
-       "6025150                          0                          0  ...      False   \n",
-       "6025165                          0                          1  ...      False   \n",
-       "6025173                          0                          1  ...      False   \n",
-       "6025182                          0                          0  ...      False   \n",
-       "6025198                          0                          1  ...      False   \n",
-       "\n",
-       "         chronic_kidney_disease  atrial_fibrillation  migraine  \\\n",
-       "eid                                                              \n",
-       "5773047                   False                False     False   \n",
-       "5773050                   False                False     False   \n",
-       "5773065                   False                False     False   \n",
-       "5773073                   False                False     False   \n",
-       "5773082                   False                False     False   \n",
-       "...                         ...                  ...       ...   \n",
-       "6025150                   False                False     False   \n",
-       "6025165                   False                False     False   \n",
-       "6025173                   False                False     False   \n",
-       "6025182                   False                False     False   \n",
-       "6025198                    True                False     False   \n",
-       "\n",
-       "         rheumatoid_arthritis  systemic_lupus_erythematosus  \\\n",
-       "eid                                                           \n",
-       "5773047                 False                         False   \n",
-       "5773050                 False                         False   \n",
-       "5773065                 False                         False   \n",
-       "5773073                 False                         False   \n",
-       "5773082                 False                         False   \n",
-       "...                       ...                           ...   \n",
-       "6025150                 False                         False   \n",
-       "6025165                 False                         False   \n",
-       "6025173                 False                         False   \n",
-       "6025182                 False                         False   \n",
-       "6025198                 False                         False   \n",
-       "\n",
-       "         severe_mental_illness  erectile_dysfunction  MACE_event_time  \\\n",
-       "eid                                                                     \n",
-       "5773047                  False                 False        12.479124   \n",
-       "5773050                  False                 False        11.011636   \n",
-       "5773065                  False                 False        12.136893   \n",
-       "5773073                   True                 False        12.558522   \n",
-       "5773082                  False                  True        10.543463   \n",
-       "...                        ...                   ...              ...   \n",
-       "6025150                  False                 False        13.253936   \n",
-       "6025165                  False                 False        12.076660   \n",
-       "6025173                  False                 False        12.035592   \n",
-       "6025182                  False                 False        10.250513   \n",
-       "6025198                  False                 False        10.677618   \n",
-       "\n",
-       "         MACE_event  \n",
-       "eid                  \n",
-       "5773047           0  \n",
-       "5773050           0  \n",
-       "5773065           0  \n",
-       "5773073           0  \n",
-       "5773082           0  \n",
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-       "6025198           0  \n",
-       "\n",
-       "[17809 rows x 40 columns]"
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-       "       1000018   1000020   1000037   1000043   1000084   1000092   1000107  \\\n",
-       "11.0  0.058033  0.139988  0.044844  0.137486  0.057704  0.091478  0.018506   \n",
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-       "\n",
-       "       5772984   5772992   5773004   5773016   5773029   5773031  \n",
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-     "text": [
-      "clinical\n",
-      "5\n",
-      "Iteration 1: norm_delta = 0.86084, step_size = 0.5000, log_lik = -304721.60878, newton_decrement = 8805.61526, seconds_since_start = 4.6\n",
-      "Iteration 2: norm_delta = 0.40803, step_size = 0.5000, log_lik = -298240.06389, newton_decrement = 2125.29212, seconds_since_start = 9.7\n",
-      "Iteration 3: norm_delta = 0.23051, step_size = 0.5000, log_lik = -296633.01666, newton_decrement = 620.81947, seconds_since_start = 14.5\n",
-      "Iteration 4: norm_delta = 0.10443, step_size = 0.6000, log_lik = -296106.92724, newton_decrement = 116.17579, seconds_since_start = 19.1\n",
-      "Iteration 5: norm_delta = 0.03230, step_size = 0.7200, log_lik = -295999.11525, newton_decrement = 10.39756, seconds_since_start = 23.8\n",
-      "Iteration 6: norm_delta = 0.00474, step_size = 0.8640, log_lik = -295988.87165, newton_decrement = 0.21437, seconds_since_start = 28.3\n",
-      "Iteration 7: norm_delta = 0.00001, step_size = 1.0000, log_lik = -295988.65708, newton_decrement = 0.00000, seconds_since_start = 33.0\n",
-      "Iteration 8: norm_delta = 0.00000, step_size = 1.0000, log_lik = -295988.65708, newton_decrement = 0.00000, seconds_since_start = 37.8\n",
-      "Convergence success after 8 iterations.\n",
-      "0.7348317370771167\n"
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-       "      <th></th>\n",
-       "      <th>age_at_recruitment</th>\n",
-       "      <th>ethnic_background_0.0</th>\n",
-       "      <th>ethnic_background_1.0</th>\n",
-       "      <th>ethnic_background_2.0</th>\n",
-       "      <th>ethnic_background_3.0</th>\n",
-       "      <th>ethnic_background_4.0</th>\n",
-       "      <th>townsend_deprivation_index_at_recruitment</th>\n",
-       "      <th>sex</th>\n",
-       "      <th>overall_health_rating_0.0</th>\n",
-       "      <th>overall_health_rating_1.0</th>\n",
-       "      <th>...</th>\n",
-       "      <th>diabetes2</th>\n",
-       "      <th>chronic_kidney_disease</th>\n",
-       "      <th>atrial_fibrillation</th>\n",
-       "      <th>migraine</th>\n",
-       "      <th>rheumatoid_arthritis</th>\n",
-       "      <th>systemic_lupus_erythematosus</th>\n",
-       "      <th>severe_mental_illness</th>\n",
-       "      <th>erectile_dysfunction</th>\n",
-       "      <th>MACE_event_time</th>\n",
-       "      <th>MACE_event</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>eid</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
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-       "      <th></th>\n",
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-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>5772905</th>\n",
-       "      <td>-1.281261</td>\n",
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-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>11.665982</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5772920</th>\n",
-       "      <td>0.324289</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>-0.182328</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>12.668036</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5772951</th>\n",
-       "      <td>0.694801</td>\n",
-       "      <td>1</td>\n",
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-       "      <td>0</td>\n",
-       "      <td>0.702923</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>12.065708</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5772966</th>\n",
-       "      <td>-0.910749</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>-0.369847</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>3.581109</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5772979</th>\n",
-       "      <td>0.818305</td>\n",
-       "      <td>1</td>\n",
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-       "      <td>0.241004</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
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-       "      <td>10.507871</td>\n",
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-       "    <tr>\n",
-       "      <th>6025147</th>\n",
-       "      <td>-1.528269</td>\n",
-       "      <td>0</td>\n",
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-       "      <td>0.056636</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
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-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>11.879535</td>\n",
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-       "      <td>-1.528269</td>\n",
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-       "      <td>False</td>\n",
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-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
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-       "      <td>False</td>\n",
-       "      <td>13.253936</td>\n",
-       "      <td>0</td>\n",
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-       "      <th>6025173</th>\n",
-       "      <td>0.200785</td>\n",
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-       "      <td>-0.153607</td>\n",
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-       "      <td>1</td>\n",
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-       "      <td>False</td>\n",
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-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
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-       "      <td>False</td>\n",
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-       "      <td>12.035592</td>\n",
-       "      <td>0</td>\n",
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-       "    <tr>\n",
-       "      <th>6025182</th>\n",
-       "      <td>0.077282</td>\n",
-       "      <td>1</td>\n",
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-       "      <td>False</td>\n",
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-       "      <td>10.250513</td>\n",
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-       "      <td>10.677618</td>\n",
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-       "<p>17809 rows × 40 columns</p>\n",
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-       "         age_at_recruitment  ethnic_background_0.0  ethnic_background_1.0  \\\n",
-       "eid                                                                         \n",
-       "5772905           -1.281261                      1                      0   \n",
-       "5772920            0.324289                      1                      0   \n",
-       "5772951            0.694801                      1                      0   \n",
-       "5772966           -0.910749                      1                      0   \n",
-       "5772979            0.818305                      1                      0   \n",
-       "...                     ...                    ...                    ...   \n",
-       "6025147           -1.528269                      0                      0   \n",
-       "6025150           -1.528269                      1                      0   \n",
-       "6025173            0.200785                      1                      0   \n",
-       "6025182            0.077282                      1                      0   \n",
-       "6025198            1.435824                      1                      0   \n",
-       "\n",
-       "         ethnic_background_2.0  ethnic_background_3.0  ethnic_background_4.0  \\\n",
-       "eid                                                                            \n",
-       "5772905                      0                      0                      0   \n",
-       "5772920                      0                      0                      0   \n",
-       "5772951                      0                      0                      0   \n",
-       "5772966                      0                      0                      0   \n",
-       "5772979                      0                      0                      0   \n",
-       "...                        ...                    ...                    ...   \n",
-       "6025147                      1                      0                      0   \n",
-       "6025150                      0                      0                      0   \n",
-       "6025173                      0                      0                      0   \n",
-       "6025182                      0                      0                      0   \n",
-       "6025198                      0                      0                      0   \n",
-       "\n",
-       "         townsend_deprivation_index_at_recruitment  sex  \\\n",
-       "eid                                                       \n",
-       "5772905                                   1.390055  0.0   \n",
-       "5772920                                  -0.182328  1.0   \n",
-       "5772951                                   0.702923  0.0   \n",
-       "5772966                                  -0.369847  0.0   \n",
-       "5772979                                   0.241004  1.0   \n",
-       "...                                            ...  ...   \n",
-       "6025147                                   0.056636  1.0   \n",
-       "6025150                                   0.459341  0.0   \n",
-       "6025173                                  -0.153607  1.0   \n",
-       "6025182                                   0.440520  1.0   \n",
-       "6025198                                  -0.187437  1.0   \n",
-       "\n",
-       "         overall_health_rating_0.0  overall_health_rating_1.0  ...  diabetes2  \\\n",
-       "eid                                                            ...              \n",
-       "5772905                          1                          0  ...      False   \n",
-       "5772920                          0                          1  ...      False   \n",
-       "5772951                          0                          1  ...      False   \n",
-       "5772966                          1                          0  ...      False   \n",
-       "5772979                          0                          0  ...      False   \n",
-       "...                            ...                        ...  ...        ...   \n",
-       "6025147                          0                          1  ...      False   \n",
-       "6025150                          0                          0  ...      False   \n",
-       "6025173                          0                          1  ...      False   \n",
-       "6025182                          0                          0  ...      False   \n",
-       "6025198                          0                          1  ...      False   \n",
-       "\n",
-       "         chronic_kidney_disease  atrial_fibrillation  migraine  \\\n",
-       "eid                                                              \n",
-       "5772905                   False                False     False   \n",
-       "5772920                   False                False     False   \n",
-       "5772951                   False                False     False   \n",
-       "5772966                   False                False     False   \n",
-       "5772979                   False                False     False   \n",
-       "...                         ...                  ...       ...   \n",
-       "6025147                   False                False     False   \n",
-       "6025150                   False                False     False   \n",
-       "6025173                   False                False     False   \n",
-       "6025182                   False                False     False   \n",
-       "6025198                    True                False     False   \n",
-       "\n",
-       "         rheumatoid_arthritis  systemic_lupus_erythematosus  \\\n",
-       "eid                                                           \n",
-       "5772905                 False                         False   \n",
-       "5772920                 False                         False   \n",
-       "5772951                 False                         False   \n",
-       "5772966                 False                         False   \n",
-       "5772979                 False                         False   \n",
-       "...                       ...                           ...   \n",
-       "6025147                 False                         False   \n",
-       "6025150                 False                         False   \n",
-       "6025173                 False                         False   \n",
-       "6025182                 False                         False   \n",
-       "6025198                 False                         False   \n",
-       "\n",
-       "         severe_mental_illness  erectile_dysfunction  MACE_event_time  \\\n",
-       "eid                                                                     \n",
-       "5772905                   True                 False        11.665982   \n",
-       "5772920                   True                 False        12.668036   \n",
-       "5772951                  False                 False        12.065708   \n",
-       "5772966                   True                 False         3.581109   \n",
-       "5772979                  False                 False        10.507871   \n",
-       "...                        ...                   ...              ...   \n",
-       "6025147                  False                 False        11.879535   \n",
-       "6025150                  False                 False        13.253936   \n",
-       "6025173                  False                 False        12.035592   \n",
-       "6025182                  False                 False        10.250513   \n",
-       "6025198                  False                 False        10.677618   \n",
-       "\n",
-       "         MACE_event  \n",
-       "eid                  \n",
-       "5772905           0  \n",
-       "5772920           1  \n",
-       "5772951           0  \n",
-       "5772966           1  \n",
-       "5772979           0  \n",
-       "...             ...  \n",
-       "6025147           0  \n",
-       "6025150           0  \n",
-       "6025173           0  \n",
-       "6025182           0  \n",
-       "6025198           0  \n",
-       "\n",
-       "[17809 rows x 40 columns]"
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-       "       1000018   1000037   1000043  1000079   1000084   1000092   1000107  \\\n",
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-      "clinical\n",
-      "6\n",
-      "Iteration 1: norm_delta = 0.85322, step_size = 0.5000, log_lik = -302327.27794, newton_decrement = 8775.73930, seconds_since_start = 4.8\n",
-      "Iteration 2: norm_delta = 0.40298, step_size = 0.5000, log_lik = -295872.36077, newton_decrement = 2104.01885, seconds_since_start = 9.7\n",
-      "Iteration 3: norm_delta = 0.22878, step_size = 0.5000, log_lik = -294281.53451, newton_decrement = 613.69144, seconds_since_start = 14.3\n",
-      "Iteration 4: norm_delta = 0.10398, step_size = 0.6000, log_lik = -293761.52022, newton_decrement = 114.71321, seconds_since_start = 18.9\n",
-      "Iteration 5: norm_delta = 0.03221, step_size = 0.7200, log_lik = -293655.07013, newton_decrement = 10.25860, seconds_since_start = 23.4\n",
-      "Iteration 6: norm_delta = 0.00473, step_size = 0.8640, log_lik = -293644.96364, newton_decrement = 0.21139, seconds_since_start = 28.0\n",
-      "Iteration 7: norm_delta = 0.00001, step_size = 1.0000, log_lik = -293644.75205, newton_decrement = 0.00000, seconds_since_start = 32.5\n",
-      "Iteration 8: norm_delta = 0.00000, step_size = 1.0000, log_lik = -293644.75205, newton_decrement = 0.00000, seconds_since_start = 37.4\n",
-      "Convergence success after 8 iterations.\n",
-      "0.735391035035003\n"
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-       "         age_at_recruitment  ethnic_background_0.0  ethnic_background_1.0  \\\n",
-       "eid                                                                         \n",
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-       "eid                                                                            \n",
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-       "eid                                                       \n",
-       "5772371                                  -0.119080  0.0   \n",
-       "5772386                                  -0.758432  0.0   \n",
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-       "eid                                                            ...              \n",
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-       "eid                                                              \n",
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-       "eid                                                           \n",
-       "5772371                 False                         False   \n",
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-       "eid                                                                     \n",
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-       "       1000018   1000020   1000037   1000043   1000079  1000084   1000092  \\\n",
-       "11.0  0.057237  0.141367  0.063974  0.135173  0.080137  0.05721  0.094709   \n",
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-       "       1000107   1000135   1000144  ...   5772235   5772244   5772256  \\\n",
-       "11.0  0.018708  0.018252  0.095704  ...  0.161399  0.011815  0.113757   \n",
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-       "[1 rows x 338355 columns]"
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-    {
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-     "text": [
-      "clinical\n",
-      "7\n",
-      "Iteration 1: norm_delta = 0.88759, step_size = 0.5000, log_lik = -302733.12849, newton_decrement = 8860.69273, seconds_since_start = 5.5\n",
-      "Iteration 2: norm_delta = 0.41170, step_size = 0.5000, log_lik = -296210.88497, newton_decrement = 2142.45257, seconds_since_start = 10.7\n",
-      "Iteration 3: norm_delta = 0.23022, step_size = 0.5000, log_lik = -294590.81994, newton_decrement = 626.11995, seconds_since_start = 16.1\n",
-      "Iteration 4: norm_delta = 0.10371, step_size = 0.6000, log_lik = -294060.22491, newton_decrement = 117.20414, seconds_since_start = 21.2\n",
-      "Iteration 5: norm_delta = 0.03196, step_size = 0.7200, log_lik = -293951.45990, newton_decrement = 10.48383, seconds_since_start = 26.9\n",
-      "Iteration 6: norm_delta = 0.00467, step_size = 0.8640, log_lik = -293941.13196, newton_decrement = 0.21564, seconds_since_start = 31.7\n",
-      "Iteration 7: norm_delta = 0.00001, step_size = 1.0000, log_lik = -293940.91613, newton_decrement = 0.00000, seconds_since_start = 36.4\n",
-      "Iteration 8: norm_delta = 0.00000, step_size = 1.0000, log_lik = -293940.91613, newton_decrement = 0.00000, seconds_since_start = 41.1\n",
-      "Convergence success after 8 iterations.\n",
-      "0.7349299168760053\n"
-     ]
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-       "  </tbody>\n",
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-       "<p>17809 rows × 40 columns</p>\n",
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-       "         age_at_recruitment  ethnic_background_0.0  ethnic_background_1.0  \\\n",
-       "eid                                                                         \n",
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-       "...                     ...                    ...                    ...   \n",
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-       "6025182            0.078386                      1                      0   \n",
-       "6025198            1.437004                      1                      0   \n",
-       "\n",
-       "         ethnic_background_2.0  ethnic_background_3.0  ethnic_background_4.0  \\\n",
-       "eid                                                                            \n",
-       "5773119                      0                      0                      0   \n",
-       "5773126                      0                      0                      0   \n",
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-       "5773155                      0                      0                      0   \n",
-       "5773160                      0                      0                      0   \n",
-       "...                        ...                    ...                    ...   \n",
-       "6025150                      0                      0                      0   \n",
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-       "6025173                      0                      0                      0   \n",
-       "6025182                      0                      0                      0   \n",
-       "6025198                      0                      0                      0   \n",
-       "\n",
-       "         townsend_deprivation_index_at_recruitment  sex  \\\n",
-       "eid                                                       \n",
-       "5773119                                  -0.734818  0.0   \n",
-       "5773126                                   1.231116  0.0   \n",
-       "5773142                                  -0.173953  1.0   \n",
-       "5773155                                  -0.593668  1.0   \n",
-       "5773160                                  -0.551456  0.0   \n",
-       "...                                            ...  ...   \n",
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-       "6025198                                  -0.187711  1.0   \n",
-       "\n",
-       "         overall_health_rating_0.0  overall_health_rating_1.0  ...  diabetes2  \\\n",
-       "eid                                                            ...              \n",
-       "5773119                          0                          1  ...      False   \n",
-       "5773126                          0                          0  ...      False   \n",
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-       "...                            ...                        ...  ...        ...   \n",
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-       "eid                                                              \n",
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-       "eid                                                           \n",
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-       "eid                                                                     \n",
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-      "clinical\n",
-      "8\n",
-      "Iteration 1: norm_delta = 0.86797, step_size = 0.5000, log_lik = -303030.33010, newton_decrement = 8870.74428, seconds_since_start = 5.1\n",
-      "Iteration 2: norm_delta = 0.40860, step_size = 0.5000, log_lik = -296511.80267, newton_decrement = 2127.11690, seconds_since_start = 10.1\n",
-      "Iteration 3: norm_delta = 0.23080, step_size = 0.5000, log_lik = -294903.35742, newton_decrement = 621.40334, seconds_since_start = 15.0\n",
-      "Iteration 4: norm_delta = 0.10437, step_size = 0.6000, log_lik = -294376.77863, newton_decrement = 116.23146, seconds_since_start = 20.2\n",
-      "Iteration 5: norm_delta = 0.03215, step_size = 0.7200, log_lik = -294268.92252, newton_decrement = 10.38349, seconds_since_start = 25.3\n",
-      "Iteration 6: norm_delta = 0.00468, step_size = 0.8640, log_lik = -294258.69409, newton_decrement = 0.21313, seconds_since_start = 30.4\n",
-      "Iteration 7: norm_delta = 0.00001, step_size = 1.0000, log_lik = -294258.48078, newton_decrement = 0.00000, seconds_since_start = 35.3\n",
-      "Iteration 8: norm_delta = 0.00000, step_size = 1.0000, log_lik = -294258.48078, newton_decrement = 0.00000, seconds_since_start = 39.9\n",
-      "Convergence success after 8 iterations.\n",
-      "0.7314659792776314\n"
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-       "         age_at_recruitment  ethnic_background_0.0  ethnic_background_1.0  \\\n",
-       "eid                                                                         \n",
-       "5774550           -0.540283                      1                      0   \n",
-       "5774565            0.200683                      1                      0   \n",
-       "5774573           -1.281248                      1                      0   \n",
-       "5774582           -0.293294                      0                      0   \n",
-       "5774598            1.065143                      1                      0   \n",
-       "...                     ...                    ...                    ...   \n",
-       "6025150           -1.528237                      1                      0   \n",
-       "6025165           -1.281248                      1                      0   \n",
-       "6025173            0.200683                      1                      0   \n",
-       "6025182            0.077189                      1                      0   \n",
-       "6025198            1.435626                      1                      0   \n",
-       "\n",
-       "         ethnic_background_2.0  ethnic_background_3.0  ethnic_background_4.0  \\\n",
-       "eid                                                                            \n",
-       "5774550                      0                      0                      0   \n",
-       "5774565                      0                      0                      0   \n",
-       "5774573                      0                      0                      0   \n",
-       "5774582                      0                      0                      1   \n",
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-       "6025182                      0                      0                      0   \n",
-       "6025198                      0                      0                      0   \n",
-       "\n",
-       "         townsend_deprivation_index_at_recruitment  sex  \\\n",
-       "eid                                                       \n",
-       "5774550                                   1.194103  0.0   \n",
-       "5774565                                   1.215520  1.0   \n",
-       "5774573                                  -1.186634  0.0   \n",
-       "5774582                                  -0.312864  0.0   \n",
-       "5774598                                   1.277487  0.0   \n",
-       "...                                            ...  ...   \n",
-       "6025150                                   0.458897  0.0   \n",
-       "6025165                                  -0.244926  0.0   \n",
-       "6025173                                  -0.153487  1.0   \n",
-       "6025182                                   0.440092  1.0   \n",
-       "6025198                                  -0.187286  1.0   \n",
-       "\n",
-       "         overall_health_rating_0.0  overall_health_rating_1.0  ...  diabetes2  \\\n",
-       "eid                                                            ...              \n",
-       "5774550                          1                          0  ...      False   \n",
-       "5774565                          0                          1  ...      False   \n",
-       "5774573                          0                          1  ...      False   \n",
-       "5774582                          0                          1  ...      False   \n",
-       "5774598                          1                          0  ...      False   \n",
-       "...                            ...                        ...  ...        ...   \n",
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-       "6025173                          0                          1  ...      False   \n",
-       "6025182                          0                          0  ...      False   \n",
-       "6025198                          0                          1  ...      False   \n",
-       "\n",
-       "         chronic_kidney_disease  atrial_fibrillation  migraine  \\\n",
-       "eid                                                              \n",
-       "5774550                   False                False     False   \n",
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-       "5774582                   False                False     False   \n",
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-       "...                         ...                  ...       ...   \n",
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-       "6025182                   False                False     False   \n",
-       "6025198                    True                False     False   \n",
-       "\n",
-       "         rheumatoid_arthritis  systemic_lupus_erythematosus  \\\n",
-       "eid                                                           \n",
-       "5774550                 False                         False   \n",
-       "5774565                 False                         False   \n",
-       "5774573                 False                         False   \n",
-       "5774582                 False                         False   \n",
-       "5774598                 False                         False   \n",
-       "...                       ...                           ...   \n",
-       "6025150                 False                         False   \n",
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-       "6025173                 False                         False   \n",
-       "6025182                 False                         False   \n",
-       "6025198                 False                         False   \n",
-       "\n",
-       "         severe_mental_illness  erectile_dysfunction  MACE_event_time  \\\n",
-       "eid                                                                     \n",
-       "5774550                  False                 False        10.524298   \n",
-       "5774565                  False                 False        11.534565   \n",
-       "5774573                  False                 False        11.633128   \n",
-       "5774582                  False                 False        11.665982   \n",
-       "5774598                  False                 False        11.969884   \n",
-       "...                        ...                   ...              ...   \n",
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-       "6025165                  False                 False        12.076660   \n",
-       "6025173                  False                 False        12.035592   \n",
-       "6025182                  False                 False        10.250513   \n",
-       "6025198                  False                 False        10.677618   \n",
-       "\n",
-       "         MACE_event  \n",
-       "eid                  \n",
-       "5774550           0  \n",
-       "5774565           0  \n",
-       "5774573           0  \n",
-       "5774582           0  \n",
-       "5774598           0  \n",
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-       "6025165           0  \n",
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-       "6025198           0  \n",
-       "\n",
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-       "      1000018   1000020   1000037   1000043   1000079   1000084   1000092  \\\n",
-       "11.0  0.05741  0.141517  0.053211  0.136765  0.078732  0.057589  0.091528   \n",
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-     "text": [
-      "clinical\n",
-      "9\n",
-      "Iteration 1: norm_delta = 0.82958, step_size = 0.5000, log_lik = -303247.13371, newton_decrement = 8850.62404, seconds_since_start = 4.6\n",
-      "Iteration 2: norm_delta = 0.40662, step_size = 0.5000, log_lik = -296741.73646, newton_decrement = 2122.14004, seconds_since_start = 9.2\n",
-      "Iteration 3: norm_delta = 0.23229, step_size = 0.5000, log_lik = -295137.12084, newton_decrement = 619.56414, seconds_since_start = 13.9\n",
-      "Iteration 4: norm_delta = 0.10585, step_size = 0.6000, log_lik = -294612.10980, newton_decrement = 115.86974, seconds_since_start = 18.7\n",
-      "Iteration 5: norm_delta = 0.03277, step_size = 0.7200, log_lik = -294504.58754, newton_decrement = 10.35656, seconds_since_start = 23.5\n",
-      "Iteration 6: norm_delta = 0.00479, step_size = 0.8640, log_lik = -294494.38520, newton_decrement = 0.21289, seconds_since_start = 28.2\n",
-      "Iteration 7: norm_delta = 0.00001, step_size = 1.0000, log_lik = -294494.17213, newton_decrement = 0.00000, seconds_since_start = 33.4\n",
-      "Iteration 8: norm_delta = 0.00000, step_size = 1.0000, log_lik = -294494.17213, newton_decrement = 0.00000, seconds_since_start = 38.1\n",
-      "Convergence success after 8 iterations.\n",
-      "0.7334943567030547\n"
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-       "         age_at_recruitment  ethnic_background_0.0  ethnic_background_1.0  \\\n",
-       "eid                                                                         \n",
-       "5773563            0.077623                      1                      0   \n",
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-       "5773580           -1.157516                      1                      0   \n",
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-       "5773606            1.189249                      1                      0   \n",
-       "...                     ...                    ...                    ...   \n",
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-       "6025198            1.436277                      1                      0   \n",
-       "\n",
-       "         ethnic_background_2.0  ethnic_background_3.0  ethnic_background_4.0  \\\n",
-       "eid                                                                            \n",
-       "5773563                      0                      0                      0   \n",
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-       "6025198                      0                      0                      0   \n",
-       "\n",
-       "         townsend_deprivation_index_at_recruitment  sex  \\\n",
-       "eid                                                       \n",
-       "5773563                                   0.119955  0.0   \n",
-       "5773575                                   0.806047  1.0   \n",
-       "5773580                                  -1.603078  0.0   \n",
-       "5773591                                  -0.503944  1.0   \n",
-       "5773606                                  -0.767200  1.0   \n",
-       "...                                            ...  ...   \n",
-       "6025150                                   0.458608  0.0   \n",
-       "6025165                                  -0.245670  0.0   \n",
-       "6025173                                  -0.154172  1.0   \n",
-       "6025182                                   0.439791  1.0   \n",
-       "6025198                                  -0.187993  1.0   \n",
-       "\n",
-       "         overall_health_rating_0.0  overall_health_rating_1.0  ...  diabetes2  \\\n",
-       "eid                                                            ...              \n",
-       "5773563                          0                          1  ...      False   \n",
-       "5773575                          0                          1  ...      False   \n",
-       "5773580                          0                          0  ...      False   \n",
-       "5773591                          1                          0  ...      False   \n",
-       "5773606                          0                          1  ...      False   \n",
-       "...                            ...                        ...  ...        ...   \n",
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-       "6025198                          0                          1  ...      False   \n",
-       "\n",
-       "         chronic_kidney_disease  atrial_fibrillation  migraine  \\\n",
-       "eid                                                              \n",
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-       "...                         ...                  ...       ...   \n",
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-       "6025198                    True                False     False   \n",
-       "\n",
-       "         rheumatoid_arthritis  systemic_lupus_erythematosus  \\\n",
-       "eid                                                           \n",
-       "5773563                 False                         False   \n",
-       "5773575                 False                         False   \n",
-       "5773580                 False                         False   \n",
-       "5773591                 False                         False   \n",
-       "5773606                 False                         False   \n",
-       "...                       ...                           ...   \n",
-       "6025150                 False                         False   \n",
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-       "6025173                 False                         False   \n",
-       "6025182                 False                         False   \n",
-       "6025198                 False                         False   \n",
-       "\n",
-       "         severe_mental_illness  erectile_dysfunction  MACE_event_time  \\\n",
-       "eid                                                                     \n",
-       "5773563                  False                 False        10.658453   \n",
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-       "5773606                   True                 False        10.880219   \n",
-       "...                        ...                   ...              ...   \n",
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-       "6025182                  False                 False        10.250513   \n",
-       "6025198                  False                 False        10.677618   \n",
-       "\n",
-       "         MACE_event  \n",
-       "eid                  \n",
-       "5773563           0  \n",
-       "5773575           1  \n",
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-       "5773591           0  \n",
-       "5773606           0  \n",
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-       "\n",
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-       "       1000018   1000020   1000037   1000043   1000079   1000092  1000107  \\\n",
-       "11.0  0.057894  0.141919  0.045861  0.134326  0.077182  0.098027  0.01871   \n",
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-     "text": [
-      "clinical_pgs\n",
-      "0\n",
-      "Iteration 1: norm_delta = 0.81393, step_size = 0.5000, log_lik = -303524.54541, newton_decrement = 9056.05660, seconds_since_start = 4.8\n",
-      "Iteration 2: norm_delta = 0.40994, step_size = 0.5000, log_lik = -296858.91749, newton_decrement = 2188.34610, seconds_since_start = 10.1\n",
-      "Iteration 3: norm_delta = 0.23405, step_size = 0.5000, log_lik = -295204.38867, newton_decrement = 638.07352, seconds_since_start = 15.2\n",
-      "Iteration 4: norm_delta = 0.10660, step_size = 0.6000, log_lik = -294663.69952, newton_decrement = 119.34498, seconds_since_start = 20.1\n",
-      "Iteration 5: norm_delta = 0.03305, step_size = 0.7200, log_lik = -294552.94598, newton_decrement = 10.68236, seconds_since_start = 25.7\n",
-      "Iteration 6: norm_delta = 0.00485, step_size = 0.8640, log_lik = -294542.42177, newton_decrement = 0.22024, seconds_since_start = 30.9\n",
-      "Iteration 7: norm_delta = 0.00001, step_size = 1.0000, log_lik = -294542.20133, newton_decrement = 0.00000, seconds_since_start = 35.9\n",
-      "Iteration 8: norm_delta = 0.00000, step_size = 1.0000, log_lik = -294542.20132, newton_decrement = 0.00000, seconds_since_start = 41.4\n",
-      "Convergence success after 8 iterations.\n",
-      "0.7406283373443374\n"
-     ]
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-       "      <td>False</td>\n",
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-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>12.358658</td>\n",
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-       "         PGS000011  PGS000057  PGS000058  PGS000059  age_at_recruitment  \\\n",
-       "eid                                                                       \n",
-       "5773759   2.007031   0.521875   0.883755  -0.145525            1.683621   \n",
-       "5773764  -1.077634  -0.959405   0.772914  -0.549035            0.942714   \n",
-       "5773777  -1.103880   0.262475  -0.804646  -1.061722            0.325291   \n",
-       "5773788   0.427973   1.134818   0.283298   1.355846           -1.773947   \n",
-       "5773796   0.565868  -0.270545  -0.949221   1.988331           -0.786070   \n",
-       "...            ...        ...        ...        ...                 ...   \n",
-       "6025147   0.546585   0.268284   1.378534  -1.684997           -1.526978   \n",
-       "6025150   0.940851   0.981582   0.151601   0.568805           -1.526978   \n",
-       "6025165   1.245484  -0.471755  -0.947980  -0.681236           -1.280009   \n",
-       "6025182  -0.155731  -0.167687  -0.644893  -0.757363            0.078322   \n",
-       "6025198  -0.237283  -0.882787   1.075431  -0.473279            1.436652   \n",
-       "\n",
-       "         ethnic_background_0.0  ethnic_background_1.0  ethnic_background_2.0  \\\n",
-       "eid                                                                            \n",
-       "5773759                      1                      0                      0   \n",
-       "5773764                      1                      0                      0   \n",
-       "5773777                      1                      0                      0   \n",
-       "5773788                      1                      0                      0   \n",
-       "5773796                      1                      0                      0   \n",
-       "...                        ...                    ...                    ...   \n",
-       "6025147                      0                      0                      1   \n",
-       "6025150                      1                      0                      0   \n",
-       "6025165                      1                      0                      0   \n",
-       "6025182                      1                      0                      0   \n",
-       "6025198                      1                      0                      0   \n",
-       "\n",
-       "         ethnic_background_3.0  ethnic_background_4.0  ...  diabetes2  \\\n",
-       "eid                                                    ...              \n",
-       "5773759                      0                      0  ...      False   \n",
-       "5773764                      0                      0  ...      False   \n",
-       "5773777                      0                      0  ...      False   \n",
-       "5773788                      0                      0  ...      False   \n",
-       "5773796                      0                      0  ...      False   \n",
-       "...                        ...                    ...  ...        ...   \n",
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-       "6025150                      0                      0  ...      False   \n",
-       "6025165                      0                      0  ...      False   \n",
-       "6025182                      0                      0  ...      False   \n",
-       "6025198                      0                      0  ...      False   \n",
-       "\n",
-       "         chronic_kidney_disease  atrial_fibrillation  migraine  \\\n",
-       "eid                                                              \n",
-       "5773759                   False                False     False   \n",
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-       "5773777                   False                False      True   \n",
-       "5773788                   False                False     False   \n",
-       "5773796                   False                False     False   \n",
-       "...                         ...                  ...       ...   \n",
-       "6025147                   False                False     False   \n",
-       "6025150                   False                False     False   \n",
-       "6025165                   False                False     False   \n",
-       "6025182                   False                False     False   \n",
-       "6025198                    True                False     False   \n",
-       "\n",
-       "         rheumatoid_arthritis  systemic_lupus_erythematosus  \\\n",
-       "eid                                                           \n",
-       "5773759                 False                         False   \n",
-       "5773764                  True                         False   \n",
-       "5773777                 False                         False   \n",
-       "5773788                 False                         False   \n",
-       "5773796                 False                         False   \n",
-       "...                       ...                           ...   \n",
-       "6025147                 False                         False   \n",
-       "6025150                 False                         False   \n",
-       "6025165                 False                         False   \n",
-       "6025182                 False                         False   \n",
-       "6025198                 False                         False   \n",
-       "\n",
-       "         severe_mental_illness  erectile_dysfunction  MACE_event_time  \\\n",
-       "eid                                                                     \n",
-       "5773759                  False                 False        12.010951   \n",
-       "5773764                  False                  True         2.989733   \n",
-       "5773777                  False                 False         2.198494   \n",
-       "5773788                  False                 False        12.342231   \n",
-       "5773796                  False                 False        12.358658   \n",
-       "...                        ...                   ...              ...   \n",
-       "6025147                  False                 False        11.879535   \n",
-       "6025150                  False                 False        13.253936   \n",
-       "6025165                  False                 False        12.076660   \n",
-       "6025182                  False                 False        10.250513   \n",
-       "6025198                  False                 False        10.677618   \n",
-       "\n",
-       "         MACE_event  \n",
-       "eid                  \n",
-       "5773759           0  \n",
-       "5773764           1  \n",
-       "5773777           1  \n",
-       "5773788           0  \n",
-       "5773796           0  \n",
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-       "6025198           0  \n",
-       "\n",
-       "[17809 rows x 44 columns]"
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-       "       1000018   1000020   1000037   1000043   1000079   1000084   1000092  \\\n",
-       "11.0  0.050346  0.145234  0.060491  0.139643  0.079925  0.057078  0.082222   \n",
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-     "text": [
-      "clinical_pgs\n",
-      "1\n",
-      "Iteration 1: norm_delta = 0.89055, step_size = 0.5000, log_lik = -301749.28339, newton_decrement = 9040.11665, seconds_since_start = 4.9\n",
-      "Iteration 2: norm_delta = 0.40804, step_size = 0.5000, log_lik = -295105.03702, newton_decrement = 2162.19112, seconds_since_start = 10.3\n",
-      "Iteration 3: norm_delta = 0.22967, step_size = 0.5000, log_lik = -293470.52354, newton_decrement = 628.62077, seconds_since_start = 15.3\n",
-      "Iteration 4: norm_delta = 0.10382, step_size = 0.6000, log_lik = -292937.95135, newton_decrement = 117.14400, seconds_since_start = 20.5\n",
-      "Iteration 5: norm_delta = 0.03202, step_size = 0.7200, log_lik = -292829.26291, newton_decrement = 10.44181, seconds_since_start = 25.5\n",
-      "Iteration 6: norm_delta = 0.00467, step_size = 0.8640, log_lik = -292818.97739, newton_decrement = 0.21418, seconds_since_start = 31.5\n",
-      "Iteration 7: norm_delta = 0.00001, step_size = 1.0000, log_lik = -292818.76302, newton_decrement = 0.00000, seconds_since_start = 37.7\n",
-      "Iteration 8: norm_delta = 0.00000, step_size = 1.0000, log_lik = -292818.76302, newton_decrement = 0.00000, seconds_since_start = 43.7\n",
-      "Convergence success after 8 iterations.\n",
-      "0.742948754209381\n"
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-       "      <th>5773895</th>\n",
-       "      <td>-0.363702</td>\n",
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-       "      <td>-0.340790</td>\n",
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-       "      <td>10.483231</td>\n",
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-       "      <th>5773928</th>\n",
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-       "         PGS000011  PGS000057  PGS000058  PGS000059  age_at_recruitment  \\\n",
-       "eid                                                                       \n",
-       "5773886  -0.201274  -0.322502   0.253023  -0.189727            1.188748   \n",
-       "5773895  -0.363702  -0.118134  -0.340790  -1.293252           -1.404329   \n",
-       "5773928   0.667596   0.827069   0.926460   0.303638           -1.033889   \n",
-       "5773935  -0.467794  -0.654600  -1.081712  -0.758674           -0.046051   \n",
-       "5773944  -0.563947  -1.293251  -1.406646  -0.151638           -0.910410   \n",
-       "...            ...        ...        ...        ...                 ...   \n",
-       "6025150   0.941076   0.980346   0.150938   0.569663           -1.527809   \n",
-       "6025165   1.246171  -0.473374  -0.947561  -0.682422           -1.280849   \n",
-       "6025173  -1.530617  -1.477683  -1.099265  -1.269521            0.200909   \n",
-       "6025182  -0.157168  -0.169226  -0.644772  -0.758674            0.077429   \n",
-       "6025198  -0.238843  -0.884515   1.073859  -0.474126            1.435708   \n",
-       "\n",
-       "         ethnic_background_0.0  ethnic_background_1.0  ethnic_background_2.0  \\\n",
-       "eid                                                                            \n",
-       "5773886                      1                      0                      0   \n",
-       "5773895                      1                      0                      0   \n",
-       "5773928                      1                      0                      0   \n",
-       "5773935                      1                      0                      0   \n",
-       "5773944                      1                      0                      0   \n",
-       "...                        ...                    ...                    ...   \n",
-       "6025150                      1                      0                      0   \n",
-       "6025165                      1                      0                      0   \n",
-       "6025173                      1                      0                      0   \n",
-       "6025182                      1                      0                      0   \n",
-       "6025198                      1                      0                      0   \n",
-       "\n",
-       "         ethnic_background_3.0  ethnic_background_4.0  ...  diabetes2  \\\n",
-       "eid                                                    ...              \n",
-       "5773886                      0                      0  ...       True   \n",
-       "5773895                      0                      0  ...      False   \n",
-       "5773928                      0                      0  ...      False   \n",
-       "5773935                      0                      0  ...      False   \n",
-       "5773944                      0                      0  ...      False   \n",
-       "...                        ...                    ...  ...        ...   \n",
-       "6025150                      0                      0  ...      False   \n",
-       "6025165                      0                      0  ...      False   \n",
-       "6025173                      0                      0  ...      False   \n",
-       "6025182                      0                      0  ...      False   \n",
-       "6025198                      0                      0  ...      False   \n",
-       "\n",
-       "         chronic_kidney_disease  atrial_fibrillation  migraine  \\\n",
-       "eid                                                              \n",
-       "5773886                   False                 True     False   \n",
-       "5773895                   False                False     False   \n",
-       "5773928                   False                False     False   \n",
-       "5773935                   False                False     False   \n",
-       "5773944                   False                False     False   \n",
-       "...                         ...                  ...       ...   \n",
-       "6025150                   False                False     False   \n",
-       "6025165                   False                False     False   \n",
-       "6025173                   False                False     False   \n",
-       "6025182                   False                False     False   \n",
-       "6025198                    True                False     False   \n",
-       "\n",
-       "         rheumatoid_arthritis  systemic_lupus_erythematosus  \\\n",
-       "eid                                                           \n",
-       "5773886                  True                         False   \n",
-       "5773895                 False                         False   \n",
-       "5773928                 False                         False   \n",
-       "5773935                 False                         False   \n",
-       "5773944                 False                         False   \n",
-       "...                       ...                           ...   \n",
-       "6025150                 False                         False   \n",
-       "6025165                 False                         False   \n",
-       "6025173                 False                         False   \n",
-       "6025182                 False                         False   \n",
-       "6025198                 False                         False   \n",
-       "\n",
-       "         severe_mental_illness  erectile_dysfunction  MACE_event_time  \\\n",
-       "eid                                                                     \n",
-       "5773886                   True                  True        11.331964   \n",
-       "5773895                  False                 False        10.483231   \n",
-       "5773928                  False                 False        13.322382   \n",
-       "5773935                  False                 False        12.821355   \n",
-       "5773944                   True                 False        12.366872   \n",
-       "...                        ...                   ...              ...   \n",
-       "6025150                  False                 False        13.253936   \n",
-       "6025165                  False                 False        12.076660   \n",
-       "6025173                  False                 False        12.035592   \n",
-       "6025182                  False                 False        10.250513   \n",
-       "6025198                  False                 False        10.677618   \n",
-       "\n",
-       "         MACE_event  \n",
-       "eid                  \n",
-       "5773886           0  \n",
-       "5773895           0  \n",
-       "5773928           0  \n",
-       "5773935           0  \n",
-       "5773944           0  \n",
-       "...             ...  \n",
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-       "6025165           0  \n",
-       "6025173           0  \n",
-       "6025182           0  \n",
-       "6025198           0  \n",
-       "\n",
-       "[17809 rows x 44 columns]"
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-       "<p>1 rows × 338355 columns</p>\n",
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-       "       1000018   1000020   1000043   1000079   1000084   1000092   1000110  \\\n",
-       "11.0  0.051708  0.140301  0.138154  0.079953  0.057551  0.082913  0.015229   \n",
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-       "       5773764   5773788  5773808   5773814   5773827  5773840   5773871  \n",
-       "11.0  0.238527  0.010776  0.02415  0.125521  0.107289  0.03844  0.083407  \n",
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-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "clinical_pgs\n",
-      "2\n",
-      "Iteration 1: norm_delta = 0.85663, step_size = 0.5000, log_lik = -302233.28004, newton_decrement = 9156.75424, seconds_since_start = 5.3\n",
-      "Iteration 2: norm_delta = 0.41373, step_size = 0.5000, log_lik = -295505.97523, newton_decrement = 2190.40235, seconds_since_start = 11.1\n",
-      "Iteration 3: norm_delta = 0.23410, step_size = 0.5000, log_lik = -293850.05090, newton_decrement = 637.46461, seconds_since_start = 17.3\n",
-      "Iteration 4: norm_delta = 0.10598, step_size = 0.6000, log_lik = -293309.94934, newton_decrement = 118.93813, seconds_since_start = 22.8\n",
-      "Iteration 5: norm_delta = 0.03270, step_size = 0.7200, log_lik = -293199.58935, newton_decrement = 10.61430, seconds_since_start = 28.2\n",
-      "Iteration 6: norm_delta = 0.00477, step_size = 0.8640, log_lik = -293189.13353, newton_decrement = 0.21794, seconds_since_start = 33.4\n",
-      "Iteration 7: norm_delta = 0.00001, step_size = 1.0000, log_lik = -293188.91540, newton_decrement = 0.00000, seconds_since_start = 39.2\n",
-      "Iteration 8: norm_delta = 0.00000, step_size = 1.0000, log_lik = -293188.91540, newton_decrement = 0.00000, seconds_since_start = 44.5\n",
-      "Convergence success after 8 iterations.\n",
-      "0.7361979604315427\n"
-     ]
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-     "data": {
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-       "      <th>PGS000011</th>\n",
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-       "      <th>PGS000059</th>\n",
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-       "         PGS000011  PGS000057  PGS000058  PGS000059  age_at_recruitment  \\\n",
-       "eid                                                                       \n",
-       "5772660   0.093215   1.159010  -0.351006   0.065219           -0.046225   \n",
-       "5772678  -0.277338  -0.015564   0.606919  -0.189011           -1.527857   \n",
-       "5772693  -0.458261  -0.576115   1.028400   0.608838            0.818060   \n",
-       "5772709  -1.428803  -1.231292   0.482911  -0.490741            1.064999   \n",
-       "5772717  -0.387607   0.904268  -1.387189   1.009435            0.941529   \n",
-       "...            ...        ...        ...        ...                 ...   \n",
-       "6025131   0.076733   0.265313  -0.477689  -1.173071           -0.787041   \n",
-       "6025147   0.545783   0.267115   1.377875  -1.686139           -1.527857   \n",
-       "6025165   1.244816  -0.472776  -0.947602  -0.681487           -1.280919   \n",
-       "6025173  -1.528300  -1.476621  -1.099388  -1.268147            0.200713   \n",
-       "6025182  -0.156667  -0.168769  -0.644650  -0.757682            0.077244   \n",
-       "\n",
-       "         ethnic_background_0.0  ethnic_background_1.0  ethnic_background_2.0  \\\n",
-       "eid                                                                            \n",
-       "5772660                      1                      0                      0   \n",
-       "5772678                      1                      0                      0   \n",
-       "5772693                      1                      0                      0   \n",
-       "5772709                      1                      0                      0   \n",
-       "5772717                      1                      0                      0   \n",
-       "...                        ...                    ...                    ...   \n",
-       "6025131                      1                      0                      0   \n",
-       "6025147                      0                      0                      1   \n",
-       "6025165                      1                      0                      0   \n",
-       "6025173                      1                      0                      0   \n",
-       "6025182                      1                      0                      0   \n",
-       "\n",
-       "         ethnic_background_3.0  ethnic_background_4.0  ...  diabetes2  \\\n",
-       "eid                                                    ...              \n",
-       "5772660                      0                      0  ...      False   \n",
-       "5772678                      0                      0  ...      False   \n",
-       "5772693                      0                      0  ...      False   \n",
-       "5772709                      0                      0  ...      False   \n",
-       "5772717                      0                      0  ...      False   \n",
-       "...                        ...                    ...  ...        ...   \n",
-       "6025131                      0                      0  ...      False   \n",
-       "6025147                      0                      0  ...      False   \n",
-       "6025165                      0                      0  ...      False   \n",
-       "6025173                      0                      0  ...      False   \n",
-       "6025182                      0                      0  ...      False   \n",
-       "\n",
-       "         chronic_kidney_disease  atrial_fibrillation  migraine  \\\n",
-       "eid                                                              \n",
-       "5772660                   False                False     False   \n",
-       "5772678                   False                False     False   \n",
-       "5772693                   False                False     False   \n",
-       "5772709                   False                False     False   \n",
-       "5772717                   False                False     False   \n",
-       "...                         ...                  ...       ...   \n",
-       "6025131                   False                False     False   \n",
-       "6025147                   False                False     False   \n",
-       "6025165                   False                False     False   \n",
-       "6025173                   False                False     False   \n",
-       "6025182                   False                False     False   \n",
-       "\n",
-       "         rheumatoid_arthritis  systemic_lupus_erythematosus  \\\n",
-       "eid                                                           \n",
-       "5772660                 False                         False   \n",
-       "5772678                 False                         False   \n",
-       "5772693                 False                         False   \n",
-       "5772709                 False                         False   \n",
-       "5772717                 False                         False   \n",
-       "...                       ...                           ...   \n",
-       "6025131                 False                         False   \n",
-       "6025147                 False                         False   \n",
-       "6025165                 False                         False   \n",
-       "6025173                 False                         False   \n",
-       "6025182                 False                         False   \n",
-       "\n",
-       "         severe_mental_illness  erectile_dysfunction  MACE_event_time  \\\n",
-       "eid                                                                     \n",
-       "5772660                  False                 False        11.956194   \n",
-       "5772678                  False                 False        12.057495   \n",
-       "5772693                  False                 False        11.288159   \n",
-       "5772709                  False                 False        11.608487   \n",
-       "5772717                  False                 False        10.431211   \n",
-       "...                        ...                   ...              ...   \n",
-       "6025131                  False                 False        10.294319   \n",
-       "6025147                  False                 False        11.879535   \n",
-       "6025165                  False                 False        12.076660   \n",
-       "6025173                  False                 False        12.035592   \n",
-       "6025182                  False                 False        10.250513   \n",
-       "\n",
-       "         MACE_event  \n",
-       "eid                  \n",
-       "5772660           0  \n",
-       "5772678           0  \n",
-       "5772693           0  \n",
-       "5772709           0  \n",
-       "5772717           0  \n",
-       "...             ...  \n",
-       "6025131           0  \n",
-       "6025147           0  \n",
-       "6025165           0  \n",
-       "6025173           0  \n",
-       "6025182           0  \n",
-       "\n",
-       "[17809 rows x 44 columns]"
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-       "      <th></th>\n",
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-       "      <th>11.0</th>\n",
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-       "      <td>0.113779</td>\n",
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-       "      <td>0.016781</td>\n",
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-       "      <td>0.04008</td>\n",
-       "      <td>0.084508</td>\n",
-       "      <td>0.01528</td>\n",
-       "      <td>0.104305</td>\n",
-       "      <td>0.157584</td>\n",
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-       "<p>1 rows × 338355 columns</p>\n",
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-       "       1000020   1000037   1000043   1000079   1000084   1000107   1000110  \\\n",
-       "11.0  0.143645  0.066114  0.137168  0.080571  0.057327  0.015005  0.015094   \n",
-       "\n",
-       "       1000128   1000135   1000144  ...   5772530   5772541   5772564  \\\n",
-       "11.0  0.015283  0.014762  0.094281  ...  0.113779  0.047515  0.114631   \n",
-       "\n",
-       "       5772577   5772588  5772596   5772601  5772626   5772642   5772655  \n",
-       "11.0  0.016781  0.052812  0.04008  0.084508  0.01528  0.104305  0.157584  \n",
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-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "clinical_pgs\n",
-      "3\n",
-      "Iteration 1: norm_delta = 0.82516, step_size = 0.5000, log_lik = -303597.59644, newton_decrement = 9094.45912, seconds_since_start = 5.8\n",
-      "Iteration 2: norm_delta = 0.41015, step_size = 0.5000, log_lik = -296912.39725, newton_decrement = 2176.98780, seconds_since_start = 11.4\n",
-      "Iteration 3: norm_delta = 0.23326, step_size = 0.5000, log_lik = -295266.68726, newton_decrement = 633.07846, seconds_since_start = 16.7\n",
-      "Iteration 4: norm_delta = 0.10585, step_size = 0.6000, log_lik = -294730.31874, newton_decrement = 118.05161, seconds_since_start = 22.5\n",
-      "Iteration 5: norm_delta = 0.03267, step_size = 0.7200, log_lik = -294620.78480, newton_decrement = 10.52766, seconds_since_start = 28.1\n",
-      "Iteration 6: norm_delta = 0.00476, step_size = 0.8640, log_lik = -294610.41472, newton_decrement = 0.21589, seconds_since_start = 34.2\n",
-      "Iteration 7: norm_delta = 0.00001, step_size = 1.0000, log_lik = -294610.19865, newton_decrement = 0.00000, seconds_since_start = 39.9\n",
-      "Iteration 8: norm_delta = 0.00000, step_size = 1.0000, log_lik = -294610.19865, newton_decrement = 0.00000, seconds_since_start = 45.3\n",
-      "Convergence success after 8 iterations.\n",
-      "0.7351190869752977\n"
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-       "eid                                                                       \n",
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-       "6025198  -0.237579  -0.883070   1.074218  -0.473377            1.435798   \n",
-       "\n",
-       "         ethnic_background_0.0  ethnic_background_1.0  ethnic_background_2.0  \\\n",
-       "eid                                                                            \n",
-       "5773031                      1                      0                      0   \n",
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-       "eid                                                    ...              \n",
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-       "eid                                                              \n",
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-       "6025198                    True                False     False   \n",
-       "\n",
-       "         rheumatoid_arthritis  systemic_lupus_erythematosus  \\\n",
-       "eid                                                           \n",
-       "5773031                 False                         False   \n",
-       "5773047                 False                         False   \n",
-       "5773050                 False                         False   \n",
-       "5773065                 False                         False   \n",
-       "5773082                 False                         False   \n",
-       "...                       ...                           ...   \n",
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-       "6025173                 False                         False   \n",
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-       "6025198                 False                         False   \n",
-       "\n",
-       "         severe_mental_illness  erectile_dysfunction  MACE_event_time  \\\n",
-       "eid                                                                     \n",
-       "5773031                   True                 False        12.895277   \n",
-       "5773047                  False                 False        12.479124   \n",
-       "5773050                  False                 False        11.011636   \n",
-       "5773065                  False                 False        12.136893   \n",
-       "5773082                  False                  True        10.543463   \n",
-       "...                        ...                   ...              ...   \n",
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-       "6025182                  False                 False        10.250513   \n",
-       "6025198                  False                 False        10.677618   \n",
-       "\n",
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-       "eid                  \n",
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-       "      <th>11.0</th>\n",
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-       "      <td>0.066414</td>\n",
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-       "       1000018   1000020   1000037   1000043   1000079   1000084   1000092  \\\n",
-       "11.0  0.050562  0.142304  0.069965  0.138803  0.079423  0.056375  0.082514   \n",
-       "\n",
-       "       1000107  1000110   1000128  ...   5772937   5772943   5772951  \\\n",
-       "11.0  0.015524  0.01513  0.009993  ...  0.066414  0.046224  0.046547   \n",
-       "\n",
-       "       5772966   5772979   5772984   5772992   5773004   5773016   5773029  \n",
-       "11.0  0.026889  0.075675  0.138425  0.021496  0.037928  0.100447  0.061939  \n",
-       "\n",
-       "[1 rows x 338355 columns]"
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-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "clinical_pgs\n",
-      "4\n",
-      "Iteration 1: norm_delta = 0.87106, step_size = 0.5000, log_lik = -302316.54463, newton_decrement = 9092.53551, seconds_since_start = 6.3\n",
-      "Iteration 2: norm_delta = 0.41156, step_size = 0.5000, log_lik = -295633.80085, newton_decrement = 2174.42596, seconds_since_start = 11.6\n",
-      "Iteration 3: norm_delta = 0.23359, step_size = 0.5000, log_lik = -293990.03191, newton_decrement = 632.30218, seconds_since_start = 17.0\n",
-      "Iteration 4: norm_delta = 0.10614, step_size = 0.6000, log_lik = -293454.32530, newton_decrement = 117.89333, seconds_since_start = 22.6\n",
-      "Iteration 5: norm_delta = 0.03282, step_size = 0.7200, log_lik = -293344.93866, newton_decrement = 10.51323, seconds_since_start = 28.3\n",
-      "Iteration 6: norm_delta = 0.00479, step_size = 0.8640, log_lik = -293334.58275, newton_decrement = 0.21563, seconds_since_start = 34.5\n",
-      "Iteration 7: norm_delta = 0.00001, step_size = 1.0000, log_lik = -293334.36694, newton_decrement = 0.00000, seconds_since_start = 39.7\n",
-      "Iteration 8: norm_delta = 0.00000, step_size = 1.0000, log_lik = -293334.36694, newton_decrement = 0.00000, seconds_since_start = 45.6\n",
-      "Convergence success after 8 iterations.\n",
-      "0.735015495787619\n"
-     ]
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-    {
-     "data": {
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-       "         PGS000011  PGS000057  PGS000058  PGS000059  age_at_recruitment  \\\n",
-       "eid                                                                       \n",
-       "5773047  -0.388727  -0.575749   0.298344  -1.156563           -0.540188   \n",
-       "5773050  -0.815984  -1.903787  -1.227988  -1.099685           -1.034344   \n",
-       "5773065   1.495869   0.546498   0.223731   0.056829            0.571662   \n",
-       "5773073   0.115564   0.776212   0.373265  -0.000048            0.942279   \n",
-       "5773082  -0.574049  -1.623025  -1.507021   0.189544            1.065818   \n",
-       "...            ...        ...        ...        ...                 ...   \n",
-       "6025150   0.941069   0.980403   0.151244   0.569175           -1.528500   \n",
-       "6025165   1.245812  -0.472052  -0.947779  -0.682210           -1.281422   \n",
-       "6025173  -1.527768  -1.475488  -1.099556  -1.268980            0.201046   \n",
-       "6025182  -0.155905  -0.168168  -0.644846  -0.758419            0.077507   \n",
-       "6025198  -0.237486  -0.882835   1.074606  -0.474030            1.436435   \n",
-       "\n",
-       "         ethnic_background_0.0  ethnic_background_1.0  ethnic_background_2.0  \\\n",
-       "eid                                                                            \n",
-       "5773047                      1                      0                      0   \n",
-       "5773050                      1                      0                      0   \n",
-       "5773065                      1                      0                      0   \n",
-       "5773073                      1                      0                      0   \n",
-       "5773082                      1                      0                      0   \n",
-       "...                        ...                    ...                    ...   \n",
-       "6025150                      1                      0                      0   \n",
-       "6025165                      1                      0                      0   \n",
-       "6025173                      1                      0                      0   \n",
-       "6025182                      1                      0                      0   \n",
-       "6025198                      1                      0                      0   \n",
-       "\n",
-       "         ethnic_background_3.0  ethnic_background_4.0  ...  diabetes2  \\\n",
-       "eid                                                    ...              \n",
-       "5773047                      0                      0  ...      False   \n",
-       "5773050                      0                      0  ...      False   \n",
-       "5773065                      0                      0  ...      False   \n",
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-       "5773082                      0                      0  ...      False   \n",
-       "...                        ...                    ...  ...        ...   \n",
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-       "6025173                      0                      0  ...      False   \n",
-       "6025182                      0                      0  ...      False   \n",
-       "6025198                      0                      0  ...      False   \n",
-       "\n",
-       "         chronic_kidney_disease  atrial_fibrillation  migraine  \\\n",
-       "eid                                                              \n",
-       "5773047                   False                False     False   \n",
-       "5773050                   False                False     False   \n",
-       "5773065                   False                False     False   \n",
-       "5773073                   False                False     False   \n",
-       "5773082                   False                False     False   \n",
-       "...                         ...                  ...       ...   \n",
-       "6025150                   False                False     False   \n",
-       "6025165                   False                False     False   \n",
-       "6025173                   False                False     False   \n",
-       "6025182                   False                False     False   \n",
-       "6025198                    True                False     False   \n",
-       "\n",
-       "         rheumatoid_arthritis  systemic_lupus_erythematosus  \\\n",
-       "eid                                                           \n",
-       "5773047                 False                         False   \n",
-       "5773050                 False                         False   \n",
-       "5773065                 False                         False   \n",
-       "5773073                 False                         False   \n",
-       "5773082                 False                         False   \n",
-       "...                       ...                           ...   \n",
-       "6025150                 False                         False   \n",
-       "6025165                 False                         False   \n",
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-       "         severe_mental_illness  erectile_dysfunction  MACE_event_time  \\\n",
-       "eid                                                                     \n",
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-       "...                        ...                   ...              ...   \n",
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-       "\n",
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-       "      1000018   1000020   1000037   1000043   1000084   1000092   1000107  \\\n",
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-     "text": [
-      "clinical_pgs\n",
-      "5\n",
-      "Iteration 1: norm_delta = 0.86450, step_size = 0.5000, log_lik = -304721.60878, newton_decrement = 9083.97648, seconds_since_start = 6.1\n",
-      "Iteration 2: norm_delta = 0.41176, step_size = 0.5000, log_lik = -298031.49196, newton_decrement = 2195.33102, seconds_since_start = 12.0\n",
-      "Iteration 3: norm_delta = 0.23280, step_size = 0.5000, log_lik = -296371.77806, newton_decrement = 639.33537, seconds_since_start = 17.1\n",
-      "Iteration 4: norm_delta = 0.10552, step_size = 0.6000, log_lik = -295830.07342, newton_decrement = 119.37258, seconds_since_start = 22.2\n",
-      "Iteration 5: norm_delta = 0.03264, step_size = 0.7200, log_lik = -295719.30378, newton_decrement = 10.66846, seconds_since_start = 27.6\n",
-      "Iteration 6: norm_delta = 0.00479, step_size = 0.8640, log_lik = -295708.79365, newton_decrement = 0.21975, seconds_since_start = 33.1\n",
-      "Iteration 7: norm_delta = 0.00001, step_size = 1.0000, log_lik = -295708.57369, newton_decrement = 0.00000, seconds_since_start = 38.6\n",
-      "Iteration 8: norm_delta = 0.00000, step_size = 1.0000, log_lik = -295708.57369, newton_decrement = 0.00000, seconds_since_start = 43.9\n",
-      "Convergence success after 8 iterations.\n",
-      "0.7384889453055903\n"
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-       "<p>17809 rows × 44 columns</p>\n",
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-       "         PGS000011  PGS000057  PGS000058  PGS000059  age_at_recruitment  \\\n",
-       "eid                                                                       \n",
-       "5772905  -0.411116  -0.501189  -0.626652   1.177541           -1.281261   \n",
-       "5772920  -0.186573  -0.118220  -0.947081  -0.093737            0.324289   \n",
-       "5772951   0.353732   0.494530  -0.218386   0.304723            0.694801   \n",
-       "5772966   0.061837  -0.655078  -0.761244  -0.930913           -0.910749   \n",
-       "5772979  -0.576725  -1.088408   0.691370   0.266775            0.818305   \n",
-       "...            ...        ...        ...        ...                 ...   \n",
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-       "\n",
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-       "eid                                                                            \n",
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-       "eid                                                    ...              \n",
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-       "eid                                                              \n",
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-       "eid                                                           \n",
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-       "eid                                                                     \n",
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-     "text": [
-      "clinical_pgs\n",
-      "6\n",
-      "Iteration 1: norm_delta = 0.85849, step_size = 0.5000, log_lik = -302327.27794, newton_decrement = 9052.24281, seconds_since_start = 5.2\n",
-      "Iteration 2: norm_delta = 0.40660, step_size = 0.5000, log_lik = -295665.38141, newton_decrement = 2172.15635, seconds_since_start = 10.6\n",
-      "Iteration 3: norm_delta = 0.23061, step_size = 0.5000, log_lik = -294023.33616, newton_decrement = 631.60217, seconds_since_start = 16.1\n",
-      "Iteration 4: norm_delta = 0.10476, step_size = 0.6000, log_lik = -293488.22013, newton_decrement = 117.79442, seconds_since_start = 22.1\n",
-      "Iteration 5: norm_delta = 0.03245, step_size = 0.7200, log_lik = -293378.91982, newton_decrement = 10.51880, seconds_since_start = 27.8\n",
-      "Iteration 6: norm_delta = 0.00476, step_size = 0.8640, log_lik = -293368.55735, newton_decrement = 0.21654, seconds_since_start = 32.8\n",
-      "Iteration 7: norm_delta = 0.00001, step_size = 1.0000, log_lik = -293368.34061, newton_decrement = 0.00000, seconds_since_start = 38.6\n",
-      "Iteration 8: norm_delta = 0.00000, step_size = 1.0000, log_lik = -293368.34061, newton_decrement = 0.00000, seconds_since_start = 44.9\n",
-      "Convergence success after 8 iterations.\n",
-      "0.7403159331283038\n"
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-       "         PGS000011  PGS000057  PGS000058  PGS000059  age_at_recruitment  \\\n",
-       "eid                                                                       \n",
-       "5772371   1.148061   1.720016   1.224771   1.080266            0.819092   \n",
-       "5772386   0.106730   0.474097  -0.287736  -0.170717            0.942628   \n",
-       "5772404   1.454590   1.923301   1.396991   1.591214            0.077877   \n",
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-       "6025198  -0.237100  -0.882067   1.074923  -0.473986            1.436772   \n",
-       "\n",
-       "         ethnic_background_0.0  ethnic_background_1.0  ethnic_background_2.0  \\\n",
-       "eid                                                                            \n",
-       "5772371                      1                      0                      0   \n",
-       "5772386                      1                      0                      0   \n",
-       "5772404                      1                      0                      0   \n",
-       "5772416                      1                      0                      0   \n",
-       "5772450                      1                      0                      0   \n",
-       "...                        ...                    ...                    ...   \n",
-       "6025147                      0                      0                      1   \n",
-       "6025150                      1                      0                      0   \n",
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-       "eid                                                    ...              \n",
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-       "\n",
-       "         chronic_kidney_disease  atrial_fibrillation  migraine  \\\n",
-       "eid                                                              \n",
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-       "...                         ...                  ...       ...   \n",
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-       "6025198                    True                False     False   \n",
-       "\n",
-       "         rheumatoid_arthritis  systemic_lupus_erythematosus  \\\n",
-       "eid                                                           \n",
-       "5772371                 False                         False   \n",
-       "5772386                 False                         False   \n",
-       "5772404                 False                         False   \n",
-       "5772416                  True                         False   \n",
-       "5772450                 False                         False   \n",
-       "...                       ...                           ...   \n",
-       "6025147                 False                         False   \n",
-       "6025150                 False                         False   \n",
-       "6025165                 False                         False   \n",
-       "6025173                 False                         False   \n",
-       "6025198                 False                         False   \n",
-       "\n",
-       "         severe_mental_illness  erectile_dysfunction  MACE_event_time  \\\n",
-       "eid                                                                     \n",
-       "5772371                   True                 False        11.978097   \n",
-       "5772386                  False                 False        12.030116   \n",
-       "5772404                  False                 False        11.534565   \n",
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-       "...                        ...                   ...              ...   \n",
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-       "6025165                  False                 False        12.076660   \n",
-       "6025173                  False                 False        12.035592   \n",
-       "6025198                  False                 False        10.677618   \n",
-       "\n",
-       "         MACE_event  \n",
-       "eid                  \n",
-       "5772371           0  \n",
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-       "       1000018   1000020   1000037   1000043   1000079   1000084   1000092  \\\n",
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-     "text": [
-      "clinical_pgs\n",
-      "7\n",
-      "Iteration 1: norm_delta = 0.88805, step_size = 0.5000, log_lik = -302733.12849, newton_decrement = 9117.16126, seconds_since_start = 6.9\n",
-      "Iteration 2: norm_delta = 0.41410, step_size = 0.5000, log_lik = -296018.77875, newton_decrement = 2207.01978, seconds_since_start = 13.3\n",
-      "Iteration 3: norm_delta = 0.23187, step_size = 0.5000, log_lik = -294350.16892, newton_decrement = 643.12910, seconds_since_start = 18.6\n",
-      "Iteration 4: norm_delta = 0.10455, step_size = 0.6000, log_lik = -293805.23430, newton_decrement = 120.12183, seconds_since_start = 24.3\n",
-      "Iteration 5: norm_delta = 0.03224, step_size = 0.7200, log_lik = -293693.77083, newton_decrement = 10.72935, seconds_since_start = 30.3\n",
-      "Iteration 6: norm_delta = 0.00471, step_size = 0.8640, log_lik = -293683.20139, newton_decrement = 0.22049, seconds_since_start = 35.8\n",
-      "Iteration 7: norm_delta = 0.00001, step_size = 1.0000, log_lik = -293682.98071, newton_decrement = 0.00000, seconds_since_start = 41.8\n",
-      "Iteration 8: norm_delta = 0.00000, step_size = 1.0000, log_lik = -293682.98071, newton_decrement = 0.00000, seconds_since_start = 47.0\n",
-      "Convergence success after 8 iterations.\n",
-      "0.739308784177118\n"
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-       "         PGS000011  PGS000057  PGS000058  PGS000059  age_at_recruitment  \\\n",
-       "eid                                                                       \n",
-       "5773119   0.808138   1.515087  -0.562825   0.216784            0.942961   \n",
-       "5773126  -0.489326  -1.648711   0.563878   0.654460           -0.662678   \n",
-       "5773142  -0.521987  -0.270928  -0.782630   0.983739           -1.897784   \n",
-       "5773155   0.557677   1.540601  -0.968924   0.454382            1.560514   \n",
-       "5773160  -0.980970  -0.599815   0.339866   0.511664           -1.033210   \n",
-       "...            ...        ...        ...        ...                 ...   \n",
-       "6025150   0.939523   0.979282   0.151303   0.568500           -1.527252   \n",
-       "6025165   1.244074  -0.472643  -0.947026  -0.681972           -1.280231   \n",
-       "6025173  -1.527763  -1.475712  -1.098708  -1.268315            0.201897   \n",
-       "6025182  -0.156762  -0.168870  -0.644285  -0.758125            0.078386   \n",
-       "6025198  -0.238292  -0.883276   1.074081  -0.473944            1.437004   \n",
-       "\n",
-       "         ethnic_background_0.0  ethnic_background_1.0  ethnic_background_2.0  \\\n",
-       "eid                                                                            \n",
-       "5773119                      1                      0                      0   \n",
-       "5773126                      1                      0                      0   \n",
-       "5773142                      1                      0                      0   \n",
-       "5773155                      1                      0                      0   \n",
-       "5773160                      1                      0                      0   \n",
-       "...                        ...                    ...                    ...   \n",
-       "6025150                      1                      0                      0   \n",
-       "6025165                      1                      0                      0   \n",
-       "6025173                      1                      0                      0   \n",
-       "6025182                      1                      0                      0   \n",
-       "6025198                      1                      0                      0   \n",
-       "\n",
-       "         ethnic_background_3.0  ethnic_background_4.0  ...  diabetes2  \\\n",
-       "eid                                                    ...              \n",
-       "5773119                      0                      0  ...      False   \n",
-       "5773126                      0                      0  ...      False   \n",
-       "5773142                      0                      0  ...      False   \n",
-       "5773155                      0                      0  ...      False   \n",
-       "5773160                      0                      0  ...      False   \n",
-       "...                        ...                    ...  ...        ...   \n",
-       "6025150                      0                      0  ...      False   \n",
-       "6025165                      0                      0  ...      False   \n",
-       "6025173                      0                      0  ...      False   \n",
-       "6025182                      0                      0  ...      False   \n",
-       "6025198                      0                      0  ...      False   \n",
-       "\n",
-       "         chronic_kidney_disease  atrial_fibrillation  migraine  \\\n",
-       "eid                                                              \n",
-       "5773119                    True                False     False   \n",
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-       "...                         ...                  ...       ...   \n",
-       "6025150                   False                False     False   \n",
-       "6025165                   False                False     False   \n",
-       "6025173                   False                False     False   \n",
-       "6025182                   False                False     False   \n",
-       "6025198                    True                False     False   \n",
-       "\n",
-       "         rheumatoid_arthritis  systemic_lupus_erythematosus  \\\n",
-       "eid                                                           \n",
-       "5773119                 False                         False   \n",
-       "5773126                 False                         False   \n",
-       "5773142                 False                         False   \n",
-       "5773155                 False                         False   \n",
-       "5773160                 False                         False   \n",
-       "...                       ...                           ...   \n",
-       "6025150                 False                         False   \n",
-       "6025165                 False                         False   \n",
-       "6025173                 False                         False   \n",
-       "6025182                 False                         False   \n",
-       "6025198                 False                         False   \n",
-       "\n",
-       "         severe_mental_illness  erectile_dysfunction  MACE_event_time  \\\n",
-       "eid                                                                     \n",
-       "5773119                  False                 False        10.861054   \n",
-       "5773126                  False                 False        11.321013   \n",
-       "5773142                  False                 False        11.055441   \n",
-       "5773155                  False                 False        12.793977   \n",
-       "5773160                  False                 False        12.046543   \n",
-       "...                        ...                   ...              ...   \n",
-       "6025150                  False                 False        13.253936   \n",
-       "6025165                  False                 False        12.076660   \n",
-       "6025173                  False                 False        12.035592   \n",
-       "6025182                  False                 False        10.250513   \n",
-       "6025198                  False                 False        10.677618   \n",
-       "\n",
-       "         MACE_event  \n",
-       "eid                  \n",
-       "5773119           0  \n",
-       "5773126           0  \n",
-       "5773142           0  \n",
-       "5773155           0  \n",
-       "5773160           0  \n",
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-       "6025198           0  \n",
-       "\n",
-       "[17809 rows x 44 columns]"
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-       "<p>1 rows × 338355 columns</p>\n",
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-       "       1000018   1000020   1000037   1000079   1000084   1000092  1000107  \\\n",
-       "11.0  0.050713  0.146715  0.055408  0.081858  0.058026  0.078962  0.01521   \n",
-       "\n",
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-       "       5773031   5773047   5773050   5773065   5773073   5773082   5773098  \n",
-       "11.0  0.021496  0.035242  0.042867  0.032257  0.106235  0.108759  0.132702  \n",
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-     "text": [
-      "clinical_pgs\n",
-      "8\n",
-      "Iteration 1: norm_delta = 0.87008, step_size = 0.5000, log_lik = -303030.33010, newton_decrement = 9127.48939, seconds_since_start = 5.3\n",
-      "Iteration 2: norm_delta = 0.41079, step_size = 0.5000, log_lik = -296319.38491, newton_decrement = 2192.09836, seconds_since_start = 10.6\n",
-      "Iteration 3: norm_delta = 0.23208, step_size = 0.5000, log_lik = -294662.06304, newton_decrement = 638.65956, seconds_since_start = 15.4\n",
-      "Iteration 4: norm_delta = 0.10495, step_size = 0.6000, log_lik = -294120.92953, newton_decrement = 119.21479, seconds_since_start = 20.6\n",
-      "Iteration 5: norm_delta = 0.03233, step_size = 0.7200, log_lik = -294010.31352, newton_decrement = 10.63562, seconds_since_start = 25.9\n",
-      "Iteration 6: norm_delta = 0.00471, step_size = 0.8640, log_lik = -293999.83707, newton_decrement = 0.21811, seconds_since_start = 31.0\n",
-      "Iteration 7: norm_delta = 0.00001, step_size = 1.0000, log_lik = -293999.61878, newton_decrement = 0.00000, seconds_since_start = 36.0\n",
-      "Iteration 8: norm_delta = 0.00000, step_size = 1.0000, log_lik = -293999.61878, newton_decrement = 0.00000, seconds_since_start = 41.2\n",
-      "Convergence success after 8 iterations.\n",
-      "0.7364618343921243\n"
-     ]
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-     "data": {
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-       "      <td>1</td>\n",
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-       "      <th>5774565</th>\n",
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-       "      <td>11.534565</td>\n",
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-       "      <th>5774573</th>\n",
-       "      <td>1.165125</td>\n",
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-       "      <td>0.948109</td>\n",
-       "      <td>0.387442</td>\n",
-       "      <td>-1.281248</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
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-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>11.633128</td>\n",
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-       "    <tr>\n",
-       "      <th>5774582</th>\n",
-       "      <td>-0.966883</td>\n",
-       "      <td>-1.438682</td>\n",
-       "      <td>0.338244</td>\n",
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-       "         PGS000011  PGS000057  PGS000058  PGS000059  age_at_recruitment  \\\n",
-       "eid                                                                       \n",
-       "5774550  -0.204321  -0.449014   0.043888  -0.113098           -0.540283   \n",
-       "5774565   0.195130  -0.397975   0.020903  -0.549303            0.200683   \n",
-       "5774573   1.165125   0.707083   0.948109   0.387442           -1.281248   \n",
-       "5774582  -0.966883  -1.438682   0.338244  -1.402303           -0.293294   \n",
-       "5774598   0.127607   0.265540   0.752617   0.963472            1.065143   \n",
-       "...            ...        ...        ...        ...                 ...   \n",
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-       "6025165   1.245677  -0.472132  -0.947966  -0.681690           -1.281248   \n",
-       "6025173  -1.527474  -1.475411  -1.099847  -1.268653            0.200683   \n",
-       "6025182  -0.155824  -0.168296  -0.644824  -0.757924            0.077189   \n",
-       "6025198  -0.237392  -0.882851   1.075813  -0.473441            1.435626   \n",
-       "\n",
-       "         ethnic_background_0.0  ethnic_background_1.0  ethnic_background_2.0  \\\n",
-       "eid                                                                            \n",
-       "5774550                      1                      0                      0   \n",
-       "5774565                      1                      0                      0   \n",
-       "5774573                      1                      0                      0   \n",
-       "5774582                      0                      0                      0   \n",
-       "5774598                      1                      0                      0   \n",
-       "...                        ...                    ...                    ...   \n",
-       "6025150                      1                      0                      0   \n",
-       "6025165                      1                      0                      0   \n",
-       "6025173                      1                      0                      0   \n",
-       "6025182                      1                      0                      0   \n",
-       "6025198                      1                      0                      0   \n",
-       "\n",
-       "         ethnic_background_3.0  ethnic_background_4.0  ...  diabetes2  \\\n",
-       "eid                                                    ...              \n",
-       "5774550                      0                      0  ...      False   \n",
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-       "5774573                      0                      0  ...      False   \n",
-       "5774582                      0                      1  ...      False   \n",
-       "5774598                      0                      0  ...      False   \n",
-       "...                        ...                    ...  ...        ...   \n",
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-       "6025182                      0                      0  ...      False   \n",
-       "6025198                      0                      0  ...      False   \n",
-       "\n",
-       "         chronic_kidney_disease  atrial_fibrillation  migraine  \\\n",
-       "eid                                                              \n",
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-       "5774582                   False                False     False   \n",
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-       "...                         ...                  ...       ...   \n",
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-       "6025165                   False                False     False   \n",
-       "6025173                   False                False     False   \n",
-       "6025182                   False                False     False   \n",
-       "6025198                    True                False     False   \n",
-       "\n",
-       "         rheumatoid_arthritis  systemic_lupus_erythematosus  \\\n",
-       "eid                                                           \n",
-       "5774550                 False                         False   \n",
-       "5774565                 False                         False   \n",
-       "5774573                 False                         False   \n",
-       "5774582                 False                         False   \n",
-       "5774598                 False                         False   \n",
-       "...                       ...                           ...   \n",
-       "6025150                 False                         False   \n",
-       "6025165                 False                         False   \n",
-       "6025173                 False                         False   \n",
-       "6025182                 False                         False   \n",
-       "6025198                 False                         False   \n",
-       "\n",
-       "         severe_mental_illness  erectile_dysfunction  MACE_event_time  \\\n",
-       "eid                                                                     \n",
-       "5774550                  False                 False        10.524298   \n",
-       "5774565                  False                 False        11.534565   \n",
-       "5774573                  False                 False        11.633128   \n",
-       "5774582                  False                 False        11.665982   \n",
-       "5774598                  False                 False        11.969884   \n",
-       "...                        ...                   ...              ...   \n",
-       "6025150                  False                 False        13.253936   \n",
-       "6025165                  False                 False        12.076660   \n",
-       "6025173                  False                 False        12.035592   \n",
-       "6025182                  False                 False        10.250513   \n",
-       "6025198                  False                 False        10.677618   \n",
-       "\n",
-       "         MACE_event  \n",
-       "eid                  \n",
-       "5774550           0  \n",
-       "5774565           0  \n",
-       "5774573           0  \n",
-       "5774582           0  \n",
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-       "6025198           0  \n",
-       "\n",
-       "[17809 rows x 44 columns]"
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-       "       1000018   1000020   1000037   1000043   1000079   1000084   1000092  \\\n",
-       "11.0  0.050906  0.143179  0.062984  0.139005  0.080869  0.057764  0.079568   \n",
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-     "text": [
-      "clinical_pgs\n",
-      "9\n",
-      "Iteration 1: norm_delta = 0.83365, step_size = 0.5000, log_lik = -303247.13371, newton_decrement = 9099.09566, seconds_since_start = 5.1\n",
-      "Iteration 2: norm_delta = 0.40970, step_size = 0.5000, log_lik = -296555.48940, newton_decrement = 2184.87160, seconds_since_start = 10.0\n",
-      "Iteration 3: norm_delta = 0.23397, step_size = 0.5000, log_lik = -294903.70587, newton_decrement = 636.11402, seconds_since_start = 15.0\n",
-      "Iteration 4: norm_delta = 0.10659, step_size = 0.6000, log_lik = -294364.74142, newton_decrement = 118.71162, seconds_since_start = 20.0\n",
-      "Iteration 5: norm_delta = 0.03300, step_size = 0.7200, log_lik = -294254.59084, newton_decrement = 10.59557, seconds_since_start = 25.2\n",
-      "Iteration 6: norm_delta = 0.00483, step_size = 0.8640, log_lik = -294244.15341, newton_decrement = 0.21760, seconds_since_start = 30.1\n",
-      "Iteration 7: norm_delta = 0.00001, step_size = 1.0000, log_lik = -294243.93562, newton_decrement = 0.00000, seconds_since_start = 35.0\n",
-      "Iteration 8: norm_delta = 0.00000, step_size = 1.0000, log_lik = -294243.93562, newton_decrement = 0.00000, seconds_since_start = 40.0\n",
-      "Convergence success after 8 iterations.\n",
-      "0.7387615340941369\n"
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-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>10.658453</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5773575</th>\n",
-       "      <td>0.305054</td>\n",
-       "      <td>0.042499</td>\n",
-       "      <td>1.440277</td>\n",
-       "      <td>-0.114978</td>\n",
-       "      <td>1.065735</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>9.442847</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5773580</th>\n",
-       "      <td>-0.094371</td>\n",
-       "      <td>0.391560</td>\n",
-       "      <td>1.025690</td>\n",
-       "      <td>0.414624</td>\n",
-       "      <td>-1.157516</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>12.955510</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5773591</th>\n",
-       "      <td>-0.529758</td>\n",
-       "      <td>-0.781643</td>\n",
-       "      <td>-0.921464</td>\n",
-       "      <td>0.889788</td>\n",
-       "      <td>0.942221</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>10.924025</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5773606</th>\n",
-       "      <td>-0.358935</td>\n",
-       "      <td>0.289542</td>\n",
-       "      <td>-0.940255</td>\n",
-       "      <td>-0.360135</td>\n",
-       "      <td>1.189249</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
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-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>10.880219</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
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-       "      <td>...</td>\n",
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-       "      <td>...</td>\n",
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-       "      <td>...</td>\n",
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-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6025150</th>\n",
-       "      <td>0.938464</td>\n",
-       "      <td>0.978161</td>\n",
-       "      <td>0.151047</td>\n",
-       "      <td>0.568284</td>\n",
-       "      <td>-1.528058</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>13.253936</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6025165</th>\n",
-       "      <td>1.242864</td>\n",
-       "      <td>-0.473190</td>\n",
-       "      <td>-0.948694</td>\n",
-       "      <td>-0.681713</td>\n",
-       "      <td>-1.281030</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>12.076660</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6025173</th>\n",
-       "      <td>-1.527596</td>\n",
-       "      <td>-1.475863</td>\n",
-       "      <td>-1.100570</td>\n",
-       "      <td>-1.267833</td>\n",
-       "      <td>0.201137</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
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-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>12.035592</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6025182</th>\n",
-       "      <td>-0.157276</td>\n",
-       "      <td>-0.169537</td>\n",
-       "      <td>-0.645563</td>\n",
-       "      <td>-0.757837</td>\n",
-       "      <td>0.077623</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>10.250513</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6025198</th>\n",
-       "      <td>-0.238765</td>\n",
-       "      <td>-0.883661</td>\n",
-       "      <td>1.075012</td>\n",
-       "      <td>-0.473764</td>\n",
-       "      <td>1.436277</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
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-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>10.677618</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>17809 rows × 44 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         PGS000011  PGS000057  PGS000058  PGS000059  age_at_recruitment  \\\n",
-       "eid                                                                       \n",
-       "5773563  -0.594864  -1.087696   1.329238  -0.530579            0.077623   \n",
-       "5773575   0.305054   0.042499   1.440277  -0.114978            1.065735   \n",
-       "5773580  -0.094371   0.391560   1.025690   0.414624           -1.157516   \n",
-       "5773591  -0.529758  -0.781643  -0.921464   0.889788            0.942221   \n",
-       "5773606  -0.358935   0.289542  -0.940255  -0.360135            1.189249   \n",
-       "...            ...        ...        ...        ...                 ...   \n",
-       "6025150   0.938464   0.978161   0.151047   0.568284           -1.528058   \n",
-       "6025165   1.242864  -0.473190  -0.948694  -0.681713           -1.281030   \n",
-       "6025173  -1.527596  -1.475863  -1.100570  -1.267833            0.201137   \n",
-       "6025182  -0.157276  -0.169537  -0.645563  -0.757837            0.077623   \n",
-       "6025198  -0.238765  -0.883661   1.075012  -0.473764            1.436277   \n",
-       "\n",
-       "         ethnic_background_0.0  ethnic_background_1.0  ethnic_background_2.0  \\\n",
-       "eid                                                                            \n",
-       "5773563                      1                      0                      0   \n",
-       "5773575                      1                      0                      0   \n",
-       "5773580                      1                      0                      0   \n",
-       "5773591                      1                      0                      0   \n",
-       "5773606                      1                      0                      0   \n",
-       "...                        ...                    ...                    ...   \n",
-       "6025150                      1                      0                      0   \n",
-       "6025165                      1                      0                      0   \n",
-       "6025173                      1                      0                      0   \n",
-       "6025182                      1                      0                      0   \n",
-       "6025198                      1                      0                      0   \n",
-       "\n",
-       "         ethnic_background_3.0  ethnic_background_4.0  ...  diabetes2  \\\n",
-       "eid                                                    ...              \n",
-       "5773563                      0                      0  ...      False   \n",
-       "5773575                      0                      0  ...      False   \n",
-       "5773580                      0                      0  ...      False   \n",
-       "5773591                      0                      0  ...      False   \n",
-       "5773606                      0                      0  ...      False   \n",
-       "...                        ...                    ...  ...        ...   \n",
-       "6025150                      0                      0  ...      False   \n",
-       "6025165                      0                      0  ...      False   \n",
-       "6025173                      0                      0  ...      False   \n",
-       "6025182                      0                      0  ...      False   \n",
-       "6025198                      0                      0  ...      False   \n",
-       "\n",
-       "         chronic_kidney_disease  atrial_fibrillation  migraine  \\\n",
-       "eid                                                              \n",
-       "5773563                   False                False     False   \n",
-       "5773575                   False                False     False   \n",
-       "5773580                   False                False     False   \n",
-       "5773591                   False                False     False   \n",
-       "5773606                   False                False     False   \n",
-       "...                         ...                  ...       ...   \n",
-       "6025150                   False                False     False   \n",
-       "6025165                   False                False     False   \n",
-       "6025173                   False                False     False   \n",
-       "6025182                   False                False     False   \n",
-       "6025198                    True                False     False   \n",
-       "\n",
-       "         rheumatoid_arthritis  systemic_lupus_erythematosus  \\\n",
-       "eid                                                           \n",
-       "5773563                 False                         False   \n",
-       "5773575                 False                         False   \n",
-       "5773580                 False                         False   \n",
-       "5773591                 False                         False   \n",
-       "5773606                 False                         False   \n",
-       "...                       ...                           ...   \n",
-       "6025150                 False                         False   \n",
-       "6025165                 False                         False   \n",
-       "6025173                 False                         False   \n",
-       "6025182                 False                         False   \n",
-       "6025198                 False                         False   \n",
-       "\n",
-       "         severe_mental_illness  erectile_dysfunction  MACE_event_time  \\\n",
-       "eid                                                                     \n",
-       "5773563                  False                 False        10.658453   \n",
-       "5773575                  False                 False         9.442847   \n",
-       "5773580                  False                 False        12.955510   \n",
-       "5773591                  False                 False        10.924025   \n",
-       "5773606                   True                 False        10.880219   \n",
-       "...                        ...                   ...              ...   \n",
-       "6025150                  False                 False        13.253936   \n",
-       "6025165                  False                 False        12.076660   \n",
-       "6025173                  False                 False        12.035592   \n",
-       "6025182                  False                 False        10.250513   \n",
-       "6025198                  False                 False        10.677618   \n",
-       "\n",
-       "         MACE_event  \n",
-       "eid                  \n",
-       "5773563           0  \n",
-       "5773575           1  \n",
-       "5773580           0  \n",
-       "5773591           0  \n",
-       "5773606           0  \n",
-       "...             ...  \n",
-       "6025150           0  \n",
-       "6025165           0  \n",
-       "6025173           0  \n",
-       "6025182           0  \n",
-       "6025198           0  \n",
-       "\n",
-       "[17809 rows x 44 columns]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
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-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>1000018</th>\n",
-       "      <th>1000020</th>\n",
-       "      <th>1000037</th>\n",
-       "      <th>1000043</th>\n",
-       "      <th>1000079</th>\n",
-       "      <th>1000092</th>\n",
-       "      <th>1000107</th>\n",
-       "      <th>1000110</th>\n",
-       "      <th>1000128</th>\n",
-       "      <th>1000135</th>\n",
-       "      <th>...</th>\n",
-       "      <th>5773445</th>\n",
-       "      <th>5773467</th>\n",
-       "      <th>5773474</th>\n",
-       "      <th>5773489</th>\n",
-       "      <th>5773490</th>\n",
-       "      <th>5773509</th>\n",
-       "      <th>5773517</th>\n",
-       "      <th>5773538</th>\n",
-       "      <th>5773546</th>\n",
-       "      <th>5773552</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>11.0</th>\n",
-       "      <td>0.051756</td>\n",
-       "      <td>0.143366</td>\n",
-       "      <td>0.055164</td>\n",
-       "      <td>0.136415</td>\n",
-       "      <td>0.079103</td>\n",
-       "      <td>0.085704</td>\n",
-       "      <td>0.015456</td>\n",
-       "      <td>0.01531</td>\n",
-       "      <td>0.012502</td>\n",
-       "      <td>0.014412</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.018737</td>\n",
-       "      <td>0.172527</td>\n",
-       "      <td>0.055127</td>\n",
-       "      <td>0.013103</td>\n",
-       "      <td>0.010185</td>\n",
-       "      <td>0.129603</td>\n",
-       "      <td>0.018464</td>\n",
-       "      <td>0.039519</td>\n",
-       "      <td>0.04868</td>\n",
-       "      <td>0.037171</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1 rows × 338356 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       1000018   1000020   1000037   1000043   1000079   1000092   1000107  \\\n",
-       "11.0  0.051756  0.143366  0.055164  0.136415  0.079103  0.085704  0.015456   \n",
-       "\n",
-       "      1000110   1000128   1000135  ...   5773445   5773467   5773474  \\\n",
-       "11.0  0.01531  0.012502  0.014412  ...  0.018737  0.172527  0.055127   \n",
-       "\n",
-       "       5773489   5773490   5773509   5773517   5773538  5773546   5773552  \n",
-       "11.0  0.013103  0.010185  0.129603  0.018464  0.039519  0.04868  0.037171  \n",
-       "\n",
-       "[1 rows x 338356 columns]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "predictions_dict = {}\n",
-    "for group in tqdm(groups):\n",
-    "    predictions_dict[group] = {}\n",
-    "    for partition in partitions:\n",
-    "        tqdm.write(group)\n",
-    "        tqdm.write(partition)\n",
-    "        predictions_dict[group][partition] = fit_predict_coxph(data.copy(), group, partition, time, event, f\"{dataset_path}/partition_{partition}/cox_{group}.p\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for group in groups:\n",
-    "    pred_dfs = [predictions_dict[group][partition] for partition in partitions]\n",
-    "    predictions_dict[group][\"complete\"] = pd.concat(pred_dfs).sort_values(\"eid\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from functools import reduce\n",
-    "prediction_dfs = [predictions_dict[group][\"complete\"] for group in groups]\n",
-    "predictions = reduce(lambda  left,right: pd.merge(left,right,on=['eid', \"partition\", \"split\"], how='left'), prediction_dfs).sort_values([\"eid\", \"partition\", \"split\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "\n",
-       "    .dataframe tbody tr th {\n",
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-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>score_COX_clinical</th>\n",
-       "      <th>0_1_Ft_COX_clinical</th>\n",
-       "      <th>0_2_Ft_COX_clinical</th>\n",
-       "      <th>0_3_Ft_COX_clinical</th>\n",
-       "      <th>0_4_Ft_COX_clinical</th>\n",
-       "      <th>0_5_Ft_COX_clinical</th>\n",
-       "      <th>0_6_Ft_COX_clinical</th>\n",
-       "      <th>0_7_Ft_COX_clinical</th>\n",
-       "      <th>0_8_Ft_COX_clinical</th>\n",
-       "      <th>...</th>\n",
-       "      <th>0_17_Ft_COX_clinical_pgs</th>\n",
-       "      <th>0_18_Ft_COX_clinical_pgs</th>\n",
-       "      <th>0_19_Ft_COX_clinical_pgs</th>\n",
-       "      <th>0_20_Ft_COX_clinical_pgs</th>\n",
-       "      <th>0_21_Ft_COX_clinical_pgs</th>\n",
-       "      <th>0_22_Ft_COX_clinical_pgs</th>\n",
-       "      <th>0_23_Ft_COX_clinical_pgs</th>\n",
-       "      <th>0_24_Ft_COX_clinical_pgs</th>\n",
-       "      <th>0_25_Ft_COX_clinical_pgs</th>\n",
-       "      <th>0_26_Ft_COX_clinical_pgs</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>0.056848</td>\n",
-       "      <td>0.003620</td>\n",
-       "      <td>0.007845</td>\n",
-       "      <td>0.012573</td>\n",
-       "      <td>0.017312</td>\n",
-       "      <td>0.022510</td>\n",
-       "      <td>0.027965</td>\n",
-       "      <td>0.033560</td>\n",
-       "      <td>0.039235</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.069412</td>\n",
-       "      <td>0.069412</td>\n",
-       "      <td>0.069412</td>\n",
-       "      <td>0.069412</td>\n",
-       "      <td>0.069412</td>\n",
-       "      <td>0.069412</td>\n",
-       "      <td>0.069412</td>\n",
-       "      <td>0.069412</td>\n",
-       "      <td>0.069412</td>\n",
-       "      <td>0.069412</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>0.058350</td>\n",
-       "      <td>0.003683</td>\n",
-       "      <td>0.007990</td>\n",
-       "      <td>0.012913</td>\n",
-       "      <td>0.017859</td>\n",
-       "      <td>0.023154</td>\n",
-       "      <td>0.028784</td>\n",
-       "      <td>0.034615</td>\n",
-       "      <td>0.040404</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.072307</td>\n",
-       "      <td>0.072307</td>\n",
-       "      <td>0.072307</td>\n",
-       "      <td>0.072307</td>\n",
-       "      <td>0.072307</td>\n",
-       "      <td>0.072307</td>\n",
-       "      <td>0.072307</td>\n",
-       "      <td>0.072307</td>\n",
-       "      <td>0.072307</td>\n",
-       "      <td>0.072307</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>0.058792</td>\n",
-       "      <td>0.003701</td>\n",
-       "      <td>0.008070</td>\n",
-       "      <td>0.012982</td>\n",
-       "      <td>0.017968</td>\n",
-       "      <td>0.023280</td>\n",
-       "      <td>0.028935</td>\n",
-       "      <td>0.034768</td>\n",
-       "      <td>0.040614</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.072175</td>\n",
-       "      <td>0.072175</td>\n",
-       "      <td>0.072175</td>\n",
-       "      <td>0.072175</td>\n",
-       "      <td>0.072175</td>\n",
-       "      <td>0.072175</td>\n",
-       "      <td>0.072175</td>\n",
-       "      <td>0.072175</td>\n",
-       "      <td>0.072175</td>\n",
-       "      <td>0.072175</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>0.056783</td>\n",
-       "      <td>0.003614</td>\n",
-       "      <td>0.007890</td>\n",
-       "      <td>0.012524</td>\n",
-       "      <td>0.017254</td>\n",
-       "      <td>0.022399</td>\n",
-       "      <td>0.027937</td>\n",
-       "      <td>0.033584</td>\n",
-       "      <td>0.039314</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.070711</td>\n",
-       "      <td>0.070711</td>\n",
-       "      <td>0.070711</td>\n",
-       "      <td>0.070711</td>\n",
-       "      <td>0.070711</td>\n",
-       "      <td>0.070711</td>\n",
-       "      <td>0.070711</td>\n",
-       "      <td>0.070711</td>\n",
-       "      <td>0.070711</td>\n",
-       "      <td>0.070711</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>0.058033</td>\n",
-       "      <td>0.003722</td>\n",
-       "      <td>0.007933</td>\n",
-       "      <td>0.012689</td>\n",
-       "      <td>0.017598</td>\n",
-       "      <td>0.022875</td>\n",
-       "      <td>0.028521</td>\n",
-       "      <td>0.034297</td>\n",
-       "      <td>0.040176</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.070499</td>\n",
-       "      <td>0.070499</td>\n",
-       "      <td>0.070499</td>\n",
-       "      <td>0.070499</td>\n",
-       "      <td>0.070499</td>\n",
-       "      <td>0.070499</td>\n",
-       "      <td>0.070499</td>\n",
-       "      <td>0.070499</td>\n",
-       "      <td>0.070499</td>\n",
-       "      <td>0.070499</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3957373</th>\n",
-       "      <td>6025198</td>\n",
-       "      <td>0.304692</td>\n",
-       "      <td>0.021952</td>\n",
-       "      <td>0.047583</td>\n",
-       "      <td>0.075156</td>\n",
-       "      <td>0.102743</td>\n",
-       "      <td>0.131628</td>\n",
-       "      <td>0.161432</td>\n",
-       "      <td>0.191187</td>\n",
-       "      <td>0.220504</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.401433</td>\n",
-       "      <td>0.401433</td>\n",
-       "      <td>0.401433</td>\n",
-       "      <td>0.401433</td>\n",
-       "      <td>0.401433</td>\n",
-       "      <td>0.401433</td>\n",
-       "      <td>0.401433</td>\n",
-       "      <td>0.401433</td>\n",
-       "      <td>0.401433</td>\n",
-       "      <td>0.401433</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3957374</th>\n",
-       "      <td>6025198</td>\n",
-       "      <td>0.300461</td>\n",
-       "      <td>0.022339</td>\n",
-       "      <td>0.047034</td>\n",
-       "      <td>0.074491</td>\n",
-       "      <td>0.101926</td>\n",
-       "      <td>0.130097</td>\n",
-       "      <td>0.159591</td>\n",
-       "      <td>0.188690</td>\n",
-       "      <td>0.217495</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.400216</td>\n",
-       "      <td>0.400216</td>\n",
-       "      <td>0.400216</td>\n",
-       "      <td>0.400216</td>\n",
-       "      <td>0.400216</td>\n",
-       "      <td>0.400216</td>\n",
-       "      <td>0.400216</td>\n",
-       "      <td>0.400216</td>\n",
-       "      <td>0.400216</td>\n",
-       "      <td>0.400216</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3957376</th>\n",
-       "      <td>6025198</td>\n",
-       "      <td>0.310522</td>\n",
-       "      <td>0.022736</td>\n",
-       "      <td>0.048882</td>\n",
-       "      <td>0.076539</td>\n",
-       "      <td>0.104753</td>\n",
-       "      <td>0.134293</td>\n",
-       "      <td>0.164534</td>\n",
-       "      <td>0.194872</td>\n",
-       "      <td>0.224480</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.411701</td>\n",
-       "      <td>0.411701</td>\n",
-       "      <td>0.411701</td>\n",
-       "      <td>0.411701</td>\n",
-       "      <td>0.411701</td>\n",
-       "      <td>0.411701</td>\n",
-       "      <td>0.411701</td>\n",
-       "      <td>0.411701</td>\n",
-       "      <td>0.411701</td>\n",
-       "      <td>0.411701</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3957377</th>\n",
-       "      <td>6025198</td>\n",
-       "      <td>0.304024</td>\n",
-       "      <td>0.022313</td>\n",
-       "      <td>0.047734</td>\n",
-       "      <td>0.075392</td>\n",
-       "      <td>0.102953</td>\n",
-       "      <td>0.131779</td>\n",
-       "      <td>0.161964</td>\n",
-       "      <td>0.191702</td>\n",
-       "      <td>0.220167</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.400954</td>\n",
-       "      <td>0.400954</td>\n",
-       "      <td>0.400954</td>\n",
-       "      <td>0.400954</td>\n",
-       "      <td>0.400954</td>\n",
-       "      <td>0.400954</td>\n",
-       "      <td>0.400954</td>\n",
-       "      <td>0.400954</td>\n",
-       "      <td>0.400954</td>\n",
-       "      <td>0.400954</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3957378</th>\n",
-       "      <td>6025198</td>\n",
-       "      <td>0.310785</td>\n",
-       "      <td>0.023107</td>\n",
-       "      <td>0.049601</td>\n",
-       "      <td>0.077679</td>\n",
-       "      <td>0.106172</td>\n",
-       "      <td>0.135355</td>\n",
-       "      <td>0.165582</td>\n",
-       "      <td>0.195784</td>\n",
-       "      <td>0.225160</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.408787</td>\n",
-       "      <td>0.408787</td>\n",
-       "      <td>0.408787</td>\n",
-       "      <td>0.408787</td>\n",
-       "      <td>0.408787</td>\n",
-       "      <td>0.408787</td>\n",
-       "      <td>0.408787</td>\n",
-       "      <td>0.408787</td>\n",
-       "      <td>0.408787</td>\n",
-       "      <td>0.408787</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>3957380 rows × 57 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "             eid  score_COX_clinical  0_1_Ft_COX_clinical  \\\n",
-       "0        1000018            0.056848             0.003620   \n",
-       "4        1000018            0.058350             0.003683   \n",
-       "5        1000018            0.058792             0.003701   \n",
-       "6        1000018            0.056783             0.003614   \n",
-       "8        1000018            0.058033             0.003722   \n",
-       "...          ...                 ...                  ...   \n",
-       "3957373  6025198            0.304692             0.021952   \n",
-       "3957374  6025198            0.300461             0.022339   \n",
-       "3957376  6025198            0.310522             0.022736   \n",
-       "3957377  6025198            0.304024             0.022313   \n",
-       "3957378  6025198            0.310785             0.023107   \n",
-       "\n",
-       "         0_2_Ft_COX_clinical  0_3_Ft_COX_clinical  0_4_Ft_COX_clinical  \\\n",
-       "0                   0.007845             0.012573             0.017312   \n",
-       "4                   0.007990             0.012913             0.017859   \n",
-       "5                   0.008070             0.012982             0.017968   \n",
-       "6                   0.007890             0.012524             0.017254   \n",
-       "8                   0.007933             0.012689             0.017598   \n",
-       "...                      ...                  ...                  ...   \n",
-       "3957373             0.047583             0.075156             0.102743   \n",
-       "3957374             0.047034             0.074491             0.101926   \n",
-       "3957376             0.048882             0.076539             0.104753   \n",
-       "3957377             0.047734             0.075392             0.102953   \n",
-       "3957378             0.049601             0.077679             0.106172   \n",
-       "\n",
-       "         0_5_Ft_COX_clinical  0_6_Ft_COX_clinical  0_7_Ft_COX_clinical  \\\n",
-       "0                   0.022510             0.027965             0.033560   \n",
-       "4                   0.023154             0.028784             0.034615   \n",
-       "5                   0.023280             0.028935             0.034768   \n",
-       "6                   0.022399             0.027937             0.033584   \n",
-       "8                   0.022875             0.028521             0.034297   \n",
-       "...                      ...                  ...                  ...   \n",
-       "3957373             0.131628             0.161432             0.191187   \n",
-       "3957374             0.130097             0.159591             0.188690   \n",
-       "3957376             0.134293             0.164534             0.194872   \n",
-       "3957377             0.131779             0.161964             0.191702   \n",
-       "3957378             0.135355             0.165582             0.195784   \n",
-       "\n",
-       "         0_8_Ft_COX_clinical  ...  0_17_Ft_COX_clinical_pgs  \\\n",
-       "0                   0.039235  ...                  0.069412   \n",
-       "4                   0.040404  ...                  0.072307   \n",
-       "5                   0.040614  ...                  0.072175   \n",
-       "6                   0.039314  ...                  0.070711   \n",
-       "8                   0.040176  ...                  0.070499   \n",
-       "...                      ...  ...                       ...   \n",
-       "3957373             0.220504  ...                  0.401433   \n",
-       "3957374             0.217495  ...                  0.400216   \n",
-       "3957376             0.224480  ...                  0.411701   \n",
-       "3957377             0.220167  ...                  0.400954   \n",
-       "3957378             0.225160  ...                  0.408787   \n",
-       "\n",
-       "         0_18_Ft_COX_clinical_pgs  0_19_Ft_COX_clinical_pgs  \\\n",
-       "0                        0.069412                  0.069412   \n",
-       "4                        0.072307                  0.072307   \n",
-       "5                        0.072175                  0.072175   \n",
-       "6                        0.070711                  0.070711   \n",
-       "8                        0.070499                  0.070499   \n",
-       "...                           ...                       ...   \n",
-       "3957373                  0.401433                  0.401433   \n",
-       "3957374                  0.400216                  0.400216   \n",
-       "3957376                  0.411701                  0.411701   \n",
-       "3957377                  0.400954                  0.400954   \n",
-       "3957378                  0.408787                  0.408787   \n",
-       "\n",
-       "         0_20_Ft_COX_clinical_pgs  0_21_Ft_COX_clinical_pgs  \\\n",
-       "0                        0.069412                  0.069412   \n",
-       "4                        0.072307                  0.072307   \n",
-       "5                        0.072175                  0.072175   \n",
-       "6                        0.070711                  0.070711   \n",
-       "8                        0.070499                  0.070499   \n",
-       "...                           ...                       ...   \n",
-       "3957373                  0.401433                  0.401433   \n",
-       "3957374                  0.400216                  0.400216   \n",
-       "3957376                  0.411701                  0.411701   \n",
-       "3957377                  0.400954                  0.400954   \n",
-       "3957378                  0.408787                  0.408787   \n",
-       "\n",
-       "         0_22_Ft_COX_clinical_pgs  0_23_Ft_COX_clinical_pgs  \\\n",
-       "0                        0.069412                  0.069412   \n",
-       "4                        0.072307                  0.072307   \n",
-       "5                        0.072175                  0.072175   \n",
-       "6                        0.070711                  0.070711   \n",
-       "8                        0.070499                  0.070499   \n",
-       "...                           ...                       ...   \n",
-       "3957373                  0.401433                  0.401433   \n",
-       "3957374                  0.400216                  0.400216   \n",
-       "3957376                  0.411701                  0.411701   \n",
-       "3957377                  0.400954                  0.400954   \n",
-       "3957378                  0.408787                  0.408787   \n",
-       "\n",
-       "         0_24_Ft_COX_clinical_pgs  0_25_Ft_COX_clinical_pgs  \\\n",
-       "0                        0.069412                  0.069412   \n",
-       "4                        0.072307                  0.072307   \n",
-       "5                        0.072175                  0.072175   \n",
-       "6                        0.070711                  0.070711   \n",
-       "8                        0.070499                  0.070499   \n",
-       "...                           ...                       ...   \n",
-       "3957373                  0.401433                  0.401433   \n",
-       "3957374                  0.400216                  0.400216   \n",
-       "3957376                  0.411701                  0.411701   \n",
-       "3957377                  0.400954                  0.400954   \n",
-       "3957378                  0.408787                  0.408787   \n",
-       "\n",
-       "         0_26_Ft_COX_clinical_pgs  \n",
-       "0                        0.069412  \n",
-       "4                        0.072307  \n",
-       "5                        0.072175  \n",
-       "6                        0.070711  \n",
-       "8                        0.070499  \n",
-       "...                           ...  \n",
-       "3957373                  0.401433  \n",
-       "3957374                  0.400216  \n",
-       "3957376                  0.411701  \n",
-       "3957377                  0.400954  \n",
-       "3957378                  0.408787  \n",
-       "\n",
-       "[3957380 rows x 57 columns]"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "predictions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:>"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "predictions.query(\"partition=='4'\").query(\"split=='valid'\").score_COX_clinical.hist(bins=100)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f0b63b2f690>"
-      ]
-     },
-     "execution_count": 38,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "predictions.query(\"partition=='0'\").query(\"split=='valid'\").score_COX_MACE_clinical.hist(bins=100)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "predictions.to_csv(f\"{data_path}/3_datasets_post/{dataset_name}/predictions_coxph.csv\", index=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 85,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "             eid  score_COX_clinical  0_1_Ft_COX_clinical  \\\n",
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-       "\n",
-       "         0_2_Ft_COX_clinical  0_3_Ft_COX_clinical  0_4_Ft_COX_clinical  \\\n",
-       "1978689             0.046786             0.075147             0.102025   \n",
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-       "1978686             0.050451             0.079213             0.107872   \n",
-       "\n",
-       "         0_5_Ft_COX_clinical  0_6_Ft_COX_clinical  0_7_Ft_COX_clinical  \\\n",
-       "1978689             0.130841             0.160260             0.189726   \n",
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-       "1978686             0.137382             0.168453             0.199037   \n",
-       "\n",
-       "         0_8_Ft_COX_clinical  ...  0_17_Ft_COX_clinical_pgs  \\\n",
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-       "\n",
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-       "1978687                  0.412981                  0.412981   \n",
-       "1978686                  0.412985                  0.412985   \n",
-       "\n",
-       "         0_20_Ft_COX_clinical_pgs  0_21_Ft_COX_clinical_pgs  \\\n",
-       "1978689                  0.396857                  0.396857   \n",
-       "1978685                  0.397130                  0.397130   \n",
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-       "1978687                  0.412981                  0.412981   \n",
-       "1978686                  0.412985                  0.412985   \n",
-       "\n",
-       "         0_22_Ft_COX_clinical_pgs  0_23_Ft_COX_clinical_pgs  \\\n",
-       "1978689                  0.396857                  0.396857   \n",
-       "1978685                  0.397130                  0.397130   \n",
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-       "1978686                  0.412985                  0.412985   \n",
-       "\n",
-       "         0_24_Ft_COX_clinical_pgs  0_25_Ft_COX_clinical_pgs  \\\n",
-       "1978689                  0.396857                  0.396857   \n",
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-       "1978687                  0.412981                  0.412981   \n",
-       "1978686                  0.412985                  0.412985   \n",
-       "\n",
-       "         0_26_Ft_COX_clinical_pgs  \n",
-       "1978689                  0.396857  \n",
-       "1978685                  0.397130  \n",
-       "1978688                  0.400358  \n",
-       "1978687                  0.412981  \n",
-       "1978686                  0.412985  \n",
-       "\n",
-       "[5 rows x 57 columns]"
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-       "             eid  score_COX_clinical partition  split\n",
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-   "outputs": [],
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-    "predictions_df = pd.read_feather(\"/data/analysis/ag-reils/ag-reils-shared/cardioRS/results/models/benchmarks/BEN-1285/predictions/predictions.feather\")"
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-       "      <td>0.017225</td>\n",
-       "      <td>0.982775</td>\n",
-       "      <td>0.029963</td>\n",
-       "      <td>0.044256</td>\n",
-       "      <td>0.955744</td>\n",
-       "      <td>0.034002</td>\n",
-       "      <td>0.076378</td>\n",
-       "      <td>0.923622</td>\n",
-       "      <td>0.036696</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.081644</td>\n",
-       "      <td>0.085517</td>\n",
-       "      <td>0.089424</td>\n",
-       "      <td>0.093668</td>\n",
-       "      <td>6024807</td>\n",
-       "      <td>test</td>\n",
-       "      <td>0</td>\n",
-       "      <td>DeepSurvivalMachine</td>\n",
-       "      <td>StandardMLP</td>\n",
-       "      <td>CVDCoreVariablesWithPGSDataModule_210212</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>395737</th>\n",
-       "      <td>0.031700</td>\n",
-       "      <td>0.023351</td>\n",
-       "      <td>0.976649</td>\n",
-       "      <td>0.039779</td>\n",
-       "      <td>0.059497</td>\n",
-       "      <td>0.940503</td>\n",
-       "      <td>0.044441</td>\n",
-       "      <td>0.101806</td>\n",
-       "      <td>0.898194</td>\n",
-       "      <td>0.047178</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.134328</td>\n",
-       "      <td>0.140790</td>\n",
-       "      <td>0.147390</td>\n",
-       "      <td>0.154029</td>\n",
-       "      <td>6025173</td>\n",
-       "      <td>test</td>\n",
-       "      <td>0</td>\n",
-       "      <td>DeepSurvivalMachine</td>\n",
-       "      <td>StandardMLP</td>\n",
-       "      <td>CVDCoreVariablesWithPGSDataModule_210212</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>395738 rows × 194 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          0_1_ft    0_1_Ft    0_1_St    0_2_ft    0_2_Ft    0_2_St    0_3_ft  \\\n",
-       "0       0.030803  0.022290  0.977710  0.039299  0.057751  0.942249  0.044319   \n",
-       "1       0.048185  0.036345  0.963655  0.058220  0.090192  0.909808  0.062842   \n",
-       "2       0.053468  0.040084  0.959916  0.064656  0.099897  0.900103  0.069494   \n",
-       "3       0.039807  0.029643  0.970357  0.049027  0.074585  0.925415  0.053833   \n",
-       "4       0.028805  0.021258  0.978742  0.036251  0.054144  0.945856  0.040689   \n",
-       "...          ...       ...       ...       ...       ...       ...       ...   \n",
-       "395733  0.018039  0.013417  0.986583  0.022897  0.034085  0.965915  0.026106   \n",
-       "395734  0.041583  0.030703  0.969297  0.051509  0.077817  0.922183  0.056635   \n",
-       "395735  0.027671  0.019840  0.980160  0.035789  0.051936  0.948064  0.040801   \n",
-       "395736  0.023525  0.017225  0.982775  0.029963  0.044256  0.955744  0.034002   \n",
-       "395737  0.031700  0.023351  0.976649  0.039779  0.059497  0.940503  0.044441   \n",
-       "\n",
-       "          0_3_Ft    0_3_St    0_4_ft  ...  0_23_Ft_calibrated  \\\n",
-       "0       0.099765  0.900235  0.047347  ...            0.139802   \n",
-       "1       0.151037  0.848963  0.064484  ...            0.249414   \n",
-       "2       0.167342  0.832658  0.070809  ...            0.308261   \n",
-       "3       0.126272  0.873728  0.056177  ...            0.189116   \n",
-       "4       0.092789  0.907211  0.043434  ...            0.114073   \n",
-       "...          ...       ...       ...  ...                 ...   \n",
-       "395733  0.058681  0.941319  0.028405  ...            0.048910   \n",
-       "395734  0.132170  0.867830  0.059054  ...            0.213297   \n",
-       "395735  0.090415  0.909585  0.044011  ...            0.124349   \n",
-       "395736  0.076378  0.923622  0.036696  ...            0.081644   \n",
-       "395737  0.101806  0.898194  0.047178  ...            0.134328   \n",
-       "\n",
-       "        0_24_Ft_calibrated  0_25_Ft_calibrated  0_26_Ft_calibrated      eid  \\\n",
-       "0                 0.146783            0.153930            0.161087  1000018   \n",
-       "1                 0.260941            0.272834            0.283636  1000020   \n",
-       "2                 0.322440            0.337083            0.349919  1000043   \n",
-       "3                 0.198088            0.207313            0.216100  1000079   \n",
-       "4                 0.119544            0.125108            0.130846  1000084   \n",
-       "...                    ...                 ...                 ...      ...   \n",
-       "395733            0.051133            0.053338            0.055936  6024701   \n",
-       "395734            0.223581            0.234180            0.244097  6024778   \n",
-       "395735            0.130700            0.137188            0.143797  6024787   \n",
-       "395736            0.085517            0.089424            0.093668  6024807   \n",
-       "395737            0.140790            0.147390            0.154029  6025173   \n",
-       "\n",
-       "        split  partition               module          net  \\\n",
-       "0       train          0  DeepSurvivalMachine  StandardMLP   \n",
-       "1       train          0  DeepSurvivalMachine  StandardMLP   \n",
-       "2       train          0  DeepSurvivalMachine  StandardMLP   \n",
-       "3       train          0  DeepSurvivalMachine  StandardMLP   \n",
-       "4       train          0  DeepSurvivalMachine  StandardMLP   \n",
-       "...       ...        ...                  ...          ...   \n",
-       "395733   test          0  DeepSurvivalMachine  StandardMLP   \n",
-       "395734   test          0  DeepSurvivalMachine  StandardMLP   \n",
-       "395735   test          0  DeepSurvivalMachine  StandardMLP   \n",
-       "395736   test          0  DeepSurvivalMachine  StandardMLP   \n",
-       "395737   test          0  DeepSurvivalMachine  StandardMLP   \n",
-       "\n",
-       "                                      datamodule  \n",
-       "0       CVDCoreVariablesWithPGSDataModule_210212  \n",
-       "1       CVDCoreVariablesWithPGSDataModule_210212  \n",
-       "2       CVDCoreVariablesWithPGSDataModule_210212  \n",
-       "3       CVDCoreVariablesWithPGSDataModule_210212  \n",
-       "4       CVDCoreVariablesWithPGSDataModule_210212  \n",
-       "...                                          ...  \n",
-       "395733  CVDCoreVariablesWithPGSDataModule_210212  \n",
-       "395734  CVDCoreVariablesWithPGSDataModule_210212  \n",
-       "395735  CVDCoreVariablesWithPGSDataModule_210212  \n",
-       "395736  CVDCoreVariablesWithPGSDataModule_210212  \n",
-       "395737  CVDCoreVariablesWithPGSDataModule_210212  \n",
-       "\n",
-       "[395738 rows x 194 columns]"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "predictions_df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "pd.set_option('display.max_rows', 100)\n",
-    "for t in range(1, 27, 1):\n",
-    "    predictions_df[[col for col in predictions_df.columns.to_list() if f\"_{t}_Ft\" in col]+[\"partition\", \"split\"]][f\"0_{t}_Ft_calibrated\"].hist(bins=100)\n",
-    "    plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "R [conda env:python]",
-   "language": "R",
-   "name": "conda-env-python-r"
-  },
-  "language_info": {
-   "codemirror_mode": "r",
-   "file_extension": ".r",
-   "mimetype": "text/x-r-source",
-   "name": "R",
-   "pygments_lexer": "r",
-   "version": "4.0.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/neuralcvd/preprocessing/ukbb_tabular/8_train_coxph_models_on_h5ad.ipynb b/neuralcvd/preprocessing/ukbb_tabular/8_train_coxph_models_on_h5ad.ipynb
deleted file mode 100644
index f84330b..0000000
--- a/neuralcvd/preprocessing/ukbb_tabular/8_train_coxph_models_on_h5ad.ipynb
+++ /dev/null
@@ -1,13519 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Cox model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The autoreload extension is already loaded. To reload it, use:\n",
-      "  %reload_ext autoreload\n"
-     ]
-    }
-   ],
-   "source": [
-    "%load_ext autoreload\n",
-    "%autoreload 2\n",
-    "\n",
-    "import os\n",
-    "from tqdm.auto import tqdm\n",
-    "\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import lifelines\n",
-    "from lifelines import CoxPHFitter\n",
-    "from sklearn.model_selection import StratifiedKFold\n",
-    "\n",
-    "from joblib import Parallel, delayed\n",
-    "from tqdm.notebook import tqdm\n",
-    "import neptune\n",
-    "import warnings\n",
-    "warnings.filterwarnings(\"ignore\")\n",
-    "import shutil\n",
-    "import anndata as ad\n",
-    "import pickle"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dataset_name = \"210226_cvd_gp_full_cluster\"\n",
-    "path = \"/data/analysis/ag-reils/steinfej/code/umbrella/pre/ukbb\"\n",
-    "data_path = \"/data/analysis/ag-reils/ag-reils-shared/cardioRS/data\"\n",
-    "dataset_path = f\"{data_path}/3_datasets_post/{dataset_name}\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_eid_map(h5ad):\n",
-    "    eids = np.expand_dims(h5ad.obs['eid'].values.astype(int), axis=-1)\n",
-    "    eid_map = pd.DataFrame(np.concatenate([eids, h5ad.X], axis=1),\n",
-    "                                columns=['eid'] + h5ad.var.index.values.tolist())\n",
-    "    eid_map = eid_map.astype({'eid': 'int32'})#.set_index('eid')\n",
-    "    return eid_map"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "partitions = [str(p) for p in range(10)]\n",
-    "splits = [\"train\", \"valid\", \"test\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a077368aa5bf4cd6834e08f11e2de6b8",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/10 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "eids_incl = pd.read_feather(f\"{dataset_path}/data_excl.feather\").eid.to_list()\n",
-    "excl = True\n",
-    "if excl==True: data_all = {partition: {split: pd.read_feather(f\"{dataset_path}/partition_{partition}/{split}/data_imputed_normalized.feather\").query(\"eid ==@eids_incl\").set_index(\"eid\") for split in splits} for partition in tqdm(partitions)}\n",
-    "else: data_all = {partition: {split: pd.read_feather(f\"{dataset_path}/partition_{partition}/{split}/data_imputed_normalized.feather\").set_index(\"eid\") for split in splits} for partition in tqdm(partitions)}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "features = {}\n",
-    "#features[\"pgs\"] = ['PGS000011', 'PGS000013', 'PGS000016', 'PGS000018', 'PGS000039', 'PGS000057', \n",
-    "#                   'PGS000058', 'PGS000059', 'PGS000116', 'PGS000117', 'PGS000192', 'PGS000296']\n",
-    "features[\"pgs\"] = ['PGS000011', 'PGS000057', 'PGS000058', 'PGS000059']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "basics = [\n",
-    "'age_at_recruitment',\n",
-    "'ethnic_background_0.0',\n",
-    "'ethnic_background_1.0',\n",
-    "'ethnic_background_2.0',#na 2 -> 5\n",
-    "'ethnic_background_3.0',\n",
-    "'ethnic_background_4.0',\n",
-    "'townsend_deprivation_index_at_recruitment',\n",
-    "'sex'\n",
-    "]\n",
-    "questionnaire = [\n",
-    "'overall_health_rating_0.0',\n",
-    "'overall_health_rating_1.0',\n",
-    "'overall_health_rating_2.0',\n",
-    "'overall_health_rating_3.0',\n",
-    "'smoking_status_0.0',\n",
-    "'smoking_status_1.0',\n",
-    "'smoking_status_2.0',\n",
-    "]\n",
-    "measurements = [\n",
-    "'body_mass_index_bmi',\n",
-    "'weight',\n",
-    "\"standing_height\",\n",
-    "'systolic_blood_pressure',\n",
-    "'diastolic_blood_pressure',\n",
-    "]\n",
-    "\n",
-    "labs = [\n",
-    "\"cholesterol\",\n",
-    "\"hdl_cholesterol\",\n",
-    "\"ldl_direct\",\n",
-    "\"triglycerides\"\n",
-    "]\n",
-    "\n",
-    "family_history = [\n",
-    "'fh_heart_disease',\n",
-    "]\n",
-    "\n",
-    "diagnoses = [\n",
-    "'diabetes1',\n",
-    "'diabetes2',\n",
-    "'chronic_kidney_disease',\n",
-    "'atrial_fibrillation',\n",
-    "'migraine',\n",
-    "'rheumatoid_arthritis',\n",
-    "'systemic_lupus_erythematosus',\n",
-    "'severe_mental_illness',\n",
-    "'erectile_dysfunction',\n",
-    "]\n",
-    "\n",
-    "medications = [\n",
-    "\"antihypertensives\",\n",
-    "\"ass\",\n",
-    "\"atypical_antipsychotics\",\n",
-    "\"glucocorticoids\"\n",
-    "]\n",
-    "\n",
-    "feature_dict = {\n",
-    "\"pgs\": [],\n",
-    "\"basics\": basics,\n",
-    "\"questionnaire\": questionnaire,\n",
-    "\"measurements\": measurements,\n",
-    "\"labs\": labs,\n",
-    "\"family_history\": family_history,\n",
-    "\"medications\": medications,\n",
-    "\"diagnoses\": diagnoses\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "features[\"clinical\"] = [f for group_list in feature_dict.values() for f in group_list]\n",
-    "features[\"clinical_pgs\"] = features[\"pgs\"]+[f for group_list in feature_dict.values() for f in group_list]"
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "source": [
-    "basics = [\n",
-    "    'age_at_recruitment',\n",
-    "    'ethnic_background',\n",
-    "    'townsend_deprivation_index_at_recruitment',\n",
-    "    'sex'\n",
-    "]\n",
-    "questionnaire = [\n",
-    "    'overall_health_rating',\n",
-    "    'smoking_status',\n",
-    "\n",
-    "    'alcohol_intake_frequency'\n",
-    "]\n",
-    "measurements = [\n",
-    "    'body_mass_index_bmi',\n",
-    "    'weight',\n",
-    "    \"standing_height\",\n",
-    "    'systolic_blood_pressure',\n",
-    "    'diastolic_blood_pressure',\n",
-    "\n",
-    "    'pulse_wave_arterial_stiffness_index',\n",
-    "    'pulse_wave_reflection_index',\n",
-    "    'waist_circumference',\n",
-    "    'hip_circumference',\n",
-    "    'trunk_fat_percentage',\n",
-    "    'body_fat_percentage',\n",
-    "    'basal_metabolic_rate',\n",
-    "    'forced_vital_capacity_fvc_best_measure',\n",
-    "    'forced_expiratory_volume_in_1second_fev1_best_measure',\n",
-    "    'fev1_fvc_ratio_zscore',\n",
-    "    'peak_expiratory_flow_pef_f3064_0_2',\n",
-    "    'peak_expiratory_flow_pef_f3064_0_1',\n",
-    "    'peak_expiratory_flow_pef',\n",
-    "    'pulse_rate'\n",
-    "]\n",
-    "labs = [\n",
-    "    'cholesterol',\n",
-    "    'hdl_cholesterol',\n",
-    "    'ldl_direct',\n",
-    "    'triglycerides',\n",
-    "\n",
-    "    'basophill_count',\n",
-    "    'basophill_percentage',\n",
-    "    'eosinophill_count',\n",
-    "    'eosinophill_percentage',\n",
-    "    'haematocrit_percentage',\n",
-    "    'haemoglobin_concentration',\n",
-    "    'high_light_scatter_reticulocyte_count',\n",
-    "    'high_light_scatter_reticulocyte_percentage',\n",
-    "    'immature_reticulocyte_fraction',\n",
-    "    'lymphocyte_count',\n",
-    "    'lymphocyte_percentage',\n",
-    "    'mean_corpuscular_haemoglobin',\n",
-    "    'mean_corpuscular_haemoglobin_concentration',\n",
-    "    'mean_corpuscular_volume',\n",
-    "    'mean_platelet_thrombocyte_volume',\n",
-    "    'mean_reticulocyte_volume',\n",
-    "    'mean_sphered_cell_volume',\n",
-    "    'monocyte_count',\n",
-    "    'monocyte_percentage',\n",
-    "    'neutrophill_count',\n",
-    "    'neutrophill_percentage',\n",
-    "    'nucleated_red_blood_cell_count',\n",
-    "    'nucleated_red_blood_cell_percentage',\n",
-    "    'platelet_count',\n",
-    "    'platelet_crit',\n",
-    "    'platelet_distribution_width',\n",
-    "    'red_blood_cell_erythrocyte_count',\n",
-    "    'red_blood_cell_erythrocyte_distribution_width',\n",
-    "    'reticulocyte_count',\n",
-    "    'reticulocyte_percentage',\n",
-    "    'white_blood_cell_leukocyte_count',\n",
-    "    'alanine_aminotransferase',\n",
-    "    'albumin',\n",
-    "    'alkaline_phosphatase',\n",
-    "    'apolipoprotein_a',\n",
-    "    'apolipoprotein_b',\n",
-    "    'aspartate_aminotransferase',\n",
-    "    'creactive_protein',\n",
-    "    'calcium',\n",
-    "    'creatinine',\n",
-    "    'cystatin_c',\n",
-    "    'direct_bilirubin',\n",
-    "    'gamma_glutamyltransferase',\n",
-    "    'glucose',\n",
-    "    'glycated_haemoglobin_hba1c',\n",
-    "    'igf1',\n",
-    "    'lipoprotein_a',\n",
-    "    'oestradiol',\n",
-    "    'phosphate',\n",
-    "    'rheumatoid_factor',\n",
-    "    'shbg',\n",
-    "    'testosterone',\n",
-    "    'total_bilirubin',\n",
-    "    'total_protein',\n",
-    "    'urate',\n",
-    "    'urea',\n",
-    "    'vitamin_d'\n",
-    "]\n",
-    "family_history = [\n",
-    "    'fh_heart_disease',\n",
-    "\n",
-    "    \"fh_alzheimer's_disease/dementia\",\n",
-    "    'fh_bowel_cancer',\n",
-    "    'fh_breast_cancer',\n",
-    "    'fh_chronic_bronchitis/emphysema',\n",
-    "    'fh_diabetes',\n",
-    "    'fh_high_blood_pressure',\n",
-    "    'fh_lung_cancer',\n",
-    "    \"fh_parkinson's_disease\",\n",
-    "    'fh_severe_depression',\n",
-    "    'fh_stroke'\n",
-    "]\n",
-    "diagnoses = [\n",
-    "    'stroke',\n",
-    "    'diabetes1',\n",
-    "    'diabetes2',\n",
-    "    'chronic_kidney_disease',\n",
-    "    'atrial_fibrillation',\n",
-    "    'migraine',\n",
-    "    'rheumatoid_arthritis',\n",
-    "    'systemic_lupus_erythematosus',\n",
-    "    'severe_mental_illness',\n",
-    "    'erectile_dysfunction',\n",
-    "\n",
-    "    'intestinal_infection',\n",
-    "    'bacterial_infection',\n",
-    "    'arthropathies',\n",
-    "    'viral_infection',\n",
-    "    'infections_specific_to_the_perinatal_period',\n",
-    "    'acute_upper_respiratory_infections',\n",
-    "    'mycoses',\n",
-    "    \"kaposi's_sarcoma\",\n",
-    "    'malaise_and_fatigue',\n",
-    "    'malignant_neoplasm_of_skin',\n",
-    "    'benign_neoplasm_of_respiratory_and_intrathoracic_organs',\n",
-    "    'benign_neoplasm_of_skin',\n",
-    "    'nonmalignant_breast_conditions',\n",
-    "    'anemias',\n",
-    "    'other_endocrine_disorders',\n",
-    "    'diabetes',\n",
-    "    'disorders_of_mineral_metabolism',\n",
-    "    'obesity',\n",
-    "    'respiratory_abnormalities',\n",
-    "    'disorders_of_lipoid_metabolism',\n",
-    "    'nonspecific_findings_on_examination_of_blood',\n",
-    "    'sleep_disorders',\n",
-    "    'male_genital_disorders',\n",
-    "    'cerebrovascular_disease',\n",
-    "    'cataract',\n",
-    "    'blindness_and_low_vision',\n",
-    "    'disorders_of_external_ear',\n",
-    "    'heart_valve_disorders',\n",
-    "    'ischemic_heart_disease',\n",
-    "    'cardiac_conduction_disorders',\n",
-    "    'heart_failure',\n",
-    "    'noninfectious_disorders_of_lymphatic_channels',\n",
-    "    'other_symptoms_of_respiratory_system',\n",
-    "    'disorders_of_stomach',\n",
-    "    'abdominal_pain',\n",
-    "    'abdominal_hernia',\n",
-    "    'liver_disease',\n",
-    "    'biliary_tract_disease',\n",
-    "    'bariatric_surgery',\n",
-    "    'complications',\n",
-    "    'other_hypertrophic_and_atrophic_conditions_of_skin',\n",
-    "    'symptoms_affecting_skin',\n",
-    "    'chronic_ulcer_of_skin',\n",
-    "    'gout',\n",
-    "    'osteoarthritis',\n",
-    "    'acquired_deformities',\n",
-    "    'pain_in_joint',\n",
-    "    'back_pain',\n",
-    "    'myalgia_and_myositis_unspecified',\n",
-    "    'symptoms_involving_nervous_and_musculoskeletal_systems',\n",
-    "    'pain_in_limb',\n",
-    "    'musculoskeletal_symptoms_referable_to_limbs',\n",
-    "    'congenital_musculoskeletal_anomalies',\n",
-    "    'other_symptoms/disorders_or_the_urinary_system',\n",
-    "    'urinary_calculus',\n",
-    "    'symptoms_involving_female_genital_tract',\n",
-    "    'other_nonmalignant_breast_conditions',\n",
-    "    'complications_of_the_puerperium',\n",
-    "    'complications_of_pregnancy',\n",
-    "    'postoperative_infection',\n",
-    "    'other_complications_of_pregnancy_nec',\n",
-    "    'excessive_vomiting_in_pregnancy',\n",
-    "    'infectious_and_parasitic_complications_affecting_pregnancy',\n",
-    "    'multiple_gestation',\n",
-    "    'late_pregnancy_and_failed_induction',\n",
-    "    'normal_delivery',\n",
-    "    'anemia_during_pregnancy',\n",
-    "    'maternal_complication_of_pregnancy_affecting_fetus_or_newborn',\n",
-    "    'gangrene',\n",
-    "    'abnormal_sputum',\n",
-    "    'symptoms_involving_head_and_neck',\n",
-    "    'nonspecific_chest_pain',\n",
-    "    'nausea_and_vomiting',\n",
-    "    'nonspecific_findings_on_examination_of_urine',\n",
-    "    'fever',\n",
-    "    'pain',\n",
-    "    'syncope_and_collapse',\n",
-    "    'shock',\n",
-    "    'hypothermia/chills',\n",
-    "    'abnormal_findings_examination_of_lungs',\n",
-    "    'contusion',\n",
-    "    'open_wound',\n",
-    "    'bone_marrow_or_stem_cell_transplant']\n",
-    "\n",
-    "medications= [\n",
-    "    'ass',\n",
-    "    'atypical_antipsychotics',\n",
-    "    'glucocorticoids',\n",
-    "\n",
-    "    'stomatological_preparations',\n",
-    "    'drugs_for_acid_related_disorders',\n",
-    "    'drugs_for_functional_gastrointestinal_disorders',\n",
-    "    'antiemetics_and_antinauseants',\n",
-    "    'bile_and_liver_therapy',\n",
-    "    'drugs_for_constipation',\n",
-    "    'antidiarrheals,_intestinal_antiinflammatory/antiinfective_agents',\n",
-    "    'antiobesity_preparations,_excl._diet_products',\n",
-    "    'digestives,_incl._enzymes',\n",
-    "    'drugs_used_in_diabetes',\n",
-    "    'vitamins',\n",
-    "    'mineral_supplements',\n",
-    "    'tonics',\n",
-    "    'anabolic_agents_for_systemic_use',\n",
-    "    'appetite_stimulants',\n",
-    "    'other_alimentary_tract_and_metabolism_products',\n",
-    "    'antithrombotic_agents',\n",
-    "    'antihemorrhagics',\n",
-    "    'antianemic_preparations',\n",
-    "    'blood_substitutes_and_perfusion_solutions',\n",
-    "    'other_hematological_agents',\n",
-    "    'cardiac_therapy',\n",
-    "    'antihypertensives',\n",
-    "    'diuretics',\n",
-    "    'peripheral_vasodilators',\n",
-    "    'vasoprotectives',\n",
-    "    'beta_blocking_agents',\n",
-    "    'calcium_channel_blockers',\n",
-    "    'agents_acting_on_the_renin-angiotensin_system',\n",
-    "    'lipid_modifying_agents',\n",
-    "    'antifungals_for_dermatological_use',\n",
-    "    'emollients_and_protectives',\n",
-    "    'preparations_for_treatment_of_wounds_and_ulcers',\n",
-    "    'antipruritics,_incl._antihistamines,_anesthetics,_etc.',\n",
-    "    'antipsoriatics',\n",
-    "    'antibiotics_and_chemotherapeutics_for_dermatological_use',\n",
-    "    'corticosteroids,_dermatological_preparations',\n",
-    "    'antiseptics_and_disinfectants',\n",
-    "    'medicated_dressings',\n",
-    "    'anti-acne_preparations',\n",
-    "    'other_dermatological_preparations',\n",
-    "    'gynecological_antiinfectives_and_antiseptics',\n",
-    "    'other_gynecologicals',\n",
-    "    'sex_hormones_and_modulators_of_the_genital_system',\n",
-    "    'urologicals',\n",
-    "    'pituitary_and_hypothalamic_hormones_and_analogues',\n",
-    "    'corticosteroids_for_systemic_use',\n",
-    "    'thyroid_therapy',\n",
-    "    'pancreatic_hormones',\n",
-    "    'calcium_homeostasis',\n",
-    "    'antibacterials_for_systemic_use',\n",
-    "    'antimycotics_for_systemic_use',\n",
-    "    'antimycobacterials',\n",
-    "    'antivirals_for_systemic_use',\n",
-    "    'immune_sera_and_immunoglobulins',\n",
-    "    'vaccines',\n",
-    "    'antineoplastic_agents',\n",
-    "    'endocrine_therapy',\n",
-    "    'immunostimulants',\n",
-    "    'immunosuppressants',\n",
-    "    'antiinflammatory_and_antirheumatic_products',\n",
-    "    'topical_products_for_joint_and_muscular_pain',\n",
-    "    'muscle_relaxants',\n",
-    "    'antigout_preparations',\n",
-    "    'drugs_for_treatment_of_bone_diseases',\n",
-    "    'other_drugs_for_disorders_of_the_musculo-skeletal_system',\n",
-    "    'anesthetics',\n",
-    "    'analgesics',\n",
-    "    'antiepileptics',\n",
-    "    'anti-parkinson_drugs',\n",
-    "    'psycholeptics',\n",
-    "    'psychoanaleptics',\n",
-    "    'other_nervous_system_drugs',\n",
-    "    'antiprotozoals',\n",
-    "    'anthelmintics',\n",
-    "    'ectoparasiticides,_incl._scabicides,_insecticides_and_repellents',\n",
-    "    'nasal_preparations',\n",
-    "    'throat_preparations',\n",
-    "    'drugs_for_obstructive_airway_diseases',\n",
-    "    'cough_and_cold_preparations',\n",
-    "    'antihistamines_for_systemic_use',\n",
-    "    'other_respiratory_system_products',\n",
-    "    'ophthalmologicals',\n",
-    "    'otologicals',\n",
-    "    'ophthalmological_and_otological_preparations',\n",
-    "    'allergens',\n",
-    "    'all_other_therapeutic_products',\n",
-    "    'diagnostic_agents',\n",
-    "    'general_nutrients',\n",
-    "    'all_other_non-therapeutic_products',\n",
-    "    'contrast_media',\n",
-    "    'diagnostic_radiopharmaceuticals',\n",
-    "    'therapeutic_radiopharmaceuticals',\n",
-    "    'surgical_dressings'\n",
-    "]\n",
-    "\n",
-    "feature_dict = {\n",
-    "    \"pgs\": [],\n",
-    "    \"basics\": basics,\n",
-    "    \"questionnaire\": questionnaire,\n",
-    "    \"measurements\": measurements,\n",
-    "    \"labs\": labs,\n",
-    "    \"family_history\": family_history,\n",
-    "    \"medications\": medications,\n",
-    "    \"diagnoses\": diagnoses\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "source": [
-    "features[\"clinical_big\"] = [f for group_list in feature_dict.values() for f in group_list]\n",
-    "features[\"clinical_big_with_pgs\"] = features[\"pgs\"]+[f for group_list in feature_dict.values() for f in group_list]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Predictions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "endpoint = \"MACE\"; event=endpoint+'_event'; time=endpoint+'_event_time'\n",
-    "groups = [\"clinical\", \"clinical_pgs\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d78503d7377446f68abd00a1ed2bbc87",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/2 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "data = {}\n",
-    "for group in tqdm(groups): \n",
-    "    data[group] = {\"features\":features[group]+[time, event]}\n",
-    "    for partition in partitions: \n",
-    "        data[group][partition] = {}\n",
-    "        for split in splits: data[group][partition][split] = data_all[partition][split].loc[:, data[group][\"features\"]].copy()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from lifelines.utils import concordance_index\n",
-    "def fit_predict_coxph(data_h5ad, group, partition, time, event, pickle_path):\n",
-    "    cph = CoxPHFitter()\n",
-    "    train_data, val_data, test_data = data_h5ad[group][partition][\"train\"], data_h5ad[group][partition][\"valid\"], data_h5ad[group][partition][\"test\"]\n",
-    "    covariates_with_tte = data[group][\"features\"]\n",
-    "    cph.fit(train_data[covariates_with_tte], duration_col=time, event_col=event, show_progress=True, step_size=0.5)\n",
-    "    pickle.dump(cph, open(pickle_path, \"wb\" ) )\n",
-    "    print(concordance_index(val_data[time], -cph.predict_partial_hazard(val_data[covariates_with_tte]), val_data[event]))\n",
-    "    #display(val_data[covariates_with_tte])\n",
-    "    surv_train = 1-cph.predict_survival_function(train_data[covariates_with_tte], times=[11]) # as years + 1\n",
-    "    surv_train_all = 1-cph.predict_survival_function(train_data[covariates_with_tte], times=[t for t in range(1,27)])\n",
-    "    display(surv_train)\n",
-    "    surv_val = 1-cph.predict_survival_function(val_data[covariates_with_tte], times=[11]) # as years + 1  \n",
-    "    surv_val_all = 1-cph.predict_survival_function(val_data[covariates_with_tte], times=[t for t in range(1,27)]) # as years + 1  \n",
-    "    surv_test = 1-cph.predict_survival_function(test_data[covariates_with_tte], times=[11]) # as years + 1  \n",
-    "    surv_test_all = 1-cph.predict_survival_function(test_data[covariates_with_tte], times=[t for t in range(1,27)]) # as years + 1  \n",
-    "    pred = {\"train\":train_data.reset_index()[[\"eid\"]],\n",
-    "            \"val\":val_data.reset_index()[[\"eid\"]],\n",
-    "           \"test\":test_data.reset_index()[[\"eid\"]],}\n",
-    "    pred[\"train\"][f\"score_COX_{group}\"] = surv_train.iloc[0].to_list()\n",
-    "    pred[\"val\"][f\"score_COX_{group}\"] = surv_val.iloc[0].to_list()\n",
-    "    pred[\"test\"][f\"score_COX_{group}\"] = surv_test.iloc[0].to_list()\n",
-    "    \n",
-    "    for t in range(1, 27):\n",
-    "        prefix = f\"0_{t}_Ft_COX_{group}\"\n",
-    "        pred[\"train\"][prefix] = surv_train_all.T[t].to_list()\n",
-    "        pred[\"val\"][prefix] = surv_val_all.T[t].to_list()\n",
-    "        pred[\"test\"][prefix] = surv_test_all.T[t].to_list()\n",
-    "        \n",
-    "    return pd.concat([pred[\"train\"].assign(partition=partition, split=\"train\"), pred[\"val\"].assign(partition=partition, split=\"valid\"), pred[\"test\"].assign(partition=partition, split=\"test\")])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d059c02085eb4b8ebdde509e671863ad",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/2 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "clinical\n",
-      "0\n",
-      "Iteration 1: norm_delta = 0.84740, step_size = 0.5000, log_lik = -302789.67372, newton_decrement = 8760.47734, seconds_since_start = 3.8\n",
-      "Iteration 2: norm_delta = 0.40220, step_size = 0.5000, log_lik = -296354.04940, newton_decrement = 2090.87293, seconds_since_start = 7.6\n",
-      "Iteration 3: norm_delta = 0.22814, step_size = 0.5000, log_lik = -294773.26721, newton_decrement = 609.12739, seconds_since_start = 11.5\n",
-      "Iteration 4: norm_delta = 0.10348, step_size = 0.6000, log_lik = -294257.15345, newton_decrement = 113.73696, seconds_since_start = 15.3\n",
-      "Iteration 5: norm_delta = 0.03201, step_size = 0.7200, log_lik = -294151.61387, newton_decrement = 10.16387, seconds_since_start = 19.1\n",
-      "Iteration 6: norm_delta = 0.00470, step_size = 0.8640, log_lik = -294141.60080, newton_decrement = 0.20941, seconds_since_start = 22.9\n",
-      "Iteration 7: norm_delta = 0.00001, step_size = 1.0000, log_lik = -294141.39119, newton_decrement = 0.00000, seconds_since_start = 26.8\n",
-      "Iteration 8: norm_delta = 0.00000, step_size = 1.0000, log_lik = -294141.39119, newton_decrement = 0.00000, seconds_since_start = 30.6\n",
-      "Convergence success after 8 iterations.\n",
-      "0.7342208911808222\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>age_at_recruitment</th>\n",
-       "      <th>ethnic_background_0.0</th>\n",
-       "      <th>ethnic_background_1.0</th>\n",
-       "      <th>ethnic_background_2.0</th>\n",
-       "      <th>ethnic_background_3.0</th>\n",
-       "      <th>ethnic_background_4.0</th>\n",
-       "      <th>townsend_deprivation_index_at_recruitment</th>\n",
-       "      <th>sex</th>\n",
-       "      <th>overall_health_rating_0.0</th>\n",
-       "      <th>overall_health_rating_1.0</th>\n",
-       "      <th>...</th>\n",
-       "      <th>diabetes2</th>\n",
-       "      <th>chronic_kidney_disease</th>\n",
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-       "      <th>severe_mental_illness</th>\n",
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-       "      <th>eid</th>\n",
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-       "         age_at_recruitment  ethnic_background_0.0  ethnic_background_1.0  \\\n",
-       "eid                                                                         \n",
-       "5774080            0.800912                      1                      0   \n",
-       "5774091            0.924494                      1                      0   \n",
-       "5774108            0.553747                      1                      0   \n",
-       "5774114           -0.187746                      1                      0   \n",
-       "5774127            0.553747                      1                      0   \n",
-       "...                     ...                    ...                    ...   \n",
-       "6025147           -1.670733                      0                      0   \n",
-       "6025150           -1.670733                      1                      0   \n",
-       "6025165           -1.423569                      1                      0   \n",
-       "6025173            0.059418                      1                      0   \n",
-       "6025182           -0.064164                      1                      0   \n",
-       "\n",
-       "         ethnic_background_2.0  ethnic_background_3.0  ethnic_background_4.0  \\\n",
-       "eid                                                                            \n",
-       "5774080                      0                      0                      0   \n",
-       "5774091                      0                      0                      0   \n",
-       "5774108                      0                      0                      0   \n",
-       "5774114                      0                      0                      0   \n",
-       "5774127                      0                      0                      0   \n",
-       "...                        ...                    ...                    ...   \n",
-       "6025147                      1                      0                      0   \n",
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-       "6025165                      0                      0                      0   \n",
-       "6025173                      0                      0                      0   \n",
-       "6025182                      0                      0                      0   \n",
-       "\n",
-       "         townsend_deprivation_index_at_recruitment  sex  \\\n",
-       "eid                                                       \n",
-       "5774080                                  -0.807565  1.0   \n",
-       "5774091                                  -0.960384  1.0   \n",
-       "5774108                                   0.461605  0.0   \n",
-       "5774114                                   0.433239  0.0   \n",
-       "5774127                                   2.563782  1.0   \n",
-       "...                                            ...  ...   \n",
-       "6025147                                   0.034317  1.0   \n",
-       "6025150                                   0.431740  0.0   \n",
-       "6025165                                  -0.263493  0.0   \n",
-       "6025173                                  -0.173169  1.0   \n",
-       "6025182                                   0.413165  1.0   \n",
-       "\n",
-       "         overall_health_rating_0.0  overall_health_rating_1.0  ...  diabetes2  \\\n",
-       "eid                                                            ...              \n",
-       "5774080                          0                          1  ...      False   \n",
-       "5774091                          0                          0  ...      False   \n",
-       "5774108                          0                          1  ...       True   \n",
-       "5774114                          1                          0  ...      False   \n",
-       "5774127                          1                          0  ...      False   \n",
-       "...                            ...                        ...  ...        ...   \n",
-       "6025147                          0                          1  ...      False   \n",
-       "6025150                          0                          0  ...      False   \n",
-       "6025165                          0                          1  ...      False   \n",
-       "6025173                          0                          1  ...      False   \n",
-       "6025182                          0                          0  ...      False   \n",
-       "\n",
-       "         chronic_kidney_disease  atrial_fibrillation  migraine  \\\n",
-       "eid                                                              \n",
-       "5774080                   False                False     False   \n",
-       "5774091                   False                False     False   \n",
-       "5774108                   False                False     False   \n",
-       "5774114                    True                False     False   \n",
-       "5774127                   False                False     False   \n",
-       "...                         ...                  ...       ...   \n",
-       "6025147                   False                False     False   \n",
-       "6025150                   False                False     False   \n",
-       "6025165                   False                False     False   \n",
-       "6025173                   False                False     False   \n",
-       "6025182                   False                False     False   \n",
-       "\n",
-       "         rheumatoid_arthritis  systemic_lupus_erythematosus  \\\n",
-       "eid                                                           \n",
-       "5774080                 False                         False   \n",
-       "5774091                 False                         False   \n",
-       "5774108                 False                         False   \n",
-       "5774114                  True                         False   \n",
-       "5774127                 False                         False   \n",
-       "...                       ...                           ...   \n",
-       "6025147                 False                         False   \n",
-       "6025150                 False                         False   \n",
-       "6025165                 False                         False   \n",
-       "6025173                 False                         False   \n",
-       "6025182                 False                         False   \n",
-       "\n",
-       "         severe_mental_illness  erectile_dysfunction  MACE_event_time  \\\n",
-       "eid                                                                     \n",
-       "5774080                  False                 False        10.866530   \n",
-       "5774091                  False                 False        11.835729   \n",
-       "5774108                  False                 False        10.546201   \n",
-       "5774114                   True                 False        10.915811   \n",
-       "5774127                  False                  True        11.367556   \n",
-       "...                        ...                   ...              ...   \n",
-       "6025147                  False                 False        11.879535   \n",
-       "6025150                  False                 False        13.253936   \n",
-       "6025165                  False                 False        12.076660   \n",
-       "6025173                  False                 False        12.035592   \n",
-       "6025182                  False                 False        10.250513   \n",
-       "\n",
-       "         MACE_event  \n",
-       "eid                  \n",
-       "5774080           0  \n",
-       "5774091           0  \n",
-       "5774108           0  \n",
-       "5774114           0  \n",
-       "5774127           0  \n",
-       "...             ...  \n",
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-       "6025182           0  \n",
-       "\n",
-       "[17756 rows x 40 columns]"
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-       "       1000018   1000020   1000037   1000043   1000092   1000107   1000110  \\\n",
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-       "      1000128   1000135   1000144  ...   5773972   5773983   5773999  \\\n",
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-      "clinical\n",
-      "1\n",
-      "Iteration 1: norm_delta = 0.84042, step_size = 0.5000, log_lik = -301918.37352, newton_decrement = 8782.58025, seconds_since_start = 3.8\n",
-      "Iteration 2: norm_delta = 0.40627, step_size = 0.5000, log_lik = -295456.62162, newton_decrement = 2114.35338, seconds_since_start = 7.7\n",
-      "Iteration 3: norm_delta = 0.23122, step_size = 0.5000, log_lik = -293857.97662, newton_decrement = 616.65662, seconds_since_start = 11.5\n",
-      "Iteration 4: norm_delta = 0.10500, step_size = 0.6000, log_lik = -293335.46432, newton_decrement = 115.17985, seconds_since_start = 15.3\n",
-      "Iteration 5: norm_delta = 0.03241, step_size = 0.7200, log_lik = -293228.59034, newton_decrement = 10.27859, seconds_since_start = 19.0\n",
-      "Iteration 6: norm_delta = 0.00472, step_size = 0.8640, log_lik = -293218.46552, newton_decrement = 0.21082, seconds_since_start = 22.8\n",
-      "Iteration 7: norm_delta = 0.00001, step_size = 1.0000, log_lik = -293218.25453, newton_decrement = 0.00000, seconds_since_start = 26.6\n",
-      "Iteration 8: norm_delta = 0.00000, step_size = 1.0000, log_lik = -293218.25453, newton_decrement = 0.00000, seconds_since_start = 30.3\n",
-      "Convergence success after 8 iterations.\n",
-      "0.7320170036068242\n"
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-       "eid                                                       \n",
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-       "eid                                                              \n",
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-       "eid                                                           \n",
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-       "      <th>1000020</th>\n",
-       "      <th>1000043</th>\n",
-       "      <th>1000079</th>\n",
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-       "      <th>5773935</th>\n",
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-       "      <th>11.0</th>\n",
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-       "<p>1 rows × 338347 columns</p>\n",
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-       "       1000018   1000020  1000043   1000079   1000084  1000092   1000107  \\\n",
-       "11.0  0.056674  0.139445  0.13621  0.080178  0.057933  0.09166  0.018393   \n",
-       "\n",
-       "       1000110   1000128  1000135  ...   5773814   5773827   5773840  5773871  \\\n",
-       "11.0  0.014255  0.013792  0.01802  ...  0.113922  0.108543  0.034103  0.06617   \n",
-       "\n",
-       "       5773886   5773895   5773928   5773935   5773944   5773961  \n",
-       "11.0  0.804845  0.013291  0.024577  0.025625  0.036494  0.033593  \n",
-       "\n",
-       "[1 rows x 338347 columns]"
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-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "clinical\n",
-      "2\n",
-      "Iteration 1: norm_delta = 0.83461, step_size = 0.5000, log_lik = -303934.60837, newton_decrement = 8899.23295, seconds_since_start = 3.8\n",
-      "Iteration 2: norm_delta = 0.40527, step_size = 0.5000, log_lik = -297388.53639, newton_decrement = 2144.25117, seconds_since_start = 7.9\n",
-      "Iteration 3: norm_delta = 0.23083, step_size = 0.5000, log_lik = -295767.17692, newton_decrement = 626.21041, seconds_since_start = 11.8\n",
-      "Iteration 4: norm_delta = 0.10507, step_size = 0.6000, log_lik = -295236.52115, newton_decrement = 117.15378, seconds_since_start = 15.7\n",
-      "Iteration 5: norm_delta = 0.03251, step_size = 0.7200, log_lik = -295127.80684, newton_decrement = 10.47061, seconds_since_start = 19.5\n",
-      "Iteration 6: norm_delta = 0.00475, step_size = 0.8640, log_lik = -295117.49239, newton_decrement = 0.21504, seconds_since_start = 23.2\n",
-      "Iteration 7: norm_delta = 0.00001, step_size = 1.0000, log_lik = -295117.27717, newton_decrement = 0.00000, seconds_since_start = 27.2\n",
-      "Iteration 8: norm_delta = 0.00000, step_size = 1.0000, log_lik = -295117.27717, newton_decrement = 0.00000, seconds_since_start = 31.1\n",
-      "Convergence success after 8 iterations.\n",
-      "0.7307608305062687\n"
-     ]
-    },
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-     "data": {
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-       "<p>17750 rows × 40 columns</p>\n",
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-       "         age_at_recruitment  ethnic_background_0.0  ethnic_background_1.0  \\\n",
-       "eid                                                                         \n",
-       "5773808           -1.670824                      1                      0   \n",
-       "5773814            0.429000                      1                      0   \n",
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-       "...                     ...                    ...                    ...   \n",
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-       "6025173            0.058443                      1                      0   \n",
-       "6025182           -0.065076                      1                      0   \n",
-       "6025198            1.293634                      1                      0   \n",
-       "\n",
-       "         ethnic_background_2.0  ethnic_background_3.0  ethnic_background_4.0  \\\n",
-       "eid                                                                            \n",
-       "5773808                      0                      0                      0   \n",
-       "5773814                      0                      0                      0   \n",
-       "5773827                      0                      0                      0   \n",
-       "5773840                      0                      0                      0   \n",
-       "5773871                      0                      0                      0   \n",
-       "...                        ...                    ...                    ...   \n",
-       "6025147                      1                      0                      0   \n",
-       "6025150                      0                      0                      0   \n",
-       "6025173                      0                      0                      0   \n",
-       "6025182                      0                      0                      0   \n",
-       "6025198                      0                      0                      0   \n",
-       "\n",
-       "         townsend_deprivation_index_at_recruitment  sex  \\\n",
-       "eid                                                       \n",
-       "5773808                                  -0.844251  1.0   \n",
-       "5773814                                   0.187428  1.0   \n",
-       "5773827                                  -0.693672  1.0   \n",
-       "5773840                                   2.262139  1.0   \n",
-       "5773871                                  -0.368867  1.0   \n",
-       "...                                            ...  ...   \n",
-       "6025147                                   0.035776  1.0   \n",
-       "6025150                                   0.433824  0.0   \n",
-       "6025173                                  -0.172037  1.0   \n",
-       "6025182                                   0.415220  1.0   \n",
-       "6025198                                  -0.205476  1.0   \n",
-       "\n",
-       "         overall_health_rating_0.0  overall_health_rating_1.0  ...  diabetes2  \\\n",
-       "eid                                                            ...              \n",
-       "5773808                          0                          1  ...      False   \n",
-       "5773814                          0                          1  ...      False   \n",
-       "5773827                          0                          1  ...      False   \n",
-       "5773840                          0                          0  ...      False   \n",
-       "5773871                          0                          1  ...      False   \n",
-       "...                            ...                        ...  ...        ...   \n",
-       "6025147                          0                          1  ...      False   \n",
-       "6025150                          0                          0  ...      False   \n",
-       "6025173                          0                          1  ...      False   \n",
-       "6025182                          0                          0  ...      False   \n",
-       "6025198                          0                          1  ...      False   \n",
-       "\n",
-       "         chronic_kidney_disease  atrial_fibrillation  migraine  \\\n",
-       "eid                                                              \n",
-       "5773808                   False                False     False   \n",
-       "5773814                   False                False      True   \n",
-       "5773827                   False                False     False   \n",
-       "5773840                   False                False     False   \n",
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-       "...                         ...                  ...       ...   \n",
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-       "         rheumatoid_arthritis  systemic_lupus_erythematosus  \\\n",
-       "eid                                                           \n",
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-       "         severe_mental_illness  erectile_dysfunction  MACE_event_time  \\\n",
-       "eid                                                                     \n",
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-       "       1000018   1000020   1000037   1000043   1000079   1000084   1000092  \\\n",
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-     "text": [
-      "clinical\n",
-      "3\n",
-      "Iteration 1: norm_delta = 0.83041, step_size = 0.5000, log_lik = -304005.99770, newton_decrement = 8799.39988, seconds_since_start = 3.8\n",
-      "Iteration 2: norm_delta = 0.41042, step_size = 0.5000, log_lik = -297537.13319, newton_decrement = 2107.94080, seconds_since_start = 7.8\n",
-      "Iteration 3: norm_delta = 0.23359, step_size = 0.5000, log_lik = -295943.30814, newton_decrement = 614.95995, seconds_since_start = 11.6\n",
-      "Iteration 4: norm_delta = 0.10610, step_size = 0.6000, log_lik = -295422.22582, newton_decrement = 114.89211, seconds_since_start = 15.5\n",
-      "Iteration 5: norm_delta = 0.03277, step_size = 0.7200, log_lik = -295315.61723, newton_decrement = 10.25611, seconds_since_start = 19.3\n",
-      "Iteration 6: norm_delta = 0.00478, step_size = 0.8640, log_lik = -295305.51441, newton_decrement = 0.21046, seconds_since_start = 23.3\n",
-      "Iteration 7: norm_delta = 0.00001, step_size = 1.0000, log_lik = -295305.30377, newton_decrement = 0.00000, seconds_since_start = 27.1\n",
-      "Iteration 8: norm_delta = 0.00000, step_size = 1.0000, log_lik = -295305.30377, newton_decrement = 0.00000, seconds_since_start = 31.1\n",
-      "Convergence success after 8 iterations.\n",
-      "0.7326336213224398\n"
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-       "<p>17778 rows × 40 columns</p>\n",
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-       "         age_at_recruitment  ethnic_background_0.0  ethnic_background_1.0  \\\n",
-       "eid                                                                         \n",
-       "5773935           -0.188933                      1                      0   \n",
-       "5773944           -1.053656                      1                      0   \n",
-       "5773956            1.046386                      1                      0   \n",
-       "5773961            0.675790                      1                      0   \n",
-       "5773972            1.169918                      1                      0   \n",
-       "...                     ...                    ...                    ...   \n",
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-       "6025165           -1.424251                      1                      0   \n",
-       "6025173            0.058131                      1                      0   \n",
-       "6025198            1.293450                      1                      0   \n",
-       "\n",
-       "         ethnic_background_2.0  ethnic_background_3.0  ethnic_background_4.0  \\\n",
-       "eid                                                                            \n",
-       "5773935                      0                      0                      0   \n",
-       "5773944                      0                      0                      0   \n",
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-       "6025173                      0                      0                      0   \n",
-       "6025198                      0                      0                      0   \n",
-       "\n",
-       "         townsend_deprivation_index_at_recruitment  sex  \\\n",
-       "eid                                                       \n",
-       "5773935                                  -0.884220  0.0   \n",
-       "5773944                                  -0.477837  0.0   \n",
-       "5773956                                  -1.042789  0.0   \n",
-       "5773961                                  -1.315573  0.0   \n",
-       "5773972                                  -0.347410  1.0   \n",
-       "...                                            ...  ...   \n",
-       "6025147                                   0.036094  1.0   \n",
-       "6025150                                   0.434258  0.0   \n",
-       "6025165                                  -0.262270  0.0   \n",
-       "6025173                                  -0.171779  1.0   \n",
-       "6025198                                  -0.205227  1.0   \n",
-       "\n",
-       "         overall_health_rating_0.0  overall_health_rating_1.0  ...  diabetes2  \\\n",
-       "eid                                                            ...              \n",
-       "5773935                          0                          1  ...      False   \n",
-       "5773944                          0                          0  ...      False   \n",
-       "5773956                          0                          0  ...      False   \n",
-       "5773961                          0                          0  ...      False   \n",
-       "5773972                          0                          1  ...      False   \n",
-       "...                            ...                        ...  ...        ...   \n",
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-       "6025198                          0                          1  ...      False   \n",
-       "\n",
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-       "eid                                                              \n",
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-       "eid                                                           \n",
-       "5773935                 False                         False   \n",
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-       "6025198                 False                         False   \n",
-       "\n",
-       "         severe_mental_illness  erectile_dysfunction  MACE_event_time  \\\n",
-       "eid                                                                     \n",
-       "5773935                  False                 False        12.821355   \n",
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-       "6025173                  False                 False        12.035592   \n",
-       "6025198                  False                 False        10.677618   \n",
-       "\n",
-       "         MACE_event  \n",
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-       "       1000020   1000037   1000043   1000079   1000084   1000092   1000107  \\\n",
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-     "text": [
-      "clinical\n",
-      "4\n",
-      "Iteration 1: norm_delta = 0.86923, step_size = 0.5000, log_lik = -302864.07070, newton_decrement = 8837.27278, seconds_since_start = 3.8\n",
-      "Iteration 2: norm_delta = 0.40674, step_size = 0.5000, log_lik = -296366.86382, newton_decrement = 2122.31770, seconds_since_start = 7.8\n",
-      "Iteration 3: norm_delta = 0.22958, step_size = 0.5000, log_lik = -294762.08582, newton_decrement = 619.79745, seconds_since_start = 11.8\n",
-      "Iteration 4: norm_delta = 0.10393, step_size = 0.6000, log_lik = -294236.86718, newton_decrement = 115.96211, seconds_since_start = 15.9\n",
-      "Iteration 5: norm_delta = 0.03211, step_size = 0.7200, log_lik = -294129.25507, newton_decrement = 10.37426, seconds_since_start = 19.7\n",
-      "Iteration 6: norm_delta = 0.00470, step_size = 0.8640, log_lik = -294119.03473, newton_decrement = 0.21365, seconds_since_start = 23.5\n",
-      "Iteration 7: norm_delta = 0.00001, step_size = 1.0000, log_lik = -294118.82089, newton_decrement = 0.00000, seconds_since_start = 27.3\n",
-      "Iteration 8: norm_delta = 0.00000, step_size = 1.0000, log_lik = -294118.82089, newton_decrement = 0.00000, seconds_since_start = 31.2\n",
-      "Convergence success after 8 iterations.\n",
-      "0.7329179574481282\n"
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-       "         age_at_recruitment  ethnic_background_0.0  ethnic_background_1.0  \\\n",
-       "eid                                                                         \n",
-       "5772854           -0.188141                      1                      0   \n",
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-       "...                     ...                    ...                    ...   \n",
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-       "\n",
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-       "eid                                                                            \n",
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-       "\n",
-       "         townsend_deprivation_index_at_recruitment  sex  \\\n",
-       "eid                                                       \n",
-       "5772854                                   0.471387  1.0   \n",
-       "5772869                                   0.116279  1.0   \n",
-       "5772881                                  -0.732901  1.0   \n",
-       "5772897                                  -0.390127  1.0   \n",
-       "5772905                                   1.355080  0.0   \n",
-       "...                                            ...  ...   \n",
-       "6025147                                   0.036270  1.0   \n",
-       "6025165                                  -0.262191  0.0   \n",
-       "6025173                                  -0.171670  1.0   \n",
-       "6025182                                   0.415948  1.0   \n",
-       "6025198                                  -0.205129  1.0   \n",
-       "\n",
-       "         overall_health_rating_0.0  overall_health_rating_1.0  ...  diabetes2  \\\n",
-       "eid                                                            ...              \n",
-       "5772854                          0                          1  ...      False   \n",
-       "5772869                          0                          1  ...      False   \n",
-       "5772881                          0                          1  ...      False   \n",
-       "5772897                          0                          1  ...      False   \n",
-       "5772905                          1                          0  ...      False   \n",
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-       "6025198                          0                          1  ...      False   \n",
-       "\n",
-       "         chronic_kidney_disease  atrial_fibrillation  migraine  \\\n",
-       "eid                                                              \n",
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-       "6025198                    True                False     False   \n",
-       "\n",
-       "         rheumatoid_arthritis  systemic_lupus_erythematosus  \\\n",
-       "eid                                                           \n",
-       "5772854                 False                         False   \n",
-       "5772869                 False                         False   \n",
-       "5772881                 False                         False   \n",
-       "5772897                 False                         False   \n",
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-       "6025182                 False                         False   \n",
-       "6025198                 False                         False   \n",
-       "\n",
-       "         severe_mental_illness  erectile_dysfunction  MACE_event_time  \\\n",
-       "eid                                                                     \n",
-       "5772854                  False                 False        12.566735   \n",
-       "5772869                  False                 False        12.353183   \n",
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-       "5772905                   True                 False        11.665982   \n",
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-       "6025182                  False                 False        10.250513   \n",
-       "6025198                  False                 False        10.677618   \n",
-       "\n",
-       "         MACE_event  \n",
-       "eid                  \n",
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-       "5772897           0  \n",
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-       "       1000018   1000020   1000037   1000043  1000079   1000084   1000092  \\\n",
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-     "text": [
-      "clinical\n",
-      "5\n",
-      "Iteration 1: norm_delta = 0.83871, step_size = 0.5000, log_lik = -303530.87696, newton_decrement = 8798.90133, seconds_since_start = 4.2\n",
-      "Iteration 2: norm_delta = 0.40518, step_size = 0.5000, log_lik = -297055.30290, newton_decrement = 2115.55485, seconds_since_start = 7.9\n",
-      "Iteration 3: norm_delta = 0.23031, step_size = 0.5000, log_lik = -295455.77368, newton_decrement = 616.93414, seconds_since_start = 11.9\n",
-      "Iteration 4: norm_delta = 0.10463, step_size = 0.6000, log_lik = -294933.01653, newton_decrement = 115.29343, seconds_since_start = 15.9\n",
-      "Iteration 5: norm_delta = 0.03236, step_size = 0.7200, log_lik = -294826.03053, newton_decrement = 10.30417, seconds_since_start = 20.0\n",
-      "Iteration 6: norm_delta = 0.00474, step_size = 0.8640, log_lik = -294815.87959, newton_decrement = 0.21200, seconds_since_start = 23.8\n",
-      "Iteration 7: norm_delta = 0.00001, step_size = 1.0000, log_lik = -294815.66740, newton_decrement = 0.00000, seconds_since_start = 27.6\n",
-      "Iteration 8: norm_delta = 0.00000, step_size = 1.0000, log_lik = -294815.66740, newton_decrement = 0.00000, seconds_since_start = 31.3\n",
-      "Convergence success after 8 iterations.\n",
-      "0.7335853485634957\n"
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-       "         age_at_recruitment  ethnic_background_0.0  ethnic_background_1.0  \\\n",
-       "eid                                                                         \n",
-       "5773935           -0.188183                      1                      0   \n",
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-       "5773956            1.047030                      1                      0   \n",
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-       "5773972            1.170551                      1                      0   \n",
-       "...                     ...                    ...                    ...   \n",
-       "6025150           -1.670438                      1                      0   \n",
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-       "6025173            0.058860                      1                      0   \n",
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-       "6025198            1.294072                      1                      0   \n",
-       "\n",
-       "         ethnic_background_2.0  ethnic_background_3.0  ethnic_background_4.0  \\\n",
-       "eid                                                                            \n",
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-       "6025198                      0                      0                      0   \n",
-       "\n",
-       "         townsend_deprivation_index_at_recruitment  sex  \\\n",
-       "eid                                                       \n",
-       "5773935                                  -0.884706  0.0   \n",
-       "5773944                                  -0.478805  0.0   \n",
-       "5773956                                  -1.043086  0.0   \n",
-       "5773961                                  -1.315546  0.0   \n",
-       "5773972                                  -0.348533  1.0   \n",
-       "...                                            ...  ...   \n",
-       "6025150                                   0.432205  0.0   \n",
-       "6025165                                  -0.263495  0.0   \n",
-       "6025173                                  -0.173111  1.0   \n",
-       "6025182                                   0.413618  1.0   \n",
-       "6025198                                  -0.206520  1.0   \n",
-       "\n",
-       "         overall_health_rating_0.0  overall_health_rating_1.0  ...  diabetes2  \\\n",
-       "eid                                                            ...              \n",
-       "5773935                          0                          1  ...      False   \n",
-       "5773944                          0                          0  ...      False   \n",
-       "5773956                          0                          0  ...      False   \n",
-       "5773961                          0                          0  ...      False   \n",
-       "5773972                          0                          1  ...      False   \n",
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-       "6025198                          0                          1  ...      False   \n",
-       "\n",
-       "         chronic_kidney_disease  atrial_fibrillation  migraine  \\\n",
-       "eid                                                              \n",
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-       "6025150                   False                False     False   \n",
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-       "6025182                   False                False     False   \n",
-       "6025198                    True                False     False   \n",
-       "\n",
-       "         rheumatoid_arthritis  systemic_lupus_erythematosus  \\\n",
-       "eid                                                           \n",
-       "5773935                 False                         False   \n",
-       "5773944                 False                         False   \n",
-       "5773956                 False                         False   \n",
-       "5773961                 False                         False   \n",
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-       "6025173                 False                         False   \n",
-       "6025182                 False                         False   \n",
-       "6025198                 False                         False   \n",
-       "\n",
-       "         severe_mental_illness  erectile_dysfunction  MACE_event_time  \\\n",
-       "eid                                                                     \n",
-       "5773935                  False                 False        12.821355   \n",
-       "5773944                   True                 False        12.366872   \n",
-       "5773956                  False                 False        12.019165   \n",
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-       "5773972                  False                 False        10.420260   \n",
-       "...                        ...                   ...              ...   \n",
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-       "6025173                  False                 False        12.035592   \n",
-       "6025182                  False                 False        10.250513   \n",
-       "6025198                  False                 False        10.677618   \n",
-       "\n",
-       "         MACE_event  \n",
-       "eid                  \n",
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-       "5773944           0  \n",
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-       "5773961           0  \n",
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-       "\n",
-       "[17786 rows x 40 columns]"
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-       "       1000018   1000020   1000037   1000043   1000079   1000084   1000092  \\\n",
-       "11.0  0.058658  0.141535  0.075851  0.135895  0.077556  0.056996  0.093573   \n",
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-       "      5773827   5773840  5773871   5773886   5773895  5773907   5773928  \n",
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-     "output_type": "stream",
-     "text": [
-      "clinical\n",
-      "6\n",
-      "Iteration 1: norm_delta = 0.85460, step_size = 0.5000, log_lik = -302584.48904, newton_decrement = 8891.78043, seconds_since_start = 3.8\n",
-      "Iteration 2: norm_delta = 0.41090, step_size = 0.5000, log_lik = -296057.11273, newton_decrement = 2124.11951, seconds_since_start = 7.8\n",
-      "Iteration 3: norm_delta = 0.23321, step_size = 0.5000, log_lik = -294451.10345, newton_decrement = 619.48442, seconds_since_start = 12.0\n",
-      "Iteration 4: norm_delta = 0.10594, step_size = 0.6000, log_lik = -293926.18102, newton_decrement = 115.78936, seconds_since_start = 16.2\n",
-      "Iteration 5: norm_delta = 0.03278, step_size = 0.7200, log_lik = -293818.73382, newton_decrement = 10.35032, seconds_since_start = 20.4\n",
-      "Iteration 6: norm_delta = 0.00480, step_size = 0.8640, log_lik = -293808.53737, newton_decrement = 0.21297, seconds_since_start = 24.5\n",
-      "Iteration 7: norm_delta = 0.00001, step_size = 1.0000, log_lik = -293808.32421, newton_decrement = 0.00000, seconds_since_start = 28.4\n",
-      "Iteration 8: norm_delta = 0.00000, step_size = 1.0000, log_lik = -293808.32420, newton_decrement = 0.00000, seconds_since_start = 32.2\n",
-      "Convergence success after 8 iterations.\n",
-      "0.7351054072686027\n"
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-       "         age_at_recruitment  ethnic_background_0.0  ethnic_background_1.0  \\\n",
-       "eid                                                                         \n",
-       "5774024            1.293981                      1                      0   \n",
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-       "5774080            0.799883                      1                      0   \n",
-       "...                     ...                    ...                    ...   \n",
-       "6025150           -1.670602                      1                      0   \n",
-       "6025165           -1.423554                      1                      0   \n",
-       "6025173            0.058738                      1                      0   \n",
-       "6025182           -0.064787                      1                      0   \n",
-       "6025198            1.293981                      1                      0   \n",
-       "\n",
-       "         ethnic_background_2.0  ethnic_background_3.0  ethnic_background_4.0  \\\n",
-       "eid                                                                            \n",
-       "5774024                      0                      0                      0   \n",
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-       "6025182                      0                      0                      0   \n",
-       "6025198                      0                      0                      0   \n",
-       "\n",
-       "         townsend_deprivation_index_at_recruitment  sex  \\\n",
-       "eid                                                       \n",
-       "5774024                                  -0.080038  0.0   \n",
-       "5774038                                   2.741456  1.0   \n",
-       "5774052                                  -1.159937  1.0   \n",
-       "5774063                                  -0.335110  0.0   \n",
-       "5774080                                  -0.807184  1.0   \n",
-       "...                                            ...  ...   \n",
-       "6025150                                   0.433342  0.0   \n",
-       "6025165                                  -0.262575  0.0   \n",
-       "6025173                                  -0.172163  1.0   \n",
-       "6025182                                   0.414749  1.0   \n",
-       "6025198                                  -0.205582  1.0   \n",
-       "\n",
-       "         overall_health_rating_0.0  overall_health_rating_1.0  ...  diabetes2  \\\n",
-       "eid                                                            ...              \n",
-       "5774024                          0                          1  ...      False   \n",
-       "5774038                          0                          0  ...      False   \n",
-       "5774052                          0                          1  ...      False   \n",
-       "5774063                          0                          1  ...      False   \n",
-       "5774080                          0                          1  ...      False   \n",
-       "...                            ...                        ...  ...        ...   \n",
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-       "6025198                          0                          1  ...      False   \n",
-       "\n",
-       "         chronic_kidney_disease  atrial_fibrillation  migraine  \\\n",
-       "eid                                                              \n",
-       "5774024                   False                False     False   \n",
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-       "...                         ...                  ...       ...   \n",
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-       "6025182                   False                False     False   \n",
-       "6025198                    True                False     False   \n",
-       "\n",
-       "         rheumatoid_arthritis  systemic_lupus_erythematosus  \\\n",
-       "eid                                                           \n",
-       "5774024                 False                         False   \n",
-       "5774038                 False                         False   \n",
-       "5774052                 False                         False   \n",
-       "5774063                 False                         False   \n",
-       "5774080                 False                         False   \n",
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-       "6025173                 False                         False   \n",
-       "6025182                 False                         False   \n",
-       "6025198                 False                         False   \n",
-       "\n",
-       "         severe_mental_illness  erectile_dysfunction  MACE_event_time  \\\n",
-       "eid                                                                     \n",
-       "5774024                  False                 False        10.921287   \n",
-       "5774038                  False                 False        11.148528   \n",
-       "5774052                  False                 False        11.561944   \n",
-       "5774063                  False                 False        11.126626   \n",
-       "5774080                  False                 False        10.866530   \n",
-       "...                        ...                   ...              ...   \n",
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-       "6025182                  False                 False        10.250513   \n",
-       "6025198                  False                 False        10.677618   \n",
-       "\n",
-       "         MACE_event  \n",
-       "eid                  \n",
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-       "[17779 rows x 40 columns]"
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-       "       1000018  1000037   1000043  1000079   1000084   1000110   1000128  \\\n",
-       "11.0  0.058709  0.05642  0.139815  0.07789  0.056702  0.014441  0.013639   \n",
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-     "text": [
-      "clinical\n",
-      "7\n",
-      "Iteration 1: norm_delta = 0.87240, step_size = 0.5000, log_lik = -302470.77013, newton_decrement = 8822.16059, seconds_since_start = 3.8\n",
-      "Iteration 2: norm_delta = 0.40721, step_size = 0.5000, log_lik = -295988.64579, newton_decrement = 2114.44720, seconds_since_start = 7.8\n",
-      "Iteration 3: norm_delta = 0.23081, step_size = 0.5000, log_lik = -294389.78193, newton_decrement = 617.82255, seconds_since_start = 11.6\n",
-      "Iteration 4: norm_delta = 0.10486, step_size = 0.6000, log_lik = -293866.21506, newton_decrement = 115.66778, seconds_since_start = 15.5\n",
-      "Iteration 5: norm_delta = 0.03244, step_size = 0.7200, log_lik = -293758.87451, newton_decrement = 10.34847, seconds_since_start = 19.3\n",
-      "Iteration 6: norm_delta = 0.00474, step_size = 0.8640, log_lik = -293748.67989, newton_decrement = 0.21286, seconds_since_start = 23.2\n",
-      "Iteration 7: norm_delta = 0.00001, step_size = 1.0000, log_lik = -293748.46684, newton_decrement = 0.00000, seconds_since_start = 27.1\n",
-      "Iteration 8: norm_delta = 0.00000, step_size = 1.0000, log_lik = -293748.46684, newton_decrement = 0.00000, seconds_since_start = 30.9\n",
-      "Convergence success after 8 iterations.\n",
-      "0.7335116712923672\n"
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-       "         age_at_recruitment  ethnic_background_0.0  ethnic_background_1.0  \\\n",
-       "eid                                                                         \n",
-       "5774203           -0.189260                      1                      0   \n",
-       "5774211           -0.312817                      1                      0   \n",
-       "5774245           -0.930601                      1                      0   \n",
-       "5774267           -1.177714                      1                      0   \n",
-       "5774274           -1.177714                      0                      1   \n",
-       "...                     ...                    ...                    ...   \n",
-       "6025150           -1.671941                      1                      0   \n",
-       "6025165           -1.424828                      1                      0   \n",
-       "6025173            0.057853                      1                      0   \n",
-       "6025182           -0.065703                      1                      0   \n",
-       "6025198            1.293421                      1                      0   \n",
-       "\n",
-       "         ethnic_background_2.0  ethnic_background_3.0  ethnic_background_4.0  \\\n",
-       "eid                                                                            \n",
-       "5774203                      0                      0                      0   \n",
-       "5774211                      0                      0                      0   \n",
-       "5774245                      0                      0                      0   \n",
-       "5774267                      0                      0                      0   \n",
-       "5774274                      0                      0                      0   \n",
-       "...                        ...                    ...                    ...   \n",
-       "6025150                      0                      0                      0   \n",
-       "6025165                      0                      0                      0   \n",
-       "6025173                      0                      0                      0   \n",
-       "6025182                      0                      0                      0   \n",
-       "6025198                      0                      0                      0   \n",
-       "\n",
-       "         townsend_deprivation_index_at_recruitment  sex  \\\n",
-       "eid                                                       \n",
-       "5774203                                   0.749576  1.0   \n",
-       "5774211                                  -1.299185  0.0   \n",
-       "5774245                                   2.044694  1.0   \n",
-       "5774267                                  -0.723440  1.0   \n",
-       "5774274                                   0.494199  0.0   \n",
-       "...                                            ...  ...   \n",
-       "6025150                                   0.433481  0.0   \n",
-       "6025165                                  -0.262766  0.0   \n",
-       "6025173                                  -0.172311  1.0   \n",
-       "6025182                                   0.414879  1.0   \n",
-       "6025198                                  -0.205746  1.0   \n",
-       "\n",
-       "         overall_health_rating_0.0  overall_health_rating_1.0  ...  diabetes2  \\\n",
-       "eid                                                            ...              \n",
-       "5774203                          0                          1  ...      False   \n",
-       "5774211                          0                          1  ...      False   \n",
-       "5774245                          0                          1  ...      False   \n",
-       "5774267                          0                          1  ...      False   \n",
-       "5774274                          0                          0  ...      False   \n",
-       "...                            ...                        ...  ...        ...   \n",
-       "6025150                          0                          0  ...      False   \n",
-       "6025165                          0                          1  ...      False   \n",
-       "6025173                          0                          1  ...      False   \n",
-       "6025182                          0                          0  ...      False   \n",
-       "6025198                          0                          1  ...      False   \n",
-       "\n",
-       "         chronic_kidney_disease  atrial_fibrillation  migraine  \\\n",
-       "eid                                                              \n",
-       "5774203                   False                False     False   \n",
-       "5774211                   False                 True     False   \n",
-       "5774245                   False                False     False   \n",
-       "5774267                   False                False     False   \n",
-       "5774274                   False                False     False   \n",
-       "...                         ...                  ...       ...   \n",
-       "6025150                   False                False     False   \n",
-       "6025165                   False                False     False   \n",
-       "6025173                   False                False     False   \n",
-       "6025182                   False                False     False   \n",
-       "6025198                    True                False     False   \n",
-       "\n",
-       "         rheumatoid_arthritis  systemic_lupus_erythematosus  \\\n",
-       "eid                                                           \n",
-       "5774203                 False                         False   \n",
-       "5774211                 False                         False   \n",
-       "5774245                 False                         False   \n",
-       "5774267                 False                         False   \n",
-       "5774274                  True                         False   \n",
-       "...                       ...                           ...   \n",
-       "6025150                 False                         False   \n",
-       "6025165                 False                         False   \n",
-       "6025173                 False                         False   \n",
-       "6025182                 False                         False   \n",
-       "6025198                 False                         False   \n",
-       "\n",
-       "         severe_mental_illness  erectile_dysfunction  MACE_event_time  \\\n",
-       "eid                                                                     \n",
-       "5774203                  False                 False         6.743326   \n",
-       "5774211                  False                 False        12.659822   \n",
-       "5774245                  False                 False        12.640657   \n",
-       "5774267                  False                 False        12.859685   \n",
-       "5774274                  False                 False        10.830938   \n",
-       "...                        ...                   ...              ...   \n",
-       "6025150                  False                 False        13.253936   \n",
-       "6025165                  False                 False        12.076660   \n",
-       "6025173                  False                 False        12.035592   \n",
-       "6025182                  False                 False        10.250513   \n",
-       "6025198                  False                 False        10.677618   \n",
-       "\n",
-       "         MACE_event  \n",
-       "eid                  \n",
-       "5774203           1  \n",
-       "5774211           0  \n",
-       "5774245           0  \n",
-       "5774267           0  \n",
-       "5774274           0  \n",
-       "...             ...  \n",
-       "6025150           0  \n",
-       "6025165           0  \n",
-       "6025173           0  \n",
-       "6025182           0  \n",
-       "6025198           0  \n",
-       "\n",
-       "[17782 rows x 40 columns]"
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-       "      <th></th>\n",
-       "      <th>1000018</th>\n",
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-       "      <th>1000079</th>\n",
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-       "      <th>11.0</th>\n",
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-       "      <td>0.01375</td>\n",
-       "      <td>0.017445</td>\n",
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-       "      <td>0.028315</td>\n",
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-       "      <td>0.039113</td>\n",
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-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1 rows × 338417 columns</p>\n",
-       "</div>"
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-       "       1000018   1000020   1000037   1000079   1000084   1000092  1000107  \\\n",
-       "11.0  0.056108  0.139779  0.054076  0.077655  0.055557  0.094508  0.01867   \n",
-       "\n",
-       "       1000110  1000128   1000135  ...   5774091  5774108   5774114   5774127  \\\n",
-       "11.0  0.014553  0.01375  0.017445  ...  0.119721  0.08067  0.089872  0.275772   \n",
-       "\n",
-       "       5774139   5774140   5774153   5774162   5774171   5774186  \n",
-       "11.0  0.018271  0.023672  0.028315  0.039754  0.068492  0.039113  \n",
-       "\n",
-       "[1 rows x 338417 columns]"
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-     "metadata": {},
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-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "clinical\n",
-      "8\n",
-      "Iteration 1: norm_delta = 0.88350, step_size = 0.5000, log_lik = -303600.50186, newton_decrement = 8863.09158, seconds_since_start = 3.9\n",
-      "Iteration 2: norm_delta = 0.40709, step_size = 0.5000, log_lik = -297083.72871, newton_decrement = 2125.68633, seconds_since_start = 7.7\n",
-      "Iteration 3: norm_delta = 0.23170, step_size = 0.5000, log_lik = -295476.42684, newton_decrement = 620.60038, seconds_since_start = 11.5\n",
-      "Iteration 4: norm_delta = 0.10545, step_size = 0.6000, log_lik = -294950.53981, newton_decrement = 116.04341, seconds_since_start = 15.3\n",
-      "Iteration 5: norm_delta = 0.03261, step_size = 0.7200, log_lik = -294842.85895, newton_decrement = 10.36572, seconds_since_start = 19.1\n",
-      "Iteration 6: norm_delta = 0.00476, step_size = 0.8640, log_lik = -294832.64800, newton_decrement = 0.21279, seconds_since_start = 23.1\n",
-      "Iteration 7: norm_delta = 0.00001, step_size = 1.0000, log_lik = -294832.43504, newton_decrement = 0.00000, seconds_since_start = 27.1\n",
-      "Iteration 8: norm_delta = 0.00000, step_size = 1.0000, log_lik = -294832.43503, newton_decrement = 0.00000, seconds_since_start = 30.8\n",
-      "Convergence success after 8 iterations.\n",
-      "0.7337164482287201\n"
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-       "eid                                                                         \n",
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-       "eid                                                                            \n",
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-       "eid                                                       \n",
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-       "eid                                                              \n",
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-       "eid                                                           \n",
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-       "eid                                                                     \n",
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-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>11.0</th>\n",
-       "      <td>0.056757</td>\n",
-       "      <td>0.141078</td>\n",
-       "      <td>0.05026</td>\n",
-       "      <td>0.139246</td>\n",
-       "      <td>0.078742</td>\n",
-       "      <td>0.058</td>\n",
-       "      <td>0.09465</td>\n",
-       "      <td>0.018437</td>\n",
-       "      <td>0.014367</td>\n",
-       "      <td>0.013738</td>\n",
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-       "      <td>0.042405</td>\n",
-       "      <td>0.039005</td>\n",
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-       "<p>1 rows × 338456 columns</p>\n",
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-       "       1000018   1000020  1000037   1000043   1000079  1000084  1000092  \\\n",
-       "11.0  0.056757  0.141078  0.05026  0.139246  0.078742    0.058  0.09465   \n",
-       "\n",
-       "       1000107   1000110   1000128  ...   5774359   5774364   5774396  \\\n",
-       "11.0  0.018437  0.014367  0.013738  ...  0.042405  0.039005  0.046237   \n",
-       "\n",
-       "       5774405   5774420  5774443   5774451   5774466   5774479   5774484  \n",
-       "11.0  0.011547  0.132303  0.01077  0.069881  0.021488  0.047245  0.043107  \n",
-       "\n",
-       "[1 rows x 338456 columns]"
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-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "clinical\n",
-      "9\n",
-      "Iteration 1: norm_delta = 0.84487, step_size = 0.5000, log_lik = -302369.03502, newton_decrement = 8802.10427, seconds_since_start = 3.8\n",
-      "Iteration 2: norm_delta = 0.40507, step_size = 0.5000, log_lik = -295905.60665, newton_decrement = 2101.24037, seconds_since_start = 7.9\n",
-      "Iteration 3: norm_delta = 0.22850, step_size = 0.5000, log_lik = -294316.81957, newton_decrement = 613.29160, seconds_since_start = 11.6\n",
-      "Iteration 4: norm_delta = 0.10333, step_size = 0.6000, log_lik = -293797.12542, newton_decrement = 114.70320, seconds_since_start = 15.4\n",
-      "Iteration 5: norm_delta = 0.03191, step_size = 0.7200, log_lik = -293690.68288, newton_decrement = 10.25953, seconds_since_start = 19.2\n",
-      "Iteration 6: norm_delta = 0.00467, step_size = 0.8640, log_lik = -293680.57560, newton_decrement = 0.21129, seconds_since_start = 23.0\n",
-      "Iteration 7: norm_delta = 0.00001, step_size = 1.0000, log_lik = -293680.36412, newton_decrement = 0.00000, seconds_since_start = 26.8\n",
-      "Iteration 8: norm_delta = 0.00000, step_size = 1.0000, log_lik = -293680.36412, newton_decrement = 0.00000, seconds_since_start = 30.6\n",
-      "Convergence success after 8 iterations.\n",
-      "0.7341210145016998\n"
-     ]
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-     "data": {
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-       "         age_at_recruitment  ethnic_background_0.0  ethnic_background_1.0  \\\n",
-       "eid                                                                         \n",
-       "5774222           -0.684344                      1                      0   \n",
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-       "...                     ...                    ...                    ...   \n",
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-       "6025182           -0.066466                      1                      0   \n",
-       "6025198            1.292867                      1                      0   \n",
-       "\n",
-       "         ethnic_background_2.0  ethnic_background_3.0  ethnic_background_4.0  \\\n",
-       "eid                                                                            \n",
-       "5774222                      0                      0                      0   \n",
-       "5774236                      0                      0                      0   \n",
-       "5774245                      0                      0                      0   \n",
-       "5774267                      0                      0                      0   \n",
-       "5774274                      0                      0                      0   \n",
-       "...                        ...                    ...                    ...   \n",
-       "6025150                      0                      0                      0   \n",
-       "6025165                      0                      0                      0   \n",
-       "6025173                      0                      0                      0   \n",
-       "6025182                      0                      0                      0   \n",
-       "6025198                      0                      0                      0   \n",
-       "\n",
-       "         townsend_deprivation_index_at_recruitment  sex  \\\n",
-       "eid                                                       \n",
-       "5774222                                  -1.075855  0.0   \n",
-       "5774236                                  -0.901500  0.0   \n",
-       "5774245                                   2.044590  1.0   \n",
-       "5774267                                  -0.724010  1.0   \n",
-       "5774274                                   0.493834  0.0   \n",
-       "...                                            ...  ...   \n",
-       "6025150                                   0.433106  0.0   \n",
-       "6025165                                  -0.263259  0.0   \n",
-       "6025173                                  -0.172788  1.0   \n",
-       "6025182                                   0.414501  1.0   \n",
-       "6025198                                  -0.206229  1.0   \n",
-       "\n",
-       "         overall_health_rating_0.0  overall_health_rating_1.0  ...  diabetes2  \\\n",
-       "eid                                                            ...              \n",
-       "5774222                          0                          0  ...      False   \n",
-       "5774236                          0                          0  ...      False   \n",
-       "5774245                          0                          1  ...      False   \n",
-       "5774267                          0                          1  ...      False   \n",
-       "5774274                          0                          0  ...      False   \n",
-       "...                            ...                        ...  ...        ...   \n",
-       "6025150                          0                          0  ...      False   \n",
-       "6025165                          0                          1  ...      False   \n",
-       "6025173                          0                          1  ...      False   \n",
-       "6025182                          0                          0  ...      False   \n",
-       "6025198                          0                          1  ...      False   \n",
-       "\n",
-       "         chronic_kidney_disease  atrial_fibrillation  migraine  \\\n",
-       "eid                                                              \n",
-       "5774222                   False                False     False   \n",
-       "5774236                   False                False     False   \n",
-       "5774245                   False                False     False   \n",
-       "5774267                   False                False     False   \n",
-       "5774274                   False                False     False   \n",
-       "...                         ...                  ...       ...   \n",
-       "6025150                   False                False     False   \n",
-       "6025165                   False                False     False   \n",
-       "6025173                   False                False     False   \n",
-       "6025182                   False                False     False   \n",
-       "6025198                    True                False     False   \n",
-       "\n",
-       "         rheumatoid_arthritis  systemic_lupus_erythematosus  \\\n",
-       "eid                                                           \n",
-       "5774222                 False                         False   \n",
-       "5774236                 False                         False   \n",
-       "5774245                 False                         False   \n",
-       "5774267                 False                         False   \n",
-       "5774274                  True                         False   \n",
-       "...                       ...                           ...   \n",
-       "6025150                 False                         False   \n",
-       "6025165                 False                         False   \n",
-       "6025173                 False                         False   \n",
-       "6025182                 False                         False   \n",
-       "6025198                 False                         False   \n",
-       "\n",
-       "         severe_mental_illness  erectile_dysfunction  MACE_event_time  \\\n",
-       "eid                                                                     \n",
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-       "\n",
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-       "       1000018   1000020   1000037   1000043   1000079   1000084   1000092  \\\n",
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-     "text": [
-      "clinical_pgs\n",
-      "0\n",
-      "Iteration 1: norm_delta = 0.85142, step_size = 0.5000, log_lik = -302789.67372, newton_decrement = 9031.28749, seconds_since_start = 4.1\n",
-      "Iteration 2: norm_delta = 0.40595, step_size = 0.5000, log_lik = -296151.00190, newton_decrement = 2159.31329, seconds_since_start = 8.2\n",
-      "Iteration 3: norm_delta = 0.23022, step_size = 0.5000, log_lik = -294518.76240, newton_decrement = 627.15448, seconds_since_start = 12.3\n",
-      "Iteration 4: norm_delta = 0.10441, step_size = 0.6000, log_lik = -293987.45099, newton_decrement = 116.82710, seconds_since_start = 16.5\n",
-      "Iteration 5: norm_delta = 0.03230, step_size = 0.7200, log_lik = -293879.05374, newton_decrement = 10.42320, seconds_since_start = 20.6\n",
-      "Iteration 6: norm_delta = 0.00474, step_size = 0.8640, log_lik = -293868.78563, newton_decrement = 0.21450, seconds_since_start = 24.7\n",
-      "Iteration 7: norm_delta = 0.00001, step_size = 1.0000, log_lik = -293868.57093, newton_decrement = 0.00000, seconds_since_start = 28.9\n",
-      "Iteration 8: norm_delta = 0.00000, step_size = 1.0000, log_lik = -293868.57093, newton_decrement = 0.00000, seconds_since_start = 33.1\n",
-      "Convergence success after 8 iterations.\n",
-      "0.7385962740563872\n"
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-       "<p>17756 rows × 44 columns</p>\n",
-       "</div>"
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-      "text/plain": [
-       "         PGS000011  PGS000057  PGS000058  PGS000059  age_at_recruitment  \\\n",
-       "eid                                                                       \n",
-       "5774080   0.504130   0.231645   0.999551   0.355905            0.800912   \n",
-       "5774091  -0.547183  -0.194042   0.555937  -0.605116            0.924494   \n",
-       "5774108   0.671261   1.873436   0.948709   0.418592            0.553747   \n",
-       "5774114   1.530158   0.927932   1.055698   0.588455           -0.187746   \n",
-       "5774127  -0.329992   1.283870   0.316242   0.740566            0.553747   \n",
-       "...            ...        ...        ...        ...                 ...   \n",
-       "6025147   0.519527   0.240938   1.351894  -1.702280           -1.670733   \n",
-       "6025150   0.912528   0.952614   0.123178   0.551615           -1.670733   \n",
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-       "\n",
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-       "eid                                                                            \n",
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-       "eid                                                    ...              \n",
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-       "eid                                                              \n",
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-       "eid                                                           \n",
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-       "eid                                                                     \n",
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-     "text": [
-      "clinical_pgs\n",
-      "1\n",
-      "Iteration 1: norm_delta = 0.84363, step_size = 0.5000, log_lik = -301918.37352, newton_decrement = 9029.07588, seconds_since_start = 4.2\n",
-      "Iteration 2: norm_delta = 0.40934, step_size = 0.5000, log_lik = -295271.53723, newton_decrement = 2178.42860, seconds_since_start = 8.4\n",
-      "Iteration 3: norm_delta = 0.23296, step_size = 0.5000, log_lik = -293624.65731, newton_decrement = 633.93448, seconds_since_start = 12.5\n",
-      "Iteration 4: norm_delta = 0.10578, step_size = 0.6000, log_lik = -293087.56072, newton_decrement = 118.20847, seconds_since_start = 16.7\n",
-      "Iteration 5: norm_delta = 0.03265, step_size = 0.7200, log_lik = -292977.88323, newton_decrement = 10.53748, seconds_since_start = 21.2\n",
-      "Iteration 6: norm_delta = 0.00476, step_size = 0.8640, log_lik = -292967.50367, newton_decrement = 0.21597, seconds_since_start = 25.4\n",
-      "Iteration 7: norm_delta = 0.00001, step_size = 1.0000, log_lik = -292967.28752, newton_decrement = 0.00000, seconds_since_start = 29.6\n",
-      "Iteration 8: norm_delta = 0.00000, step_size = 1.0000, log_lik = -292967.28752, newton_decrement = 0.00000, seconds_since_start = 34.1\n",
-      "Convergence success after 8 iterations.\n",
-      "0.7371736332303399\n"
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-       "         PGS000011  PGS000057  PGS000058  PGS000059  age_at_recruitment  \\\n",
-       "eid                                                                       \n",
-       "5773972   0.155023   2.021287   1.119648   1.306834            1.170062   \n",
-       "5773983   0.045591   1.002189   0.669587   0.360903            1.540685   \n",
-       "5773999  -0.288240   0.721837  -0.352591   0.533248           -0.312430   \n",
-       "5774009   2.853341   1.919377   1.044842   1.779799            0.922980   \n",
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-       "6025150   0.911089   0.951234   0.123185   0.550534           -1.671381   \n",
-       "6025165   1.214556  -0.498583  -0.977576  -0.698169           -1.424299   \n",
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-       "6025198  -0.262530  -0.908620   1.048006  -0.490435            1.293603   \n",
-       "\n",
-       "         ethnic_background_0.0  ethnic_background_1.0  ethnic_background_2.0  \\\n",
-       "eid                                                                            \n",
-       "5773972                      1                      0                      0   \n",
-       "5773983                      1                      0                      0   \n",
-       "5773999                      1                      0                      0   \n",
-       "5774009                      1                      0                      0   \n",
-       "5774017                      1                      0                      0   \n",
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-       "6025147                      0                      0                      1   \n",
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-       "\n",
-       "         ethnic_background_3.0  ethnic_background_4.0  ...  diabetes2  \\\n",
-       "eid                                                    ...              \n",
-       "5773972                      0                      0  ...      False   \n",
-       "5773983                      0                      0  ...      False   \n",
-       "5773999                      0                      0  ...      False   \n",
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-       "\n",
-       "         chronic_kidney_disease  atrial_fibrillation  migraine  \\\n",
-       "eid                                                              \n",
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-       "...                         ...                  ...       ...   \n",
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-       "6025198                    True                False     False   \n",
-       "\n",
-       "         rheumatoid_arthritis  systemic_lupus_erythematosus  \\\n",
-       "eid                                                           \n",
-       "5773972                 False                         False   \n",
-       "5773983                 False                         False   \n",
-       "5773999                 False                         False   \n",
-       "5774009                 False                         False   \n",
-       "5774017                 False                         False   \n",
-       "...                       ...                           ...   \n",
-       "6025147                 False                         False   \n",
-       "6025150                 False                         False   \n",
-       "6025165                 False                         False   \n",
-       "6025182                 False                         False   \n",
-       "6025198                 False                         False   \n",
-       "\n",
-       "         severe_mental_illness  erectile_dysfunction  MACE_event_time  \\\n",
-       "eid                                                                     \n",
-       "5773972                  False                 False        10.420260   \n",
-       "5773983                  False                 False        12.364134   \n",
-       "5773999                  False                 False        11.342916   \n",
-       "5774009                  False                 False        11.463381   \n",
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-       "...                        ...                   ...              ...   \n",
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-       "6025150                  False                 False        13.253936   \n",
-       "6025165                  False                 False        12.076660   \n",
-       "6025182                  False                 False        10.250513   \n",
-       "6025198                  False                 False        10.677618   \n",
-       "\n",
-       "         MACE_event  \n",
-       "eid                  \n",
-       "5773972           0  \n",
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-       "5774009           0  \n",
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-       "       1000018   1000020   1000043   1000079   1000084   1000092   1000107  \\\n",
-       "11.0  0.050676  0.141886  0.138537  0.082222  0.058141  0.079901  0.015138   \n",
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-     "text": [
-      "clinical_pgs\n",
-      "2\n",
-      "Iteration 1: norm_delta = 0.83836, step_size = 0.5000, log_lik = -303934.60837, newton_decrement = 9164.67613, seconds_since_start = 4.0\n",
-      "Iteration 2: norm_delta = 0.40845, step_size = 0.5000, log_lik = -297189.61863, newton_decrement = 2211.37629, seconds_since_start = 8.2\n",
-      "Iteration 3: norm_delta = 0.23265, step_size = 0.5000, log_lik = -295517.75359, newton_decrement = 644.14683, seconds_since_start = 12.4\n",
-      "Iteration 4: norm_delta = 0.10586, step_size = 0.6000, log_lik = -294971.96435, newton_decrement = 120.27386, seconds_since_start = 16.5\n",
-      "Iteration 5: norm_delta = 0.03275, step_size = 0.7200, log_lik = -294860.36274, newton_decrement = 10.73591, seconds_since_start = 20.7\n",
-      "Iteration 6: norm_delta = 0.00478, step_size = 0.8640, log_lik = -294849.78726, newton_decrement = 0.22031, seconds_since_start = 24.9\n",
-      "Iteration 7: norm_delta = 0.00001, step_size = 1.0000, log_lik = -294849.56676, newton_decrement = 0.00000, seconds_since_start = 29.3\n",
-      "Iteration 8: norm_delta = 0.00000, step_size = 1.0000, log_lik = -294849.56676, newton_decrement = 0.00000, seconds_since_start = 33.8\n",
-      "Convergence success after 8 iterations.\n",
-      "0.7351214359762639\n"
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-       "      <th>5773840</th>\n",
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-       "      <td>False</td>\n",
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-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
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-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
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-       "         PGS000011  PGS000057  PGS000058  PGS000059  age_at_recruitment  \\\n",
-       "eid                                                                       \n",
-       "5773808  -1.812692  -1.087006  -0.549280  -0.058962           -1.670824   \n",
-       "5773814   0.088857   0.072109   1.226257   0.417502            0.429000   \n",
-       "5773827   0.585491  -0.270818  -0.078308  -0.357968            0.676039   \n",
-       "5773840   0.419020   0.796243   0.816419   1.008088           -1.053228   \n",
-       "5773871   2.490407   1.869502  -0.031807   0.928714            0.429000   \n",
-       "...            ...        ...        ...        ...                 ...   \n",
-       "6025147   0.517928   0.240124   1.351553  -1.702714           -1.670824   \n",
-       "6025150   0.910754   0.951965   0.123146   0.550484           -1.670824   \n",
-       "6025173  -1.548184  -1.500397  -1.129791  -1.285205            0.058443   \n",
-       "6025182  -0.181822  -0.194956  -0.674304  -0.775329           -0.065076   \n",
-       "6025198  -0.263076  -0.908596   1.048086  -0.491321            1.293634   \n",
-       "\n",
-       "         ethnic_background_0.0  ethnic_background_1.0  ethnic_background_2.0  \\\n",
-       "eid                                                                            \n",
-       "5773808                      1                      0                      0   \n",
-       "5773814                      1                      0                      0   \n",
-       "5773827                      1                      0                      0   \n",
-       "5773840                      1                      0                      0   \n",
-       "5773871                      1                      0                      0   \n",
-       "...                        ...                    ...                    ...   \n",
-       "6025147                      0                      0                      1   \n",
-       "6025150                      1                      0                      0   \n",
-       "6025173                      1                      0                      0   \n",
-       "6025182                      1                      0                      0   \n",
-       "6025198                      1                      0                      0   \n",
-       "\n",
-       "         ethnic_background_3.0  ethnic_background_4.0  ...  diabetes2  \\\n",
-       "eid                                                    ...              \n",
-       "5773808                      0                      0  ...      False   \n",
-       "5773814                      0                      0  ...      False   \n",
-       "5773827                      0                      0  ...      False   \n",
-       "5773840                      0                      0  ...      False   \n",
-       "5773871                      0                      0  ...      False   \n",
-       "...                        ...                    ...  ...        ...   \n",
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-       "6025173                      0                      0  ...      False   \n",
-       "6025182                      0                      0  ...      False   \n",
-       "6025198                      0                      0  ...      False   \n",
-       "\n",
-       "         chronic_kidney_disease  atrial_fibrillation  migraine  \\\n",
-       "eid                                                              \n",
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-       "...                         ...                  ...       ...   \n",
-       "6025147                   False                False     False   \n",
-       "6025150                   False                False     False   \n",
-       "6025173                   False                False     False   \n",
-       "6025182                   False                False     False   \n",
-       "6025198                    True                False     False   \n",
-       "\n",
-       "         rheumatoid_arthritis  systemic_lupus_erythematosus  \\\n",
-       "eid                                                           \n",
-       "5773808                 False                         False   \n",
-       "5773814                 False                         False   \n",
-       "5773827                 False                         False   \n",
-       "5773840                 False                         False   \n",
-       "5773871                 False                         False   \n",
-       "...                       ...                           ...   \n",
-       "6025147                 False                         False   \n",
-       "6025150                 False                         False   \n",
-       "6025173                 False                         False   \n",
-       "6025182                 False                         False   \n",
-       "6025198                 False                         False   \n",
-       "\n",
-       "         severe_mental_illness  erectile_dysfunction  MACE_event_time  \\\n",
-       "eid                                                                     \n",
-       "5773808                  False                 False        10.685832   \n",
-       "5773814                  False                 False        13.100616   \n",
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-       "5773840                  False                 False        11.789185   \n",
-       "5773871                  False                 False        11.222450   \n",
-       "...                        ...                   ...              ...   \n",
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-       "6025150                  False                 False        13.253936   \n",
-       "6025173                  False                 False        12.035592   \n",
-       "6025182                  False                 False        10.250513   \n",
-       "6025198                  False                 False        10.677618   \n",
-       "\n",
-       "         MACE_event  \n",
-       "eid                  \n",
-       "5773808           0  \n",
-       "5773814           0  \n",
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-       "5773840           0  \n",
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-       "\n",
-       "[17750 rows x 44 columns]"
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-       "       1000018   1000020   1000037   1000043   1000079   1000084   1000092  \\\n",
-       "11.0  0.051739  0.143324  0.063317  0.142969  0.082017  0.059062  0.080513   \n",
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-     "text": [
-      "clinical_pgs\n",
-      "3\n",
-      "Iteration 1: norm_delta = 0.83402, step_size = 0.5000, log_lik = -304005.99770, newton_decrement = 9072.55473, seconds_since_start = 4.0\n",
-      "Iteration 2: norm_delta = 0.41374, step_size = 0.5000, log_lik = -297332.54431, newton_decrement = 2176.30449, seconds_since_start = 8.3\n",
-      "Iteration 3: norm_delta = 0.23546, step_size = 0.5000, log_lik = -295687.34149, newton_decrement = 632.79383, seconds_since_start = 12.3\n",
-      "Iteration 4: norm_delta = 0.10692, step_size = 0.6000, log_lik = -295151.23722, newton_decrement = 117.89940, seconds_since_start = 16.5\n",
-      "Iteration 5: norm_delta = 0.03301, step_size = 0.7200, log_lik = -295041.85007, newton_decrement = 10.50411, seconds_since_start = 21.0\n",
-      "Iteration 6: norm_delta = 0.00481, step_size = 0.8640, log_lik = -295031.50347, newton_decrement = 0.21525, seconds_since_start = 25.1\n",
-      "Iteration 7: norm_delta = 0.00001, step_size = 1.0000, log_lik = -295031.28803, newton_decrement = 0.00000, seconds_since_start = 29.2\n",
-      "Iteration 8: norm_delta = 0.00000, step_size = 1.0000, log_lik = -295031.28803, newton_decrement = 0.00000, seconds_since_start = 33.4\n",
-      "Convergence success after 8 iterations.\n",
-      "0.7377430041881108\n"
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-       "      <th>5773961</th>\n",
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-       "         PGS000011  PGS000057  PGS000058  PGS000059  age_at_recruitment  \\\n",
-       "eid                                                                       \n",
-       "5773935  -0.490263  -0.678900  -1.113311  -0.774262           -0.188933   \n",
-       "5773944  -0.585952  -1.316175  -1.439081  -0.168532           -1.053656   \n",
-       "5773956   0.831714  -0.373008   1.337478  -0.036029            1.046386   \n",
-       "5773961   0.189128   0.730102   0.475269  -0.585194            0.675790   \n",
-       "5773972   0.155354   2.023145   1.119482   1.307935            1.169918   \n",
-       "...            ...        ...        ...        ...                 ...   \n",
-       "6025147   0.518852   0.240575   1.351386  -1.701416           -1.671315   \n",
-       "6025150   0.911811   0.952523   0.122509   0.551218           -1.671315   \n",
-       "6025165   1.215434  -0.498064  -0.978815  -0.698175           -1.424251   \n",
-       "6025173  -1.547960  -1.500210  -1.130909  -1.284011            0.058131   \n",
-       "6025198  -0.262416  -0.908319   1.047803  -0.490326            1.293450   \n",
-       "\n",
-       "         ethnic_background_0.0  ethnic_background_1.0  ethnic_background_2.0  \\\n",
-       "eid                                                                            \n",
-       "5773935                      1                      0                      0   \n",
-       "5773944                      1                      0                      0   \n",
-       "5773956                      1                      0                      0   \n",
-       "5773961                      1                      0                      0   \n",
-       "5773972                      1                      0                      0   \n",
-       "...                        ...                    ...                    ...   \n",
-       "6025147                      0                      0                      1   \n",
-       "6025150                      1                      0                      0   \n",
-       "6025165                      1                      0                      0   \n",
-       "6025173                      1                      0                      0   \n",
-       "6025198                      1                      0                      0   \n",
-       "\n",
-       "         ethnic_background_3.0  ethnic_background_4.0  ...  diabetes2  \\\n",
-       "eid                                                    ...              \n",
-       "5773935                      0                      0  ...      False   \n",
-       "5773944                      0                      0  ...      False   \n",
-       "5773956                      0                      0  ...      False   \n",
-       "5773961                      0                      0  ...      False   \n",
-       "5773972                      0                      0  ...      False   \n",
-       "...                        ...                    ...  ...        ...   \n",
-       "6025147                      0                      0  ...      False   \n",
-       "6025150                      0                      0  ...      False   \n",
-       "6025165                      0                      0  ...      False   \n",
-       "6025173                      0                      0  ...      False   \n",
-       "6025198                      0                      0  ...      False   \n",
-       "\n",
-       "         chronic_kidney_disease  atrial_fibrillation  migraine  \\\n",
-       "eid                                                              \n",
-       "5773935                   False                False     False   \n",
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-       "5773956                   False                False     False   \n",
-       "5773961                   False                False     False   \n",
-       "5773972                   False                False     False   \n",
-       "...                         ...                  ...       ...   \n",
-       "6025147                   False                False     False   \n",
-       "6025150                   False                False     False   \n",
-       "6025165                   False                False     False   \n",
-       "6025173                   False                False     False   \n",
-       "6025198                    True                False     False   \n",
-       "\n",
-       "         rheumatoid_arthritis  systemic_lupus_erythematosus  \\\n",
-       "eid                                                           \n",
-       "5773935                 False                         False   \n",
-       "5773944                 False                         False   \n",
-       "5773956                 False                         False   \n",
-       "5773961                 False                         False   \n",
-       "5773972                 False                         False   \n",
-       "...                       ...                           ...   \n",
-       "6025147                 False                         False   \n",
-       "6025150                 False                         False   \n",
-       "6025165                 False                         False   \n",
-       "6025173                 False                         False   \n",
-       "6025198                 False                         False   \n",
-       "\n",
-       "         severe_mental_illness  erectile_dysfunction  MACE_event_time  \\\n",
-       "eid                                                                     \n",
-       "5773935                  False                 False        12.821355   \n",
-       "5773944                   True                 False        12.366872   \n",
-       "5773956                  False                 False        12.019165   \n",
-       "5773961                  False                 False        12.709103   \n",
-       "5773972                  False                 False        10.420260   \n",
-       "...                        ...                   ...              ...   \n",
-       "6025147                  False                 False        11.879535   \n",
-       "6025150                  False                 False        13.253936   \n",
-       "6025165                  False                 False        12.076660   \n",
-       "6025173                  False                 False        12.035592   \n",
-       "6025198                  False                 False        10.677618   \n",
-       "\n",
-       "         MACE_event  \n",
-       "eid                  \n",
-       "5773935           0  \n",
-       "5773944           0  \n",
-       "5773956           0  \n",
-       "5773961           0  \n",
-       "5773972           0  \n",
-       "...             ...  \n",
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-       "6025165           0  \n",
-       "6025173           0  \n",
-       "6025198           0  \n",
-       "\n",
-       "[17778 rows x 44 columns]"
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-       "<p>1 rows × 338322 columns</p>\n",
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-       "       1000020   1000037   1000043  1000079   1000084   1000092   1000107  \\\n",
-       "11.0  0.143886  0.065885  0.141765  0.07961  0.057475  0.083325  0.015389   \n",
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-       "       5773814   5773827   5773871   5773886   5773895   5773907   5773928  \n",
-       "11.0  0.128839  0.107794  0.092878  0.810978  0.012141  0.094387  0.027914  \n",
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-    {
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-     "output_type": "stream",
-     "text": [
-      "clinical_pgs\n",
-      "4\n",
-      "Iteration 1: norm_delta = 0.87203, step_size = 0.5000, log_lik = -302864.07070, newton_decrement = 9093.83811, seconds_since_start = 4.2\n",
-      "Iteration 2: norm_delta = 0.40954, step_size = 0.5000, log_lik = -296174.71296, newton_decrement = 2186.56763, seconds_since_start = 8.4\n",
-      "Iteration 3: norm_delta = 0.23141, step_size = 0.5000, log_lik = -294521.63404, newton_decrement = 636.68630, seconds_since_start = 12.6\n",
-      "Iteration 4: norm_delta = 0.10486, step_size = 0.6000, log_lik = -293982.17890, newton_decrement = 118.85252, seconds_since_start = 16.7\n",
-      "Iteration 5: norm_delta = 0.03242, step_size = 0.7200, log_lik = -293871.89388, newton_decrement = 10.61692, seconds_since_start = 20.8\n",
-      "Iteration 6: norm_delta = 0.00475, step_size = 0.8640, log_lik = -293861.43487, newton_decrement = 0.21844, seconds_since_start = 24.9\n",
-      "Iteration 7: norm_delta = 0.00001, step_size = 1.0000, log_lik = -293861.21624, newton_decrement = 0.00000, seconds_since_start = 29.1\n",
-      "Iteration 8: norm_delta = 0.00000, step_size = 1.0000, log_lik = -293861.21624, newton_decrement = 0.00000, seconds_since_start = 33.6\n",
-      "Convergence success after 8 iterations.\n",
-      "0.7377586361103405\n"
-     ]
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-       "         PGS000011  PGS000057  PGS000058  PGS000059  age_at_recruitment  \\\n",
-       "eid                                                                       \n",
-       "5772854  -0.831473  -0.475716  -1.808277  -0.846440           -0.188141   \n",
-       "5772869  -0.355422   0.365418  -0.441591  -0.667018            0.058844   \n",
-       "5772881   2.001596   2.274987   2.729080   0.639929           -1.299574   \n",
-       "5772897   0.025154   0.671285  -2.087384  -0.207070            0.429322   \n",
-       "5772905  -0.434826  -0.526694  -0.655950   1.155849           -1.423067   \n",
-       "...            ...        ...        ...        ...                 ...   \n",
-       "6025147   0.519168   0.239772   1.351487  -1.702124           -1.670052   \n",
-       "6025165   1.215796  -0.498806  -0.977772  -0.698864           -1.423067   \n",
-       "6025173  -1.547780  -1.500870  -1.129805  -1.284712            0.058844   \n",
-       "6025182  -0.180865  -0.195338  -0.674328  -0.774953           -0.064648   \n",
-       "6025198  -0.262152  -0.909028   1.048027  -0.491012            1.293770   \n",
-       "\n",
-       "         ethnic_background_0.0  ethnic_background_1.0  ethnic_background_2.0  \\\n",
-       "eid                                                                            \n",
-       "5772854                      1                      0                      0   \n",
-       "5772869                      1                      0                      0   \n",
-       "5772881                      1                      0                      0   \n",
-       "5772897                      1                      0                      0   \n",
-       "5772905                      1                      0                      0   \n",
-       "...                        ...                    ...                    ...   \n",
-       "6025147                      0                      0                      1   \n",
-       "6025165                      1                      0                      0   \n",
-       "6025173                      1                      0                      0   \n",
-       "6025182                      1                      0                      0   \n",
-       "6025198                      1                      0                      0   \n",
-       "\n",
-       "         ethnic_background_3.0  ethnic_background_4.0  ...  diabetes2  \\\n",
-       "eid                                                    ...              \n",
-       "5772854                      0                      0  ...      False   \n",
-       "5772869                      0                      0  ...      False   \n",
-       "5772881                      0                      0  ...      False   \n",
-       "5772897                      0                      0  ...      False   \n",
-       "5772905                      0                      0  ...      False   \n",
-       "...                        ...                    ...  ...        ...   \n",
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-       "6025173                      0                      0  ...      False   \n",
-       "6025182                      0                      0  ...      False   \n",
-       "6025198                      0                      0  ...      False   \n",
-       "\n",
-       "         chronic_kidney_disease  atrial_fibrillation  migraine  \\\n",
-       "eid                                                              \n",
-       "5772854                   False                False     False   \n",
-       "5772869                   False                False     False   \n",
-       "5772881                   False                False     False   \n",
-       "5772897                   False                False     False   \n",
-       "5772905                   False                False     False   \n",
-       "...                         ...                  ...       ...   \n",
-       "6025147                   False                False     False   \n",
-       "6025165                   False                False     False   \n",
-       "6025173                   False                False     False   \n",
-       "6025182                   False                False     False   \n",
-       "6025198                    True                False     False   \n",
-       "\n",
-       "         rheumatoid_arthritis  systemic_lupus_erythematosus  \\\n",
-       "eid                                                           \n",
-       "5772854                 False                         False   \n",
-       "5772869                 False                         False   \n",
-       "5772881                 False                         False   \n",
-       "5772897                 False                         False   \n",
-       "5772905                 False                         False   \n",
-       "...                       ...                           ...   \n",
-       "6025147                 False                         False   \n",
-       "6025165                 False                         False   \n",
-       "6025173                 False                         False   \n",
-       "6025182                 False                         False   \n",
-       "6025198                 False                         False   \n",
-       "\n",
-       "         severe_mental_illness  erectile_dysfunction  MACE_event_time  \\\n",
-       "eid                                                                     \n",
-       "5772854                  False                 False        12.566735   \n",
-       "5772869                  False                 False        12.353183   \n",
-       "5772881                  False                 False        10.710472   \n",
-       "5772897                  False                 False        12.429843   \n",
-       "5772905                   True                 False        11.665982   \n",
-       "...                        ...                   ...              ...   \n",
-       "6025147                  False                 False        11.879535   \n",
-       "6025165                  False                 False        12.076660   \n",
-       "6025173                  False                 False        12.035592   \n",
-       "6025182                  False                 False        10.250513   \n",
-       "6025198                  False                 False        10.677618   \n",
-       "\n",
-       "         MACE_event  \n",
-       "eid                  \n",
-       "5772854           1  \n",
-       "5772869           0  \n",
-       "5772881           0  \n",
-       "5772897           0  \n",
-       "5772905           0  \n",
-       "...             ...  \n",
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-       "6025173           0  \n",
-       "6025182           0  \n",
-       "6025198           0  \n",
-       "\n",
-       "[17721 rows x 44 columns]"
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-       "<p>1 rows × 338386 columns</p>\n",
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-       "       1000018  1000020   1000037   1000043   1000079  1000084   1000092  \\\n",
-       "11.0  0.051065  0.14108  0.069313  0.138425  0.079897   0.0577  0.083881   \n",
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-     "text": [
-      "clinical_pgs\n",
-      "5\n",
-      "Iteration 1: norm_delta = 0.84122, step_size = 0.5000, log_lik = -303530.87696, newton_decrement = 9072.72533, seconds_since_start = 4.2\n",
-      "Iteration 2: norm_delta = 0.40846, step_size = 0.5000, log_lik = -296850.41665, newton_decrement = 2183.21770, seconds_since_start = 8.4\n",
-      "Iteration 3: norm_delta = 0.23223, step_size = 0.5000, log_lik = -295200.02498, newton_decrement = 634.71457, seconds_since_start = 12.7\n",
-      "Iteration 4: norm_delta = 0.10546, step_size = 0.6000, log_lik = -294662.27731, newton_decrement = 118.34724, seconds_since_start = 16.9\n",
-      "Iteration 5: norm_delta = 0.03261, step_size = 0.7200, log_lik = -294552.46665, newton_decrement = 10.56167, seconds_since_start = 21.1\n",
-      "Iteration 6: norm_delta = 0.00477, step_size = 0.8640, log_lik = -294542.06240, newton_decrement = 0.21709, seconds_since_start = 25.3\n",
-      "Iteration 7: norm_delta = 0.00001, step_size = 1.0000, log_lik = -294541.84512, newton_decrement = 0.00000, seconds_since_start = 29.5\n",
-      "Iteration 8: norm_delta = 0.00000, step_size = 1.0000, log_lik = -294541.84512, newton_decrement = 0.00000, seconds_since_start = 33.8\n",
-      "Convergence success after 8 iterations.\n",
-      "0.7392211080216978\n"
-     ]
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-       "      <th>PGS000011</th>\n",
-       "      <th>PGS000057</th>\n",
-       "      <th>PGS000058</th>\n",
-       "      <th>PGS000059</th>\n",
-       "      <th>age_at_recruitment</th>\n",
-       "      <th>ethnic_background_0.0</th>\n",
-       "      <th>ethnic_background_1.0</th>\n",
-       "      <th>ethnic_background_2.0</th>\n",
-       "      <th>ethnic_background_3.0</th>\n",
-       "      <th>ethnic_background_4.0</th>\n",
-       "      <th>...</th>\n",
-       "      <th>diabetes2</th>\n",
-       "      <th>chronic_kidney_disease</th>\n",
-       "      <th>atrial_fibrillation</th>\n",
-       "      <th>migraine</th>\n",
-       "      <th>rheumatoid_arthritis</th>\n",
-       "      <th>systemic_lupus_erythematosus</th>\n",
-       "      <th>severe_mental_illness</th>\n",
-       "      <th>erectile_dysfunction</th>\n",
-       "      <th>MACE_event_time</th>\n",
-       "      <th>MACE_event</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>eid</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>5773935</th>\n",
-       "      <td>-0.489797</td>\n",
-       "      <td>-0.678411</td>\n",
-       "      <td>-1.111549</td>\n",
-       "      <td>-0.775433</td>\n",
-       "      <td>-0.188183</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>12.821355</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5773944</th>\n",
-       "      <td>-0.585514</td>\n",
-       "      <td>-1.315263</td>\n",
-       "      <td>-1.437158</td>\n",
-       "      <td>-0.169347</td>\n",
-       "      <td>-1.052831</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>12.366872</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5773956</th>\n",
-       "      <td>0.832557</td>\n",
-       "      <td>-0.372722</td>\n",
-       "      <td>1.338028</td>\n",
-       "      <td>-0.036766</td>\n",
-       "      <td>1.047030</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>12.019165</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5773961</th>\n",
-       "      <td>0.189787</td>\n",
-       "      <td>0.729656</td>\n",
-       "      <td>0.476245</td>\n",
-       "      <td>-0.586254</td>\n",
-       "      <td>0.676466</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>12.709103</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5773972</th>\n",
-       "      <td>0.156004</td>\n",
-       "      <td>2.021840</td>\n",
-       "      <td>1.120140</td>\n",
-       "      <td>1.307987</td>\n",
-       "      <td>1.170551</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>10.420260</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6025150</th>\n",
-       "      <td>0.912677</td>\n",
-       "      <td>0.951929</td>\n",
-       "      <td>0.123660</td>\n",
-       "      <td>0.550825</td>\n",
-       "      <td>-1.670438</td>\n",
-       "      <td>1</td>\n",
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-       "      <td>0</td>\n",
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-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
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-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>13.253936</td>\n",
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-       "    <tr>\n",
-       "      <th>6025165</th>\n",
-       "      <td>1.216387</td>\n",
-       "      <td>-0.497695</td>\n",
-       "      <td>-0.977119</td>\n",
-       "      <td>-0.699301</td>\n",
-       "      <td>-1.423395</td>\n",
-       "      <td>1</td>\n",
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-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
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-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>12.076660</td>\n",
-       "      <td>0</td>\n",
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-       "    <tr>\n",
-       "      <th>6025173</th>\n",
-       "      <td>-1.547796</td>\n",
-       "      <td>-1.499176</td>\n",
-       "      <td>-1.129139</td>\n",
-       "      <td>-1.285481</td>\n",
-       "      <td>0.058860</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
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-       "      <td>0</td>\n",
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-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>12.035592</td>\n",
-       "      <td>0</td>\n",
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-       "    <tr>\n",
-       "      <th>6025182</th>\n",
-       "      <td>-0.180581</td>\n",
-       "      <td>-0.194404</td>\n",
-       "      <td>-0.673702</td>\n",
-       "      <td>-0.775433</td>\n",
-       "      <td>-0.064661</td>\n",
-       "      <td>1</td>\n",
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-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>10.250513</td>\n",
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-       "    <tr>\n",
-       "      <th>6025198</th>\n",
-       "      <td>-0.261885</td>\n",
-       "      <td>-0.907678</td>\n",
-       "      <td>1.048496</td>\n",
-       "      <td>-0.491331</td>\n",
-       "      <td>1.294072</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
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-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>10.677618</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>17786 rows × 44 columns</p>\n",
-       "</div>"
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-      "text/plain": [
-       "         PGS000011  PGS000057  PGS000058  PGS000059  age_at_recruitment  \\\n",
-       "eid                                                                       \n",
-       "5773935  -0.489797  -0.678411  -1.111549  -0.775433           -0.188183   \n",
-       "5773944  -0.585514  -1.315263  -1.437158  -0.169347           -1.052831   \n",
-       "5773956   0.832557  -0.372722   1.338028  -0.036766            1.047030   \n",
-       "5773961   0.189787   0.729656   0.476245  -0.586254            0.676466   \n",
-       "5773972   0.156004   2.021840   1.120140   1.307987            1.170551   \n",
-       "...            ...        ...        ...        ...                 ...   \n",
-       "6025150   0.912677   0.951929   0.123660   0.550825           -1.670438   \n",
-       "6025165   1.216387  -0.497695  -0.977119  -0.699301           -1.423395   \n",
-       "6025173  -1.547796  -1.499176  -1.129139  -1.285481            0.058860   \n",
-       "6025182  -0.180581  -0.194404  -0.673702  -0.775433           -0.064661   \n",
-       "6025198  -0.261885  -0.907678   1.048496  -0.491331            1.294072   \n",
-       "\n",
-       "         ethnic_background_0.0  ethnic_background_1.0  ethnic_background_2.0  \\\n",
-       "eid                                                                            \n",
-       "5773935                      1                      0                      0   \n",
-       "5773944                      1                      0                      0   \n",
-       "5773956                      1                      0                      0   \n",
-       "5773961                      1                      0                      0   \n",
-       "5773972                      1                      0                      0   \n",
-       "...                        ...                    ...                    ...   \n",
-       "6025150                      1                      0                      0   \n",
-       "6025165                      1                      0                      0   \n",
-       "6025173                      1                      0                      0   \n",
-       "6025182                      1                      0                      0   \n",
-       "6025198                      1                      0                      0   \n",
-       "\n",
-       "         ethnic_background_3.0  ethnic_background_4.0  ...  diabetes2  \\\n",
-       "eid                                                    ...              \n",
-       "5773935                      0                      0  ...      False   \n",
-       "5773944                      0                      0  ...      False   \n",
-       "5773956                      0                      0  ...      False   \n",
-       "5773961                      0                      0  ...      False   \n",
-       "5773972                      0                      0  ...      False   \n",
-       "...                        ...                    ...  ...        ...   \n",
-       "6025150                      0                      0  ...      False   \n",
-       "6025165                      0                      0  ...      False   \n",
-       "6025173                      0                      0  ...      False   \n",
-       "6025182                      0                      0  ...      False   \n",
-       "6025198                      0                      0  ...      False   \n",
-       "\n",
-       "         chronic_kidney_disease  atrial_fibrillation  migraine  \\\n",
-       "eid                                                              \n",
-       "5773935                   False                False     False   \n",
-       "5773944                   False                False     False   \n",
-       "5773956                   False                False     False   \n",
-       "5773961                   False                False     False   \n",
-       "5773972                   False                False     False   \n",
-       "...                         ...                  ...       ...   \n",
-       "6025150                   False                False     False   \n",
-       "6025165                   False                False     False   \n",
-       "6025173                   False                False     False   \n",
-       "6025182                   False                False     False   \n",
-       "6025198                    True                False     False   \n",
-       "\n",
-       "         rheumatoid_arthritis  systemic_lupus_erythematosus  \\\n",
-       "eid                                                           \n",
-       "5773935                 False                         False   \n",
-       "5773944                 False                         False   \n",
-       "5773956                 False                         False   \n",
-       "5773961                 False                         False   \n",
-       "5773972                 False                         False   \n",
-       "...                       ...                           ...   \n",
-       "6025150                 False                         False   \n",
-       "6025165                 False                         False   \n",
-       "6025173                 False                         False   \n",
-       "6025182                 False                         False   \n",
-       "6025198                 False                         False   \n",
-       "\n",
-       "         severe_mental_illness  erectile_dysfunction  MACE_event_time  \\\n",
-       "eid                                                                     \n",
-       "5773935                  False                 False        12.821355   \n",
-       "5773944                   True                 False        12.366872   \n",
-       "5773956                  False                 False        12.019165   \n",
-       "5773961                  False                 False        12.709103   \n",
-       "5773972                  False                 False        10.420260   \n",
-       "...                        ...                   ...              ...   \n",
-       "6025150                  False                 False        13.253936   \n",
-       "6025165                  False                 False        12.076660   \n",
-       "6025173                  False                 False        12.035592   \n",
-       "6025182                  False                 False        10.250513   \n",
-       "6025198                  False                 False        10.677618   \n",
-       "\n",
-       "         MACE_event  \n",
-       "eid                  \n",
-       "5773935           0  \n",
-       "5773944           0  \n",
-       "5773956           0  \n",
-       "5773961           0  \n",
-       "5773972           0  \n",
-       "...             ...  \n",
-       "6025150           0  \n",
-       "6025165           0  \n",
-       "6025173           0  \n",
-       "6025182           0  \n",
-       "6025198           0  \n",
-       "\n",
-       "[17786 rows x 44 columns]"
-      ]
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-     "output_type": "display_data"
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-       "<p>1 rows × 338474 columns</p>\n",
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-       "       1000018   1000020   1000037   1000043   1000079   1000084   1000092  \\\n",
-       "11.0  0.051869  0.143412  0.086845  0.137761  0.079809  0.057252  0.081141   \n",
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-       "       1000107   1000110   1000128  ...   5773788   5773796   5773814  \\\n",
-       "11.0  0.015235  0.015485  0.015103  ...  0.011322  0.011908  0.129816   \n",
-       "\n",
-       "      5773827   5773840   5773871   5773886   5773895   5773907   5773928  \n",
-       "11.0  0.10873  0.038682  0.108642  0.791478  0.011928  0.092871  0.027278  \n",
-       "\n",
-       "[1 rows x 338474 columns]"
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-     "text": [
-      "clinical_pgs\n",
-      "6\n",
-      "Iteration 1: norm_delta = 0.85899, step_size = 0.5000, log_lik = -302584.48904, newton_decrement = 9175.49386, seconds_since_start = 4.5\n",
-      "Iteration 2: norm_delta = 0.41476, step_size = 0.5000, log_lik = -295844.59451, newton_decrement = 2195.75284, seconds_since_start = 8.8\n",
-      "Iteration 3: norm_delta = 0.23540, step_size = 0.5000, log_lik = -294184.68819, newton_decrement = 638.65516, seconds_since_start = 13.4\n",
-      "Iteration 4: norm_delta = 0.10687, step_size = 0.6000, log_lik = -293643.58442, newton_decrement = 119.15072, seconds_since_start = 17.6\n",
-      "Iteration 5: norm_delta = 0.03305, step_size = 0.7200, log_lik = -293533.02514, newton_decrement = 10.63882, seconds_since_start = 21.8\n",
-      "Iteration 6: norm_delta = 0.00484, step_size = 0.8640, log_lik = -293522.54476, newton_decrement = 0.21875, seconds_since_start = 26.0\n",
-      "Iteration 7: norm_delta = 0.00001, step_size = 1.0000, log_lik = -293522.32582, newton_decrement = 0.00000, seconds_since_start = 30.1\n",
-      "Iteration 8: norm_delta = 0.00000, step_size = 1.0000, log_lik = -293522.32581, newton_decrement = 0.00000, seconds_since_start = 34.3\n",
-      "Convergence success after 8 iterations.\n",
-      "0.7402728130703884\n"
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-       "         PGS000011  PGS000057  PGS000058  PGS000059  age_at_recruitment  \\\n",
-       "eid                                                                       \n",
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-       "...            ...        ...        ...        ...                 ...   \n",
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-       "6025198  -0.261661  -0.907893   1.048292  -0.490531            1.293981   \n",
-       "\n",
-       "         ethnic_background_0.0  ethnic_background_1.0  ethnic_background_2.0  \\\n",
-       "eid                                                                            \n",
-       "5774024                      1                      0                      0   \n",
-       "5774038                      0                      0                      0   \n",
-       "5774052                      1                      0                      0   \n",
-       "5774063                      1                      0                      0   \n",
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-       "         ethnic_background_3.0  ethnic_background_4.0  ...  diabetes2  \\\n",
-       "eid                                                    ...              \n",
-       "5774024                      0                      0  ...      False   \n",
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-       "\n",
-       "         chronic_kidney_disease  atrial_fibrillation  migraine  \\\n",
-       "eid                                                              \n",
-       "5774024                   False                False     False   \n",
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-       "6025198                    True                False     False   \n",
-       "\n",
-       "         rheumatoid_arthritis  systemic_lupus_erythematosus  \\\n",
-       "eid                                                           \n",
-       "5774024                 False                         False   \n",
-       "5774038                 False                         False   \n",
-       "5774052                 False                         False   \n",
-       "5774063                 False                         False   \n",
-       "5774080                 False                         False   \n",
-       "...                       ...                           ...   \n",
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-       "6025198                 False                         False   \n",
-       "\n",
-       "         severe_mental_illness  erectile_dysfunction  MACE_event_time  \\\n",
-       "eid                                                                     \n",
-       "5774024                  False                 False        10.921287   \n",
-       "5774038                  False                 False        11.148528   \n",
-       "5774052                  False                 False        11.561944   \n",
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-       "5774080                  False                 False        10.866530   \n",
-       "...                        ...                   ...              ...   \n",
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-       "6025182                  False                 False        10.250513   \n",
-       "6025198                  False                 False        10.677618   \n",
-       "\n",
-       "         MACE_event  \n",
-       "eid                  \n",
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-       "5774038           0  \n",
-       "5774052           0  \n",
-       "5774063           0  \n",
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-       "[17779 rows x 44 columns]"
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-       "       1000018   1000037   1000043   1000079   1000084   1000110   1000128  \\\n",
-       "11.0  0.051752  0.066423  0.141857  0.080201  0.057029  0.015115  0.016137   \n",
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-       "       1000135   1000144   1000156  ...   5773928   5773935   5773944  \\\n",
-       "11.0  0.014387  0.047692  0.018374  ...  0.027321  0.021459  0.030926   \n",
-       "\n",
-       "      5773956   5773961   5773972   5773983   5773999   5774009   5774017  \n",
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-     "text": [
-      "clinical_pgs\n",
-      "7\n",
-      "Iteration 1: norm_delta = 0.87694, step_size = 0.5000, log_lik = -302470.77013, newton_decrement = 9088.93091, seconds_since_start = 4.2\n",
-      "Iteration 2: norm_delta = 0.41027, step_size = 0.5000, log_lik = -295788.78995, newton_decrement = 2181.67103, seconds_since_start = 8.6\n",
-      "Iteration 3: norm_delta = 0.23285, step_size = 0.5000, log_lik = -294139.35384, newton_decrement = 635.75866, seconds_since_start = 12.9\n",
-      "Iteration 4: norm_delta = 0.10593, step_size = 0.6000, log_lik = -293600.65171, newton_decrement = 118.79866, seconds_since_start = 17.1\n",
-      "Iteration 5: norm_delta = 0.03281, step_size = 0.7200, log_lik = -293490.41299, newton_decrement = 10.61637, seconds_since_start = 21.3\n",
-      "Iteration 6: norm_delta = 0.00480, step_size = 0.8640, log_lik = -293479.95472, newton_decrement = 0.21822, seconds_since_start = 25.5\n",
-      "Iteration 7: norm_delta = 0.00001, step_size = 1.0000, log_lik = -293479.73630, newton_decrement = 0.00000, seconds_since_start = 29.9\n",
-      "Iteration 8: norm_delta = 0.00000, step_size = 1.0000, log_lik = -293479.73630, newton_decrement = 0.00000, seconds_since_start = 34.5\n",
-      "Convergence success after 8 iterations.\n",
-      "0.739204341277305\n"
-     ]
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-       "         PGS000011  PGS000057  PGS000058  PGS000059  age_at_recruitment  \\\n",
-       "eid                                                                       \n",
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-       "...            ...        ...        ...        ...                 ...   \n",
-       "6025150   0.912296   0.951932   0.121856   0.551442           -1.671941   \n",
-       "6025165   1.215934  -0.497920  -0.979015  -0.698979           -1.424828   \n",
-       "6025173  -1.547596  -1.499559  -1.131047  -1.285297            0.057853   \n",
-       "6025182  -0.180704  -0.194581  -0.675573  -0.775129           -0.065703   \n",
-       "6025198  -0.261989  -0.907968   1.046769  -0.490959            1.293421   \n",
-       "\n",
-       "         ethnic_background_0.0  ethnic_background_1.0  ethnic_background_2.0  \\\n",
-       "eid                                                                            \n",
-       "5774203                      1                      0                      0   \n",
-       "5774211                      1                      0                      0   \n",
-       "5774245                      1                      0                      0   \n",
-       "5774267                      1                      0                      0   \n",
-       "5774274                      0                      1                      0   \n",
-       "...                        ...                    ...                    ...   \n",
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-       "         ethnic_background_3.0  ethnic_background_4.0  ...  diabetes2  \\\n",
-       "eid                                                    ...              \n",
-       "5774203                      0                      0  ...      False   \n",
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-       "...                        ...                    ...  ...        ...   \n",
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-       "6025182                      0                      0  ...      False   \n",
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-       "eid                                                              \n",
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-       "eid                                                           \n",
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-       "eid                                                                     \n",
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-       "       1000018   1000020   1000037   1000079   1000084   1000092   1000107  \\\n",
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-     "text": [
-      "clinical_pgs\n",
-      "8\n",
-      "Iteration 1: norm_delta = 0.88714, step_size = 0.5000, log_lik = -303600.50186, newton_decrement = 9129.51890, seconds_since_start = 4.5\n",
-      "Iteration 2: norm_delta = 0.41037, step_size = 0.5000, log_lik = -296884.25230, newton_decrement = 2191.76682, seconds_since_start = 9.1\n",
-      "Iteration 3: norm_delta = 0.23379, step_size = 0.5000, log_lik = -295227.28380, newton_decrement = 637.90573, seconds_since_start = 13.6\n",
-      "Iteration 4: norm_delta = 0.10647, step_size = 0.6000, log_lik = -294686.81198, newton_decrement = 118.99481, seconds_since_start = 18.1\n",
-      "Iteration 5: norm_delta = 0.03294, step_size = 0.7200, log_lik = -294576.40232, newton_decrement = 10.61255, seconds_since_start = 22.3\n",
-      "Iteration 6: norm_delta = 0.00481, step_size = 0.8640, log_lik = -294565.94862, newton_decrement = 0.21763, seconds_since_start = 26.5\n",
-      "Iteration 7: norm_delta = 0.00001, step_size = 1.0000, log_lik = -294565.73082, newton_decrement = 0.00000, seconds_since_start = 30.7\n",
-      "Iteration 8: norm_delta = 0.00000, step_size = 1.0000, log_lik = -294565.73082, newton_decrement = 0.00000, seconds_since_start = 35.1\n",
-      "Convergence success after 8 iterations.\n",
-      "0.7389123318005372\n"
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-       "         PGS000011  PGS000057  PGS000058  PGS000059  age_at_recruitment  \\\n",
-       "eid                                                                       \n",
-       "5774504  -1.195535  -0.270892  -0.728970  -1.040175            0.676477   \n",
-       "5774516   0.375675   0.085736   0.801015   0.973959           -1.177732   \n",
-       "5774531  -0.629394  -1.136989  -0.745352  -1.053770            0.923704   \n",
-       "5774550  -0.228892  -0.474680   0.014961  -0.130907           -0.683276   \n",
-       "5774565   0.169057  -0.423733  -0.008039  -0.566598            0.058407   \n",
-       "...            ...        ...        ...        ...                 ...   \n",
-       "6025150   0.912106   0.951832   0.122957   0.551491           -1.672187   \n",
-       "6025165   1.215656  -0.497755  -0.977525  -0.698828           -1.424959   \n",
-       "6025173  -1.547073  -1.499211  -1.129503  -1.285099            0.058407   \n",
-       "6025182  -0.180577  -0.194472  -0.674190  -0.774972           -0.065207   \n",
-       "6025198  -0.261839  -0.907728   1.047544  -0.490825            1.294546   \n",
-       "\n",
-       "         ethnic_background_0.0  ethnic_background_1.0  ethnic_background_2.0  \\\n",
-       "eid                                                                            \n",
-       "5774504                      1                      0                      0   \n",
-       "5774516                      1                      0                      0   \n",
-       "5774531                      1                      0                      0   \n",
-       "5774550                      1                      0                      0   \n",
-       "5774565                      1                      0                      0   \n",
-       "...                        ...                    ...                    ...   \n",
-       "6025150                      1                      0                      0   \n",
-       "6025165                      1                      0                      0   \n",
-       "6025173                      1                      0                      0   \n",
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-       "6025198                      1                      0                      0   \n",
-       "\n",
-       "         ethnic_background_3.0  ethnic_background_4.0  ...  diabetes2  \\\n",
-       "eid                                                    ...              \n",
-       "5774504                      0                      0  ...      False   \n",
-       "5774516                      0                      0  ...      False   \n",
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-       "6025198                      0                      0  ...      False   \n",
-       "\n",
-       "         chronic_kidney_disease  atrial_fibrillation  migraine  \\\n",
-       "eid                                                              \n",
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-       "...                         ...                  ...       ...   \n",
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-       "6025182                   False                False     False   \n",
-       "6025198                    True                False     False   \n",
-       "\n",
-       "         rheumatoid_arthritis  systemic_lupus_erythematosus  \\\n",
-       "eid                                                           \n",
-       "5774504                  True                         False   \n",
-       "5774516                 False                         False   \n",
-       "5774531                 False                         False   \n",
-       "5774550                 False                         False   \n",
-       "5774565                 False                         False   \n",
-       "...                       ...                           ...   \n",
-       "6025150                 False                         False   \n",
-       "6025165                 False                         False   \n",
-       "6025173                 False                         False   \n",
-       "6025182                 False                         False   \n",
-       "6025198                 False                         False   \n",
-       "\n",
-       "         severe_mental_illness  erectile_dysfunction  MACE_event_time  \\\n",
-       "eid                                                                     \n",
-       "5774504                  False                 False        11.534565   \n",
-       "5774516                  False                 False        12.725530   \n",
-       "5774531                  False                 False        10.305270   \n",
-       "5774550                  False                 False        10.524298   \n",
-       "5774565                  False                 False        11.534565   \n",
-       "...                        ...                   ...              ...   \n",
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-       "6025165                  False                 False        12.076660   \n",
-       "6025173                  False                 False        12.035592   \n",
-       "6025182                  False                 False        10.250513   \n",
-       "6025198                  False                 False        10.677618   \n",
-       "\n",
-       "         MACE_event  \n",
-       "eid                  \n",
-       "5774504           0  \n",
-       "5774516           0  \n",
-       "5774531           0  \n",
-       "5774550           0  \n",
-       "5774565           0  \n",
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-       "6025198           0  \n",
-       "\n",
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-       "      1000018   1000020   1000037   1000043   1000079   1000084   1000092  \\\n",
-       "11.0  0.05061  0.143079  0.060355  0.141412  0.080694  0.058133  0.082066   \n",
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-     "text": [
-      "clinical_pgs\n",
-      "9\n",
-      "Iteration 1: norm_delta = 0.84912, step_size = 0.5000, log_lik = -302369.03502, newton_decrement = 9055.19678, seconds_since_start = 4.5\n",
-      "Iteration 2: norm_delta = 0.40874, step_size = 0.5000, log_lik = -295716.00432, newton_decrement = 2165.35354, seconds_since_start = 9.1\n",
-      "Iteration 3: norm_delta = 0.23041, step_size = 0.5000, log_lik = -294079.00524, newton_decrement = 630.23031, seconds_since_start = 13.8\n",
-      "Iteration 4: norm_delta = 0.10411, step_size = 0.6000, log_lik = -293545.03016, newton_decrement = 117.61004, seconds_since_start = 18.3\n",
-      "Iteration 5: norm_delta = 0.03214, step_size = 0.7200, log_lik = -293435.89933, newton_decrement = 10.50382, seconds_since_start = 22.7\n",
-      "Iteration 6: norm_delta = 0.00471, step_size = 0.8640, log_lik = -293425.55177, newton_decrement = 0.21610, seconds_since_start = 27.1\n",
-      "Iteration 7: norm_delta = 0.00001, step_size = 1.0000, log_lik = -293425.33547, newton_decrement = 0.00000, seconds_since_start = 31.8\n",
-      "Iteration 8: norm_delta = 0.00000, step_size = 1.0000, log_lik = -293425.33547, newton_decrement = 0.00000, seconds_since_start = 36.4\n",
-      "Convergence success after 8 iterations.\n",
-      "0.7386060269302596\n"
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-       "      <td>-1.285353</td>\n",
-       "      <td>0.057110</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
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-       "      <td>False</td>\n",
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-       "      <td>False</td>\n",
-       "      <td>12.035592</td>\n",
-       "      <td>0</td>\n",
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-       "    <tr>\n",
-       "      <th>6025182</th>\n",
-       "      <td>-0.180224</td>\n",
-       "      <td>-0.194594</td>\n",
-       "      <td>-0.674892</td>\n",
-       "      <td>-0.775175</td>\n",
-       "      <td>-0.066466</td>\n",
-       "      <td>1</td>\n",
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-       "      <td>...</td>\n",
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-       "      <td>10.250513</td>\n",
-       "      <td>0</td>\n",
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-       "    <tr>\n",
-       "      <th>6025198</th>\n",
-       "      <td>-0.261494</td>\n",
-       "      <td>-0.908259</td>\n",
-       "      <td>1.047948</td>\n",
-       "      <td>-0.491000</td>\n",
-       "      <td>1.292867</td>\n",
-       "      <td>1</td>\n",
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-       "      <td>10.677618</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>17778 rows × 44 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         PGS000011  PGS000057  PGS000058  PGS000059  age_at_recruitment  \\\n",
-       "eid                                                                       \n",
-       "5774222  -0.643001  -1.418119  -0.197678   0.479207           -0.684344   \n",
-       "5774236   0.795482   1.436640   1.805986  -0.282605            1.045715   \n",
-       "5774245  -1.664676  -0.016178  -1.093623  -0.067969           -0.931495   \n",
-       "5774267  -0.866094  -0.501750   0.377235   0.227945           -1.178647   \n",
-       "5774274  -1.923866  -1.127756   1.210451   0.151347           -1.178647   \n",
-       "...            ...        ...        ...        ...                 ...   \n",
-       "6025150   0.912574   0.952367   0.122767   0.551421           -1.672949   \n",
-       "6025165   1.216156  -0.498052  -0.978422  -0.699024           -1.425798   \n",
-       "6025173  -1.546862  -1.500081  -1.130497  -1.285353            0.057110   \n",
-       "6025182  -0.180224  -0.194594  -0.674892  -0.775175           -0.066466   \n",
-       "6025198  -0.261494  -0.908259   1.047948  -0.491000            1.292867   \n",
-       "\n",
-       "         ethnic_background_0.0  ethnic_background_1.0  ethnic_background_2.0  \\\n",
-       "eid                                                                            \n",
-       "5774222                      1                      0                      0   \n",
-       "5774236                      1                      0                      0   \n",
-       "5774245                      1                      0                      0   \n",
-       "5774267                      1                      0                      0   \n",
-       "5774274                      0                      1                      0   \n",
-       "...                        ...                    ...                    ...   \n",
-       "6025150                      1                      0                      0   \n",
-       "6025165                      1                      0                      0   \n",
-       "6025173                      1                      0                      0   \n",
-       "6025182                      1                      0                      0   \n",
-       "6025198                      1                      0                      0   \n",
-       "\n",
-       "         ethnic_background_3.0  ethnic_background_4.0  ...  diabetes2  \\\n",
-       "eid                                                    ...              \n",
-       "5774222                      0                      0  ...      False   \n",
-       "5774236                      0                      0  ...      False   \n",
-       "5774245                      0                      0  ...      False   \n",
-       "5774267                      0                      0  ...      False   \n",
-       "5774274                      0                      0  ...      False   \n",
-       "...                        ...                    ...  ...        ...   \n",
-       "6025150                      0                      0  ...      False   \n",
-       "6025165                      0                      0  ...      False   \n",
-       "6025173                      0                      0  ...      False   \n",
-       "6025182                      0                      0  ...      False   \n",
-       "6025198                      0                      0  ...      False   \n",
-       "\n",
-       "         chronic_kidney_disease  atrial_fibrillation  migraine  \\\n",
-       "eid                                                              \n",
-       "5774222                   False                False     False   \n",
-       "5774236                   False                False     False   \n",
-       "5774245                   False                False     False   \n",
-       "5774267                   False                False     False   \n",
-       "5774274                   False                False     False   \n",
-       "...                         ...                  ...       ...   \n",
-       "6025150                   False                False     False   \n",
-       "6025165                   False                False     False   \n",
-       "6025173                   False                False     False   \n",
-       "6025182                   False                False     False   \n",
-       "6025198                    True                False     False   \n",
-       "\n",
-       "         rheumatoid_arthritis  systemic_lupus_erythematosus  \\\n",
-       "eid                                                           \n",
-       "5774222                 False                         False   \n",
-       "5774236                 False                         False   \n",
-       "5774245                 False                         False   \n",
-       "5774267                 False                         False   \n",
-       "5774274                  True                         False   \n",
-       "...                       ...                           ...   \n",
-       "6025150                 False                         False   \n",
-       "6025165                 False                         False   \n",
-       "6025173                 False                         False   \n",
-       "6025182                 False                         False   \n",
-       "6025198                 False                         False   \n",
-       "\n",
-       "         severe_mental_illness  erectile_dysfunction  MACE_event_time  \\\n",
-       "eid                                                                     \n",
-       "5774222                  False                 False        12.851472   \n",
-       "5774236                  False                 False        11.518138   \n",
-       "5774245                  False                 False        12.640657   \n",
-       "5774267                  False                 False        12.859685   \n",
-       "5774274                  False                 False        10.830938   \n",
-       "...                        ...                   ...              ...   \n",
-       "6025150                  False                 False        13.253936   \n",
-       "6025165                  False                 False        12.076660   \n",
-       "6025173                  False                 False        12.035592   \n",
-       "6025182                  False                 False        10.250513   \n",
-       "6025198                  False                 False        10.677618   \n",
-       "\n",
-       "         MACE_event  \n",
-       "eid                  \n",
-       "5774222           0  \n",
-       "5774236           0  \n",
-       "5774245           0  \n",
-       "5774267           0  \n",
-       "5774274           0  \n",
-       "...             ...  \n",
-       "6025150           0  \n",
-       "6025165           0  \n",
-       "6025173           0  \n",
-       "6025182           0  \n",
-       "6025198           0  \n",
-       "\n",
-       "[17778 rows x 44 columns]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
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-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>1000018</th>\n",
-       "      <th>1000020</th>\n",
-       "      <th>1000037</th>\n",
-       "      <th>1000043</th>\n",
-       "      <th>1000079</th>\n",
-       "      <th>1000084</th>\n",
-       "      <th>1000092</th>\n",
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-       "      <th>1000110</th>\n",
-       "      <th>1000128</th>\n",
-       "      <th>...</th>\n",
-       "      <th>5774108</th>\n",
-       "      <th>5774114</th>\n",
-       "      <th>5774127</th>\n",
-       "      <th>5774140</th>\n",
-       "      <th>5774153</th>\n",
-       "      <th>5774162</th>\n",
-       "      <th>5774171</th>\n",
-       "      <th>5774186</th>\n",
-       "      <th>5774203</th>\n",
-       "      <th>5774211</th>\n",
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-       "      <th>11.0</th>\n",
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-       "      <td>0.14068</td>\n",
-       "      <td>0.05024</td>\n",
-       "      <td>0.137169</td>\n",
-       "      <td>0.079798</td>\n",
-       "      <td>0.056645</td>\n",
-       "      <td>0.080797</td>\n",
-       "      <td>0.015229</td>\n",
-       "      <td>0.015166</td>\n",
-       "      <td>0.012089</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.093826</td>\n",
-       "      <td>0.11241</td>\n",
-       "      <td>0.2979</td>\n",
-       "      <td>0.033315</td>\n",
-       "      <td>0.030649</td>\n",
-       "      <td>0.038146</td>\n",
-       "      <td>0.068191</td>\n",
-       "      <td>0.048319</td>\n",
-       "      <td>0.07966</td>\n",
-       "      <td>0.042945</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1 rows × 338451 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       1000018  1000020  1000037   1000043   1000079   1000084   1000092  \\\n",
-       "11.0  0.051245  0.14068  0.05024  0.137169  0.079798  0.056645  0.080797   \n",
-       "\n",
-       "       1000107   1000110   1000128  ...   5774108  5774114  5774127   5774140  \\\n",
-       "11.0  0.015229  0.015166  0.012089  ...  0.093826  0.11241   0.2979  0.033315   \n",
-       "\n",
-       "       5774153   5774162   5774171   5774186  5774203   5774211  \n",
-       "11.0  0.030649  0.038146  0.068191  0.048319  0.07966  0.042945  \n",
-       "\n",
-       "[1 rows x 338451 columns]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "predictions_dict = {}\n",
-    "for group in tqdm(groups):\n",
-    "    predictions_dict[group] = {}\n",
-    "    for partition in partitions:\n",
-    "        tqdm.write(group)\n",
-    "        tqdm.write(partition)\n",
-    "        predictions_dict[group][partition] = fit_predict_coxph(data.copy(), group, partition, time, event, f\"{dataset_path}/partition_{partition}/cox_{group}.p\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for group in groups:\n",
-    "    pred_dfs = [predictions_dict[group][partition] for partition in partitions]\n",
-    "    predictions_dict[group][\"complete\"] = pd.concat(pred_dfs).sort_values(\"eid\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from functools import reduce\n",
-    "prediction_dfs = [predictions_dict[group][\"complete\"] for group in groups]\n",
-    "predictions = reduce(lambda  left,right: pd.merge(left,right,on=['eid', \"partition\", \"split\"], how='left'), prediction_dfs).sort_values([\"eid\", \"partition\", \"split\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [
-    {
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-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>eid</th>\n",
-       "      <th>score_COX_clinical</th>\n",
-       "      <th>0_1_Ft_COX_clinical</th>\n",
-       "      <th>0_2_Ft_COX_clinical</th>\n",
-       "      <th>0_3_Ft_COX_clinical</th>\n",
-       "      <th>0_4_Ft_COX_clinical</th>\n",
-       "      <th>0_5_Ft_COX_clinical</th>\n",
-       "      <th>0_6_Ft_COX_clinical</th>\n",
-       "      <th>0_7_Ft_COX_clinical</th>\n",
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-       "      <th>...</th>\n",
-       "      <th>0_17_Ft_COX_clinical_pgs</th>\n",
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-       "      <th>0_23_Ft_COX_clinical_pgs</th>\n",
-       "      <th>0_24_Ft_COX_clinical_pgs</th>\n",
-       "      <th>0_25_Ft_COX_clinical_pgs</th>\n",
-       "      <th>0_26_Ft_COX_clinical_pgs</th>\n",
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-       "      <td>0.072135</td>\n",
-       "      <td>0.072135</td>\n",
-       "      <td>0.072135</td>\n",
-       "      <td>0.072135</td>\n",
-       "      <td>0.072135</td>\n",
-       "      <td>0.072135</td>\n",
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-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>1000018</td>\n",
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-       "      <td>0.007781</td>\n",
-       "      <td>0.012503</td>\n",
-       "      <td>0.017271</td>\n",
-       "      <td>0.022374</td>\n",
-       "      <td>0.027922</td>\n",
-       "      <td>0.033559</td>\n",
-       "      <td>0.039247</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.070567</td>\n",
-       "      <td>0.070567</td>\n",
-       "      <td>0.070567</td>\n",
-       "      <td>0.070567</td>\n",
-       "      <td>0.070567</td>\n",
-       "      <td>0.070567</td>\n",
-       "      <td>0.070567</td>\n",
-       "      <td>0.070567</td>\n",
-       "      <td>0.070567</td>\n",
-       "      <td>0.070567</td>\n",
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-       "      <td>1000018</td>\n",
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-       "      <td>0.003746</td>\n",
-       "      <td>0.008057</td>\n",
-       "      <td>0.012858</td>\n",
-       "      <td>0.017804</td>\n",
-       "      <td>0.023122</td>\n",
-       "      <td>0.028774</td>\n",
-       "      <td>0.034591</td>\n",
-       "      <td>0.040414</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.072634</td>\n",
-       "      <td>0.072634</td>\n",
-       "      <td>0.072634</td>\n",
-       "      <td>0.072634</td>\n",
-       "      <td>0.072634</td>\n",
-       "      <td>0.072634</td>\n",
-       "      <td>0.072634</td>\n",
-       "      <td>0.072634</td>\n",
-       "      <td>0.072634</td>\n",
-       "      <td>0.072634</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1000018</td>\n",
-       "      <td>0.057880</td>\n",
-       "      <td>0.003672</td>\n",
-       "      <td>0.007944</td>\n",
-       "      <td>0.012735</td>\n",
-       "      <td>0.017621</td>\n",
-       "      <td>0.022866</td>\n",
-       "      <td>0.028458</td>\n",
-       "      <td>0.034155</td>\n",
-       "      <td>0.039957</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.071070</td>\n",
-       "      <td>0.071070</td>\n",
-       "      <td>0.071070</td>\n",
-       "      <td>0.071070</td>\n",
-       "      <td>0.071070</td>\n",
-       "      <td>0.071070</td>\n",
-       "      <td>0.071070</td>\n",
-       "      <td>0.071070</td>\n",
-       "      <td>0.071070</td>\n",
-       "      <td>0.071070</td>\n",
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-       "      <th>3</th>\n",
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-       "      <td>0.057609</td>\n",
-       "      <td>0.003662</td>\n",
-       "      <td>0.007943</td>\n",
-       "      <td>0.012691</td>\n",
-       "      <td>0.017556</td>\n",
-       "      <td>0.022699</td>\n",
-       "      <td>0.028280</td>\n",
-       "      <td>0.034041</td>\n",
-       "      <td>0.039812</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.070298</td>\n",
-       "      <td>0.070298</td>\n",
-       "      <td>0.070298</td>\n",
-       "      <td>0.070298</td>\n",
-       "      <td>0.070298</td>\n",
-       "      <td>0.070298</td>\n",
-       "      <td>0.070298</td>\n",
-       "      <td>0.070298</td>\n",
-       "      <td>0.070298</td>\n",
-       "      <td>0.070298</td>\n",
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-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3957371</th>\n",
-       "      <td>6025198</td>\n",
-       "      <td>0.301366</td>\n",
-       "      <td>0.021968</td>\n",
-       "      <td>0.047153</td>\n",
-       "      <td>0.074526</td>\n",
-       "      <td>0.101769</td>\n",
-       "      <td>0.130468</td>\n",
-       "      <td>0.160208</td>\n",
-       "      <td>0.189207</td>\n",
-       "      <td>0.217862</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.402898</td>\n",
-       "      <td>0.402898</td>\n",
-       "      <td>0.402898</td>\n",
-       "      <td>0.402898</td>\n",
-       "      <td>0.402898</td>\n",
-       "      <td>0.402898</td>\n",
-       "      <td>0.402898</td>\n",
-       "      <td>0.402898</td>\n",
-       "      <td>0.402898</td>\n",
-       "      <td>0.402898</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3957374</th>\n",
-       "      <td>6025198</td>\n",
-       "      <td>0.307027</td>\n",
-       "      <td>0.022363</td>\n",
-       "      <td>0.047995</td>\n",
-       "      <td>0.075923</td>\n",
-       "      <td>0.103533</td>\n",
-       "      <td>0.132864</td>\n",
-       "      <td>0.163087</td>\n",
-       "      <td>0.192882</td>\n",
-       "      <td>0.222204</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.400709</td>\n",
-       "      <td>0.400709</td>\n",
-       "      <td>0.400709</td>\n",
-       "      <td>0.400709</td>\n",
-       "      <td>0.400709</td>\n",
-       "      <td>0.400709</td>\n",
-       "      <td>0.400709</td>\n",
-       "      <td>0.400709</td>\n",
-       "      <td>0.400709</td>\n",
-       "      <td>0.400709</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3957375</th>\n",
-       "      <td>6025198</td>\n",
-       "      <td>0.301556</td>\n",
-       "      <td>0.022088</td>\n",
-       "      <td>0.047178</td>\n",
-       "      <td>0.074604</td>\n",
-       "      <td>0.101969</td>\n",
-       "      <td>0.130606</td>\n",
-       "      <td>0.160212</td>\n",
-       "      <td>0.189818</td>\n",
-       "      <td>0.218427</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.401917</td>\n",
-       "      <td>0.401917</td>\n",
-       "      <td>0.401917</td>\n",
-       "      <td>0.401917</td>\n",
-       "      <td>0.401917</td>\n",
-       "      <td>0.401917</td>\n",
-       "      <td>0.401917</td>\n",
-       "      <td>0.401917</td>\n",
-       "      <td>0.401917</td>\n",
-       "      <td>0.401917</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3957377</th>\n",
-       "      <td>6025198</td>\n",
-       "      <td>0.304523</td>\n",
-       "      <td>0.022163</td>\n",
-       "      <td>0.047792</td>\n",
-       "      <td>0.075470</td>\n",
-       "      <td>0.103378</td>\n",
-       "      <td>0.132394</td>\n",
-       "      <td>0.161997</td>\n",
-       "      <td>0.191878</td>\n",
-       "      <td>0.220810</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.405716</td>\n",
-       "      <td>0.405716</td>\n",
-       "      <td>0.405716</td>\n",
-       "      <td>0.405716</td>\n",
-       "      <td>0.405716</td>\n",
-       "      <td>0.405716</td>\n",
-       "      <td>0.405716</td>\n",
-       "      <td>0.405716</td>\n",
-       "      <td>0.405716</td>\n",
-       "      <td>0.405716</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3957372</th>\n",
-       "      <td>6025198</td>\n",
-       "      <td>0.300658</td>\n",
-       "      <td>0.022115</td>\n",
-       "      <td>0.047290</td>\n",
-       "      <td>0.074274</td>\n",
-       "      <td>0.101894</td>\n",
-       "      <td>0.130143</td>\n",
-       "      <td>0.159139</td>\n",
-       "      <td>0.188676</td>\n",
-       "      <td>0.217285</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.396430</td>\n",
-       "      <td>0.396430</td>\n",
-       "      <td>0.396430</td>\n",
-       "      <td>0.396430</td>\n",
-       "      <td>0.396430</td>\n",
-       "      <td>0.396430</td>\n",
-       "      <td>0.396430</td>\n",
-       "      <td>0.396430</td>\n",
-       "      <td>0.396430</td>\n",
-       "      <td>0.396430</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>3957380 rows × 57 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "             eid  score_COX_clinical  0_1_Ft_COX_clinical  \\\n",
-       "0        1000018            0.058397             0.003733   \n",
-       "8        1000018            0.056674             0.003620   \n",
-       "1        1000018            0.058533             0.003746   \n",
-       "4        1000018            0.057880             0.003672   \n",
-       "3        1000018            0.057609             0.003662   \n",
-       "...          ...                 ...                  ...   \n",
-       "3957371  6025198            0.301366             0.021968   \n",
-       "3957374  6025198            0.307027             0.022363   \n",
-       "3957375  6025198            0.301556             0.022088   \n",
-       "3957377  6025198            0.304523             0.022163   \n",
-       "3957372  6025198            0.300658             0.022115   \n",
-       "\n",
-       "         0_2_Ft_COX_clinical  0_3_Ft_COX_clinical  0_4_Ft_COX_clinical  \\\n",
-       "0                   0.008150             0.013001             0.017885   \n",
-       "8                   0.007781             0.012503             0.017271   \n",
-       "1                   0.008057             0.012858             0.017804   \n",
-       "4                   0.007944             0.012735             0.017621   \n",
-       "3                   0.007943             0.012691             0.017556   \n",
-       "...                      ...                  ...                  ...   \n",
-       "3957371             0.047153             0.074526             0.101769   \n",
-       "3957374             0.047995             0.075923             0.103533   \n",
-       "3957375             0.047178             0.074604             0.101969   \n",
-       "3957377             0.047792             0.075470             0.103378   \n",
-       "3957372             0.047290             0.074274             0.101894   \n",
-       "\n",
-       "         0_5_Ft_COX_clinical  0_6_Ft_COX_clinical  0_7_Ft_COX_clinical  \\\n",
-       "0                   0.023166             0.028781             0.034546   \n",
-       "8                   0.022374             0.027922             0.033559   \n",
-       "1                   0.023122             0.028774             0.034591   \n",
-       "4                   0.022866             0.028458             0.034155   \n",
-       "3                   0.022699             0.028280             0.034041   \n",
-       "...                      ...                  ...                  ...   \n",
-       "3957371             0.130468             0.160208             0.189207   \n",
-       "3957374             0.132864             0.163087             0.192882   \n",
-       "3957375             0.130606             0.160212             0.189818   \n",
-       "3957377             0.132394             0.161997             0.191878   \n",
-       "3957372             0.130143             0.159139             0.188676   \n",
-       "\n",
-       "         0_8_Ft_COX_clinical  ...  0_17_Ft_COX_clinical_pgs  \\\n",
-       "0                   0.040420  ...                  0.072135   \n",
-       "8                   0.039247  ...                  0.070567   \n",
-       "1                   0.040414  ...                  0.072634   \n",
-       "4                   0.039957  ...                  0.071070   \n",
-       "3                   0.039812  ...                  0.070298   \n",
-       "...                      ...  ...                       ...   \n",
-       "3957371             0.217862  ...                  0.402898   \n",
-       "3957374             0.222204  ...                  0.400709   \n",
-       "3957375             0.218427  ...                  0.401917   \n",
-       "3957377             0.220810  ...                  0.405716   \n",
-       "3957372             0.217285  ...                  0.396430   \n",
-       "\n",
-       "         0_18_Ft_COX_clinical_pgs  0_19_Ft_COX_clinical_pgs  \\\n",
-       "0                        0.072135                  0.072135   \n",
-       "8                        0.070567                  0.070567   \n",
-       "1                        0.072634                  0.072634   \n",
-       "4                        0.071070                  0.071070   \n",
-       "3                        0.070298                  0.070298   \n",
-       "...                           ...                       ...   \n",
-       "3957371                  0.402898                  0.402898   \n",
-       "3957374                  0.400709                  0.400709   \n",
-       "3957375                  0.401917                  0.401917   \n",
-       "3957377                  0.405716                  0.405716   \n",
-       "3957372                  0.396430                  0.396430   \n",
-       "\n",
-       "         0_20_Ft_COX_clinical_pgs  0_21_Ft_COX_clinical_pgs  \\\n",
-       "0                        0.072135                  0.072135   \n",
-       "8                        0.070567                  0.070567   \n",
-       "1                        0.072634                  0.072634   \n",
-       "4                        0.071070                  0.071070   \n",
-       "3                        0.070298                  0.070298   \n",
-       "...                           ...                       ...   \n",
-       "3957371                  0.402898                  0.402898   \n",
-       "3957374                  0.400709                  0.400709   \n",
-       "3957375                  0.401917                  0.401917   \n",
-       "3957377                  0.405716                  0.405716   \n",
-       "3957372                  0.396430                  0.396430   \n",
-       "\n",
-       "         0_22_Ft_COX_clinical_pgs  0_23_Ft_COX_clinical_pgs  \\\n",
-       "0                        0.072135                  0.072135   \n",
-       "8                        0.070567                  0.070567   \n",
-       "1                        0.072634                  0.072634   \n",
-       "4                        0.071070                  0.071070   \n",
-       "3                        0.070298                  0.070298   \n",
-       "...                           ...                       ...   \n",
-       "3957371                  0.402898                  0.402898   \n",
-       "3957374                  0.400709                  0.400709   \n",
-       "3957375                  0.401917                  0.401917   \n",
-       "3957377                  0.405716                  0.405716   \n",
-       "3957372                  0.396430                  0.396430   \n",
-       "\n",
-       "         0_24_Ft_COX_clinical_pgs  0_25_Ft_COX_clinical_pgs  \\\n",
-       "0                        0.072135                  0.072135   \n",
-       "8                        0.070567                  0.070567   \n",
-       "1                        0.072634                  0.072634   \n",
-       "4                        0.071070                  0.071070   \n",
-       "3                        0.070298                  0.070298   \n",
-       "...                           ...                       ...   \n",
-       "3957371                  0.402898                  0.402898   \n",
-       "3957374                  0.400709                  0.400709   \n",
-       "3957375                  0.401917                  0.401917   \n",
-       "3957377                  0.405716                  0.405716   \n",
-       "3957372                  0.396430                  0.396430   \n",
-       "\n",
-       "         0_26_Ft_COX_clinical_pgs  \n",
-       "0                        0.072135  \n",
-       "8                        0.070567  \n",
-       "1                        0.072634  \n",
-       "4                        0.071070  \n",
-       "3                        0.070298  \n",
-       "...                           ...  \n",
-       "3957371                  0.402898  \n",
-       "3957374                  0.400709  \n",
-       "3957375                  0.401917  \n",
-       "3957377                  0.405716  \n",
-       "3957372                  0.396430  \n",
-       "\n",
-       "[3957380 rows x 57 columns]"
-      ]
-     },
-     "execution_count": 41,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "predictions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:>"
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "predictions.query(\"partition=='4'\").query(\"split=='valid'\").score_COX_clinical.hist(bins=100)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "AttributeError",
-     "evalue": "'DataFrame' object has no attribute 'score_COX_MACE_clinical'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-17-aaf6d021f7c8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpredictions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mquery\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"partition=='0'\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mquery\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"split=='valid'\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscore_COX_MACE_clinical\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbins\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m/data/analysis/ag-reils/ag-reils-shared/deps/miniconda3/envs/pl1.x/lib/python3.7/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   5137\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5138\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5139\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5140\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5141\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'score_COX_MACE_clinical'"
-     ]
-    }
-   ],
-   "source": [
-    "predictions.query(\"partition=='0'\").query(\"split=='valid'\").score_COX_MACE_clinical.hist(bins=100)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "predictions.to_csv(f\"{data_path}/3_datasets_post/{dataset_name}/predictions_coxph.csv\", index=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 85,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "             eid  score_COX_clinical  0_1_Ft_COX_clinical  \\\n",
-       "1978689  6025198            0.302564             0.021615   \n",
-       "1978685  6025198            0.300681             0.021327   \n",
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-       "1978687  6025198            0.308601             0.022987   \n",
-       "1978686  6025198            0.314298             0.023260   \n",
-       "\n",
-       "         0_2_Ft_COX_clinical  0_3_Ft_COX_clinical  0_4_Ft_COX_clinical  \\\n",
-       "1978689             0.046786             0.075147             0.102025   \n",
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-       "1978688             0.047657             0.075360             0.103045   \n",
-       "1978687             0.048860             0.076584             0.104698   \n",
-       "1978686             0.050451             0.079213             0.107872   \n",
-       "\n",
-       "         0_5_Ft_COX_clinical  0_6_Ft_COX_clinical  0_7_Ft_COX_clinical  \\\n",
-       "1978689             0.130841             0.160260             0.189726   \n",
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-       "1978688             0.132320             0.162574             0.192677   \n",
-       "1978687             0.133883             0.163955             0.193831   \n",
-       "1978686             0.137382             0.168453             0.199037   \n",
-       "\n",
-       "         0_8_Ft_COX_clinical  ...  0_17_Ft_COX_clinical_pgs  \\\n",
-       "1978689             0.218453  ...                  0.396857   \n",
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-       "\n",
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-       "1978689                  0.396857                  0.396857   \n",
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-       "1978687                  0.412981                  0.412981   \n",
-       "1978686                  0.412985                  0.412985   \n",
-       "\n",
-       "         0_20_Ft_COX_clinical_pgs  0_21_Ft_COX_clinical_pgs  \\\n",
-       "1978689                  0.396857                  0.396857   \n",
-       "1978685                  0.397130                  0.397130   \n",
-       "1978688                  0.400358                  0.400358   \n",
-       "1978687                  0.412981                  0.412981   \n",
-       "1978686                  0.412985                  0.412985   \n",
-       "\n",
-       "         0_22_Ft_COX_clinical_pgs  0_23_Ft_COX_clinical_pgs  \\\n",
-       "1978689                  0.396857                  0.396857   \n",
-       "1978685                  0.397130                  0.397130   \n",
-       "1978688                  0.400358                  0.400358   \n",
-       "1978687                  0.412981                  0.412981   \n",
-       "1978686                  0.412985                  0.412985   \n",
-       "\n",
-       "         0_24_Ft_COX_clinical_pgs  0_25_Ft_COX_clinical_pgs  \\\n",
-       "1978689                  0.396857                  0.396857   \n",
-       "1978685                  0.397130                  0.397130   \n",
-       "1978688                  0.400358                  0.400358   \n",
-       "1978687                  0.412981                  0.412981   \n",
-       "1978686                  0.412985                  0.412985   \n",
-       "\n",
-       "         0_26_Ft_COX_clinical_pgs  \n",
-       "1978689                  0.396857  \n",
-       "1978685                  0.397130  \n",
-       "1978688                  0.400358  \n",
-       "1978687                  0.412981  \n",
-       "1978686                  0.412985  \n",
-       "\n",
-       "[5 rows x 57 columns]"
-      ]
-     },
-     "execution_count": 85,
-     "metadata": {},
-     "output_type": "execute_result"
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-   ],
-   "source": [
-    "predictions.tail()"
-   ]
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-   "execution_count": 87,
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-   "outputs": [
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-       "             eid  score_COX_clinical partition  split\n",
-       "0        1000018            0.058907         0  train\n",
-       "2        1000018            0.058406         1   test\n",
-       "3        1000018            0.058947         2  train\n",
-       "4        1000018            0.057233         3  train\n",
-       "1        1000018            0.058583         4  train\n",
-       "...          ...                 ...       ...    ...\n",
-       "1978689  6025198            0.302564         0  valid\n",
-       "1978685  6025198            0.300681         1   test\n",
-       "1978688  6025198            0.307658         2  valid\n",
-       "1978687  6025198            0.308601         3  valid\n",
-       "1978686  6025198            0.314298         4  valid\n",
-       "\n",
-       "[1978690 rows x 4 columns]"
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-     },
-     "execution_count": 87,
-     "metadata": {},
-     "output_type": "execute_result"
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-    "predictions[[\"eid\", \"score_COX_clinical\", 'partition', 'split']]"
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-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "predictions_df = pd.read_feather(\"/data/analysis/ag-reils/ag-reils-shared/cardioRS/results/models/benchmarks/BEN-1285/predictions/predictions.feather\")"
-   ]
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-  {
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-   "execution_count": 19,
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-   "outputs": [
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-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0.053468</td>\n",
-       "      <td>0.040084</td>\n",
-       "      <td>0.959916</td>\n",
-       "      <td>0.064656</td>\n",
-       "      <td>0.099897</td>\n",
-       "      <td>0.900103</td>\n",
-       "      <td>0.069494</td>\n",
-       "      <td>0.167342</td>\n",
-       "      <td>0.832658</td>\n",
-       "      <td>0.070809</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.308261</td>\n",
-       "      <td>0.322440</td>\n",
-       "      <td>0.337083</td>\n",
-       "      <td>0.349919</td>\n",
-       "      <td>1000043</td>\n",
-       "      <td>train</td>\n",
-       "      <td>0</td>\n",
-       "      <td>DeepSurvivalMachine</td>\n",
-       "      <td>StandardMLP</td>\n",
-       "      <td>CVDCoreVariablesWithPGSDataModule_210212</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>0.039807</td>\n",
-       "      <td>0.029643</td>\n",
-       "      <td>0.970357</td>\n",
-       "      <td>0.049027</td>\n",
-       "      <td>0.074585</td>\n",
-       "      <td>0.925415</td>\n",
-       "      <td>0.053833</td>\n",
-       "      <td>0.126272</td>\n",
-       "      <td>0.873728</td>\n",
-       "      <td>0.056177</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.189116</td>\n",
-       "      <td>0.198088</td>\n",
-       "      <td>0.207313</td>\n",
-       "      <td>0.216100</td>\n",
-       "      <td>1000079</td>\n",
-       "      <td>train</td>\n",
-       "      <td>0</td>\n",
-       "      <td>DeepSurvivalMachine</td>\n",
-       "      <td>StandardMLP</td>\n",
-       "      <td>CVDCoreVariablesWithPGSDataModule_210212</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>0.028805</td>\n",
-       "      <td>0.021258</td>\n",
-       "      <td>0.978742</td>\n",
-       "      <td>0.036251</td>\n",
-       "      <td>0.054144</td>\n",
-       "      <td>0.945856</td>\n",
-       "      <td>0.040689</td>\n",
-       "      <td>0.092789</td>\n",
-       "      <td>0.907211</td>\n",
-       "      <td>0.043434</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.114073</td>\n",
-       "      <td>0.119544</td>\n",
-       "      <td>0.125108</td>\n",
-       "      <td>0.130846</td>\n",
-       "      <td>1000084</td>\n",
-       "      <td>train</td>\n",
-       "      <td>0</td>\n",
-       "      <td>DeepSurvivalMachine</td>\n",
-       "      <td>StandardMLP</td>\n",
-       "      <td>CVDCoreVariablesWithPGSDataModule_210212</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>395733</th>\n",
-       "      <td>0.018039</td>\n",
-       "      <td>0.013417</td>\n",
-       "      <td>0.986583</td>\n",
-       "      <td>0.022897</td>\n",
-       "      <td>0.034085</td>\n",
-       "      <td>0.965915</td>\n",
-       "      <td>0.026106</td>\n",
-       "      <td>0.058681</td>\n",
-       "      <td>0.941319</td>\n",
-       "      <td>0.028405</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.048910</td>\n",
-       "      <td>0.051133</td>\n",
-       "      <td>0.053338</td>\n",
-       "      <td>0.055936</td>\n",
-       "      <td>6024701</td>\n",
-       "      <td>test</td>\n",
-       "      <td>0</td>\n",
-       "      <td>DeepSurvivalMachine</td>\n",
-       "      <td>StandardMLP</td>\n",
-       "      <td>CVDCoreVariablesWithPGSDataModule_210212</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>395734</th>\n",
-       "      <td>0.041583</td>\n",
-       "      <td>0.030703</td>\n",
-       "      <td>0.969297</td>\n",
-       "      <td>0.051509</td>\n",
-       "      <td>0.077817</td>\n",
-       "      <td>0.922183</td>\n",
-       "      <td>0.056635</td>\n",
-       "      <td>0.132170</td>\n",
-       "      <td>0.867830</td>\n",
-       "      <td>0.059054</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.213297</td>\n",
-       "      <td>0.223581</td>\n",
-       "      <td>0.234180</td>\n",
-       "      <td>0.244097</td>\n",
-       "      <td>6024778</td>\n",
-       "      <td>test</td>\n",
-       "      <td>0</td>\n",
-       "      <td>DeepSurvivalMachine</td>\n",
-       "      <td>StandardMLP</td>\n",
-       "      <td>CVDCoreVariablesWithPGSDataModule_210212</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>395735</th>\n",
-       "      <td>0.027671</td>\n",
-       "      <td>0.019840</td>\n",
-       "      <td>0.980160</td>\n",
-       "      <td>0.035789</td>\n",
-       "      <td>0.051936</td>\n",
-       "      <td>0.948064</td>\n",
-       "      <td>0.040801</td>\n",
-       "      <td>0.090415</td>\n",
-       "      <td>0.909585</td>\n",
-       "      <td>0.044011</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.124349</td>\n",
-       "      <td>0.130700</td>\n",
-       "      <td>0.137188</td>\n",
-       "      <td>0.143797</td>\n",
-       "      <td>6024787</td>\n",
-       "      <td>test</td>\n",
-       "      <td>0</td>\n",
-       "      <td>DeepSurvivalMachine</td>\n",
-       "      <td>StandardMLP</td>\n",
-       "      <td>CVDCoreVariablesWithPGSDataModule_210212</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>395736</th>\n",
-       "      <td>0.023525</td>\n",
-       "      <td>0.017225</td>\n",
-       "      <td>0.982775</td>\n",
-       "      <td>0.029963</td>\n",
-       "      <td>0.044256</td>\n",
-       "      <td>0.955744</td>\n",
-       "      <td>0.034002</td>\n",
-       "      <td>0.076378</td>\n",
-       "      <td>0.923622</td>\n",
-       "      <td>0.036696</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.081644</td>\n",
-       "      <td>0.085517</td>\n",
-       "      <td>0.089424</td>\n",
-       "      <td>0.093668</td>\n",
-       "      <td>6024807</td>\n",
-       "      <td>test</td>\n",
-       "      <td>0</td>\n",
-       "      <td>DeepSurvivalMachine</td>\n",
-       "      <td>StandardMLP</td>\n",
-       "      <td>CVDCoreVariablesWithPGSDataModule_210212</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>395737</th>\n",
-       "      <td>0.031700</td>\n",
-       "      <td>0.023351</td>\n",
-       "      <td>0.976649</td>\n",
-       "      <td>0.039779</td>\n",
-       "      <td>0.059497</td>\n",
-       "      <td>0.940503</td>\n",
-       "      <td>0.044441</td>\n",
-       "      <td>0.101806</td>\n",
-       "      <td>0.898194</td>\n",
-       "      <td>0.047178</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.134328</td>\n",
-       "      <td>0.140790</td>\n",
-       "      <td>0.147390</td>\n",
-       "      <td>0.154029</td>\n",
-       "      <td>6025173</td>\n",
-       "      <td>test</td>\n",
-       "      <td>0</td>\n",
-       "      <td>DeepSurvivalMachine</td>\n",
-       "      <td>StandardMLP</td>\n",
-       "      <td>CVDCoreVariablesWithPGSDataModule_210212</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>395738 rows × 194 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          0_1_ft    0_1_Ft    0_1_St    0_2_ft    0_2_Ft    0_2_St    0_3_ft  \\\n",
-       "0       0.030803  0.022290  0.977710  0.039299  0.057751  0.942249  0.044319   \n",
-       "1       0.048185  0.036345  0.963655  0.058220  0.090192  0.909808  0.062842   \n",
-       "2       0.053468  0.040084  0.959916  0.064656  0.099897  0.900103  0.069494   \n",
-       "3       0.039807  0.029643  0.970357  0.049027  0.074585  0.925415  0.053833   \n",
-       "4       0.028805  0.021258  0.978742  0.036251  0.054144  0.945856  0.040689   \n",
-       "...          ...       ...       ...       ...       ...       ...       ...   \n",
-       "395733  0.018039  0.013417  0.986583  0.022897  0.034085  0.965915  0.026106   \n",
-       "395734  0.041583  0.030703  0.969297  0.051509  0.077817  0.922183  0.056635   \n",
-       "395735  0.027671  0.019840  0.980160  0.035789  0.051936  0.948064  0.040801   \n",
-       "395736  0.023525  0.017225  0.982775  0.029963  0.044256  0.955744  0.034002   \n",
-       "395737  0.031700  0.023351  0.976649  0.039779  0.059497  0.940503  0.044441   \n",
-       "\n",
-       "          0_3_Ft    0_3_St    0_4_ft  ...  0_23_Ft_calibrated  \\\n",
-       "0       0.099765  0.900235  0.047347  ...            0.139802   \n",
-       "1       0.151037  0.848963  0.064484  ...            0.249414   \n",
-       "2       0.167342  0.832658  0.070809  ...            0.308261   \n",
-       "3       0.126272  0.873728  0.056177  ...            0.189116   \n",
-       "4       0.092789  0.907211  0.043434  ...            0.114073   \n",
-       "...          ...       ...       ...  ...                 ...   \n",
-       "395733  0.058681  0.941319  0.028405  ...            0.048910   \n",
-       "395734  0.132170  0.867830  0.059054  ...            0.213297   \n",
-       "395735  0.090415  0.909585  0.044011  ...            0.124349   \n",
-       "395736  0.076378  0.923622  0.036696  ...            0.081644   \n",
-       "395737  0.101806  0.898194  0.047178  ...            0.134328   \n",
-       "\n",
-       "        0_24_Ft_calibrated  0_25_Ft_calibrated  0_26_Ft_calibrated      eid  \\\n",
-       "0                 0.146783            0.153930            0.161087  1000018   \n",
-       "1                 0.260941            0.272834            0.283636  1000020   \n",
-       "2                 0.322440            0.337083            0.349919  1000043   \n",
-       "3                 0.198088            0.207313            0.216100  1000079   \n",
-       "4                 0.119544            0.125108            0.130846  1000084   \n",
-       "...                    ...                 ...                 ...      ...   \n",
-       "395733            0.051133            0.053338            0.055936  6024701   \n",
-       "395734            0.223581            0.234180            0.244097  6024778   \n",
-       "395735            0.130700            0.137188            0.143797  6024787   \n",
-       "395736            0.085517            0.089424            0.093668  6024807   \n",
-       "395737            0.140790            0.147390            0.154029  6025173   \n",
-       "\n",
-       "        split  partition               module          net  \\\n",
-       "0       train          0  DeepSurvivalMachine  StandardMLP   \n",
-       "1       train          0  DeepSurvivalMachine  StandardMLP   \n",
-       "2       train          0  DeepSurvivalMachine  StandardMLP   \n",
-       "3       train          0  DeepSurvivalMachine  StandardMLP   \n",
-       "4       train          0  DeepSurvivalMachine  StandardMLP   \n",
-       "...       ...        ...                  ...          ...   \n",
-       "395733   test          0  DeepSurvivalMachine  StandardMLP   \n",
-       "395734   test          0  DeepSurvivalMachine  StandardMLP   \n",
-       "395735   test          0  DeepSurvivalMachine  StandardMLP   \n",
-       "395736   test          0  DeepSurvivalMachine  StandardMLP   \n",
-       "395737   test          0  DeepSurvivalMachine  StandardMLP   \n",
-       "\n",
-       "                                      datamodule  \n",
-       "0       CVDCoreVariablesWithPGSDataModule_210212  \n",
-       "1       CVDCoreVariablesWithPGSDataModule_210212  \n",
-       "2       CVDCoreVariablesWithPGSDataModule_210212  \n",
-       "3       CVDCoreVariablesWithPGSDataModule_210212  \n",
-       "4       CVDCoreVariablesWithPGSDataModule_210212  \n",
-       "...                                          ...  \n",
-       "395733  CVDCoreVariablesWithPGSDataModule_210212  \n",
-       "395734  CVDCoreVariablesWithPGSDataModule_210212  \n",
-       "395735  CVDCoreVariablesWithPGSDataModule_210212  \n",
-       "395736  CVDCoreVariablesWithPGSDataModule_210212  \n",
-       "395737  CVDCoreVariablesWithPGSDataModule_210212  \n",
-       "\n",
-       "[395738 rows x 194 columns]"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "predictions_df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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vPKgoJNWDMwTNCI81SPVjIWjGWQ5SPVgImlUT7U6yKKTusxDUE5xFSN3XM4UQEQPAPwFHAF/NzPVdjqQucRYhdUdPFEJEHAF8GfhdYA/wvYjYkpmPdjeZekmnzl6yWKTWeqIQgDOBkcz8EUBEDAErAQtBHTdWLJ8/Yz+Xz8IpshaQ6iIys9sZiIiLgYHM/NNy/9PAWZn5mXHrrQHWlLu/BjzextMcD7zQgbizpW55oX6Z65YX6pfZvDOv3cy/lJnvbrWgV2YI0WLsoKbKzA3Ahmk9QcT2zOybzrbdULe8UL/MdcsL9cts3pnXycy98knlPcBJTfcXA892KYskHZZ6pRC+ByyNiJMj4h3AILCly5kk6bDSE7uMMnN/RHwG+Fcap51+LTN3dfhpprWrqYvqlhfql7lueaF+mc078zqWuScOKkuSuq9XdhlJkrrMQpAkAXOwECJiICIej4iRiFjbYnlExHVl+cMR8eFu5GzKM1XeX4+I+yLi9Yj4QjcyjsszVd5Ly/v6cER8NyI+2I2c4zJNlXllyftQRGyPiN/qRs6mPJPmbVrvIxHxRvkcT1dVeI/7I+Ll8h4/FBFf7EbOpjxTvscl80MRsSsi/n22M47LMtX7+xdN7+0j5d/FcW0/UWbOmS8aB6SfBH4ZeAfwA+DUceucD2yl8dmHs4EHejzve4CPAOuAL9Tg/f0osLDc/ng33982Ms/nreNpHwB+2Mt5m9a7G/gOcHEN3uN+4I5u5mwz77E0rpTwvnL/Pb2cd9z6nwDuns5zzbUZwpuXwMjM/wPGLoHRbCVwUzbcDxwbESfMdtBiyryZuTczvwf8rBsBx6mS97uZua/cvZ/GZ0q6qUrm0Sz/JwFH0+JDkbOoyr9hgD8HvgHsnc1wE6iauVdUyfuHwDcz82lo/H84yxmbtfv+fgr4+nSeaK4VwonA/zTd31PG2l1ntvRSlirazbuaxmysmypljohPRsQPgTuBP5mlbK1MmTciTgQ+CXxlFnNNpuq/i9+MiB9ExNaIOG12orVUJe+vAgsjYjgidkTEZbOW7mCV/7+LiHcBAzR+WWhbT3wOoYOqXAKj0mUyZkkvZamict6IWE6jELq6P57ql0X5FvCtiPht4G+Bc2c62ASq5P1H4KrMfCOi1eqzrkrm79O4hs5oRJwPfBtYOtPBJlAl7zxgGbACOAq4LyLuz8z/nulwLbTzc+ITwH9m5ovTeaK5VghVLoHRS5fJ6KUsVVTKGxEfAL4KfDwzfzxL2SbS1nucmfdGxK9ExPGZ2Y2LnFXJ2wcMlTI4Hjg/IvZn5rdnJeHBpsycmT9puv2diLi+x9/jPcALmfkq8GpE3At8EOhGIbTzb3iQae4uAubcQeV5wI+Ak3nr4Mtp49a5gAMPKj/Yy3mb1v1run9Qucr7+z5gBPhot/89tJH5FN46qPxh4Jmx+72Yd9z6N9L9g8pV3uP3Nr3HZwJP9/J7DLwf2FbWfRfwCHB6r+Yt6y0AXgSOnu5zzakZQk5wCYyI+LOy/Cs0zso4n8YPrf8F/riX80bEe4HtwDHAzyPiczTOMPjJRI/bzbzAF4FfBK4vv8Huzy5ePbJi5t8HLouInwE/Bf4gy/9hPZq3p1TMfDFwRUTsp/EeD/bye5yZj0XEvwAPAz+n8VccH+nVvGXVTwL/lo1ZzbR46QpJEjD3zjKSJE2ThSBJAiwESVJhIUiSAAtBklRYCJIkwEKQJBX/D/8h2YbfTS1iAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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eHxHHgbOSlqdjrh3RZ/hYa4DnYjzXsszMbNKKXDL6LPBFoFfSSyn2R8BWYKekDcBbwK0AEXFY0k7gFbI7lO6IiPOp3+3AQ8B0snmF3Sm+HXhEUh/ZyKBzcqdlZmbjVbYgRMT3KX2NH2DFKH22AFtKxA+QTUiPjL9HKihmZlYbH8oV06aKl9Y0s0bmj64wMzPABcHMzBIXBDMzA1wQzMwscUEwMzPAdxlNGd9xZGaNxiMEMzMDXBDMzCxxQTAzM8AFwczMEk8qV4EnmM2sEXiEYGZmgAuCmZklLghmZga4IJiZWVJkCc1vSzop6VAudreko5JeSl835PbdJalP0hFJ1+firZJ607770zKapKU2H0vxFyXNr/A5mplZAUVGCA8Bq0rE74uIa9LXMwCSriZb/nJx6vOgpMtS+23ARrI1lhfljrkBOB0RC4H7gHsneC5mZjYJRZbQfGEcr9pXA90RcQ54M62RvExSPzAjIvYCSHoYuJlsTeXVwN2p/+PAA5IUETGO82gYvgXVzOrVZN6H8BVJa4EDwJ0RcRqYC+zLtRlIsZ+n7ZFx0vcfA0TEkKQzwMeBUyN/oKSNZKMMmpub6enpKZzs4ODg++3vXDpUuN9UKpV/Ps965jwrr1FydZ6VVU95TrQgbAPuASJ9/zrwJUAl2sYYccrsuzAY0QV0AbS1tUV7e3vhhHt6ehhuvz73Kr2W+m9rvyiWz7OeOc/Ka5RcnWdl1VOeE7rLKCJORMT5iPgF8E1gWdo1AMzLNW0BjqV4S4n4BX0kTQNmAm9PJC8zM5u4CRUESXNyD28Bhu9A2gV0pjuHFpBNHu+PiOPAWUnL091Fa4Enc33Wpe01wHOX6vyBmVk9K3vJSNJ3gHbgKkkDwJ8A7ZKuIbu00w98GSAiDkvaCbwCDAF3RMT5dKjbye5Ymk42mbw7xbcDj6QJ6LfJ7lIyM7MqK3KX0RdKhLeP0X4LsKVE/ACwpET8PeDWcnmYmdnU8juVzcwM8Mdf15Tfk2Bm9cQjBDMzA1wQzMwscUEwMzPAcwh1Y3g+4c6lQ7TXNhUz+5DyCMHMzAAXBDMzS1wQzMwMcEEwM7PEBcHMzADfZVSX/A5mM6sFjxDMzAxwQTAzs8QFwczMABcEMzNLyhYESd+WdFLSoVzsSknPSno9fZ+d23eXpD5JRyRdn4u3SupN++5PS2mSltt8LMVflDS/wudoZmYFFBkhPASsGhHbDOyJiEXAnvQYSVeTLYG5OPV5UNJlqc82YCPZOsuLcsfcAJyOiIXAfcC9Ez2ZS9H8zU+//2VmNpXKFoSIeIFsreO81cCOtL0DuDkX746IcxHxJtAHLJM0B5gREXsjIoCHR/QZPtbjwIrh0YOZmVWPsr/PZRpll3Geiogl6fE7ETErt/90RMyW9ACwLyIeTfHtwG6gH9gaEStT/DpgU0TclC5FrYqIgbTvDeDaiDhVIo+NZKMMmpubW7u7uwuf6ODgIE1NTQD0Hj1TuF+1NU+HEz8rvW/p3JnVTWYM+eeznjVKntA4uTrPyqp2nh0dHQcjoq3Uvkq/Ma3UK/sYIz5Wn4uDEV1AF0BbW1u0t7cXTqynp4fh9uvr+PLLnUuH+Hpv6V9L/23t1U1mDPnns541Sp7QOLk6z8qqpzwnepfRiXQZiPT9ZIoPAPNy7VqAYyneUiJ+QR9J04CZXHyJyszMpthEC8IuYF3aXgc8mYt3pjuHFpBNHu+PiOPAWUnL0/zA2hF9ho+1BnguilzH+hDyBLOZTaWyl4wkfQdoB66SNAD8CbAV2ClpA/AWcCtARByWtBN4BRgC7oiI8+lQt5PdsTSdbF5hd4pvBx6R1Ec2MuisyJmZmdm4lC0IEfGFUXatGKX9FmBLifgBYEmJ+HukgmJmZrXjdyqbmRnggmBmZonXQ2hQXjPBzCrNIwQzMwNcEMzMLHFBMDMzwHMIlwTPJ5hZJXiEYGZmgAuCmZklLghmZga4IJiZWeJJ5UuMJ5jNbKI8QjAzM8AFwczMEl8yuoT58pGZjYdHCGZmBkyyIEjql9Qr6SVJB1LsSknPSno9fZ+da3+XpD5JRyRdn4u3puP0Sbo/LbNpZmZVVIkRQkdEXBMRbenxZmBPRCwC9qTHSLqabHnMxcAq4EFJl6U+24CNZGswL0r7zcysiqZiDmE12RrMADuAHmBTindHxDngzbSG8jJJ/cCMiNgLIOlh4GY+WHPZKsDzCWZWjiJi4p2lN4HTQAD/LSK6JL0TEbNybU5HxGxJDwD7IuLRFN9O9ke/H9gaEStT/DpgU0TcVOLnbSQbSdDc3Nza3d1dONfBwUGampoA6D16ZgJnWx3N0+HEz6b2ZyydO3PSx8g/n/WsUfKExsnVeVZWtfPs6Og4mLuic4HJjhA+GxHHJH0SeFbSa2O0LTUvEGPELw5GdAFdAG1tbdHe3l440Z6eHobbr8+9Wq43dy4d4uu9U3vzV/9t7ZM+Rv75rGeNkic0Tq7Os7LqKc9J/eWJiGPp+0lJTwDLgBOS5kTEcUlzgJOp+QAwL9e9BTiW4i0l4jZFfPnIzEqZ8KSypCskfWx4G/iXwCFgF7AuNVsHPJm2dwGdkn5Z0gKyyeP9EXEcOCtpebq7aG2uj5mZVclkRgjNwBPpDtFpwH+PiL+R9ANgp6QNwFvArQARcVjSTuAVYAi4IyLOp2PdDjwETCebV/CEcpV4tGBmwyZcECLiH4BPl4j/BFgxSp8twJYS8QPAkonmYmZmk+d3KpuZGeDPMrIcXz4y+3DzCMHMzACPEGwUHi2Yffh4hGBmZoBHCFaARwtmHw4eIZiZGeARgo3T8GjhzqVD73+krZldGlwQbMLmj/iQQF9OMmtsvmRkZmaARwhWQZ58NmtsLgg2JVwczBqPC4JNORcHs8bggmBV5eJgVr9cEKxmRt6lNMyFwqw2XBCs7rhQmNVG3RQESauA/wpcBnwrIrbWOCWrMy4UZlOrLgqCpMuAbwD/AhgAfiBpV0S8UtvMrBGMVigge0f1+jH2u5iYfaAuCgKwDOhLy3IiqRtYTbb+stmUGauYTDUXI6s3ioha54CkNcCqiPj36fEXgWsj4isj2m0ENqaH/xg4Mo4fcxVwqgLpTjXnWVmNkic0Tq7Os7Kqnec/iohPlNpRLyMElYhdVKkiogvomtAPkA5ERNtE+laT86ysRskTGidX51lZ9ZRnvXyW0QAwL/e4BThWo1zMzD6U6qUg/ABYJGmBpF8COoFdNc7JzOxDpS4uGUXEkKSvAP+D7LbTb0fE4Qr/mAldaqoB51lZjZInNE6uzrOy6ibPuphUNjOz2quXS0ZmZlZjLghmZgZcYgVB0ipJRyT1SdpcYr8k3Z/2vyzpd2qRZ8qlXK7/RNJeSeckfa0WOaY8yuV5W3ouX5b095I+Xad5rk45viTpgKR/Vo955tr9rqTz6T06NVHgOW2XdCY9py9J+uN6zDO1aU85Hpb0d9XOMeVQ7vn8T7nn8lD6/V9Z1SQj4pL4IpuMfgP4DeCXgB8BV49ocwOwm+x9D8uBF+s4108CvwtsAb5Wx3n+HjA7bf9BLZ7Tgnk28cGc2W8Dr9Vjnrl2zwHPAGvq+HffDjxVi/zGmecssk89+PX0+JP1mOeI9p8Dnqt2npfSCOH9j7+IiP8HDH/8Rd5q4OHI7ANmSZpT7UQpkGtEnIyIHwA/r0F+w4rk+fcRcTo93Ef2HpJqK5LnYKT/acAVlHjjYxUU+TcK8B+B7wInq5ncCEVzrbUief4b4HsR8RZk/7eqnCOM//n8AvCdqmSWcykVhLnAj3OPB1JsvG2qoV7yKGe8eW4gG4FVW6E8Jd0i6TXgaeBLVcotr2yekuYCtwB/WcW8Sin6u/+nkn4kabekxdVJ7QJF8vwUMFtSj6SDktZWLbsPFP6/JOlXgFVkLwqqqi7eh1AhRT7+otBHZFRBveRRTuE8JXWQFYRaXJsv+tEnTwBPSPrnwD3AyqlObIQief4XYFNEnJdKNa+aIrn+kOxzcQYl3QD8NbBoqhMboUie04BWYAUwHdgraV9E/J+pTi5nPP/nPwf8r4h4ewrzKelSKghFPv6iXj4io17yKKdQnpJ+G/gW8AcR8ZMq5ZY3ruczIl6Q9JuSroqIan6oWJE824DuVAyuAm6QNBQRf12VDD9QNteI+Glu+xlJD9bpczoAnIqId4F3Jb0AfBqoZkEYz7/RTmpwuQi4pCaVpwH/ACzgg0mbxSPa3MiFk8r76zXXXNu7qd2kcpHn9NeBPuD36vx3v5APJpV/Bzg6/Lie8hzR/iFqN6lc5Dn91dxzugx4qx6fU+C3gD2p7a8Ah4Al9ZZnajcTeBu4oha/90tmhBCjfPyFpP+Q9v8l2V0bN5D9Afu/wL+r11wl/SpwAJgB/ELSH5LdlfDT0Y5bizyBPwY+DjyYXtUORZU/ubFgnv8aWCvp58DPgM9H+h9YZ3nWhYK5rgFulzRE9px21uNzGhGvSvob4GXgF2QrMh6qtzxT01uA/xnZaKbq/NEVZmYGXFp3GZmZ2SS4IJiZGeCCYGZmiQuCmZkBLghmZpa4IJiZGeCCYGZmyf8HgNvA4hA6yAEAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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AVXUkyf3AM8AscGNVvdvOdQNwN3AancVkF5Q3OT/RLG0+Kw6Eqvoh8IlF2v8SuPQkx+wH9i/Sfgi4cKV9kSStnvcy0rpztCBtDt66QpIEGAiSpMYpIw2U00fSxuUIQZIEGAiSpMYpIw2N00fSxuIIQZIEOELQBuFoQRo+RwiSJMARgjYgRwvScBgI2tAMB2lwnDKSJAGOELSJOFqQ1pcjBEkS4AhBm5SjBWntOUKQJAEGgiSpccpIm57TR9LaMBA0UgwHaeUMBI0sw0FaHgNBY8FwkHpzUVmSBDhC0BiaGy3s2znL1HC7Im0oBoLGmlNJ0gkGgtQYDhp3BoK0iO5w6GZQaJQZCNIyOIrQKDMQpBVyFKFRYyBIa2xhUBgQ2iwMBGmdOZLQZrFhAiHJbuDfAKcAf1BVtwy5S9K6OllQnIwBovW2IQIhySnAvwf+MXAU+G6Sg1X1zHB7Jm0cyw2QboaJ+rEhAgG4BJipqh8CJLkPuAIwEKQ10CtM9u2c5bpVBE43w2fzSlUNuw8kuQrYXVX/rD3/LPDLVfW5BfvtBfa2p38LeK6P058NvLaG3d3MrMV81uMEazHfKNfjr1XVRxZ7YaOMELJI2/uSqqoOAAeWdeLkUFVNrrRjo8RazGc9TrAW841rPTbK3U6PAud2Pd8GvDykvkjSWNoogfBdYEeS85J8ANgDHBxynyRprGyIKaOqmk3yOeC/0rns9CtVdWSNTr+sKaYRZy3msx4nWIv5xrIeG2JRWZI0fBtlykiSNGQGgiQJGKFASLI7yXNJZpLctMjrSXJ7e/37Sf7uMPo5CH3U4jOtBt9P8mdJPjGMfg5Kr3p07ff3krzbPhczkvqpRZKpJE8lOZLkvw+6j4PUx7+VM5L85yR/0erx68Po58BU1aZ/0FmI/t/AXwc+APwFcP6CfS4HHqLzmYddwBPD7vcQa/H3gTPb9q+Oai36rUfXfo8C3wauGna/h/h34xfo3CHgF9vzjw6730OuxxeBW9v2R4DXgQ8Mu+/r9RiVEcJ7t76oqv8HzN36otsVwL3V8TjwC0nOGXRHB6BnLarqz6rqjfb0cTqf+xhV/fzdAPiXwDeAVwfZuQHrpxb/FPhmVb0IUFXjXo8Cfj5JgA/RCYTZwXZzcEYlELYCL3U9P9ralrvPKFju+7yezshpVPWsR5KtwKeA3xtgv4ahn78bfxM4M8l0ku8luWZgvRu8furx74CP0/mg7GHg81X1s8F0b/A2xOcQ1kA/t77o6/YYI6Dv95nkV+gEwj9Y1x4NVz/1+NfAF6rq3c4vgiOrn1psAS4GLgVOAx5L8nhV/a/17twQ9FOPy4CngH8E/A3g4ST/o6p+vM59G4pRCYR+bn0xLrfH6Ot9Jvk7wB8Av1pVfzmgvg1DP/WYBO5rYXA2cHmS2ar6TwPp4eD0++/ktap6G3g7yXeATwCjGAj91OPXgVuqs4gwk+R54G8DTw6mi4M1KlNG/dz64iBwTbvaaBfwZlUdG3RHB6BnLZL8IvBN4LMj+ptft571qKrzqmp7VW0HHgD+xQiGAfT37+RB4B8m2ZLkrwK/DDw74H4OSj/1eJHOaIkkE3TusvzDgfZygEZihFAnufVFkn/eXv89OlePXA7MAP+XTvKPnD5r8a+As4A72m/FszWid3bssx5joZ9aVNWzSf4Y+D7wMzrfXvj08Hq9fvr8u/El4O4kh+lMMX2hqkb1ttjeukKS1DEqU0aSpFUyECRJgIEgSWoMBEkSYCBIkhoDQZIEGAiSpOb/A3LqilfAkpYcAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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H+yRJfaUrpowycywirgW+R3Xa6Tczc988HX5GU0w9yhpUrEPFOlSswwRdsagsSeq8bpkykiR1mIEgSQJ6JBAiYk1EvBARIxGxpcHzERG3lOefiYhf7UQ/F1oLdbiivP5nIuL7EXF+J/q50JrVoW67X4uIo+VzMD2nlTpExHBEPB0R+yLiX9vdx4XWwr+JkyLiHyPiB6UGX+hEP7tGZi7qG9Ui9H8DPwccC/wAOGfCNpcAD1J93mE18Hin+92hOvw6cHK5/5l+rUPddo8ADwCXdbrfHfp7+BjV1QA+Xh6f3ul+d6AGXwRuKvdPA94Aju103zt164URwvuXvcjM/wPGL3tRby1wR1Z2Ax+LiDPa3dEF1rQOmfn9zDxcHu6m+rxHr2nl7wHgT4BvA4fa2bk2aqUOvwfcm5kvA2Rmr9WilRok8NGICOAjVIEw1t5udo9eCIRlwCt1j0dL20y3Wexm+ho3Uo2aek3TOkTEMuBzwNfb2K92a+Xv4ReAkyOiFhFPRcT6tvWuPVqpwdeAT1B9EHYvcF1m/qQ93es+XfE5hDlq5bIXLV0aY5Fr+TVGxKepAuE3FrRHndFKHf4SuD4zj1ZvDHtSK3VYAlwIXAQcDzwWEbsz878WunNt0koNLgaeBn4L+HngoYj4t8z88QL3rSv1QiC0ctmLfrg0RkuvMSJ+GfgG8JnM/FGb+tZOrdRhCLirhMFS4JKIGMvMf2hLD9uj1X8Xr2fm28DbEfEocD7QK4HQSg2+ANyY1SLCSES8BPwS8ER7uthdemHKqJXLXuwC1pezjVYDb2bmwXZ3dIE1rUNEfBy4F7iyh94FTtS0Dpl5VmYOZuYgcA/wxz0WBtDav4v7gN+MiCUR8TPAJ4Hn29zPhdRKDV6mGiEREQNUV1F+sa297CKLfoSQU1z2IiL+qDz/daozSS4BRoD/pXpX0FNarMOfA6cCt5Z3x2PZY1d7bLEOPa+VOmTm8xHxXeAZ4CdU31T4bOd6Pb9a/Fv4CrA9IvZSTTFdn5n9eElswEtXSJKKXpgykiTNAwNBkgQYCJKkwkCQJAEGgiSpMBAkSYCBIEkq/h/4+Lil9hAhGQAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "pd.set_option('display.max_rows', 100)\n",
-    "for t in range(1, 27, 1):\n",
-    "    predictions_df[[col for col in predictions_df.columns.to_list() if f\"_{t}_Ft\" in col]+[\"partition\", \"split\"]][f\"0_{t}_Ft_calibrated\"].hist(bins=100)\n",
-    "    plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python [conda env:miniconda3-pl1.x]",
-   "language": "python",
-   "name": "conda-env-miniconda3-pl1.x-py"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/neuralcvd/preprocessing/ukbb_tabular/9_Check_h5ad.ipynb b/neuralcvd/preprocessing/ukbb_tabular/9_Check_h5ad.ipynb
deleted file mode 100644
index 676aa85..0000000
--- a/neuralcvd/preprocessing/ukbb_tabular/9_Check_h5ad.ipynb
+++ /dev/null
@@ -1,8163 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 3. Check h5ad"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The autoreload extension is already loaded. To reload it, use:\n",
-      "  %reload_ext autoreload\n"
-     ]
-    }
-   ],
-   "source": [
-    "%load_ext autoreload\n",
-    "%autoreload 2\n",
-    "\n",
-    "import os\n",
-    "from tqdm.auto import tqdm\n",
-    "import matplotlib.pyplot as pl\n",
-    "import pathlib\n",
-    "import anndata as ad\n",
-    "\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import lifelines\n",
-    "from lifelines import CoxPHFitter\n",
-    "from sklearn.model_selection import StratifiedKFold\n",
-    "from collections import OrderedDict\n",
-    "\n",
-    "\n",
-    "from joblib import Parallel, delayed\n",
-    "from tqdm.notebook import tqdm\n",
-    "import neptune\n",
-    "import warnings\n",
-    "warnings.filterwarnings(\"ignore\")\n",
-    "import shutil\n",
-    "\n",
-    "#project_path = \"/home/steinfej/projects/uk_biobank/\"\n",
-    "#dataset_path = \"data/datasets/\"\n",
-    "project_name = \"cvd_massive_excl_emb_ind\"\n",
-    "shared_path = \"/data/analysis/ag-reils/ag-reils-shared/cardioRS/data/3_datasets_post\"\n",
-    "from utils import setup, nanstr_to_nan, h5ad_ensure_dimensionality, h5ad_fillna"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Read data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Utils"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "h5ad_fp = f\"{shared_path}/{project_name}/prep/derived.filtered.transformed.h5ad\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "h5ad_fp = f\"{shared_path}/{project_name}/partition_0/valid/data.normalized.scores.h5ad\"\n",
-    "data = ad.read_h5ad(h5ad_fp)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AnnData object with n_obs × n_vars = 39888 × 4167\n",
-       "    obs: 'eid'\n",
-       "    var: 'dtype', 'isTarget', 'based_on', 'aggr_fn', 'recoding', 'NewLevels'"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#h5ad_fp = f\"{shared_path}/{project_name}/prep/derived.filtered.transformed.h5ad\"\n",
-    "h5ad_fp_train = f\"{shared_path}/{project_name}/partition_0/train/data.normalized.h5ad\"\n",
-    "h5ad_fp_valid = f\"{shared_path}/{project_name}/partition_0/valid/data.normalized.h5ad\"\n",
-    "h5ad_fp_test = f\"{shared_path}/{project_name}/partition_0/test/data.normalized.h5ad\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_train = ad.read_h5ad(h5ad_fp_train)\n",
-    "data_valid = ad.read_h5ad(h5ad_fp_valid)\n",
-    "data_test = ad.read_h5ad(h5ad_fp_test)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_complete = data_train.concatenate(data_valid, data_test)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_complete.write_h5ad(f\"{shared_path}/cvd_massive_complete/complete.data.normalized.h5ad\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from pprint import pprint"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "measurements = ['body_mass_index_bmi',\n",
-    "  'weight',\n",
-    "  'pulse_wave_arterial_stiffness_index',\n",
-    "  'pulse_wave_reflection_index',\n",
-    "  'waist_circumference',\n",
-    "  'hip_circumference',\n",
-    "  'trunk_fat_percentage',\n",
-    "  'body_fat_percentage',\n",
-    "  'basal_metabolic_rate',\n",
-    "  'forced_vital_capacity_fvc_best_measure',\n",
-    "  'forced_expiratory_volume_in_1second_fev1_best_measure',\n",
-    "  'fev1_fvc_ratio_zscore',\n",
-    "  'peak_expiratory_flow_pef_f3064_0_2',\n",
-    "  'peak_expiratory_flow_pef_f3064_0_1',\n",
-    "  'peak_expiratory_flow_pef',\n",
-    "  'systolic_blood_pressure',\n",
-    "  'diastolic_blood_pressure',\n",
-    "  'pulse_rate']\n",
-    "labs =  ['basophill_count',\n",
-    "  'basophill_percentage',\n",
-    "  'eosinophill_count',\n",
-    "  'eosinophill_percentage',\n",
-    "  'haematocrit_percentage',\n",
-    "  'haemoglobin_concentration',\n",
-    "  'high_light_scatter_reticulocyte_count',\n",
-    "  'high_light_scatter_reticulocyte_percentage',\n",
-    "  'immature_reticulocyte_fraction',\n",
-    "  'lymphocyte_count',\n",
-    "  'lymphocyte_percentage',\n",
-    "  'mean_corpuscular_haemoglobin',\n",
-    "  'mean_corpuscular_haemoglobin_concentration',\n",
-    "  'mean_corpuscular_volume',\n",
-    "  'mean_platelet_thrombocyte_volume',\n",
-    "  'mean_reticulocyte_volume',\n",
-    "  'mean_sphered_cell_volume',\n",
-    "  'monocyte_count',\n",
-    "  'monocyte_percentage',\n",
-    "  'neutrophill_count',\n",
-    "  'neutrophill_percentage',\n",
-    "  'nucleated_red_blood_cell_count',\n",
-    "  'nucleated_red_blood_cell_percentage',\n",
-    "  'platelet_count',\n",
-    "  'platelet_crit',\n",
-    "  'platelet_distribution_width',\n",
-    "  'red_blood_cell_erythrocyte_count',\n",
-    "  'red_blood_cell_erythrocyte_distribution_width',\n",
-    "  'reticulocyte_count',\n",
-    "  'reticulocyte_percentage',\n",
-    "  'white_blood_cell_leukocyte_count',\n",
-    "  'alanine_aminotransferase',\n",
-    "  'albumin',\n",
-    "  'alkaline_phosphatase',\n",
-    "  'apolipoprotein_a',\n",
-    "  'apolipoprotein_b',\n",
-    "  'aspartate_aminotransferase',\n",
-    "  'creactive_protein',\n",
-    "  'calcium',\n",
-    "  'cholesterol',\n",
-    "  'creatinine',\n",
-    "  'cystatin_c',\n",
-    "  'direct_bilirubin',\n",
-    "  'gamma_glutamyltransferase',\n",
-    "  'glucose',\n",
-    "  'glycated_haemoglobin_hba1c',\n",
-    "  'hdl_cholesterol',\n",
-    "  'igf1',\n",
-    "  'ldl_direct',\n",
-    "  'lipoprotein_a',\n",
-    "  'oestradiol',\n",
-    "  'phosphate',\n",
-    "  'rheumatoid_factor',\n",
-    "  'shbg',\n",
-    "  'testosterone',\n",
-    "  'total_bilirubin',\n",
-    "  'total_protein',\n",
-    "  'triglycerides',\n",
-    "  'urate',\n",
-    "  'urea',\n",
-    "  'vitamin_d']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "content = measurements + labs\n",
-    "content_original = [f\"{e}_original\" for e in content]\n",
-    "df_original = pd.DataFrame(data=data[:,content_original].X, columns = content_original)\n",
-    "df_scaled = pd.DataFrame(data=data[:, content].X, columns = content )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>body_mass_index_bmi_original</th>\n",
-       "      <th>weight_original</th>\n",
-       "      <th>pulse_wave_arterial_stiffness_index_original</th>\n",
-       "      <th>pulse_wave_reflection_index_original</th>\n",
-       "      <th>waist_circumference_original</th>\n",
-       "      <th>hip_circumference_original</th>\n",
-       "      <th>trunk_fat_percentage_original</th>\n",
-       "      <th>body_fat_percentage_original</th>\n",
-       "      <th>basal_metabolic_rate_original</th>\n",
-       "      <th>forced_vital_capacity_fvc_best_measure_original</th>\n",
-       "      <th>...</th>\n",
-       "      <th>phosphate_original</th>\n",
-       "      <th>rheumatoid_factor_original</th>\n",
-       "      <th>shbg_original</th>\n",
-       "      <th>testosterone_original</th>\n",
-       "      <th>total_bilirubin_original</th>\n",
-       "      <th>total_protein_original</th>\n",
-       "      <th>triglycerides_original</th>\n",
-       "      <th>urate_original</th>\n",
-       "      <th>urea_original</th>\n",
-       "      <th>vitamin_d_original</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>26.555700</td>\n",
-       "      <td>63.799999</td>\n",
-       "      <td>7.27700</td>\n",
-       "      <td>80.0</td>\n",
-       "      <td>85.000000</td>\n",
-       "      <td>107.000000</td>\n",
-       "      <td>37.500000</td>\n",
-       "      <td>39.500000</td>\n",
-       "      <td>5012.0</td>\n",
-       "      <td>3.21</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.422</td>\n",
-       "      <td>38.200001</td>\n",
-       "      <td>70.110001</td>\n",
-       "      <td>1.560000</td>\n",
-       "      <td>7.41</td>\n",
-       "      <td>71.970001</td>\n",
-       "      <td>1.247</td>\n",
-       "      <td>221.300003</td>\n",
-       "      <td>5.48</td>\n",
-       "      <td>70.699997</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>22.746500</td>\n",
-       "      <td>70.699997</td>\n",
-       "      <td>12.30770</td>\n",
-       "      <td>45.0</td>\n",
-       "      <td>87.800003</td>\n",
-       "      <td>94.400002</td>\n",
-       "      <td>33.400002</td>\n",
-       "      <td>28.700001</td>\n",
-       "      <td>6171.0</td>\n",
-       "      <td>4.19</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.264</td>\n",
-       "      <td>11.800000</td>\n",
-       "      <td>55.310001</td>\n",
-       "      <td>12.237000</td>\n",
-       "      <td>8.07</td>\n",
-       "      <td>78.449997</td>\n",
-       "      <td>1.906</td>\n",
-       "      <td>374.700012</td>\n",
-       "      <td>5.28</td>\n",
-       "      <td>35.900002</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>29.567900</td>\n",
-       "      <td>95.800003</td>\n",
-       "      <td>11.11110</td>\n",
-       "      <td>78.0</td>\n",
-       "      <td>98.000000</td>\n",
-       "      <td>104.000000</td>\n",
-       "      <td>27.600000</td>\n",
-       "      <td>25.600000</td>\n",
-       "      <td>8711.0</td>\n",
-       "      <td>4.14</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.928</td>\n",
-       "      <td>19.000000</td>\n",
-       "      <td>31.629999</td>\n",
-       "      <td>11.398000</td>\n",
-       "      <td>8.65</td>\n",
-       "      <td>69.699997</td>\n",
-       "      <td>5.184</td>\n",
-       "      <td>322.799988</td>\n",
-       "      <td>6.67</td>\n",
-       "      <td>63.599998</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>25.015600</td>\n",
-       "      <td>60.099998</td>\n",
-       "      <td>10.98160</td>\n",
-       "      <td>52.0</td>\n",
-       "      <td>86.000000</td>\n",
-       "      <td>94.000000</td>\n",
-       "      <td>34.200001</td>\n",
-       "      <td>36.500000</td>\n",
-       "      <td>4879.0</td>\n",
-       "      <td>2.34</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.201</td>\n",
-       "      <td>30.799999</td>\n",
-       "      <td>6.750000</td>\n",
-       "      <td>0.975000</td>\n",
-       "      <td>6.29</td>\n",
-       "      <td>72.419998</td>\n",
-       "      <td>1.740</td>\n",
-       "      <td>230.199997</td>\n",
-       "      <td>8.16</td>\n",
-       "      <td>60.400002</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>29.363199</td>\n",
-       "      <td>88.900002</td>\n",
-       "      <td>11.91490</td>\n",
-       "      <td>50.0</td>\n",
-       "      <td>106.000000</td>\n",
-       "      <td>104.000000</td>\n",
-       "      <td>28.700001</td>\n",
-       "      <td>27.000000</td>\n",
-       "      <td>7991.0</td>\n",
-       "      <td>3.63</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.218</td>\n",
-       "      <td>11.000000</td>\n",
-       "      <td>27.209999</td>\n",
-       "      <td>7.057000</td>\n",
-       "      <td>9.52</td>\n",
-       "      <td>78.919998</td>\n",
-       "      <td>2.847</td>\n",
-       "      <td>428.399994</td>\n",
-       "      <td>4.50</td>\n",
-       "      <td>26.100000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>319103</th>\n",
-       "      <td>21.936300</td>\n",
-       "      <td>59.000000</td>\n",
-       "      <td>12.19700</td>\n",
-       "      <td>76.0</td>\n",
-       "      <td>71.000000</td>\n",
-       "      <td>88.000000</td>\n",
-       "      <td>22.200001</td>\n",
-       "      <td>19.299999</td>\n",
-       "      <td>5786.0</td>\n",
-       "      <td>4.50</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.136</td>\n",
-       "      <td>11.500000</td>\n",
-       "      <td>67.930000</td>\n",
-       "      <td>20.207001</td>\n",
-       "      <td>15.56</td>\n",
-       "      <td>73.589996</td>\n",
-       "      <td>0.965</td>\n",
-       "      <td>262.600006</td>\n",
-       "      <td>3.65</td>\n",
-       "      <td>31.900000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>319104</th>\n",
-       "      <td>25.349300</td>\n",
-       "      <td>72.400002</td>\n",
-       "      <td>5.47619</td>\n",
-       "      <td>39.0</td>\n",
-       "      <td>86.000000</td>\n",
-       "      <td>105.000000</td>\n",
-       "      <td>37.000000</td>\n",
-       "      <td>37.500000</td>\n",
-       "      <td>5782.0</td>\n",
-       "      <td>3.87</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.057</td>\n",
-       "      <td>13.500000</td>\n",
-       "      <td>61.590000</td>\n",
-       "      <td>1.059000</td>\n",
-       "      <td>12.93</td>\n",
-       "      <td>72.099998</td>\n",
-       "      <td>0.730</td>\n",
-       "      <td>298.799988</td>\n",
-       "      <td>4.21</td>\n",
-       "      <td>41.599998</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>319105</th>\n",
-       "      <td>24.227501</td>\n",
-       "      <td>62.799999</td>\n",
-       "      <td>11.12660</td>\n",
-       "      <td>65.0</td>\n",
-       "      <td>74.000000</td>\n",
-       "      <td>94.000000</td>\n",
-       "      <td>28.799999</td>\n",
-       "      <td>32.099998</td>\n",
-       "      <td>5381.0</td>\n",
-       "      <td>3.47</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.996</td>\n",
-       "      <td>16.299999</td>\n",
-       "      <td>73.379997</td>\n",
-       "      <td>0.652000</td>\n",
-       "      <td>11.19</td>\n",
-       "      <td>74.199997</td>\n",
-       "      <td>1.442</td>\n",
-       "      <td>220.199997</td>\n",
-       "      <td>4.01</td>\n",
-       "      <td>72.699997</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>319106</th>\n",
-       "      <td>29.142500</td>\n",
-       "      <td>104.099998</td>\n",
-       "      <td>8.91509</td>\n",
-       "      <td>63.0</td>\n",
-       "      <td>108.000000</td>\n",
-       "      <td>104.000000</td>\n",
-       "      <td>32.099998</td>\n",
-       "      <td>27.500000</td>\n",
-       "      <td>9330.0</td>\n",
-       "      <td>4.80</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.986</td>\n",
-       "      <td>17.200001</td>\n",
-       "      <td>24.480000</td>\n",
-       "      <td>10.951000</td>\n",
-       "      <td>9.95</td>\n",
-       "      <td>70.650002</td>\n",
-       "      <td>5.756</td>\n",
-       "      <td>353.600006</td>\n",
-       "      <td>4.42</td>\n",
-       "      <td>45.900002</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>319107</th>\n",
-       "      <td>29.598801</td>\n",
-       "      <td>102.400002</td>\n",
-       "      <td>16.03450</td>\n",
-       "      <td>74.0</td>\n",
-       "      <td>110.000000</td>\n",
-       "      <td>108.000000</td>\n",
-       "      <td>31.500000</td>\n",
-       "      <td>30.000000</td>\n",
-       "      <td>8849.0</td>\n",
-       "      <td>4.26</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.163</td>\n",
-       "      <td>20.600000</td>\n",
-       "      <td>45.090000</td>\n",
-       "      <td>15.030000</td>\n",
-       "      <td>11.85</td>\n",
-       "      <td>70.620003</td>\n",
-       "      <td>2.327</td>\n",
-       "      <td>454.799988</td>\n",
-       "      <td>5.14</td>\n",
-       "      <td>20.200001</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>319108 rows × 79 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        body_mass_index_bmi_original  weight_original  \\\n",
-       "0                          26.555700        63.799999   \n",
-       "1                          22.746500        70.699997   \n",
-       "2                          29.567900        95.800003   \n",
-       "3                          25.015600        60.099998   \n",
-       "4                          29.363199        88.900002   \n",
-       "...                              ...              ...   \n",
-       "319103                     21.936300        59.000000   \n",
-       "319104                     25.349300        72.400002   \n",
-       "319105                     24.227501        62.799999   \n",
-       "319106                     29.142500       104.099998   \n",
-       "319107                     29.598801       102.400002   \n",
-       "\n",
-       "        pulse_wave_arterial_stiffness_index_original  \\\n",
-       "0                                            7.27700   \n",
-       "1                                           12.30770   \n",
-       "2                                           11.11110   \n",
-       "3                                           10.98160   \n",
-       "4                                           11.91490   \n",
-       "...                                              ...   \n",
-       "319103                                      12.19700   \n",
-       "319104                                       5.47619   \n",
-       "319105                                      11.12660   \n",
-       "319106                                       8.91509   \n",
-       "319107                                      16.03450   \n",
-       "\n",
-       "        pulse_wave_reflection_index_original  waist_circumference_original  \\\n",
-       "0                                       80.0                     85.000000   \n",
-       "1                                       45.0                     87.800003   \n",
-       "2                                       78.0                     98.000000   \n",
-       "3                                       52.0                     86.000000   \n",
-       "4                                       50.0                    106.000000   \n",
-       "...                                      ...                           ...   \n",
-       "319103                                  76.0                     71.000000   \n",
-       "319104                                  39.0                     86.000000   \n",
-       "319105                                  65.0                     74.000000   \n",
-       "319106                                  63.0                    108.000000   \n",
-       "319107                                  74.0                    110.000000   \n",
-       "\n",
-       "        hip_circumference_original  trunk_fat_percentage_original  \\\n",
-       "0                       107.000000                      37.500000   \n",
-       "1                        94.400002                      33.400002   \n",
-       "2                       104.000000                      27.600000   \n",
-       "3                        94.000000                      34.200001   \n",
-       "4                       104.000000                      28.700001   \n",
-       "...                            ...                            ...   \n",
-       "319103                   88.000000                      22.200001   \n",
-       "319104                  105.000000                      37.000000   \n",
-       "319105                   94.000000                      28.799999   \n",
-       "319106                  104.000000                      32.099998   \n",
-       "319107                  108.000000                      31.500000   \n",
-       "\n",
-       "        body_fat_percentage_original  basal_metabolic_rate_original  \\\n",
-       "0                          39.500000                         5012.0   \n",
-       "1                          28.700001                         6171.0   \n",
-       "2                          25.600000                         8711.0   \n",
-       "3                          36.500000                         4879.0   \n",
-       "4                          27.000000                         7991.0   \n",
-       "...                              ...                            ...   \n",
-       "319103                     19.299999                         5786.0   \n",
-       "319104                     37.500000                         5782.0   \n",
-       "319105                     32.099998                         5381.0   \n",
-       "319106                     27.500000                         9330.0   \n",
-       "319107                     30.000000                         8849.0   \n",
-       "\n",
-       "        forced_vital_capacity_fvc_best_measure_original  ...  \\\n",
-       "0                                                  3.21  ...   \n",
-       "1                                                  4.19  ...   \n",
-       "2                                                  4.14  ...   \n",
-       "3                                                  2.34  ...   \n",
-       "4                                                  3.63  ...   \n",
-       "...                                                 ...  ...   \n",
-       "319103                                             4.50  ...   \n",
-       "319104                                             3.87  ...   \n",
-       "319105                                             3.47  ...   \n",
-       "319106                                             4.80  ...   \n",
-       "319107                                             4.26  ...   \n",
-       "\n",
-       "        phosphate_original  rheumatoid_factor_original  shbg_original  \\\n",
-       "0                    1.422                   38.200001      70.110001   \n",
-       "1                    1.264                   11.800000      55.310001   \n",
-       "2                    0.928                   19.000000      31.629999   \n",
-       "3                    1.201                   30.799999       6.750000   \n",
-       "4                    1.218                   11.000000      27.209999   \n",
-       "...                    ...                         ...            ...   \n",
-       "319103               1.136                   11.500000      67.930000   \n",
-       "319104               1.057                   13.500000      61.590000   \n",
-       "319105               0.996                   16.299999      73.379997   \n",
-       "319106               0.986                   17.200001      24.480000   \n",
-       "319107               1.163                   20.600000      45.090000   \n",
-       "\n",
-       "        testosterone_original  total_bilirubin_original  \\\n",
-       "0                    1.560000                      7.41   \n",
-       "1                   12.237000                      8.07   \n",
-       "2                   11.398000                      8.65   \n",
-       "3                    0.975000                      6.29   \n",
-       "4                    7.057000                      9.52   \n",
-       "...                       ...                       ...   \n",
-       "319103              20.207001                     15.56   \n",
-       "319104               1.059000                     12.93   \n",
-       "319105               0.652000                     11.19   \n",
-       "319106              10.951000                      9.95   \n",
-       "319107              15.030000                     11.85   \n",
-       "\n",
-       "        total_protein_original  triglycerides_original  urate_original  \\\n",
-       "0                    71.970001                   1.247      221.300003   \n",
-       "1                    78.449997                   1.906      374.700012   \n",
-       "2                    69.699997                   5.184      322.799988   \n",
-       "3                    72.419998                   1.740      230.199997   \n",
-       "4                    78.919998                   2.847      428.399994   \n",
-       "...                        ...                     ...             ...   \n",
-       "319103               73.589996                   0.965      262.600006   \n",
-       "319104               72.099998                   0.730      298.799988   \n",
-       "319105               74.199997                   1.442      220.199997   \n",
-       "319106               70.650002                   5.756      353.600006   \n",
-       "319107               70.620003                   2.327      454.799988   \n",
-       "\n",
-       "        urea_original  vitamin_d_original  \n",
-       "0                5.48           70.699997  \n",
-       "1                5.28           35.900002  \n",
-       "2                6.67           63.599998  \n",
-       "3                8.16           60.400002  \n",
-       "4                4.50           26.100000  \n",
-       "...               ...                 ...  \n",
-       "319103           3.65           31.900000  \n",
-       "319104           4.21           41.599998  \n",
-       "319105           4.01           72.699997  \n",
-       "319106           4.42           45.900002  \n",
-       "319107           5.14           20.200001  \n",
-       "\n",
-       "[319108 rows x 79 columns]"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_original"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
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-       "    .dataframe thead th {\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>body_mass_index_bmi</th>\n",
-       "      <th>weight</th>\n",
-       "      <th>pulse_wave_arterial_stiffness_index</th>\n",
-       "      <th>pulse_wave_reflection_index</th>\n",
-       "      <th>waist_circumference</th>\n",
-       "      <th>hip_circumference</th>\n",
-       "      <th>trunk_fat_percentage</th>\n",
-       "      <th>body_fat_percentage</th>\n",
-       "      <th>basal_metabolic_rate</th>\n",
-       "      <th>forced_vital_capacity_fvc_best_measure</th>\n",
-       "      <th>...</th>\n",
-       "      <th>phosphate</th>\n",
-       "      <th>rheumatoid_factor</th>\n",
-       "      <th>shbg</th>\n",
-       "      <th>testosterone</th>\n",
-       "      <th>total_bilirubin</th>\n",
-       "      <th>total_protein</th>\n",
-       "      <th>triglycerides</th>\n",
-       "      <th>urate</th>\n",
-       "      <th>urea</th>\n",
-       "      <th>vitamin_d</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>93770</th>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>1.697369</td>\n",
-       "      <td>-0.993863</td>\n",
-       "      <td>-0.603288</td>\n",
-       "      <td>2.233255</td>\n",
-       "      <td>2.656092</td>\n",
-       "      <td>1.030125</td>\n",
-       "      <td>1.637406</td>\n",
-       "      <td>0.570121</td>\n",
-       "      <td>-0.515888</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.616273</td>\n",
-       "      <td>-0.206852</td>\n",
-       "      <td>0.325754</td>\n",
-       "      <td>-0.768892</td>\n",
-       "      <td>-0.688897</td>\n",
-       "      <td>-0.897459</td>\n",
-       "      <td>0.675306</td>\n",
-       "      <td>0.083443</td>\n",
-       "      <td>-0.928118</td>\n",
-       "      <td>-0.761460</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>316865</th>\n",
-       "      <td>0.481531</td>\n",
-       "      <td>0.242866</td>\n",
-       "      <td>-0.540830</td>\n",
-       "      <td>0.084680</td>\n",
-       "      <td>1.005116</td>\n",
-       "      <td>0.115456</td>\n",
-       "      <td>0.695786</td>\n",
-       "      <td>0.185122</td>\n",
-       "      <td>0.137530</td>\n",
-       "      <td>0.685692</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.766222</td>\n",
-       "      <td>0.062543</td>\n",
-       "      <td>-0.720949</td>\n",
-       "      <td>0.630528</td>\n",
-       "      <td>0.232489</td>\n",
-       "      <td>0.143004</td>\n",
-       "      <td>0.240612</td>\n",
-       "      <td>1.099900</td>\n",
-       "      <td>-0.157219</td>\n",
-       "      <td>-1.110865</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>270154</th>\n",
-       "      <td>-0.211962</td>\n",
-       "      <td>-0.329925</td>\n",
-       "      <td>-0.162804</td>\n",
-       "      <td>-0.458453</td>\n",
-       "      <td>0.084011</td>\n",
-       "      <td>-0.436856</td>\n",
-       "      <td>0.398596</td>\n",
-       "      <td>0.591761</td>\n",
-       "      <td>-0.608726</td>\n",
-       "      <td>0.033988</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.183454</td>\n",
-       "      <td>-0.594780</td>\n",
-       "      <td>0.076522</td>\n",
-       "      <td>-0.836271</td>\n",
-       "      <td>-0.638598</td>\n",
-       "      <td>-0.713848</td>\n",
-       "      <td>2.042763</td>\n",
-       "      <td>-0.447713</td>\n",
-       "      <td>-0.538775</td>\n",
-       "      <td>-1.608648</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>266822</th>\n",
-       "      <td>0.041533</td>\n",
-       "      <td>-0.407155</td>\n",
-       "      <td>-0.916272</td>\n",
-       "      <td>-0.241199</td>\n",
-       "      <td>-1.451163</td>\n",
-       "      <td>0.446843</td>\n",
-       "      <td>0.299533</td>\n",
-       "      <td>0.580143</td>\n",
-       "      <td>-0.636971</td>\n",
-       "      <td>-1.299970</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.853351</td>\n",
-       "      <td>-0.691762</td>\n",
-       "      <td>-0.382607</td>\n",
-       "      <td>-0.858069</td>\n",
-       "      <td>-0.579154</td>\n",
-       "      <td>0.728114</td>\n",
-       "      <td>-1.042678</td>\n",
-       "      <td>-0.638776</td>\n",
-       "      <td>1.330072</td>\n",
-       "      <td>1.875831</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>188910</th>\n",
-       "      <td>-0.240454</td>\n",
-       "      <td>0.217123</td>\n",
-       "      <td>-0.514518</td>\n",
-       "      <td>-0.530870</td>\n",
-       "      <td>-0.069506</td>\n",
-       "      <td>-0.436856</td>\n",
-       "      <td>-0.492975</td>\n",
-       "      <td>-0.848905</td>\n",
-       "      <td>0.619921</td>\n",
-       "      <td>0.033988</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.664540</td>\n",
-       "      <td>-0.196076</td>\n",
-       "      <td>-0.812496</td>\n",
-       "      <td>1.317357</td>\n",
-       "      <td>0.719474</td>\n",
-       "      <td>0.737905</td>\n",
-       "      <td>-0.166358</td>\n",
-       "      <td>0.497414</td>\n",
-       "      <td>-0.671152</td>\n",
-       "      <td>-0.747101</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50011</th>\n",
-       "      <td>1.822759</td>\n",
-       "      <td>1.735984</td>\n",
-       "      <td>0.714802</td>\n",
-       "      <td>0.265724</td>\n",
-       "      <td>1.312150</td>\n",
-       "      <td>2.877017</td>\n",
-       "      <td>1.463527</td>\n",
-       "      <td>1.579315</td>\n",
-       "      <td>0.613231</td>\n",
-       "      <td>-0.811191</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.133471</td>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>0.747637</td>\n",
-       "      <td>-0.845188</td>\n",
-       "      <td>-0.377958</td>\n",
-       "      <td>-0.508202</td>\n",
-       "      <td>-0.762454</td>\n",
-       "      <td>-0.629860</td>\n",
-       "      <td>0.551386</td>\n",
-       "      <td>0.593083</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>124938</th>\n",
-       "      <td>-1.305707</td>\n",
-       "      <td>-0.432899</td>\n",
-       "      <td>-0.926827</td>\n",
-       "      <td>-0.313617</td>\n",
-       "      <td>-0.299782</td>\n",
-       "      <td>-0.657781</td>\n",
-       "      <td>-1.954159</td>\n",
-       "      <td>-1.987496</td>\n",
-       "      <td>0.463831</td>\n",
-       "      <td>1.245751</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.241401</td>\n",
-       "      <td>-0.729477</td>\n",
-       "      <td>-0.003538</td>\n",
-       "      <td>1.947212</td>\n",
-       "      <td>-0.252211</td>\n",
-       "      <td>0.439232</td>\n",
-       "      <td>-0.711954</td>\n",
-       "      <td>-1.024724</td>\n",
-       "      <td>1.088679</td>\n",
-       "      <td>0.186241</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>54036</th>\n",
-       "      <td>-0.447699</td>\n",
-       "      <td>-0.735384</td>\n",
-       "      <td>-0.529246</td>\n",
-       "      <td>-0.386035</td>\n",
-       "      <td>-0.299782</td>\n",
-       "      <td>0.004993</td>\n",
-       "      <td>0.931061</td>\n",
-       "      <td>0.882218</td>\n",
-       "      <td>-1.059899</td>\n",
-       "      <td>-0.913020</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.328871</td>\n",
-       "      <td>-0.686374</td>\n",
-       "      <td>-0.421244</td>\n",
-       "      <td>-0.799279</td>\n",
-       "      <td>-1.079858</td>\n",
-       "      <td>0.368235</td>\n",
-       "      <td>0.905031</td>\n",
-       "      <td>-1.149552</td>\n",
-       "      <td>-0.118285</td>\n",
-       "      <td>-0.249318</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>43019</th>\n",
-       "      <td>-0.921214</td>\n",
-       "      <td>-1.295303</td>\n",
-       "      <td>2.164339</td>\n",
-       "      <td>0.482977</td>\n",
-       "      <td>-0.990611</td>\n",
-       "      <td>-1.431018</td>\n",
-       "      <td>-0.146253</td>\n",
-       "      <td>0.173504</td>\n",
-       "      <td>-1.280654</td>\n",
-       "      <td>-0.536254</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.791214</td>\n",
-       "      <td>-0.535513</td>\n",
-       "      <td>-0.249985</td>\n",
-       "      <td>-0.854106</td>\n",
-       "      <td>-0.839794</td>\n",
-       "      <td>1.234880</td>\n",
-       "      <td>0.282200</td>\n",
-       "      <td>-0.299957</td>\n",
-       "      <td>-1.628936</td>\n",
-       "      <td>-1.015138</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>243869</th>\n",
-       "      <td>-0.056721</td>\n",
-       "      <td>-0.593795</td>\n",
-       "      <td>0.496730</td>\n",
-       "      <td>0.012262</td>\n",
-       "      <td>-0.299782</td>\n",
-       "      <td>-0.105469</td>\n",
-       "      <td>0.237618</td>\n",
-       "      <td>0.556907</td>\n",
-       "      <td>-0.801980</td>\n",
-       "      <td>-0.342779</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.553795</td>\n",
-       "      <td>0.234955</td>\n",
-       "      <td>1.071360</td>\n",
-       "      <td>-0.864180</td>\n",
-       "      <td>-0.725478</td>\n",
-       "      <td>0.955792</td>\n",
-       "      <td>-0.136652</td>\n",
-       "      <td>-1.251453</td>\n",
-       "      <td>-0.087137</td>\n",
-       "      <td>0.176669</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1000 rows × 79 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        body_mass_index_bmi    weight  pulse_wave_arterial_stiffness_index  \\\n",
-       "93770              3.000000  1.697369                            -0.993863   \n",
-       "316865             0.481531  0.242866                            -0.540830   \n",
-       "270154            -0.211962 -0.329925                            -0.162804   \n",
-       "266822             0.041533 -0.407155                            -0.916272   \n",
-       "188910            -0.240454  0.217123                            -0.514518   \n",
-       "...                     ...       ...                                  ...   \n",
-       "50011              1.822759  1.735984                             0.714802   \n",
-       "124938            -1.305707 -0.432899                            -0.926827   \n",
-       "54036             -0.447699 -0.735384                            -0.529246   \n",
-       "43019             -0.921214 -1.295303                             2.164339   \n",
-       "243869            -0.056721 -0.593795                             0.496730   \n",
-       "\n",
-       "        pulse_wave_reflection_index  waist_circumference  hip_circumference  \\\n",
-       "93770                     -0.603288             2.233255           2.656092   \n",
-       "316865                     0.084680             1.005116           0.115456   \n",
-       "270154                    -0.458453             0.084011          -0.436856   \n",
-       "266822                    -0.241199            -1.451163           0.446843   \n",
-       "188910                    -0.530870            -0.069506          -0.436856   \n",
-       "...                             ...                  ...                ...   \n",
-       "50011                      0.265724             1.312150           2.877017   \n",
-       "124938                    -0.313617            -0.299782          -0.657781   \n",
-       "54036                     -0.386035            -0.299782           0.004993   \n",
-       "43019                      0.482977            -0.990611          -1.431018   \n",
-       "243869                     0.012262            -0.299782          -0.105469   \n",
-       "\n",
-       "        trunk_fat_percentage  body_fat_percentage  basal_metabolic_rate  \\\n",
-       "93770               1.030125             1.637406              0.570121   \n",
-       "316865              0.695786             0.185122              0.137530   \n",
-       "270154              0.398596             0.591761             -0.608726   \n",
-       "266822              0.299533             0.580143             -0.636971   \n",
-       "188910             -0.492975            -0.848905              0.619921   \n",
-       "...                      ...                  ...                   ...   \n",
-       "50011               1.463527             1.579315              0.613231   \n",
-       "124938             -1.954159            -1.987496              0.463831   \n",
-       "54036               0.931061             0.882218             -1.059899   \n",
-       "43019              -0.146253             0.173504             -1.280654   \n",
-       "243869              0.237618             0.556907             -0.801980   \n",
-       "\n",
-       "        forced_vital_capacity_fvc_best_measure  ...  phosphate  \\\n",
-       "93770                                -0.515888  ...   0.616273   \n",
-       "316865                                0.685692  ...   0.766222   \n",
-       "270154                                0.033988  ...  -0.183454   \n",
-       "266822                               -1.299970  ...   1.853351   \n",
-       "188910                                0.033988  ...  -0.664540   \n",
-       "...                                        ...  ...        ...   \n",
-       "50011                                -0.811191  ...  -0.133471   \n",
-       "124938                                1.245751  ...   0.241401   \n",
-       "54036                                -0.913020  ...   0.328871   \n",
-       "43019                                -0.536254  ...   0.791214   \n",
-       "243869                               -0.342779  ...   0.553795   \n",
-       "\n",
-       "        rheumatoid_factor      shbg  testosterone  total_bilirubin  \\\n",
-       "93770           -0.206852  0.325754     -0.768892        -0.688897   \n",
-       "316865           0.062543 -0.720949      0.630528         0.232489   \n",
-       "270154          -0.594780  0.076522     -0.836271        -0.638598   \n",
-       "266822          -0.691762 -0.382607     -0.858069        -0.579154   \n",
-       "188910          -0.196076 -0.812496      1.317357         0.719474   \n",
-       "...                   ...       ...           ...              ...   \n",
-       "50011            3.000000  0.747637     -0.845188        -0.377958   \n",
-       "124938          -0.729477 -0.003538      1.947212        -0.252211   \n",
-       "54036           -0.686374 -0.421244     -0.799279        -1.079858   \n",
-       "43019           -0.535513 -0.249985     -0.854106        -0.839794   \n",
-       "243869           0.234955  1.071360     -0.864180        -0.725478   \n",
-       "\n",
-       "        total_protein  triglycerides     urate      urea  vitamin_d  \n",
-       "93770       -0.897459       0.675306  0.083443 -0.928118  -0.761460  \n",
-       "316865       0.143004       0.240612  1.099900 -0.157219  -1.110865  \n",
-       "270154      -0.713848       2.042763 -0.447713 -0.538775  -1.608648  \n",
-       "266822       0.728114      -1.042678 -0.638776  1.330072   1.875831  \n",
-       "188910       0.737905      -0.166358  0.497414 -0.671152  -0.747101  \n",
-       "...               ...            ...       ...       ...        ...  \n",
-       "50011       -0.508202      -0.762454 -0.629860  0.551386   0.593083  \n",
-       "124938       0.439232      -0.711954 -1.024724  1.088679   0.186241  \n",
-       "54036        0.368235       0.905031 -1.149552 -0.118285  -0.249318  \n",
-       "43019        1.234880       0.282200 -0.299957 -1.628936  -1.015138  \n",
-       "243869       0.955792      -0.136652 -1.251453 -0.087137   0.176669  \n",
-       "\n",
-       "[1000 rows x 79 columns]"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "ERROR! Session/line number was not unique in database. History logging moved to new session 2143\n"
-     ]
-    }
-   ],
-   "source": [
-    "df_scaled.sample(1000)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import plotly.io as pio\n",
-    "png_renderer = pio.renderers[\"png\"]\n",
-    "png_renderer.width = 2000\n",
-    "png_renderer.height = 500\n",
-    "\n",
-    "pio.renderers.default = \"png\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df = pd.concat([df_original, df_scaled]).sample(50000)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
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rRMIAPal2+Pj6qnLTfhrZt7V9+OLA9z+qcceRmjS0k+3zlp2fauX692y9zfhq3GmURvVrY2e2m4dLni/2mF3hwfi+MX+EcgUGJDouzLhu0S1Ui6cNtKH5tg/26pXFb9kHSsz3TrEqwXZ8jejT2j4kYY7Yh0xMLc19zS9AA3JkU89hs1SlXHFVKP30FfU3Y2HFW+9q3YLRyh2UU+Z7a8marVo0dYC9XlLjkgD9mj+6eQMCCCCAAAIIXKcAn3+uDtBv1Ocf8wBx++bVVbzoI5oStlof7Tlg//u5TpXSWrX+Pf3w8+/2YWE+/1y9GpsZznz+Sd3PP9f5I4PTEEAAAQQQQACBTC1AgJ6py0/nEcjcAr/+fkxtek3Q9v9f4toEd3PH97Yzt+MfH+89oOVrd2h2aA8bQprA7N3VU2yoaQ5Pgde0+W8oT+5AtWrwkg31ar70vJ3VbA4TeA4Mnad1C0dfEaCbkO33oyc0oEsT+z4zG3zmwrVaOjPEY8HM9etVK2NnHye8fnIDdDO7vUvIdBscxl+uOqVt+uzLg5q1aK0NC82s6ccffkDGYsfqyVqz8QP998cj9hdI8Y/IqEt2pvqezXOVVG3MgwyDxr0at0y5mTH/dKX22rNlnp3lf63jWrVMGKB76rep+6HDRzRhSHDcbT2NhaadR2tQ92Z2VvW1AvS+o+YoT1CgOrWqZVcIiD2cBuhDxi+wM/9jDxPqvzK2p85fuJikq5kpvend3Xa1hPvvvj3u3KTamFgNEgboSbXDPCwwbOJCG2THHs27jrEPrJgAveug6apduZR9iMAcsxetU3RMjH1QwwTb5r1mv3ez+sGLJYokORwWr95iZ6gP7No07j21Wg/WuEHtVejBu21YbgLy+D8LYgP0z786pLlL1mvexD5x3//mD2Z7hvj1N3/+89hJDenZwr7PtK9Bh+F6b83Uq9oVf1wSoF/ru5jXEUAAAQQQQOB6Bfj8c3WAfqM+/5gHiGNipE4ta6p2m8Fq36y6fdB32sguMv/dXPGFZ+wDl3z+STxA5/NP6n7+ud6fGZyHAAIIIIAAAghkZgEC9MxcffqOAAJq1HGkBnVvbsNXMyN8ddjl2btrNr6vHbs+U0xMjF3SO1/eILv8uQkCJ89dpSUz/hdmxw+8zF5nYUs32Jmzvj4+dvnmhjVeVOtGlW1oapYFNzNrzWFmH9RrN1S71s24IkCfOGel1m/5yM5aNUd0dLSdCb9y7jB1HTzDhrbxj3LPF1XfTo3s9Vs3rKznnn70qusnFqCXqdtD6xZeuYS7OTE2IDTt69OxoZ3V66lNiQ0jM1O5TL0e+mDtdNVoGWKXN+8xbJZ6tKuvZW9us788Kl/qKbv0+fK123XxYoS9zOdfHdTeLfPsnxOrjVl2u/eI2br15v8t2W7qY2azm2W0EztSUsuEAbqnfpu6h4dHqkub2nG3jT8WzC8ow5a+LbMEunnW4qtvD2v+5L52Ce9rBejnL4Rr8arNdob9C889qd7BDezqAk4D9IRjt0arQfaXemYpfU+ub235txat2myXfzcPAZgVA5JqY2I1SBigJ9WOX44c1cr179oHWWIPM7M8dgl3E0CbGfDZ/P3sy2Z7ADOW+ndubL8eMeU1u6S+mXUf+4BLYu0xs+rz58utlxtXiXu5Q7/JalK7vF547gkboO/bNv+Ka8QG6Gbmfv32w+xDHLlzBarTwKlqUb+i/b5OGKDHHx9mq4earQbZ73dPP2MI0PmHCQEEEEAAAQRSU4DPP6nz+cc8QDx3yQaN7NdGPYbMtKuJVWjUxz5A/FLjvnpj/kj7YDWffxIP0Pn8k7qff1LzZwrXRgABBBBAAAEE3CpAgO7WytIvBBBIlsDSN7bprxOnlTVrFruvtJkpbkLaWYvWaeGUfnYJ6Pc/3q9VG3bGBehmSb7Xpg+Mu378wMsErubDvwkZzfLuE19ZaYPd2AC9RsWSqlbhOXvuNwd/srN+E85Af23NVh0/ccaGpik5TChrrm3uYY79X/9XIyYvttf/z96v9dqaLXHBpHkwoEjFdtr5xtQr9kCPfz+zn3Zw/8nauGSc3tr67xS3ySxjWKdqaZlQevG0ATL9MjOd1276UG/MG2FvZWb9L589WPfdfbt9rXTtbnEBemK1+fbQz7ZP5mGC5BwpreWb7+ySuYdZrt8cnmqRWNAZ/+9M2NuiXqW4etdvP1zDerdMVoAe2zezPPuoaUsVlCvAbjXQf0yYypa8/iXcE47d2AA9PDwiWa7GZ9WG9+IeNDHtTNjGxOqSMEBPqh3/nL+okVNei1uG3lyrdc9xalSznJ2BblZHaFjzRbu8esLD7EXeZdAMFby/gEo+XVgNa5ZNcogsXLnJPtjgaQb6/u2vXnF+bIBu/tLU2TzccNcdt9i2mG0aYv8+/h7osX82r8UP0D2NSwL05Hxn8x4EEEAAAQQQuF4BPv+kzucf8wBx+Ya91bFlTZ089bddScr8t2uFF562D8aaz2TmwV8+/yQeoPP55/KD/Kn1+ed6f15wHgIIIIAAAgggkJkFCNAzc/XpOwII2GWVX+49wYbdZilrs7ezWbLaLLdnZpybGa5m1m9EZFSyAvSBofP14H0F7MxWM4O7ZfdQNaj+vxnoF8MjbIidPZu/QsbNV4Hbbrp6D/SjJ9S8W6jmjutlr2XaYEJ+sxe5p8ME6GaJ87AJfeTvn1W9hs+2e2ib5a3Nns+tuo/VO8vG29m7m979xC4l+O/1M68I0M2e3zmy+dvZESbQrtl6sFaHDbMzxFPaJrOMoZnFX638c/YBgh9/+UM9h822e0ybJbpNmxoFj9S2lWbv9mxasGKTXRp7z+Yw283EamP2kDez9oNb1LSBqnkQ4Kdf/7QBfGJHSmv57oef6/W3dmjB5H72cmaZ76T6fa0AvXyDXnYv+ccevl9mRooZZ2YmSnJmoJvl/c1S4uYw+6z/8ttRu+T92JnLbb2CW9Swr8VvQ3L2QE8quL6nwK1Juppg2gTF/v5+dt9u8/1gZvwn1cbE6pDcAP2u22+2v1QcG9Le7mVuHmYwe6CPHxwctwe62Vd8xqhudoWG02fO2SXczXL3TbuMVscWNVS40P1qGDzCrhJx+y3/W6kgfrvMyhCteoy1WwyYZdq379qrmQvX6a2Fo+34jB+Wx54X/+/qvDxEC6b0u2J5/YT1SDg+4gfonsYlATr/MCGAAAIIIIBAagrw+Sf1Pv+YB4jNlkQj+ra2/82/esNOvb7uXT1b9GH74Caff6qpWJHEA3Q+/6Tu55/U/JnCtRFAAAEEEEAAAbcKEKC7tbL0CwEEki3wcq8JMntpx84qN8Fx9yGz9NORP+3s8eZ1K+rt7R8nK0A/ePiIDaajoi7ZwNv8ssRHPjZANktEmzDZzGo+dfqsnilSSMN6tVKO7P5XLOFuGm5mjE+au8oG5/5+We0MhjpVSnvskwnQnyxcUO/9+3OdPP23ihV5OO765kSzpOC2D/Yof97cNpw1+y6/OrnvFQH6vgOHNHj8Att+P7+satmgkupXK2Pvm9I2meXYm3cNtXtuP3DPHfYaLzXpZ5fIjp35a2bob3pvt923un71MvaXTPH3v05YG3MNE36OnrbEhrkmQC9f+mkN/f99phMCpbSWZsZwx4FT9fORowpuXkP1qr2QZL+vFaC/8+5uTZ23RjkDsqvIowUVERmpJnXKJytADxk7X7s//0bZ/P111x03K3RgOzsWzUMI3QbP0KXoaE0Z3lkffvKlnQHeuXVtG2gPmbAwbvb2n3+dVKvu4+xy5uaIH2LHOsXOQDd7myflagLrBa+/Y8dDYM4AW7uijxVUUm1MbJAmN0A37TDvNasMmNno/7r/TuUOCrSzvM0DE+Yw3z8r1r1rH/AwwXloSDv7gMKB7w5r4pCO9j3rt35k920Pm/C/pRATtuu9j/bJPFBw8WK47ipwi4b3bh2357mnAD06OkYdB0yxy2+ah1Fy5cyhWi+Vskv5J1zCPakZ6J7GJQF6sn9080YEEEAAAQQQuE4BPv/kintg8kZ+/jGft15ft8NuY2W2EzIPJ5uHas3Dn+VKFbXV4vNP4gE6n39S//PPdf644DQEEEAAAQQQQCDTChCgZ9rS03EEEHCbgAnQ2zWtZmfuciCAQOoImG0aTHDfqmFl+WXNIrMnetPOozVvYh/dXeCW1LkpV0UAAQQQQAABBBC4SoDPPwwKBBBAAAEEEEAAAQQQSC0BAvTUkuW6CCCAgJcFzC+Q2jZJelm81GiOWV7c7AUd/zBLjJvlrb15mJkjo6YuueqWM8d0t8vkp9djQOg8Hfzh1yua93yxx9SrQ4P02mSP7Upv/UmN8Wn2sqxTpZTKPn95FtFvfx5Xi/9facGsNsCBAAIIIIAAAggg4B2BzPz5JzX+O9cbVUtvnxec9tlt/XHqwfkIIIAAAggggICbBAjQ3VRN+oIAAplaIC1+gZSpwel8phQw2zSYhzXCIyLk6+OjbNn81bFFTRV/6pFM6UGnEUAAAQQQQACBtBLg809ayXNfBBBAAAEEEEAAAQTcL0CA7v4a00MEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgWQIEKAnA4m3IIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgi4X4AA3f01pocIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAskQIEBPBpKnt/x+4oLDK3A6AggggAACCCCAAAIIuEUgh38W5c3ln2bdOXU2QhciLqXZ/bkxAggggAACCCCAAAIIpC+BO/LnSF8NojUIIIBABhAgQHdYJAJ0h4CcjgACCCCAAAIIIICAiwQI0F1UTLqCAAIIIIAAAggggIALBAjQXVBEuoAAAl4XIEB3SE6A7hCQ0xFAAAEEEEAAAQQQcJEAAbqLiklXEEAAAQQQQAABBBBwgQABuguKSBcQQMDrAgToDskJ0B0CcjoCCCCAAAIIIIAAAi4SIEB3UTHpCgIIIIAAAggggAACLhAgQHdBEekCAgh4XYAA3SE5AbpDQE5HAAEEEEAAAQQQQMBFAgToLiomXUEAAQQQQAABBBBAwAUCBOguKCJdQAABrwsQoDskJ0B3CMjpCCCAAAIIIIAAAgi4SIAA3UXFpCsIIIAAAggggAACCLhAgADdBUWkCwgg4HUBAnSH5AToDgE5HQEEEEAAAQQQQAABFwkQoLuomHQFAQQQQAABBBBAAAEXCGTWAP3JCm21f/uriVawfd9JatukmooVKeSCCtMFBBBIDQECdIeqBOgOATkdAQQQQAABBBBAAAEXCRCgu6iYdAUBBBBAAAEEEEAAARcIuC1A7zlslprXq6SijxX0WB0CdBcMXrqAQBoKEKA7xCdAdwjI6QgggAACCCCAAAIIuEiAAN1FxaQrCCCAAAIIIIAAAgi4QMBtAfpX3x7W/ffcoZwB2T1Wp0jFdtq3bX6i72EGugsGNl1AIJUFCNAdAhOgOwTkdAQQQAABBBBAAAEEXCRAgO6iYtIVBBBAAAEEEEAAAQRcIJAeAvTjJ8+oRqsQffDmdPn5ZbWqi1dt0S+/H9PQni0UOmOZdu3+UlFRUXr8kQc0fnCw/LJm0fzlGxWYM4c+3nNAn+z7VqvmDtPYmcvjll//8psf7LmnzpyTr6+vQro1U6lnH7PXf7ZqR7VvVl2rN+zUpehoNapZVm2bVLWvJQzQzX3Wb/1Ily5Fq2md8mpWt4ILKk8XEEDAiQABuhM9SQToDgE5HQEEEEAAAQQQQAABFwkQoLuomHQFAQQQQAABBBBAAAEXCKSHAN0wtu0zUc3rVtQLzz1hVRt3GqU+wQ311OP/0r4Dh/TYw/fbv2/VfZya16uoSmWesQH6sje3a3T/tnHBePzw+9jx07oYHq67C9yqDz/5UhPnrNKGxWPsdcwM9Jb1K6lHu3o6e+68WnYfq36dG6t40UeuCNC37PxUK9e/p7689NMAACAASURBVLAJvRUVdcm2a1S/NnrikQdcUH26gAAC1yuQqQL0CxcjNHzSIn3xzQ+Kjo7WiyWLqH/nJvL19dGpM2c1MHSevvz2sPLlCdLIvm3i9tDw9BoB+vUOPc5DAAEEEEAAAQQQQMB9AgTo7qspPUIAAQQQQAABBBBAICMLpJcAfd3mD7Vn/3cKHdhOf/51Us06j9b2VZPl4+NzBe+0+W8oT+5AtWrwkg3QDx0+oglDguPek9Ty65FRl1Siemft2TzXvtfsgb595STdnD+P/XrJmq068sdxhXRrekWA3nXQdNWuXEplny9q3zd70TpFx8Soa5s6GbnstB0BBBwKZKoA3fyw/e2P4xrWu6V9kii4/xQ1qFFGlcoUU/8xYSpw203q0rqODnx3WL1GvKKNS8YqezZ/j68RoDscgZyOAAIIIIAAAggggICLBAjQXVRMuoIAAggggAACCCCAgAsE0kuAbmaBV20+QO+unqIVb72ro8dPqW/HRjITH8OWbtCB73+Ur4+PfvntmBrWeFGtG1W2AXp4eKS6tKkdV4n4AbpZ1n352u26eDHCvv75Vwe1d8s8+2cToO/ZHGaXgjfHxh3/0c6P9mnysE5XBOgNOgzXydNnlc3fz74vPCJSFUo/rf6dG7ug+nQBAQSuVyBTBeiT5q7SbTfni9u/YtTUJXrkX/fap4tK1OisnW9MU47s/tay6+AZqlultEoXfyLJ18qUeJIl3K935HEeAggggAACCCCAAAIuFCBAd2FR6RICCCCAAAIIIIAAAhlYIL0E6LG5S4PqL2resg0a2LWpzWcmzlmp8xfCNah7M2XNkkUTX1mpm/LljgvQIyIi1bn11QH6Qw/cpcpN+2n57MG67+7bdf7CRZWu3e2KAH3r6xN16815bfUWr96i3/88cdUM9C4h09Ww5osq9ezjGbjKNB0BBG60QKYK0M2TSx0HTFFw8xo6d/6Ctn2wR7NDe9r9L5p1HWOX84g9poSttsuEVC33XJKvtWlUhQD9Ro9IrocAAggggAACCCCAQAYWIEDPwMWj6QgggAACCCCAAAIIuFAgPQXoZr9xk8uYZdnfXjLWag8Mna8H7yuglxtXkdnTvGX3UJmQPXYGelIB+h235Vej4JHatnKSAnJk04IVmzR3yXo769wcZg/0JrXL2X3Wz/5zQc26jLHheWJ7oJsZ8TNGdVPuoJw6feacXcI9X55cLhwNdAkBBJIrkKkC9JiYGE0OW61t7+/RqTPnNKJPa1Up96x+PnJUXUKmxf3ANnivLH5L0dExql6xRJKvmWVDLoRfSq4170MAAQQQcLFAdIzke+WWTS7uLV1DAAEEEEhKIEZSQLbLSwSmxXHqbIQuRPAZJS3suScCCCCAAAIIIIAAAulRID0F6BfDI1SqVje1alApblb5wcNH1HfUHLvt7p2336xniz4sH/lcM0AvVqSQna2+6b3dypcnSPWrl9HqDTu1dsEoW4Z67YapctlntX7Lv3X+YrhdFr5d02r2tYT7qL+2ZqtWrHvXzmLPExSo0JB2KvzQfemxnLQJAQS8JJCpAvTpr76p03+f0+DuzXXy9N/qOWy23QO9eNFH1TB4uF3CPfYYP3uFXSakeoUSSb5mnog6de7y3hocCCCAAAKZXMAkJgTomXwQ0H0EEEBAyurrq1wBWdOMggA9zei5MQIIIIAAAggggAAC6VIgPQXo6RKIRiGAAAKJCGSqAL1Ks/6aP6mvCtx2k6X4aM8BrTRLc4zuppI1umjLiokKCgywrwX3n6L61cqo7PNFknytXKmiLOHOtxUCCCCAAAIIIIAAAgjECbCEO4MBAQQQQAABBBBAAAEE0pMAAXp6qgZtQQCBjCKQqQL0TgOnqtSzj6txrXJ2efbxs19XQI7s6t62roZOXKj8eXOra5s6OvDdYXUZNF2bl09QzoDsHl/7/cSFNK310JFZNXJoVJq2gZsjgAACCCCAAAIIIIDAZYH0GqAHdKyg83O2UyYEEEAAAQQQQAABBBDIZAIE6Jms4HQXAQRuiECmCtB///O4Rk59Tb/+/pfMfuhFChfU4B4tlCO7v/4+d14hofP1+YGDCgrMqSE9W6jkM4UtsqfXCNBvyDjkIggggAACCCCAAAIIuEKAAN0VZaQTCCCAAAIIIIAAAgi4RoAA3TWlpCMIIOBFgUwVoKeGKwF6aqhyTQQQQAABBBBAAAEEMqYAAXrGrButRgABBBBAAAEEEEDArQIE6G6tLP1CAIHUFCBAd6hLgO4QkNMRQAABBBBAAAEEEHCRAAG6i4pJVxBAAAEEEEAAAQQQcIEAAboLikgXEEDA6wIE6A7JCdAdAnI6AggggAACCCCAAAIuEiBAd1Ex6QoCCCCAAAIIIIAAAi4QIEB3QRHpAgIIeF2AAN0hOQG6Q0BORwABBBBAAAEEEEDARQIE6C4qJl1BAAEEEEAAAQQQQMAFAgToLigiXUAAAa8LEKA7JE+LAH3oyKwaOTTKtjz+nx12hdMRQAABBBBAAAEEEEDAoQABukNATkcAAQQQQAABBBBAAIEbKpARA/QvDsRo5vzLGUhyjice9VHX9lmT81begwACCCRLgAA9WUxJv4kA3SEgpyOAAAIIIIAAAggg4CKBtA7QIyIvyS+r71WiZxqWUu5VH7pImq4ggAACCHgSiIiKln8i/x6ghgACCCCQ+QR8fHwyXKczSoA+f/lGRUREqnPr2hnOOCM2OLW8/zpxWj2Hzdax46fUoXkNlS/9lHoNm60jf/ylRjXLqnWjyhmO6/Avf6jH0FnasHhMhmv79Tb4yQpttX/7q9d7+lXnEaA7pCRAdwjI6QgggAACCCCAAAIIuEggrQP0U2cjdCHi0lWiAR0r6Pyc7S6SpisIIIAAAggggAACCCCQHAFmoCdH6frek1qB7vW1Jm3P+venX+mpxx9Sjuz+KW7INwd/0syF6zRnXE+P5zrx3vHhZypf6il7/ZiYGL3UpJ82LhkrP7+smjb/Dfn7+6lTy5qKjo7RkjVb9cexExrYtan92tc3bR5CSdjOlMCmlwC957BZal6vkoo+VjDJ5jvpZ/yLEqCnZIR44b0E6F5A5hYIIIAAAggggAACCGQQAQL0DFIomokAAggggAACCCCAQCYRIEBPvUI7CXRTr1Xev7IJQBt3GmUD8Ly5c6W4ARcuRujg4V/1xCMPeDz3er3/PndebXqO1xvzR8Rdf/dn36j4U4/Yr03IW71iSZUtWcR+PWLKa3ry0QdVs1LJFPflRp8Qv50puXZ6CdC/+vaw7r/nDuUMyO6x+dfbz/gXJUBPyQjxwnsJ0L2AzC0QQAABBBBAAAEEEMggAgToGaRQNBMBBBBAAAEEEEAAgUwiQIDuudBffPOD5i/bqMDAHDr21ymdO39B3V6uq+eLPabwiEiVrNFZe7fMi7tI/9FhKvt8UVUq84ziB7p79n+n8bNX6J/zFyT5KKRbU5V69nGdv3BRI6cu0Rdf/1fZs/krpFszPfNkoUQbNWfJeoWHR6pHu3r29dY9x+neO2/TsN6t7Ncm6K1ctriKPVlIA0Ln6cdf/lBU1CU1qlVW7ZpW0+hpS3X7rfn1cuMq9v2RUZdUpm53vbVwtA0wk9sOc+7pM+cSvYd5rVqLgerZrr7GzFiqYkUeVv48QVr25nY9cO8dCsqVU4unDfDY74TnN65VTlPCVuu16QNtu9dt/lALV2zSxYhI5c8bpElDO+rO22++wjsxwKhLlzRq6hJ9uu87RUZGqtCD92jy8E4K7j9Z+7/+QQ/eW8Da9e3USLFh6yuvrdeyN7Ypd1BO3XHrTSpV/HF7b1Or3EGBWjNvuMo36KXm9SvpP3sP6PjJv1Wk8IMa1L15XBPMOFi/9SNduhStpnXKq1ndCva119e9q9fX7bDL/OcKDNDssT10U77cV7VxVmj3JAdp/FDYUztM3yfMXqn3PvpcuXPlVK2XnteajR/ELeG+a/cXmhK2RhfDI1SsSCEN7t5cvll81bDDCPUJbqjnnn5UZ/7+R3XbDtGCKf11z523Jtqmc/9cUOiMZdr7xffKksVX9auXUZtGl8ebcQjMmUMf7zmgT/Z9q1Vzh2nszOVq26SaveeJU39r0LhX7ffId//9VZFRUXZVAFPf5Pbzy29+sPc/deacfH197fdTqWcfs/cnQE9n/6ilVYBuGEYOjdLQkVnt/3MggAACCCCAAAIIIIBA2gsQoKd9DWgBAggggAACCCCAAAII/E+AAN3zaDABetPOo21Q+nDBe+y+1+brt5eMVTZ/v2QH6PXaDdPYkHYqeN+dOnP2H5m954MCA2xoHRiQXb06NNAPP/+utr0n6J2l4xSQ4+oZuZ99edAuJ750Zoj+OX/RtiMiMlKblo23nXihTne9tWi0cucK1P6vD6noY//S8ZNnbKBt3vPzkT9tYLk6bLh9v1lW3YTBC6f2T1E7zLlm6fLE7pEvTy4VqxJsHw4Y0ae1DUzN8WK9Hlq7YFTcDHRP/U54vqlB/AD9+x9+1W235LNB8OS5q3XhYrgG92h+zQB958f7tGHrR5o6oottk6mlCd5/+e2ougyaccV+4PHD1q6Dpqtu1RdUpsST9rwhExbaoL16xRL2a/Perm3q2AcTTEjepNMoG8I//cRD2rLzU61c/57CJvS2DzOYmfij+rXR/XffrlqtB2vT8vF2HP3253EVuO0mJdXGpEZp/HZ6asfqDTu1eecnChvf2wbjfUbMkZmFbvZA/+PYSTXrPFrLZg2yriFjX7UPO7RtUlXfHvpZfUfN1ZuvjrTB/gP33OFxz/ehExfahyR6d2ig8xfC1b7vJLWoX1GVyhSz9TEPUozu3zYu1DavxwboJvg29WhRv5L+++Nv6jNyjh3Pscax+5d76uex46d1MTxcdxe4VR9+8qUmzlkVV1cC9HT2Lx8BejorCM1BAAEEEEAAAQQQQCANBQjQ0xCfWyOAAAIIIIAAAggggMBVAgTongeFCW9NKLh+0Zi4N3YdPMMu321C4uTOQO87ao7yBAWqU6taVyxjXrxaJ21dMdGGweZo2X2sOrasqeJFLy8fHv+IjIyyIfkHa6dr58f7tWf/tzJh8pCeLeSXNat6DJ0VFzjGP88E7YO6N7MPAFRs3NfOADdhrenX4w8/oHrVXlBK2pGYWOw9HvnXvTZQNl7xZyknDNA93S/h+QkD9Pj3/3jvAS1fu0OzQ3tcM0A/9OMRdQmZboP92OXZzbVuRID+3pqpMg8PmMMs8V74oftUt2ppmfC9duVSdlUCc8xetE7RMTHq1LKWqjTrr5ebVFXtl563e62bI6k2JjVKEwboSbUjuP8U1a9WRuVKXW6HWRI9dOZyGy6bPd1/P3pCA7o0sa+ZGfozF661D2qYwzy0YcbZqdN/a/nsIXZmeVKHefhhx+op9uEQc2zftdc+tDBzTHdbn0OHj2jCkOC40+MH6C/3nmBXd4hdqt9c69NNc+17k9vPK75foi6pRPXO2rP56msk2YEUvOATYzYn4LhuAQL066bjRAQQQAABBBBAAAEEXCdAgO66ktIhBBBAAAEEEEAAAQQytAABuufymfB24isr7ezc2GPYpEV6pOA9qlW51FUBer9Rc1Wu1FNXLeFuZuMuXrVZazft0gvPPanewQ3sUu7PVO6ge++6Le7aZma5WXa64gtPJ9ows1d3t7Z19cbGD+x7vj30i/z8sthZ5ybkNEvD//r7MYUtfVtH/zolHx/J7DM9f3JfG+qamdx5cgfaWb7lG/TW+sVjbPie0nZ4uocJO/dtm29n2cce8QN0Y+HpfgnPTxigr9n4vnbs+kwmvjTLiufLG2T3V0/OHuiff3VIc5esl5mp3KdjQ7sU/40I0OP31yyVX/C+AmpYs6wadBiuk6fP2lnm5jDL/lco/bT6d26s3/88rjlLNtil3009zP/MkVgbkxqlCYPlpNpRv/1wDe3ZQo89fL+9lAnqe4+YYwP0iXNWav2Wj+wy9eaIjo62D3SsnDvMfv3XidN6sV5PjRnQ1uO+72bslqrVVZ9vmx/XXLOk+ujpS+2qB6Y+ZguCLm1qx71+5Qz05fYhhOAWNWRqPmH2Ci2fPdi+N7n9NEvDL1+7XRcvRvy/5cG4LRaYgZ7O/qkiQE9nBaE5CCCAAAIIIIAAAgikoQABehric2sEEEAAAQQQQAABBBC4SoAA3fOgMEGeCcXNLPHYo12fSWpY80W7pPezVTrqs63/2wO9bZ+JdqZvwj3QY881+12PmrZUQbkC1LdjIzvz28waDsiRLVmj0+yDnt3fX6s27LSzzc0+5ybgN0tvm1nOZs9oE9q2qFdJ1So8Z69pwtNhvVvaAP27//6i4ZMXq0fbelr65jY7c9scKW2Hp3skFlSWrd/TLgOeN/flWdqe7pfw/PgB+gf/+UKzFq3Twin97L7h73+831okN0CPRf76+5/s3ucbl4yzS+p3GTTd0RLuscuLm+vHD9DNjHczVsxqBUkdZpn9jgOmqkvr2nrhuSfi3ha/jbHhdsJrJAyWk2qHmYHeqGbZuGXozR7lZhl9E6C/tmarjp848/8PdVzdyv5jwhQYkMPuW75m3gjlyO6faFfMAw2xM9BjV1RIOAPdjP/OrRMP0M3DEHVeHqJ7777NPhDSs3093XXHLfZeyennSy8+q8pN+9nQ/b67b9f5CxdVunY3AvRk/WRJgzcRoKcBOrdEAAEEEEAAAQQQQCCdChCgp9PC0CwEEEAAAQQQQAABBDKpAAG658Kb8NbsaT1zdDcbUJsZtSaM3LJiol2munzD3gob30sP3FvA7qldo2WIxoa0vypAN8F1oQfvtjcz+0CbWc9mprkJMc3sZLMHul/WLPrlt2O6/ZZ8cUt6J2yd2Qfd7BV9y015bWhsjpea9JOvr49WvDLUziIu36CX3efbzDY27zdLY5sZ9CZAN0eNVoP04L0F7HLeVcsVt3+X0nZ4ukdiAboJRs0+5WZf9mvdz1OAvm7zh9r6/h7bdzObu//oMEVERiUrQDd7fefI5m9n4JtwtWbrwVodNky+vr56qXFfvbtmStze8yndAz2p4Nrsgb7irXc1Y1Q3W5vTZ87ZJdxzZM+mo3+dtKsPmP3kew2frarli6twofsTbWPsgwcJx0NygmUzE97sw77zo32aNaa7fHx9NDB0nr7/4cjlPdCPnlDzbqGaO66XHryvgHU1s87NfuS7dn9hZ8kvnzVYsxatlZllPrBr0yS/acz+8Lly5rB7wMfugd6sbgVVLvtsoisExJ+BvuPDz/TJ599oUPfmV10/Of0sWewxNQoeqW0rJ9kHUhas2GRXG9izOcxejxno6ewfOQL0dFYQmoMAAggggAACCCCAQBoKpMcAPaBjBStyfs72NJTh1ggggAACCCCAAAIIIJAWAgTontVNgD513hrdfkt+7f/6kLJkyWL3ijZLf5vDzIieseBNu+R1/nxB8vfzU+niT1wVoIeMna/dn3+jbP7+uuuOmxU6sJ1uypfbBrlmBvmu3V8qIjLSBqphE/okOSPd7INuZm+bgNLMKDbH4PEL9M3Bn7R2wSj79Tvv7rZtzhmQXUUeLWiv26RO+bgA3cxiX/D6O9q1bmbcfVLaDk/3SCyo3PbBXo2btdw6rVs42mO/PQXopp3dh8zST0f+tH7N61bU29s/TlaAvu/AIWsVFXXJPqDQskElu1qAOSbPXa2NOz62tTN7pN+oAN1c28zwXrHuXdvnPEGBCg1pp7sL3Kr2fSbq+Km/7YMTJZ4ubB+o+PLbH5JsY2IjNTnBsgnQI6MuacLs1/Xev/cpMDCHmtYur+Xrdti96s3xn71fa9LcVTY49/fLqk6taqlSmWJ2Rvis0O4qeN+dMrPH67UfrhF9WqlI4YKJfuOYgH3k1Nf0yeff2pnqdau+oJcbV7HL+Se2xH78AN087NG65zhlz+avrFmz6IF77tDw3q3swynJ7af5Xtr03m7lyxOk+tXLaPWGnXHfFwToafEvjId7EqCns4LQHAQQQAABBBBAAAEE0lCAAD0N8bk1AggggAACCCCAAAIIXCVAgO55UMQG6IunDWD0IIBAKgmYZezb9p5oH4S4/db8NvBftf49ffXdYY0f1CGV7urssj4xZtF6jusWIEC/bjpORAABBBBAAAEEEEDAdQIE6K4rKR1CAAEEEEAAAQQQQCBDC2TEAN2b4PH33/bWfc0s84bBI666XYfmNezMdm8d6aUdKe2vWSJ/7aZdV5xmlkBfMKVfSi+VLt5vZs6PmrrkqrbMHNNdBW67yettTI32fHvoZw2btMhuQ5Ali69d1n7a/DV2ef0e7ep5vY/JuSEBenKUPLyHAN0hIKcjgAACCCCAAAIIIOAiAQJ0FxWTriCAAAIIIIAAAggg4AIBAnTPRUyLAN0Fw4ouIJBiAbMVgtmn3Syvf+lStJ56/CH1Dm6gbP5+Kb6WN04gQHeoTIDuEJDTEUAAAQQQQAABBBBwkQABuouKSVcQQAABBBBAAAEEEHCBAAG6C4pIFxBAwOsCBOgOyQnQHQJyOgIIIIAAAggggAACLhIgQHdRMekKAggggAACCCCAAAIuECBAd0ER6QICCHhdgADdITkBukNATkcAAQQQQAABBBBAwEUCBOguKiZdQQABBBBAAAEEEEDABQIE6C4oIl1AAAGvCxCgOyQnQHcIyOkIIIAAAggggAACCLhIgADdRcWkKwgggAACCCCAAAIIuECAAN0FRaQLCCDgdQECdIfkBOgOATkdAQQQQAABBBBAAAEXCRCgu6iYdAUBBBBAAAEEEEAAARcIEKC7oIh0AQEEvC5AgO6QnADdISCnI4AAAggggAACCCDgIgECdBcVk64ggAACCCCAAAIIIOACgYwYoEfu/bf+mTAg2fp+T5VUzv7jk/1+3ogAAghcS4AA/VpC13idAN0hIKcjgAACCCCAAAIIIOAiAQJ0FxWTriCAAAIIIIAAAggg4AIBAvTUK+L85RsVERGpzq1rp95NuHKcQGp5/3XitHoOm61jx0+pQ/MaKl/6KfUaNltH/vhLjWqWVetGlTNcFQ7/8od6DJ2lDYvHZLi2p5cGE6A7rAQBukNATkcAAQQQQAABBBBAwEUCBOguKiZdQQABBBBAAAEEEEDABQIE6KlXxNQKdFOvxal35X9/+pWeevwh5cjun+KbfHPwJ81cuE5zxvX0eK4T7x0ffqbypZ6y14+JidFLTfpp45Kx8vPLqmnz35C/v586tayp6OgYLVmzVX8cO6GBXZvar319fVLcpxtxQsJ2puSaBOgp0Ur8vQToDg0J0B0CcjoCCCCAAAIIIIAAAi4SIEB3UTHpCgIIIIAAAggggAACLhAgQE+9IjoJdFOvVd6/sgl6G3caZQPwvLlzpbgBFy5G6ODhX/XEIw94PPd6vf8+d15teo7XG/NHxF1/92ffqPhTj9ivew6bpeoVS6psySL26xFTXtOTjz6ompVKprgvN/qE+O1MybUJ0FOilfh7CdAdGhKgOwTkdAQQQAABBBBAAAEEXCRAgO6iYtIVBBBAAAEEEEAAAQRcIECA7rmIX3zzg+Yv26jAwBw69tcpnTt/Qd1erqvniz2m8IhIlazRWXu3zIu7SP/RYSr7fFFVKvOM4ge6e/Z/p/GzV+if8xck+SikW1OVevZxnb9wUSOnLtEXX/9X2bP5K6RbMz3zZKFEGzVnyXqFh0eqR7t69vXWPcfp3jtv07DerezXJuitXLa4ij1ZSANC5+nHX/5QVNQlNapVVu2aVtPoaUt1+6359XLjKvb9kVGXVKZud721cLRyBmRPdjvMuafPnEv0Hua1ai0Gqme7+hozY6mKFXlY+fMEadmb2/XAvXcoKFdOLZ42wGO/E57fuFY5TQlbrdemD7TtXrf5Qy1csUkXIyKVP2+QJg3tqDtvv/kK78QAoy5d0qipS/Tpvu8UGRmpQg/eo8nDOym4/2Tt//oHPXhvAWvXt1MjPVmhrfZvf1WvvLZey97YptxBOXXHrTepVPHH7b1NrXIHBWrNvOEq36CXmtevpP/sPaDjJ/9WkcIPalD35nFNMONg/daPdOlStJrWKa9mdSvY115f965eX7fDLvOfKzBAs8f20E35cl/Vxlmh3ZMcpLHtNG/w1A7T9wmzV+q9jz5X7lw5Veul57Vm4wdxS7jv2v2FpoSt0cXwCBUrUkiDuzeXbxZfNewwQn2CG+q5px/Vmb//Ud22Q7RgSn/dc+etLvjp56wLBOjO/ESA7hCQ0xFAAAEEEEAAAQQQcJEAAbqLiklXEEAAAQQQQAABBBBwgQABuucimgC9aefRNih9uOA9dt9r8/XbS8Yqm79fsgP0eu2GaWxIOxW8706dOfuPfHx8FBQYYEPrwIDs6tWhgX74+Xe17T1B7ywdp4Ac2a9q2GdfHrTLiS+dGaJ/zl+07YiIjNSmZePte1+o011vLRqt3LkCtf/rQyr62L90/OQZG2ib9/x85E+Nnblcq8OG2/ebZdVNGLxwav8UtcOca5YuT+we+fLkUrEqwfbhgBF9WiswZw57rxfr9dDaBaPiZqB76nfC800N4gfo3//wq267JZ8NgifPXa0LF8M1uEfzawboOz/epw1bP9LUEV1sm0wtTfD+y29H1WXQjCv2A48fTHcdNF11q76gMiWetOcNmbDQBu3VK5awX5v3dm1Txz6YYELyJp1G2RD+6Sce0padn2rl+vcUNqG3fZjBzMQf1a+N7r/7dtVqPViblo+34+i3P4+rwG03Kak2JjVK47fTUztWb9ipzTs/Udj43jYY7zNijswsdLMH+h/HTqpZ59FaNmuQdQ0Z+6p92KFtk6r69tDP6jtqrt58daQN9h+4544Mued7avyoJkB3qEqA7hCQ0xFAAAEEEEAAAQQQcJEAAbqLiklXEEAAAQQQQAABBBBwgQABuucimvB26MSFWr9oTNwbEW6GfgAAIABJREFUuw6eYZfvNiFxcmeg9x01R3mCAtWpVa0rljEvXq2Ttq6YaMNgc7TsPlYdW9ZU8aKXlw+Pf0RGRtmQ/IO107Xz4/3as/9bmTB5SM8W8suaVT2GzrIBesLDBO2DujezDwBUbNzXzgA3Ya3p1+MPP6B61V5QStqRmFjsPR751702UDZe8WcpJwzQPd0v4fkJA/T49/947wEtX7tDs0N7XDNAP/TjEXUJmW6D/djl2c21bkSA/t6aqTIPD5jDLPFe+KH7VLdqaZnwvXblUnZVAnPMXrRO0TEx6tSylqo066+Xm1RV7Zeet3utmyOpNiY1ShMG6Em1I7j/FNWvVkblSl1uh1n6PXTmchugmz3dfz96QgO6NLGvmRn6MxeutQ9qmMM8tGHG2anTf2v57CHKksXXBT/5nHeBAN2hIQG6Q0BORwABBBBAAAEEEEDARQIE6C4qJl1BAAEEEEAAAQQQQMAFAgTonotowtuJr6y0s3Njj2GTFumRgveoVuVSVwXo/UbNVblST121hPv5C+FavGqz1m7apReee1K9gxvYpdyfqdxB9951W9y1zcxys4x7xReeTrRhZq/ubm3r6o2NH9j3fHvoF/n5ZbGzzk3IaZaG//X3Ywpb+raO/nVKPj7SV98e1vzJfW2oa2Zy58kdqBb1K6l8g95av3iMDd9T2g5P9zCh7r5t8+0s+9gjfoBuLDzdL+H5CQP0NRvf145dn8nsrW6WFc+XN8jur56cPdA//+qQ5i5Zr2PHT6tPx4Z2Kf4bEaDH769ZKr/gfQXUsGZZNegwXCdPn7WzzM1hlv2vUPpp9e/cWL//eVxzlmywS7+bepj/mSOxNiY1ShMG6Em1o3774Rras4Uee/h+eykT1PceMccG6BPnrNT6LR/ZZerNER0dbR/oWDl3mP36rxOn9WK9nhozoG262Pc9vfzYJUB3WAkCdIeAnI4AAggggAACCCCAgIsECNBdVEy6ggACCCCAAAIIIICACwQI0D0X0YS3JhQ3s8Rjj3Z9JqlhzRftkt7PVumoz7b+bw/0tn0m2pm+CfdAjz3X7Hc9atpSBeUKUN+OjezMbzNrOCBHtmSNJrMPenZ/f63asNPONjf7nJuA3yy9bWY5ly/1lA1tW9SrpGoVnrPXNOHpsN4tbYD+3X9/0fDJi9WjbT0tfXObnbltjpS2w9M94oe6sZ0qW7+nXQY8b+7Ls7Q93S/h+fED9A/+84VmLVqnhVP62X3D3/94v7VIboAe256vv//J7n2+cck4u6R+l0HTHS3hbvZLjz3iB+hmxrsZK2a1gqQOs8x+xwFT1aV1bb3w3BNxb4vfxthwO+E1EgboSbXDzEBvVLNs3DL0e7/43i7bbwL019Zs1fETZ/7/oY6rW9l/TJgCA3Lok33fas28EcqR3T9ZY9XtbyJAd1hhAnSHgJyOAAIIIIAAAggggICLBAjQXVRMuoIAAggggAACCCCAgAsECNA9F9GEt2ZP65mju9mA+stvfpAJI7esmGj3MC/fsLfCxvfSA/cWsHtq12gZorEh7a8K0E1wXejBu+3Nlr253c56NjPNTYhpZiebPdD9smbRL78d0+235Itb0jth68w+6KEzlumWm/La0NgcLzXpJ19fH614ZaidRVy+QS+7z7eZbWze/3LvCXYGvQnQzVGj1SA9eG8Bu5x31XLF7d+ltB2e7pFYgF7n5SF2n3KzL/u17ucpQF+3+UNtfX+P7buZzd1/dJgiIqOSFaCbvb5zZPO3M/DPX7iomq0Ha3XYMPn6+uqlxn317popcXvPp3QP9KSCa7MH+oq33tWMUd1sbU6fOWeXcM+RPZuO/nXSrj5g9pPvNXy2qpYvrsKF7k+0jbEPHiQcD8kN0M0+7Ds/2qdZY7rLx9dHA0Pn6fsfjlzeA/3oCTXvFqq543rpwfsKWFcz69zsD79r9xd2lvzyWYM1a9FamRUSBnZt6oKffM67QIDu0JAA3SEgpyOAAAIIIIAAAggg4CIBAnQXFZOuIIAAAggggAACCCDgAgECdM9FNAH61HlrdPst+bX/60PKkiWL3SvaLP1tDjMjesaCN+2S1/nzBcnfz0+liz9xVYAeMna+dn/+jbL5++uuO25W6MB2uilfbhvkmhnku3Z/qYjISBuohk3ok+SMdLMPupm93bdTIzuj2ByDxy/QNwd/0toFo+zX77y727Y5Z0B2FXm0oL1ukzrl4wJ0M4t9wevvaNe6mXH3SWk7PN0jsQB92wd7NW7Wcuu0buFoj/32FKCbdnYfMks/HfnT+jWvW1Fvb/84WQH6vgOHrFVU1CX7gELLBpXsagHmmDx3tTbu+NjWzuyRfqMCdHNtM8N7xbp3bZ/zBAUqNKSd7i5wq9r3majjp/62D06UeLqwfaDiy29/SLKNiY3U5AbokVGXNGH263rv3/sUGJhDTWuX1/J1O+xe9eb4z96vNWnuKhuc+/tlVadWtVSpTDGZBx9mhXZXwfvulFk9oV774RrRp5WKFC7ogp9+zrqQqQL00rW72QEce1yKjtGTjz6gRVMH6NSZs/aJjC+/Pax8eYI0sm8bFX3s8gDx9BoBurMByNkIIIAAAggggAACCLhJgADdTdWkLwgggAACCCCAAAIIZHwBAnTPNYwN0BdPG5Dxi00PEEDghglkqgA9odq0+W/YPRReblxFZo3/ArfdpC6t6+jAd4fVa8Qr2rhkrLJn8/f4GgH6DRuLXAgBBBBAAAEEEEAAgQwvQICe4UtIBxBAAAEEEEAAAQQQcJVARgzQvVmA+Ptve+u+ZpZ5w+ARV92uQ/Madma7t4700o6U9tcskb92064rTjNLoC+Y0i+ll0oX7zcz50dNXXJVW2aO6W5zS460Eci0AfrF8AhVadZf6xaMtiF6iRqdtfONacqR3d9WouvgGapbpbRdziGp18qUeFIE6GkzcLkrAggggAACCCCAAALpUYAAPT1WhTYhgAACCCCAAAIIIJB5BQjQPdc+LQL0zDsa6TkCGUcg0wboqzfs1NcHf7J7HRz965SadR2j7SsnxVVuSthq5ckdqKrlnkvytTaNqhCgZ5yxTksRQAABBBBAAAEEEEh1AQL0VCfmBggggAACCCCAAAIIIJACAQL0FGDxVgQQQOD/BTJlgB4TE6MaLUM0eXhn/ev+O/XzkaPqEjJNby8ZGzcwXln8lqKjY1S9YokkX+vSprbOh0d5fTB16xdj7zljgo/Mn83/cyCAAAIIeFcg4U/eS9ExyuLLz2PvViH93O3yv8wcCCCAgBQTI+XMnjXNKE6djdCFiEtX3D+gYwX79fk529OsXdwYAQQQQAABBBBAAAEE0kaAAD1t3LkrAghkbIFMGaB/+MmXWrBikxZPG2Crd+z4aTUMHm6XcI89xs9eoZvy5Vb1CiWSfM3snW5+QeXto+/gywHNxNExMn82/8+BAAIIIOBdgYQ/ec3DWT4+BOjerUL6uRuVTz+1oCUIpLWAeZgqKKdfmjWDAD3N6LkxAggggAACCCCAAALpUoAAPV2WhUYhgEA6F8iUAXrbPhPVqGZZlS/1lC2PCT1K1uiiLSsmKigwwP5dcP8pql+tjMo+XyTJ18qVKsoS7ul8gNM8BBBAAAEEEEAAAQS8KcAS7t7U5l4IIIAAAggggAACCCBwLQEC9GsJ8ToCCCBwtUCmC9D/++NvCh4wRVtfn6gsWXzjRIZOXKj8eXOra5s6OvDdYXUZNF2bl09QzoDs8vTa7ycueH1cDR15eUnIkUOjZP5s/p8DAQQQQAABBBBAAAEE0l6AAD3ta0ALEEAAAQQQQAABBBBA4H8CBOiMBgQQQCDlApkuQB8yYaHuves2meXX4x9/nzuvkND5+vzAQQUF5tSQni1U8pnC9i2eXiNAT/mg4wwEEEAAAQQQQAABBNwqQIDu1srSLwQQQAABBBBAAAEEMqYAAXrGrButRgCBtBXIdAH6jeYmQL/RolwPAQQQQAABBBBAAIGMK0CAnnFrR8sRQAABBBBAAAEEEHCjAAG6G6tKnxBAILUFCNAdChOgOwTkdAQQQAABBBBAAAEEXCRAgO6iYtIVBBBAAAEEEEAAAQRcIECA7oIi0gUEEPC6AAG6Q3ICdIeAnI4AAggggAACCCCAgIsECNBdVEy6ggACCCCAAAIIIICACwQI0F1QRLqAAAJeFyBAd0hOgO4QkNMRQAABBBBAAAEEEHCRAAG6i4pJVxBAAAEEEEAAAQQQcIEAAboLikgXEEDA6wIE6A7JCdAdAnI6AggggAACCCCAAAIuEiBAd1Ex6QoCCCCAAAIIIIAAAi4QIEB3QRHpAgIIeF2AAN0heVoG6LFNHzk0ymEvOB0BBBBAAAEEEEAAAQRuhAAB+o1Q5BoIIIAAAggggAACCCBwowQI0G+UJNdBAIHMJECA7rDa6SFAN10gRHdYSE5HAAEEEEAAAQQQQOAGCBCg3wBELoEAAggggAACCCCAAAI3TIAA/YZRciEEEMhEAgToDotNgO4QkNMRQAABBBBAAAEEEHCRAAG6i4pJVxBAAAEEEEAAAQQQcIEAAboLikgXEEDA6wIE6A7JCdAdAnI6AggggAACCCCAAAIuEiBAd1Ex6QoCCCCAAAIIIIAAAi4QIEB3QRHpAgIIeF2AAN0hOQG6Q0BORwABBBBAAAEEEEDARQIE6C4qJl1BAAEEEEAAAQQQQMAFAgToLigiXUAAAa8LEKA7JCdAdwjI6QgggAACCCCAAAIIuEiAAN1FxaQrCCCAAAIIIIAAAgi4QIAA3QVFpAsIIOB1AQJ0h+QE6A4BOR0BBBBAAAEEEEAAARcJEKC7qJh0BQEEEEAAAQQQQAABFwgQoLugiHQBAQS8LkCA7pCcAN0hIKcjgAACCCCAAAIIIOAiAQJ0FxWTriCAAAIIIIAAAggg4AIBAnQXFJEuIICA1wUI0B2SE6A7BOR0BBBAAAEEEEAAAQRcJECA7qJi0hUEEEAAAQQQQAABBFwgQIDugiLSBQQQ8LoAAbpDcgJ0h4CcjgACCCCAAAIIIICAiwQI0F1UTLqCAAIIIIAAAggggIALBAjQXVBEuoAAAl4XIEB3SE6A7hCQ0xFAAAEEEEAAAQQQcJEAAbqLiklXEEAAAQQQQAABBBBwgQABuguKSBcQQMDrAgToDskJ0B0CcjoCCCCAAAIIIIAAAi4SIEB3UTHpCgIIIIAAAggggAACLhAgQHdBEekCAgh4XYAA3SE5AbpDQE5HAAEEEEAAAQQQQMBFAgToLiomXUEAAQQQQAABBBBAwAUCBOguKCJdQAABrwsQoDskJ0B3CMjpCCCAAAIIIIAAAgi4SIAA3UXFpCsIIIAAAggggAACCLhAgADdBUWkCwgg4HUBAnSH5AToDgE5HQEEEEAAAQQQQAABFwkQoLuomHQFAQQQQAABBBBAAAEXCBCgu6CIdAEBBLwuQIDukJwA3SEgpyOAAAIIIIAAAggg4CIBAnQXFZOuIIAAAggggAACCCDgAgECdBcUkS4ggIDXBQjQHZIToDsE5HQEEEAAAQQQQAABBFwkQIDuomLSFQQQQAABBBBAAAEEXCBAgO6CItIFBBDwugABukNyAnSHgJyOAAIIIIAAAggggICLBAjQXVRMuoIAAggggAACCCCAgAsECNBdUES6gAACXhcgQHdIToDuEJDTEUAAAQQQQAABBBBwkQABuouKSVcQQAABBBBAAAEEEHCBAAG6C4pIFxBAwOsCBOgOyQnQHQJyOgIIIIAAAggggAACLhIgQHdRMekKAggggAACCCCAAAIuECBAd0ER6QICCHhdgADdITkBukNATkcAAQQQQAABBBBAwEUCBOguKiZdQQABBBBAAAEEEEDABQIE6C4oIl1AAAGvCxCgOyQnQHcIyOkIIIAAAggggAACCLhIgADdRcWkKwgggAACCCCAAAIIuECAAN0FRaQLCCDgdQECdIfk6SVAN90YOTTKYW84HQEEEEAAAQQQQAABBJwIEKA70eNcBBBAAAEEEEAAAQQQuNECBOg3WpTrIYBAZhAgQHdYZQJ0h4CcjgACCCCAAAIIIICAiwTSOkA/fS5S4ZGXrhDN1qG8/To8bIeLpOkKAggggIBHAR9JMRghgAACCCAg3Zo3OwwIIIAAAikUIEBPIVjCtxOgOwTkdAQQQAABBBBAAAEEXCSQ1gH6xYhL8svqe4Xo2Ual7Ne5Vn7oImm6ggACCCDgSSDyUrT8slz57wFiCCCAAAKZUyCLr3mqigMBBBBAICUCBOgp0UrkvQToDgE5HQEEEEAAAQQQQAABFwmkdYB+6myELkRcOQM9oGMFK3x+znYXSdMVBBBAAAEEEEAAAQQQSI4AS7gnR4n3IIAAAlcKEKA7HBEE6A4BOR0BBBBAAAEEEEAAARcJEKC7qJh0BQEEEEAAAQQQQAABFwgQoLugiHQBAQS8LkCA7pCcAN0hIKcjgAACCCCAAAIIIOAiAQJ0FxWTriCAAAIIIIAAAggg4AIBAnQXFJEuIICA1wUI0B2SE6A7BOR0BBBAAAEEEEAAAQRcJECA7qJi0hUEEEAAAQQQQAABBFwgQIDugiLSBQQQ8LpApgvQf/ntmCa+skJfffejsmbx1cq5w3RTvtw6deasBobO05ffHla+PEEa2beNij5W0BbE02sE6F4fs9wQAQQQQAABBBBAAIF0K0CAnm5LQ8MQQAABBBBAAAEEEMiUAgTombLsdBoBBBwKZKoA/dw/F9Sgw3D17tBQZZ8vIh8fnzi+/mPCVOC2m9SldR0d+O6weo14RRuXjFX2bP7y9BoBusMRyOkIIIAAAggggAACCLhIgADdRcWkKwgggAACCCCAAAIIuECAAN0FRaQLCCDgdYFMFaAve3O7jv51Sr2DG1wBHR0doxI1OmvnG9OUI7u/fa3r4BmqW6W0Shd/IsnXypR4UukpQDftHjk0yuuDiBsigAACCCCAAAIIIIDAZYH0HKCb9p2fs51SIYAAAggggAACCCCAQCYSIEDPRMWmqwggcMMEMlWA3n3ITBUqeLc+3vO1zp47r2eLPqzeHRro1JlzatZ1jLavnBQHOyVstfLkDlTVcs8l+VqbRlUI0G/YUORCCCCAAAIIIIAAAghkfAEC9IxfQ3qAAAIIIIAAAggggICbBAjQ3VRN+oIAAt4SyFQBeqOOIxUUGKDxgzsoV2CABoyZp0IP3q0KpZ9Wl5BpenvJ2Dj3Vxa/JTMzvXrFEkm+1qVNbZ0P9/6M7279YjyOjxkT/rc0vbcGEvdBAAEEMrtAdIzky4/fzD4M6D8CCCAgyUcB2bKkmcSpsxG6EHHpivsHdKwQ9zUz0NOsNNwYAQQQQAABBBBAAIE0ESBATxN2booAAhlcIFMF6M26jJEJvYsXfcSW7bMvDyps6QaN7t9WDYOH2yXcY4/xs1fopny5Vb1CiSRfe7lxFZ3+J9LrQ6BPiOdbTgr1epO4IQIIIJDpBWJiYuTjQ4Ke6QcCAAggkOkFsvj4KFdA1jRzIEBPM3pujAACCCCAAAIIIIBAuhQgQE+XZaFRCCCQzgUyVYDef0yY3dO8arnitiz/2fu1lq/doZljuqlkjS7asmKinaFujuD+U1S/WhmVfb5Ikq+VK1U03S3hbtrOPujp/LuO5iGAAAIIIIAAAgi4ViC9L+Fu4JmF7trhR8cQQAABBBBAAAEEELhKgACdQYEAAgikXCBTBei7P/tGoTOXa9msQcqRzV89h81WyWKF1bhWOQ2duFD58+ZW1zZ1dOC7w+oyaLo2L5+gnAHZPb72+4kLKVd3eMbQkZ5ntBCgOwTmdAQQQAABBBBAAAEErlOAAP064TgNAQQQQAABBBBAAAEEUkWAAD1VWLkoAgi4XCBTBeimlq++/o5eX7dDPvJRxTLPqHdwA2XNkkV/nzuvkND5+vzAQQUF5tSQni1U8pnCtvyeXiNAd/l3CN1DAAEEEEAAAQQQQCAFAgToKcDirQgggAACCCCAAAIIIJDqAgToqU7MDRBAwIUCmS5Av9E1JEC/0aJcDwEEEEAAAQQQQACBjCuQEQJ0o8sy7hl3jNFyBBBAAAEEEEAAAQRSIkCAnhIt3osAAghcFiBAdzgSCNAdAnI6AggggAACCCCAAAIuEiBAd1Ex6QoCCCCAAAIIIIAAAi4QIEB3QRHpAgIIeF2AAN0hOQG6Q0BORwABBBBAAAEEEEDARQIE6C4qJl1BAAEEEEAAAQQQQMAFAgToLigiXUAAAa8LEKA7JCdAdwjI6QgggAACCCCAAAIIuEiAAN1FxaQrCCCAAAIIIIAAAgi4QIAA3QVFpAsIIOB1AQJ0h+TpOUAfOjKrRg6NcthDTkcAAQQQQAABBBBAAIHkChCgJ1eK9yGAAAIIIIAAAggggIA3BAjQvaHMPRBAwG0CBOgOK5oRA3SCdYdF53QEEEAAAQQQQAABBJIQIEBnaCCAAAIIIIAAAggggEB6EiBAT0/VoC0IIJBRBAjQHVaKAN0hIKcjgAACCCCAAAIIIOAiAQJ0FxWTriCAAAIIIIAAAggg4AIBAnQXFJEuIICA1wUI0B2Sp9cA3cwyN0diS7gzA91h0TkdAQQQQAABBBBAAIEkBAjQGRoIIIAAAggggAACCCCQngQI0NNTNWgLAghkFAECdIeVIkB3CMjpCCCAAAIIIIAAAgi4SIAA3UXFpCsIIIAAAggggAACCLhAgADdBUWkCwgg4HUBAnSH5AToDgE5HQEEEEAAAQQQQAABFwkQoLuomHQFAQT+j707j5eqrvsA/mVfBSXqsSwrH+2pJym1TANFkrQkl9xySU3RDPWSgiGKingVEFECFxBRc8fcS0sNU8v2R60UFa3QTM01VPSibPd5zRg3WS5wOTPnzvzmPf+UzvzO+X3fn+PA5cOcIUCAAAECBAgkIKBATyBEIxAgkLuAAj0juQI9I6DlBAgQIECAAAECBBISUKAnFKZRCBAgQIAAAQIECCQgoEBPIEQjECCQu4ACPSO5Aj0joOUECBAgQIAAAQIEEhJQoCcUplEIECBAgAABAgQIJCCgQE8gRCMQIJC7gAI9I3k1Feij69tH/ejFsex/M45uOQECBAgQIECAAAECKwhUS4Fe2HbDtFnyI0CAAAECBAgQIEAgcQEFeuIBG48AgbIIKNAzslZLgV4ozQsPBXrGwC0nQIAAAQIECBAgsBoBBbrLgwABAgQIECBAgACBShJQoFdSGvZCgEC1CCjQMyZViQX6e0cqFOaFhwI9Y9CWEyBAgAABAgQIEFgLAQX6WiB5CQECBAgQIECAAAECuQko0HOjdiICBBISUKBnDFOBnhHQcgIECBAgQIAAAQIJCSjQEwrTKAQIECBAgAABAgQSEFCgJxCiEQgQyF1AgZ6RvBoK9GWfPi+M6hbuGQO3nAABAgQIECBAgMBqBBToLg8CBAgQIECAAAECBCpJQIFeSWnYCwEC1SKgQM+YlAI9I6DlBAgQIECAAAECBBISUKAnFKZRCBAgQIAAAQIECCQgoEBPIEQjECCQu4ACPSO5Aj0joOUECBAgQIAAAQIEEhJQoCcUplEIECBAgAABAgQIJCCgQE8gRCMQIJC7gAI9I7kCPSOg5QQIECBAgAABAgQSElCgJxSmUQgQIECAAAECBAgkIKBATyBEIxAgkLuAAj0juQI9I6DlBAgQIECAAAECBBISUKAnFKZRCBAgQIAAAQIECCQgoEBPIEQjECCQu4ACPSN5pRfoK45XP3pxjK5vH4X/9SBAgAABAgQIECBAoLQCCvTSejoaAQIECBAgQIAAAQLZBBTo2fysJkCgNgUU6BlzV6BnBLScAAECBAgQIECAQEICCvSEwjQKAQIECBAgQIAAgQQEFOgJhGgEAgRyF1CgZyRXoGcEtJwAAQIECBAgQIBAQgIK9ITCNAoBAgQIECBAgACBBAQU6AmEaAQCBHIXUKBnJM+7QC/cfj3Lwy3cs+hZS4AAAQIECBAgQGD1Agp0VwgBAgQIECBAgAABApUkoECvpDTshQCBahFQoGdMSoGeEdByAgQIECBAgAABAgkJKNATCtMoBAgQIECAAAECBBIQUKAnEKIRCBDIXUCBnpG82gr0ZeMWPonuQYAAAQIECBAgQIBAaQUU6KX1dDQCBAgQIECAAAECBLIJKNCz+VlNgEBtCijQM+auQM8IaDkBAgQIECBAgACBhAQU6AmFaRQCBAgQIECAAAECCQgo0BMI0QgECOQuoEDPSK5AzwhoOQECBAgQIECAAIGEBBToCYVpFAIECBAgQIAAAQIJCCjQEwjRCAQI5C6gQM9IrkDPCGg5AQIECBAgQIAAgYQEFOgJhWkUAgQIECBAgAABAgkIKNATCNEIBAjkLqBAz0iuQM8IaDkBAgQIECBAgACBhAQU6AmFaRQCBAgQIECAAAECCQgo0BMI0QgECOQuoEDPSK5AzwhoOQECBAgQIECAAIGEBBToCYVpFAIECBAgQIAAAQIJCCjQEwjRCAQI5C6gQM9IrkDPCGg5AQIECBAgQIAAgYQEFOgJhWkUAgQIECBAgAABAgkIKNATCNEIBAjkLqBAz0iuQM8IaDkBAgQIECBAgACBhAQU6AmFaRQCBAgQIECAAAECCQgo0BMI0QgECOQuUFMF+kOPPBmHHndWdOzQvgn63hsnx3rdu8a81+fHSeMujocfnxu91u8R9SMGx1Z9Niu+bnXPKdBzv2adkAABAgQIECBAgEDFCijQKzYaGyNAgAABAgQIECBQkwIK9JqM3dAECGQUqKkCfdYvH4jfPfhYnDrskJXYRo6dHhtt2DvqDtsrZs+ZG8NPnxq3Xzk+OnfqGKt7ToGe8Qq0nAABAgQIECBAgEBCAtVUoBfYG6bNSkjfKAQIECBAgAABAgQIrCigQHdNECBAoOUCNVWg//BH98S/Xp8fRx2yx3JSS5c2Rt/dj4nCp9G7dO7IccpyAAAgAElEQVRYfG7oKefF3oP6R/9tP9vscwP6bhEK9JZfdFYQIECAAAECBAgQSFVAgZ5qsuYiQIAAAQIECBAgUJ0CCvTqzM2uCRBoXYGaKtB/cN0dccUNd0Wnjh2iS+dO8Y3dvxQH7jkwXnx5Xhw0dGzMuu6cpjQmTb8+1u/ZPb428IvNPjd4/0EK9Na9fp2dAAECBAgQIECAQEUJKNArKg6bIUCAAAECBAgQIFDzAgr0mr8EABAgsA4CNVWgL1myNN5ZuCi6dukUzzz3YvFT5nWH7Rmf2OQjUTdqctx25fgmwqmX3xqFT6bvtnPfZp+rG7xnvPX24nVgX/clx45sXPfF71k5ZUKbkhzHQQgQIEDgXYHGxog23lpdDgQIEKh5gTbRJrp2btdqDvPmL4wFC5csd/6uR+3U7H7cwr3VonJiAgQIECBAgAABArkIKNBzYXYSAgQSE6ipAn3F7K6/7b7i950Xvvd8vyFjirdwX/aYcOHM6N2rZ+y2U99mnzv8gEHx+luLcr0kjh9VmtOdO640x3EUAgQIEHhXoDEao1CaeBAgQIBAbQu0a9smundp32oICvRWo3diAgQIECBAgAABAhUpoECvyFhsigCBCheo6QJ95q0/j6eeeSFOGnpg9Nu9Lu6cOTF6dO9ajGzIyEmx764DYsfttmz2uYHbb1XVt3AfXd8+6kfn+wn6Cv/vwfYIECBAgAABAgQIZBJwC/dMfBYTIECAAAECBAgQIFBiAQV6iUEdjgCBmhCoqQL9dw8+Fn0+tUl069o5nn/hlTjyhHPjtOHfiq23+GSMnnhZvG+DnjF08F7vfir95ClxxzVnF1+7uueef3VBrhdKofQuxaNQnCvQSyHpGAQIECBAgAABAgT+I6BAdzUQIECAAAECBAgQIFBJAgr0SkrDXggQqBaBmirQZ1xze1xz893FbHqs1y2OOHBQ7L5zv+I/v/FmQ4waNyMemv1k9OjeLU4ddkj023rzNT5XrQX6sgvUJ9Cr5T9V+yRAgAABAgQIEKgGAQV6NaRkjwQIECBAgAABAgRqR0CBXjtZm5QAgdIJ1FSBXjq2/xxJgV4OVcckQIAAAQIECBAgUJ0CCvTqzM2uCRAgQIAAAQIECKQqoEBPNVlzESBQTgEFekZdBXpGQMsJECBAgAABAgQIJCSgQE8oTKMQIECAAAECBAgQSEBAgZ5AiEYgQCB3AQV6RnIFekZAywkQIECAAAECBAgkJKBATyhMoxAgQIAAAQIECBBIQECBnkCIRiBAIHcBBXpGcgV6RkDLCRAgQIAAAQIECCQkoEBPKEyjECBAgAABAgQIEEhAQIGeQIhGIEAgdwEFekZyBXpGQMsJECBAgAABAgQIJCSgQE8oTKMQIECAAAECBAgQSEBAgZ5AiEYgQCB3AQV6RnIFekZAywkQIECAAAECBAgkJKBATyhMoxAgQIAAAQIECBBIQECBnkCIRiBAIHcBBXpG8hQK9NH17aN+9OKMEpYTIECAAAECBAgQIKBAdw0QIECAAAECBAgQIFBJAgr0SkrDXggQqBYBBXrGpKq9QF82vgI944VgOQECBAgQIECAAIGIUKC7DAgQIECAAAECBAgQqCQBBXolpWEvBAhUi4ACPWNSCvSMgJYTIECAAAECBAgQSEhAgZ5QmEYhQIAAAQIECBAgkICAAj2BEI1AgEDuAgr0jOQK9IyAlhMgQIAAAQIECBBISKC1C/TX31wYCxcvXU60w5FfblZ40cV3J6RvFAIEog0DAu8KNDZGtHE9uBwI1KZAY22ObermBd6/fmc8BAgQINBCAQV6C8FWfLkCPSOg5QQIECBAgAABAgQSEmjtAn3BO0uiXbvlG5OGA/s3K9z12l8mpG8UAgRCaeIi+LfA4qVLo33btjwIEKhFAX95phZTX+3MHdv79cBFQYAAgZYKKNBbKrbC6xXoGQEtJ0CAAAECBAgQIJCQQGsX6PPmL4wFC5csJ9r1qJ2aFW6YNishfaMQIECAAAECBAgQILCigFu4uyYIECDQcoHcCvS7738wvrz951ba4TsLF8U9v3oodtlxm5bvvgJWKNArIARbIECAAAECBAgQIFAhAgr0CgnCNggQIECAAAECBAgQKAoo0F0IBAgQaLlAbgV6392Pid/8+MKVdrh4yZL4wi5D4qGfzWj57itghQK9AkKwBQIECBAgQIAAAQIVIqBAr5AgbIMAAQIECBAgQIAAgaKAAt2FQIAAgZYLlL1A/8tTzxZ3dfDQcXHV+aOW2+GSJUvjl7/7c9x4+y/iZ9ed0/LdV8AKBXoFhGALBAgQIECAAAECBCpEQIFeIUHYBgECBAgQIECAAAECRQEFuguBAAECLRcoe4F+yoRL47cPPBovvPyv6NCh/XI7bNe2bWz0wffHiXUHRN/Pb97y3VfACgV6BYRgCwQIECBAgAABAgQqRECBXiFB2AYBAgQIECBAgAABAkUBBboLgQABAi0XKHuBXtjSokWLY/+j6uOmS+pbvsMKX6FAr/CAbI8AAQIECBAgQIBAjgIK9ByxnYoAAQIECBAgQIAAgTUKKNDXSOQFBAgQWEkglwK9cNbCd523b9cuuQgU6MlFaiACBAgQIECAAAEC6yygQF9nOgsJECBAgAABAgQIECiDgAK9DKgOSYBA8gK5FegFyYcf+1s8+uTT8eZbC1aC/fY3d61KbAV6VcZm0wQIECBAgAABAgTKIqBALwurgxIgQIAAAQIECBAgsI4CCvR1hLOMAIGaFsitQL/k2p/EjGtujy033zQ6d+q0Evrk+rqqDEKBXpWx2TQBAgQIECBAgACBsggo0MvC6qAECBAgQIAAAQIECKyjgAJ9HeEsI0CgpgVyK9B32v97cfnkE2OjDXsnBa5ATypOwxAgQIAAAQIECBDIJKBAz8RnMQECBAgQIECAAAECJRZQoJcY1OEIEKgJgdwK9J33/1787LpzkkNVoCcXqYEIECBAgAABAgQIrLOAAn2d6SwkQIAAAQIECBAgQKAMAgr0MqA6JAECyQvkVqAfetxZMeb4Q+NjH9kwKVQFelJxGoYAAQIECBAgQIBAJgEFeiY+iwkQIECAAAECBAgQKLGAAr3EoA5HgEBNCORWoN96569iyiU3xl6D+sdHPvSB6Nihw3LAgwZuU5XgCvSqjM2mCRAgQIAAAQIECJRFQIFeFlYHJUCAAAECBAgQIEBgHQUU6OsIZxkBAjUtkFuBvu+RY1YLfcPFq3++UlNSoFdqMvZFgAABAgQIECBAIH8BBXr+5s5IgAABAgQIECBAgEDzAgp0VwcBAgRaLpBbgd7yrVXHCgV6deRklwQIECBAgAABAgTyEFCg56HsHAQIECBAgAABAgQIrK2AAn1tpbyOAAEC/xFQoGe8GhToGQEtJ0CAAAECBAgQIJCQgAI9oTCNQoAAAQIECBAgQCABAQV6AiEagQCB3AVyK9C/tM9xqx3u3hsn5z58KU6oQC+FomMQIECAAAECBAgQSENAgZ5GjqYgQIAAAQIECBAgkIqAAj2VJM1BgECeArkV6H/445zl5npn4aL4x/MvxbW33B3HHPr12GXHbfKcu2TnUqCXjNKBCBAgQIAAAQIECFS9gAK96iM0AAECBAgQIECAAIGkBBToScVpGAIEchLIrUBvbp4XX54Xx512QcycempOI5f2NKkU6AWV+tGLS4vjaAQIECBAgAABAgRqTECBXmOBG5cAAQIECBAgQIBAhQso0Cs8INsjQKAiBVq9QC+o7LT/92LWdedUJNCaNqVAX5OQ5wkQIECAAAECBAjUjoACvXayNikBAgQIECBAgACBahBQoFdDSvZIgEClCbR6gT7nr8/EcaMviDuvPbvSbNZqPwr0tWLyIgIECBAgQIAAAQI1IaBAr4mYDUmAAAECBAgQIECgagQU6FUTlY0SIFBBArkV6AfVjV1p7LffWRhz//58nPTdb8a+uw6oIJa134oCfe2tvJIAAQIECBAgQIBA6gIK9NQTNh8BAgQIECBAgACB6hJQoFdXXnZLgEBlCORWoN9yx/0rTdyta5f45KYbx8YbfaAyNNZhFwr0dUCzhAABAgQIECBAgECiAgr0RIM1FgECBAgQIECAAIEqFVCgV2lwtk2AQKsK5Fagt+qUqzj55Bk3xqxfPhA/ueqs4rPzXp8fJ427OB5+fG70Wr9H1I8YHFv12WyNzynQKy1Z+yFAgAABAgQIECDQegIK9Nazd2YCBAgQIECAAAECBFYWUKC7KggQINBygVwL9Lca3o7CJ9H/+tRz0RiNsclHPxR77bJ9rNe9a8t3nmHFI3Oeigsuuzme/efLTQX6yLHTY6MNe0fdYXvF7DlzY/jpU+P2K8dH504dY3XPKdAzBGEpAQIECBAgQIAAgcQEFOiJBWocAgQIECBAgAABAlUuoECv8gBtnwCBVhHIrUCf89dn4tvfmxgf3/iD8b+f+Fi0adMmHn3iqfjr08/FjIkj4tP/87FcABYuXBQHDx0Xpw4/JEaeOb1YoC9d2hh9dz8m7r1xcnTp3LG4j6GnnBd7D+of/bf9bLPPDei7RSjQc4nNSQgQIECAAAECBAhUhYACvSpiskkCBAgQIECAAAECNSOgQK+ZqA1KgEAJBXIr0A8eOja+3P/z8a19v7Lc9q+44a64+5cPxlXnjyrhWM0f6tyLro8NP9ArBg3cJg6qG1ss0F98eV4cNHRszLrunKaFk6ZfH+v37B5fG/jFZp8bvP8gBXouqTkJAQIECBAgQIAAgeoQUKBXR052SYAAAQIECBAgQKBWBBTotZK0OQkQKKVAbgX6FjsdEfffct5Kt2t/482G6L/nd+NPsy4p5VyrPNbDj/0tplx6U1xyzoh47Y03mwr0vz/7YtSNmhy3XTm+ad3Uy28tfjJ9t537Nvtc3eA94623F5d93+89wbEjG8t2vikT2pTt2A5MgACB1AUaGyPaeBtNPWbzESBAYI0CbaJNdO3cbo2vK9cL5s1fGAsWLlnu8F2P2qnZ0zVMm1WurTguAQIECBAgQIAAAQIVIKBAr4AQbIEAgaoTyK1A326PoXH1BSfHxz6y4XJIf/v78zF42IT4xc1Tyor3zsJFccjQcTHp9GOK33U+7/X5TQX6S6+8FvsNGVO8hfuyx4QLZ0bvXj1jt536Nvvc4QcMitffWlTWfa948OPL+EH9c8flOoqTESBAICmBxmiMQmniQYAAAQK1LdCubZvo3qV9qyEo0FuN3okJECBAgAABAgQIVKSAAr0iY7EpAgQqXCC3Av2sC66NPz/2txg19JvxyU03jsbGxnj8r8/E2ClXRb+t+8SxR+xdVqoHH34yjhxxTrRv/+6nQQrnb1jwTnTr2jluufSM2Ofbp8WdMydGj+5di88PGTkp9t11QOy43ZbRb/e6VT43cPutcr2F++j68v5BXP3ofD9NX9bAHZwAAQIECBAgQIBAKwi4hXsroDslAQIECBAgQIAAAQLNCijQXRwECBBouUBuBfrChYuKt0+/9pafR+H/Fx6dO3WM/b++Yww/8hvRrl3blu8+w4r3fgK9cJjREy+L923QM4YO3itmz5kbdSdPiTuuObtYsK/uuedfXZBhFy1bqkBvmZdXEyBAgAABAgQIEMhbQIGet7jzESBAgAABAgQIECCwOgEFuuuDAAECLRfIrUBftrVFixbHs/98ORYvWRIf+dAHiiV6azxWLNAL38U+atyMeGj2k9Gje7c4ddgh0W/rzYtbW91zCvTWSM85CRAgQIAAAQIECFSmgAK9MnOxKwIECBAgQIAAAQK1KqBAr9XkzU2AQBaB3Ar0K264K/pv85n4+MYfXG6/s594Kh55fG4c8PWBWeZotbUK9Fajd2ICBAgQIECAAAECFSegQK+4SGyIAAECBAgQIECAQE0LKNBrOn7DEyCwjgK5Feh9dz8mrr7glNhkhQL9H8+/FAcefUbcf+v56zhC6y5ToLeuv7MTIECAAAECBAgQqCQBBXolpWEvBAgQIECAAAECBAgo0F0DBAgQaLlAbgX6Fl8+PH523bnxgd7rL7fLf702P3bc57j4092Xtnz3FbBCgV4BIdgCAQIECBAgQIAAgQoRUKBXSBC2QYAAAQIECBAgQIBAUUCB7kIgQIBAywVyK9D3+87pscvAbeLQb3x1uV1ec/Pdccsd98eNM05v+e4rYIUCvQJCsAUCBAgQIECAAAECFSKgQK+QIGyDAAECBAgQIECAAIGigALdhUCAAIGWC+RWoP/fn+bEUSdOii03/0R8+n8+Vtzp43/5ezzw5yfigrHHxhc//+mW774CVijQKyAEWyBAgAABAgQIECBQIQIK9AoJwjYIECBAgAABAgQIECgKKNBdCAQIEGi5QG4FemFrr/zr9bj+tvviqWeej6VLG+NjH9kw9t1tQGz4/l4t33mFrFCgV0gQtkGAAAECBAgQIECgAgQU6BUQgi0QIECAAAECBAgQINAkoEB3MRAgQKDlArkW6C3fXuWvUKBXfkZ2SIAAAQIECBAgQCAvAQV6XtLOQ4AAAQIECBAgQIDA2ggo0NdGyWsIECCwvIACPeMVoUDPCGg5AQIECBAgQIAAgYQEFOgJhWkUAgQIECBAgAABAgkIKNATCNEIBAjkLqBAz0iuQM8IaDkBAgQIECBAgACBhAQU6AmFaRQCBAgQIECAAAECCQgo0BMI0QgECOQuoEDPSK5AzwhoOQECBAgQIECAAIGEBBToCYVpFAIECBAgQIAAAQIJCCjQEwjRCAQI5C6gQM9IrkDPCGg5AQIECBAgQIAAgYQEFOgJhWkUAgQIECBAgAABAgkIKNATCNEIBAjkLqBAz0iuQM8IaDkBAgQIECBAgACBhAQU6AmFaRQCBAgQIECAAAECCQgo0BMI0QgECOQuoEDPSK5AzwhoOQECBAgQIECAAIGEBBToCYVpFAIECBAgQIAAAQIJCCjQEwjRCAQI5C6gQM9IrkDPCGg5AQIECBAgQIAAgYQEFOgJhWkUAgQIECBAgAABAgkIKNATCNEIBAjkLqBAz0iuQM8IaDkBAgQIECBAgACBhAQU6AmFaRQCBAgQIECAAAECCQgo0BMI0QgECOQuoEDPSK5AzwhoOQECBAgQIECAAIGEBBToCYVpFAIECBAgQIAAAQIJCCjQEwjRCAQI5C6gQM9IrkDPCGg5AQIECBAgQIAAgYQEFOgJhWkUAgQIECBAgAABAgkIKNATCNEIBAjkLqBAz0iuQM8IaDkBAgQIECBAgACBhAQU6AmFaRQCBAgQIECAAAECCQgo0BMI0QgECOQuoEDPSK5AzwhoOQECBAgQIECAAIGEBBToCYVpFAIECBAgQIAAAQIJCCjQEwjRCAQI5C6gQM9InlKBvoyifvTijCqWEyBAgAABAgQIEKhNAQV6beZuagIECBAgQIAAAQKVKqBAr9Rk7IsAgUoWUKBnTEeBnhHQcgIECBAgQIAAAQIJCSjQEwrTKAQIECBAgAABAgQSEFCgJxCiEQgQyF1AgZ6RXIGeEdByAgQIECBAgAABAgkJKNATCtMoBAgQIECAAAECBBIQUKAnEKIRCBDIXUCBnpFcgZ4R0HICBAgQIECAAAECCQko0BMK0ygECBAgQIAAAQIEEhBQoCcQohEIEMhdQIGekVyBnhHQcgIECBAgQIAAAQIJCSjQEwrTKAQIECBAgAABAgQSEFCgJxCiEQgQyF1AgZ6RXIGeEdByAgQIECBAgAABAgkJKNATCtMoBAgQIECAAAECBBIQUKAnEKIRCBDIXUCBnpFcgZ4R0HICBAgQIECAAAECCQko0BMK0ygECBAgQIAAAQIEEhBQoCcQohEIEMhdQIGekVyBnhHQcgIECBAgQIAAAQIJCSjQEwrTKAQIECBAgAABAgQSEFCgJxCiEQgQyF1AgZ6RXIGeEdByAgQIECBAgAABAgkJKNATCtMoBAgQIECAAAECBBIQUKAnEKIRCBDIXUCBnpFcgZ4R0HICBAgQIECAAAECCQko0BMK0ygECBAgQIAAAQIEEhBQoCcQohEIEMhdQIGekVyBnhHQcgIECBAgQIAAAQIJCSjQEwrTKAQIECBAgAABAgQSEFCgJxCiEQgQyF1AgZ6RXIGeEdByAgQIECBAgAABAgkJKNATCtMoBAgQIECAAAECBBIQUKAnEKIRCBDIXUCBnpFcgZ4R0HICBAgQIECAAAECCQko0BMK0ygECBAgQIAAAQIEEhBQoCcQohEIEMhdQIGekVyBnhHQcgIECBAgQIAAAQIJCSjQEwrTKAQIECBAgAABAgQSEFCgJxCiEQgQyF1AgZ6RXIGeEdByAgQIECBAgAABAgkJtHaBPr9hUSxa0ricaNvDd2xWeOml9ySkbxQCBAgQIECAAAECBFYU6LVeRygECBAg0EKBmirQf/PA7LjwB7fGM8+9GJ07d4r999gxDj9gUJFs3uvz46RxF8fDj8+NXuv3iPoRg2OrPput8bkUC/TC0PWjF7fwUvJyAgQIECBAgAABAgRau0Bf8M6SaNNm+RzePmiHZoPpfPUvhEaAAAECCQosWRrRrm2CgxmJAAECBFos0LljuxavsYAAAQK1LlBTBfptP/tNfGqzj8amH98oXnv9zTjg6Po46+TvxGf/979j5NjpsdGGvaPusL1i9py5Mfz0qXH7leOjc6eOq31OgV7r/wmZnwABAgQIECBAgMB/BFq7QJ83f2EsWLhkuUi6HrVTsxE1TJslPgIECBAgQIAAAQIEEhZwC/eEwzUaAQJlE6ipAn1FxWGnXRA777B1fGXAF6Lv7sfEvTdOji6d372dydBTzou9B/WP/tt+ttnnBvTdIhToZbs2HZgAAQIECBAgQIBA1Qko0KsuMhsmQIAAAQIECBAgkLSAAj3peA1HgECZBGqyQF+6tDF+++CjcebkK+PaqafGwoWL46ChY2PWdec0MU+afn2s37N7fG3gF5t9bvD+gxToZbowHZYAAQIECBAgQIBANQoo0KsxNXsmQIAAAQIECBAgkK6AAj3dbE1GgED5BGquQD9z8lVxyx33R/v27eKUYw+O3XbuG39/9sWoGzU5brtyfJP01MtvjULRXni+uefqBu8Zby7I77vCjzuxsXxXwgpHnnzWCl+cmNuZnYgAAQLVKVB4h/bOWZ3Z2TUBAgRKKVD4/vFunduX8pAtOpZbuLeIy4sJECBAgAABAgQIJC+gQE8+YgMSIFAGgZor0JcZPvPcS3HyWTNi76/tEH0/v3nsN2RM8Rbuyx4TLpwZvXv1jN126tvsc4cfMCjeaFhUhlhWfcjhJ+V2qpj0n79LkN9JnYkAAQJVLNDYGFEoTTwIECBAoLYF2rZpE927KNBr+yowPQECBAgQIECAAIHKEVCgV04WdkKAQPUI1GyBXojohtvvi0cenxunf++w6Ld7Xdw5c2L06N61mN6QkZNi310HxI7bbdnscwO338ot3KvnWrdTAgQIECBAgAABAmUXcAv3shM7AQECBAgQIECAAAECLRBQoLcAy0sJECDwb4GaKtAf+PMTseXmm0W7dm3jtdffjGFjLih+x/k+u+4QoydeFu/boGcMHbxXzJ4zN+pOnhJ3XHN2dOvaebXPPf/qgtwuptH1+X2SpX50fremzw3QiQgQIECAAAECBAiUWUCBXmZghydAgAABAgQIECBAoEUCCvQWcXkxAQIEigI1VaCPHDs9fvfgY8UCvXOnjrHHV7aLIw/aNdq0aRNvvNkQo8bNiIdmPxk9uneLU4cdEv223ryItLrnFOj+SyJAgAABAgQIECBAYJmAAt21QIAAAQIECBAgQIBAJQko0CspDXshQKBaBGqqQC9HKAr0cqg6JgECBAgQIECAAIHqFKi2Ar2g3DBtVnVi2zUBAgQIECBAgAABAmsUUKCvkcgLCBAgsJKAAj3jRaFAzwhoOQECBAgQIECAAIGEBBToCYVpFAIECBAgQIAAAQIJCCjQEwjRCAQI5C6gQM9IrkDPCGg5AQIECBAgQIAAgYQEFOgJhWkUAgQIECBAgAABAgkIKNATCNEIBAjkLqBAz0iuQM8IaDkBAgQIECBAgACBhAQU6AmFaRQCBAgQIECAAAECCQgo0BMI0QgECOQuoEDPSK5AzwhoOQECBAgQIECAAIGEBBToCYVpFAIECBAgQIAAAQIJCCjQEwjRCAQI5C6gQM9IrkDPCGg5AQIECBAgQIAAgYQEFOgJhWkUAgQIECBAgAABAgkIKNATCNEIBAjkLqBAz0iuQM8IaDkBAgQIECBAgACBhAQU6AmFaRQCBAgQIECAAAECCQgo0BMI0QgECOQuoEDPSK5AzwhoOQECBAgQIECAAIGEBBToCYVpFAIECBAgQIAAAQIJCCjQEwjRCAQI5C6gQM9IrkDPCGg5AQIECBAgQIAAgYQEFOgJhWkUAgQIECBAgAABAgkIKNATCNEIBAjkLqBAz0iuQM8IaDkBAgQIECBAgACBhAQU6AmFaRQCBAgQIECAAAECCQgo0BMI0QgECOQuoEDPSK5AzwhoOQECBAgQIECAAIGEBBToCYVpFAIECBAgQIAAAQIJCCjQEwjRCAQI5C6gQM9IrkDPCGg5AQIECBAgQIAAgYQEFOgJhWkUAgQIECBAgAABAgkIKNATCNEIBAjkLqBAz0iuQM8IaDkBAgQIECBAgACBhAQU6AmFaRQCBAgQIECAAAECCQgo0BMI0QgECOQuoEDPSK5AzwhoOQECBAgQIECAAIGEBBToCYVpFAIECBAgQIAAAQIJCCjQEwjRCAQI5C6gQM9IrkDPCGg5AQIECBAgQIAAgYQEFOgJhWkUAgQIECBAgAABAgkIKNATCNEIBAjkLqBAz0iuQM8IaDkBAgQIECBAgACBhAQU6AmFaRQCBAgQIECAAAECCQgo0BMI0QgECOQuoEDPSJ5qgV5gqR+9OKOO5T+W0pwAACAASURBVAQIECBAgAABAgRqS0CBXlt5m5YAAQIECBAgQIBApQso0Cs9IfsjQKASBRToGVNRoGcEtJwAAQIECBAgQIBAQgIK9ITCNAoBAgQIECBAgACBBAQU6AmEaAQCBHIXUKBnJFegZwS0nAABAgQIECBAgEBCAgr0hMI0CgECBAgQIECAAIEEBBToCYRoBAIEchdQoGckV6BnBLScAAECBAgQIECAQEICCvSEwjQKAQIECBAgQIAAgQQEFOgJhGgEAgRyF1CgZyRXoGcEtJwAAQIECBAgQIBAQgIK9ITCNAoBAgQIECBAgACBBAQU6AmEaAQCBHIXUKBnJFegZwS0nAABAgQIECBAgEBCAgr0hMI0CgECBAgQIECAAIEEBBToCYRoBAIEchdQoGckV6BnBLScAAECBAgQIECAQEICCvSEwjQKAQIECBAgQIAAgQQEFOgJhGgEAgRyF1CgZyRPuUAv0NSPXpxRyHICBAgQIECAAAECtSOgQK+drE1KgAABAgQIECBAoBoEFOjVkJI9EiBQaQIK9IyJKNAzAlpOgAABAgQIECBAICEBBXpCYRqFAAECBAgQIECAQAICCvQEQjQCAQK5CyjQM5Ir0DMCWk6AAAECBAgQIEAgIQEFekJhGoUAAQIECBAgQIBAAgIK9ARCNAIBArkLKNAzkivQMwJaToAAAQIECBAgQCAhgWos0Av8DdNmJZSCUQgQIECAAAECBAgQWCagQHctECBAoOUCCvSWmy23Iq8CfXR9+4w7XbflvgN93dysIkCAAAECBAgQqE0BBXpt5m5qAgQIECBAgAABApUqoECv1GTsiwCBShZQoGdMR4GeEdByAgQIECBAgAABAgkJKNATCtMoBAgQIECAAAECBBIQUKAnEKIRCBDIXUCBnpFcgZ4R0HICBAgQIECAAAECCQko0BMK0ygECBAgQIAAAQIEEhBQoCcQohEIEMhdQIGekVyBnhHQcgIECBAgQIAAAQIJCSjQEwrTKAQIECBAgAABAgQSEFCgJxCiEQgQyF1AgZ6RXIGeEdByAgQIECBAgAABAgkJKNATCtMoBAgQIECAAAECBBIQUKAnEKIRCBDIXUCBnpFcgZ4R0HICBAgQIECAAAECCQko0BMK0ygECBAgQIAAAQIEEhBQoCcQohEIEMhdQIGekVyBnhHQcgIECBAgQIAAAQIJCSjQEwrTKAQIECBAgAABAgQSEFCgJxCiEQgQyF2gpgr0R594OiZNvz6enPuP6NK5Uxy63y5x4J4Di+jzXp8fJ427OB5+fG70Wr9H1I8YHFv12WyNzynQc79mnZAAAQIECBAgQIBAxQoo0Cs2GhsjQIAAAQIECBAgUJMCCvSajN3QBAhkFKipAv2WO+6Pj31kw9hy883i5Vdfi298Z0zMmDgiNv34RjFy7PTYaMPeUXfYXjF7ztwYfvrUuP3K8dG5U8fVPqdAz3gFWk6AAAECBAgQIEAgIQEFekJhGoUAAQIECBAgQIBAAgIK9ARCNAIBArkL1FSBvqLu0JOnxB5f3S527LdV9N39mLj3xsnRpXPH4suGnnJe7D2of/Tf9rPNPjeg7xahQM/9mnVCAgQIECBAgAABAhUroECv2GhsjAABAgQIECBAgEBNCijQazJ2QxMgkFGgZgv0RYsWxy4HjYyrzhsVbdu2jYOGjo1Z153TxFm41fv6PbvH1wZ+sdnnBu8/SIGe8QK0nAABAgQIECBAgEBKAgr0lNI0CwECBAgQIECAAIHqF1CgV3+GJiBAIH+Bmi3Qz7v0pnir4e04aeg34+/Pvhh1oybHbVeOb0pg6uW3xtKljbHbzn2bfa5u8J7x5oJFuaR23Im5nGalk0w+q3XO66wEqkOgTXVs0y5zEVja2Bht27gmcsF2EgIVJ9BYcTuyodYUaBPdu7RvtQ3Mm78wFixcstz5ux610xr30zBt1hpf4wUECBAgQIAAAQIECFSfgAK9+jKzYwIEWl+gJgv0H/7onph1/4Mxbfyw6NChfbz0ymux35AxxVu4L3tMuHBm9O7VM3bbqW+zzx1+wKB4/a18CvTjR7XOxXLuuNY5r7MSqA4BhUl15JTPLpc2RrTVn+eD7SwEKk7Af/wVF0krbqjwa8F6XTu02g4U6K1G78QECBAgQIAAAQIEKlJAgV6RsdgUAQIVLlBzBfqP7vp13Hj7L2L62cOja5fOxXgaGxuj3+51cefMidGje9fivxsyclLsu+uA2HG7LZt9buD2W7mFe4Vf4LZHgAABAgQIECBAIE8Bt3DPU9u5CBAgQIAAAQIECBBYk4ACfU1CnidAgMDKAjVVoN913x/i6pvujosmDI9uXd8tz5c9Rk+8LN63Qc8YOnivmD1nbtSdPCXuuObs4utW99zzry7I5boaXd86t4GsH704l/mchAABAgQIECBAgEAKAgr0FFI0AwECBAgQIECAAIF0BBTo6WRpEgIE8hOoqQJ9h72OjVfnvRHv/Yraflv3KRbqb7zZEKPGzYiHZj8ZPbp3i1OHHRL9tt68mMTqnlOg53exOhMBAgQIECBAgACBShdQoFd6QvZHgAABAgQIECBAoLYEFOi1lbdpCRAojUBNFeilIVv+KAr0cqg6JgECBAgQIECAAIHqFFCgV2dudk2AAAECBAgQIEAgVQEFeqrJmosAgXIKKNAz6irQMwJaToAAAQIECBAgQCAhAQV6QmEahQABAgQIECBAgEACAgr0BEI0AgECuQso0DOSp16gF3h8D3rGi8RyAgQIECBAgACBmhFQoNdM1AYlQIAAAQIECBAgUBUCCvSqiMkmCRCoMAEFesZAFOgZAS0nQIAAAQIECBAgkJCAAj2hMI1CgAABAgQIECBAIAEBBXoCIRqBAIHcBRToGckV6BkBLSdAgAABAgQIECCQkIACPaEwjUKAAAECBAgQIEAgAQEFegIhGoEAgdwFFOgZyWuhQC8QuY17xgvFcgIECBAgQIAAgZoQUKDXRMyGJECAAAECBAgQIFA1Agr0qonKRgkQqCABBXrGMBToGQEtJ0CAAAECBAgQIJCQQLUW6IUIGqbNSigJoxAgQIAAAQIECBAgUBBQoLsOCBAg0HIBBXrLzZZboUDPCGg5AQIECBAgQIAAgYQEFOgJhWkUAgQIECBAgAABAgkIKNATCNEIBAjkLqBAz0iuQM8IaDkBAgQIECBAgACBhAQU6AmFaRQCBAgQIECAAAECCQgo0BMI0QgECOQuoEDPSK5AzwhoOQECBAgQIECAAIGEBBToCYVpFAIECBAgQIAAAQIJCCjQEwjRCAQI5C6gQM9IrkDPCGg5AQIECBAgQIAAgYQEFOgJhWkUAgQIECBAgAABAgkIKNATCNEIBAjkLqBAz0iuQM8IaDkBAgQIECBAgACBhAQU6AmFaRQCBAgQIECAAAECCQgo0BMI0QgECOQuoEDPSK5AzwhoOQECBAgQIECAAIGEBBToCYVpFAIECBAgQIAAAQIJCCjQEwjRCAQI5C6gQM9IrkDPCGg5AQIECBAgQIAAgYQEFOgJhWkUAgQIECBAgAABAgkIKNATCNEIBAjkLqBAz0heKwV6gal+9OKMWpYTIECAAAECBAgQSFugtQv0NxcsjiVLG5dDbjzsS2uF3uYH967V67yIAAECBCpfoLExok2byt+nHRIgQIBA+QV6dutQ/pM4AwECBBITUKBnDLSWCvQClRI94wVjOQECBAgQIECAQNICrV2gN7y9JBpj+QJ90SED1sq8w5X3rdXrvIgAAQIEKl9AgV75GdkhAQIE8hLo1rl9XqdyHgIECCQjoEDPGKUCPSOg5QQIECBAgAABAgQSEmjtAn3e/IWxYOGS5US7HrXTWgk3TJu1Vq/zIgIECBAgQIAAAQIEqkfALdyrJys7JUCgcgQU6BmzUKBnBLScAAECBAgQIECAQEICCvSEwjQKAQIECBAgQIAAgQQEFOgJhGgEAgRyF1CgZyRXoGcEtJwAAQIECBAgQIBAQgIK9ITCNAoBAgQIECBAgACBBAQU6AmEaAQCBHIXUKBnJFegZwS0nAABAgQIECBAgEBCAgr0hMI0CgECBAgQIECAAIEEBBToCYRoBAIEchdQoGckV6BnBLScAAECBAgQIECAQEICCvSEwjQKAQIECBAgQIAAgQQEFOgJhGgEAgRyF1CgZyRXoGcEtJwAAQIECBAgQIBAQgIK9ITCNAoBAgQIECBAgACBBAQU6AmEaAQCBHIXUKBnJFegZwS0nAABAgQIECBAgEBCAgr0hMI0CgECBAgQIECAAIEEBBToCYRoBAIEchdQoGckV6BnBLScAAECBAgQIECAQEICCvSEwjQKAQIECBAgQIAAgQQEFOgJhGgEAgRyF1CgZyRXoGcEtJwAAQIECBAgQIBAQgIK9ITCNAoBAgQIECBAgACBBAQU6AmEaAQCBHIXUKBnJFegZwS0nAABAgQIECBAgEBCAgr0hMI0CgECBAgQIECAAIEEBBToCYRoBAIEchdQoGckV6BnBLScAAECBAgQIECAQEICCvSEwjQKAQIECBAgQIAAgQQEFOgJhGgEAgRyF1CgZyRXoGcEtJwAAQIECBAgQIBAQgIK9ITCNAoBAgQIECBAgACBBAQU6AmEaAQCBHIXUKBnJK+1Ar3AVT96cUY1ywkQIECAAAECBAikKaBATzNXUxEgQIAAAQIECBCoVgEFerUmZ98ECLSmgAI9o74CPSOg5QQIECBAgAABAgQSElCgJxSmUQgQIECAAAECBAgkIKBATyBEIxAgkLuAAj0juQI9I6DlBAgQIECAAAECBBISUKAnFKZRCBAgQIAAAQIECCQgoEBPIEQjECCQu4ACPSO5Aj0joOUECBAgQIAAAQIEEhJQoCcUplEIECBAgAABAgQIJCCgQE8gRCMQIJC7gAI9I7kCPSOg5QQIECBAgAABAgQSElCgJxSmUQgQIECAAAECBAgkIKBATyBEIxAgkLuAAj0juQI9I6DlBAgQIECAAAECBBISUKAnFKZRCBAgQIAAAQIECCQgoEBPIEQjECCQu0DNFegNC96OkWdOL0KfP/bYJvB5r8+Pk8ZdHA8/Pjd6rd8j6kcMjq36bFZ8fnXPKdBzv2adkAABAgQIECBAgEDFCijQKzYaGyNAgAABAgQIECBQkwIK9JqM3dAECGQUqKkC/Z8v/SvqRk2OLT69abz0yrzlCvSRY6fHRhv2jrrD9orZc+bG8NOnxu1Xjo/OnTrG6p5ToGe8Ai0nQIAAAQIECBAgkJCAAj2hMI1CgAABAgQIECBAIAEBBXoCIRqBAIHcBWqqQH+r4e144m//iIWLFsU1N81qKtCXLm2MvrsfE/feODm6dO5YDGHoKefF3oP6R/9tP9vscwP6bhG1WqCPrm8f9aMX537BOiEBAgQIECBAgACBShZQoFdyOvZGgAABAgQIECBAoPYEFOi1l7mJCRDILlBTBfoyrt8+8Ghce8vdTQX6iy/Pi4OGjo1Z153TJDpp+vWxfs/u8bWBX2z2ucH7D1KgZ78GHYEAAQIECBAgQIBAMgIK9GSiNAgBAgQIECBAgACBJAQU6EnEaAgCBHIWUKBHxN+ffbF4a/fbrhzfxD/18luj8Mn03Xbu2+xzdYP3jPkLFuUS2bATczlNi07y/bNa9HIvJkCAQNICjY1tok2bxqRnNBwBAgQIrFmgbZs20a1z+zW/sEyvmDd/YSxYuGS5o3c9aqe1OlvDtFlr9TovIkCAAAECBAgQIECgegQU6NWTlZ0SIFA5Agr0iHjplddivyFjirdwX/aYcOHM6N2rZ+y2U99mnzv8gEExvyGf25gPO6nySpnvj29TOVeynRAgQKCVBZY2NkahNPEgQIAAgdoWKPxS0L1LdRboheSU6LV9/ZqeAAECBAgQIEAgPQEFenqZmogAgfILKNAjorGxMfrtXhd3zpwYPbp3LaoPGTkp9t11QOy43ZbNPjdw+61q8hbuyy5L34Fe/v9AnYEAAQIECBAgQKC6BKr5Fu4K9Oq61uyWAAECBAgQIECAwNoIKNDXRslrCBAgsLyAAv3fHqMnXhbv26BnDB28V8yeMzfqTp4Sd1xzdnTr2jlW99zzry7I5ZoaXd96n2JpbkAFei7ROwkBAgQIECBAgEAVCSjQqygsWyVAgAABAgQIECBQAwIK9BoI2YgECJRcQIH+b9I33myIUeNmxEOzn4we3bvFqcMOiX5bb158dnXPKdBLfk06IAECBAgQIECAAIGqFVCgV210Nk6AAAECBAgQIEAgSQEFepKxGooAgTIL1GSBXkpTBXopNR2LAAECBAgQIECAQHULKNCrOz+7J0CAAAECBAgQIJCagAI9tUTNQ4BAHgIK9IzKCvSMgJYTIECAAAECBAgQSEhAgZ5QmEYhQIAAAQIECBAgkICAAj2BEI1AgEDuAgr0jOQK9IyAlhMgQIAAAQIECBBISECBnlCYRiFAgAABAgQIECCQgIACPYEQjUCAQO4CCvSM5Ar0jICWEyBAgAABAgQIEEhIQIGeUJhGIUCAAAECBAgQIJCAgAI9gRCNQIBA7gIK9IzkCvSMgJYTIECAAAECBAgQSEhAgZ5QmEYhQIAAAQIECBAgkICAAj2BEI1AgEDuAgr0jOQK9IyAlhMgQIAAAQIECBBISECBnlCYRiFAgAABAgQIECCQgIACPYEQjUCAQO4CCvSM5Ar0jICWEyBAgAABAgQIEEhIQIGeUJhGIUCAAAECBAgQIJCAgAI9gRCNQIBA7gIK9IzktV6gj65vH/WjF2dUtJwAAQIECBAgQIBAGgIK9DRyNAUBAgQIECBAgACBVAQU6KkkaQ4CBPIUUKBn1K7lAn0ZnQI940VkOQECBAgQIECAQDIClVagdz1qpxbZNkyb1aLXezEBAgQIECBAgAABApUtoECv7HzsjgCByhRQoGfMJY8CvfAp70p+KNArOR17I0CAAAECBAgQyFNAgZ6ntnMRIECAAAECBAgQILAmAQX6moQ8T4AAgZUFFOgZr4pyF+iVXp4X+BToGS8iywkQIECAAAECBJIRUKAnE6VBCBAgQIAAAQIECCQhoEBPIkZDECCQs4ACPSN4uQr0Zd8trkDPGJDlBAgQIECAAAECBHIUqPYCvUDlNu45XjBORYAAAQIECBAgQKDMAgr0MgM7PAECSQoo0DPGWs4CPePWclvuE+i5UTsRAQIECBAgQIBAhQso0Cs8INsjQIAAAQIECBAgUGMCCvQaC9y4BAiURECBnpFRge4W7hkvIcsJECBAgAABAgQSElCgJxSmUQgQIECAAAECBAgkIKBATyBEIxAgkLuAAj0juQL93QJ92S3nM3JaToAAAQIECBAgQKCqBRToVR2fzRMgQIAAAQIECBBITkCBnlykBiJAIAcBBXpGZAX68gW6Ij3jBWU5AQIECBAgQIBAVQso0Ks6PpsnQIAAAQIECBAgkJyAAj25SA1EgEAOAgr0jMgKdAV6xkvIcgIECBAgQIAAgYQEFOgJhWkUAgQIECBAgAABAgkIKNATCNEIBAjkLqBAz0hejgK98Cnuan4UbunuQYAAAQIECBAgQKAWBSqpQO961E7rFEHDtFnrtM4iAgQIECBAgAABAgQqT0CBXnmZ2BEBApUvoEDPmJECfWVABXrGi8pyAgQIECBAgACBqhVQoFdtdDZOgAABAgQIECBAIEkBBXqSsRqKAIEyCyjQMwIr0BXoGS8hywkQIECAAAECBBISUKAnFKZRCBAgQIAAAQIECCQgoEBPIEQjECCQu4ACPSN5qQv0ar99+3s5fRI948VlOQECBAgQIECAQNUJKNCrLjIbJkCAAAECBAgQIJC0gAI96XgNR4BAmQQU6BlhFejNAyrQM15clhMgQIAAAQIECFSdgAK96iKzYQIECBAgQIAAAQJJCyjQk47XcAQIlElAgZ4RVoGuQM94CVlOgAABAgQIECCQkEClFOhdj9ppnVUbps1a57UWEiBAgAABAgQIECBQWQIK9MrKw24IEKgOAQV6xpwU6Ksv0Au3pPdJ9IwXmeUECBAgQIAAAQJVI5BCgb4MW5FeNZedjRIgQIAAAQIECBBoVkCB7uIgQIBAywUU6C03W26FAn3NgAr0NRt5BQECBAgQIECAQBoCCvQ0cjQFAQIECBAgQIAAgVQEFOipJGkOAgTyFFCgZ9RWoK8ZsFCg+yT6mp28ggABAgQIECBAoPoFFOjVn6EJCBAgQIAAAQIECKQkoEBPKU2zECCQl4ACPaO0An3NgAr0NRt5BQECBAgQIECAQBoCCvQ0cjQFAQIECBAgQIAAgVQEFOipJGkOAgTyFFCgZ9RWoK8ZUIG+ZiOvIECAAAECBAgQSENAgZ5GjqYgQIAAAQIECBAgkIqAAj2VJM1BgECeAgr0jNoK9DUDKtDXbOQVBAgQIECAAAECaQgo0NPI0RQECBAgQIAAAQIEUhFQoKeSpDkIEMhTQIGeUbuUBXrhe8JTfCjQU0zVTAQIECBAgAABAqsSSKlAL8zXMG2WoAkQIECAAAECBAgQqGIBBXoVh2frBAi0moACPSO9Ar1lgIUy3YMAAQIECBAgQIBAqgIK9FSTNRcBAgQIECBAgACB6hRQoFdnbnZNgEDrCijQM/or0FsOqERvuZkVBAgQIECAAAEC1SGgQK+OnOySAAECBAgQIECAQK0IKNBrJWlzEiBQSgEFekZNBXrLARXoLTezggABAgQIECBAoDoEFOjVkZNdEiBAgAABAgQIEKgVAQV6rSRtTgIESimgQM+oqUBfd0BF+rrbWUmAAAECBAgQIFCZAgr0yszFrggQIECAAAECBAjUqoACvVaTNzcBAlkEFOhroTfv9flx0riL4+HH50av9XtE/YjBsVWfzYorS1Wgj65vvxY7Se8lSvT0MjURAQIECBAgQKCWBRTotZy+2QkQIECAAAECBAhUnoACvfIysSMCBCpfQIG+FhmNHDs9Ntqwd9QdtlfMnjM3hp8+NW6/cnx07tQxU4FeKM2XFcgK9LUIwksIECBAgAABAgQIVLiAAr3CA7I9AgQIECBAgAABAjUmoECvscCNS4BASQQU6GtgXLq0Mfrufkzce+Pk6NK5Y/HVQ085L/Ye1D8G9N1CgZ7xMnzvJ9Df+xcKMh7WcgIECBAgQIAAAQKtIqBAbxV2JyVAgAABAgQIECBAoBkBBbpLgwABAi0XUKCvwezFl+fFQUPHxqzrzml65aTp18f6PbvH4P0HrVOBvqwo9gn0KH4C/72fvl/2z83d2l3J3vL/yK0gQIAAAQIECBDIT6ASCvQ2h+9YsoEbps0q2bEciAABAgQIECBAgACB/AUU6PmbOyMBAtUvoEBfQ4Z/f/bFqBs1OW67cnzTK6defmsUPpleN3jPdboCjjh20Tqtq6VFl0zp0DTuMq/Cvyv8//c+V0smZk1HwHWcTpYmIUCAAAECKwosXLw0OrZv22owDT+9MRZePrlk51//+l+V7FgORIAAAQIECBAgQIAAAQIECBCoBgEF+hpSeumV12K/IWOKt3Bf9phw4czo3atnHH7AoGrI2B4JECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAYC0EFOhrQGpsbIx+u9fFnTMnRo/uXYuvHjJyUuy764AYuP1Wa0HsJQQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBQDQIK9LVIafTEy+J9G/SMoYP3itlz5kbdyVPijmvOjm5dO6/Fai8hQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgWoQUKCvRUpvvNkQo8bNiIdmPxk9uneLU4cdEv223nwtVnoJAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECFSLgAK9TEktXdoYZ11wbfz057+LDh3axXcO3j3232PHMp3NYQmUTuChR56MQ487Kzp2aN900HtvnBzrde8aruvSOTtSeQQaFrwdI8+cXjz4+WOPbTrJ6q7dea/Pj5PGXRwPPz43eq3fI+pHDI6t+mxWng06KoG1FPjFb/8cI86YFleeNyo+uenGTau2+PLh0b59u6Z/Pmf00TGg7xbFf77ljvvj/MtujoULF8eXt/9c8S/8tWvXdi3P6GUESivw6BNPx6Tp18eTc/8RXTp3ikP32yUO3HNg8SSre9/1nlzaHFY8mveJ8vo6evkE/PpXPltHLr9Ac7+va+492c/d5c/EGVou8PdnX4xjRk2Ob+w2IA7Z9ytNB7joyh/HRVf9ONr/++eOT2zykbh26qnF5x+Z81ScMuGSePnV1+KT/71xTDjlO/H+963f8pNbQaAEAq/86/U496Lr49f/90jxZ+rtt/lMnHLcIdHh3z9fe08uAbJDECBAIDEBBXqZAr3pJ78slucXjj8uGha8EwcPHRtnnzIkPv0/HyvTGR2WQGkEZv3ygfjdg48Vi5cVH67r0hg7SnkE/vnSv6Ju1OTY4tObxkuvzFuuQF/dtTty7PTYaMPeUXfYu1/TMfz0qXH7leOjc6eO5dmooxJYg8DlP7wz7v3NH2PB2+8U/0LHsgL99TfeKv5+4sdXjFvpCE//44UYPHxCXH3+yfH+3hsU/yLJZ/53kzj0G1/lTaBVBAp/APWxj2wYW26+WfEPTb/xnTExY+KI2PTjG8Xq3ne9J5cvLu8T5bN15PIK+PWvvL6OXl6B5n5ft7r3ZD93lzcTR2+5wO//+HiMnXxVbPrxD8cWn/7v5Qr0ceddHZ/7zCfiKwO+sNyBlyxZGoMOGlksKLffpk9cfdOs+O2Dj8aF445r+QasIFACgT89+tcovPfuutMXY+mSpTH0lPOKfxn9gK8PLP775n6e9p5cAnyHIECAQJUKKNDLFNyQkefGwft8pelW71fd+LN44aV/xYij9y/TGR2WQGkEfvije+Jfr8+Pow7ZY6UDJGjR5wAAGI9JREFUuq5LY+wo5RF4q+HteOJv/4iFixbFNTfNWq5Ab+7aPX7IftF392OicJeFLp3fLcwLP0TtPah/06d6y7NbRyXQvEDhD6i2/PSmcfjxE+PkYw9qKtDnPvPPOPP7V8Zl3x+50uJLZ/405r/ZEMd9e5/ic3P++kycevZlccPFY1ATqAiBoSdPiT2+ul3s2G+rZt93+2/7We/JZUzL+0QZcR26rAJ+/Ssrr4OXWaC539et7j3Zz91lDsXhWyzw16eei+7du8QNt90XPdfrtlyBfvzpU+PAPb9cLNHf+3jk8bkx/vxrmj6NXrizwg57fTd+evWE4h0OPQi0tsCVN9wVz/7zlRj13W+G9+TWTsP5CRAgUJkCCvQy5bLLN0+IS889IT60Ye/iGe7//SNxzc2z4qIJw8t0RoclUBqBH1x3R1xxw13RqWOH4i1Xv7H7l5puueq6Lo2xo5RX4LcPPBrX3nL3cgV6c9fu6d87LA4aOjZmXXdO06YKtxxev2f3GLz/oPJu1NEJrEHgoLqxccpxBzcV6LOfeCoOH3528baHS5Ysie23+WyxMO/apVOMnnhZbNXnE/H1r25XPOo7CxdF392OiQfvupgzgVYXWLRocexy0Mi46rxR0bZt22bfd7828Ivek8uYlveJMuI6dFkF/PpXVl4Hz0lgxd/Xre492c/dOYXiNC0WmHLJTbFBz+7LFehHnfj9ePof/4xFi5fEhu/vFUMP3yu22fJTcfus3xY/cT72xCOazrP/UfVx8rEHR59PfrzF57aAQKkFho+ZGjv227L4iXTvyaXWdTwCBAikIaBAL1OOA/Y+Lm66pD7et0GP4hn+709zit9LWvguUw8ClSxQuM1WoXgpFDLPPPdi8dO4dYftGTv1/3y4ris5OXtbJrCqAr25a/eMEw4v3vb9tivHNwFOvfzWKPzt+LrBe0Il0KoCK/5Ba2Ezb761ILp36xJvvNkQ9ZOuiF7rrxejvntQnHDGRbHjdlvFV7/0n1snfnrAoTH73h9EmzZtWnUOJydw3qU3ReEuIScN/WYUvj+zuffd3Xbu6z25jJeL94ky4jp02QX8+ld2Yicos8CKv69b3Xvyl/YZ5s+TypyHw6+bwKoK9LffWRjt2rUrfgf6//3piRg+5sK4Ycbp8as/PByP/+WZGP2erwc89Liz4qhv7VEs2D0ItKbAbx6YHeddclNcfeEp0b5du9X+PO09uTWTcm4CBAi0roACvUz+u3xzZEw/e3hsvNF/Fc9wz6//GNf/+J64aMLxZTqjwxIoj8D1t91X/F7owvfwuq7LY+yopRVY9SfQV/2eXD/i8NhvyJjiLdyXPSZcODN69+oZhx/gE+ilTcbRWiqwqgL9vccofE/bUSdOijuuOTtOO+cH0eeTm8Q+u+5QfEmhaOi/53fjoZ/NaOlpvZ5ASQUKXw0z6/4HY9r4YdGhQ/t46ZXXmn3f3W2nvt6TS6q//MG8T5QR16FzFfDrX67cTlYigRV/X7e692Q/d5cI3WFKLrCqAn3Fk4w4Y1rssO0WEW0i7v/dwzHhlO80vWTvI0bHmOMPjT6f2qTke3NAAmsr8Micp2LUuIvj4nNGxAc/0Ku4zHvy2up5HQECBGpLQIFepryPGTU59t11QNN36F523U/jxZfnFT9540GgmgRm3vrzeOqZF4rfCeS6rqbkanevqyrQm7t2T6w7MPrtXhd3zpwYPf79PWxDRk4qvn8P3H6r2kU0eUUIrKlA/9vTz8X36qfFLZedGZdff2e8/MprMeLo/Yt7L/yhwGkTL4ubLz2jImaxidoU+NFdv44bb/9F8S+Vdu3SuYjQ2NjY7Pvujttt6T25jJeK94ky4jp0rgJ+/cuV28lKJLDi7+tW957s5+4SoTtMyQXWpkAvfAJ90MBt40P/9b447ZzL44aLxxT3sXjJkthuj6Fx17UTo2ePbiXfmwMSWBuBJ/72jxhRPy0mnzE0Ntn4g01LvCevjZ7XECBAoPYEFOhlyrzwXT+33HF/XDj+uGhY8E5885gz48yRh8fnPvOJMp3RYQmURuB3Dz5W/NvA3bp2judfeCWOPOHcOG34t2LrLT5Z/A4r13VpnB2lfAKrKtBXd+0WvuvqfRv0jKGD9yrebaHu5CnFT/QW/hvwINCaAiv+QetjTz4dG6zfo/i35Au/tzj5rBmx2SYfiaO/tUc898Ir8a3vjit+Vcz7e28QI8+8KDbb5MNx1CF7tOYIzl3DAnfd94e4+qa746IJw1d6P13d+6735PJdNN4nymfryOUV8OtfeX0dPR+BFX9ft7r3ZD9355OJs7RcYFUF+n2/+VNst02f4m2wC19fOXLs9Ljl0jNjve5dY7dvnVT8INF2X+gTV980K+751UNx2fdHtvzEVhAogcDcZ/4Zw0+7MM4dc3T890c/tNwRvSeXANghCBAgkKCAAr2MoZ570fVx6533F7979ND9vhqD93c74DJyO3SJBGZcc3tcc/PdxaP1WK9bHHHgoNh9535NR3ddlwjaYcomsKoCvXCy5q7dwndJjxo3Ix6a/WT06N4tTh12SPTbevOy7c+BCaytwIp/0Hr/7x+JceddHQ0L3o7OnToWv++87rA9i7fFLjx++vPfx7kX/TAWvPNObL/NZ+KMEYOjY8cOa3s6ryNQUoEd9jo2Xp33RrRp85/D9tu6T7FQX937rvfkksaw0sG8T5TX19HLI+DXv/K4Omq+Aqu6s9Dq3pP93J1vPs62dgKrKtC/e+p58cdH/lL8mWSjDXvHCUcf0HSL9sKnfUeNnxH/fPHV2OSjH4qzTj4yPvzB96/dybyKQIkFCl8vUHjfbdv2Pz+gdOrYIR648+I1/jztPbnEYTgcAQIEqkRAgV4lQdkmAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECJRXQIFeXl9HJ0CAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIEqEVCgV0lQtkmAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEC5RVQoJfX19EJECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoEoEFOhVEpRtEiBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEB5BRTo5fV1dAIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBCoEgEFepUEZZsECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgUF4BBXp5fR2dAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBKpEQIFeJUHZJgECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAiUV0CBXl5fRydAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBKhFQoFdJULZJgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAuUVUKCX19fRCRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKBKBBToVRKUbRIgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAeQUU6OX1dXQCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQqBIBBXqVBGWbBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIFBeAQV6eX0dnQABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgSqRECBXiVB2SYBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIlFdAgV5eX0cnQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgSoRUKBXSVC2SYAAAQKVKdCw4O3Yepch8Yubp0TvXj0rc5N2RYAAAQIECBAgQIBAzQjse+SYOGy/XWLQwG1qZmaDEiBAgAABAgQIECilgAK9lJqORYAAAQI1J6BAr7nIDUyAAAECBAgQIECgogUU6BUdj80RIECAAAECBAhUgYACvQpCskUCBAgQqFwBBXrlZmNnBAgQIECAAAECBGpRQIFei6mbmQABAgQIECBAoJQCCvRSajoWAQIECFSlwB6HnRyH7POV2Ptr/Zv2f8sd98dlM38at105Ph594umYPOPGePTJp2Lp0sbY7gt94owTDo8unTvGigX6qPEz4r/e3yuOPWLvpmNd+INb4pnnXooJp3yn+O9efHle1H//ivjtA49Grw16xDd2GxDf/uau0aZNm6r0s2kCBAgQIECAAAECBEonMHbKVfHGmw0x4eR3f34oPJ574ZX46oEj4p4bvh/t27eLCRfOjN8/9FjMf7MhPrXZR6N+xOD4+MYfLL72vQV64eeam37yy7j6gpObjvWHP86JkWMvintvnNz0766/7b6Ycc3tMe+1N6LPpzaJU4d9Kzb59/FKN5kjESBAgAABAgQIEKgOAQV6deRklwQIECBQRoHCHxT9/o+PxyXnjGg6y3dOODc+95lPxJEH7VYsv5957sXYqs9mseDthXHUiZNily9tE4ftv8s6FegHHn1GsYQ/ZN+vxMuvvhZDTzmv+P8LRboHAQIECBAgQIAAAQK1LfDnx/4WRxw/MX71o/OjU8cORYxLrv1J/O6hx4o/syxavCTuuvcP0XfrzaNrl05x1vnXxouvzItpZw0rvralBfqv/vBIjJ1ydXz/9GNi440+ENf96J744Y/ujR9fMa7p/LWdiOkJECBAgAABAgRqTUCBXmuJm5cAAQIEVhL454uvxlcPPCHuvWly9Fp/vXj9jbei/57fjZ9cfVZ8+IPvX+n106+6Lf7+7Asx7qRvt7hAf+iRv0ThU+p3Xnt203GvveXncdd9f4grppwkHQIECBAgQIAAAQIECBR/PvneUfvFl7f/XFFj7yNGx8H77Bxf/+p2K+kUfsY44cyL4u4fnlt8rqUF+uHHnx1f/8p2sdvOfZuO3W+Puvj+mLr4wpaflAYBAgQIECBAgACBmhNQoNdc5AYmQIAAgVUJHDx0XOz65W1jvz12jBtv/0Xceuevmm5zOPuJp+IH190RT//jhWhsbIxX570Rn//s/8S5px3d4gL9htvvi9PPvaJ428WmR2NjfPC/escd10wQDgECBAgQIECAAAECBOL8y24u/vxR+JmjcDesrx92SvzylvOie7cu8dIrr8Ul194ef3r0r7Fo0eJ4+51F8VbDguLzhUdLC/QBex8Xr70+P+I9Xym1dOnSGDvyiOVKdbEQIECAAAECBAgQqBUBBXqtJG1OAgQIEFitwPU/vjd+es/v4/LJJ8YR35tY/KTH/nvsWPzDqV0POTFOOe7g+NrAL0a7dm1j6hU/ir89/dxaF+iTpl9f/N7zwneg//BH98TNP70/fjj9NIkQIECAAAECBAgQIEBglQJPPfPP2PfI0+L+Wy+Iq268K+b89R8xaczRxdcWPo1e+HqpYUfuG127dI4H/vxEDB9z4VoX6Pf//pEYPfHSpu9AL9x968yRh0f/bT8rDQIECBAgQIAAAQIEIkKB7jIgQIAAAQIRxdu2f2mf4+KmS+qLfyD18xsmxQY914t7fvVQjDv/mqbbIRawTjjjoliydOkqC/SxU66KDu3bxwnHHNDkOuy0C6Jjhw7FAr3wh1tDRp4bP7/h+9FzvW7sCRAgQIAAAQIECBAgsEqBwifJv3PwbjHtih/FMYftGTv22zLmv9kQ2+56dPzsunNiow17F9cV7nJ1/qU3r7JA/9kvHoiLr74tbpxxetM5Cl8hNeOa25oK9MOGnRWf/d9N47hv7yMJAgQIECBAgAABAgQU6K4BAgQIECDwH4G6UVPinYWLokOHdjF1/LDiE3P++kzsP+T0uOr8k2OzTT4cd9//YIybcnV88fOfXmWBfssd98elM38aM6eeGut17xqF7yMsfKfgzv0/XyzQC49vHnNm9FivW4w4ev/o1XO9mPvM8/H2Owuj7+c3FwcBAgQIECBAgAABAgSKAlfccFfc//uH47Enno5f3DwlOnRoX/xKqX6718Uxh309vrHbl+LRJ5+O0RN/ULwF+6pu4V64/fseh50SP7zotPjEJh8u3hmr8PNJ4Zbv9944uXieX//f7Dj21PNizPGHxXZf6BNvNiyI3z74aOyxc7/o2LGDNAgQIECAAAECBAjUnIBPoNdc5AYmQIAAgeYE7rz3D3H86VPj7FOHxNcGbtv0souu/HFcddPP4p13FsaAvlvGzjtsHXfd94dVFuiLlyyJsVOujl/+9s/Rtl3b4h9Sbbn5ZvGXuc82Feiv/Ov1OOuCa4t/GLZw0eL48Ia9Y+jhe8fOO3xeOAQIECBAgAABAgQIECgKvPzqa7HjvsNi70E7xJjvHdqkct9v/hTjz78mXnxlXvzvZh+N0cO/FUNGTor7bnq3EH/vd6AX/rnwNVKXXXdHLG1sLP4F3m9946sxcdrMpgK98Jof/+zXMePq2+OZ516Kbt06x+f6fCImjTmmWNp7ECBAgAABAgQIEKg1AQV6rSVuXgIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBYpYAC3YVBgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgR8B7prgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIvCvgE+iuBAIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgoEB3DRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgXcFfALdlUCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBBTorgECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIPCugE+guxIIECBAgAABAgQIECBAgAABAgQIECBA4P/bt4MSAAAABGL9WytcjVUY9xMJECBAgAABAgQIGNA1QIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEEvBAVwIBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEDCga4AAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECCTgga4EAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBgQNcAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBIwANdCQQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAwICuAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgkIAHuhIIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgIABXQMECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQCABD3QlECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABA7oGCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAAh7oSiBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgZ0DRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgQQ80JVAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQM6BogQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIJeKArgQABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIGNA1QIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEEvBAVwIBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEDCga4AAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECCTgga4EAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBgQNcAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBIwANdCQQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAwICuAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgkIAHuhIIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgIABXQMECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQCABD3QlECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABA7oGCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAAh7oSiBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgZ0DRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgQQ80JVAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQuMJDmoRE00eTPAAAAAElFTkSuQmCC"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "for col in range(3):\n",
-    "    import plotly.express as px\n",
-    "    fig = px.histogram(df, x=[content_original[col]] + [content[col]], facet_col = \"variable\")\n",
-    "    fig.update_yaxes(matches=None)\n",
-    "    fig.show()\n",
-    "\n",
-    "    #fig = px.histogram(df_scaled.sample(50000), x=content[col])\n",
-    "    #fig.show(renderer=\"png\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "ERROR! Session/line number was not unique in database. History logging moved to new session 2142\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "ERROR:root:Internal Python error in the inspect module.\n",
-      "Below is the traceback from this internal error.\n",
-      "\n",
-      "ERROR:root:Internal Python error in the inspect module.\n",
-      "Below is the traceback from this internal error.\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Traceback (most recent call last):\n",
-      "  File \"/home/steinfej/miniconda3/envs/python/lib/python3.7/site-packages/IPython/core/interactiveshell.py\", line 3343, in run_code\n",
-      "    exec(code_obj, self.user_global_ns, self.user_ns)\n",
-      "  File \"<ipython-input-13-d4f86b43bccd>\", line 2, in <module>\n",
-      "    fig = px.histogram(df_original.sample(1000), x=content_original)\n",
-      "  File \"/home/steinfej/miniconda3/envs/python/lib/python3.7/site-packages/plotly/express/_chart_types.py\", line 456, in histogram\n",
-      "    layout_patch=dict(barmode=barmode, barnorm=barnorm),\n",
-      "  File \"/home/steinfej/miniconda3/envs/python/lib/python3.7/site-packages/plotly/express/_core.py\", line 2053, in make_figure\n",
-      "    fig.update_layout(template=args[\"template\"], overwrite=True)\n",
-      "  File \"/home/steinfej/miniconda3/envs/python/lib/python3.7/site-packages/plotly/basedatatypes.py\", line 965, in update_layout\n",
-      "    self.layout.update(dict1, overwrite=overwrite, **kwargs)\n",
-      "  File \"/home/steinfej/miniconda3/envs/python/lib/python3.7/site-packages/plotly/basedatatypes.py\", line 4256, in update\n",
-      "    BaseFigure._perform_update(self, kwargs, overwrite=overwrite)\n",
-      "  File \"/home/steinfej/miniconda3/envs/python/lib/python3.7/site-packages/plotly/basedatatypes.py\", line 3349, in _perform_update\n",
-      "    plotly_obj[key] = val\n",
-      "  File \"/home/steinfej/miniconda3/envs/python/lib/python3.7/site-packages/plotly/basedatatypes.py\", line 4995, in __setitem__\n",
-      "    super(BaseLayoutHierarchyType, self).__setitem__(prop, value)\n",
-      "  File \"/home/steinfej/miniconda3/envs/python/lib/python3.7/site-packages/plotly/basedatatypes.py\", line 4011, in __setitem__\n",
-      "    self._set_compound_prop(prop, value)\n",
-      "  File \"/home/steinfej/miniconda3/envs/python/lib/python3.7/site-packages/plotly/basedatatypes.py\", line 4396, in _set_compound_prop\n",
-      "    val = validator.validate_coerce(val, skip_invalid=self._skip_invalid)\n",
-      "  File \"/home/steinfej/miniconda3/envs/python/lib/python3.7/site-packages/_plotly_utils/basevalidators.py\", line 2740, in validate_coerce\n",
-      "    v, skip_invalid=skip_invalid\n",
-      "  File \"/home/steinfej/miniconda3/envs/python/lib/python3.7/site-packages/_plotly_utils/basevalidators.py\", line 2450, in validate_coerce\n",
-      "    v = self.data_class(v)\n",
-      "  File \"/home/steinfej/miniconda3/envs/python/lib/python3.7/site-packages/plotly/graph_objs/layout/_template.py\", line 323, in __init__\n",
-      "    self[\"layout\"] = _v\n",
-      "  File \"/home/steinfej/miniconda3/envs/python/lib/python3.7/site-packages/plotly/basedatatypes.py\", line 4011, in __setitem__\n",
-      "    self._set_compound_prop(prop, value)\n",
-      "  File \"/home/steinfej/miniconda3/envs/python/lib/python3.7/site-packages/plotly/basedatatypes.py\", line 4396, in _set_compound_prop\n",
-      "    val = validator.validate_coerce(val, skip_invalid=self._skip_invalid)\n",
-      "  File \"/home/steinfej/miniconda3/envs/python/lib/python3.7/site-packages/_plotly_utils/basevalidators.py\", line 2446, in validate_coerce\n",
-      "    v = self.data_class(v, skip_invalid=skip_invalid, _validate=_validate)\n",
-      "  File \"/home/steinfej/miniconda3/envs/python/lib/python3.7/site-packages/plotly/graph_objs/_layout.py\", line 6026, in __init__\n",
-      "    self[\"ternary\"] = _v\n",
-      "  File \"/home/steinfej/miniconda3/envs/python/lib/python3.7/site-packages/plotly/basedatatypes.py\", line 4995, in __setitem__\n",
-      "    super(BaseLayoutHierarchyType, self).__setitem__(prop, value)\n",
-      "  File \"/home/steinfej/miniconda3/envs/python/lib/python3.7/site-packages/plotly/basedatatypes.py\", line 4011, in __setitem__\n",
-      "    self._set_compound_prop(prop, value)\n",
-      "  File \"/home/steinfej/miniconda3/envs/python/lib/python3.7/site-packages/plotly/basedatatypes.py\", line 4396, in _set_compound_prop\n",
-      "    val = validator.validate_coerce(val, skip_invalid=self._skip_invalid)\n",
-      "  File \"/home/steinfej/miniconda3/envs/python/lib/python3.7/site-packages/_plotly_utils/basevalidators.py\", line 2446, in validate_coerce\n",
-      "    v = self.data_class(v, skip_invalid=skip_invalid, _validate=_validate)\n",
-      "  File \"/home/steinfej/miniconda3/envs/python/lib/python3.7/site-packages/_plotly_utils/basevalidators.py\", line 2416, in data_class\n",
-      "    self._data_class = getattr(module, self.data_class_str)\n",
-      "  File \"/home/steinfej/miniconda3/envs/python/lib/python3.7/site-packages/_plotly_utils/importers.py\", line 36, in __getattr__\n",
-      "    class_module = importlib.import_module(rel_module, parent_name)\n",
-      "  File \"/home/steinfej/miniconda3/envs/python/lib/python3.7/importlib/__init__.py\", line 127, in import_module\n",
-      "    return _bootstrap._gcd_import(name[level:], package, level)\n",
-      "  File \"<frozen importlib._bootstrap>\", line 1006, in _gcd_import\n",
-      "  File \"<frozen importlib._bootstrap>\", line 983, in _find_and_load\n",
-      "  File \"<frozen importlib._bootstrap>\", line 967, in _find_and_load_unlocked\n",
-      "  File \"<frozen importlib._bootstrap>\", line 677, in _load_unlocked\n",
-      "  File \"<frozen importlib._bootstrap_external>\", line 724, in exec_module\n",
-      "  File \"<frozen importlib._bootstrap_external>\", line 818, in get_code\n",
-      "  File \"<frozen importlib._bootstrap_external>\", line 917, in get_data\n",
-      "KeyboardInterrupt\n",
-      "\n",
-      "During handling of the above exception, another exception occurred:\n",
-      "\n",
-      "Traceback (most recent call last):\n",
-      "  File \"/home/steinfej/miniconda3/envs/python/lib/python3.7/site-packages/IPython/core/interactiveshell.py\", line 2044, in showtraceback\n",
-      "    stb = value._render_traceback_()\n",
-      "AttributeError: 'KeyboardInterrupt' object has no attribute '_render_traceback_'\n",
-      "\n",
-      "During handling of the above exception, another exception occurred:\n",
-      "\n",
-      "Traceback (most recent call last):\n",
-      "  File \"/home/steinfej/miniconda3/envs/python/lib/python3.7/site-packages/IPython/core/ultratb.py\", line 1169, in get_records\n",
-      "    return _fixed_getinnerframes(etb, number_of_lines_of_context, tb_offset)\n",
-      "  File \"/home/steinfej/miniconda3/envs/python/lib/python3.7/site-packages/IPython/core/ultratb.py\", line 316, in wrapped\n",
-      "    return f(*args, **kwargs)\n",
-      "  File \"/home/steinfej/miniconda3/envs/python/lib/python3.7/site-packages/IPython/core/ultratb.py\", line 350, in _fixed_getinnerframes\n",
-      "    records = fix_frame_records_filenames(inspect.getinnerframes(etb, context))\n",
-      "  File \"/home/steinfej/miniconda3/envs/python/lib/python3.7/inspect.py\", line 1502, in getinnerframes\n",
-      "    frameinfo = (tb.tb_frame,) + getframeinfo(tb, context)\n",
-      "  File \"/home/steinfej/miniconda3/envs/python/lib/python3.7/inspect.py\", line 1460, in getframeinfo\n",
-      "    filename = getsourcefile(frame) or getfile(frame)\n",
-      "  File \"/home/steinfej/miniconda3/envs/python/lib/python3.7/inspect.py\", line 696, in getsourcefile\n",
-      "    if getattr(getmodule(object, filename), '__loader__', None) is not None:\n",
-      "  File \"/home/steinfej/miniconda3/envs/python/lib/python3.7/inspect.py\", line 742, in getmodule\n",
-      "    os.path.realpath(f)] = module.__name__\n",
-      "  File \"/home/steinfej/miniconda3/envs/python/lib/python3.7/posixpath.py\", line 395, in realpath\n",
-      "    path, ok = _joinrealpath(filename[:0], filename, {})\n",
-      "  File \"/home/steinfej/miniconda3/envs/python/lib/python3.7/posixpath.py\", line 429, in _joinrealpath\n",
-      "    if not islink(newpath):\n",
-      "  File \"/home/steinfej/miniconda3/envs/python/lib/python3.7/posixpath.py\", line 171, in islink\n",
-      "    st = os.lstat(path)\n",
-      "KeyboardInterrupt\n",
-      "Traceback (most recent call last):\n",
-      "  File \"/home/steinfej/miniconda3/envs/python/lib/python3.7/site-packages/IPython/core/interactiveshell.py\", line 3343, in run_code\n",
-      "    exec(code_obj, self.user_global_ns, self.user_ns)\n",
-      "  File \"<ipython-input-13-d4f86b43bccd>\", line 2, in <module>\n",
-      "    fig = px.histogram(df_original.sample(1000), x=content_original)\n",
-      "  File \"/home/steinfej/miniconda3/envs/python/lib/python3.7/site-packages/plotly/express/_chart_types.py\", line 456, in histogram\n",
-      "    layout_patch=dict(barmode=barmode, barnorm=barnorm),\n",
-      "  File \"/home/steinfej/miniconda3/envs/python/lib/python3.7/site-packages/plotly/express/_core.py\", line 2053, in make_figure\n",
-      "    fig.update_layout(template=args[\"template\"], overwrite=True)\n",
-      "  File \"/home/steinfej/miniconda3/envs/python/lib/python3.7/site-packages/plotly/basedatatypes.py\", line 965, in update_layout\n",
-      "    self.layout.update(dict1, overwrite=overwrite, **kwargs)\n",
-      "  File \"/home/steinfej/miniconda3/envs/python/lib/python3.7/site-packages/plotly/basedatatypes.py\", line 4256, in update\n",
-      "    BaseFigure._perform_update(self, kwargs, overwrite=overwrite)\n",
-      "  File \"/home/steinfej/miniconda3/envs/python/lib/python3.7/site-packages/plotly/basedatatypes.py\", line 3349, in _perform_update\n",
-      "    plotly_obj[key] = val\n",
-      "  File \"/home/steinfej/miniconda3/envs/python/lib/python3.7/site-packages/plotly/basedatatypes.py\", line 4995, in __setitem__\n",
-      "    super(BaseLayoutHierarchyType, self).__setitem__(prop, value)\n",
-      "  File \"/home/steinfej/miniconda3/envs/python/lib/python3.7/site-packages/plotly/basedatatypes.py\", line 4011, in __setitem__\n",
-      "    self._set_compound_prop(prop, value)\n",
-      "  File \"/home/steinfej/miniconda3/envs/python/lib/python3.7/site-packages/plotly/basedatatypes.py\", line 4396, in _set_compound_prop\n",
-      "    val = validator.validate_coerce(val, skip_invalid=self._skip_invalid)\n",
-      "  File \"/home/steinfej/miniconda3/envs/python/lib/python3.7/site-packages/_plotly_utils/basevalidators.py\", line 2740, in validate_coerce\n",
-      "    v, skip_invalid=skip_invalid\n",
-      "  File \"/home/steinfej/miniconda3/envs/python/lib/python3.7/site-packages/_plotly_utils/basevalidators.py\", line 2450, in validate_coerce\n",
-      "    v = self.data_class(v)\n",
-      "  File \"/home/steinfej/miniconda3/envs/python/lib/python3.7/site-packages/plotly/graph_objs/layout/_template.py\", line 323, in __init__\n",
-      "    self[\"layout\"] = _v\n",
-      "  File \"/home/steinfej/miniconda3/envs/python/lib/python3.7/site-packages/plotly/basedatatypes.py\", line 4011, in __setitem__\n",
-      "    self._set_compound_prop(prop, value)\n",
-      "  File \"/home/steinfej/miniconda3/envs/python/lib/python3.7/site-packages/plotly/basedatatypes.py\", line 4396, in _set_compound_prop\n",
-      "    val = validator.validate_coerce(val, skip_invalid=self._skip_invalid)\n",
-      "  File \"/home/steinfej/miniconda3/envs/python/lib/python3.7/site-packages/_plotly_utils/basevalidators.py\", line 2446, in validate_coerce\n",
-      "    v = self.data_class(v, skip_invalid=skip_invalid, _validate=_validate)\n",
-      "  File \"/home/steinfej/miniconda3/envs/python/lib/python3.7/site-packages/plotly/graph_objs/_layout.py\", line 6026, in __init__\n",
-      "    self[\"ternary\"] = _v\n",
-      "  File \"/home/steinfej/miniconda3/envs/python/lib/python3.7/site-packages/plotly/basedatatypes.py\", line 4995, in __setitem__\n",
-      "    super(BaseLayoutHierarchyType, self).__setitem__(prop, value)\n",
-      "  File \"/home/steinfej/miniconda3/envs/python/lib/python3.7/site-packages/plotly/basedatatypes.py\", line 4011, in __setitem__\n",
-      "    self._set_compound_prop(prop, value)\n",
-      "  File \"/home/steinfej/miniconda3/envs/python/lib/python3.7/site-packages/plotly/basedatatypes.py\", line 4396, in _set_compound_prop\n",
-      "    val = validator.validate_coerce(val, skip_invalid=self._skip_invalid)\n",
-      "  File \"/home/steinfej/miniconda3/envs/python/lib/python3.7/site-packages/_plotly_utils/basevalidators.py\", line 2446, in validate_coerce\n",
-      "    v = self.data_class(v, skip_invalid=skip_invalid, _validate=_validate)\n",
-      "  File \"/home/steinfej/miniconda3/envs/python/lib/python3.7/site-packages/_plotly_utils/basevalidators.py\", line 2416, in data_class\n",
-      "    self._data_class = getattr(module, self.data_class_str)\n",
-      "  File \"/home/steinfej/miniconda3/envs/python/lib/python3.7/site-packages/_plotly_utils/importers.py\", line 36, in __getattr__\n",
-      "    class_module = importlib.import_module(rel_module, parent_name)\n",
-      "  File \"/home/steinfej/miniconda3/envs/python/lib/python3.7/importlib/__init__.py\", line 127, in import_module\n",
-      "    return _bootstrap._gcd_import(name[level:], package, level)\n",
-      "  File \"<frozen importlib._bootstrap>\", line 1006, in _gcd_import\n",
-      "  File \"<frozen importlib._bootstrap>\", line 983, in _find_and_load\n",
-      "  File \"<frozen importlib._bootstrap>\", line 967, in _find_and_load_unlocked\n",
-      "  File \"<frozen importlib._bootstrap>\", line 677, in _load_unlocked\n",
-      "  File \"<frozen importlib._bootstrap_external>\", line 724, in exec_module\n",
-      "  File \"<frozen importlib._bootstrap_external>\", line 818, in get_code\n",
-      "  File \"<frozen importlib._bootstrap_external>\", line 917, in get_data\n",
-      "KeyboardInterrupt\n",
-      "\n",
-      "During handling of the above exception, another exception occurred:\n",
-      "\n",
-      "Traceback (most recent call last):\n",
-      "  File \"/home/steinfej/miniconda3/envs/python/lib/python3.7/site-packages/IPython/core/interactiveshell.py\", line 2044, in showtraceback\n",
-      "    stb = value._render_traceback_()\n",
-      "AttributeError: 'KeyboardInterrupt' object has no attribute '_render_traceback_'\n",
-      "\n",
-      "During handling of the above exception, another exception occurred:\n",
-      "\n",
-      "Traceback (most recent call last):\n",
-      "  File \"/home/steinfej/miniconda3/envs/python/lib/python3.7/site-packages/IPython/core/interactiveshell.py\", line 3263, in run_ast_nodes\n",
-      "    if (await self.run_code(code, result,  async_=asy)):\n",
-      "  File \"/home/steinfej/miniconda3/envs/python/lib/python3.7/site-packages/IPython/core/interactiveshell.py\", line 3360, in run_code\n",
-      "    self.showtraceback(running_compiled_code=True)\n",
-      "  File \"/home/steinfej/miniconda3/envs/python/lib/python3.7/site-packages/IPython/core/interactiveshell.py\", line 2047, in showtraceback\n",
-      "    value, tb, tb_offset=tb_offset)\n",
-      "  File \"/home/steinfej/miniconda3/envs/python/lib/python3.7/site-packages/IPython/core/ultratb.py\", line 1436, in structured_traceback\n",
-      "    self, etype, value, tb, tb_offset, number_of_lines_of_context)\n",
-      "  File \"/home/steinfej/miniconda3/envs/python/lib/python3.7/site-packages/IPython/core/ultratb.py\", line 1336, in structured_traceback\n",
-      "    self, etype, value, tb, tb_offset, number_of_lines_of_context\n",
-      "  File \"/home/steinfej/miniconda3/envs/python/lib/python3.7/site-packages/IPython/core/ultratb.py\", line 1193, in structured_traceback\n",
-      "    tb_offset)\n",
-      "  File \"/home/steinfej/miniconda3/envs/python/lib/python3.7/site-packages/IPython/core/ultratb.py\", line 1150, in format_exception_as_a_whole\n",
-      "    last_unique, recursion_repeat = find_recursion(orig_etype, evalue, records)\n",
-      "  File \"/home/steinfej/miniconda3/envs/python/lib/python3.7/site-packages/IPython/core/ultratb.py\", line 451, in find_recursion\n",
-      "    return len(records), 0\n",
-      "TypeError: object of type 'NoneType' has no len()\n",
-      "\n",
-      "During handling of the above exception, another exception occurred:\n",
-      "\n",
-      "Traceback (most recent call last):\n",
-      "  File \"/home/steinfej/miniconda3/envs/python/lib/python3.7/site-packages/IPython/core/interactiveshell.py\", line 2044, in showtraceback\n",
-      "    stb = value._render_traceback_()\n",
-      "AttributeError: 'TypeError' object has no attribute '_render_traceback_'\n",
-      "\n",
-      "During handling of the above exception, another exception occurred:\n",
-      "\n",
-      "Traceback (most recent call last):\n",
-      "  File \"/home/steinfej/miniconda3/envs/python/lib/python3.7/site-packages/IPython/core/ultratb.py\", line 1169, in get_records\n",
-      "    return _fixed_getinnerframes(etb, number_of_lines_of_context, tb_offset)\n",
-      "  File \"/home/steinfej/miniconda3/envs/python/lib/python3.7/site-packages/IPython/core/ultratb.py\", line 316, in wrapped\n",
-      "    return f(*args, **kwargs)\n",
-      "  File \"/home/steinfej/miniconda3/envs/python/lib/python3.7/site-packages/IPython/core/ultratb.py\", line 350, in _fixed_getinnerframes\n",
-      "    records = fix_frame_records_filenames(inspect.getinnerframes(etb, context))\n",
-      "  File \"/home/steinfej/miniconda3/envs/python/lib/python3.7/inspect.py\", line 1502, in getinnerframes\n",
-      "    frameinfo = (tb.tb_frame,) + getframeinfo(tb, context)\n",
-      "  File \"/home/steinfej/miniconda3/envs/python/lib/python3.7/inspect.py\", line 1460, in getframeinfo\n",
-      "    filename = getsourcefile(frame) or getfile(frame)\n",
-      "  File \"/home/steinfej/miniconda3/envs/python/lib/python3.7/inspect.py\", line 696, in getsourcefile\n",
-      "    if getattr(getmodule(object, filename), '__loader__', None) is not None:\n",
-      "  File \"/home/steinfej/miniconda3/envs/python/lib/python3.7/inspect.py\", line 742, in getmodule\n",
-      "    os.path.realpath(f)] = module.__name__\n",
-      "  File \"/home/steinfej/miniconda3/envs/python/lib/python3.7/posixpath.py\", line 395, in realpath\n",
-      "    path, ok = _joinrealpath(filename[:0], filename, {})\n",
-      "  File \"/home/steinfej/miniconda3/envs/python/lib/python3.7/posixpath.py\", line 429, in _joinrealpath\n",
-      "    if not islink(newpath):\n",
-      "  File \"/home/steinfej/miniconda3/envs/python/lib/python3.7/posixpath.py\", line 171, in islink\n",
-      "    st = os.lstat(path)\n",
-      "KeyboardInterrupt\n"
-     ]
-    },
-    {
-     "ename": "TypeError",
-     "evalue": "object of type 'NoneType' has no len()",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "    \u001b[0;31m[... skipping hidden 1 frame]\u001b[0m\n",
-      "\u001b[0;32m<ipython-input-13-d4f86b43bccd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mplotly\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexpress\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistogram\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_original\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcontent_original\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/python/lib/python3.7/site-packages/plotly/express/_chart_types.py\u001b[0m in \u001b[0;36mhistogram\u001b[0;34m(data_frame, x, y, color, facet_row, facet_col, facet_col_wrap, facet_row_spacing, facet_col_spacing, hover_name, hover_data, animation_frame, animation_group, category_orders, labels, color_discrete_sequence, color_discrete_map, marginal, opacity, orientation, barmode, barnorm, histnorm, log_x, log_y, range_x, range_y, histfunc, cumulative, nbins, title, template, width, height)\u001b[0m\n\u001b[1;32m    455\u001b[0m         ),\n\u001b[0;32m--> 456\u001b[0;31m         \u001b[0mlayout_patch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbarmode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbarmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbarnorm\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbarnorm\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    457\u001b[0m     )\n",
-      "\u001b[0;32m~/miniconda3/envs/python/lib/python3.7/site-packages/plotly/express/_core.py\u001b[0m in \u001b[0;36mmake_figure\u001b[0;34m(args, constructor, trace_patch, layout_patch)\u001b[0m\n\u001b[1;32m   2052\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m\"template\"\u001b[0m \u001b[0;32min\u001b[0m \u001b[0margs\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"template\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2053\u001b[0;31m         \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate_layout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtemplate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"template\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moverwrite\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2054\u001b[0m     \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mframes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mframe_list\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mframes\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/python/lib/python3.7/site-packages/plotly/basedatatypes.py\u001b[0m in \u001b[0;36mupdate_layout\u001b[0;34m(self, dict1, overwrite, **kwargs)\u001b[0m\n\u001b[1;32m    964\u001b[0m         \"\"\"\n\u001b[0;32m--> 965\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayout\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdict1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moverwrite\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moverwrite\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    966\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/python/lib/python3.7/site-packages/plotly/basedatatypes.py\u001b[0m in \u001b[0;36mupdate\u001b[0;34m(self, dict1, overwrite, **kwargs)\u001b[0m\n\u001b[1;32m   4255\u001b[0m                 \u001b[0mBaseFigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_perform_update\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moverwrite\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moverwrite\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4256\u001b[0;31m                 \u001b[0mBaseFigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_perform_update\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moverwrite\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moverwrite\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4257\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/python/lib/python3.7/site-packages/plotly/basedatatypes.py\u001b[0m in \u001b[0;36m_perform_update\u001b[0;34m(plotly_obj, update_obj, overwrite)\u001b[0m\n\u001b[1;32m   3348\u001b[0m                     \u001b[0;31m# Don't recurse and assign property as-is\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3349\u001b[0;31m                     \u001b[0mplotly_obj\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mval\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3350\u001b[0m                     \u001b[0;32mcontinue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/python/lib/python3.7/site-packages/plotly/basedatatypes.py\u001b[0m in \u001b[0;36m__setitem__\u001b[0;34m(self, prop, value)\u001b[0m\n\u001b[1;32m   4994\u001b[0m             \u001b[0;31m# Set as ordinary property\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4995\u001b[0;31m             \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mBaseLayoutHierarchyType\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__setitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4996\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/python/lib/python3.7/site-packages/plotly/basedatatypes.py\u001b[0m in \u001b[0;36m__setitem__\u001b[0;34m(self, prop, value)\u001b[0m\n\u001b[1;32m   4010\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalidator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mCompoundValidator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4011\u001b[0;31m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_compound_prop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4012\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/python/lib/python3.7/site-packages/plotly/basedatatypes.py\u001b[0m in \u001b[0;36m_set_compound_prop\u001b[0;34m(self, prop, val)\u001b[0m\n\u001b[1;32m   4395\u001b[0m         \u001b[0mvalidator\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_validator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprop\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4396\u001b[0;31m         \u001b[0mval\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalidator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalidate_coerce\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mskip_invalid\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_skip_invalid\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4397\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/python/lib/python3.7/site-packages/_plotly_utils/basevalidators.py\u001b[0m in \u001b[0;36mvalidate_coerce\u001b[0;34m(self, v, skip_invalid)\u001b[0m\n\u001b[1;32m   2739\u001b[0m         return super(BaseTemplateValidator, self).validate_coerce(\n\u001b[0;32m-> 2740\u001b[0;31m             \u001b[0mv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mskip_invalid\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mskip_invalid\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2741\u001b[0m         )\n",
-      "\u001b[0;32m~/miniconda3/envs/python/lib/python3.7/site-packages/_plotly_utils/basevalidators.py\u001b[0m in \u001b[0;36mvalidate_coerce\u001b[0;34m(self, v, skip_invalid, _validate)\u001b[0m\n\u001b[1;32m   2449\u001b[0m             \u001b[0;31m# Copy object\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2450\u001b[0;31m             \u001b[0mv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata_class\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2451\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/python/lib/python3.7/site-packages/plotly/graph_objs/layout/_template.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, arg, data, layout, **kwargs)\u001b[0m\n\u001b[1;32m    322\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0m_v\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 323\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"layout\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_v\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    324\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/python/lib/python3.7/site-packages/plotly/basedatatypes.py\u001b[0m in \u001b[0;36m__setitem__\u001b[0;34m(self, prop, value)\u001b[0m\n\u001b[1;32m   4010\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalidator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mCompoundValidator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4011\u001b[0;31m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_compound_prop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4012\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/python/lib/python3.7/site-packages/plotly/basedatatypes.py\u001b[0m in \u001b[0;36m_set_compound_prop\u001b[0;34m(self, prop, val)\u001b[0m\n\u001b[1;32m   4395\u001b[0m         \u001b[0mvalidator\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_validator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprop\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4396\u001b[0;31m         \u001b[0mval\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalidator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalidate_coerce\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mskip_invalid\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_skip_invalid\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4397\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/python/lib/python3.7/site-packages/_plotly_utils/basevalidators.py\u001b[0m in \u001b[0;36mvalidate_coerce\u001b[0;34m(self, v, skip_invalid, _validate)\u001b[0m\n\u001b[1;32m   2445\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2446\u001b[0;31m             \u001b[0mv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata_class\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mskip_invalid\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mskip_invalid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_validate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_validate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2447\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/python/lib/python3.7/site-packages/plotly/graph_objs/_layout.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, arg, activeshape, angularaxis, annotations, annotationdefaults, autosize, bargap, bargroupgap, barmode, barnorm, boxgap, boxgroupgap, boxmode, calendar, clickmode, coloraxis, colorscale, colorway, datarevision, direction, dragmode, editrevision, extendfunnelareacolors, extendpiecolors, extendsunburstcolors, extendtreemapcolors, font, funnelareacolorway, funnelgap, funnelgroupgap, funnelmode, geo, grid, height, hiddenlabels, hiddenlabelssrc, hidesources, hoverdistance, hoverlabel, hovermode, images, imagedefaults, legend, mapbox, margin, meta, metasrc, modebar, newshape, orientation, paper_bgcolor, piecolorway, plot_bgcolor, polar, radialaxis, scene, selectdirection, selectionrevision, separators, shapes, shapedefaults, showlegend, sliders, sliderdefaults, spikedistance, sunburstcolorway, template, ternary, title, titlefont, transition, treemapcolorway, uirevision, uniformtext, updatemenus, updatemenudefaults, violingap, violingroupgap, violinmode, waterfallgap, waterfallgroupgap, waterfallmode, width, xaxis, yaxis, **kwargs)\u001b[0m\n\u001b[1;32m   6025\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0m_v\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6026\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"ternary\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_v\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   6027\u001b[0m         \u001b[0m_v\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"title\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/python/lib/python3.7/site-packages/plotly/basedatatypes.py\u001b[0m in \u001b[0;36m__setitem__\u001b[0;34m(self, prop, value)\u001b[0m\n\u001b[1;32m   4994\u001b[0m             \u001b[0;31m# Set as ordinary property\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4995\u001b[0;31m             \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mBaseLayoutHierarchyType\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__setitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4996\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/python/lib/python3.7/site-packages/plotly/basedatatypes.py\u001b[0m in \u001b[0;36m__setitem__\u001b[0;34m(self, prop, value)\u001b[0m\n\u001b[1;32m   4010\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalidator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mCompoundValidator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4011\u001b[0;31m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_compound_prop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4012\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/python/lib/python3.7/site-packages/plotly/basedatatypes.py\u001b[0m in \u001b[0;36m_set_compound_prop\u001b[0;34m(self, prop, val)\u001b[0m\n\u001b[1;32m   4395\u001b[0m         \u001b[0mvalidator\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_validator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprop\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4396\u001b[0;31m         \u001b[0mval\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalidator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalidate_coerce\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mskip_invalid\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_skip_invalid\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4397\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/python/lib/python3.7/site-packages/_plotly_utils/basevalidators.py\u001b[0m in \u001b[0;36mvalidate_coerce\u001b[0;34m(self, v, skip_invalid, _validate)\u001b[0m\n\u001b[1;32m   2445\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2446\u001b[0;31m             \u001b[0mv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata_class\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mskip_invalid\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mskip_invalid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_validate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_validate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2447\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/python/lib/python3.7/site-packages/_plotly_utils/basevalidators.py\u001b[0m in \u001b[0;36mdata_class\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   2415\u001b[0m             \u001b[0mmodule\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimport_module\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodule_str\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2416\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data_class\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodule\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata_class_str\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2417\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/python/lib/python3.7/site-packages/_plotly_utils/importers.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(import_name)\u001b[0m\n\u001b[1;32m     35\u001b[0m             \u001b[0mclass_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimport_name\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 36\u001b[0;31m             \u001b[0mclass_module\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimportlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimport_module\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrel_module\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparent_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     37\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclass_module\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclass_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/python/lib/python3.7/importlib/__init__.py\u001b[0m in \u001b[0;36mimport_module\u001b[0;34m(name, package)\u001b[0m\n\u001b[1;32m    126\u001b[0m             \u001b[0mlevel\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 127\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_bootstrap\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_gcd_import\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpackage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    128\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/python/lib/python3.7/importlib/_bootstrap.py\u001b[0m in \u001b[0;36m_gcd_import\u001b[0;34m(name, package, level)\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/python/lib/python3.7/importlib/_bootstrap.py\u001b[0m in \u001b[0;36m_find_and_load\u001b[0;34m(name, import_)\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/python/lib/python3.7/importlib/_bootstrap.py\u001b[0m in \u001b[0;36m_find_and_load_unlocked\u001b[0;34m(name, import_)\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/python/lib/python3.7/importlib/_bootstrap.py\u001b[0m in \u001b[0;36m_load_unlocked\u001b[0;34m(spec)\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/python/lib/python3.7/importlib/_bootstrap_external.py\u001b[0m in \u001b[0;36mexec_module\u001b[0;34m(self, module)\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/python/lib/python3.7/importlib/_bootstrap_external.py\u001b[0m in \u001b[0;36mget_code\u001b[0;34m(self, fullname)\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/python/lib/python3.7/importlib/_bootstrap_external.py\u001b[0m in \u001b[0;36mget_data\u001b[0;34m(self, path)\u001b[0m\n",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m: ",
-      "\nDuring handling of the above exception, another exception occurred:\n",
-      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[0;32m~/miniconda3/envs/python/lib/python3.7/site-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mshowtraceback\u001b[0;34m(self, exc_tuple, filename, tb_offset, exception_only, running_compiled_code)\u001b[0m\n\u001b[1;32m   2043\u001b[0m                         \u001b[0;31m# in the engines. This should return a list of strings.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2044\u001b[0;31m                         \u001b[0mstb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_render_traceback_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2045\u001b[0m                     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mAttributeError\u001b[0m: 'KeyboardInterrupt' object has no attribute '_render_traceback_'",
-      "\nDuring handling of the above exception, another exception occurred:\n",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m~/miniconda3/envs/python/lib/python3.7/site-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mrun_ast_nodes\u001b[0;34m(self, nodelist, cell_name, interactivity, compiler, result)\u001b[0m\n\u001b[1;32m   3262\u001b[0m                         \u001b[0masy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompare\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3263\u001b[0;31m                     \u001b[0;32mif\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;32mawait\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_code\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m  \u001b[0masync_\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0masy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3264\u001b[0m                         \u001b[0;32mreturn\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "    \u001b[0;31m[... skipping hidden 1 frame]\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/python/lib/python3.7/site-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mshowtraceback\u001b[0;34m(self, exc_tuple, filename, tb_offset, exception_only, running_compiled_code)\u001b[0m\n\u001b[1;32m   2046\u001b[0m                         stb = self.InteractiveTB.structured_traceback(etype,\n\u001b[0;32m-> 2047\u001b[0;31m                                             value, tb, tb_offset=tb_offset)\n\u001b[0m\u001b[1;32m   2048\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/python/lib/python3.7/site-packages/IPython/core/ultratb.py\u001b[0m in \u001b[0;36mstructured_traceback\u001b[0;34m(self, etype, value, tb, tb_offset, number_of_lines_of_context)\u001b[0m\n\u001b[1;32m   1435\u001b[0m         return FormattedTB.structured_traceback(\n\u001b[0;32m-> 1436\u001b[0;31m             self, etype, value, tb, tb_offset, number_of_lines_of_context)\n\u001b[0m\u001b[1;32m   1437\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/python/lib/python3.7/site-packages/IPython/core/ultratb.py\u001b[0m in \u001b[0;36mstructured_traceback\u001b[0;34m(self, etype, value, tb, tb_offset, number_of_lines_of_context)\u001b[0m\n\u001b[1;32m   1335\u001b[0m             return VerboseTB.structured_traceback(\n\u001b[0;32m-> 1336\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0metype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtb_offset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumber_of_lines_of_context\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1337\u001b[0m             )\n",
-      "\u001b[0;32m~/miniconda3/envs/python/lib/python3.7/site-packages/IPython/core/ultratb.py\u001b[0m in \u001b[0;36mstructured_traceback\u001b[0;34m(self, etype, evalue, etb, tb_offset, number_of_lines_of_context)\u001b[0m\n\u001b[1;32m   1192\u001b[0m         formatted_exception = self.format_exception_as_a_whole(etype, evalue, etb, number_of_lines_of_context,\n\u001b[0;32m-> 1193\u001b[0;31m                                                                tb_offset)\n\u001b[0m\u001b[1;32m   1194\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/python/lib/python3.7/site-packages/IPython/core/ultratb.py\u001b[0m in \u001b[0;36mformat_exception_as_a_whole\u001b[0;34m(self, etype, evalue, etb, number_of_lines_of_context, tb_offset)\u001b[0m\n\u001b[1;32m   1149\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1150\u001b[0;31m         \u001b[0mlast_unique\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrecursion_repeat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfind_recursion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0morig_etype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrecords\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1151\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/python/lib/python3.7/site-packages/IPython/core/ultratb.py\u001b[0m in \u001b[0;36mfind_recursion\u001b[0;34m(etype, value, records)\u001b[0m\n\u001b[1;32m    450\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_recursion_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0metype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrecords\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 451\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrecords\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    452\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mTypeError\u001b[0m: object of type 'NoneType' has no len()",
-      "\nDuring handling of the above exception, another exception occurred:\n",
-      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[0;32m~/miniconda3/envs/python/lib/python3.7/site-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mshowtraceback\u001b[0;34m(self, exc_tuple, filename, tb_offset, exception_only, running_compiled_code)\u001b[0m\n\u001b[1;32m   2043\u001b[0m                         \u001b[0;31m# in the engines. This should return a list of strings.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2044\u001b[0;31m                         \u001b[0mstb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_render_traceback_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2045\u001b[0m                     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mAttributeError\u001b[0m: 'TypeError' object has no attribute '_render_traceback_'",
-      "\nDuring handling of the above exception, another exception occurred:\n",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m~/miniconda3/envs/python/lib/python3.7/site-packages/IPython/core/async_helpers.py\u001b[0m in \u001b[0;36m_pseudo_sync_runner\u001b[0;34m(coro)\u001b[0m\n\u001b[1;32m     66\u001b[0m     \"\"\"\n\u001b[1;32m     67\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 68\u001b[0;31m         \u001b[0mcoro\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     69\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mStopIteration\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     70\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/python/lib/python3.7/site-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mrun_cell_async\u001b[0;34m(self, raw_cell, store_history, silent, shell_futures)\u001b[0m\n\u001b[1;32m   3070\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3071\u001b[0m                 has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n\u001b[0;32m-> 3072\u001b[0;31m                        interactivity=interactivity, compiler=compiler, result=result)\n\u001b[0m\u001b[1;32m   3073\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3074\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlast_execution_succeeded\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_raised\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/python/lib/python3.7/site-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mrun_ast_nodes\u001b[0;34m(self, nodelist, cell_name, interactivity, compiler, result)\u001b[0m\n\u001b[1;32m   3280\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3281\u001b[0m                 \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merror_before_exec\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexc_info\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3282\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshowtraceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3283\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3284\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/python/lib/python3.7/site-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mshowtraceback\u001b[0;34m(self, exc_tuple, filename, tb_offset, exception_only, running_compiled_code)\u001b[0m\n\u001b[1;32m   2045\u001b[0m                     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2046\u001b[0m                         stb = self.InteractiveTB.structured_traceback(etype,\n\u001b[0;32m-> 2047\u001b[0;31m                                             value, tb, tb_offset=tb_offset)\n\u001b[0m\u001b[1;32m   2048\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2049\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_showtraceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0metype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/python/lib/python3.7/site-packages/IPython/core/ultratb.py\u001b[0m in \u001b[0;36mstructured_traceback\u001b[0;34m(self, etype, value, tb, tb_offset, number_of_lines_of_context)\u001b[0m\n\u001b[1;32m   1434\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1435\u001b[0m         return FormattedTB.structured_traceback(\n\u001b[0;32m-> 1436\u001b[0;31m             self, etype, value, tb, tb_offset, number_of_lines_of_context)\n\u001b[0m\u001b[1;32m   1437\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1438\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/python/lib/python3.7/site-packages/IPython/core/ultratb.py\u001b[0m in \u001b[0;36mstructured_traceback\u001b[0;34m(self, etype, value, tb, tb_offset, number_of_lines_of_context)\u001b[0m\n\u001b[1;32m   1334\u001b[0m             \u001b[0;31m# Verbose modes need a full traceback\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1335\u001b[0m             return VerboseTB.structured_traceback(\n\u001b[0;32m-> 1336\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0metype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtb_offset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumber_of_lines_of_context\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1337\u001b[0m             )\n\u001b[1;32m   1338\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mmode\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'Minimal'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/python/lib/python3.7/site-packages/IPython/core/ultratb.py\u001b[0m in \u001b[0;36mstructured_traceback\u001b[0;34m(self, etype, evalue, etb, tb_offset, number_of_lines_of_context)\u001b[0m\n\u001b[1;32m   1209\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0mevalue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1210\u001b[0m             formatted_exceptions += self.format_exception_as_a_whole(etype, evalue, etb, lines_of_context,\n\u001b[0;32m-> 1211\u001b[0;31m                                                                      chained_exceptions_tb_offset)\n\u001b[0m\u001b[1;32m   1212\u001b[0m             \u001b[0mexception\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_parts_of_chained_exception\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1213\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/python/lib/python3.7/site-packages/IPython/core/ultratb.py\u001b[0m in \u001b[0;36mformat_exception_as_a_whole\u001b[0;34m(self, etype, evalue, etb, number_of_lines_of_context, tb_offset)\u001b[0m\n\u001b[1;32m   1148\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1149\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1150\u001b[0;31m         \u001b[0mlast_unique\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrecursion_repeat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfind_recursion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0morig_etype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrecords\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1151\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1152\u001b[0m         \u001b[0mframes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat_records\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrecords\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlast_unique\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrecursion_repeat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/python/lib/python3.7/site-packages/IPython/core/ultratb.py\u001b[0m in \u001b[0;36mfind_recursion\u001b[0;34m(etype, value, records)\u001b[0m\n\u001b[1;32m    449\u001b[0m     \u001b[0;31m# first frame (from in to out) that looks different.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    450\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_recursion_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0metype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrecords\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 451\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrecords\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    452\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    453\u001b[0m     \u001b[0;31m# Select filename, lineno, func_name to track frames with\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mTypeError\u001b[0m: object of type 'NoneType' has no len()"
-     ]
-    }
-   ],
-   "source": [
-    "import plotly.express as px\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for lab in labs:\n",
-    "    X = data[:1000, labs]\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(1000, 61)"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "X.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1440x2160 with 70 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import seaborn as sns\n",
-    "sns.set(rc={'figure.figsize':(20,30)})\n",
-    "import matplotlib.pyplot as plt\n",
-    "fig, axes = plt.subplots(nrows=7, ncols=10);"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "    \u001b[0;31m[... skipping hidden 1 frame]\u001b[0m\n",
-      "\u001b[0;32m<ipython-input-16-a5854f66ea24>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m     \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m//\u001b[0m\u001b[0;36m7\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m%\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m~/miniconda3/envs/python/lib/python3.7/site-packages/seaborn/distributions.py\u001b[0m in \u001b[0;36mhistplot\u001b[0;34m(data, x, y, hue, weights, stat, bins, binwidth, binrange, discrete, cumulative, common_bins, common_norm, multiple, element, fill, shrink, kde, kde_kws, line_kws, thresh, pthresh, pmax, cbar, cbar_ax, cbar_kws, palette, hue_order, hue_norm, color, log_scale, legend, ax, **kwargs)\u001b[0m\n\u001b[1;32m   1391\u001b[0m         \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1392\u001b[0;31m         \u001b[0mvariables\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_DistributionPlotter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_semantics\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlocals\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1393\u001b[0m     )\n",
-      "\u001b[0;32m~/miniconda3/envs/python/lib/python3.7/site-packages/seaborn/distributions.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, variables)\u001b[0m\n\u001b[1;32m    107\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 108\u001b[0;31m         \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvariables\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvariables\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    109\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/python/lib/python3.7/site-packages/seaborn/_core.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, variables)\u001b[0m\n\u001b[1;32m    603\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 604\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0massign_variables\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvariables\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    605\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/python/lib/python3.7/site-packages/seaborn/_core.py\u001b[0m in \u001b[0;36massign_variables\u001b[0;34m(self, data, variables)\u001b[0m\n\u001b[1;32m    662\u001b[0m             plot_data, variables = self._assign_variables_wideform(\n\u001b[0;32m--> 663\u001b[0;31m                 \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mvariables\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    664\u001b[0m             )\n",
-      "\u001b[0;32m~/miniconda3/envs/python/lib/python3.7/site-packages/seaborn/_core.py\u001b[0m in \u001b[0;36m_assign_variables_wideform\u001b[0;34m(self, data, **kwargs)\u001b[0m\n\u001b[1;32m    719\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 720\u001b[0;31m             \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0matleast_1d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mobject\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    721\u001b[0m         flat = not any(\n",
-      "\u001b[0;32m~/miniconda3/envs/python/lib/python3.7/site-packages/numpy/core/_asarray.py\u001b[0m in \u001b[0;36masarray\u001b[0;34m(a, dtype, order)\u001b[0m\n\u001b[1;32m     84\u001b[0m     \"\"\"\n\u001b[0;32m---> 85\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     86\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/python/lib/python3.7/site-packages/anndata/_core/anndata.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, index)\u001b[0m\n\u001b[1;32m   1087\u001b[0m         \u001b[0moidx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvidx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_normalize_indices\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1088\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mAnnData\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moidx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moidx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvidx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvidx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0masview\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1089\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/python/lib/python3.7/site-packages/anndata/_core/anndata.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, X, obs, var, uns, obsm, varm, layers, raw, dtype, shape, filename, filemode, asview, obsp, varp, oidx, vidx)\u001b[0m\n\u001b[1;32m    304\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"`X` has to be an AnnData object.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 305\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_init_as_view\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moidx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvidx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    306\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/python/lib/python3.7/site-packages/anndata/_core/anndata.py\u001b[0m in \u001b[0;36m_init_as_view\u001b[0;34m(self, adata_ref, oidx, vidx)\u001b[0m\n\u001b[1;32m    357\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_remove_unused_categories\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0madata_ref\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobs_sub\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0muns_new\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 358\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_remove_unused_categories\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0madata_ref\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvar\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvar_sub\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0muns_new\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    359\u001b[0m         \u001b[0;31m# set attributes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/python/lib/python3.7/site-packages/anndata/_core/anndata.py\u001b[0m in \u001b[0;36m_remove_unused_categories\u001b[0;34m(self, df_full, df_sub, uns)\u001b[0m\n\u001b[1;32m   1092\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1093\u001b[0;31m         \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdf_full\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1094\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_categorical\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_full\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m: ",
-      "\nDuring handling of the above exception, another exception occurred:\n",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
-     ]
-    }
-   ],
-   "source": [
-    "for i in range(X.shape[1]):\n",
-    "    sns.histplot(X[:, i],ax=axes[i//7,i%10])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'PGS': ['PGS000011',\n",
-      "         'PGS000013',\n",
-      "         'PGS000016',\n",
-      "         'PGS000018',\n",
-      "         'PGS000039',\n",
-      "         'PGS000057',\n",
-      "         'PGS000058',\n",
-      "         'PGS000059',\n",
-      "         'PGS000116',\n",
-      "         'PGS000117',\n",
-      "         'PGS000192',\n",
-      "         'PGS000296'],\n",
-      " 'basics': ['age_at_recruitment',\n",
-      "            'townsend_deprivation_index_at_recruitment',\n",
-      "            'ethnic_background_0.0',\n",
-      "            'ethnic_background_1.0',\n",
-      "            'ethnic_background_2.0',\n",
-      "            'ethnic_background_3.0',\n",
-      "            'ethnic_background_4.0',\n",
-      "            'ethnic_background',\n",
-      "            'sex'],\n",
-      " 'diagnoses': ['coronary_heart_disease',\n",
-      "               'myocardial_infarction',\n",
-      "               'stroke',\n",
-      "               'diabetes1',\n",
-      "               'diabetes2',\n",
-      "               'chronic_kidney_disease',\n",
-      "               'atrial_fibrillation',\n",
-      "               'migraine',\n",
-      "               'rheumatoid_arthritis',\n",
-      "               'systemic_lupus_erythematosus',\n",
-      "               'severe_mental_illness',\n",
-      "               'erectile_dysfunction',\n",
-      "               'hypertensive_disorder_systemic_arterial',\n",
-      "               'hyperlipidemia',\n",
-      "               'depressive_disorder',\n",
-      "               'gastroesophageal_reflux_disease',\n",
-      "               'diabetes_mellitus_type_2',\n",
-      "               'essential_hypertension',\n",
-      "               'obesity',\n",
-      "               'diabetes_mellitus',\n",
-      "               'asthma',\n",
-      "               'coronary_arteriosclerosis',\n",
-      "               'allergic_rhinitis',\n",
-      "               'hypothyroidism',\n",
-      "               'upper_respiratory_infection',\n",
-      "               'hypercholesterolemia',\n",
-      "               'backache',\n",
-      "               'abdominal_pain',\n",
-      "               'osteoarthritis',\n",
-      "               'low_back_pain',\n",
-      "               'anemia',\n",
-      "               'anxiety',\n",
-      "               'urinary_tract_infectious_disease',\n",
-      "               'chronic_obstructive_lung_disease',\n",
-      "               'pneumonia',\n",
-      "               'chest_pain',\n",
-      "               'congestive_heart_failure',\n",
-      "               'headache',\n",
-      "               'pregnant',\n",
-      "               'knee_pain',\n",
-      "               'osteoporosis',\n",
-      "               'polyp_of_colon',\n",
-      "               'otitis_media',\n",
-      "               'sinusitis',\n",
-      "               'cough',\n",
-      "               'sleep_apnea',\n",
-      "               'insomnia',\n",
-      "               'inflammatory_disorder_due_to_increased_blood_urate_level',\n",
-      "               'tobacco_dependence_syndrome',\n",
-      "               'malignant_tumor_of_prostate',\n",
-      "               'constipation',\n",
-      "               'hearing_loss',\n",
-      "               'fatigue',\n",
-      "               'obstructive_sleep_apnea_syndrome',\n",
-      "               'malignant_neoplasm_of_breast',\n",
-      "               'delivery_normal',\n",
-      "               'irritable_bowel_syndrome',\n",
-      "               'tobacco_user',\n",
-      "               'neck_pain',\n",
-      "               'cerebrovascular_accident',\n",
-      "               'asthenia',\n",
-      "               'shoulder_pain',\n",
-      "               'acne_vulgaris',\n",
-      "               'benign_prostatic_hyperplasia',\n",
-      "               'dyspnea',\n",
-      "               'carpal_tunnel_syndrome',\n",
-      "               'bronchitis',\n",
-      "               'pharyngitis',\n",
-      "               'arthritis',\n",
-      "               'diarrhea',\n",
-      "               'dizziness',\n",
-      "               'alcohol_abuse',\n",
-      "               'dementia',\n",
-      "               'eczema',\n",
-      "               'syncope',\n",
-      "               'acute_sinusitis',\n",
-      "               'iron_deficiency_anemia',\n",
-      "               'allergic_rhinitis_caused_by_pollen',\n",
-      "               'gastritis',\n",
-      "               'cataract',\n",
-      "               'hematuria_syndrome',\n",
-      "               'disorder_of_the_peripheral_nervous_system',\n",
-      "               'viral_hepatitis_type_c',\n",
-      "               'palpitations',\n",
-      "               'eruption_of_skin',\n",
-      "               'diabetes_mellitus_type_1',\n",
-      "               'renal_failure_syndrome',\n",
-      "               'routine_antenatal_care',\n",
-      "               'peripheral_vascular_disease',\n",
-      "               'hyperglycemia',\n",
-      "               'seizure_disorder',\n",
-      "               'fever',\n",
-      "               'osteoarthritis_of_knee',\n",
-      "               'actinic_keratosis',\n",
-      "               'urinary_incontinence',\n",
-      "               'hemorrhoids',\n",
-      "               'seizure',\n",
-      "               'laceration_-_injury',\n",
-      "               'glaucoma',\n",
-      "               'body_mass_index_30+_-_obesity',\n",
-      "               'breast_lump',\n",
-      "               'viral_disease',\n",
-      "               'abnormal_cervical_smear',\n",
-      "               'cellulitis',\n",
-      "               'senile_hyperkeratosis',\n",
-      "               'anxiety_disorder',\n",
-      "               'vertigo',\n",
-      "               'dysphagia',\n",
-      "               'fall_on_same_level_from_slipping_tripping_or_stumbling',\n",
-      "               'edema',\n",
-      "               'malignant_neoplasm_of_colon',\n",
-      "               'hip_pain',\n",
-      "               'posttraumatic_stress_disorder',\n",
-      "               'inflammatory_dermatosis',\n",
-      "               'psoriasis',\n",
-      "               'myopia',\n",
-      "               'motor_vehicle_traffic_accident',\n",
-      "               'senile_cataract',\n",
-      "               'heart_murmur',\n",
-      "               'liver_function_tests_abnormal',\n",
-      "               'angina',\n",
-      "               'impaired_fasting_glycemia',\n",
-      "               'chronic_ischemic_heart_disease',\n",
-      "               'chronic_sinusitis',\n",
-      "               'menopause_present',\n",
-      "               'basal_cell_carcinoma_of_skin',\n",
-      "               'screening_procedure',\n",
-      "               'raised_prostate_specific_antigen',\n",
-      "               'impaired_glucose_tolerance',\n",
-      "               'smoker',\n",
-      "               'hypertriglyceridemia',\n",
-      "               'irregular_periods',\n",
-      "               'herpes_zoster',\n",
-      "               'sensorineural_hearing_loss',\n",
-      "               'rectal_hemorrhage',\n",
-      "               'history_of_surgery',\n",
-      "               'peptic_ulcer',\n",
-      "               'tinnitus',\n",
-      "               'bipolar_disorder',\n",
-      "               'vitamin_d_deficiency',\n",
-      "               'transient_ischemic_attack',\n",
-      "               'streptococcal_sore_throat',\n",
-      "               'onychomycosis',\n",
-      "               'deep_venous_thrombosis',\n",
-      "               'presbyopia',\n",
-      "               'neonatal_jaundice',\n",
-      "               'bacterial_vaginosis',\n",
-      "               'impacted_cerumen',\n",
-      "               'foot_pain',\n",
-      "               'sciatica',\n",
-      "               'vomiting',\n",
-      "               'history_of_polyp_of_colon',\n",
-      "               'benign_prostatic_hypertrophy_with_outflow_obstruction',\n",
-      "               'type_ii_diabetes_mellitus_without_complication',\n",
-      "               'calculus_in_biliary_tract',\n",
-      "               'epigastric_pain',\n",
-      "               'late_effects_of_cerebrovascular_disease',\n",
-      "               'gastroenteritis',\n",
-      "               'pulmonary_embolism',\n",
-      "               'inguinal_hernia',\n",
-      "               'verruca_vulgaris',\n",
-      "               'sepsis',\n",
-      "               'disorder_of_kidney_due_to_diabetes_mellitus',\n",
-      "               'nausea_and_vomiting',\n",
-      "               'hyperthyroidism',\n",
-      "               'abscess',\n",
-      "               'dental_caries',\n",
-      "               'gastrointestinal_hemorrhage',\n",
-      "               'rosacea',\n",
-      "               \"parkinson's_disease\",\n",
-      "               'menorrhagia',\n",
-      "               'malignant_tumor_of_lung',\n",
-      "               'joint_pain',\n",
-      "               'morbid_obesity',\n",
-      "               'hiatal_hernia',\n",
-      "               'arthralgia_of_the_ankle_and/or_foot',\n",
-      "               'restless_legs',\n",
-      "               'thrombocytopenic_disorder',\n",
-      "               'old_myocardial_infarction',\n",
-      "               'neuropathy',\n",
-      "               'cardiomyopathy',\n",
-      "               'atopic_dermatitis',\n",
-      "               'pain_in_pelvis',\n",
-      "               'contact_dermatitis',\n",
-      "               'indigestion',\n",
-      "               'nicotine_dependence',\n",
-      "               'sprain_of_ankle',\n",
-      "               'degenerative_disorder_of_macula',\n",
-      "               'exacerbation_of_asthma',\n",
-      "               'alcohol_dependence',\n",
-      "               'hypokalemia',\n",
-      "               'mitral_valve_regurgitation',\n",
-      "               'hyponatremia',\n",
-      "               'abdominal_aortic_aneurysm',\n",
-      "               'cyst_of_ovary',\n",
-      "               'otitis_externa',\n",
-      "               'threatened_abortion',\n",
-      "               'scoliosis_deformity_of_spine',\n",
-      "               'seborrheic_dermatitis',\n",
-      "               'spinal_stenosis',\n",
-      "               'dysmenorrhea',\n",
-      "               'acute_otitis_media',\n",
-      "               \"alzheimer's_disease\",\n",
-      "               'neuropathy_due_to_diabetes_mellitus',\n",
-      "               'acute_pharyngitis',\n",
-      "               'degeneration_of_intervertebral_disc',\n",
-      "               'attention_deficit_hyperactivity_disorder_predominantly_inattentive_type',\n",
-      "               'unplanned_pregnancy',\n",
-      "               'secondary_erectile_dysfunction',\n",
-      "               'spinal_stenosis_of_lumbar_region',\n",
-      "               'accidental_physical_contact',\n",
-      "               'proteinuria',\n",
-      "               'urticaria',\n",
-      "               'genital_herpes_simplex',\n",
-      "               'malignant_neoplasm_of_female_breast',\n",
-      "               'nausea',\n",
-      "               'chronic_rhinitis',\n",
-      "               'multiple_sclerosis',\n",
-      "               'chronic_kidney_disease_stage_3',\n",
-      "               'panic_disorder',\n",
-      "               'attention_deficit_hyperactivity_disorder',\n",
-      "               'amnesia',\n",
-      "               'otalgia',\n",
-      "               'tremor',\n",
-      "               'retention_of_urine',\n",
-      "               \"non-hodgkin's_lymphoma\",\n",
-      "               'alcoholism',\n",
-      "               'dysuria',\n",
-      "               'generalized_anxiety_disorder',\n",
-      "               'paroxysmal_atrial_fibrillation',\n",
-      "               'peripheral_venous_insufficiency',\n",
-      "               'nonproliferative_retinopathy_due_to_diabetes_mellitus',\n",
-      "               'shoulder_joint_pain',\n",
-      "               'moderate_recurrent_major_depression',\n",
-      "               'diverticulitis',\n",
-      "               'solitary_nodule_of_lung',\n",
-      "               'hyperkalemia',\n",
-      "               'recurrent_major_depressive_episodes',\n",
-      "               'multiple_myeloma',\n",
-      "               'regular_astigmatism',\n",
-      "               'secondary_malignant_neoplasm_of_liver',\n",
-      "               'ulcerative_colitis',\n",
-      "               'vaginitis',\n",
-      "               'acute_renal_failure_syndrome',\n",
-      "               'amenorrhea',\n",
-      "               'tendinitis',\n",
-      "               'rhinitis',\n",
-      "               'bleeding_from_nose',\n",
-      "               \"crohn's_disease\",\n",
-      "               'nuclear_senile_cataract',\n",
-      "               'muscle_pain',\n",
-      "               'epidermoid_cyst',\n",
-      "               'impaired_cognition',\n",
-      "               'acute_exacerbation_of_chronic_obstructive_airways_disease',\n",
-      "               'eustachian_tube_disorder',\n",
-      "               'internal_hemorrhoids',\n",
-      "               'substance_abuse',\n",
-      "               'melanocytic_nevus',\n",
-      "               'pain',\n",
-      "               \"barrett's_esophagus\",\n",
-      "               'cerebrovascular_disease',\n",
-      "               'malignant_melanoma',\n",
-      "               'folliculitis',\n",
-      "               'family_history_of_diabetes_mellitus',\n",
-      "               'mitral_valve_prolapse',\n",
-      "               'chronic_hepatitis_c',\n",
-      "               'hypermetropia',\n",
-      "               'endometriosis',\n",
-      "               'gestational_diabetes_mellitus',\n",
-      "               'cirrhosis_of_liver',\n",
-      "               'injury_of_head',\n",
-      "               'dehydration',\n",
-      "               'herpes_simplex',\n",
-      "               'fracture_of_bone',\n",
-      "               'overweight',\n",
-      "               'right_inguinal_hernia',\n",
-      "               'adjustment_disorder',\n",
-      "               'tinea_pedis',\n",
-      "               'aortic_valve_stenosis',\n",
-      "               'viral_hepatitis_type_b',\n",
-      "               'umbilical_hernia',\n",
-      "               'ingrowing_nail',\n",
-      "               'postmenopausal_bleeding',\n",
-      "               'goiter',\n",
-      "               'secondary_malignant_neoplasm_of_bone',\n",
-      "               'pulmonary_emphysema',\n",
-      "               'left_inguinal_hernia',\n",
-      "               'snoring',\n",
-      "               'polycystic_ovary_syndrome',\n",
-      "               'polycystic_ovary',\n",
-      "               'microscopic_hematuria',\n",
-      "               'pain_in_wrist',\n",
-      "               'pleural_effusion',\n",
-      "               'false_labor',\n",
-      "               'bleeding_from_vagina',\n",
-      "               'acquired_hypothyroidism',\n",
-      "               'premature_rupture_of_membranes',\n",
-      "               'human_immunodeficiency_virus_infection',\n",
-      "               'contusion',\n",
-      "               'secondary_malignant_neoplasm_of_lung',\n",
-      "               'problem_situation_relating_to_social_and_personal_history',\n",
-      "               'hypercalcemia',\n",
-      "               'occlusion_of_carotid_artery',\n",
-      "               'prolonged_second_stage_of_labor',\n",
-      "               'lipoma',\n",
-      "               'poisoning_caused_by_drug_and/or_medicinal_substance',\n",
-      "               'paranoid_schizophrenia',\n",
-      "               'hypersensitivity_reaction',\n",
-      "               'dysthymia',\n",
-      "               'fetal_or_neonatal_effect_of_maternal_premature_rupture_of_membrane',\n",
-      "               'noncompliance_with_treatment',\n",
-      "               'falls',\n",
-      "               'muscle_strain',\n",
-      "               'schizophrenia',\n",
-      "               'left_bundle_branch_block',\n",
-      "               'elevated_blood_pressure_reading_without_diagnosis_of_hypertension',\n",
-      "               'disorder_of_nervous_system_due_to_type_2_diabetes_mellitus',\n",
-      "               'preinfarction_syndrome',\n",
-      "               'conduction_disorder_of_the_heart',\n",
-      "               'lateral_epicondylitis',\n",
-      "               'burn',\n",
-      "               'pyelonephritis',\n",
-      "               'intermittent_claudication',\n",
-      "               'varicose_veins_of_lower_extremity',\n",
-      "               'hemiplegia',\n",
-      "               'chronic_pain',\n",
-      "               'bronchiolitis',\n",
-      "               'mild_persistent_asthma',\n",
-      "               'secondary_malignant_neoplasm_of_lymph_node',\n",
-      "               'mixed_hyperlipidemia',\n",
-      "               'female_urinary_stress_incontinence',\n",
-      "               'localized_primary_osteoarthritis_of_the_ankle_and/or_foot',\n",
-      "               'hypesthesia',\n",
-      "               'hand_pain',\n",
-      "               'psychotic_disorder',\n",
-      "               'menopausal_syndrome',\n",
-      "               'acute_myocardial_infarction_of_anterior_wall',\n",
-      "               'bronchiectasis',\n",
-      "               'lumbar_radiculopathy',\n",
-      "               'osteoarthritis_of_hip',\n",
-      "               'deviated_nasal_septum',\n",
-      "               'paresthesia',\n",
-      "               'hypoglycemia',\n",
-      "               'deep_venous_thrombosis_of_lower_extremity',\n",
-      "               'complication_of_surgical_procedure',\n",
-      "               'mild_intermittent_asthma',\n",
-      "               'generalized_ischemic_myocardial_dysfunction',\n",
-      "               'lumbar_spondylosis',\n",
-      "               'acute_tonsillitis',\n",
-      "               'esophagitis',\n",
-      "               'aortic_valve_regurgitation',\n",
-      "               'malignant_neoplasm_of_skin',\n",
-      "               'altered_mental_status',\n",
-      "               'atrial_flutter',\n",
-      "               'alopecia',\n",
-      "               'high_risk_pregnancy',\n",
-      "               'missed_abortion',\n",
-      "               'malignant_tumor_of_urinary_bladder',\n",
-      "               'pulmonary_hypertension',\n",
-      "               'nasal_congestion',\n",
-      "               'hemoptysis',\n",
-      "               'viral_gastroenteritis',\n",
-      "               'right_upper_quadrant_pain',\n",
-      "               'primary_open_angle_glaucoma',\n",
-      "               'malignant_tumor_of_ovary',\n",
-      "               'incomplete_emptying_of_bladder',\n",
-      "               'pruritic_disorder',\n",
-      "               'fibrocystic_breast_changes',\n",
-      "               'tinea_corporis',\n",
-      "               'acute_pancreatitis',\n",
-      "               'acute_myocardial_infarction',\n",
-      "               'cerebral_hemorrhage',\n",
-      "               'urge_incontinence_of_urine',\n",
-      "               'megaloblastic_anemia_due_to_vitamin_b>12<_deficiency',\n",
-      "               'chronic_lymphoid_leukemia_disease',\n",
-      "               'disorder_of_lymphatic_system',\n",
-      "               'diverticular_disease',\n",
-      "               'tonsillitis',\n",
-      "               'tear_film_insufficiency',\n",
-      "               'abnormal_gait',\n",
-      "               'assault',\n",
-      "               'accidental_fall',\n",
-      "               'orthostatic_hypotension',\n",
-      "               'pancreatitis',\n",
-      "               'closed_fracture_of_distal_end_of_radius',\n",
-      "               'first_degree_perineal_laceration',\n",
-      "               'not_for_resuscitation',\n",
-      "               'gastroesophageal_reflux_disease_with_esophagitis',\n",
-      "               'intracranial_injury',\n",
-      "               \"bell's_palsy\",\n",
-      "               'bradycardia',\n",
-      "               'polymyalgia_rheumatica',\n",
-      "               'dysplasia_of_cervix',\n",
-      "               'serous_otitis_media',\n",
-      "               'angioedema',\n",
-      "               'venous_varices',\n",
-      "               'spasm',\n",
-      "               'sleep_disorder',\n",
-      "               'pain_of_breast',\n",
-      "               'malabsorption_syndrome_due_to_intolerance_to_lactose',\n",
-      "               'tachycardia',\n",
-      "               'acute_conjunctivitis',\n",
-      "               'malignant_lymphoma',\n",
-      "               'supraventricular_tachycardia',\n",
-      "               'hyperparathyroidism',\n",
-      "               'periodontitis',\n",
-      "               'history_of_malignant_neoplasm_of_prostate',\n",
-      "               'renal_disorder_due_to_type_2_diabetes_mellitus',\n",
-      "               'nerve_root_disorder',\n",
-      "               'nonexudative_age-related_macular_degeneration',\n",
-      "               'history_of_primary_malignant_neoplasm_of_skin',\n",
-      "               'tension-type_headache',\n",
-      "               'chronic_pain_syndrome',\n",
-      "               'abnormal_weight_loss',\n",
-      "               'pure_hypercholesterolemia',\n",
-      "               'acute_upper_respiratory_infection',\n",
-      "               'inflammatory_disease_of_liver',\n",
-      "               'colitis',\n",
-      "               'prolonged_pregnancy',\n",
-      "               'major_depression_single_episode',\n",
-      "               'croup',\n",
-      "               'family_history_of_malignant_neoplasm_of_breast',\n",
-      "               'skin_tag',\n",
-      "               'metabolic_syndrome_x',\n",
-      "               'adverse_reaction_caused_by_drug',\n",
-      "               'retinopathy_due_to_diabetes_mellitus',\n",
-      "               'female_infertility',\n",
-      "               'open-angle_glaucoma',\n",
-      "               \"tietze's_disease\",\n",
-      "               'postpartum_care',\n",
-      "               'gallbladder_calculus',\n",
-      "               'sarcoidosis',\n",
-      "               'neurogenic_bladder',\n",
-      "               'tobacco_dependence_in_remission',\n",
-      "               'wheezing',\n",
-      "               'elevated_levels_of_transaminase_&_lactic_acid_dehydrogenase',\n",
-      "               'ascites',\n",
-      "               'low_blood_pressure',\n",
-      "               'bursitis',\n",
-      "               'miscarriage',\n",
-      "               'methicillin_resistant_staphylococcus_aureus_carrier',\n",
-      "               'urethritis',\n",
-      "               'noninfectious_gastroenteritis',\n",
-      "               'malignant_tumor_of_thyroid_gland',\n",
-      "               'pressure_ulcer',\n",
-      "               'verruca_plantaris',\n",
-      "               'anemia_of_chronic_disorder',\n",
-      "               'hernia_of_anterior_abdominal_wall',\n",
-      "               'degeneration_of_lumbar_intervertebral_disc',\n",
-      "               'right_lower_quadrant_pain',\n",
-      "               'infertile',\n",
-      "               'hernia_of_abdominal_wall',\n",
-      "               'benign_paroxysmal_positional_vertigo',\n",
-      "               'pityriasis_versicolor',\n",
-      "               'injury_of_lower_leg',\n",
-      "               'dry_skin',\n",
-      "               'impacted_tooth',\n",
-      "               'acquired_trigger_finger',\n",
-      "               'chronic_osteoarthritis',\n",
-      "               'acute_stress_disorder',\n",
-      "               'peripheral_circulatory_disorder_due_to_type_2_diabetes_mellitus',\n",
-      "               'respiratory_failure',\n",
-      "               'allergic_disposition',\n",
-      "               'stress',\n",
-      "               'atrophic_vaginitis',\n",
-      "               'degeneration_of_lumbosacral_intervertebral_disc',\n",
-      "               'astigmatism',\n",
-      "               'sick_sinus_syndrome',\n",
-      "               'cocaine_abuse',\n",
-      "               'intermittent_asthma',\n",
-      "               'cervical_radiculopathy',\n",
-      "               'atherosclerosis_of_coronary_artery',\n",
-      "               'methicillin_resistant_staphylococcus_aureus_infection',\n",
-      "               'female_pelvic_inflammatory_disease',\n",
-      "               'pain_in_eye',\n",
-      "               'cervical_spondylosis',\n",
-      "               'prematurity_of_infant',\n",
-      "               'postoperative_care',\n",
-      "               'postmature_infancy',\n",
-      "               'temporomandibular_joint_disorder',\n",
-      "               'nocturia',\n",
-      "               'adjustment_disorder_with_depressed_mood',\n",
-      "               'visual_impairment',\n",
-      "               'male_hypogonadism',\n",
-      "               'closed_intertrochanteric_fracture',\n",
-      "               'pain_in_limb',\n",
-      "               'complication_occurring_during_pregnancy',\n",
-      "               'sprain_of_knee',\n",
-      "               'history_of_malignant_neoplasm_of_colon',\n",
-      "               'fracture_of_ankle',\n",
-      "               'injury_of_hand',\n",
-      "               'postoperative_wound_infection',\n",
-      "               'congestive_cardiomyopathy',\n",
-      "               'nocturnal_enuresis',\n",
-      "               'proliferative_retinopathy_due_to_diabetes_mellitus',\n",
-      "               'major_depressive_disorder',\n",
-      "               'fall_on_same_level_from_slipping',\n",
-      "               'chronic_bronchitis',\n",
-      "               'advanced_maternal_age_gravida',\n",
-      "               'recurrent_major_depression_in_full_remission',\n",
-      "               'pregnancy-induced_hypertension',\n",
-      "               'epididymitis',\n",
-      "               'impetigo',\n",
-      "               'schizoaffective_disorder',\n",
-      "               'candidiasis_of_mouth',\n",
-      "               'mild_recurrent_major_depression',\n",
-      "               'cerebral_palsy',\n",
-      "               'squamous_cell_carcinoma_of_skin',\n",
-      "               'hypogonadism',\n",
-      "               'greater_trochanteric_pain_syndrome',\n",
-      "               \"graves'_disease\",\n",
-      "               'malignant_neoplastic_disease',\n",
-      "               'moderate_persistent_asthma',\n",
-      "               'steatosis_of_liver',\n",
-      "               'obsessive-compulsive_disorder',\n",
-      "               'mood_disorder',\n",
-      "               'degeneration_of_cervical_intervertebral_disc',\n",
-      "               'corneal_abrasion',\n",
-      "               'anal_fissure',\n",
-      "               'heart_failure',\n",
-      "               'adjustment_disorder_with_mixed_emotional_features',\n",
-      "               'febrile_convulsion',\n",
-      "               'degenerative_joint_disease_of_hand',\n",
-      "               'chronic_type_b_viral_hepatitis',\n",
-      "               'blepharitis',\n",
-      "               'cyst',\n",
-      "               'heartburn',\n",
-      "               'history_of_malignant_melanoma',\n",
-      "               'intellectual_disability',\n",
-      "               'exudative_age-related_macular_degeneration',\n",
-      "               'hypoxia',\n",
-      "               'influenza',\n",
-      "               'intervertebral_disc_prolapse',\n",
-      "               'swelling_of_first_metatarsophalangeal_joint_of_hallux',\n",
-      "               'genuine_stress_incontinence',\n",
-      "               'opioid_dependence',\n",
-      "               'antepartum_hemorrhage',\n",
-      "               'pneumothorax',\n",
-      "               'kidney_disease',\n",
-      "               'atopic_conjunctivitis',\n",
-      "               'dermatophytosis',\n",
-      "               'influenza_caused_by_influenza_a_virus',\n",
-      "               'cystitis',\n",
-      "               'moderate_major_depression_single_episode',\n",
-      "               'furuncle',\n",
-      "               'cannabis_abuse',\n",
-      "               'conductive_hearing_loss',\n",
-      "               'neutropenic_disorder',\n",
-      "               'injury_of_finger',\n",
-      "               'intestinal_obstruction',\n",
-      "               'lymphadenopathy',\n",
-      "               'mass_of_pelvic_structure',\n",
-      "               'myelodysplastic_syndrome',\n",
-      "               'jaundice',\n",
-      "               'severe_recurrent_major_depression_without_psychotic_features',\n",
-      "               'inflammation_of_cervix',\n",
-      "               'sickle_cell_trait',\n",
-      "               'squamous_cell_carcinoma',\n",
-      "               'small_bowel_obstruction',\n",
-      "               'premenstrual_tension_syndrome',\n",
-      "               'fracture_of_proximal_end_of_femur',\n",
-      "               'developmental_delay',\n",
-      "               'drug_abuse',\n",
-      "               'left_lower_quadrant_pain',\n",
-      "               'hordeolum',\n",
-      "               'disorder_of_lung',\n",
-      "               'migraine_without_aura',\n",
-      "               'open-angle_glaucoma_-_borderline',\n",
-      "               'disorder_of_refraction',\n",
-      "               'acute_appendicitis',\n",
-      "               'amyloidosis',\n",
-      "               \"hodgkin's_disease\",\n",
-      "               'contraception_care',\n",
-      "               'hydrocele_of_testis',\n",
-      "               'cellulitis_of_foot',\n",
-      "               'pancytopenia',\n",
-      "               'pain_in_elbow',\n",
-      "               'peripheral_nerve_disease',\n",
-      "               'eating_disorder',\n",
-      "               'hernia_of_abdominal_cavity',\n",
-      "               'secondary_malignant_neoplasm_of_brain',\n",
-      "               'late_effects_of_respiratory_tuberculosis',\n",
-      "               'alcohol_intoxication',\n",
-      "               'abrasion',\n",
-      "               'breech_presentation',\n",
-      "               'premature_labor',\n",
-      "               'family_history:_cardiovascular_disease',\n",
-      "               \"raynaud's_phenomenon\",\n",
-      "               'history_of_fall',\n",
-      "               'posterior_rhinorrhea',\n",
-      "               'acute_gastritis',\n",
-      "               'polyp_of_nasal_cavity',\n",
-      "               'essential_tremor',\n",
-      "               'candidiasis',\n",
-      "               'ocular_hypertension',\n",
-      "               'aortic_valve_disorder',\n",
-      "               'diaper_rash',\n",
-      "               'cholangitis',\n",
-      "               'primary_malignant_neoplasm_of_lung',\n",
-      "               'impingement_syndrome_of_shoulder_region',\n",
-      "               'idiopathic_urticaria',\n",
-      "               'arthritis_of_knee',\n",
-      "               'ulcer_of_duodenum',\n",
-      "               'peripheral_neuropathy_due_to_diabetes_mellitus',\n",
-      "               'hypervolemia',\n",
-      "               'cholecystitis',\n",
-      "               'deficiency_of_glucose-6-phosphate_dehydrogenase',\n",
-      "               'acute_pyelonephritis',\n",
-      "               'musculoskeletal_chest_pain',\n",
-      "               'melena',\n",
-      "               'dog_bite',\n",
-      "               'cigarette_smoker',\n",
-      "               'infectious_mononucleosis',\n",
-      "               'hypertensive_renal_failure',\n",
-      "               'cocaine_dependence',\n",
-      "               'abdominal_aortic_aneurysm_without_rupture',\n",
-      "               'right_bundle_branch_block',\n",
-      "               'alcoholic_cirrhosis',\n",
-      "               'fibrosis_of_lung',\n",
-      "               'neoplasm_of_uncertain_behavior_of_skin',\n",
-      "               'malignant_melanoma_of_skin',\n",
-      "               'urgent_desire_to_urinate',\n",
-      "               'bursitis_of_hip',\n",
-      "               'chronic_alcoholism_in_remission',\n",
-      "               'chronic_pancreatitis',\n",
-      "               'gastroparesis',\n",
-      "               'ectopic_pregnancy',\n",
-      "               'muscle_weakness',\n",
-      "               'recurrent_major_depression',\n",
-      "               'pilonidal_cyst',\n",
-      "               'pain_in_toe',\n",
-      "               'pulmonary_tuberculosis',\n",
-      "               'celiac_disease',\n",
-      "               'cramp_in_lower_leg',\n",
-      "               'secondary_malignant_neoplasm_of_pleura',\n",
-      "               'fracture_of_hand',\n",
-      "               'cyst_of_breast',\n",
-      "               'nephrotic_syndrome',\n",
-      "               'polyp_of_nasal_sinus',\n",
-      "               'chondromalacia_of_patella',\n",
-      "               'spinal_stenosis_in_cervical_region',\n",
-      "               'disorder_of_artery',\n",
-      "               'vitiligo',\n",
-      "               'female_cystocele',\n",
-      "               'dysphasia',\n",
-      "               'retinal_disorder',\n",
-      "               'epiretinal_membrane',\n",
-      "               'recurrent_major_depression_in_partial_remission',\n",
-      "               'infection_caused_by_trichomonas',\n",
-      "               'osteomyelitis',\n",
-      "               'polyp_of_nasal_cavity_and/or_nasal_sinus',\n",
-      "               'mass_of_neck',\n",
-      "               'fall_from_one_level_to_another',\n",
-      "               'idiopathic_thrombocytopenic_purpura',\n",
-      "               'complete_miscarriage',\n",
-      "               'gastric_ulcer',\n",
-      "               'papilloma_of_skin',\n",
-      "               'fetal_or_neonatal_effect_of_breech_delivery_and_extraction',\n",
-      "               'secondary_malignant_neoplastic_disease',\n",
-      "               'hypoxemia',\n",
-      "               'paraplegia',\n",
-      "               'perforation_of_tympanic_membrane',\n",
-      "               'ventricular_tachycardia',\n",
-      "               'mixed_incontinence',\n",
-      "               'disorder_of_eye_due_to_type_2_diabetes_mellitus',\n",
-      "               'trigeminal_neuralgia',\n",
-      "               'retinal_detachment',\n",
-      "               'leukopenia',\n",
-      "               'vitreous_hemorrhage',\n",
-      "               'ischemic_ulcer',\n",
-      "               'intramural_leiomyoma_of_uterus',\n",
-      "               'viral_hepatitis_type_a',\n",
-      "               \"ménière's_disease\",\n",
-      "               'fracture_of_phalanx_of_hand',\n",
-      "               'muscle_atrophy',\n",
-      "               'incontinence_of_feces',\n",
-      "               'mitral_valve_disorder',\n",
-      "               'atherosclerosis_of_arteries_of_the_extremities',\n",
-      "               'spondylosis',\n",
-      "               'pterygium',\n",
-      "               'history_of_peptic_ulcer',\n",
-      "               'ulnar_neuropathy',\n",
-      "               'lung_mass',\n",
-      "               'foreign_body_in_respiratory_tract',\n",
-      "               'chronic_kidney_disease_stage_4',\n",
-      "               'myocardial_ischemia',\n",
-      "               'non-toxic_multinodular_goiter',\n",
-      "               'pain_in_finger',\n",
-      "               'cervical_spondylosis_without_myelopathy',\n",
-      "               'body_mass_index_25-29_-_overweight',\n",
-      "               'clouded_consciousness',\n",
-      "               'mixed_conductive_and_sensorineural_hearing_loss',\n",
-      "               'tooth_eruption_disorder',\n",
-      "               'hyperuricemia',\n",
-      "               'closed_fracture_of_neck_of_femur',\n",
-      "               'bipolar_ii_disorder',\n",
-      "               'disturbance_in_sleep_behavior',\n",
-      "               'relationship_problems',\n",
-      "               'sprain_of_wrist',\n",
-      "               'personality_disorder',\n",
-      "               'external_hemorrhoids',\n",
-      "               'abnormal_vision',\n",
-      "               'hyperprolactinemia',\n",
-      "               'hemochromatosis',\n",
-      "               'lumbosacral_radiculopathy',\n",
-      "               'heart_valve_disorder',\n",
-      "               'cardiac_arrest',\n",
-      "               'infection_caused_by_molluscum_contagiosum',\n",
-      "               'chronic_kidney_disease_stage_2',\n",
-      "               'secondary_malignant_neoplasm_of_peritoneum',\n",
-      "               'thoracic_back_pain',\n",
-      "               'blood_in_urine',\n",
-      "               'adhesive_capsulitis_of_shoulder',\n",
-      "               'diplopia',\n",
-      "               \"sjögren's_syndrome\",\n",
-      "               'ureteric_stone',\n",
-      "               'bronchospasm',\n",
-      "               'chronic_fatigue_syndrome',\n",
-      "               'cannabis_dependence',\n",
-      "               'neck_sprain',\n",
-      "               'multinodular_goiter',\n",
-      "               'ptosis_of_eyelid',\n",
-      "               'failure_to_thrive',\n",
-      "               'torticollis',\n",
-      "               'acute_bronchiolitis',\n",
-      "               'viral_exanthem',\n",
-      "               'talipes_planus',\n",
-      "               'idiopathic_peripheral_neuropathy',\n",
-      "               'foreign_body_in_pharynx',\n",
-      "               'jaw_pain',\n",
-      "               'renal_impairment',\n",
-      "               'ataxia',\n",
-      "               'age-related_macular_degeneration',\n",
-      "               'uterine_prolapse',\n",
-      "               'renal_mass',\n",
-      "               'pneumonitis',\n",
-      "               'coordination_problem',\n",
-      "               'blindness_-_both_eyes',\n",
-      "               'primary_hyperparathyroidism',\n",
-      "               'musculoskeletal_pain',\n",
-      "               'mycosis',\n",
-      "               'primigravida',\n",
-      "               'urethral_stricture',\n",
-      "               'leukocytosis',\n",
-      "               'ventricular_premature_complex',\n",
-      "               'ulcer_of_foot_due_to_diabetes_mellitus',\n",
-      "               'chronic_headache_disorder',\n",
-      "               'hemangioma',\n",
-      "               'lymphedema',\n",
-      "               'postmenopausal_state',\n",
-      "               'chronic_ulcer_of_skin',\n",
-      "               'left_heart_failure',\n",
-      "               'excessive_and_frequent_menstruation',\n",
-      "               'thrombocytosis',\n",
-      "               'disorder_of_liver',\n",
-      "               'disorder_of_carotid_artery',\n",
-      "               'altered_bowel_function',\n",
-      "               'abscess_of_foot',\n",
-      "               'malignant_tumor_of_head_and/or_neck',\n",
-      "               'streptococcus_group_b_infection_of_the_infant',\n",
-      "               'concussion_injury_of_brain',\n",
-      "               'feeding_problems_in_newborn',\n",
-      "               'bipolar_i_disorder',\n",
-      "               'viral_pharyngitis',\n",
-      "               'lower_respiratory_tract_infection',\n",
-      "               'hydronephrosis',\n",
-      "               'borderline_personality_disorder',\n",
-      "               'esophageal_varices',\n",
-      "               'hypersomnia',\n",
-      "               'sensorineural_hearing_loss_bilateral',\n",
-      "               'varicocele',\n",
-      "               'subarachnoid_intracranial_hemorrhage',\n",
-      "               'incisional_hernia',\n",
-      "               'varicella',\n",
-      "               'pain_in_testicle',\n",
-      "               'transplant_follow-up',\n",
-      "               'tinea_cruris',\n",
-      "               'laryngitis',\n",
-      "               'hypertrophy_of_nail',\n",
-      "               'crushed_in_between_objects',\n",
-      "               'amblyopia',\n",
-      "               'polyp_of_cervix',\n",
-      "               'cyst_of_kidney',\n",
-      "               'hepatic_encephalopathy',\n",
-      "               'blood_glucose_abnormal',\n",
-      "               'postherpetic_neuralgia',\n",
-      "               'frank_hematuria',\n",
-      "               'cramp',\n",
-      "               'interstitial_lung_disease',\n",
-      "               'complete_atrioventricular_block',\n",
-      "               'malignant_tumor_of_kidney',\n",
-      "               'otitis',\n",
-      "               'septic_shock',\n",
-      "               'disorder_of_thyroid_gland',\n",
-      "               'hypertrophic_cardiomyopathy',\n",
-      "               'respiratory_distress_syndrome_in_the_newborn',\n",
-      "               'infectious_gastroenteritis',\n",
-      "               'subdural_intracranial_hemorrhage',\n",
-      "               'hepatitis_b_carrier',\n",
-      "               'manic_bipolar_i_disorder',\n",
-      "               'secondary_pulmonary_hypertension',\n",
-      "               'history_of_thrombosis',\n",
-      "               'gonorrhea',\n",
-      "               'derangement_of_knee',\n",
-      "               'appendicitis',\n",
-      "               'polyneuropathy_due_to_diabetes_mellitus',\n",
-      "               'neonatal_hypoglycemia',\n",
-      "               'prolonged_rupture_of_membranes',\n",
-      "               'vasomotor_rhinitis',\n",
-      "               'renal_disorder_due_to_type_1_diabetes_mellitus',\n",
-      "               'tuberculosis',\n",
-      "               'feeding_problem',\n",
-      "               'chronic_tonsillitis',\n",
-      "               'history_of_tuberculosis',\n",
-      "               'acute_duodenal_ulcer_with_hemorrhage',\n",
-      "               'hammer_toe',\n",
-      "               'malignant_tumor_of_cervix',\n",
-      "               'prolapsed_lumbar_intervertebral_disc',\n",
-      "               'hematemesis',\n",
-      "               'perianal_abscess',\n",
-      "               'nonvenomous_insect_bite',\n",
-      "               'spondylolisthesis',\n",
-      "               'malignant_tumor_of_esophagus',\n",
-      "               'aphthous_ulcer_of_mouth',\n",
-      "               'ventricular_septal_defect',\n",
-      "               'oropharyngeal_dysphagia',\n",
-      "               'injury_of_knee',\n",
-      "               'traumatic_brain_injury',\n",
-      "               'osteoarthritis_of_glenohumeral_joint',\n",
-      "               'fetal_or_neonatal_effect_of_maternal_medical_problem',\n",
-      "               'portal_hypertension',\n",
-      "               'duodenitis',\n",
-      "               'non-toxic_uninodular_goiter',\n",
-      "               'acute_cholecystitis',\n",
-      "               'renal_colic',\n",
-      "               'atrial_arrhythmia',\n",
-      "               'mass_of_body_structure',\n",
-      "               'nuclear_cataract',\n",
-      "               'drug-induced_hypoglycemia',\n",
-      "               'disorder_of_brain',\n",
-      "               'cystoid_macular_edema',\n",
-      "               'malocclusion_of_teeth',\n",
-      "               'aortic_aneurysm',\n",
-      "               'cyst_of_meibomian_gland',\n",
-      "               'allergy_to_drug',\n",
-      "               'malignant_tumor_of_nasopharynx',\n",
-      "               'history_of_musculoskeletal_disease',\n",
-      "               'respiratory_tract_infection',\n",
-      "               'hemiplegia_as_late_effect_of_cerebrovascular_disease',\n",
-      "               'lower_abdominal_pain',\n",
-      "               'chronic_maxillary_sinusitis',\n",
-      "               'cervical_lymphadenopathy',\n",
-      "               'common_cold',\n",
-      "               'administrative_procedure',\n",
-      "               'psychosexual_dysfunction',\n",
-      "               'hypertensive_renal_disease',\n",
-      "               'ketoacidosis_due_to_diabetes_mellitus',\n",
-      "               'polyp_of_corpus_uteri',\n",
-      "               'delirium',\n",
-      "               'pityriasis_rosea',\n",
-      "               'cutis_laxa',\n",
-      "               'arthralgia_of_the_pelvic_region_and_thigh',\n",
-      "               'thyroiditis',\n",
-      "               'fracture_of_rib',\n",
-      "               'chronic_serous_otitis_media',\n",
-      "               'sarcoma',\n",
-      "               'rheumatic_fever',\n",
-      "               'complication_of_implant',\n",
-      "               'atrial_septal_defect',\n",
-      "               'disorder_of_skeletal_and/or_smooth_muscle',\n",
-      "               'plagiocephaly',\n",
-      "               'contusion_of_knee',\n",
-      "               'clostridioides_difficile_infection',\n",
-      "               'traumatic_injury',\n",
-      "               'physical_examination_procedure',\n",
-      "               'disorder_of_lipid_metabolism',\n",
-      "               'tricuspid_valve_regurgitation',\n",
-      "               'intertrigo',\n",
-      "               'acute_myeloid_leukemia_disease',\n",
-      "               'upper_gastrointestinal_hemorrhage',\n",
-      "               'breathing-related_sleep_disorder',\n",
-      "               'intestinal_adhesions_with_obstruction',\n",
-      "               \"raynaud's_disease\",\n",
-      "               'paralytic_syndrome_of_dominant_side_as_late_effect_of_stroke',\n",
-      "               'acute_gastric_ulcer_with_hemorrhage',\n",
-      "               'esotropia',\n",
-      "               'closed_fracture_of_metacarpal_bone',\n",
-      "               'syphilis',\n",
-      "               'parkinsonism',\n",
-      "               'cut_-_accidental',\n",
-      "               'disorder_of_joint_of_spine',\n",
-      "               'constipation_due_to_outlet_obstruction',\n",
-      "               'psoriasis_with_arthropathy',\n",
-      "               'amyotrophic_lateral_sclerosis',\n",
-      "               'twin_pregnancy',\n",
-      "               'closed_fracture_of_pelvis',\n",
-      "               'infection_caused_by_antimicrobial_resistant_bacteria',\n",
-      "               'enteritis_caused_by_rotavirus',\n",
-      "               'digestive_system_reflux',\n",
-      "               'unemployed',\n",
-      "               'closed_fracture_of_metatarsal_bone',\n",
-      "               'pneumonia_caused_by_pseudomonas',\n",
-      "               'hydrocephalus',\n",
-      "               'family_history_of_cancer',\n",
-      "               'closed_fracture_of_patella',\n",
-      "               'rectal_polyp',\n",
-      "               'animal_bite_wound',\n",
-      "               'acute_cholangitis',\n",
-      "               'disorder_of_intestine',\n",
-      "               'pleurisy',\n",
-      "               'family_history_of_prostate_cancer',\n",
-      "               'cerebral_atherosclerosis',\n",
-      "               'fracture_of_radius',\n",
-      "               'acute_duodenal_ulcer',\n",
-      "               'hypomagnesemia',\n",
-      "               'hyperbilirubinemia',\n",
-      "               'complete_trisomy_21_syndrome',\n",
-      "               'cervical_spondylosis_with_myelopathy',\n",
-      "               'pressure_ulcer_of_buttock',\n",
-      "               'cervical_arthritis',\n",
-      "               'radiology_result_abnormal',\n",
-      "               'complication_of_transplanted_kidney',\n",
-      "               'acute_bronchiolitis_caused_by_respiratory_syncytial_virus',\n",
-      "               'pleuritic_pain',\n",
-      "               'abnormal_findings_on_diagnostic_imaging_of_lung',\n",
-      "               'paralytic_syndrome_of_nondominant_side_as_late_effect_of_stroke',\n",
-      "               'benign_neoplasm_of_colon',\n",
-      "               'complication_of_renal_dialysis',\n",
-      "               'failed_induction_of_labor',\n",
-      "               'herniation_of_nucleus_pulposus',\n",
-      "               'myasthenia_gravis',\n",
-      "               'neoplasm_of_brain',\n",
-      "               'short_stature_disorder',\n",
-      "               'hypertrophy_of_nasal_turbinates',\n",
-      "               'lymphadenitis',\n",
-      "               'excessive_cerumen_in_ear_canal',\n",
-      "               'cognitive_disorder',\n",
-      "               'postconcussion_syndrome',\n",
-      "               'polyuria',\n",
-      "               'herniation_of_rectum_into_vagina',\n",
-      "               'hallucinations',\n",
-      "               'nutritional_deficiency_disorder',\n",
-      "               'epicondylitis',\n",
-      "               'gangrenous_disorder',\n",
-      "               'iritis',\n",
-      "               'disorder_due_to_type_2_diabetes_mellitus',\n",
-      "               'cortical_senile_cataract',\n",
-      "               'disorder_of_rotator_cuff',\n",
-      "               'exanthema_subitum',\n",
-      "               'intrauterine_pregnancy',\n",
-      "               'abdominal_mass',\n",
-      "               'hirsutism',\n",
-      "               'acute_maxillary_sinusitis',\n",
-      "               'localization-related_epilepsy',\n",
-      "               'human_papillomavirus_infection',\n",
-      "               'hand_joint_inflamed',\n",
-      "               'hypertrophy_of_breast',\n",
-      "               'cervical_intraepithelial_neoplasia',\n",
-      "               'collapse_of_vertebra',\n",
-      "               'bacteremia',\n",
-      "               'hypertensive_heart_disease',\n",
-      "               'mixed_bipolar_i_disorder',\n",
-      "               'malaise_and_fatigue',\n",
-      "               'insect_bite_-_wound',\n",
-      "               'arteriosclerotic_vascular_disease',\n",
-      "               'thyrotoxicosis',\n",
-      "               'renal_artery_stenosis',\n",
-      "               'aspiration_pneumonia',\n",
-      "               'carcinoma_in_situ_of_breast',\n",
-      "               'polyneuropathy_due_to_type_2_diabetes_mellitus',\n",
-      "               'abdominal_pregnancy',\n",
-      "               'onychogryposis',\n",
-      "               'fracture_of_tibia',\n",
-      "               'compression_fracture_of_vertebral_column',\n",
-      "               'closed_supracondylar_fracture_of_humerus',\n",
-      "               'dysarthria',\n",
-      "               'maternal_pyrexia_in_labor',\n",
-      "               'synovial_cyst_of_knee',\n",
-      "               'lichen_simplex_chronicus',\n",
-      "               'inflamed_seborrheic_keratosis',\n",
-      "               'fracture_of_clavicle',\n",
-      "               'prostate_specific_antigen_abnormal',\n",
-      "               'chronic_purulent_otitis_media',\n",
-      "               'abdominal_abscess',\n",
-      "               'excessive_vomiting_in_pregnancy',\n",
-      "               'postoperative_hypothyroidism',\n",
-      "               'allergy_to_food',\n",
-      "               'non-toxic_nodular_goiter',\n",
-      "               'preterm_premature_rupture_of_membranes',\n",
-      "               'prostate_mass',\n",
-      "               'reduced_libido',\n",
-      "               'abnormal_cervical_papanicolaou_smear',\n",
-      "               'infectious_disease',\n",
-      "               'keratosis_pilaris',\n",
-      "               'oligomenorrhea',\n",
-      "               \"cyst_of_bartholin's_gland_duct\",\n",
-      "               'undifferentiated_schizophrenia',\n",
-      "               'open_wound',\n",
-      "               'alcohol_withdrawal_syndrome',\n",
-      "               'respiratory_syncytial_virus_infection',\n",
-      "               'urinary_bladder_stone',\n",
-      "               'fetal_or_neonatal_effect_of_multiple_pregnancy',\n",
-      "               'vesicoureteric_reflux',\n",
-      "               'hyperhidrosis',\n",
-      "               'temporal_arteritis',\n",
-      "               'erythrocytosis',\n",
-      "               'spontaneous_pneumothorax',\n",
-      "               'aphasia',\n",
-      "               'bleeding',\n",
-      "               'heterozygous_thalassemia',\n",
-      "               'chronic_ulcer_of_lower_extremity',\n",
-      "               'disorder_of_nervous_system_due_to_diabetes_mellitus',\n",
-      "               'family_history_of_disorder',\n",
-      "               'injury_of_upper_extremity',\n",
-      "               'mild_major_depression_single_episode',\n",
-      "               'parainfluenza',\n",
-      "               'balanitis',\n",
-      "               'hypertrophy_of_tonsils',\n",
-      "               'fracture_of_humerus',\n",
-      "               'mild_nonproliferative_retinopathy_due_to_diabetes_mellitus',\n",
-      "               'disorder_of_cardiovascular_system',\n",
-      "               'dyssomnia',\n",
-      "               'transitory_tachypnea_of_newborn',\n",
-      "               'hypocalcemia',\n",
-      "               'gastroduodenitis',\n",
-      "               'spinal_cord_disorder',\n",
-      "               'alveolar_hypoventilation',\n",
-      "               'hand_joint_pain',\n",
-      "               'vasculitis',\n",
-      "               'inflammation_of_sacroiliac_joint',\n",
-      "               'patent_ductus_arteriosus',\n",
-      "               'radial_styloid_tenosynovitis',\n",
-      "               'current_tear_of_medial_cartilage_and/or_meniscus_of_knee',\n",
-      "               'closed_fracture_of_lateral_malleolus',\n",
-      "               'iron_deficiency',\n",
-      "               'chronic_constipation',\n",
-      "               'noncompliance_with_medication_regimen',\n",
-      "               'fibroadenosis_of_breast',\n",
-      "               'sprain_of_spinal_ligament',\n",
-      "               'hemiplegia_as_late_effect_of_cerebrovascular_accident',\n",
-      "               'achilles_tendinitis',\n",
-      "               \"gilbert's_syndrome\",\n",
-      "               'knee_joint_effusion',\n",
-      "               'left_upper_quadrant_pain',\n",
-      "               'disorder_of_nervous_system_due_to_type_1_diabetes_mellitus',\n",
-      "               'panic_disorder_without_agoraphobia',\n",
-      "               'large_liver',\n",
-      "               'hyperparathyroidism_due_to_renal_insufficiency',\n",
-      "               'macrocytic_anemia',\n",
-      "               'chronic_pulmonary_heart_disease',\n",
-      "               'hypovolemia',\n",
-      "               'suicide_attempt',\n",
-      "               'puncture_wound_-_injury',\n",
-      "               'chronic_post-traumatic_stress_disorder',\n",
-      "               'congenital_heart_disease',\n",
-      "               'acute_otitis_externa',\n",
-      "               'autistic_disorder',\n",
-      "               'aneurysm_of_thoracic_aorta',\n",
-      "               'ultrasound_scan_abnormal',\n",
-      "               'fracture_of_phalanx_of_foot',\n",
-      "               'excessive_fetal_growth_affecting_management_of_mother',\n",
-      "               'fracture_of_mandible',\n",
-      "               'disorder_of_lower_extremity',\n",
-      "               'radiation_therapy_complication',\n",
-      "               'malignant_tumor_of_pancreas',\n",
-      "               'exotropia',\n",
-      "               'fractured_nasal_bones',\n",
-      "               'drowsy',\n",
-      "               'nonunion_of_fracture',\n",
-      "               'residual_cognitive_deficit_as_late_effect_of_cerebrovascular_accident',\n",
-      "               'asbestosis',\n",
-      "               'autonomic_neuropathy_due_to_type_2_diabetes_mellitus',\n",
-      "               'splenomegaly',\n",
-      "               'closed_fracture_of_shaft_of_femur',\n",
-      "               'closed_fracture_of_clavicle',\n",
-      "               'open_wound_of_finger',\n",
-      "               'chronic_prostatitis',\n",
-      "               'malignant_tumor_of_stomach',\n",
-      "               'patellar_tendonitis',\n",
-      "               'neoplastic_disease',\n",
-      "               'scar',\n",
-      "               'hematoma',\n",
-      "               'syndrome_of_inappropriate_vasopressin_secretion',\n",
-      "               'administrative_reason_for_encounter',\n",
-      "               'viral_gastroenteritis_caused_by_norwalk-like_agent',\n",
-      "               'disorder_of_stoma',\n",
-      "               'cardiomegaly',\n",
-      "               'chronic_mucoid_otitis_media',\n",
-      "               'inflammatory_disorder_of_breast',\n",
-      "               'non-infective_non-allergic_rhinitis',\n",
-      "               'fracture_of_fibula',\n",
-      "               'incomplete_miscarriage',\n",
-      "               'periapical_abscess',\n",
-      "               'motor_vehicle_on_road_in_collision_with_pedestrian',\n",
-      "               'chronic_duodenal_ulcer',\n",
-      "               'retinal_edema',\n",
-      "               'kyphosis_deformity_of_spine',\n",
-      "               'nummular_eczema',\n",
-      "               'adjustment_disorder_with_anxious_mood',\n",
-      "               'bipolar_affective_disorder_current_episode_depression',\n",
-      "               'pre-eclampsia',\n",
-      "               'degenerative_joint_disease_of_ankle_and/or_foot',\n",
-      "               'mitral_valve_stenosis',\n",
-      "               'induratio_penis_plastica',\n",
-      "               'malignant_tumor_of_testis',\n",
-      "               'systemic_sclerosis',\n",
-      "               'polycythemia_vera',\n",
-      "               'chronic_otitis_media',\n",
-      "               'retinopathy_of_prematurity',\n",
-      "               'foreign_body_in_ear',\n",
-      "               'teething_syndrome',\n",
-      "               'drug_dependence',\n",
-      "               'sprain_of_ligament_of_finger',\n",
-      "               'sprain_of_ligament_of_thumb',\n",
-      "               'atypical_squamous_cells_of_undetermined_significance_on_cervical_papanicolaou_smear',\n",
-      "               'low_tension_glaucoma',\n",
-      "               'complication_due_to_diabetes_mellitus',\n",
-      "               'dizziness_and_giddiness',\n",
-      "               'cough_variant_asthma',\n",
-      "               'fracture_of_foot',\n",
-      "               'haemophilus_influenzae_pneumonia',\n",
-      "               'delusional_disorder',\n",
-      "               'retinal_lattice_degeneration',\n",
-      "               'pericardial_effusion',\n",
-      "               'chronic_kidney_disease_stage_5',\n",
-      "               'disorder_of_eye_due_to_diabetes_mellitus',\n",
-      "               'primary_biliary_cholangitis',\n",
-      "               'dislocation_of_shoulder_joint',\n",
-      "               'hemangioma_of_skin',\n",
-      "               'basal_cell_carcinoma_of_face',\n",
-      "               'disease_caused_by_adenovirus',\n",
-      "               'exposure_to_communicable_disease',\n",
-      "               'poisoning_caused_by_anticoagulant',\n",
-      "               'complex_regional_pain_syndrome',\n",
-      "               'enlargement_of_tonsil_or_adenoid',\n",
-      "               'hidradenitis',\n",
-      "               'acute_lymphoid_leukemia_disease',\n",
-      "               'not_up_to_date_with_immunizations',\n",
-      "               'labyrinthitis',\n",
-      "               'peripheral_angiopathy_due_to_diabetes_mellitus',\n",
-      "               'chronic_tension-type_headache',\n",
-      "               'fetal_or_neonatal_effect_of_maternal_anesthesia_and/or_analgesia',\n",
-      "               'closed_fracture_of_ankle',\n",
-      "               'narcolepsy',\n",
-      "               'congenital_cystic_kidney_disease',\n",
-      "               'inflammation_of_bursa_of_olecranon',\n",
-      "               'eruption_caused_by_drug',\n",
-      "               'metabolic_acidosis',\n",
-      "               'disorder_of_autonomic_nervous_system',\n",
-      "               'mallory-weiss_syndrome',\n",
-      "               'chronic_interstitial_cystitis',\n",
-      "               'alcoholic_liver_damage',\n",
-      "               'alopecia_areata',\n",
-      "               'trichiasis',\n",
-      "               'orchitis_and_epididymitis',\n",
-      "               'perimenopausal_disorder',\n",
-      "               'injury_of_eye_region',\n",
-      "               'shoulder_joint_inflamed',\n",
-      "               'graft-versus-host_disease',\n",
-      "               'end-stage_renal_disease',\n",
-      "               'hallux_valgus',\n",
-      "               'dystrophia_unguium',\n",
-      "               'senile_dementia',\n",
-      "               'type_ii_diabetes_mellitus_with_ulcer',\n",
-      "               'keratitis',\n",
-      "               'plantar_fascial_fibromatosis',\n",
-      "               'neurogenic_bowel',\n",
-      "               'complication_of_procedure',\n",
-      "               'homeless',\n",
-      "               'cerebral_embolism',\n",
-      "               'chronic_duodenal_ulcer_with_hemorrhage',\n",
-      "               'lichen_planus',\n",
-      "               'abnormal_sexual_function',\n",
-      "               'meningitis',\n",
-      "               'leukemia_disease',\n",
-      "               'bursitis_of_shoulder',\n",
-      "               'venous_retinal_branch_occlusion',\n",
-      "               'abscess_of_skin_and/or_subcutaneous_tissue',\n",
-      "               'anomaly_of_tooth_position',\n",
-      "               'bacterial_conjunctivitis',\n",
-      "               'malignant_tumor_of_ascending_colon',\n",
-      "               'aftercare',\n",
-      "               'neuralgia',\n",
-      "               'vulvovaginitis',\n",
-      "               'pulmonary_edema',\n",
-      "               'visual_disturbance',\n",
-      "               'arthritis_of_spine',\n",
-      "               'contusion_of_hip',\n",
-      "               'anxiety_state',\n",
-      "               'anorectal_fistula',\n",
-      "               'uveitis',\n",
-      "               'wound_dehiscence',\n",
-      "               'fracture_of_pelvis',\n",
-      "               'cocaine_dependence_in_remission',\n",
-      "               'granulomatosis_with_polyangiitis',\n",
-      "               'head_and_neck_injury',\n",
-      "               'vascular_dementia',\n",
-      "               'severe_major_depression_single_episode_without_psychotic_features',\n",
-      "               'anemia_of_pregnancy',\n",
-      "               'vocal_cord_palsy',\n",
-      "               'arthropathy',\n",
-      "               'history_of_pulmonary_embolus',\n",
-      "               'herpangina',\n",
-      "               'idiopathic_scoliosis',\n",
-      "               'inflammatory_polyarthropathy',\n",
-      "               'mass_of_pancreas',\n",
-      "               'peritonitis',\n",
-      "               'stomatitis',\n",
-      "               'vitreous_degeneration',\n",
-      "               'central_retinal_vein_occlusion',\n",
-      "               'closed_fracture_of_carpal_bone',\n",
-      "               'social_phobia',\n",
-      "               'atelectasis',\n",
-      "               'discharge_from_nipple',\n",
-      "               'biceps_tendinitis',\n",
-      "               'keratosis',\n",
-      "               'scarlet_fever',\n",
-      "               'acute_urinary_tract_infection',\n",
-      "               'nasal_discharge',\n",
-      "               'mood_disorder_due_to_a_general_medical_condition',\n",
-      "               'concussion_with_no_loss_of_consciousness',\n",
-      "               'malignant_lymphoma_of_extranodal_and/or_solid_organ_site',\n",
-      "               'staphylococcal_pneumonia',\n",
-      "               'anorectal_disorder',\n",
-      "               'sepsis_caused_by_escherichia_coli',\n",
-      "               'chronic_gastric_ulcer_with_hemorrhage',\n",
-      "               'galactorrhea_not_associated_with_childbirth',\n",
-      "               'anemia_in_neoplastic_disease',\n",
-      "               'lumbar_sprain',\n",
-      "               'hip_joint_inflamed',\n",
-      "               'sprain_of_shoulder',\n",
-      "               'anisometropia',\n",
-      "               'tricuspid_incompetence_non-rheumatic',\n",
-      "               'expressive_language_disorder',\n",
-      "               'chronic_diarrhea',\n",
-      "               'cervical_intraepithelial_neoplasia_grade_1',\n",
-      "               'epidemic_vertigo',\n",
-      "               'disorder_of_kidney_and/or_ureter',\n",
-      "               'incontinence',\n",
-      "               'closed_fracture_of_surgical_neck_of_humerus',\n",
-      "               'bacterial_enteritis',\n",
-      "               'closed_fracture_of_femur',\n",
-      "               'meralgia_paresthetica',\n",
-      "               'corneal_ulcer',\n",
-      "               'foreign_body',\n",
-      "               'ecchymosis',\n",
-      "               'panic_disorder_with_agoraphobia',\n",
-      "               'mild_intellectual_disability',\n",
-      "               'single_episode_of_major_depression_in_full_remission',\n",
-      "               'streptococcal_tonsillitis',\n",
-      "               'malignant_tumor_of_lower_third_of_esophagus',\n",
-      "               'malignant_neoplasm_of_upper-outer_quadrant_of_female_breast',\n",
-      "               'primary_sclerosing_cholangitis',\n",
-      "               'malignant_tumor_of_large_intestine',\n",
-      "               'second_degree_perineal_laceration',\n",
-      "               'thyroid_function_tests_abnormal',\n",
-      "               'closed_fracture_of_calcaneus',\n",
-      "               'bulimia_nervosa',\n",
-      "               'sexually_transmitted_infectious_disease',\n",
-      "               'injury_of_lower_extremity',\n",
-      "               'herpesvirus_infection',\n",
-      "               'worried_well',\n",
-      "               'peritonsillar_abscess',\n",
-      "               'rhd_negative',\n",
-      "               'rhegmatogenous_retinal_detachment',\n",
-      "               'conjunctival_hemorrhage',\n",
-      "               'central_sleep_apnea_syndrome',\n",
-      "               'delayed_milestone',\n",
-      "               'viral_enteritis',\n",
-      "               'retained_foreign_body_in_eye',\n",
-      "               'chronic_anemia',\n",
-      "               'family_history_of_stroke',\n",
-      "               'malignant_tumor_of_larynx',\n",
-      "               'cystic_fibrosis',\n",
-      "               'perioral_dermatitis',\n",
-      "               'closed_fracture_of_upper_end_of_humerus',\n",
-      "               'pyuria',\n",
-      "               'herpetic_gingivostomatitis',\n",
-      "               'fracture_of_tooth',\n",
-      "               'familial_hypercholesterolemia',\n",
-      "               'dysphagia_as_a_late_effect_of_cerebrovascular_accident',\n",
-      "               'migraine_with_aura',\n",
-      "               'hyperpigmentation_of_skin',\n",
-      "               'peripheral_vertigo',\n",
-      "               'benign_neoplasm_of_skin',\n",
-      "               'neoplasm_of_uncertain_behavior_of_brain_and/or_spinal_cord',\n",
-      "               'cellulitis_and_abscess_of_face',\n",
-      "               'accident_caused_by_hot_liquid_and_vapor_including_steam',\n",
-      "               'fracture_of_face_bones',\n",
-      "               'infection_caused_by_methicillin_susceptible_staphylococcus_aureus',\n",
-      "               'disorder_of_joint_of_ankle_and/or_foot',\n",
-      "               'obstruction_co-occurrent_and_due_to_left_inguinal_hernia',\n",
-      "               'obstruction_co-occurrent_and_due_to_right_inguinal_hernia',\n",
-      "               'opioid_abuse',\n",
-      "               'keratoconus',\n",
-      "               'tenosynovitis',\n",
-      "               'disorder_of_breast',\n",
-      "               'bilateral_inguinal_hernia',\n",
-      "               'bleeding_esophageal_varices',\n",
-      "               'brachial_plexus_disorder',\n",
-      "               'endometriosis_of_pelvis',\n",
-      "               'benign_neoplasm_of_skin_of_face',\n",
-      "               'moderate_depression',\n",
-      "               'epidermoid_cyst_of_skin',\n",
-      "               'anorexia_nervosa',\n",
-      "               'oppositional_defiant_disorder',\n",
-      "               'closed_fracture_of_upper_end_of_tibia',\n",
-      "               'anatomical_narrow_angle_glaucoma',\n",
-      "               'chloasma',\n",
-      "               'secondary_polycythemia',\n",
-      "               'chronic_myeloid_leukemia_disease',\n",
-      "               'dystonia',\n",
-      "               'lumbosacral_spondylosis_without_myelopathy',\n",
-      "               'fracture_of_femur',\n",
-      "               'liver_mass',\n",
-      "               'closed_fracture_of_tibia_and_fibula',\n",
-      "               'educational_problem',\n",
-      "               'oligohydramnios',\n",
-      "               'swollen_abdomen',\n",
-      "               'degenerative_joint_disease_of_shoulder_region',\n",
-      "               'pediculosis_capitis',\n",
-      "               'cephalhematoma_due_to_birth_trauma',\n",
-      "               'inflammatory_bowel_disease',\n",
-      "               'closed_fracture_of_phalanx_of_foot',\n",
-      "               'abscess_of_breast',\n",
-      "               'secondary_cataract',\n",
-      "               'metatarsal_bone_fracture',\n",
-      "               'rheumatic_mitral_stenosis',\n",
-      "               'opioid_withdrawal',\n",
-      "               'multiple_pregnancy',\n",
-      "               'tuberculosis_of_pleura',\n",
-      "               'acute_viral_pharyngitis',\n",
-      "               'meibomian_gland_dysfunction',\n",
-      "               'major_depression_single_episode_in_partial_remission',\n",
-      "               'acute_type_b_viral_hepatitis',\n",
-      "               'cardiogenic_shock',\n",
-      "               'blindness_of_one_eye',\n",
-      "               'first_degree_atrioventricular_block',\n",
-      "               'closed_fracture_of_nasal_bones',\n",
-      "               'undescended_testicle',\n",
-      "               'malignant_tumor_of_oral_cavity',\n",
-      "               'bacterial_pneumonia',\n",
-      "               'perirectal_abscess',\n",
-      "               'cervical_intraepithelial_neoplasia_grade_2',\n",
-      "               'malignant_tumor_of_cecum',\n",
-      "               'cerebral_infarction_due_to_embolism_of_cerebral_arteries',\n",
-      "               'panniculitis',\n",
-      "               'maculopapular_eruption',\n",
-      "               'aphakia',\n",
-      "               'medial_epicondylitis_of_elbow_joint',\n",
-      "               'open_wound_of_hand',\n",
-      "               'diastolic_dysfunction',\n",
-      "               'asteatosis_cutis',\n",
-      "               'secondary_malignant_neoplasm_of_skin',\n",
-      "               'chest_injury',\n",
-      "               'macular_edema_due_to_diabetes_mellitus',\n",
-      "               'cerebral_infarction',\n",
-      "               'closed_fracture_of_base_of_neck_of_femur',\n",
-      "               'toxic_diffuse_goiter',\n",
-      "               'acute_appendicitis_with_generalized_peritonitis',\n",
-      "               'rheumatic_mitral_regurgitation',\n",
-      "               'non-traumatic_intracerebral_ventricular_hemorrhage',\n",
-      "               'congenital_pneumonia',\n",
-      "               'discoid_lupus_erythematosus',\n",
-      "               'blood_coagulation_disorder',\n",
-      "               'neurofibromatosis_syndrome',\n",
-      "               'strabismus',\n",
-      "               'quadriplegia',\n",
-      "               'infection_of_skin',\n",
-      "               'cannabis_dependence_in_remission',\n",
-      "               'cellulitis_of_face',\n",
-      "               'talipes_equinovarus',\n",
-      "               'callosity',\n",
-      "               'benign_neoplastic_disease',\n",
-      "               'rhabdomyolysis',\n",
-      "               'placenta_previa',\n",
-      "               'atherosclerosis_of_artery',\n",
-      "               'secondary_malignant_neoplasm_of_female_breast',\n",
-      "               'pneumonia_caused_by_klebsiella_pneumoniae',\n",
-      "               'poisoning_caused_by_sedative_and/or_hypnotic',\n",
-      "               'essential_thrombocythemia',\n",
-      "               'heart_block',\n",
-      "               'sprain_of_ligament_of_lower_limb',\n",
-      "               'microcytic_anemia',\n",
-      "               'dental_consultation_and_report',\n",
-      "               'severe_persistent_asthma',\n",
-      "               'hyperplasia_of_prostate',\n",
-      "               'skin_ulcer',\n",
-      "               'sinus_node_dysfunction',\n",
-      "               'testicular_hypofunction',\n",
-      "               'benign_neoplasm_of_skin_of_trunk',\n",
-      "               'atrial_fibrillation_and_flutter',\n",
-      "               'respiratory_distress',\n",
-      "               'anticoagulant_adverse_reaction',\n",
-      "               'disorder_of_gallbladder',\n",
-      "               'rheumatic_heart_disease',\n",
-      "               'history_of_malignant_neoplasm_of_cervix',\n",
-      "               'foot-drop',\n",
-      "               'chronic_hepatitis',\n",
-      "               'blood_chemistry_abnormal',\n",
-      "               'malaise',\n",
-      "               'nonulcer_dyspepsia',\n",
-      "               'viral_conjunctivitis',\n",
-      "               'calcaneal_spur',\n",
-      "               'lupus_erythematosus',\n",
-      "               'benign_tumor_of_eyelid',\n",
-      "               'laceration_of_finger',\n",
-      "               'moderate_nonproliferative_retinopathy_due_to_diabetes_mellitus',\n",
-      "               'malignant_tumor_of_head_of_pancreas',\n",
-      "               'autoimmune_hepatitis',\n",
-      "               'chronic_kidney_disease_stage_1',\n",
-      "               'papular_eruption',\n",
-      "               'low_birth_weight_infant',\n",
-      "               'neoplasm_of_uncertain_behavior_of_soft_tissues',\n",
-      "               'esophageal_dysmotility',\n",
-      "               'hereditary_factor_viii_deficiency_disease',\n",
-      "               'clostridium_difficile_colitis',\n",
-      "               'cyst_of_liver',\n",
-      "               'monoclonal_gammopathy',\n",
-      "               'disorder_of_peripheral_autonomic_nervous_system',\n",
-      "               'developmental_academic_disorder',\n",
-      "               'hypoadrenalism',\n",
-      "               'degenerative_progressive_high_myopia',\n",
-      "               'solar_degeneration',\n",
-      "               'overactive_bladder',\n",
-      "               \"hodgkin's_disease_nodular_sclerosis\",\n",
-      "               'single_live_birth',\n",
-      "               'fracture_of_radial_head',\n",
-      "               'high_birth_weight',\n",
-      "               'local_infection_of_wound',\n",
-      "               'tubal_pregnancy',\n",
-      "               'fetal_or_neonatal_effect_of_placenta_previa',\n",
-      "               'poisoning_caused_by_analgesic_drug',\n",
-      "               'vernal_conjunctivitis',\n",
-      "               'injury_of_brachial_plexus',\n",
-      "               'eosinophil_count_raised',\n",
-      "               'hypospadias',\n",
-      "               'aneurysm',\n",
-      "               'cellulitis_of_hand',\n",
-      "               'occlusion_of_ureter',\n",
-      "               'lobar_pneumonia',\n",
-      "               'chronic_respiratory_failure',\n",
-      "               'myeloproliferative_disorder',\n",
-      "               'subclinical_hypothyroidism',\n",
-      "               \"crohn's_disease_of_small_intestine\",\n",
-      "               'acute_lymphoid_leukemia_in_remission',\n",
-      "               'peripheral_circulatory_disorder_due_to_type_1_diabetes_mellitus',\n",
-      "               'hypertensive_heart_failure',\n",
-      "               'bladder_neck_obstruction',\n",
-      "               'closed_fracture_of_rib',\n",
-      "               'injury_of_foot',\n",
-      "               'female_hypogonadism_syndrome',\n",
-      "               'visual_field_scotoma',\n",
-      "               'ulcer_of_heel',\n",
-      "               'intervertebral_disc_disorder',\n",
-      "               'dermatomyositis',\n",
-      "               'periodic_limb_movement_disorder',\n",
-      "               'closed_fracture_of_fibula',\n",
-      "               'ganglion_of_wrist',\n",
-      "               'gingivitis',\n",
-      "               'systemic_lupus_erythematosus_glomerulonephritis_syndrome',\n",
-      "               'rheumatic_mitral_stenosis_with_regurgitation',\n",
-      "               'disorder_of_skin_and/or_subcutaneous_tissue',\n",
-      "               'drug-induced_disorder_of_liver',\n",
-      "               'suicidal_thoughts',\n",
-      "               'cluster_headache_syndrome',\n",
-      "               'closed_bimalleolar_fracture',\n",
-      "               'ventricular_fibrillation',\n",
-      "               'neoplasm_of_pituitary_gland',\n",
-      "               'myoclonus',\n",
-      "               'pressure_ulcer_of_heel',\n",
-      "               'cytomegalovirus_infection',\n",
-      "               'closed_fracture_of_shaft_of_radius',\n",
-      "               'thalassemia',\n",
-      "               'diastolic_heart_failure',\n",
-      "               'bullous_pemphigoid',\n",
-      "               'closed_fracture_of_shaft_of_humerus',\n",
-      "               'deficiency_of_macronutrients',\n",
-      "               'abscess_of_liver',\n",
-      "               'neonatal_jaundice_associated_with_preterm_delivery',\n",
-      "               \"crohn's_disease_of_large_bowel\",\n",
-      "               'gastric_polyp',\n",
-      "               'subsequent_myocardial_infarction',\n",
-      "               'generalized_epilepsy',\n",
-      "               'multiparous',\n",
-      "               'pericarditis',\n",
-      "               'mass_of_adrenal_gland',\n",
-      "               'shoulder_joint_unstable',\n",
-      "               'proctitis',\n",
-      "               'optic_atrophy',\n",
-      "               'mass_of_testicle',\n",
-      "               'neoplasm_of_liver',\n",
-      "               'burn_of_hand',\n",
-      "               'malignant_neoplasm_of_brain',\n",
-      "               'chronic_gastritis',\n",
-      "               'benign_neoplasm_of_prostate',\n",
-      "               'malignant_neoplasm_of_corpus_uteri_excluding_isthmus',\n",
-      "               'malignant_neoplasm_of_gastrointestinal_tract',\n",
-      "               'traumatic_arthropathy',\n",
-      "               'recurrent_left_inguinal_hernia',\n",
-      "               'recurrent_right_inguinal_hernia',\n",
-      "               'cholesteatoma',\n",
-      "               'guillain-barré_syndrome',\n",
-      "               'sprain_of_medial_collateral_ligament_of_knee',\n",
-      "               'lichen_sclerosus_et_atrophicus',\n",
-      "               'pain_in_thoracic_spine',\n",
-      "               'tear_of_medial_meniscus_of_knee',\n",
-      "               'retinal_detachment_with_retinal_defect',\n",
-      "               'skin_ulcer_of_calf',\n",
-      "               'acute_atopic_conjunctivitis',\n",
-      "               'livebirth',\n",
-      "               'spinal_cord_compression',\n",
-      "               'mammography',\n",
-      "               \"crohn's_disease_of_small_and_large_intestines\",\n",
-      "               'endometriosis_of_uterus',\n",
-      "               'myositis',\n",
-      "               'retinal_hemorrhage',\n",
-      "               'polyneuropathy',\n",
-      "               'acquired_nasolacrimal_duct_stenosis',\n",
-      "               'tooth_disorder',\n",
-      "               'malignant_neoplasm_of_uterus',\n",
-      "               'acanthosis_nigricans',\n",
-      "               'fracture_of_tibia_and_fibula',\n",
-      "               'exostosis',\n",
-      "               'endometritis',\n",
-      "               'primary_malignant_neoplasm_of_skin_of_face',\n",
-      "               'paroxysmal_tachycardia',\n",
-      "               'polyp_of_vocal_cord_or_larynx',\n",
-      "               'pulmonary_sarcoidosis',\n",
-      "               'blister_of_skin_and/or_mucosa',\n",
-      "               'congenital_dislocation_of_hip',\n",
-      "               'cellulitis_of_toe',\n",
-      "               'secondary_malignant_neoplasm_of_soft_tissues',\n",
-      "               'neonatal_jaundice_due_to_delayed_conjugation',\n",
-      "               'nondependent_cocaine_abuse_in_remission',\n",
-      "               'placenta_previa_with_hemorrhage',\n",
-      "               'fetal_or_neonatal_effect_of_maternal_oligohydramnios',\n",
-      "               'gallbladder_and_bile_duct_calculi',\n",
-      "               'secondary_hypertension',\n",
-      "               'pneumonia_caused_by_gram_negative_bacteria',\n",
-      "               'periodic_leg_movements_of_sleep',\n",
-      "               'epiphora',\n",
-      "               'aplastic_anemia',\n",
-      "               'injury_of_shoulder_region',\n",
-      "               'diabetes_insipidus',\n",
-      "               'hepatitis_c_carrier',\n",
-      "               'facial_laceration',\n",
-      "               'closed_fracture_of_humerus',\n",
-      "               'thrombophlebitis',\n",
-      "               'cerebellar_ataxia',\n",
-      "               'xerostomia',\n",
-      "               'mass_of_thyroid_gland',\n",
-      "               'angle-closure_glaucoma',\n",
-      "               'concussion_with_less_than_1_hour_loss_of_consciousness',\n",
-      "               'retinal_defect',\n",
-      "               'anterior_horn_cell_disease',\n",
-      "               'pruritus_ani',\n",
-      "               'juvenile_chronic_arthritis',\n",
-      "               'somatization_disorder',\n",
-      "               'fracture_of_vertebral_column',\n",
-      "               \"gilles_de_la_tourette's_syndrome\",\n",
-      "               'hypernatremia',\n",
-      "               'erythrocyte_sedimentation_rate_raised',\n",
-      "               'lesion_of_vulva',\n",
-      "               'pneumonia_caused_by_mycoplasma_pneumoniae',\n",
-      "               'disorder_of_the_larynx',\n",
-      "               'vulvitis',\n",
-      "               'adjustment_disorder_with_mixed_disturbance_of_emotions_and_conduct',\n",
-      "               'hypopituitarism',\n",
-      "               'atherosclerosis_of_aorta',\n",
-      "               'dementia_associated_with_another_disease',\n",
-      "               'anogenital_warts',\n",
-      "               'fracture_of_calcaneus',\n",
-      "               'acute_poliomyelitis',\n",
-      "               'wound',\n",
-      "               'disorder_due_to_type_1_diabetes_mellitus',\n",
-      "               'secondary_malignant_neoplasm_of_ovary',\n",
-      "               'history_of_head_injury',\n",
-      "               'gallbladder_calculus_with_acute_cholecystitis_and_no_obstruction',\n",
-      "               'accidents_caused_by_cutting_and_piercing_instruments_or_objects',\n",
-      "               'infected_hand',\n",
-      "               'elbow_fracture',\n",
-      "               'pervasive_developmental_disorder',\n",
-      "               'severe_nonproliferative_retinopathy_with_clinically_significant_macular_edema_due_to_diabetes_mellitus',\n",
-      "               'refractory_migraine_without_aura',\n",
-      "               'disorder_of_bone',\n",
-      "               'achalasia_of_esophagus',\n",
-      "               'venous_thrombosis',\n",
-      "               'macrocephaly',\n",
-      "               'normal_pressure_hydrocephalus',\n",
-      "               'hypertensive_retinopathy',\n",
-      "               'traumatic_subdural_intracranial_hemorrhage',\n",
-      "               'malabsorption_syndrome',\n",
-      "               'demyelinating_disease_of_central_nervous_system',\n",
-      "               'optic_neuritis',\n",
-      "               'autoimmune_thyroiditis',\n",
-      "               'macular_hole',\n",
-      "               'telangiectasia_disorder',\n",
-      "               'post-laminectomy_syndrome',\n",
-      "               'mass_in_head_or_neck',\n",
-      "               'closed_fracture_of_shaft_of_tibia',\n",
-      "               'disorder_of_prostate',\n",
-      "               'median_neuropathy',\n",
-      "               \"abscess_of_bartholin's_gland\",\n",
-      "               'acute_respiratory_distress_syndrome',\n",
-      "               'atypical_facial_pain',\n",
-      "               'acute_myeloid_leukemia_in_remission',\n",
-      "               'benign_neoplasm_of_cerebral_meninges',\n",
-      "               'liver_cell_carcinoma',\n",
-      "               'assault_by_striking_by_blunt_or_thrown_object',\n",
-      "               'history_of_disorder',\n",
-      "               'onychomycosis_caused_by_dermatophyte',\n",
-      "               'gravid_uterus_size_for_dates_discrepancy',\n",
-      "               'underweight',\n",
-      "               'recurrent_dislocation_of_shoulder_region',\n",
-      "               'ectropion_of_eyelid',\n",
-      "               'injury_of_ankle',\n",
-      "               'obsessive_compulsive_personality_disorder',\n",
-      "               'acute_non-suppurative_otitis_media_-_serous',\n",
-      "               'family_problems',\n",
-      "               'communicating_hydrocephalus',\n",
-      "               'disorder_of_pancreas',\n",
-      "               'precocious_puberty',\n",
-      "               'pulmonary_aspiration',\n",
-      "               'contusion_of_elbow',\n",
-      "               'elderly_primigravida',\n",
-      "               'lesion_of_brain',\n",
-      "               'cyst_of_pancreas',\n",
-      "               'malignant_tumor_of_descending_colon',\n",
-      "               'alcoholic_fatty_liver',\n",
-      "               'paralytic_ileus',\n",
-      "               'burn_of_lower_limb',\n",
-      "               'secondary_hyperparathyroidism',\n",
-      "               'melanocytic_nevus_of_face',\n",
-      "               'neoplasm_of_ovary',\n",
-      "               'legal_problem',\n",
-      "               'acute_non-st_segment_elevation_myocardial_infarction',\n",
-      "               'drug-induced_mood_disorder',\n",
-      "               'rheumatic_tricuspid_valve_regurgitation',\n",
-      "               'rheumatic_aortic_regurgitation',\n",
-      "               'microcephalus',\n",
-      "               'erythema_nodosum',\n",
-      "               'postpartum_depression',\n",
-      "               'renovascular_hypertension',\n",
-      "               'aneurysm_of_iliac_artery',\n",
-      "               'encopresis',\n",
-      "               'closed_fracture_of_navicular_bone_of_wrist',\n",
-      "               'closed_fracture_of_facial_bone',\n",
-      "               'closed_fracture_of_olecranon_process_of_ulna',\n",
-      "               'antisocial_personality_disorder',\n",
-      "               'open_wound_of_lower_limb',\n",
-      "               'ketoacidosis_due_to_type_2_diabetes_mellitus',\n",
-      "               'arteriosclerosis_of_coronary_artery_bypass_graft',\n",
-      "               'peripheral_nerve_entrapment_syndrome',\n",
-      "               'corneal_opacity',\n",
-      "               'autosomal_dominant_polycystic_kidney_disease',\n",
-      "               'aortic_incompetence_non-rheumatic',\n",
-      "               'closed_fracture_proximal_humerus_greater_tuberosity',\n",
-      "               'bladder_outflow_obstruction',\n",
-      "               'blow_out_fracture_of_orbit',\n",
-      "               'scalp_laceration',\n",
-      "               'congenital_malformation',\n",
-      "               'squamous_cell_carcinoma_of_skin_of_face',\n",
-      "               'itching',\n",
-      "               'choroidal_retinal_neovascularization',\n",
-      "               'anemia_due_to_and_following_chemotherapy',\n",
-      "               'anal_polyp',\n",
-      "               'adult_failure_to_thrive_syndrome',\n",
-      "               'anaphylaxis',\n",
-      "               'urinary_tract_obstruction',\n",
-      "               'amaurosis_fugax',\n",
-      "               'lesion_of_ulnar_nerve',\n",
-      "               'congenital_pectus_excavatum',\n",
-      "               'acute_myocardial_infarction_of_inferior_wall',\n",
-      "               'acute_prostatitis',\n",
-      "               'arthralgia_of_temporomandibular_joint',\n",
-      "               'neurofibromatosis_type_1',\n",
-      "               'allergic_contact_dermatitis',\n",
-      "               'periodontal_disease',\n",
-      "               'ulcer_of_esophagus',\n",
-      "               'fasciitis',\n",
-      "               'disorder_of_eye_proper',\n",
-      "               'traumatic_and/or_non-traumatic_injury',\n",
-      "               'atherosclerosis_of_bypass_graft_of_limb',\n",
-      "               'idiopathic_pulmonary_fibrosis',\n",
-      "               'lipids_abnormal',\n",
-      "               'acute_peptic_ulcer',\n",
-      "               'closed_traumatic_subdural_intracranial_hemorrhage',\n",
-      "               'residual_schizophrenia',\n",
-      "               'subtrochanteric_fracture_of_femur',\n",
-      "               'hemolytic_disease_of_fetus_or_newborn_due_to_abo_immunization',\n",
-      "               'acute_pulmonary_edema',\n",
-      "               'closed_fracture_of_vertebral_column',\n",
-      "               'disorder_of_respiratory_system',\n",
-      "               'senile_entropion',\n",
-      "               'primary_malignant_neoplasm_of_rectosigmoid_junction',\n",
-      "               'tenosynovitis_of_hand',\n",
-      "               'sprain_of_upper_extremity',\n",
-      "               'primary_hyperaldosteronism',\n",
-      "               'hand_foot_and_mouth_disease',\n",
-      "               'history_of_malignant_neoplasm_of_ovary',\n",
-      "               'atherosclerosis_of_renal_artery',\n",
-      "               'contusion_of_hand',\n",
-      "               'hidradenitis_suppurativa',\n",
-      "               'lymphocytosis',\n",
-      "               'hypercalciuria',\n",
-      "               'contusion_of_foot',\n",
-      "               'accidental_needle_stick_injury',\n",
-      "               'pseudoexfoliation_glaucoma',\n",
-      "               'flushing',\n",
-      "               'lesion_of_liver',\n",
-      "               'injury_of_tendon_of_the_rotator_cuff_of_shoulder',\n",
-      "               'malignant_tumor_of_transverse_colon',\n",
-      "               'supernumerary_tooth',\n",
-      "               'melanocytic_nevus_of_skin',\n",
-      "               'hemiplegia_of_nondominant_side',\n",
-      "               'multigravida_of_advanced_maternal_age',\n",
-      "               'bacterial_arthritis',\n",
-      "               'burn_of_upper_limb',\n",
-      "               'viral_pneumonia',\n",
-      "               'polyhydramnios',\n",
-      "               'assault_by_cutting_and_stabbing_instruments',\n",
-      "               'physical_child_abuse',\n",
-      "               'disorder_of_oral_soft_tissues',\n",
-      "               'postpartum_hemorrhage',\n",
-      "               'von_willebrand_disorder',\n",
-      "               'muscular_dystrophy',\n",
-      "               'intracranial_hemorrhage',\n",
-      "               'osteitis_deformans',\n",
-      "               'congenital_arteriovenous_malformation',\n",
-      "               'entropion_of_eyelid',\n",
-      "               'malignant_tumor_of_tongue',\n",
-      "               'sprain_of_foot',\n",
-      "               'cor_pulmonale',\n",
-      "               'male_pattern_alopecia',\n",
-      "               'fracture_of_thoracic_spine',\n",
-      "               'neoplasm_of_skin',\n",
-      "               'victim_of_physical_abuse',\n",
-      "               'absence_of_breast',\n",
-      "               'mass_of_parotid_gland',\n",
-      "               'slow_transit_constipation',\n",
-      "               'malignant_tumor_of_tonsil',\n",
-      "               'malignant_tumor_of_hepatic_flexure',\n",
-      "               \"iatrogenic_cushing's_disease\",\n",
-      "               'ankle_ulcer',\n",
-      "               'fracture_malunion',\n",
-      "               'hemiplegia_of_dominant_side',\n",
-      "               'hepatic_failure',\n",
-      "               'disorder_of_eye_due_to_type_1_diabetes_mellitus',\n",
-      "               'tetralogy_of_fallot',\n",
-      "               'neoplasm_of_uncertain_behavior_of_meninges',\n",
-      "               'fracture_of_cervical_spine',\n",
-      "               'bipolar_affective_disorder_currently_manic_severe_with_psychosis',\n",
-      "               'non-rheumatic_mitral_regurgitation',\n",
-      "               'pneumonia_caused_by_respiratory_syncytial_virus',\n",
-      "               'mental_disorders_during_pregnancy_childbirth_and_the_puerperium',\n",
-      "               'closed_fracture_pubis',\n",
-      "               'open_wound_of_face',\n",
-      "               'puerperal_pyrexia',\n",
-      "               'nontraumatic_rotator_cuff_tear',\n",
-      "               'complication_of_urinary_catheter',\n",
-      "               'posterior_subcapsular_polar_senile_cataract',\n",
-      "               'female_pelvic_peritoneal_adhesions',\n",
-      "               'prepatellar_bursitis',\n",
-      "               'closed_fracture_of_phalanx_of_finger',\n",
-      "               'atrial_premature_complex',\n",
-      "               'hypoparathyroidism',\n",
-      "               \"marfan's_syndrome\",\n",
-      "               'renal_hypertension',\n",
-      "               'central_serous_chorioretinopathy',\n",
-      "               'intervertebral_disc_disorder_of_lumbar_region_with_myelopathy',\n",
-      "               'history_of_malignant_lymphoma',\n",
-      "               'fecal_impaction',\n",
-      "               'autonomic_neuropathy_due_to_diabetes_mellitus',\n",
-      "               'fracture_of_lumbar_spine',\n",
-      "               'neoplasm_of_parotid_gland',\n",
-      "               'lyme_disease',\n",
-      "               'corneal_foreign_body',\n",
-      "               'abducens_nerve_disorder',\n",
-      "               'carbuncle',\n",
-      "               'pain_at_rest_due_to_peripheral_vascular_disease',\n",
-      "               'incipient_cataract',\n",
-      "               'fracture_of_ulna',\n",
-      "               'severe_major_depression_with_psychotic_features',\n",
-      "               'esophageal_varices_without_bleeding',\n",
-      "               'congenital_anomaly_of_eye',\n",
-      "               'abnormal_findings_on_antenatal_screening_of_mother',\n",
-      "               'closed_fracture_of_intracapsular_section_of_femur',\n",
-      "               'accident_caused_by_glass_edge',\n",
-      "               'dehiscence_of_surgical_wound',\n",
-      "               'obstructive_hydrocephalus',\n",
-      "               'calculus_of_kidney_and_ureter',\n",
-      "               'shock',\n",
-      "               'lesion_of_bladder',\n",
-      "               'colostomy_present',\n",
-      "               'radiographic_imaging_procedure',\n",
-      "               'primary_malignant_neoplasm_of_the_peritoneum',\n",
-      "               'incomplete_quadriplegia_due_to_spinal_cord_lesion_between_first_and_fourth_cervical_vertebra',\n",
-      "               'history_of_malignant_neoplasm_of_thyroid',\n",
-      "               'tic_disorder',\n",
-      "               'histoplasmosis',\n",
-      "               'polymyositis',\n",
-      "               'mumps',\n",
-      "               'moderate_intellectual_disability',\n",
-      "               'coarctation_of_aorta',\n",
-      "               'exophthalmos',\n",
-      "               'immunoglobulin_a_vasculitis',\n",
-      "               'inflammatory_neuropathy',\n",
-      "               'parastomal_hernia',\n",
-      "               'cellulitis_of_finger',\n",
-      "               'empyema',\n",
-      "               'primary_degenerative_dementia_of_the_alzheimer_type_senile_onset',\n",
-      "               'dysenteric_diarrhea',\n",
-      "               'developmental_disorder',\n",
-      "               'congenital_stenosis_of_pulmonary_valve',\n",
-      "               'sudden_hearing_loss',\n",
-      "               'bipolar_affective_disorder_current_episode_manic',\n",
-      "               'open_wound_of_anterior_abdominal_wall',\n",
-      "               'drug-induced_hypotension',\n",
-      "               'deficiency_anemias',\n",
-      "               'blind_or_low_vision_-_both_eyes',\n",
-      "               'acute_angle-closure_glaucoma',\n",
-      "               'hypothermia',\n",
-      "               'lower_urinary_tract_infectious_disease',\n",
-      "               'rickets',\n",
-      "               'disruptive_behavior_disorder',\n",
-      "               'hypertensive_heart_disease_without_congestive_heart_failure',\n",
-      "               'gastric_hemorrhage',\n",
-      "               'disorder_of_menstruation',\n",
-      "               'acquired_deformity_of_distal_interphalangeal_joint_of_finger_due_to_trauma',\n",
-      "               'pyogenic_granuloma',\n",
-      "               'chronic_cholecystitis',\n",
-      "               'laceration_of_hand',\n",
-      "               'erythema_multiforme',\n",
-      "               'aseptic_necrosis_of_bone',\n",
-      "               'sialoadenitis',\n",
-      "               'hypoplasia_of_maxillary_bone',\n",
-      "               'round_hole_of_retina_without_detachment',\n",
-      "               'impulse_control_disorder',\n",
-      "               'articular_disc_disorder_of_temporomandibular_joint',\n",
-      "               'neoplasm_of_bladder',\n",
-      "               'schwannoma',\n",
-      "               'labyrinthine_disorder',\n",
-      "               'shoulder_stiff',\n",
-      "               'lymphoproliferative_disorder',\n",
-      "               'diaphragmatic_hernia',\n",
-      "               'periumbilical_pain',\n",
-      "               'foreign_body_in_skin',\n",
-      "               'traumatic_dislocation_of_joint_of_finger',\n",
-      "               'deep_venous_thrombosis_of_upper_extremity',\n",
-      "               'respiratory_condition_of_fetus_or_newborn',\n",
-      "               'prosthetic_joint_dislocation',\n",
-      "               'macular_drusen',\n",
-      "               'fracture_of_distal_end_of_radius',\n",
-      "               'neurogenic_claudication',\n",
-      "               'sacral_dimple',\n",
-      "               'post_infarct_angina',\n",
-      "               'malignant_tumor_of_sigmoid_colon',\n",
-      "               'thrombotic_stroke',\n",
-      "               'impairment_of_balance',\n",
-      "               'child_abuse',\n",
-      "               'sepsis_caused_by_gram_negative_bacteria',\n",
-      "               'retinal_neovascularization',\n",
-      "               'spasmodic_torticollis',\n",
-      "               'retinitis_pigmentosa',\n",
-      "               'hereditary_motor_and_sensory_neuropathy',\n",
-      "               'cervical_disc_disorder',\n",
-      "               'loose_body_in_knee',\n",
-      "               'cleft_palate',\n",
-      "               'closed_fracture_of_acetabulum',\n",
-      "               'postoperative_seroma',\n",
-      "               'hyperplasia_of_mandibular_bone',\n",
-      "               'uremia',\n",
-      "               'rectal_prolapse',\n",
-      "               'empyema_of_pleura',\n",
-      "               'premenstrual_dysphoric_disorder',\n",
-      "               'hemolytic_anemia',\n",
-      "               'spina_bifida',\n",
-      "               'closed_fracture_of_head_of_radius',\n",
-      "               'closed_fracture_of_radius',\n",
-      "               'burn_of_foot',\n",
-      "               'injury_of_wrist',\n",
-      "               'viral_bronchitis',\n",
-      "               'horseshoe_retinal_tear_without_detachment',\n",
-      "               'impaired_renal_function_disorder',\n",
-      "               'biliary_stricture',\n",
-      "               'cachexia',\n",
-      "               'ptosis_of_eyebrow',\n",
-      "               'calcific_tendinitis_of_shoulder',\n",
-      "               'chronic_laryngitis',\n",
-      "               'mixed_bipolar_i_disorder_in_partial_remission',\n",
-      "               'seasonal_allergic_rhinitis',\n",
-      "               'simple_obesity',\n",
-      "               'incomplete_quadriplegia_due_to_spinal_cord_lesion_between_fifth_and_seventh_cervical_vertebra',\n",
-      "               'diffuse_lewy_body_disease',\n",
-      "               'megaloblastic_anemia_due_to_folate_deficiency',\n",
-      "               'contracture_of_joint_of_hand',\n",
-      "               'atrophy_of_edentulous_alveolar_ridge',\n",
-      "               'retained_placenta',\n",
-      "               'disorder_of_mouth',\n",
-      "               'apnea_in_the_newborn',\n",
-      "               'pneumonia_and_influenza',\n",
-      "               'fracture_of_clavicle_due_to_birth_trauma',\n",
-      "               'functional_disorder_of_bladder',\n",
-      "               'menopausal_and_postmenopausal_disorders',\n",
-      "               'alcohol-induced_organic_mental_disorder',\n",
-      "               'calcification_of_breast',\n",
-      "               'disorder_of_tooth_development',\n",
-      "               'effusion_of_joint',\n",
-      "               'nightmares',\n",
-      "               'smoke_inhalation_injury',\n",
-      "               'acute_duodenal_ulcer_with_perforation',\n",
-      "               'hemianopia',\n",
-      "               'mechanical_complication_due_to_urethral_indwelling_catheter',\n",
-      "               'physical_therapy_procedure',\n",
-      "               'hypertrophy_of_adenoids',\n",
-      "               'ischemic_optic_neuropathy',\n",
-      "               'ankle_joint_inflamed',\n",
-      "               'mononeuritis',\n",
-      "               'glomerulonephritis',\n",
-      "               'allergic_urticaria',\n",
-      "               'blepharospasm',\n",
-      "               'acquired_hallux_rigidus',\n",
-      "               'membranous_glomerulonephritis',\n",
-      "               'diffuse_spasm_of_esophagus',\n",
-      "               'opioid_dependence_in_remission',\n",
-      "               'fourth_nerve_palsy',\n",
-      "               'antiphospholipid_syndrome',\n",
-      "               'hypnotic_or_anxiolytic_dependence',\n",
-      "               'reduced_fetal_movement',\n",
-      "               'lesion_of_eyelid',\n",
-      "               'vaginal_wall_prolapse',\n",
-      "               'ventricular_arrhythmia',\n",
-      "               'fussy_infant',\n",
-      "               'depressed_bipolar_i_disorder_in_partial_remission',\n",
-      "               'presbycusis',\n",
-      "               'hypophosphatemia',\n",
-      "               'otorrhea',\n",
-      "               'hyperventilation',\n",
-      "               'rheumatic_aortic_stenosis',\n",
-      "               'acquired_thrombocytopenia',\n",
-      "               'metabolic_disease',\n",
-      "               'benign_neoplasm_of_meninges',\n",
-      "               'acute_peptic_ulcer_with_hemorrhage',\n",
-      "               'waldenström_macroglobulinemia',\n",
-      "               'recurrent_sinusitis',\n",
-      "               'claustrophobia',\n",
-      "               'head_burn',\n",
-      "               'burn_of_face',\n",
-      "               'hemorrhage_of_rectum_and_anus',\n",
-      "               'acute_asthma',\n",
-      "               'gestational_proteinuria',\n",
-      "               'rapid_eye_movement_sleep_behavior_disorder',\n",
-      "               'embolism_from_thrombosis_of_vein_of_lower_extremity',\n",
-      "               'retinal_scar',\n",
-      "               'infection_caused_by_escherichia_coli',\n",
-      "               'onycholysis',\n",
-      "               'dermal_mycosis',\n",
-      "               'contusion_of_shoulder_region',\n",
-      "               'family_history_of_polyp_of_colon',\n",
-      "               'spermatocele',\n",
-      "               'endocarditis',\n",
-      "               'granuloma_annulare',\n",
-      "               'benign_intracranial_hypertension',\n",
-      "               'stridor',\n",
-      "               'complex_regional_pain_syndrome_of_lower_limb',\n",
-      "               'postoperative_urethral_stricture',\n",
-      "               'consultation',\n",
-      "               'hypertensive_heart_and_renal_disease_with',\n",
-      "               'closed_fracture_distal_humerus_lateral_epicondyle',\n",
-      "               'osteoarthritis_of_wrist',\n",
-      "               'pathological_fracture_of_vertebra',\n",
-      "               'malignant_tumor_of_oropharynx',\n",
-      "               'compression_fracture_of_lumbar_spine',\n",
-      "               'family_history_of_malignant_neoplasm_of_ovary',\n",
-      "               'chronic_diastolic_heart_failure',\n",
-      "               'abnormal_cervical_papanicolaou_smear_with_positive_human_papillomavirus_deoxyribonucleic_acid_test',\n",
-      "               'exposure_to_mycobacterium_tuberculosis',\n",
-      "               'delay_when_starting_to_pass_urine',\n",
-      "               'upper_respiratory_tract_obstruction',\n",
-      "               'abscess_of_vulva',\n",
-      "               'acromegaly',\n",
-      "               'benign_neoplasm_of_thyroid_gland',\n",
-      "               'poisoning_caused_by_opiate_and/or_related_narcotic',\n",
-      "               'primary_bacterial_peritonitis',\n",
-      "               'burn_of_multiple_sites_of_trunk',\n",
-      "               'injury_of_abdomen',\n",
-      "               'malignant_neoplasm_of_central_part_of_female_breast',\n",
-      "               'fracture_of_metacarpal_bone',\n",
-      "               'maternal_infection',\n",
-      "               'urine_cytology_abnormal',\n",
-      "               'pneumonia_caused_by_streptococcus',\n",
-      "               'acquired_absence_of_teeth',\n",
-      "               'peripheral_arterial_occlusive_disease',\n",
-      "               'midline_cystocele',\n",
-      "               'foreign_body_in_esophagus',\n",
-      "               'fetal_or_neonatal_effect_of_malpresentation_malposition_and/or_disproportion_during_labor_and/or_delivery',\n",
-      "               'swelling',\n",
-      "               'acquired_hallux_valgus',\n",
-      "               'vasospasm',\n",
-      "               'upper_abdominal_pain',\n",
-      "               'lumbosacral_spondylosis',\n",
-      "               'toxic_multinodular_goiter',\n",
-      "               'female_infertility_of_tubal_origin',\n",
-      "               'chronic_osteomyelitis',\n",
-      "               'cervico-occipital_neuralgia',\n",
-      "               'pinguecula',\n",
-      "               'multiple_myeloma_in_remission',\n",
-      "               'hearing_disorder',\n",
-      "               'malignant_neoplasm_of_lateral_wall_of_urinary_bladder',\n",
-      "               'excoriation_of_skin',\n",
-      "               'thumb_injury',\n",
-      "               'malignant_melanoma_of_trunk',\n",
-      "               'mass_of_ovary',\n",
-      "               'partial_seizure',\n",
-      "               'common_peroneal_neuropathy',\n",
-      "               'primary_cutaneous_t-cell_lymphoma',\n",
-      "               'facial_nerve_disorder',\n",
-      "               'disorder_of_vocal_cord',\n",
-      "               'stenosis_of_lacrimal_canaliculi',\n",
-      "               'contusion_of_lower_limb',\n",
-      "               'increased_lipid',\n",
-      "               'polyradiculoneuropathy',\n",
-      "               'malignant_neoplasm_of_upper-inner_quadrant_of_female_breast',\n",
-      "               'contracture_of_multiple_joints',\n",
-      "               'family_history:_raised_blood_lipids',\n",
-      "               'laceration_of_skin',\n",
-      "               'mild_depression',\n",
-      "               'screening_for_disorder',\n",
-      "               'disorder_of_esophagus',\n",
-      "               'febrile_disorder',\n",
-      "               'psychophysiologic_insomnia',\n",
-      "               'mental_disorder_caused_by_drug',\n",
-      "               'closed_fracture_of_shaft_of_ulna',\n",
-      "               'disorder_of_ovary',\n",
-      "               'disseminated_intravascular_coagulation',\n",
-      "               'acute_febrile_mucocutaneous_lymph_node_syndrome',\n",
-      "               'neoplasm_of_uncertain_behavior_of_endocrine_gland',\n",
-      "               'candidiasis_of_the_esophagus',\n",
-      "               'closed_fracture_of_foot',\n",
-      "               'chronic_pharyngitis',\n",
-      "               'sprain_of_hip',\n",
-      "               'closed_fracture_of_cervical_spine',\n",
-      "               'closed_fracture_of_the_distal_humerus',\n",
-      "               'mass_of_chest_wall',\n",
-      "               'loss_of_sense_of_smell',\n",
-      "               'depressed_bipolar_i_disorder',\n",
-      "               'heart_disease',\n",
-      "               'bullous_keratopathy',\n",
-      "               'hyphema',\n",
-      "               'syringomyelia',\n",
-      "               'irradiation_cystitis',\n",
-      "               'bone_pain',\n",
-      "               'closed_fracture_of_medial_malleolus',\n",
-      "               'family_history_of_osteoporosis',\n",
-      "               'portal_vein_thrombosis',\n",
-      "               'diverticulum_of_bladder',\n",
-      "               'unstable_knee',\n",
-      "               'thrombosed_external_hemorrhoids',\n",
-      "               'acute_pain',\n",
-      "               'severe_intellectual_disability',\n",
-      "               'chronic_hypoxemic_respiratory_failure',\n",
-      "               'refractory_epilepsy',\n",
-      "               'nephritis',\n",
-      "               'phlebitis',\n",
-      "               'hemoglobinopathy',\n",
-      "               'benign_neoplasm_of_brain',\n",
-      "               'mass_of_mediastinum',\n",
-      "               'disorder_of_knee',\n",
-      "               'primary_gout',\n",
-      "               'nutritional_disorder',\n",
-      "               'fetal_or_neonatal_effect_of_maternal_polyhydramnios',\n",
-      "               'disorder_of_stomach',\n",
-      "               'bacterial_endocarditis',\n",
-      "               'closed_supracondylar_fracture_of_femur',\n",
-      "               'neonatal_polycythemia',\n",
-      "               'disorder_of_eye_region',\n",
-      "               'pedal_cycle_accident',\n",
-      "               'hemoglobin_ss_disease_with_crisis',\n",
-      "               'fistula',\n",
-      "               'oral_phase_dysphagia',\n",
-      "               'localized_infection_of_skin_and/or_subcutaneous_tissue',\n",
-      "               'chill',\n",
-      "               'high_risk_pregnancy_due_to_history_of_preterm_labor',\n",
-      "               'maternal_obesity_syndrome',\n",
-      "               'nontraumatic_hemoperitoneum',\n",
-      "               'mechanical_complication_due_to_breast_prosthesis',\n",
-      "               'pneumonia_caused_by_escherichia_coli',\n",
-      "               'apraxia',\n",
-      "               'varicose_veins_of_lower_extremity_with_ulcer_and_inflammation',\n",
-      "               'neonatal_jaundice_due_to_delayed_conjugation_from_breast_milk_inhibitor',\n",
-      "               'secondary_malignant_neoplasm_of_intra-abdominal_lymph_nodes',\n",
-      "               'otosclerosis',\n",
-      "               'disorder_of_adrenal_gland',\n",
-      "               'pancreatic_insufficiency',\n",
-      "               'encephalitis',\n",
-      "               'arthropathy_associated_with_a_neurological_disorder',\n",
-      "               'complex_regional_pain_syndrome_of_upper_limb',\n",
-      "               'derangement_of_medial_meniscus',\n",
-      "               'hypogammaglobulinemia',\n",
-      "               'financial_problem',\n",
-      "               'c-reactive_protein_abnormal',\n",
-      "               'congenital_pes_planus',\n",
-      "               'disorder_of_blood_vessel',\n",
-      "               'male_infertility',\n",
-      "               'complication_of_transplanted_liver',\n",
-      "               'glaucoma_associated_with_ocular_inflammation',\n",
-      "               'autoimmune_hemolytic_anemia',\n",
-      "               'ketoacidosis_due_to_type_1_diabetes_mellitus',\n",
-      "               'history_of_malignant_neoplasm_of_tongue',\n",
-      "               'history_of_malignant_neoplasm_of_esophagus',\n",
-      "               'arteriovenous_fistula',\n",
-      "               'early_satiety',\n",
-      "               'hypoplasia_of_mandibular_bone',\n",
-      "               'disorder_of_eyelid',\n",
-      "               'acute_laryngitis',\n",
-      "               'bronchopulmonary_dysplasia_of_newborn',\n",
-      "               'perforation_of_nasal_septum',\n",
-      "               'herpes_zoster_without_complication',\n",
-      "               'neoplasm_of_kidney',\n",
-      "               'chronic_peptic_ulcer',\n",
-      "               'coagulation/bleeding_tests_abnormal',\n",
-      "               'sclerosing_cholangitis',\n",
-      "               'tophus',\n",
-      "               'malignant_tumor_of_lesser_curve_of_stomach',\n",
-      "               'abnormal_sputum',\n",
-      "               'homonymous_hemianopia',\n",
-      "               'malignant_tumor_of_urinary_tract_proper',\n",
-      "               'hematometra',\n",
-      "               'victim_of_abuse',\n",
-      "               'basal_cell_carcinoma_of_truncal_skin',\n",
-      "               'disorder_of_hematopoietic_structure',\n",
-      "               'ulcerative_pancolitis',\n",
-      "               'severe_pre-eclampsia',\n",
-      "               'vitamin_b_deficiency',\n",
-      "               'disorder_of_bursa_of_shoulder_region',\n",
-      "               'helicobacter-associated_disease',\n",
-      "               'infection_of_gastrointestinal_tract',\n",
-      "               'thoracic_aortic_aneurysm_without_rupture',\n",
-      "               'disorder_of_tendon_of_shoulder_region',\n",
-      "               'pneumoconiosis_caused_by_silica',\n",
-      "               'vomiting_of_pregnancy',\n",
-      "               'primary_malignant_neoplasm_of_female_genital_organ',\n",
-      "               'corneal_edema',\n",
-      "               'hemospermia',\n",
-      "               'disorder_of_urinary_bladder',\n",
-      "               'mass_of_scrotum',\n",
-      "               'fracture_of_neck_of_femur',\n",
-      "               'malignant_neoplasm_of_posterior_wall_of_urinary_bladder',\n",
-      "               \"asperger's_disorder\",\n",
-      "               'insomnia_disorder_related_to_another_mental_disorder',\n",
-      "               'ulcer_of_mouth',\n",
-      "               'closed_fracture_of_femur_distal_end',\n",
-      "               \"closed_colles'_fracture\",\n",
-      "               'mass_of_axilla',\n",
-      "               'malignant_tumor_of_rectosigmoid_junction',\n",
-      "               'acute_ischemic_heart_disease',\n",
-      "               'iliotibial_band_friction_syndrome',\n",
-      "               'stricture_of_bile_duct',\n",
-      "               'mass_of_thoracic_structure',\n",
-      "               'hypercortisolism',\n",
-      "               'white_blood_cell_disorder',\n",
-      "               'nystagmus',\n",
-      "               'contusion_of_finger',\n",
-      "               'alcohol_withdrawal_delirium',\n",
-      "               'acute_leukemia_disease',\n",
-      "               'psychoactive_substance-induced_organic_mental_disorder',\n",
-      "               'history_of_respiratory_disease',\n",
-      "               'amniotic_fluid_-meconium_stain',\n",
-      "               'convergence_insufficiency',\n",
-      "               'non-neoplastic_nevus',\n",
-      "               'drug-induced_gastrointestinal_disturbance',\n",
-      "               'urinary_tract_infection_following_delivery',\n",
-      "               'fetal_or_neonatal_effect_of_maternal_complication_of_pregnancy',\n",
-      "               'closed_fracture_finger_proximal_phalanx',\n",
-      "               'closed_fracture_finger_middle_phalanx',\n",
-      "               'tenderness',\n",
-      "               'disorder_of_endocrine_system',\n",
-      "               'infective_eczematoid_dermatitis',\n",
-      "               'primary_focal_hyperhidrosis',\n",
-      "               'fracture_of_tibial_plateau',\n",
-      "               'malignant_adenomatous_neoplasm',\n",
-      "               'malignant_carcinoid_tumor',\n",
-      "               'chronic_duodenal_ulcer_with_perforation',\n",
-      "               'alpha_thalassemia',\n",
-      "               'mechanical_ptosis',\n",
-      "               'failed_medical_induction_of_labor',\n",
-      "               'benign_neoplasm_of_adrenal_gland',\n",
-      "               'pseudocyst_of_pancreas',\n",
-      "               'neoplasm_of_adrenal_gland',\n",
-      "               'abnormal_involuntary_movement',\n",
-      "               'post_poliomyelitis_syndrome',\n",
-      "               'anoxic_encephalopathy',\n",
-      "               'perforation_of_intestine',\n",
-      "               'chondromalacia',\n",
-      "               'respiratory_arrest',\n",
-      "               'history_of_poliomyelitis',\n",
-      "               'congenital_malformation_of_genital_organs',\n",
-      "               'depressed_bipolar_i_disorder_in_full_remission',\n",
-      "               'transient_global_amnesia',\n",
-      "               'radiation_proctitis',\n",
-      "               'alcoholic_hepatitis',\n",
-      "               'contracture_of_knee_joint',\n",
-      "               'erythema',\n",
-      "               'dislocation_of_patellofemoral_joint',\n",
-      "               'abrasion_of_lower_limb',\n",
-      "               'alpha-1-antitrypsin_deficiency',\n",
-      "               'mass_of_lower_limb',\n",
-      "               'bladder_dysfunction',\n",
-      "               'placental_abruption',\n",
-      "               'chronic_atrial_fibrillation',\n",
-      "               'retraction_of_eyelid',\n",
-      "               'irregular_astigmatism',\n",
-      "               'contusion_of_toe',\n",
-      "               'fracture_of_skull',\n",
-      "               'foreign_body_in_nose',\n",
-      "               'closed_fracture_of_distal_phalanx_of_finger',\n",
-      "               'steinert_myotonic_dystrophy_syndrome',\n",
-      "               'episcleritis',\n",
-      "               'stenosis_of_cervix',\n",
-      "               'infective_otitis_externa',\n",
-      "               'refractive_amblyopia',\n",
-      "               'acquired_scoliosis',\n",
-      "               'neoplasm_of_breast',\n",
-      "               'chronic_inflammatory_demyelinating_polyradiculoneuropathy',\n",
-      "               'gastric_ulcer_with_hemorrhage',\n",
-      "               'family_history:_breast_disease',\n",
-      "               'history_of_artificial_joint',\n",
-      "               'neutropenia',\n",
-      "               'malignant_neoplasm_of_lower-outer_quadrant_of_female_breast',\n",
-      "               'emotionally_unstable_personality_disorder',\n",
-      "               'nondependent_cannabis_abuse_in_remission',\n",
-      "               'chronic_obstructive_pulmonary_disease_with_acute_lower_respiratory_infection',\n",
-      "               'non-obstructive_reflux-associated_chronic_pyelonephritis',\n",
-      "               'accidents_caused_by_knives_swords_and_daggers',\n",
-      "               'mononeuropathy_due_to_diabetes_mellitus',\n",
-      "               'dissection_of_thoracic_aorta',\n",
-      "               'measurement_of_respiratory_function',\n",
-      "               'immunodeficiency_disorder',\n",
-      "               'perforation_of_small_intestine',\n",
-      "               'simple_goiter',\n",
-      "               'fracture_of_greater_trochanter',\n",
-      "               'primate_erythroparvovirus_1_infection',\n",
-      "               'corpus_luteum_cyst',\n",
-      "               'memory_impairment',\n",
-      "               'avascular_necrosis_of_bone',\n",
-      "               'acute_st_segment_elevation_myocardial_infarction',\n",
-      "               'gastrointestinal_stromal_tumor',\n",
-      "               'snapping_thumb_syndrome',\n",
-      "               'major_depression_in_remission',\n",
-      "               'abnormal_posture',\n",
-      "               'injury_of_digital_nerve',\n",
-      "               'exposure_to_human_immunodeficiency_virus',\n",
-      "               'generalized-onset_seizures',\n",
-      "               'complication_associated_with_device',\n",
-      "               'arteriosclerotic_gangrene',\n",
-      "               'fracture_of_shaft_of_femur',\n",
-      "               'abscess_of_lung',\n",
-      "               'contusion_of_head',\n",
-      "               'chronic_mastoiditis',\n",
-      "               'congenital_hydrocele',\n",
-      "               'poisoning_caused_by_antidepressant',\n",
-      "               'epilepsy',\n",
-      "               'mycobacteriosis',\n",
-      "               'anisocoria',\n",
-      "               'microcytosis_red_cells',\n",
-      "               'recurrent_erosion_of_cornea',\n",
-      "               'lacunar_infarction',\n",
-      "               'hypogonadotropic_hypogonadism',\n",
-      "               'contusion_of_wrist',\n",
-      "               'cellulitis_of_periorbital_region',\n",
-      "               'derangement_of_meniscus_of_knee_joint',\n",
-      "               'injury_of_face',\n",
-      "               'radial_neuropathy',\n",
-      "               'orbital_cellulitis',\n",
-      "               'cardiomyopathy_associated_with_another_disorder',\n",
-      "               'open_wound_of_scalp',\n",
-      "               'tachypnea',\n",
-      "               'foreign_body_-_finger',\n",
-      "               'facial_palsy',\n",
-      "               'premature_beats',\n",
-      "               'dissection_of_aorta',\n",
-      "               'alternating_exotropia',\n",
-      "               'chandler_syndrome',\n",
-      "               'stuttering',\n",
-      "               'sickling_disorder_due_to_hemoglobin_s',\n",
-      "               'prolapse_of_vaginal_vault_after_hysterectomy',\n",
-      "               'mass_of_urinary_bladder',\n",
-      "               'cyst_of_jaw',\n",
-      "               'pain_in_forearm',\n",
-      "               'acquired_hemolytic_anemia',\n",
-      "               'quadriplegic_cerebral_palsy',\n",
-      "               'tibialis_tendinitis',\n",
-      "               'chronic_otitis_externa',\n",
-      "               'cellulitis_of_abdominal_wall',\n",
-      "               'late_effect_of_spinal_cord_injury',\n",
-      "               'chronic_pansinusitis',\n",
-      "               'photosensitivity',\n",
-      "               'benign_neoplasm_of_soft_tissues_of_upper_limb',\n",
-      "               'tuberculous_adenitis',\n",
-      "               'melanoma_in_situ_of_skin',\n",
-      "               'rheumatic_aortic_stenosis_with_regurgitation',\n",
-      "               'non-rheumatic_mitral_valve_stenosis',\n",
-      "               'fetal_or_neonatal_effect_of_maternal_infection',\n",
-      "               'neonatal_dacryocystitis_and_conjunctivitis',\n",
-      "               'traumatic_rupture_of_lumbar_intervertebral_disc',\n",
-      "               'late_effect_of_motor_vehicle_accident',\n",
-      "               'subglottic_stenosis',\n",
-      "               'mixed_receptive-expressive_language_disorder',\n",
-      "               'accident_caused_by_machinery',\n",
-      "               'hemangioma_of_skin_and_subcutaneous_tissue',\n",
-      "               'acute_schizophrenia-like_psychotic_disorder',\n",
-      "               'soft_tissue_injury',\n",
-      "               'traction_detachment_of_retina',\n",
-      "               'toxic_effect_of_lead_compound',\n",
-      "               'basal_cell_carcinoma_of_nose',\n",
-      "               'refractory_migraine_with_aura',\n",
-      "               'passive_smoker',\n",
-      "               'permanent_atrial_fibrillation',\n",
-      "               'complete_quadriplegia_due_to_spinal_cord_lesion_between_fifth_and_seventh_cervical_vertebra',\n",
-      "               'closed_fracture_of_one_rib',\n",
-      "               'calculus_of_bile_duct_with_obstruction',\n",
-      "               'brain_injury_without_open_intracranial_wound_and_with_loss_of_consciousness',\n",
-      "               'fracture_of_acetabulum',\n",
-      "               'noninflammatory_disorder_of_fallopian_tube',\n",
-      "               'congenital_anomaly_of_cerebrovascular_system',\n",
-      "               'acquired_deformity_of_hip',\n",
-      "               'acute_infectious_conjunctivitis',\n",
-      "               'prolapse_of_female_genital_organs',\n",
-      "               'employment_problem',\n",
-      "               'neutropenia_due_to_and_following_chemotherapy',\n",
-      "               'frontal_sinusitis',\n",
-      "               'primary_malignant_neoplasm_of_ovary',\n",
-      "               'rupture_of_achilles_tendon',\n",
-      "               'autoimmune_disease',\n",
-      "               'disorder_of_shoulder_region',\n",
-      "               'exposure_keratoconjunctivitis',\n",
-      "               'laboratory_test',\n",
-      "               'chronic_urinary_tract_infection',\n",
-      "               'congenital_iodine_deficiency_syndrome',\n",
-      "               'injury_of_toe',\n",
-      "               'osteomyelitis_of_ankle_and/or_foot',\n",
-      "               'disturbance_in_speech',\n",
-      "               'malignant_tumor_of_ureter',\n",
-      "               'respiratory_insufficiency',\n",
-      "               'leukoplakia_of_oral_mucosa',\n",
-      "               'aphasia_as_late_effect_of_cerebrovascular_disease',\n",
-      "               'sepsis_caused_by_staphylococcus',\n",
-      "               'osteomalacia',\n",
-      "               'closed_fracture_of_orbital_floor',\n",
-      "               'rheumatic_disease_of_tricuspid_valve',\n",
-      "               'acute_cystitis',\n",
-      "               'vascular_insufficiency_of_intestine',\n",
-      "               'gastric_varices',\n",
-      "               'defective_dental_restoration',\n",
-      "               'sequela_of_infection_caused_by_human_poliovirus',\n",
-      "               'inactive_tuberculosis',\n",
-      "               'injury_of_elbow',\n",
-      "               'open_wound_of_wrist',\n",
-      "               'neoplasm_of_lung',\n",
-      "               'neoplasm_of_prostate',\n",
-      "               'chronic_myelomonocytic_leukemia',\n",
-      "               'history_of_urinary_stone',\n",
-      "               'malignant_tumor_of_trigone_of_urinary_bladder',\n",
-      "               'lupus_anticoagulant_disorder',\n",
-      "               'non-traumatic_intracranial_subdural_hemorrhage',\n",
-      "               'cerebral_edema',\n",
-      "               'lumbar_spina_bifida_with_hydrocephalus',\n",
-      "               \"ebstein's_anomaly_of_tricuspid_valve\",\n",
-      "               'closed_fracture_distal_tibia',\n",
-      "               'syndrome_of_infant_of_diabetic_mother',\n",
-      "               'late_effect_of_accidental_injury',\n",
-      "               'multiple_system_atrophy',\n",
-      "               'common_variable_agammaglobulinemia',\n",
-      "               'tear_of_meniscus_of_knee',\n",
-      "               'acute_osteomyelitis_of_ankle_and/or_foot',\n",
-      "               'nodular_lymphoma',\n",
-      "               'history_of_pregnancy',\n",
-      "               'localized_edema',\n",
-      "               'occlusion_of_artery',\n",
-      "               'lesion_of_penis',\n",
-      "               'transient_synovitis_of_hip',\n",
-      "               'ileostomy_present',\n",
-      "               'bipolar_i_disorder_most_recent_episode_hypomanic',\n",
-      "               'posterior_subcapsular_cataract',\n",
-      "               'erythromelalgia',\n",
-      "               'pneumoconiosis',\n",
-      "               'hereditary_factor_ix_deficiency_disease',\n",
-      "               'cramp_in_limb',\n",
-      "               'ileal_pouchitis',\n",
-      "               'polyp',\n",
-      "               'mass_of_tongue',\n",
-      "               'glycosuria',\n",
-      "               'neonatal_acne',\n",
-      "               'abnormal_auditory_perception',\n",
-      "               'malaria',\n",
-      "               'intermittent_exotropia',\n",
-      "               'costal_chondritis',\n",
-      "               'peptic_ulcer_with_hemorrhage',\n",
-      "               'retained_dental_root',\n",
-      "               'recurrent_major_depression_in_remission',\n",
-      "               'closed_fracture_of_fifth_metatarsal_bone',\n",
-      "               'feeding_disorder_of_infancy_or_early_childhood',\n",
-      "               'poisoning_caused_by_wasp_sting',\n",
-      "               'cyclothymia',\n",
-      "               'muscle_fasciculation',\n",
-      "               'disorder_of_phosphorus_metabolism',\n",
-      "               'herpes_zoster_ophthalmicus',\n",
-      "               'keratoconjunctivitis',\n",
-      "               'shoulder_girdle_dystocia',\n",
-      "               'pulmonic_valve_regurgitation',\n",
-      "               'benign_neoplasm_of_ovary',\n",
-      "               'benign_neoplasm_of_skin_of_upper_limb',\n",
-      "               'malignant_lymphoma_of_lymph_nodes_of_multiple_sites',\n",
-      "               'open_wound_of_forearm',\n",
-      "               'open_wound_of_foot',\n",
-      "               'transverse_myelopathy_syndrome',\n",
-      "               'cervical_incompetence',\n",
-      "               'malignant_tumor_of_body_of_pancreas',\n",
-      "               'chronic_active_hepatitis',\n",
-      "               'closed_fracture_lumbar_vertebra',\n",
-      "               'secondary_parkinsonism',\n",
-      "               'atrioventricular_block',\n",
-      "               'acute_hepatitis_c',\n",
-      "               'organic_mood_disorder',\n",
-      "               'osteoarthritis_of_elbow',\n",
-      "               'congenital_ptosis',\n",
-      "               'intra-abdominal_and_pelvic_swelling_mass_and_lump',\n",
-      "               'perinatal_disorder_of_growth_and/or_development',\n",
-      "               'groin_mass',\n",
-      "               'malignant_tumor_of_vulva',\n",
-      "               'malignant_tumor_of_maxillary_sinus',\n",
-      "               'pyloric_stenosis',\n",
-      "               'motor_neuron_disease',\n",
-      "               'edema_of_optic_disc',\n",
-      "               'hyperplasia_of_maxillary_bone',\n",
-      "               'cleft_palate_with_cleft_lip',\n",
-      "               'tongue_tie',\n",
-      "               'acute_severe_exacerbation_of_asthma',\n",
-      "               'mental_disorder',\n",
-      "               'cheilitis',\n",
-      "               'ankle_instability',\n",
-      "               'insomnia_disorder_related_to_known_organic_factor',\n",
-      "               'dilated_cardiomyopathy_caused_by_alcohol',\n",
-      "               'secondary_physiologic_amenorrhea',\n",
-      "               'idiopathic_peripheral_autonomic_neuropathy',\n",
-      "               'cystic_fibrosis_of_the_lung',\n",
-      "               'asymptomatic_human_immunodeficiency_virus_infection',\n",
-      "               'overlapping_malignant_neoplasm_of_bronchus_and_lung',\n",
-      "               'intraductal_carcinoma_in_situ_of_breast',\n",
-      "               'mixed_bipolar_i_disorder_in_full_remission',\n",
-      "               'visual_field_defect',\n",
-      "               'fracture_of_multiple_ribs',\n",
-      "               'paraphimosis',\n",
-      "               'family_history:_neoplasm_of_ovary',\n",
-      "               'history_of_malaria',\n",
-      "               'primary_hyperoxaluria',\n",
-      "               'compulsive_gambling',\n",
-      "               'esophageal_varices_associated_with_another_disorder',\n",
-      "               'synovitis_and_tenosynovitis',\n",
-      "               'bone_cyst',\n",
-      "               'derangement_of_lateral_meniscus',\n",
-      "               'accidentally_struck_by_or_against_objects_or_persons',\n",
-      "               'eosinophilic_esophagitis',\n",
-      "               'spasm_of_bladder',\n",
-      "               'congenital_laryngomalacia',\n",
-      "               'embryonic_cyst_of_broad_ligament',\n",
-      "               'prostatic_intraepithelial_neoplasia',\n",
-      "               'malignant_tumor_of_salivary_gland',\n",
-      "               'fracture_of_radial_neck',\n",
-      "               'chronic_osteomyelitis_of_ankle_and/or_foot',\n",
-      "               'malignant_melanoma_of_lower_limb',\n",
-      "               'current_knee_cartilage_tear',\n",
-      "               'rheumatoid_arthritis_of_hand_joint',\n",
-      "               'scoliosis_of_lumbar_spine',\n",
-      "               'deformity_of_hand',\n",
-      "               'lesion_of_nose',\n",
-      "               'squamous_cell_carcinoma_of_mouth',\n",
-      "               'obstruction_of_nasolacrimal_duct',\n",
-      "               'vertebral_artery_syndrome',\n",
-      "               'malignant_tumor_of_gallbladder',\n",
-      "               'malignant_tumor_of_parotid_gland',\n",
-      "               'malignant_tumor_of_splenic_flexure',\n",
-      "               'systolic_dysfunction',\n",
-      "               'perineal_laceration_during_delivery',\n",
-      "               'solar_erythema',\n",
-      "               'adult_victim_of_abuse',\n",
-      "               'plasmacytoma',\n",
-      "               'nodular_goiter',\n",
-      "               'closed_fracture_of_bone',\n",
-      "               'malignant_neoplasm_of_bone',\n",
-      "               'late_effect_of_traumatic_injury_to_brain',\n",
-      "               'acquired_absence_of_all_teeth',\n",
-      "               'anemia_of_prematurity',\n",
-      "               'cerebral_herniation',\n",
-      "               'nasopharyngitis',\n",
-      "               'mastoiditis',\n",
-      "               'ulcer_of_rectum',\n",
-      "               'movement_disorder',\n",
-      "               'burn_of_neck',\n",
-      "               'manic_bipolar_i_disorder_in_partial_remission',\n",
-      "               'basilar_artery_syndrome',\n",
-      "               'degeneration_of_thoracic_intervertebral_disc',\n",
-      "               'pressure_ulcer_of_lower_back',\n",
-      "               'perinatal_intraventricular_hemorrhage',\n",
-      "               'traumatic_and/or_non-traumatic_injury_of_back',\n",
-      "               'inflammatory_disorder_of_bone_of_jaw',\n",
-      "               'injury_of_trigeminal_nerve',\n",
-      "               'polyneuropathy_caused_by_drug',\n",
-      "               'infestation_caused_by_anoplura',\n",
-      "               'severe_major_depression_without_psychotic_features',\n",
-      "               'polyradiculopathy',\n",
-      "               'hereditary_corneal_dystrophy',\n",
-      "               'dependent_personality_disorder',\n",
-      "               'closed_fracture_of_forearm',\n",
-      "               'secondary_malignant_neoplasm_of_intrapelvic_lymph_nodes',\n",
-      "               'closed_fracture_of_multiple_ribs',\n",
-      "               'developmental_speech_disorder',\n",
-      "               'history_of_pneumonia',\n",
-      "               'coxsackie_virus_disease',\n",
-      "               'traumatic_arthropathy_of_the_ankle_and/or_foot',\n",
-      "               'endometrial_hyperplasia',\n",
-      "               'retinal_drusen',\n",
-      "               'wrist_joint_inflamed',\n",
-      "               'lesion_of_tongue',\n",
-      "               'attention_deficit_hyperactivity_disorder_combined_type',\n",
-      "               'central_retinal_artery_occlusion',\n",
-      "               'turner_syndrome',\n",
-      "               'primary_angle-closure_glaucoma',\n",
-      "               'vaginal_enterocele',\n",
-      "               'neuroma',\n",
-      "               'aspergillosis',\n",
-      "               'congenital_cystic_disease_of_liver',\n",
-      "               'moderate_major_depression',\n",
-      "               'contusion_of_thigh',\n",
-      "               'postoperative_hemorrhage',\n",
-      "               'irritant_contact_dermatitis',\n",
-      "               'sprain_of_sacroiliac_region',\n",
-      "               'occupational_disorder',\n",
-      "               'disorder_of_uterus',\n",
-      "               'brain_disorder_resulting_from_a_period_of_impaired_oxygen_delivery_to_the_brain',\n",
-      "               'toxic_effect_of_carbon_monoxide',\n",
-      "               'malignant_neoplasm_of_lower-inner_quadrant_of_female_breast',\n",
-      "               'cataplexy_and_narcolepsy',\n",
-      "               'noninflammatory_cervical_disorder',\n",
-      "               'umbilical_granuloma',\n",
-      "               'wrist_joint_pain',\n",
-      "               'radial_polydactyly',\n",
-      "               'infections_specific_to_perinatal_period',\n",
-      "               'fracture_of_mandible_closed',\n",
-      "               'acute_periodontitis',\n",
-      "               'injury_of_unknown_intent_due_to_fall_from_height',\n",
-      "               'pressure_ulcer_of_sacral_region',\n",
-      "               'deformity_of_foot',\n",
-      "               'persistent_pulmonary_hypertension_of_the_newborn',\n",
-      "               'uninodular_goiter',\n",
-      "               'breastfeeding_problem_in_the_newborn',\n",
-      "               'self_poisoning_caused_by_carbon_monoxide',\n",
-      "               'malignant_mesothelioma_of_pleura',\n",
-      "               'chronic_allergic_conjunctivitis',\n",
-      "               'follicular_cyst_of_ovary',\n",
-      "               'pure_hyperglyceridemia',\n",
-      "               'autonomic_neuropathy',\n",
-      "               'laceration_of_lip',\n",
-      "               'puncture_wound_of_skin',\n",
-      "               'acute_epiglottitis',\n",
-      "               'abnormal_vaginal_bleeding',\n",
-      "               'myelosclerosis_with_myeloid_metaplasia',\n",
-      "               'closed_fracture_of_distal_fibula',\n",
-      "               'manic_bipolar_i_disorder_in_full_remission',\n",
-      "               'hemothorax',\n",
-      "               'closed_fracture_of_malar_and/or_maxillary_bones',\n",
-      "               'tonic-clonic_epilepsy',\n",
-      "               'complication_of_medical_care',\n",
-      "               'carcinoid_syndrome',\n",
-      "               'primary_malignant_neoplasm_of_pancreas',\n",
-      "               'primary_malignant_neoplasm_of_soft_tissues',\n",
-      "               'secondary_peripheral_neuropathy',\n",
-      "               'cesarean_wound_disruption',\n",
-      "               'spider_bite_wound',\n",
-      "               'synovitis',\n",
-      "               'severe_major_depression_single_episode_with_psychotic_features',\n",
-      "               'mass_of_head',\n",
-      "               'pathological_fracture_due_to_osteoporosis',\n",
-      "               'chronic_peptic_ulcer_with_hemorrhage',\n",
-      "               'thyrotoxicosis_factitia',\n",
-      "               'hepatorenal_syndrome',\n",
-      "               'parent-child_problem',\n",
-      "               'anxiety_disorder_of_childhood',\n",
-      "               'chronic_periodontitis',\n",
-      "               'bleeding_ulcer_of_esophagus',\n",
-      "               'residual_foreign_body_in_soft_tissue',\n",
-      "               'bleeding_from_anus',\n",
-      "               'concussion_with_loss_of_consciousness',\n",
-      "               'trigeminal_nerve_disorder',\n",
-      "               'hereditary_peripheral_neuropathy',\n",
-      "               'acute_respiratory_failure',\n",
-      "               'closed_trimalleolar_fracture',\n",
-      "               'contusion_of_upper_limb',\n",
-      "               'obstruction_co-occurrent_and_due_to_left_femoral_hernia',\n",
-      "               'obstruction_co-occurrent_and_due_to_right_femoral_hernia',\n",
-      "               'injury_of_neck',\n",
-      "               'benign_neoplasm_of_skin_of_lower_limb',\n",
-      "               'malignant_melanoma_of_skin_of_face',\n",
-      "               'seborrheic_dermatitis_of_scalp',\n",
-      "               'closed_fracture_of_acromial_end_of_clavicle',\n",
-      "               'phantom_limb',\n",
-      "               'localized_primary_osteoarthritis_of_the_shoulder_region',\n",
-      "               'elbow_joint_pain',\n",
-      "               'odontogenic_cyst',\n",
-      "               'post-infective_arthritis',\n",
-      "               'synovial_cyst',\n",
-      "               'disorder_of_iron_metabolism',\n",
-      "               'infection_and/or_inflammatory_reaction_due_to_internal_prosthetic_device_implant_and/or_graft',\n",
-      "               'hepatosplenomegaly',\n",
-      "               'pityriasis_alba',\n",
-      "               'retinoschisis',\n",
-      "               'injury_of_ulnar_nerve',\n",
-      "               'lipodystrophy',\n",
-      "               'scleritis',\n",
-      "               'breath_smells_unpleasant',\n",
-      "               'osteochondritis_dissecans',\n",
-      "               'carcinoma_in_situ_of_uterine_cervix',\n",
-      "               'chronic_lymphoid_leukemia_in_remission',\n",
-      "               'malignant_melanoma_of_skin_of_trunk',\n",
-      "               'compartment_syndrome',\n",
-      "               'avascular_necrosis_of_the_capital_femoral_epiphysis',\n",
-      "               'chorioamnionitis',\n",
-      "               'contusion_of_face',\n",
-      "               'torsion_of_ovary',\n",
-      "               'vulval_pain',\n",
-      "               \"sézary's_disease_of_lymph_nodes_of_multiple_sites\",\n",
-      "               'hyperosmolar_coma_due_to_type_2_diabetes_mellitus',\n",
-      "               'fall',\n",
-      "               'collagenous_colitis',\n",
-      "               'cataract_secondary_to_ocular_disease',\n",
-      "               'low_vision_both_eyes',\n",
-      "               'disorder_of_soft_tissue',\n",
-      "               'acute_gastric_ulcer_with_perforation',\n",
-      "               'osteolysis',\n",
-      "               'postphlebitic_syndrome',\n",
-      "               'foreign_body_in_hand',\n",
-      "               'acute_drug_intoxication',\n",
-      "               'cellulitis_of_pinna',\n",
-      "               'chronic_pelvic_inflammatory_disease',\n",
-      "               'post-inflammatory_hyperpigmentation',\n",
-      "               'contracture_of_elbow_joint',\n",
-      "               'acquired_deformity_of_joint_of_foot',\n",
-      "               'psychoactive_substance_dependence',\n",
-      "               'accidental_exposure_to_metallic_lead',\n",
-      "               'benign_neoplasm_of_respiratory_system',\n",
-      "               'calculus_of_gallbladder_with_cholecystitis',\n",
-      "               'enterobiasis',\n",
-      "               'self_inflicted_injury',\n",
-      "               'b-cell_acute_lymphoblastic_leukemia',\n",
-      "               'closed_fracture_of_shaft_of_bone_of_forearm',\n",
-      "               'superficial_injury_of_forearm',\n",
-      "               'pathological_fracture_-_upper_arm',\n",
-      "               'vaginal_lesion',\n",
-      "               'scoliosis_of_thoracic_spine',\n",
-      "               'secondary_hemorrhage_postprocedure',\n",
-      "               'postmastectomy_lymphedema_syndrome',\n",
-      "               'disorganized_schizophrenia',\n",
-      "               'postoperative_complication',\n",
-      "               'disorder_of_function_of_stomach',\n",
-      "               'abnormal_blood_pressure',\n",
-      "               'chronic_anterior_uveitis',\n",
-      "               'sprain_of_ligament',\n",
-      "               'basal_cell_carcinoma_of_ear',\n",
-      "               'body_mass_index_40+_-_severely_obese',\n",
-      "               'pathological_fracture_of_femur',\n",
-      "               'intermittent_explosive_disorder',\n",
-      "               'fracture_of_lower_leg',\n",
-      "               'restrictive_cardiomyopathy',\n",
-      "               'accidental_poisoning_caused_by_carbon_monoxide',\n",
-      "               'mass_lesion_of_brain',\n",
-      "               'punctate_keratitis',\n",
-      "               'history_of_non-hodgkins_lymphoma',\n",
-      "               'recurrent_bacterial_infection',\n",
-      "               'atypical_squamous_cells_on_cervical_papanicolaou_smear_cannot_exclude_high_grade_squamous_intraepithelial_lesion',\n",
-      "               'atherosclerosis_of_autologous_vein_bypass_graft_of_limb',\n",
-      "               'cocaine-induced_organic_mental_disorder',\n",
-      "               'influenza_with_non-respiratory_manifestation',\n",
-      "               'parainfluenza_virus_pneumonia',\n",
-      "               'pruritus_of_vulva',\n",
-      "               'agoraphobia',\n",
-      "               'autonomic_neuropathy_due_to_type_1_diabetes_mellitus',\n",
-      "               'food_poisoning',\n",
-      "               'skin_sensation_disturbance',\n",
-      "               'functional_disorder_of_intestine',\n",
-      "               'bipolar_disorder_in_remission',\n",
-      "               'dacryocystitis',\n",
-      "               'chronic_tubotympanic_suppurative_otitis_media',\n",
-      "               'lower_gastrointestinal_hemorrhage',\n",
-      "               'benign_neoplasm_of_cranial_nerve',\n",
-      "               'secondary_malignant_neoplasm_of_adrenal_gland',\n",
-      "               'neoplasm_of_uncertain_behavior_of_bladder',\n",
-      "               'scarred_macula',\n",
-      "               'hypertrophic_condition_of_skin',\n",
-      "               'history_of_cardiovascular_disease',\n",
-      "               'malignant_tumor_of_peritoneum',\n",
-      "               'myelofibrosis',\n",
-      "               'pressure_ulcer_of_ankle',\n",
-      "               'osteogenesis_imperfecta',\n",
-      "               'narcissistic_personality_disorder',\n",
-      "               'sprain_of_hand',\n",
-      "               'malignant_neoplasm_of_liver',\n",
-      "               'combined_form_of_senile_cataract',\n",
-      "               'hairy_cell_leukemia',\n",
-      "               'retroperitoneal_lymphadenopathy',\n",
-      "               'vascular_headache',\n",
-      "               'disorder_of_lumbar_spine',\n",
-      "               'hematologic_neoplasm',\n",
-      "               'noncompliance_with_dietary_regimen',\n",
-      "               'ruptured_abdominal_aortic_aneurysm',\n",
-      "               'esophageal_bleeding',\n",
-      "               'right_femoral_hernia',\n",
-      "               'left_femoral_hernia',\n",
-      "               'learning_difficulties',\n",
-      "               'abnormal_weight_gain',\n",
-      "               'serum_cholesterol_raised',\n",
-      "               'renal_function_tests_abnormal',\n",
-      "               'renal_osteodystrophy',\n",
-      "               'arthritis_associated_with_another_disorder',\n",
-      "               'mediastinal_emphysema',\n",
-      "               'enteric_campylobacteriosis',\n",
-      "               'chronic_viral_hepatitis_b_without_delta-agent',\n",
-      "               'malignant_tumor_of_glottis',\n",
-      "               'malignant_tumor_of_supraglottis',\n",
-      "               'malignant_neoplasm_of_connective_and_soft_tissue_of_hip_and_lower_limb',\n",
-      "               'mood_swings',\n",
-      "               'mixed_bipolar_affective_disorder',\n",
-      "               'current_tear_of_semilunar_cartilage',\n",
-      "               'pulmonary_stenosis_non-rheumatic',\n",
-      "               'torsion_of_appendix_of_testis',\n",
-      "               'infection_of_obstetric_surgical_wound',\n",
-      "               'localized_morphea',\n",
-      "               'hemarthrosis_of_knee',\n",
-      "               'calculus_in_urethra',\n",
-      "               'transplanted_organ_rejection',\n",
-      "               'osler_hemorrhagic_telangiectasia_syndrome',\n",
-      "               'neurosarcoidosis',\n",
-      "               'discitis',\n",
-      "               'delayed_and/or_secondary_postpartum_hemorrhage',\n",
-      "               'rupture_of_globe',\n",
-      "               'basal_cell_carcinoma_of_eyelid',\n",
-      "               'stenosis_of_nasolacrimal_duct',\n",
-      "               'thoracoabdominal_aortic_aneurysm',\n",
-      "               'cerebral_arteriovenous_malformation',\n",
-      "               'bolus_obstruction_of_intestine',\n",
-      "               'liver_transplant_rejection',\n",
-      "               'coronary_artery_spasm',\n",
-      "               'perinatal_jaundice_from_excessive_hemolysis',\n",
-      "               'nasal_deviation',\n",
-      "               'ligament_rupture',\n",
-      "               'endometriosis_of_ovary',\n",
-      "               'symbolic_dysfunction',\n",
-      "               'epicranial_subaponeurotic_hemorrhage',\n",
-      "               'atrophy_of_vagina',\n",
-      "               'lower_limb_nerve_lesion',\n",
-      "               'multiple_endocrine_neoplasia_type_1',\n",
-      "               'urinary_tract_infection_in_pregnancy',\n",
-      "               'severe_nonproliferative_retinopathy_due_to_diabetes_mellitus',\n",
-      "               'chronic_gastric_ulcer_with_perforation',\n",
-      "               'nephrosclerosis',\n",
-      "               'floppy_infant_syndrome',\n",
-      "               'bullous_myringitis',\n",
-      "               'polyotia',\n",
-      "               'contusion_of_eye',\n",
-      "               'systemic_mast_cell_disease',\n",
-      "               'disorder_of_pituitary_gland',\n",
-      "               'internal_hordeolum',\n",
-      "               'open_fracture_of_tibia_and_fibula',\n",
-      "               'mononeuropathy_due_to_type_2_diabetes_mellitus',\n",
-      "               'embolism_from_thrombosis_of_vein_of_distal_lower_extremity',\n",
-      "               'iatrogenic_pneumothorax',\n",
-      "               'idiopathic_hypersomnia_without_long_sleep_time',\n",
-      "               'glossitis',\n",
-      "               'severe_mixed_bipolar_i_disorder_without_psychotic_features',\n",
-      "               'pyonephrosis',\n",
-      "               'closed_fracture_of_distal_end_of_ulna',\n",
-      "               'acidosis',\n",
-      "               'ischemia',\n",
-      "               'parasomnia',\n",
-      "               'acquired_pancytopenia',\n",
-      "               'prurigo_nodularis',\n",
-      "               'abscess_of_buttock',\n",
-      "               'interstitial_pneumonia',\n",
-      "               'beta_thalassemia',\n",
-      "               'peritoneal_adhesion',\n",
-      "               'pyoderma',\n",
-      "               'struck_by_falling_object',\n",
-      "               'tuberous_sclerosis_syndrome',\n",
-      "               'chronic_gingivitis',\n",
-      "               'heavy-for-dates_at_birth_regardless_of_gestation_period',\n",
-      "               'stevens-johnson_syndrome',\n",
-      "               'chronic_tophaceous_gout',\n",
-      "               'open_fracture_of_phalanx_of_foot',\n",
-      "               'renal_disease_in_pregnancy_and/or_puerperium_without_hypertension',\n",
-      "               'thrombosed_hemorrhoids',\n",
-      "               'obstruction_co-occurrent_and_due_to_recurrent_right_inguinal_hernia',\n",
-      "               'obstruction_co-occurrent_and_due_to_recurrent_left_inguinal_hernia',\n",
-      "               'derangement_of_posterior_horn_of_lateral_meniscus',\n",
-      "               'alcoholic_polyneuropathy',\n",
-      "               'collagen_disease',\n",
-      "               'chews_tobacco',\n",
-      "               'fall_from_ladder',\n",
-      "               'contracture_of_joint_of_shoulder_region',\n",
-      "               'benign_neoplasm_of_pituitary_gland',\n",
-      "               'biventricular_congestive_heart_failure',\n",
-      "               'primary_malignant_neoplasm_of_thyroid_gland',\n",
-      "               'secondary_malignant_neoplasm_of_large_intestine',\n",
-      "               'open_fracture_of_phalanx_of_finger',\n",
-      "               'malignant_tumor_of_hypopharynx',\n",
-      "               'serous_detachment_of_retinal_pigment_epithelium',\n",
-      "               'absence_seizure',\n",
-      "               'respiratory_obstruction',\n",
-      "               'nocturnal_muscle_spasm',\n",
-      "               'lobular_carcinoma_in_situ_of_breast',\n",
-      "               'ovarian_failure',\n",
-      "               'neoplasm_of_oropharynx',\n",
-      "               'neoplasm_of_rectum',\n",
-      "               'abscess_of_axilla',\n",
-      "               'history_of_injury',\n",
-      "               'rheumatoid_factor_positive',\n",
-      "               'biliary_cirrhosis',\n",
-      "               'malignant_tumor_of_middle_third_of_esophagus',\n",
-      "               \"pituitary-dependent_cushing's_disease\",\n",
-      "               'recurrent_depression',\n",
-      "               'nonorganic_insomnia',\n",
-      "               'cauda_equina_syndrome',\n",
-      "               'anal_skin_tag',\n",
-      "               'stenosis_of_urinary_meatus',\n",
-      "               'hypertrophic_scar',\n",
-      "               'deliveries_by_cesarean',\n",
-      "               'neuropathic_arthropathy_due_to_diabetes_mellitus',\n",
-      "               'sacroiliac_disorder',\n",
-      "               'capsular_cataract',\n",
-      "               'pain_in_scrotum',\n",
-      "               'fetal_or_neonatal_effect_of_vacuum_extraction_delivery',\n",
-      "               'neonatal_necrotizing_enterocolitis',\n",
-      "               'postoperative_pain',\n",
-      "               'closed_fracture_of_medial_condyle_of_humerus',\n",
-      "               'platelet_disorder',\n",
-      "               'frontotemporal_dementia',\n",
-      "               'brainstem_death',\n",
-      "               'mania',\n",
-      "               'motility_disorder_of_intestine',\n",
-      "               'mass_of_pituitary',\n",
-      "               'deficiency_of_anterior_cruciate_ligament',\n",
-      "               'seronegative_rheumatoid_arthritis',\n",
-      "               'microscopic_polyarteritis_nodosa',\n",
-      "               'bone_necrosis',\n",
-      "               'placenta_previa_partialis',\n",
-      "               'self-injurious_behavior',\n",
-      "               'merkel_cell_carcinoma',\n",
-      "               'trichilemmal_cyst',\n",
-      "               'benign_neoplasm_of_ear',\n",
-      "               'subarachnoid_hemorrhage_due_to_traumatic_injury',\n",
-      "               'iliopsoas_abscess',\n",
-      "               'hypo-osmolality_and_or_hyponatremia',\n",
-      "               'neonatal_hypocalcemia',\n",
-      "               'breasts_asymmetrical',\n",
-      "               'burping',\n",
-      "               'fracture_of_phalanx_of_thumb',\n",
-      "               'disorder_of_smell',\n",
-      "               'extreme_immaturity',\n",
-      "               'benign_tumor_of_external_ear',\n",
-      "               'family_disruption',\n",
-      "               'immune_thrombocytopenia',\n",
-      "               'closed_fracture_of_scapula',\n",
-      "               'mass_of_skin',\n",
-      "               'lesion_of_lip',\n",
-      "               'mononeuritis_multiplex',\n",
-      "               'acute_gingivitis',\n",
-      "               'cyanosis',\n",
-      "               \"takayasu's_disease\",\n",
-      "               'tuberculosis_of_vertebral_column',\n",
-      "               'malignant_tumor_of_anal_canal',\n",
-      "               'malignant_tumor_of_nasal_cavity',\n",
-      "               'dysplasia_of_vagina',\n",
-      "               'hypovolemic_shock',\n",
-      "               'bronchopneumonia',\n",
-      "               'drug-induced_hyperpyrexia',\n",
-      "               'flaccid_neurogenic_bladder',\n",
-      "               'family_history_of_malignant_neoplasm_of_genital_structure',\n",
-      "               'chronic_systolic_heart_failure',\n",
-      "               'complete_quadriplegia_due_to_spinal_cord_lesion_between_first_and_fourth_cervical_vertebra',\n",
-      "               'disorder_of_joint_of_shoulder_region',\n",
-      "               'exposure_to_viral_hepatitis',\n",
-      "               'spondylolysis_of_cervical_spine',\n",
-      "               'low_lying_placenta',\n",
-      "               'left_sided_ulcerative_colitis',\n",
-      "               'foreign_body_in_lower_limb',\n",
-      "               'sepsis_caused_by_streptococcus',\n",
-      "               'torsion_of_spermatic_cord',\n",
-      "               'acute_cor_pulmonale',\n",
-      "               'fracture_of_patella',\n",
-      "               'sciatic_neuropathy',\n",
-      "               'alcohol-induced_mood_disorder',\n",
-      "               'retinal_vascular_disorder',\n",
-      "               'tuberculosis_of_meninges',\n",
-      "               'lagophthalmos',\n",
-      "               'coccidioidomycosis',\n",
-      "               'disorder_of_uterine_cervix',\n",
-      "               'bacterial_infection_caused_by_pseudomonas',\n",
-      "               'lumbar_spondylosis_with_myelopathy',\n",
-      "               'closed_fracture_of_coronoid_process_of_ulna',\n",
-      "               'mucocele_of_salivary_gland',\n",
-      "               'cryptogenic_organizing_pneumonia',\n",
-      "               'chronic_ethmoidal_sinusitis',\n",
-      "               'closed_fracture_of_neck_of_metacarpal_bone',\n",
-      "               'blind_loop_syndrome',\n",
-      "               'iridocyclitis',\n",
-      "               'convulsions_in_the_newborn',\n",
-      "               'hematoma_of_pinna',\n",
-      "               'spinal_cord_injury',\n",
-      "               'mural_thrombus_of_heart',\n",
-      "               'allergy_to_peanut',\n",
-      "               'carcinoma_in_situ_of_bladder',\n",
-      "               'acute_promyelocytic_leukemia_fab_m3',\n",
-      "               'ankylosis_of_joint',\n",
-      "               'neoplasm_of_pelvis',\n",
-      "               'neoplasm_of_larynx',\n",
-      "               'neoplasm_of_small_intestine',\n",
-      "               'neoplasm_of_retroperitoneum',\n",
-      "               'separation_anxiety',\n",
-      "               'disorder_of_elbow',\n",
-      "               'disorder_of_sacrum',\n",
-      "               'autonomic_dysreflexia',\n",
-      "               'congenital_spondylolisthesis',\n",
-      "               'hypothermia_of_newborn',\n",
-      "               'family_history:_glaucoma',\n",
-      "               'history_of_radiation_exposure',\n",
-      "               'pain_in_female_genitalia',\n",
-      "               'raised_serum_calcium_level',\n",
-      "               'antenatal_ultrasound_scan_abnormal',\n",
-      "               'fluid_volume_disorder',\n",
-      "               'alcohol_intoxication_delirium',\n",
-      "               'malignant_melanoma_of_scalp_and/or_neck',\n",
-      "               'hemangioma_of_intra-abdominal_structure',\n",
-      "               'uterovaginal_prolapse',\n",
-      "               'iodine_hypothyroidism',\n",
-      "               'nondependent_opioid_abuse_in_remission',\n",
-      "               'chronic_adenotonsillitis',\n",
-      "               'endometriosis_of_pelvic_peritoneum',\n",
-      "               'localized_secondary_osteoarthritis_of_the_shoulder_region',\n",
-      "               'clicking_hip',\n",
-      "               'transient_synovitis',\n",
-      "               'drowning_and_non-fatal_immersion',\n",
-      "               'mechanical_lagophthalmos',\n",
-      "               'episodic_tension-type_headache',\n",
-      "               'motor_neuropathy_with_multiple_conduction_block',\n",
-      "               'menstrual_migraine',\n",
-      "               'fracture_of_upper_limb',\n",
-      "               'metatarsus_adductus',\n",
-      "               'postoperative_intestinal_obstruction',\n",
-      "               'pseudo-obstruction_of_intestine',\n",
-      "               'polymorphic_light_eruption',\n",
-      "               'accident_due_to_contact_with_hot_or_corrosive_substance',\n",
-      "               'neurosyphilis',\n",
-      "               'sprain_of_ligament_of_elbow',\n",
-      "               'bimalleolar_fracture_of_ankle',\n",
-      "               'pruritus_of_genital_organs',\n",
-      "               'benign_neoplasm_of_nose_middle_ear_and_accessory_sinuses',\n",
-      "               'disorder_of_sleep-wake_cycle',\n",
-      "               'thoracic_back_sprain',\n",
-      "               'superficial_injury_of_wrist',\n",
-      "               'fall_on_same_level_due_to_impact_against_another_person',\n",
-      "               'family_history:_arthritis',\n",
-      "               'metastasis_to_digestive_organs',\n",
-      "               'non-suppurative_otitis_media',\n",
-      "               'peripheral_arteriovenous_malformation',\n",
-      "               'developmental_language_disorder',\n",
-      "               'dementia_associated_with_alcoholism',\n",
-      "               'left_sided_abdominal_pain',\n",
-      "               'mass_of_vulva',\n",
-      "               'progressive_supranuclear_ophthalmoplegia',\n",
-      "               'motorcycle_accident',\n",
-      "               'deformity_of_knee_joint',\n",
-      "               'gastrostomy_present',\n",
-      "               'vulvar_vestibulitis',\n",
-      "               'caffeine-related_disorder',\n",
-      "               'cryoglobulinemia',\n",
-      "               'varicella-zoster_virus_infection',\n",
-      "               'localized_primary_osteoarthritis_of_elbow',\n",
-      "               'lymphocytic-plasmacytic_colitis',\n",
-      "               'idiopathic_progressive_polyneuropathy',\n",
-      "               'foreign_body_in_digestive_tract',\n",
-      "               'drusen_of_optic_disc',\n",
-      "               'postoperative_infection',\n",
-      "               'traumatic_cataract',\n",
-      "               'pityriasis',\n",
-      "               'strabismic_amblyopia',\n",
-      "               'malignant_tumor_of_palate',\n",
-      "               'malignant_tumor_of_neck',\n",
-      "               'right_ventricular_failure',\n",
-      "               'fracture_of_distal_phalanx_of_finger',\n",
-      "               'lumbosacral_stenosis',\n",
-      "               'malignant_neoplasm_of_skin_of_trunk',\n",
-      "               'macular_retinal_edema',\n",
-      "               'premature_menopause',\n",
-      "               'laryngomalacia',\n",
-      "               'cholesteatoma_of_attic',\n",
-      "               'follow-up_encounter',\n",
-      "               'telogen_effluvium',\n",
-      "               'infective_arthritis',\n",
-      "               'acute_and_chronic_cholecystitis',\n",
-      "               'ehlers-danlos_syndrome',\n",
-      "               'generalized_atherosclerosis',\n",
-      "               'squamous_cell_carcinoma_of_skin_of_lower_extremity',\n",
-      "               'acute_osteomyelitis',\n",
-      "               'juvenile_rheumatoid_arthritis',\n",
-      "               'acquired_kyphosis',\n",
-      "               'fall_from_playground_equipment',\n",
-      "               'disorder_of_pinna',\n",
-      "               'saint_louis_encephalitis_virus_infection',\n",
-      "               'deformity',\n",
-      "               'peripheral_vascular_disorder_due_to_diabetes_mellitus',\n",
-      "               'cataract_of_eye_due_to_diabetes_mellitus_type_1',\n",
-      "               'thoracic_radiculopathy',\n",
-      "               'chronic_organic_mental_disorder',\n",
-      "               'cerebral_vasculitis',\n",
-      "               'transformed_migraine',\n",
-      "               'infection_of_amputation_stump',\n",
-      "               'closed_fracture_of_tibial_plateau',\n",
-      "               'congenital_anomaly_of_peripheral_blood_vessel',\n",
-      "               'intentional_poisoning_caused_by_drug',\n",
-      "               'chronic_pain_due_to_injury',\n",
-      "               'increased_cancer_antigen_125',\n",
-      "               'hereditary_thrombophilia',\n",
-      "               'stimulant_abuse',\n",
-      "               'carcinoid_tumor',\n",
-      "               'latent_syphilis',\n",
-      "               'sepsis_caused_by_staphylococcus_aureus',\n",
-      "               'mesenteric_lymphadenitis',\n",
-      "               'cyst_of_eyelid',\n",
-      "               'closed_fracture_of_shaft_of_metacarpal_bone',\n",
-      "               'miliary_tuberculosis',\n",
-      "               'typhoid_fever',\n",
-      "               'intussusception_of_intestine',\n",
-      "               'arterial_retinal_branch_occlusion',\n",
-      "               'myocarditis',\n",
-      "               'disorder_of_anterior_pituitary',\n",
-      "               'arteritis',\n",
-      "               'ileitis',\n",
-      "               'necrotizing_fasciitis',\n",
-      "               'foreign_body_in_conjunctival_sac',\n",
-      "               'derangement_of_posterior_horn_of_medial_meniscus',\n",
-      "               'megaloblastic_anemia',\n",
-      "               'peroneal_tendinitis',\n",
-      "               'simple_phobia',\n",
-      "               'staphylococcal_infectious_disease',\n",
-      "               'viral_meningitis',\n",
-      "               'dribbling_of_urine',\n",
-      "               'disorder_of_magnesium_metabolism',\n",
-      "               'peripheral_retinal_degeneration',\n",
-      "               'orthopnea',\n",
-      "               'tachyarrhythmia',\n",
-      "               'nonobliterative_otosclerosis_involving_oval_window',\n",
-      "               'distorted_body_image',\n",
-      "               'pruritic_rash',\n",
-      "               'inflammation_of_small_intestine',\n",
-      "               'arterial_thrombosis',\n",
-      "               'langerhans_cell_histiocytosis',\n",
-      "               'closed_fracture_of_sternum',\n",
-      "               'latent_syphilis_with_positive_serology',\n",
-      "               'disturbance_of_temperature_regulation_of_newborn',\n",
-      "               'mass_of_retroperitoneal_structure',\n",
-      "               'cholelithiasis_without_obstruction',\n",
-      "               'anophthalmos',\n",
-      "               'inclusion_body_myositis',\n",
-      "               'nodular_degeneration_of_cornea',\n",
-      "               'supraventricular_arrhythmia',\n",
-      "               'prickly_heat',\n",
-      "               'stenosis_of_lacrimal_punctum',\n",
-      "               'opioid_intoxication',\n",
-      "               'placenta_previa_without_hemorrhage',\n",
-      "               'fibromuscular_dysplasia_of_wall_of_artery',\n",
-      "               'thigh_pain',\n",
-      "               'late_effect_of_radiation',\n",
-      "               'congenital_cataract',\n",
-      "               'postgastric_surgery_syndrome',\n",
-      "               'hemarthrosis',\n",
-      "               'parapharyngeal_abscess',\n",
-      "               'acute_suppurative_otitis_media_with_spontaneous_rupture_of_ear_drum',\n",
-      "               'disorder_of_skeletal_system',\n",
-      "               'brain_injury_without_open_intracranial_wound',\n",
-      "               'benign_neoplasm_of_choroid',\n",
-      "               'benign_neoplasm_of_orbit',\n",
-      "               'neurofibromatosis_type_2',\n",
-      "               'chronic_leukemia_disease',\n",
-      "               'dermatitis_of_eyelid',\n",
-      "               'herpes_simplex_keratitis',\n",
-      "               'secondary_malignant_neoplasm_of_bladder',\n",
-      "               'neoplasm_of_uncertain_behavior_of_kidney',\n",
-      "               'neoplasm_of_uncertain_behavior_of_nervous_system',\n",
-      "               'neoplasm_of_uncertain_behavior_of_parotid_gland',\n",
-      "               \"dieulafoy's_vascular_malformation\",\n",
-      "               'myelodysplastic_syndrome:_refractory_anemia_without_ringed_sideroblasts_without_excess_blasts',\n",
-      "               'injury_of_penis',\n",
-      "               'dermatitis_herpetiformis',\n",
-      "               'diastasis_of_muscle',\n",
-      "               'ureteritis',\n",
-      "               'hemolytic_uremic_syndrome',\n",
-      "               'breast_finding',\n",
-      "               'hodgkin_lymphoma_lymphocyte-rich',\n",
-      "               'open_wound_of_toe',\n",
-      "               'neoplasm_of_submaxillary_gland',\n",
-      "               'intertrochanteric_fracture',\n",
-      "               'traumatic_and/or_non-traumatic_brain_injury',\n",
-      "               'disorder_of_cervical_spine',\n",
-      "               'hemifacial_spasm',\n",
-      "               'lymphangitis',\n",
-      "               'measles',\n",
-      "               'urethral_fistula',\n",
-      "               'disorder_of_cornea',\n",
-      "               'polyarteritis_nodosa',\n",
-      "               'acute_pericarditis',\n",
-      "               'history_of_diabetes_mellitus',\n",
-      "               'pneumococcal_infectious_disease',\n",
-      "               'mitochondrial_myopathy',\n",
-      "               'hereditary_coagulation_factor_deficiency',\n",
-      "               'trichotillomania',\n",
-      "               'hypochondriasis',\n",
-      "               'endophthalmitis',\n",
-      "               'congenital_stenosis_of_aortic_valve',\n",
-      "               'late_latent_syphilis',\n",
-      "               'cyclical_vomiting_syndrome',\n",
-      "               'malignant_tumor_of_tail_of_pancreas',\n",
-      "               'cerebral_venous_sinus_thrombosis',\n",
-      "               'drug-induced_immune_thrombocytopenia',\n",
-      "               'stimulus_deprivation_amblyopia',\n",
-      "               'cerebral_infarction_due_to_thrombosis_of_cerebral_arteries',\n",
-      "               'spontaneous_tension_pneumothorax',\n",
-      "               'paroxysmal_nocturnal_hemoglobinuria',\n",
-      "               'monocytosis',\n",
-      "               'atrophy_of_kidney',\n",
-      "               'rhinophyma',\n",
-      "               'pre-existing_type_2_diabetes_mellitus',\n",
-      "               'abnormal_biochemical_finding_on_antenatal_screening_of_mother',\n",
-      "               'congenital_anomaly_of_skin',\n",
-      "               'carbuncle_of_back',\n",
-      "               'abscess_of_face',\n",
-      "               'localized_primary_osteoarthritis_of_the_hand',\n",
-      "               'osteomyelitis_of_vertebra',\n",
-      "               'aneurysmal_bone_cyst',\n",
-      "               'alcoholic_gastritis',\n",
-      "               'congenital_atresia_of_the_pulmonary_valve',\n",
-      "               'hypospadias_penile',\n",
-      "               'hypospadias_balanic',\n",
-      "               'gingivostomatitis',\n",
-      "               'fetal_or_neonatal_effect_of_prolapsed_cord',\n",
-      "               'vertical_heterophoria',\n",
-      "               'psychologic_conversion_disorder',\n",
-      "               'closed_fracture_thoracic_vertebra',\n",
-      "               'abrasion_of_face',\n",
-      "               'osteomyelitis_of_lower_leg',\n",
-      "               'chronic_meningitis',\n",
-      "               'bite_of_nonvenomous_snakes_and_lizards',\n",
-      "               'accident_caused_by_electric_current',\n",
-      "               'perineal_pain',\n",
-      "               'speech_and_language_disorder',\n",
-      "               'conjunctival_foreign_body',\n",
-      "               'chronic_non-suppurative_otitis_media',\n",
-      "               'disorder_of_nasal_cavity',\n",
-      "               'asbestos-induced_pleural_plaque',\n",
-      "               'postoperative_cardiac_complication',\n",
-      "               'carotid_artery_aneurysm',\n",
-      "               'microscopic_colitis',\n",
-      "               'angiodysplasia_of_intestine',\n",
-      "               'malignant_ascites',\n",
-      "               'renal_transplant_rejection',\n",
-      "               'acne_keloid',\n",
-      "               'knee_pyogenic_arthritis',\n",
-      "               'seropositive_rheumatoid_arthritis',\n",
-      "               'enthesopathy_of_foot_region',\n",
-      "               'erythroderma_neonatorum',\n",
-      "               'epstein-barr_virus_disease',\n",
-      "               'rectal_mass',\n",
-      "               'atrophic_vulva',\n",
-      "               'hand_joint_stiff',\n",
-      "               'chiari_malformation',\n",
-      "               'polyp_of_intestine',\n",
-      "               'malignant_neoplasm_of_unknown_origin',\n",
-      "               'traumatic_injury_of_common_peroneal_nerve',\n",
-      "               'rupture_of_muscle',\n",
-      "               'undescended_testes_-_bilateral',\n",
-      "               'malignant_tumor_of_greater_curve_of_stomach',\n",
-      "               'benign_tumor_of_breast',\n",
-      "               'malignant_melanoma_of_head_and_neck',\n",
-      "               'edema_generalized',\n",
-      "               'leukoplakia',\n",
-      "               'open_wound_of_abdominal_wall',\n",
-      "               'superficial_vein_thrombosis',\n",
-      "               'large_cell_anaplastic_lymphoma',\n",
-      "               'pain_in_cervical_spine',\n",
-      "               'clavicle_injury',\n",
-      "               'lesion_of_cervix',\n",
-      "               'lesion_of_external_ear',\n",
-      "               'keratoconjunctivitis_sicca',\n",
-      "               'glossodynia',\n",
-      "               'abscess_of_back',\n",
-      "               'profound_intellectual_disability',\n",
-      "               'dislocation_of_hip_joint_prosthesis',\n",
-      "               'disseminated_idiopathic_skeletal_hyperostosis',\n",
-      "               'multiple_personality_disorder',\n",
-      "               'fracture_of_navicular_bone_of_wrist',\n",
-      "               'facial_spasm',\n",
-      "               'compensatory_emphysema',\n",
-      "               'benign_mucous_membrane_pemphigoid',\n",
-      "               'band-shaped_keratopathy',\n",
-      "               'frostbite_of_foot',\n",
-      "               'congenital_syphilis',\n",
-      "               'chronic_arthritis',\n",
-      "               'ischiorectal_abscess',\n",
-      "               'malignant_tumor_of_anterior_two-thirds_of_tongue',\n",
-      "               'malignant_tumor_of_appendix',\n",
-      "               'malignant_tumor_of_vagina',\n",
-      "               'malignant_tumor_of_renal_pelvis',\n",
-      "               'malignant_tumor_of_urethra',\n",
-      "               'malignant_tumor_of_penis',\n",
-      "               'late_effect_of_fracture_of_lower_extremities',\n",
-      "               'motion_sickness',\n",
-      "               'infection_caused_by_mycobacterium_avium-intracellulare_group',\n",
-      "               \"meckel's_diverticulum\",\n",
-      "               'extrinsic_allergic_alveolitis',\n",
-      "               'acute_hepatitis',\n",
-      "               'contusion_of_abdominal_wall',\n",
-      "               'rumination_disorder',\n",
-      "               'primary_insomnia',\n",
-      "               'alternating_esotropia',\n",
-      "               'thiamine_deficiency',\n",
-      "               'acquired_plantar_keratoderma',\n",
-      "               'delayed_puberty',\n",
-      "               'congenital_vascular_malformation',\n",
-      "               'dermatitis_of_external_ear',\n",
-      "               'partial_thickness_burn',\n",
-      "               'obstruction_of_colon',\n",
-      "               'complex_regional_pain_syndrome_type_ii_upper_limb',\n",
-      "               'adenoviral_pneumonia',\n",
-      "               'acute_follicular_conjunctivitis',\n",
-      "               'anemia_due_to_blood_loss',\n",
-      "               'neonatal_candidiasis',\n",
-      "               'disorder_of_right_cardiac_ventricle',\n",
-      "               'hydatidiform_mole_benign',\n",
-      "               'systolic_heart_failure',\n",
-      "               'benign_neoplasm_of_oral_cavity',\n",
-      "               'alcohol-induced_psychosis',\n",
-      "               'foreign_body_of_neck',\n",
-      "               'chemical_burn',\n",
-      "               'central_pain_syndrome',\n",
-      "               'chronic_hypercapnic_respiratory_failure',\n",
-      "               'mass_of_shoulder_region',\n",
-      "               'disorder_of_tendon_of_biceps',\n",
-      "               'crushing_injury_of_foot',\n",
-      "               'atypical_squamous_cells_of_undetermined_significance_on_vaginal_papanicolaou_smear',\n",
-      "               'nonspecific_tuberculin_test_reaction',\n",
-      "               'flaccid_hemiplegia_of_nondominant_side',\n",
-      "               'mass_of_foot',\n",
-      "               'neoplasm_of_lymphoid_system_structure',\n",
-      "               'sleep_hypoventilation',\n",
-      "               'hypersomnia_disorder_related_to_a_known_organic_factor',\n",
-      "               'edema_of_face',\n",
-      "               'degenerative_joint_disease_of_pelvis',\n",
-      "               'erysipelas',\n",
-      "               'paralysis',\n",
-      "               'contusion_of_lower_leg',\n",
-      "               'anemia_in_mother_complicating_pregnancy_childbirth_and/or_puerperium',\n",
-      "               'tarsal_tunnel_syndrome',\n",
-      "               'chronic_nasopharyngitis',\n",
-      "               'lymphocytopenia',\n",
-      "               'disorder_of_external_ear',\n",
-      "               'pure_red_cell_aplasia',\n",
-      "               'corneal_erosion',\n",
-      "               'crushing_injury_of_hand',\n",
-      "               'developmental_reading_disorder',\n",
-      "               'chorioretinal_scar',\n",
-      "               'posttraumatic_headache',\n",
-      "               'closed_fracture_of_base_of_metacarpal_bone_other_than_first_metacarpal',\n",
-      "               'arterial_embolism',\n",
-      "               'contusion_of_ankle',\n",
-      "               'organic_delusional_disorder',\n",
-      "               'craniosynostosis_syndrome',\n",
-      "               'alternating_exotropia_with_v_pattern',\n",
-      "               'hypersplenism',\n",
-      "               'vaccine_refused_by_patient',\n",
-      "               'disorder_of_conjunctiva',\n",
-      "               'paralytic_lagophthalmos',\n",
-      "               'bowing_deformity_of_lower_limb',\n",
-      "               'pre-existing_type_1_diabetes_mellitus_in_pregnancy',\n",
-      "               'aggressive_behavior',\n",
-      "               'simple_chronic_bronchitis',\n",
-      "               'bundle_branch_block',\n",
-      "               'sedative_abuse',\n",
-      "               'congenital_nystagmus',\n",
-      "               'rectovaginal_fistula',\n",
-      "               'subluxation_of_lens',\n",
-      "               'hiccoughs',\n",
-      "               'closed_fracture_of_femoral_condyle_of_femur',\n",
-      "               'acute-on-chronic_respiratory_failure',\n",
-      "               'tracheostomy_complication',\n",
-      "               'urethral_caruncle',\n",
-      "               'fracture_of_shaft_of_tibia',\n",
-      "               'monoarthritis',\n",
-      "               'mastitis_associated_with_lactation',\n",
-      "               'dissection_of_thoracoabdominal_aorta',\n",
-      "               'precordial_pain',\n",
-      "               'disorder_of_refraction_and/or_accommodation',\n",
-      "               'relapsing_polychondritis',\n",
-      "               'bullous_dermatosis',\n",
-      "               'recurrent_ulcer_of_mouth',\n",
-      "               'recurrent_pterygium',\n",
-      "               'enteritis_caused_by_radiation',\n",
-      "               'mucopurulent_chronic_bronchitis',\n",
-      "               'crushing_injury_of_toe',\n",
-      "               'classical_phenylketonuria',\n",
-      "               'congenital_dislocation_of_left_hip',\n",
-      "               'congenital_dislocation_of_right_hip',\n",
-      "               'tetanus',\n",
-      "               'adhesive_middle_ear_disease',\n",
-      "               'spina_bifida_of_lumbar_region',\n",
-      "               'erb-duchenne_paralysis',\n",
-      "               'enophthalmos',\n",
-      "               'sleep-wake_schedule_disorder_delayed_phase_type',\n",
-      "               'atrophic_gastritis',\n",
-      "               'schizoaffective_disorder_depressive_type',\n",
-      "               'epiphora_due_to_insufficient_drainage',\n",
-      "               'ulceration_of_intestine',\n",
-      "               'hypertensive_heart_and_renal_disease',\n",
-      "               'traumatic_urethral_stricture',\n",
-      "               'cholesteatoma_of_middle_ear',\n",
-      "               'acquired_deformity_of_ankle_and/or_foot',\n",
-      "               'chronic_congestive_heart_failure',\n",
-      "               'edema_of_eyelid',\n",
-      "               'vesicovaginal_fistula',\n",
-      "               'pneumothorax_due_to_trauma',\n",
-      "               'burning_sensation',\n",
-      "               'benign_neoplasm_of_skin_of_lip',\n",
-      "               'benign_neoplasm_of_soft_tissues_of_lower_limb',\n",
-      "               'malignant_lymphoma_of_lymph_nodes_of_head_face_and/or_neck',\n",
-      "               'primary_malignant_neoplasm_of_prostate',\n",
-      "               'secondary_malignant_neoplasm_of_central_nervous_system',\n",
-      "               'secondary_malignant_neoplasm_of_intrathoracic_lymph_nodes',\n",
-      "               'secondary_malignant_neoplasm_of_mediastinum',\n",
-      "               'secondary_malignant_neoplasm_of_retroperitoneum',\n",
-      "               \"kaposi's_sarcoma\",\n",
-      "               'hepatoblastoma',\n",
-      "               \"follicular_non-hodgkin's_lymphoma_large_cell\",\n",
-      "               'organic_mental_disorder',\n",
-      "               \"fuchs'_heterochromic_cyclitis\",\n",
-      "               'necrotizing_vasculitis',\n",
-      "               'mycosis_fungoides',\n",
-      "               'disorder_of_neck',\n",
-      "               'crowding_of_teeth',\n",
-      "               'cholangiectasis',\n",
-      "               'injury_of_forearm',\n",
-      "               'crushing_injury',\n",
-      "               'neoplasm_of_stomach',\n",
-      "               'neoplasm_of_thyroid_gland',\n",
-      "               'eye_infection',\n",
-      "               'infectious_disorder_of_kidney',\n",
-      "               'bladder_pain',\n",
-      "               'family_history:_gastrointestinal_disease',\n",
-      "               'family_history:_congenital_anomaly',\n",
-      "               'chronic_disease_of_tonsils_and/or_adenoids',\n",
-      "               'abnormal_defecation',\n",
-      "               'acquired_deformity_of_nose',\n",
-      "               'disorder_of_appendix',\n",
-      "               'ethmoidal_sinusitis',\n",
-      "               'malignant_tumor_of_cardia',\n",
-      "               'malignant_tumor_of_pancreatic_duct',\n",
-      "               'malignant_melanoma_of_ear_and/or_external_auditory_canal',\n",
-      "               'malignant_tumor_of_vault_of_bladder',\n",
-      "               'corticobasal_degeneration',\n",
-      "               'type_i_diabetes_mellitus_with_ulcer',\n",
-      "               'lesion_of_radial_nerve',\n",
-      "               'recurrent_acute_tonsillitis',\n",
-      "               'rectal_abscess',\n",
-      "               'café_au_lait_spot',\n",
-      "               'systemic_onset_juvenile_chronic_arthritis',\n",
-      "               'recurrent_subluxation_of_the_patella',\n",
-      "               'spasm_of_back_muscles',\n",
-      "               'congenital_pigmentary_skin_anomalies',\n",
-      "               'tricuspid_valve_disorder',\n",
-      "               'fracture_of_orbital_floor',\n",
-      "               'infestation_caused_by_pediculus',\n",
-      "               'chronic_glomerulonephritis',\n",
-      "               'sexual_abuse',\n",
-      "               'retractile_testis',\n",
-      "               'spastic_cerebral_palsy',\n",
-      "               'giant_papillary_conjunctivitis',\n",
-      "               'thrombophilia',\n",
-      "               'obstruction_of_biliary_tree',\n",
-      "               'chronic_pericarditis',\n",
-      "               'vulvodynia',\n",
-      "               'contracture_of_wrist_joint',\n",
-      "               'sprain_of_interphalangeal_joint_of_toe',\n",
-      "               'knee_stiff',\n",
-      "               'hip_stiff',\n",
-      "               'peripheral_neuralgia',\n",
-      "               'squamous_cell_carcinoma_of_lip',\n",
-      "               'laceration_of_liver',\n",
-      "               'fracture_of_shaft_of_radius_and/or_ulna',\n",
-      "               'slipped_upper_femoral_epiphysis',\n",
-      "               'ventricular_hypertrophy',\n",
-      "               'dilatation_of_aorta',\n",
-      "               'history_of_gastrointestinal_disease',\n",
-      "               'excessive_eating_-_polyphagia',\n",
-      "               'thoracic_spondylosis_without_myelopathy',\n",
-      "               'chorea',\n",
-      "               'electroencephalogram_abnormal',\n",
-      "               'orchitis',\n",
-      "               'injury_of_nail',\n",
-      "               'history_of_heart_disorder',\n",
-      "               'pertussis',\n",
-      "               'anterior_cord_syndrome',\n",
-      "               'synovitis/tenosynovitis_-_knee',\n",
-      "               'elbow_joint_unstable',\n",
-      "               'deformity_of_hip_joint',\n",
-      "               'lesion_of_testis',\n",
-      "               'lesion_of_vocal_cord',\n",
-      "               'congenital_deformity_of_foot',\n",
-      "               'infective_discitis',\n",
-      "               'decreased_range_of_knee_movement',\n",
-      "               \"behcet's_syndrome\",\n",
-      "               'foreign_body_in_vagina',\n",
-      "               'mixed_bipolar_i_disorder_in_remission',\n",
-      "               'aneurysm_of_renal_artery',\n",
-      "               'malignant_tumor_of_floor_of_mouth',\n",
-      "               'malignant_tumor_of_spinal_cord',\n",
-      "               'malignant_tumor_of_mediastinum',\n",
-      "               'malignant_tumor_of_face',\n",
-      "               'guttate_psoriasis',\n",
-      "               'adverse_reaction_caused_by_food',\n",
-      "               'disorder_of_endocrine_ovary',\n",
-      "               'congenital_pelviureteric_junction_obstruction',\n",
-      "               'avoidant_personality_disorder',\n",
-      "               'osteoarthrosis_of_the_carpometacarpal_joint_of_the_thumb',\n",
-      "               'contusion_of_forearm',\n",
-      "               'pseudofolliculitis_barbae',\n",
-      "               'erythroderma',\n",
-      "               'congenital_malformation_syndrome',\n",
-      "               'lymphangioma',\n",
-      "               'basal_cell_carcinoma_of_lower_extremity',\n",
-      "               'squamous_cell_carcinoma_of_skin_of_trunk',\n",
-      "               'widespread_metastatic_malignant_neoplastic_disease',\n",
-      "               'complex_regional_pain_syndrome_type_ii_lower_limb',\n",
-      "               'subluxation_of_radial_head',\n",
-      "               'narrow_angle',\n",
-      "               'gangrene_due_to_type_2_diabetes_mellitus',\n",
-      "               'chronic_ulcer_of_ankle',\n",
-      "               'tubulointerstitial_nephritis',\n",
-      "               'anemia_caused_by_medication',\n",
-      "               'history_of_malignant_neoplasm_of_ureter',\n",
-      "               'posterior_uveitis',\n",
-      "               'cataract_of_eye_due_to_diabetes_mellitus',\n",
-      "               'arthritis_of_elbow',\n",
-      "               'premature_ejaculation',\n",
-      "               'stress_fracture_of_tibia',\n",
-      "               'hypertensive_urgency',\n",
-      "               'cellulitis_of_buttock',\n",
-      "               'acute_exacerbation_of_bronchiectasis',\n",
-      "               'nondiabetic_proliferative_retinopathy',\n",
-      "               'cellulitis_of_trunk',\n",
-      "               'congenital_hydrocephalus',\n",
-      "               'disorder_of_diaphragm',\n",
-      "               'dermatitis_caused_by_radiation',\n",
-      "               'congenital_anomaly_of_skull',\n",
-      "               'acquired_genu_valgum',\n",
-      "               'stiff-man_syndrome',\n",
-      "               'chronic_ulcerative_rectosigmoiditis',\n",
-      "               'articular_cartilage_disorder',\n",
-      "               'injury_to_brachial_plexus_as_birth_trauma',\n",
-      "               'partial_thickness_burn_of_hand',\n",
-      "               'disorder_of_pericardium',\n",
-      "               'localized_amyloidosis',\n",
-      "               'traumatic_dislocation_of_knee_joint',\n",
-      "               \"huntington's_chorea\",\n",
-      "               'pseudohypoparathyroidism',\n",
-      "               'contusion_of_nose',\n",
-      "               'abscess_of_neck',\n",
-      "               'glaucoma_associated_with_ocular_trauma',\n",
-      "               'capsulitis',\n",
-      "               'congenital_anomaly_of_esophagus',\n",
-      "               'decrease_in_height',\n",
-      "               'urgent_desire_for_stool',\n",
-      "               'generalized_seborrheic_dermatitis_of_infants',\n",
-      "               'congenital_subaortic_stenosis',\n",
-      "               'thickening_of_pleura',\n",
-      "               'central_corneal_ulcer',\n",
-      "               'congenital_disorder_due_to_abnormality_of_chromosome_number_or_structure',\n",
-      "               'pauciarticular_juvenile_rheumatoid_arthritis',\n",
-      "               'double_outlet_right_ventricle',\n",
-      "               'congenital_anomaly_of_spine',\n",
-      "               'agenesis_of_left_kidney',\n",
-      "               'agenesis_of_right_kidney',\n",
-      "               'extrapyramidal_disease',\n",
-      "               'neurotrophic_keratoconjunctivitis',\n",
-      "               'disorder_of_optic_nerve',\n",
-      "               'congenital_biliary_atresia',\n",
-      "               'multiple_cranial_nerve_palsy',\n",
-      "               'contracture_of_joint',\n",
-      "               \"tailor's_bunion\",\n",
-      "               'mesangiocapillary_glomerulonephritis',\n",
-      "               'prolapsed_internal_hemorrhoids',\n",
-      "               'sleep_walking_disorder',\n",
-      "               'cyst_of_nasal_sinus',\n",
-      "               'bacterial_infectious_disease',\n",
-      "               'congenital_anomaly_of_nervous_system',\n",
-      "               'sleep_terror_disorder',\n",
-      "               'acute_frontal_sinusitis',\n",
-      "               'benign_neoplasm_of_pancreas',\n",
-      "               'primary_malignant_neoplasm_of_soft_tissues_of_lower_limb',\n",
-      "               'neoplasm_of_uncertain_behavior_of_neck',\n",
-      "               'injury_of_peroneal_nerve',\n",
-      "               \"dupuytren's_disease\",\n",
-      "               'stem_cell_donor',\n",
-      "               'endemic_goiter',\n",
-      "               'diplegic_cerebral_palsy'],\n",
-      " 'diagnoses_emb': ['node2vec_available',\n",
-      "                   'node2vec_0',\n",
-      "                   'node2vec_1',\n",
-      "                   'node2vec_2',\n",
-      "                   'node2vec_3',\n",
-      "                   'node2vec_4',\n",
-      "                   'node2vec_5',\n",
-      "                   'node2vec_6',\n",
-      "                   'node2vec_7',\n",
-      "                   'node2vec_8',\n",
-      "                   'node2vec_9',\n",
-      "                   'node2vec_10',\n",
-      "                   'node2vec_11',\n",
-      "                   'node2vec_12',\n",
-      "                   'node2vec_13',\n",
-      "                   'node2vec_14',\n",
-      "                   'node2vec_15',\n",
-      "                   'node2vec_16',\n",
-      "                   'node2vec_17',\n",
-      "                   'node2vec_18',\n",
-      "                   'node2vec_19',\n",
-      "                   'node2vec_20',\n",
-      "                   'node2vec_21',\n",
-      "                   'node2vec_22',\n",
-      "                   'node2vec_23',\n",
-      "                   'node2vec_24',\n",
-      "                   'node2vec_25',\n",
-      "                   'node2vec_26',\n",
-      "                   'node2vec_27',\n",
-      "                   'node2vec_28',\n",
-      "                   'node2vec_29',\n",
-      "                   'node2vec_30',\n",
-      "                   'node2vec_31',\n",
-      "                   'node2vec_32',\n",
-      "                   'node2vec_33',\n",
-      "                   'node2vec_34',\n",
-      "                   'node2vec_35',\n",
-      "                   'node2vec_36',\n",
-      "                   'node2vec_37',\n",
-      "                   'node2vec_38',\n",
-      "                   'node2vec_39',\n",
-      "                   'node2vec_40',\n",
-      "                   'node2vec_41',\n",
-      "                   'node2vec_42',\n",
-      "                   'node2vec_43',\n",
-      "                   'node2vec_44',\n",
-      "                   'node2vec_45',\n",
-      "                   'node2vec_46',\n",
-      "                   'node2vec_47',\n",
-      "                   'node2vec_48',\n",
-      "                   'node2vec_49',\n",
-      "                   'node2vec_50',\n",
-      "                   'node2vec_51',\n",
-      "                   'node2vec_52',\n",
-      "                   'node2vec_53',\n",
-      "                   'node2vec_54',\n",
-      "                   'node2vec_55',\n",
-      "                   'node2vec_56',\n",
-      "                   'node2vec_57',\n",
-      "                   'node2vec_58',\n",
-      "                   'node2vec_59',\n",
-      "                   'node2vec_60',\n",
-      "                   'node2vec_61',\n",
-      "                   'node2vec_62',\n",
-      "                   'node2vec_63',\n",
-      "                   'node2vec_64',\n",
-      "                   'node2vec_65',\n",
-      "                   'node2vec_66',\n",
-      "                   'node2vec_67',\n",
-      "                   'node2vec_68',\n",
-      "                   'node2vec_69',\n",
-      "                   'node2vec_70',\n",
-      "                   'node2vec_71',\n",
-      "                   'node2vec_72',\n",
-      "                   'node2vec_73',\n",
-      "                   'node2vec_74',\n",
-      "                   'node2vec_75',\n",
-      "                   'node2vec_76',\n",
-      "                   'node2vec_77',\n",
-      "                   'node2vec_78',\n",
-      "                   'node2vec_79',\n",
-      "                   'node2vec_80',\n",
-      "                   'node2vec_81',\n",
-      "                   'node2vec_82',\n",
-      "                   'node2vec_83',\n",
-      "                   'node2vec_84',\n",
-      "                   'node2vec_85',\n",
-      "                   'node2vec_86',\n",
-      "                   'node2vec_87',\n",
-      "                   'node2vec_88',\n",
-      "                   'node2vec_89',\n",
-      "                   'node2vec_90',\n",
-      "                   'node2vec_91',\n",
-      "                   'node2vec_92',\n",
-      "                   'node2vec_93',\n",
-      "                   'node2vec_94',\n",
-      "                   'node2vec_95',\n",
-      "                   'node2vec_96',\n",
-      "                   'node2vec_97',\n",
-      "                   'node2vec_98',\n",
-      "                   'node2vec_99',\n",
-      "                   'node2vec_100',\n",
-      "                   'node2vec_101',\n",
-      "                   'node2vec_102',\n",
-      "                   'node2vec_103',\n",
-      "                   'node2vec_104',\n",
-      "                   'node2vec_105',\n",
-      "                   'node2vec_106',\n",
-      "                   'node2vec_107',\n",
-      "                   'node2vec_108',\n",
-      "                   'node2vec_109',\n",
-      "                   'node2vec_110',\n",
-      "                   'node2vec_111',\n",
-      "                   'node2vec_112',\n",
-      "                   'node2vec_113',\n",
-      "                   'node2vec_114',\n",
-      "                   'node2vec_115',\n",
-      "                   'node2vec_116',\n",
-      "                   'node2vec_117',\n",
-      "                   'node2vec_118',\n",
-      "                   'node2vec_119',\n",
-      "                   'node2vec_120',\n",
-      "                   'node2vec_121',\n",
-      "                   'node2vec_122',\n",
-      "                   'node2vec_123',\n",
-      "                   'node2vec_124',\n",
-      "                   'node2vec_125',\n",
-      "                   'node2vec_126',\n",
-      "                   'node2vec_127',\n",
-      "                   'node2vec_128',\n",
-      "                   'node2vec_129',\n",
-      "                   'node2vec_130',\n",
-      "                   'node2vec_131',\n",
-      "                   'node2vec_132',\n",
-      "                   'node2vec_133',\n",
-      "                   'node2vec_134',\n",
-      "                   'node2vec_135',\n",
-      "                   'node2vec_136',\n",
-      "                   'node2vec_137',\n",
-      "                   'node2vec_138',\n",
-      "                   'node2vec_139',\n",
-      "                   'node2vec_140',\n",
-      "                   'node2vec_141',\n",
-      "                   'node2vec_142',\n",
-      "                   'node2vec_143',\n",
-      "                   'node2vec_144',\n",
-      "                   'node2vec_145',\n",
-      "                   'node2vec_146',\n",
-      "                   'node2vec_147',\n",
-      "                   'node2vec_148',\n",
-      "                   'node2vec_149',\n",
-      "                   'node2vec_150',\n",
-      "                   'node2vec_151',\n",
-      "                   'node2vec_152',\n",
-      "                   'node2vec_153',\n",
-      "                   'node2vec_154',\n",
-      "                   'node2vec_155',\n",
-      "                   'node2vec_156',\n",
-      "                   'node2vec_157',\n",
-      "                   'node2vec_158',\n",
-      "                   'node2vec_159',\n",
-      "                   'node2vec_160',\n",
-      "                   'node2vec_161',\n",
-      "                   'node2vec_162',\n",
-      "                   'node2vec_163',\n",
-      "                   'node2vec_164',\n",
-      "                   'node2vec_165',\n",
-      "                   'node2vec_166',\n",
-      "                   'node2vec_167',\n",
-      "                   'node2vec_168',\n",
-      "                   'node2vec_169',\n",
-      "                   'node2vec_170',\n",
-      "                   'node2vec_171',\n",
-      "                   'node2vec_172',\n",
-      "                   'node2vec_173',\n",
-      "                   'node2vec_174',\n",
-      "                   'node2vec_175',\n",
-      "                   'node2vec_176',\n",
-      "                   'node2vec_177',\n",
-      "                   'node2vec_178',\n",
-      "                   'node2vec_179',\n",
-      "                   'node2vec_180',\n",
-      "                   'node2vec_181',\n",
-      "                   'node2vec_182',\n",
-      "                   'node2vec_183',\n",
-      "                   'node2vec_184',\n",
-      "                   'node2vec_185',\n",
-      "                   'node2vec_186',\n",
-      "                   'node2vec_187',\n",
-      "                   'node2vec_188',\n",
-      "                   'node2vec_189',\n",
-      "                   'node2vec_190',\n",
-      "                   'node2vec_191',\n",
-      "                   'node2vec_192',\n",
-      "                   'node2vec_193',\n",
-      "                   'node2vec_194',\n",
-      "                   'node2vec_195',\n",
-      "                   'node2vec_196',\n",
-      "                   'node2vec_197',\n",
-      "                   'node2vec_198',\n",
-      "                   'node2vec_199'],\n",
-      " 'family_history': [\"fh_alzheimer's_disease/dementia\",\n",
-      "                    'fh_bowel_cancer',\n",
-      "                    'fh_breast_cancer',\n",
-      "                    'fh_chronic_bronchitis/emphysema',\n",
-      "                    'fh_diabetes',\n",
-      "                    'fh_heart_disease',\n",
-      "                    'fh_high_blood_pressure',\n",
-      "                    'fh_lung_cancer',\n",
-      "                    \"fh_parkinson's_disease\",\n",
-      "                    'fh_severe_depression',\n",
-      "                    'fh_stroke'],\n",
-      " 'labs': ['basophill_count',\n",
-      "          'basophill_percentage',\n",
-      "          'eosinophill_count',\n",
-      "          'eosinophill_percentage',\n",
-      "          'haematocrit_percentage',\n",
-      "          'haemoglobin_concentration',\n",
-      "          'high_light_scatter_reticulocyte_count',\n",
-      "          'high_light_scatter_reticulocyte_percentage',\n",
-      "          'immature_reticulocyte_fraction',\n",
-      "          'lymphocyte_count',\n",
-      "          'lymphocyte_percentage',\n",
-      "          'mean_corpuscular_haemoglobin',\n",
-      "          'mean_corpuscular_haemoglobin_concentration',\n",
-      "          'mean_corpuscular_volume',\n",
-      "          'mean_platelet_thrombocyte_volume',\n",
-      "          'mean_reticulocyte_volume',\n",
-      "          'mean_sphered_cell_volume',\n",
-      "          'monocyte_count',\n",
-      "          'monocyte_percentage',\n",
-      "          'neutrophill_count',\n",
-      "          'neutrophill_percentage',\n",
-      "          'nucleated_red_blood_cell_count',\n",
-      "          'nucleated_red_blood_cell_percentage',\n",
-      "          'platelet_count',\n",
-      "          'platelet_crit',\n",
-      "          'platelet_distribution_width',\n",
-      "          'red_blood_cell_erythrocyte_count',\n",
-      "          'red_blood_cell_erythrocyte_distribution_width',\n",
-      "          'reticulocyte_count',\n",
-      "          'reticulocyte_percentage',\n",
-      "          'white_blood_cell_leukocyte_count',\n",
-      "          'alanine_aminotransferase',\n",
-      "          'albumin',\n",
-      "          'alkaline_phosphatase',\n",
-      "          'apolipoprotein_a',\n",
-      "          'apolipoprotein_b',\n",
-      "          'aspartate_aminotransferase',\n",
-      "          'creactive_protein',\n",
-      "          'calcium',\n",
-      "          'cholesterol',\n",
-      "          'creatinine',\n",
-      "          'cystatin_c',\n",
-      "          'direct_bilirubin',\n",
-      "          'gamma_glutamyltransferase',\n",
-      "          'glucose',\n",
-      "          'glycated_haemoglobin_hba1c',\n",
-      "          'hdl_cholesterol',\n",
-      "          'igf1',\n",
-      "          'ldl_direct',\n",
-      "          'lipoprotein_a',\n",
-      "          'oestradiol',\n",
-      "          'phosphate',\n",
-      "          'rheumatoid_factor',\n",
-      "          'shbg',\n",
-      "          'testosterone',\n",
-      "          'total_bilirubin',\n",
-      "          'total_protein',\n",
-      "          'triglycerides',\n",
-      "          'urate',\n",
-      "          'urea',\n",
-      "          'vitamin_d'],\n",
-      " 'measurements': ['body_mass_index_bmi',\n",
-      "                  'weight',\n",
-      "                  'pulse_wave_arterial_stiffness_index',\n",
-      "                  'pulse_wave_reflection_index',\n",
-      "                  'waist_circumference',\n",
-      "                  'hip_circumference',\n",
-      "                  'standing_height',\n",
-      "                  'trunk_fat_percentage',\n",
-      "                  'body_fat_percentage',\n",
-      "                  'basal_metabolic_rate',\n",
-      "                  'forced_vital_capacity_fvc_best_measure',\n",
-      "                  'forced_expiratory_volume_in_1second_fev1_best_measure',\n",
-      "                  'fev1_fvc_ratio_zscore',\n",
-      "                  'peak_expiratory_flow_pef_f3064_0_2',\n",
-      "                  'peak_expiratory_flow_pef_f3064_0_1',\n",
-      "                  'peak_expiratory_flow_pef',\n",
-      "                  'systolic_blood_pressure',\n",
-      "                  'diastolic_blood_pressure',\n",
-      "                  'pulse_rate'],\n",
-      " 'medications': ['stomatological_preparations',\n",
-      "                 'drugs_for_acid_related_disorders',\n",
-      "                 'drugs_for_functional_gastrointestinal_disorders',\n",
-      "                 'antiemetics_and_antinauseants',\n",
-      "                 'bile_and_liver_therapy',\n",
-      "                 'drugs_for_constipation',\n",
-      "                 'antidiarrheals,_intestinal_antiinflammatory/antiinfective_agents',\n",
-      "                 'antiobesity_preparations,_excl._diet_products',\n",
-      "                 'digestives,_incl._enzymes',\n",
-      "                 'drugs_used_in_diabetes',\n",
-      "                 'vitamins',\n",
-      "                 'mineral_supplements',\n",
-      "                 'tonics',\n",
-      "                 'anabolic_agents_for_systemic_use',\n",
-      "                 'appetite_stimulants',\n",
-      "                 'other_alimentary_tract_and_metabolism_products',\n",
-      "                 'antithrombotic_agents',\n",
-      "                 'antihemorrhagics',\n",
-      "                 'antianemic_preparations',\n",
-      "                 'blood_substitutes_and_perfusion_solutions',\n",
-      "                 'other_hematological_agents',\n",
-      "                 'cardiac_therapy',\n",
-      "                 'antihypertensives',\n",
-      "                 'diuretics',\n",
-      "                 'peripheral_vasodilators',\n",
-      "                 'vasoprotectives',\n",
-      "                 'beta_blocking_agents',\n",
-      "                 'calcium_channel_blockers',\n",
-      "                 'agents_acting_on_the_renin-angiotensin_system',\n",
-      "                 'lipid_modifying_agents',\n",
-      "                 'antifungals_for_dermatological_use',\n",
-      "                 'emollients_and_protectives',\n",
-      "                 'preparations_for_treatment_of_wounds_and_ulcers',\n",
-      "                 'antipruritics,_incl._antihistamines,_anesthetics,_etc.',\n",
-      "                 'antipsoriatics',\n",
-      "                 'antibiotics_and_chemotherapeutics_for_dermatological_use',\n",
-      "                 'corticosteroids,_dermatological_preparations',\n",
-      "                 'antiseptics_and_disinfectants',\n",
-      "                 'medicated_dressings',\n",
-      "                 'anti-acne_preparations',\n",
-      "                 'other_dermatological_preparations',\n",
-      "                 'gynecological_antiinfectives_and_antiseptics',\n",
-      "                 'other_gynecologicals',\n",
-      "                 'sex_hormones_and_modulators_of_the_genital_system',\n",
-      "                 'urologicals',\n",
-      "                 'pituitary_and_hypothalamic_hormones_and_analogues',\n",
-      "                 'corticosteroids_for_systemic_use',\n",
-      "                 'thyroid_therapy',\n",
-      "                 'pancreatic_hormones',\n",
-      "                 'calcium_homeostasis',\n",
-      "                 'antibacterials_for_systemic_use',\n",
-      "                 'antimycotics_for_systemic_use',\n",
-      "                 'antimycobacterials',\n",
-      "                 'antivirals_for_systemic_use',\n",
-      "                 'immune_sera_and_immunoglobulins',\n",
-      "                 'vaccines',\n",
-      "                 'antineoplastic_agents',\n",
-      "                 'endocrine_therapy',\n",
-      "                 'immunostimulants',\n",
-      "                 'immunosuppressants',\n",
-      "                 'antiinflammatory_and_antirheumatic_products',\n",
-      "                 'topical_products_for_joint_and_muscular_pain',\n",
-      "                 'muscle_relaxants',\n",
-      "                 'antigout_preparations',\n",
-      "                 'drugs_for_treatment_of_bone_diseases',\n",
-      "                 'other_drugs_for_disorders_of_the_musculo-skeletal_system',\n",
-      "                 'anesthetics',\n",
-      "                 'analgesics',\n",
-      "                 'antiepileptics',\n",
-      "                 'anti-parkinson_drugs',\n",
-      "                 'psycholeptics',\n",
-      "                 'psychoanaleptics',\n",
-      "                 'other_nervous_system_drugs',\n",
-      "                 'antiprotozoals',\n",
-      "                 'anthelmintics',\n",
-      "                 'ectoparasiticides,_incl._scabicides,_insecticides_and_repellents',\n",
-      "                 'nasal_preparations',\n",
-      "                 'throat_preparations',\n",
-      "                 'drugs_for_obstructive_airway_diseases',\n",
-      "                 'cough_and_cold_preparations',\n",
-      "                 'antihistamines_for_systemic_use',\n",
-      "                 'other_respiratory_system_products',\n",
-      "                 'ophthalmologicals',\n",
-      "                 'otologicals',\n",
-      "                 'ophthalmological_and_otological_preparations',\n",
-      "                 'allergens',\n",
-      "                 'all_other_therapeutic_products',\n",
-      "                 'diagnostic_agents',\n",
-      "                 'general_nutrients',\n",
-      "                 'all_other_non-therapeutic_products',\n",
-      "                 'contrast_media',\n",
-      "                 'diagnostic_radiopharmaceuticals',\n",
-      "                 'therapeutic_radiopharmaceuticals',\n",
-      "                 'surgical_dressings',\n",
-      "                 'statins',\n",
-      "                 'ass',\n",
-      "                 'atypical_antipsychotics',\n",
-      "                 'glucocorticoids'],\n",
-      " 'questionnaire': ['overall_health_rating_0.0',\n",
-      "                   'overall_health_rating_1.0',\n",
-      "                   'overall_health_rating_2.0',\n",
-      "                   'overall_health_rating_3.0',\n",
-      "                   'smoking_status_0.0',\n",
-      "                   'smoking_status_1.0',\n",
-      "                   'smoking_status_2.0',\n",
-      "                   'alcohol_intake_frequency_0.0',\n",
-      "                   'alcohol_intake_frequency_1.0',\n",
-      "                   'alcohol_intake_frequency_2.0',\n",
-      "                   'alcohol_intake_frequency_3.0',\n",
-      "                   'alcohol_intake_frequency_4.0',\n",
-      "                   'alcohol_intake_frequency_5.0',\n",
-      "                   'overall_health_rating',\n",
-      "                   'smoking_status',\n",
-      "                   'alcohol_intake_frequency']}\n"
-     ]
-    }
-   ],
-   "source": [
-    "isTarget=False\n",
-    "feature_dict = {}\n",
-    "for group in list(data.var[data.var.isTarget==isTarget].based_on.unique()):\n",
-    "    group_features = [x for x in list(data.var[(data.var.isTarget==isTarget) & (data.var.based_on == group)].index) if \"_scaled\" not in x]\n",
-    "    feature_dict[group] = group_features\n",
-    "pprint(feature_dict)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
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-       "        text-align: right;\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>dtype</th>\n",
-       "      <th>isTarget</th>\n",
-       "      <th>based_on</th>\n",
-       "      <th>aggr_fn</th>\n",
-       "      <th>recoding</th>\n",
-       "      <th>NewLevels</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>PGS000011</th>\n",
-       "      <td>numeric</td>\n",
-       "      <td>False</td>\n",
-       "      <td>PGS</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>{}</td>\n",
-       "      <td></td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>PGS000013</th>\n",
-       "      <td>numeric</td>\n",
-       "      <td>False</td>\n",
-       "      <td>PGS</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>{}</td>\n",
-       "      <td></td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>PGS000016</th>\n",
-       "      <td>numeric</td>\n",
-       "      <td>False</td>\n",
-       "      <td>PGS</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>{}</td>\n",
-       "      <td></td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>PGS000018</th>\n",
-       "      <td>numeric</td>\n",
-       "      <td>False</td>\n",
-       "      <td>PGS</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>{}</td>\n",
-       "      <td></td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>PGS000039</th>\n",
-       "      <td>numeric</td>\n",
-       "      <td>False</td>\n",
-       "      <td>PGS</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>{}</td>\n",
-       "      <td></td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>score_SCORE</th>\n",
-       "      <td>numeric</td>\n",
-       "      <td>True</td>\n",
-       "      <td>nan</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>{}</td>\n",
-       "      <td></td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>score_ASCVD</th>\n",
-       "      <td>numeric</td>\n",
-       "      <td>True</td>\n",
-       "      <td>nan</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>{}</td>\n",
-       "      <td></td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>score_QRISK3</th>\n",
-       "      <td>numeric</td>\n",
-       "      <td>True</td>\n",
-       "      <td>nan</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>{}</td>\n",
-       "      <td></td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>score_COX_MACE_clinical</th>\n",
-       "      <td>numeric</td>\n",
-       "      <td>True</td>\n",
-       "      <td>nan</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>{}</td>\n",
-       "      <td></td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>score_COX_MACE_clinical_with_pgs</th>\n",
-       "      <td>numeric</td>\n",
-       "      <td>True</td>\n",
-       "      <td>nan</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>{}</td>\n",
-       "      <td></td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>4073 rows × 6 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                    dtype  isTarget based_on  aggr_fn  \\\n",
-       "PGS000011                         numeric     False      PGS      NaN   \n",
-       "PGS000013                         numeric     False      PGS      NaN   \n",
-       "PGS000016                         numeric     False      PGS      NaN   \n",
-       "PGS000018                         numeric     False      PGS      NaN   \n",
-       "PGS000039                         numeric     False      PGS      NaN   \n",
-       "...                                   ...       ...      ...      ...   \n",
-       "score_SCORE                       numeric      True      nan      NaN   \n",
-       "score_ASCVD                       numeric      True      nan      NaN   \n",
-       "score_QRISK3                      numeric      True      nan      NaN   \n",
-       "score_COX_MACE_clinical           numeric      True      nan      NaN   \n",
-       "score_COX_MACE_clinical_with_pgs  numeric      True      nan      NaN   \n",
-       "\n",
-       "                                 recoding NewLevels  \n",
-       "PGS000011                              {}            \n",
-       "PGS000013                              {}            \n",
-       "PGS000016                              {}            \n",
-       "PGS000018                              {}            \n",
-       "PGS000039                              {}            \n",
-       "...                                   ...       ...  \n",
-       "score_SCORE                            {}            \n",
-       "score_ASCVD                            {}            \n",
-       "score_QRISK3                           {}            \n",
-       "score_COX_MACE_clinical                {}            \n",
-       "score_COX_MACE_clinical_with_pgs       {}            \n",
-       "\n",
-       "[4073 rows x 6 columns]"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data.var"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "test = [\n",
-    "                'age_at_recruitment', 'standing_height', 'townsend_deprivation_index_at_recruitment',\n",
-    "                'diastolic_blood_pressure_automated_reading', 'systolic_blood_pressure_automated_reading',\n",
-    "                'body_mass_index_bmi','weight', 'cholesterol', 'hdl_cholesterol', 'ldl_direct', 'triglycerides',\n",
-    "                'overall_health_rating_0.0', 'overall_health_rating_1.0', 'overall_health_rating_2.0',\n",
-    "                'overall_health_rating_3.0', 'smoking_status_0.0', 'smoking_status_1.0', 'smoking_status_2.0',\n",
-    "                'ethnic_background_0.0', 'ethnic_background_1.0', 'ethnic_background_2.0', 'ethnic_background_3.0',\n",
-    "                'ethnic_background_4.0', 'sex', 'coronary_heart_disease', 'myocardial_infarction', 'stroke',\n",
-    "                'diabetes1', 'diabetes2', 'chronic_kidney_disease', 'atrial_fibrillation', 'migraine',\n",
-    "                'rheumatoid_arthritis', 'systemic_lupus_erythematosus', 'severe_mental_illness', 'erectile_dysfunction',\n",
-    "                'antihypertensives', 'statins', 'ass', 'atypical_antipsychotics', 'glucocorticoids']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "41"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(test)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'diagnoses': ['hypoglycemic_event_due_to_diabetes'],\n",
-      " 'endpoints_death': ['death_allcause_event',\n",
-      "                     'death_allcause_event_time',\n",
-      "                     'death_cvd_event',\n",
-      "                     'death_cvd_event_time'],\n",
-      " 'endpoints_hospital': ['myocardial_infarction_event',\n",
-      "                        'myocardial_infarction_event_time',\n",
-      "                        'stroke_event',\n",
-      "                        'stroke_event_time',\n",
-      "                        'cancer_breast_event',\n",
-      "                        'cancer_breast_event_time',\n",
-      "                        'diabetes_event',\n",
-      "                        'diabetes_event_time',\n",
-      "                        'atrial_fibrillation_event',\n",
-      "                        'atrial_fibrillation_event_time',\n",
-      "                        'copd_event',\n",
-      "                        'copd_event_time',\n",
-      "                        'dementia_event',\n",
-      "                        'dementia_event_time'],\n",
-      " 'nan': ['score_SCORE',\n",
-      "         'score_ASCVD',\n",
-      "         'score_QRISK3',\n",
-      "         'score_COX_MACE_clinical',\n",
-      "         'score_COX_MACE_clinical_with_pgs'],\n",
-      " 'score_ASCVD': ['ASCVD_event', 'ASCVD_event_time'],\n",
-      " 'score_MACE': ['MACE_event', 'MACE_event_time'],\n",
-      " 'score_QRISK3': ['QRISK3_event', 'QRISK3_event_time'],\n",
-      " 'score_SCORE': ['SCORE_event', 'SCORE_event_time']}\n"
-     ]
-    }
-   ],
-   "source": [
-    "isTarget=True\n",
-    "feature_dict = {}\n",
-    "for group in list(data.var[data.var.isTarget==isTarget].based_on.unique()):\n",
-    "    group_features = [x for x in list(data.var[(data.var.isTarget==isTarget) & (data.var.based_on == group)].index) if \"_scaled\" not in x]\n",
-    "    feature_dict[group] = group_features\n",
-    "pprint(feature_dict)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "output_categorical=False"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "        if output_categorical:\n",
-    "            basics = [\n",
-    "                'age_at_recruitment',\n",
-    "                'ethnic_background',\n",
-    "                'sex'\n",
-    "            ]\n",
-    "            questionnaire = [\n",
-    "                'overall_health_rating',\n",
-    "                'smoking_status',\n",
-    "                'alcohol_intake_frequency']\n",
-    "        else:\n",
-    "            basics = [\n",
-    "                'age_at_recruitment',\n",
-    "                'ethnic_background_0.0',\n",
-    "                'ethnic_background_1.0',\n",
-    "                'ethnic_background_2.0',\n",
-    "                'ethnic_background_3.0',\n",
-    "                'ethnic_background_4.0',\n",
-    "                'sex'\n",
-    "            ]\n",
-    "            questionnaire = [\n",
-    "                'overall_health_rating_0.0',\n",
-    "                'overall_health_rating_1.0',\n",
-    "                'overall_health_rating_2.0',\n",
-    "                'overall_health_rating_3.0',\n",
-    "                'smoking_status_0.0',\n",
-    "                'smoking_status_1.0',\n",
-    "                'smoking_status_2.0',\n",
-    "                'alcohol_intake_frequency_0.0',\n",
-    "                'alcohol_intake_frequency_1.0',\n",
-    "                'alcohol_intake_frequency_2.0',\n",
-    "                'alcohol_intake_frequency_3.0',\n",
-    "                'alcohol_intake_frequency_4.0',\n",
-    "                'alcohol_intake_frequency_5.0'\n",
-    "            ]\n",
-    "        measurements = [\n",
-    "            'body_mass_index_bmi',\n",
-    "            'weight',\n",
-    "            'pulse_wave_arterial_stiffness_index',\n",
-    "            'pulse_wave_reflection_index',\n",
-    "            'waist_circumference',\n",
-    "            'hip_circumference',\n",
-    "            'trunk_fat_percentage',\n",
-    "            'body_fat_percentage',\n",
-    "            'basal_metabolic_rate',\n",
-    "            'forced_vital_capacity_fvc_best_measure',\n",
-    "            'forced_expiratory_volume_in_1second_fev1_best_measure',\n",
-    "            'fev1_fvc_ratio_zscore',\n",
-    "            'peak_expiratory_flow_pef_f3064_0_2',\n",
-    "            'peak_expiratory_flow_pef_f3064_0_1',\n",
-    "            'peak_expiratory_flow_pef',\n",
-    "            'systolic_blood_pressure',\n",
-    "            'diastolic_blood_pressure',\n",
-    "            'pulse_rate'\n",
-    "        ]\n",
-    "        labs = [\n",
-    "            'basophill_count',\n",
-    "            'basophill_percentage',\n",
-    "            'eosinophill_count',\n",
-    "            'eosinophill_percentage',\n",
-    "            'haematocrit_percentage',\n",
-    "            'haemoglobin_concentration',\n",
-    "            'high_light_scatter_reticulocyte_count',\n",
-    "            'high_light_scatter_reticulocyte_percentage',\n",
-    "            'immature_reticulocyte_fraction',\n",
-    "            'lymphocyte_count',\n",
-    "            'lymphocyte_percentage',\n",
-    "            'mean_corpuscular_haemoglobin',\n",
-    "            'mean_corpuscular_haemoglobin_concentration',\n",
-    "            'mean_corpuscular_volume',\n",
-    "            'mean_platelet_thrombocyte_volume',\n",
-    "            'mean_reticulocyte_volume',\n",
-    "            'mean_sphered_cell_volume',\n",
-    "            'monocyte_count',\n",
-    "            'monocyte_percentage',\n",
-    "            'neutrophill_count',\n",
-    "            'neutrophill_percentage',\n",
-    "            'nucleated_red_blood_cell_count',\n",
-    "            'nucleated_red_blood_cell_percentage',\n",
-    "            'platelet_count',\n",
-    "            'platelet_crit',\n",
-    "            'platelet_distribution_width',\n",
-    "            'red_blood_cell_erythrocyte_count',\n",
-    "            'red_blood_cell_erythrocyte_distribution_width',\n",
-    "            'reticulocyte_count',\n",
-    "            'reticulocyte_percentage',\n",
-    "            'white_blood_cell_leukocyte_count',\n",
-    "            'alanine_aminotransferase',\n",
-    "            'albumin',\n",
-    "            'alkaline_phosphatase',\n",
-    "            'apolipoprotein_a',\n",
-    "            'apolipoprotein_b',\n",
-    "            'aspartate_aminotransferase',\n",
-    "            'creactive_protein',\n",
-    "            'calcium',\n",
-    "            'cholesterol',\n",
-    "            'creatinine',\n",
-    "            'cystatin_c',\n",
-    "            'direct_bilirubin',\n",
-    "            'gamma_glutamyltransferase',\n",
-    "            'glucose',\n",
-    "            'glycated_haemoglobin_hba1c',\n",
-    "            'hdl_cholesterol',\n",
-    "            'igf1',\n",
-    "            'ldl_direct',\n",
-    "            'lipoprotein_a',\n",
-    "            'oestradiol',\n",
-    "            'phosphate',\n",
-    "            'rheumatoid_factor',\n",
-    "            'shbg',\n",
-    "            'testosterone',\n",
-    "            'total_bilirubin',\n",
-    "            'total_protein',\n",
-    "            'triglycerides',\n",
-    "            'urate',\n",
-    "            'urea',\n",
-    "            'vitamin_d'\n",
-    "        ]\n",
-    "        family_history = [\n",
-    "            \"fh_alzheimer's_disease/dementia\",\n",
-    "            'fh_bowel_cancer',\n",
-    "            'fh_breast_cancer',\n",
-    "            'fh_chronic_bronchitis/emphysema',\n",
-    "            'fh_diabetes',\n",
-    "            'fh_heart_disease',\n",
-    "            'fh_high_blood_pressure',\n",
-    "            'fh_lung_cancer',\n",
-    "            \"fh_parkinson's_disease\",\n",
-    "            'fh_severe_depression',\n",
-    "            'fh_stroke'\n",
-    "        ]\n",
-    "        diagnoses = [\n",
-    "            'intestinal_infection',\n",
-    "            'bacterial_infection',\n",
-    "            'arthropathies',\n",
-    "            'viral_infection',\n",
-    "            'infections_specific_to_the_perinatal_period',\n",
-    "            'acute_upper_respiratory_infections',\n",
-    "            'mycoses',\n",
-    "            \"kaposi's_sarcoma\",\n",
-    "            'malaise_and_fatigue',\n",
-    "            'malignant_neoplasm_of_skin',\n",
-    "            'benign_neoplasm_of_respiratory_and_intrathoracic_organs',\n",
-    "            'benign_neoplasm_of_skin',\n",
-    "            'nonmalignant_breast_conditions',\n",
-    "            'anemias',\n",
-    "            'other_endocrine_disorders',\n",
-    "            'diabetes',\n",
-    "            'disorders_of_mineral_metabolism',\n",
-    "            'obesity',\n",
-    "            'respiratory_abnormalities',\n",
-    "            'disorders_of_lipoid_metabolism',\n",
-    "            'nonspecific_findings_on_examination_of_blood',\n",
-    "            'sleep_disorders',\n",
-    "            'male_genital_disorders',\n",
-    "            'cerebrovascular_disease',\n",
-    "            'cataract',\n",
-    "            'blindness_and_low_vision',\n",
-    "            'disorders_of_external_ear',\n",
-    "            'heart_valve_disorders',\n",
-    "            'ischemic_heart_disease',\n",
-    "            'cardiac_conduction_disorders',\n",
-    "            'heart_failure',\n",
-    "            'noninfectious_disorders_of_lymphatic_channels',\n",
-    "            'other_symptoms_of_respiratory_system',\n",
-    "            'disorders_of_stomach',\n",
-    "            'abdominal_pain',\n",
-    "            'abdominal_hernia',\n",
-    "            'liver_disease',\n",
-    "            'biliary_tract_disease',\n",
-    "            'bariatric_surgery',\n",
-    "            'complications',\n",
-    "            'other_hypertrophic_and_atrophic_conditions_of_skin',\n",
-    "            'symptoms_affecting_skin',\n",
-    "            'chronic_ulcer_of_skin',\n",
-    "            'gout',\n",
-    "            'osteoarthritis',\n",
-    "            'acquired_deformities',\n",
-    "            'pain_in_joint',\n",
-    "            'back_pain',\n",
-    "            'myalgia_and_myositis_unspecified',\n",
-    "            'symptoms_involving_nervous_and_musculoskeletal_systems',\n",
-    "            'pain_in_limb',\n",
-    "            'musculoskeletal_symptoms_referable_to_limbs',\n",
-    "            'congenital_musculoskeletal_anomalies',\n",
-    "            'other_symptoms/disorders_or_the_urinary_system',\n",
-    "            'urinary_calculus',\n",
-    "            'symptoms_involving_female_genital_tract',\n",
-    "            'other_nonmalignant_breast_conditions',\n",
-    "            'complications_of_the_puerperium',\n",
-    "            'complications_of_pregnancy',\n",
-    "            'postoperative_infection',\n",
-    "            'other_complications_of_pregnancy_nec',\n",
-    "            'excessive_vomiting_in_pregnancy',\n",
-    "            'infectious_and_parasitic_complications_affecting_pregnancy',\n",
-    "            'multiple_gestation',\n",
-    "            'late_pregnancy_and_failed_induction',\n",
-    "            'normal_delivery',\n",
-    "            'anemia_during_pregnancy',\n",
-    "            'maternal_complication_of_pregnancy_affecting_fetus_or_newborn',\n",
-    "            'gangrene',\n",
-    "            'abnormal_sputum',\n",
-    "            'symptoms_involving_head_and_neck',\n",
-    "            'nonspecific_chest_pain',\n",
-    "            'nausea_and_vomiting',\n",
-    "            'nonspecific_findings_on_examination_of_urine',\n",
-    "            'fever',\n",
-    "            'pain',\n",
-    "            'syncope_and_collapse',\n",
-    "            'shock',\n",
-    "            'hypothermia/chills',\n",
-    "            'abnormal_findings_examination_of_lungs',\n",
-    "            'contusion',\n",
-    "            'open_wound',\n",
-    "            'bone_marrow_or_stem_cell_transplant']\n",
-    "        medications=[\n",
-    "            'stomatological_preparations',\n",
-    "            'drugs_for_acid_related_disorders',\n",
-    "            'drugs_for_functional_gastrointestinal_disorders',\n",
-    "            'antiemetics_and_antinauseants',\n",
-    "            'bile_and_liver_therapy',\n",
-    "            'drugs_for_constipation',\n",
-    "            'antidiarrheals,_intestinal_antiinflammatory/antiinfective_agents',\n",
-    "            'antiobesity_preparations,_excl._diet_products',\n",
-    "            'digestives,_incl._enzymes',\n",
-    "            'drugs_used_in_diabetes',\n",
-    "            'vitamins',\n",
-    "            'mineral_supplements',\n",
-    "            'tonics',\n",
-    "            'anabolic_agents_for_systemic_use',\n",
-    "            'appetite_stimulants',\n",
-    "            'other_alimentary_tract_and_metabolism_products',\n",
-    "            'antithrombotic_agents',\n",
-    "            'antihemorrhagics',\n",
-    "            'antianemic_preparations',\n",
-    "            'blood_substitutes_and_perfusion_solutions',\n",
-    "            'other_hematological_agents',\n",
-    "            'cardiac_therapy',\n",
-    "            'antihypertensives',\n",
-    "            'diuretics',\n",
-    "            'peripheral_vasodilators',\n",
-    "            'vasoprotectives',\n",
-    "            'beta_blocking_agents',\n",
-    "            'calcium_channel_blockers',\n",
-    "            'agents_acting_on_the_renin-angiotensin_system',\n",
-    "            'lipid_modifying_agents',\n",
-    "            'antifungals_for_dermatological_use',\n",
-    "            'emollients_and_protectives',\n",
-    "            'preparations_for_treatment_of_wounds_and_ulcers',\n",
-    "            'antipruritics,_incl._antihistamines,_anesthetics,_etc.',\n",
-    "            'antipsoriatics',\n",
-    "            'antibiotics_and_chemotherapeutics_for_dermatological_use',\n",
-    "            'corticosteroids,_dermatological_preparations',\n",
-    "            'antiseptics_and_disinfectants',\n",
-    "            'medicated_dressings',\n",
-    "            'anti-acne_preparations',\n",
-    "            'other_dermatological_preparations',\n",
-    "            'gynecological_antiinfectives_and_antiseptics',\n",
-    "            'other_gynecologicals',\n",
-    "            'sex_hormones_and_modulators_of_the_genital_system',\n",
-    "            'urologicals',\n",
-    "            'pituitary_and_hypothalamic_hormones_and_analogues',\n",
-    "            'corticosteroids_for_systemic_use',\n",
-    "            'thyroid_therapy',\n",
-    "            'pancreatic_hormones',\n",
-    "            'calcium_homeostasis',\n",
-    "            'antibacterials_for_systemic_use',\n",
-    "            'antimycotics_for_systemic_use',\n",
-    "            'antimycobacterials',\n",
-    "            'antivirals_for_systemic_use',\n",
-    "            'immune_sera_and_immunoglobulins',\n",
-    "            'vaccines',\n",
-    "            'antineoplastic_agents',\n",
-    "            'endocrine_therapy',\n",
-    "            'immunostimulants',\n",
-    "            'immunosuppressants',\n",
-    "            'antiinflammatory_and_antirheumatic_products',\n",
-    "            'topical_products_for_joint_and_muscular_pain',\n",
-    "            'muscle_relaxants',\n",
-    "            'antigout_preparations',\n",
-    "            'drugs_for_treatment_of_bone_diseases',\n",
-    "            'other_drugs_for_disorders_of_the_musculo-skeletal_system',\n",
-    "            'anesthetics',\n",
-    "            'analgesics',\n",
-    "            'antiepileptics',\n",
-    "            'anti-parkinson_drugs',\n",
-    "            'psycholeptics',\n",
-    "            'psychoanaleptics',\n",
-    "            'other_nervous_system_drugs',\n",
-    "            'antiprotozoals',\n",
-    "            'anthelmintics',\n",
-    "            'ectoparasiticides,_incl._scabicides,_insecticides_and_repellents',\n",
-    "            'nasal_preparations',\n",
-    "            'throat_preparations',\n",
-    "            'drugs_for_obstructive_airway_diseases',\n",
-    "            'cough_and_cold_preparations',\n",
-    "            'antihistamines_for_systemic_use',\n",
-    "            'other_respiratory_system_products',\n",
-    "            'ophthalmologicals',\n",
-    "            'otologicals',\n",
-    "            'ophthalmological_and_otological_preparations',\n",
-    "            'allergens',\n",
-    "            'all_other_therapeutic_products',\n",
-    "            'diagnostic_agents',\n",
-    "            'general_nutrients',\n",
-    "            'all_other_non-therapeutic_products',\n",
-    "            'contrast_media',\n",
-    "            'diagnostic_radiopharmaceuticals',\n",
-    "            'therapeutic_radiopharmaceuticals',\n",
-    "            'surgical_dressings'\n",
-    "        ]\n",
-    "\n",
-    "        pgs = [\n",
-    "            'PGS000011',\n",
-    "             'PGS000013',\n",
-    "             'PGS000016',\n",
-    "             'PGS000018',\n",
-    "             'PGS000039',\n",
-    "             'PGS000057',\n",
-    "             'PGS000058',\n",
-    "             'PGS000059',\n",
-    "             'PGS000116',\n",
-    "             'PGS000117',\n",
-    "             'PGS000192',\n",
-    "             'PGS000296']\n",
-    "        \n",
-    "        feature_dict = {\n",
-    "            \"pgs\": pgs,\n",
-    "            \"basics\": basics,\n",
-    "            \"questionnaire\": questionnaire ,\n",
-    "            \"measurements\": measurements,\n",
-    "            \"labs\": labs,\n",
-    "            \"family_history\": family_history,\n",
-    "            \"medications\": medications,\n",
-    "            \"diagnoses\": diagnoses,\n",
-    "        }\n",
-    "\n",
-    "        #features = self.pgs + self.features"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "features = [f for group_list in feature_dict.values() for f in group_list]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([0.3990433 , 0.4383982 , 0.85513496, ...,        nan,        nan,\n",
-       "              nan], dtype=float32)"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data.X[1, :]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AnnData object with n_obs × n_vars = 319108 × 4073\n",
-       "    obs: 'eid'\n",
-       "    var: 'dtype', 'isTarget', 'based_on', 'aggr_fn', 'recoding', 'NewLevels'"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "PGS000011\n",
-      "PGS000013\n",
-      "PGS000016\n",
-      "PGS000018\n",
-      "PGS000039\n",
-      "PGS000057\n",
-      "PGS000058\n",
-      "PGS000059\n",
-      "PGS000116\n",
-      "PGS000117\n",
-      "PGS000192\n",
-      "PGS000296\n",
-      "age_at_recruitment\n",
-      "ethnic_background_0.0\n",
-      "ethnic_background_1.0\n",
-      "ethnic_background_2.0\n",
-      "ethnic_background_3.0\n",
-      "ethnic_background_4.0\n",
-      "sex\n",
-      "overall_health_rating_0.0\n",
-      "overall_health_rating_1.0\n",
-      "overall_health_rating_2.0\n",
-      "overall_health_rating_3.0\n",
-      "smoking_status_0.0\n",
-      "smoking_status_1.0\n",
-      "smoking_status_2.0\n",
-      "alcohol_intake_frequency_0.0\n",
-      "alcohol_intake_frequency_1.0\n",
-      "alcohol_intake_frequency_2.0\n",
-      "alcohol_intake_frequency_3.0\n",
-      "alcohol_intake_frequency_4.0\n",
-      "alcohol_intake_frequency_5.0\n",
-      "body_mass_index_bmi\n",
-      "weight\n",
-      "pulse_wave_arterial_stiffness_index\n",
-      "pulse_wave_reflection_index\n",
-      "waist_circumference\n",
-      "hip_circumference\n",
-      "trunk_fat_percentage\n",
-      "body_fat_percentage\n",
-      "basal_metabolic_rate\n",
-      "forced_vital_capacity_fvc_best_measure\n",
-      "forced_expiratory_volume_in_1second_fev1_best_measure\n",
-      "fev1_fvc_ratio_zscore\n",
-      "peak_expiratory_flow_pef_f3064_0_2\n",
-      "peak_expiratory_flow_pef_f3064_0_1\n",
-      "peak_expiratory_flow_pef\n",
-      "systolic_blood_pressure\n",
-      "diastolic_blood_pressure\n",
-      "pulse_rate\n",
-      "basophill_count\n",
-      "basophill_percentage\n",
-      "eosinophill_count\n",
-      "eosinophill_percentage\n",
-      "haematocrit_percentage\n",
-      "haemoglobin_concentration\n",
-      "high_light_scatter_reticulocyte_count\n",
-      "high_light_scatter_reticulocyte_percentage\n",
-      "immature_reticulocyte_fraction\n",
-      "lymphocyte_count\n",
-      "lymphocyte_percentage\n",
-      "mean_corpuscular_haemoglobin\n",
-      "mean_corpuscular_haemoglobin_concentration\n",
-      "mean_corpuscular_volume\n",
-      "mean_platelet_thrombocyte_volume\n",
-      "mean_reticulocyte_volume\n",
-      "mean_sphered_cell_volume\n",
-      "monocyte_count\n",
-      "monocyte_percentage\n",
-      "neutrophill_count\n",
-      "neutrophill_percentage\n",
-      "nucleated_red_blood_cell_count\n",
-      "nucleated_red_blood_cell_percentage\n",
-      "platelet_count\n",
-      "platelet_crit\n",
-      "platelet_distribution_width\n",
-      "red_blood_cell_erythrocyte_count\n",
-      "red_blood_cell_erythrocyte_distribution_width\n",
-      "reticulocyte_count\n",
-      "reticulocyte_percentage\n",
-      "white_blood_cell_leukocyte_count\n",
-      "alanine_aminotransferase\n",
-      "albumin\n",
-      "alkaline_phosphatase\n",
-      "apolipoprotein_a\n",
-      "apolipoprotein_b\n",
-      "aspartate_aminotransferase\n",
-      "creactive_protein\n",
-      "calcium\n",
-      "cholesterol\n",
-      "creatinine\n",
-      "cystatin_c\n",
-      "direct_bilirubin\n",
-      "gamma_glutamyltransferase\n",
-      "glucose\n",
-      "glycated_haemoglobin_hba1c\n",
-      "hdl_cholesterol\n",
-      "igf1\n",
-      "ldl_direct\n",
-      "lipoprotein_a\n",
-      "oestradiol\n",
-      "phosphate\n",
-      "rheumatoid_factor\n",
-      "shbg\n",
-      "testosterone\n",
-      "total_bilirubin\n",
-      "total_protein\n",
-      "triglycerides\n",
-      "urate\n",
-      "urea\n",
-      "vitamin_d\n",
-      "fh_alzheimer's_disease/dementia\n",
-      "fh_bowel_cancer\n",
-      "fh_breast_cancer\n",
-      "fh_chronic_bronchitis/emphysema\n",
-      "fh_diabetes\n",
-      "fh_heart_disease\n",
-      "fh_high_blood_pressure\n",
-      "fh_lung_cancer\n",
-      "fh_parkinson's_disease\n",
-      "fh_severe_depression\n",
-      "fh_stroke\n",
-      "stomatological_preparations\n",
-      "drugs_for_acid_related_disorders\n",
-      "drugs_for_functional_gastrointestinal_disorders\n",
-      "antiemetics_and_antinauseants\n",
-      "bile_and_liver_therapy\n",
-      "drugs_for_constipation\n",
-      "antidiarrheals,_intestinal_antiinflammatory/antiinfective_agents\n",
-      "antiobesity_preparations,_excl._diet_products\n",
-      "digestives,_incl._enzymes\n",
-      "drugs_used_in_diabetes\n",
-      "vitamins\n",
-      "mineral_supplements\n",
-      "tonics\n",
-      "anabolic_agents_for_systemic_use\n",
-      "appetite_stimulants\n",
-      "other_alimentary_tract_and_metabolism_products\n",
-      "antithrombotic_agents\n",
-      "antihemorrhagics\n",
-      "antianemic_preparations\n",
-      "blood_substitutes_and_perfusion_solutions\n",
-      "other_hematological_agents\n",
-      "cardiac_therapy\n",
-      "antihypertensives\n",
-      "diuretics\n",
-      "peripheral_vasodilators\n",
-      "vasoprotectives\n",
-      "beta_blocking_agents\n",
-      "calcium_channel_blockers\n",
-      "agents_acting_on_the_renin-angiotensin_system\n",
-      "lipid_modifying_agents\n",
-      "antifungals_for_dermatological_use\n",
-      "emollients_and_protectives\n",
-      "preparations_for_treatment_of_wounds_and_ulcers\n",
-      "antipruritics,_incl._antihistamines,_anesthetics,_etc.\n",
-      "antipsoriatics\n",
-      "antibiotics_and_chemotherapeutics_for_dermatological_use\n",
-      "corticosteroids,_dermatological_preparations\n",
-      "antiseptics_and_disinfectants\n",
-      "medicated_dressings\n",
-      "anti-acne_preparations\n",
-      "other_dermatological_preparations\n",
-      "gynecological_antiinfectives_and_antiseptics\n",
-      "other_gynecologicals\n",
-      "sex_hormones_and_modulators_of_the_genital_system\n",
-      "urologicals\n",
-      "pituitary_and_hypothalamic_hormones_and_analogues\n",
-      "corticosteroids_for_systemic_use\n",
-      "thyroid_therapy\n",
-      "pancreatic_hormones\n",
-      "calcium_homeostasis\n",
-      "antibacterials_for_systemic_use\n",
-      "antimycotics_for_systemic_use\n",
-      "antimycobacterials\n",
-      "antivirals_for_systemic_use\n",
-      "immune_sera_and_immunoglobulins\n",
-      "vaccines\n",
-      "antineoplastic_agents\n",
-      "endocrine_therapy\n",
-      "immunostimulants\n",
-      "immunosuppressants\n",
-      "antiinflammatory_and_antirheumatic_products\n",
-      "topical_products_for_joint_and_muscular_pain\n",
-      "muscle_relaxants\n",
-      "antigout_preparations\n",
-      "drugs_for_treatment_of_bone_diseases\n",
-      "other_drugs_for_disorders_of_the_musculo-skeletal_system\n",
-      "anesthetics\n",
-      "analgesics\n",
-      "antiepileptics\n",
-      "anti-parkinson_drugs\n",
-      "psycholeptics\n",
-      "psychoanaleptics\n",
-      "other_nervous_system_drugs\n",
-      "antiprotozoals\n",
-      "anthelmintics\n",
-      "ectoparasiticides,_incl._scabicides,_insecticides_and_repellents\n",
-      "nasal_preparations\n",
-      "throat_preparations\n",
-      "drugs_for_obstructive_airway_diseases\n",
-      "cough_and_cold_preparations\n",
-      "antihistamines_for_systemic_use\n",
-      "other_respiratory_system_products\n",
-      "ophthalmologicals\n",
-      "otologicals\n",
-      "ophthalmological_and_otological_preparations\n",
-      "allergens\n",
-      "all_other_therapeutic_products\n",
-      "diagnostic_agents\n",
-      "general_nutrients\n",
-      "all_other_non-therapeutic_products\n",
-      "contrast_media\n",
-      "diagnostic_radiopharmaceuticals\n",
-      "therapeutic_radiopharmaceuticals\n",
-      "surgical_dressings\n",
-      "intestinal_infection\n",
-      "bacterial_infection\n",
-      "arthropathies\n",
-      "viral_infection\n",
-      "infections_specific_to_the_perinatal_period\n",
-      "acute_upper_respiratory_infections\n",
-      "mycoses\n",
-      "kaposi's_sarcoma\n",
-      "malaise_and_fatigue\n",
-      "malignant_neoplasm_of_skin\n",
-      "benign_neoplasm_of_respiratory_and_intrathoracic_organs\n",
-      "benign_neoplasm_of_skin\n",
-      "nonmalignant_breast_conditions\n",
-      "anemias\n",
-      "other_endocrine_disorders\n",
-      "diabetes\n",
-      "disorders_of_mineral_metabolism\n",
-      "obesity\n",
-      "respiratory_abnormalities\n",
-      "disorders_of_lipoid_metabolism\n",
-      "nonspecific_findings_on_examination_of_blood\n",
-      "sleep_disorders\n",
-      "male_genital_disorders\n",
-      "cerebrovascular_disease\n",
-      "cataract\n",
-      "blindness_and_low_vision\n",
-      "disorders_of_external_ear\n",
-      "heart_valve_disorders\n",
-      "ischemic_heart_disease\n",
-      "cardiac_conduction_disorders\n",
-      "heart_failure\n",
-      "noninfectious_disorders_of_lymphatic_channels\n",
-      "other_symptoms_of_respiratory_system\n",
-      "disorders_of_stomach\n",
-      "abdominal_pain\n",
-      "abdominal_hernia\n",
-      "liver_disease\n",
-      "biliary_tract_disease\n",
-      "bariatric_surgery\n",
-      "complications\n",
-      "other_hypertrophic_and_atrophic_conditions_of_skin\n",
-      "symptoms_affecting_skin\n",
-      "chronic_ulcer_of_skin\n",
-      "gout\n",
-      "osteoarthritis\n",
-      "acquired_deformities\n",
-      "pain_in_joint\n",
-      "back_pain\n",
-      "myalgia_and_myositis_unspecified\n",
-      "symptoms_involving_nervous_and_musculoskeletal_systems\n",
-      "pain_in_limb\n",
-      "musculoskeletal_symptoms_referable_to_limbs\n",
-      "congenital_musculoskeletal_anomalies\n",
-      "other_symptoms/disorders_or_the_urinary_system\n",
-      "urinary_calculus\n",
-      "symptoms_involving_female_genital_tract\n",
-      "other_nonmalignant_breast_conditions\n",
-      "complications_of_the_puerperium\n",
-      "complications_of_pregnancy\n",
-      "postoperative_infection\n",
-      "other_complications_of_pregnancy_nec\n",
-      "excessive_vomiting_in_pregnancy\n",
-      "infectious_and_parasitic_complications_affecting_pregnancy\n",
-      "multiple_gestation\n",
-      "late_pregnancy_and_failed_induction\n",
-      "normal_delivery\n",
-      "anemia_during_pregnancy\n",
-      "maternal_complication_of_pregnancy_affecting_fetus_or_newborn\n",
-      "gangrene\n",
-      "abnormal_sputum\n",
-      "symptoms_involving_head_and_neck\n",
-      "nonspecific_chest_pain\n",
-      "nausea_and_vomiting\n",
-      "nonspecific_findings_on_examination_of_urine\n",
-      "fever\n",
-      "pain\n",
-      "syncope_and_collapse\n",
-      "shock\n",
-      "hypothermia/chills\n",
-      "abnormal_findings_examination_of_lungs\n",
-      "contusion\n",
-      "open_wound\n",
-      "bone_marrow_or_stem_cell_transplant\n"
-     ]
-    }
-   ],
-   "source": [
-    "for group, group_list in feature_dict.items():\n",
-    "    for f in group_list:\n",
-    "        print(f)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'pgs': ['PGS000011',\n",
-       "  'PGS000013',\n",
-       "  'PGS000016',\n",
-       "  'PGS000018',\n",
-       "  'PGS000039',\n",
-       "  'PGS000057',\n",
-       "  'PGS000058',\n",
-       "  'PGS000059',\n",
-       "  'PGS000116',\n",
-       "  'PGS000117',\n",
-       "  'PGS000192',\n",
-       "  'PGS000296'],\n",
-       " 'basics': ['age_at_recruitment',\n",
-       "  'ethnic_background_0.0',\n",
-       "  'ethnic_background_1.0',\n",
-       "  'ethnic_background_2.0',\n",
-       "  'ethnic_background_3.0',\n",
-       "  'ethnic_background_4.0',\n",
-       "  'sex'],\n",
-       " 'questionnaire': ['overall_health_rating_0.0',\n",
-       "  'overall_health_rating_1.0',\n",
-       "  'overall_health_rating_2.0',\n",
-       "  'overall_health_rating_3.0',\n",
-       "  'smoking_status_0.0',\n",
-       "  'smoking_status_1.0',\n",
-       "  'smoking_status_2.0',\n",
-       "  'alcohol_intake_frequency_0.0',\n",
-       "  'alcohol_intake_frequency_1.0',\n",
-       "  'alcohol_intake_frequency_2.0',\n",
-       "  'alcohol_intake_frequency_3.0',\n",
-       "  'alcohol_intake_frequency_4.0',\n",
-       "  'alcohol_intake_frequency_5.0'],\n",
-       " 'measurements': ['body_mass_index_bmi',\n",
-       "  'weight',\n",
-       "  'pulse_wave_arterial_stiffness_index',\n",
-       "  'pulse_wave_reflection_index',\n",
-       "  'waist_circumference',\n",
-       "  'hip_circumference',\n",
-       "  'trunk_fat_percentage',\n",
-       "  'body_fat_percentage',\n",
-       "  'basal_metabolic_rate',\n",
-       "  'forced_vital_capacity_fvc_best_measure',\n",
-       "  'forced_expiratory_volume_in_1second_fev1_best_measure',\n",
-       "  'fev1_fvc_ratio_zscore',\n",
-       "  'peak_expiratory_flow_pef_f3064_0_2',\n",
-       "  'peak_expiratory_flow_pef_f3064_0_1',\n",
-       "  'peak_expiratory_flow_pef',\n",
-       "  'systolic_blood_pressure',\n",
-       "  'diastolic_blood_pressure',\n",
-       "  'pulse_rate'],\n",
-       " 'labs': ['basophill_count',\n",
-       "  'basophill_percentage',\n",
-       "  'eosinophill_count',\n",
-       "  'eosinophill_percentage',\n",
-       "  'haematocrit_percentage',\n",
-       "  'haemoglobin_concentration',\n",
-       "  'high_light_scatter_reticulocyte_count',\n",
-       "  'high_light_scatter_reticulocyte_percentage',\n",
-       "  'immature_reticulocyte_fraction',\n",
-       "  'lymphocyte_count',\n",
-       "  'lymphocyte_percentage',\n",
-       "  'mean_corpuscular_haemoglobin',\n",
-       "  'mean_corpuscular_haemoglobin_concentration',\n",
-       "  'mean_corpuscular_volume',\n",
-       "  'mean_platelet_thrombocyte_volume',\n",
-       "  'mean_reticulocyte_volume',\n",
-       "  'mean_sphered_cell_volume',\n",
-       "  'monocyte_count',\n",
-       "  'monocyte_percentage',\n",
-       "  'neutrophill_count',\n",
-       "  'neutrophill_percentage',\n",
-       "  'nucleated_red_blood_cell_count',\n",
-       "  'nucleated_red_blood_cell_percentage',\n",
-       "  'platelet_count',\n",
-       "  'platelet_crit',\n",
-       "  'platelet_distribution_width',\n",
-       "  'red_blood_cell_erythrocyte_count',\n",
-       "  'red_blood_cell_erythrocyte_distribution_width',\n",
-       "  'reticulocyte_count',\n",
-       "  'reticulocyte_percentage',\n",
-       "  'white_blood_cell_leukocyte_count',\n",
-       "  'alanine_aminotransferase',\n",
-       "  'albumin',\n",
-       "  'alkaline_phosphatase',\n",
-       "  'apolipoprotein_a',\n",
-       "  'apolipoprotein_b',\n",
-       "  'aspartate_aminotransferase',\n",
-       "  'creactive_protein',\n",
-       "  'calcium',\n",
-       "  'cholesterol',\n",
-       "  'creatinine',\n",
-       "  'cystatin_c',\n",
-       "  'direct_bilirubin',\n",
-       "  'gamma_glutamyltransferase',\n",
-       "  'glucose',\n",
-       "  'glycated_haemoglobin_hba1c',\n",
-       "  'hdl_cholesterol',\n",
-       "  'igf1',\n",
-       "  'ldl_direct',\n",
-       "  'lipoprotein_a',\n",
-       "  'oestradiol',\n",
-       "  'phosphate',\n",
-       "  'rheumatoid_factor',\n",
-       "  'shbg',\n",
-       "  'testosterone',\n",
-       "  'total_bilirubin',\n",
-       "  'total_protein',\n",
-       "  'triglycerides',\n",
-       "  'urate',\n",
-       "  'urea',\n",
-       "  'vitamin_d'],\n",
-       " 'family_history': [\"fh_alzheimer's_disease/dementia\",\n",
-       "  'fh_bowel_cancer',\n",
-       "  'fh_breast_cancer',\n",
-       "  'fh_chronic_bronchitis/emphysema',\n",
-       "  'fh_diabetes',\n",
-       "  'fh_heart_disease',\n",
-       "  'fh_high_blood_pressure',\n",
-       "  'fh_lung_cancer',\n",
-       "  \"fh_parkinson's_disease\",\n",
-       "  'fh_severe_depression',\n",
-       "  'fh_stroke'],\n",
-       " 'medications': ['stomatological_preparations',\n",
-       "  'drugs_for_acid_related_disorders',\n",
-       "  'drugs_for_functional_gastrointestinal_disorders',\n",
-       "  'antiemetics_and_antinauseants',\n",
-       "  'bile_and_liver_therapy',\n",
-       "  'drugs_for_constipation',\n",
-       "  'antidiarrheals,_intestinal_antiinflammatory/antiinfective_agents',\n",
-       "  'antiobesity_preparations,_excl._diet_products',\n",
-       "  'digestives,_incl._enzymes',\n",
-       "  'drugs_used_in_diabetes',\n",
-       "  'vitamins',\n",
-       "  'mineral_supplements',\n",
-       "  'tonics',\n",
-       "  'anabolic_agents_for_systemic_use',\n",
-       "  'appetite_stimulants',\n",
-       "  'other_alimentary_tract_and_metabolism_products',\n",
-       "  'antithrombotic_agents',\n",
-       "  'antihemorrhagics',\n",
-       "  'antianemic_preparations',\n",
-       "  'blood_substitutes_and_perfusion_solutions',\n",
-       "  'other_hematological_agents',\n",
-       "  'cardiac_therapy',\n",
-       "  'antihypertensives',\n",
-       "  'diuretics',\n",
-       "  'peripheral_vasodilators',\n",
-       "  'vasoprotectives',\n",
-       "  'beta_blocking_agents',\n",
-       "  'calcium_channel_blockers',\n",
-       "  'agents_acting_on_the_renin-angiotensin_system',\n",
-       "  'lipid_modifying_agents',\n",
-       "  'antifungals_for_dermatological_use',\n",
-       "  'emollients_and_protectives',\n",
-       "  'preparations_for_treatment_of_wounds_and_ulcers',\n",
-       "  'antipruritics,_incl._antihistamines,_anesthetics,_etc.',\n",
-       "  'antipsoriatics',\n",
-       "  'antibiotics_and_chemotherapeutics_for_dermatological_use',\n",
-       "  'corticosteroids,_dermatological_preparations',\n",
-       "  'antiseptics_and_disinfectants',\n",
-       "  'medicated_dressings',\n",
-       "  'anti-acne_preparations',\n",
-       "  'other_dermatological_preparations',\n",
-       "  'gynecological_antiinfectives_and_antiseptics',\n",
-       "  'other_gynecologicals',\n",
-       "  'sex_hormones_and_modulators_of_the_genital_system',\n",
-       "  'urologicals',\n",
-       "  'pituitary_and_hypothalamic_hormones_and_analogues',\n",
-       "  'corticosteroids_for_systemic_use',\n",
-       "  'thyroid_therapy',\n",
-       "  'pancreatic_hormones',\n",
-       "  'calcium_homeostasis',\n",
-       "  'antibacterials_for_systemic_use',\n",
-       "  'antimycotics_for_systemic_use',\n",
-       "  'antimycobacterials',\n",
-       "  'antivirals_for_systemic_use',\n",
-       "  'immune_sera_and_immunoglobulins',\n",
-       "  'vaccines',\n",
-       "  'antineoplastic_agents',\n",
-       "  'endocrine_therapy',\n",
-       "  'immunostimulants',\n",
-       "  'immunosuppressants',\n",
-       "  'antiinflammatory_and_antirheumatic_products',\n",
-       "  'topical_products_for_joint_and_muscular_pain',\n",
-       "  'muscle_relaxants',\n",
-       "  'antigout_preparations',\n",
-       "  'drugs_for_treatment_of_bone_diseases',\n",
-       "  'other_drugs_for_disorders_of_the_musculo-skeletal_system',\n",
-       "  'anesthetics',\n",
-       "  'analgesics',\n",
-       "  'antiepileptics',\n",
-       "  'anti-parkinson_drugs',\n",
-       "  'psycholeptics',\n",
-       "  'psychoanaleptics',\n",
-       "  'other_nervous_system_drugs',\n",
-       "  'antiprotozoals',\n",
-       "  'anthelmintics',\n",
-       "  'ectoparasiticides,_incl._scabicides,_insecticides_and_repellents',\n",
-       "  'nasal_preparations',\n",
-       "  'throat_preparations',\n",
-       "  'drugs_for_obstructive_airway_diseases',\n",
-       "  'cough_and_cold_preparations',\n",
-       "  'antihistamines_for_systemic_use',\n",
-       "  'other_respiratory_system_products',\n",
-       "  'ophthalmologicals',\n",
-       "  'otologicals',\n",
-       "  'ophthalmological_and_otological_preparations',\n",
-       "  'allergens',\n",
-       "  'all_other_therapeutic_products',\n",
-       "  'diagnostic_agents',\n",
-       "  'general_nutrients',\n",
-       "  'all_other_non-therapeutic_products',\n",
-       "  'contrast_media',\n",
-       "  'diagnostic_radiopharmaceuticals',\n",
-       "  'therapeutic_radiopharmaceuticals',\n",
-       "  'surgical_dressings'],\n",
-       " 'diagnoses': ['intestinal_infection',\n",
-       "  'bacterial_infection',\n",
-       "  'arthropathies',\n",
-       "  'viral_infection',\n",
-       "  'infections_specific_to_the_perinatal_period',\n",
-       "  'acute_upper_respiratory_infections',\n",
-       "  'mycoses',\n",
-       "  \"kaposi's_sarcoma\",\n",
-       "  'malaise_and_fatigue',\n",
-       "  'malignant_neoplasm_of_skin',\n",
-       "  'benign_neoplasm_of_respiratory_and_intrathoracic_organs',\n",
-       "  'benign_neoplasm_of_skin',\n",
-       "  'nonmalignant_breast_conditions',\n",
-       "  'anemias',\n",
-       "  'other_endocrine_disorders',\n",
-       "  'diabetes',\n",
-       "  'disorders_of_mineral_metabolism',\n",
-       "  'obesity',\n",
-       "  'respiratory_abnormalities',\n",
-       "  'disorders_of_lipoid_metabolism',\n",
-       "  'nonspecific_findings_on_examination_of_blood',\n",
-       "  'sleep_disorders',\n",
-       "  'male_genital_disorders',\n",
-       "  'cerebrovascular_disease',\n",
-       "  'cataract',\n",
-       "  'blindness_and_low_vision',\n",
-       "  'disorders_of_external_ear',\n",
-       "  'heart_valve_disorders',\n",
-       "  'ischemic_heart_disease',\n",
-       "  'cardiac_conduction_disorders',\n",
-       "  'heart_failure',\n",
-       "  'noninfectious_disorders_of_lymphatic_channels',\n",
-       "  'other_symptoms_of_respiratory_system',\n",
-       "  'disorders_of_stomach',\n",
-       "  'abdominal_pain',\n",
-       "  'abdominal_hernia',\n",
-       "  'liver_disease',\n",
-       "  'biliary_tract_disease',\n",
-       "  'bariatric_surgery',\n",
-       "  'complications',\n",
-       "  'other_hypertrophic_and_atrophic_conditions_of_skin',\n",
-       "  'symptoms_affecting_skin',\n",
-       "  'chronic_ulcer_of_skin',\n",
-       "  'gout',\n",
-       "  'osteoarthritis',\n",
-       "  'acquired_deformities',\n",
-       "  'pain_in_joint',\n",
-       "  'back_pain',\n",
-       "  'myalgia_and_myositis_unspecified',\n",
-       "  'symptoms_involving_nervous_and_musculoskeletal_systems',\n",
-       "  'pain_in_limb',\n",
-       "  'musculoskeletal_symptoms_referable_to_limbs',\n",
-       "  'congenital_musculoskeletal_anomalies',\n",
-       "  'other_symptoms/disorders_or_the_urinary_system',\n",
-       "  'urinary_calculus',\n",
-       "  'symptoms_involving_female_genital_tract',\n",
-       "  'other_nonmalignant_breast_conditions',\n",
-       "  'complications_of_the_puerperium',\n",
-       "  'complications_of_pregnancy',\n",
-       "  'postoperative_infection',\n",
-       "  'other_complications_of_pregnancy_nec',\n",
-       "  'excessive_vomiting_in_pregnancy',\n",
-       "  'infectious_and_parasitic_complications_affecting_pregnancy',\n",
-       "  'multiple_gestation',\n",
-       "  'late_pregnancy_and_failed_induction',\n",
-       "  'normal_delivery',\n",
-       "  'anemia_during_pregnancy',\n",
-       "  'maternal_complication_of_pregnancy_affecting_fetus_or_newborn',\n",
-       "  'gangrene',\n",
-       "  'abnormal_sputum',\n",
-       "  'symptoms_involving_head_and_neck',\n",
-       "  'nonspecific_chest_pain',\n",
-       "  'nausea_and_vomiting',\n",
-       "  'nonspecific_findings_on_examination_of_urine',\n",
-       "  'fever',\n",
-       "  'pain',\n",
-       "  'syncope_and_collapse',\n",
-       "  'shock',\n",
-       "  'hypothermia/chills',\n",
-       "  'abnormal_findings_examination_of_lungs',\n",
-       "  'contusion',\n",
-       "  'open_wound',\n",
-       "  'bone_marrow_or_stem_cell_transplant']}"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "feature_dict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['PGS000011', 'PGS000013', 'PGS000016', 'PGS000018', 'PGS000039',\n",
-       "       'PGS000057', 'PGS000058', 'PGS000059', 'PGS000116', 'PGS000117',\n",
-       "       'PGS000192', 'PGS000296', 'PGS000011_scaled', 'PGS000013_scaled',\n",
-       "       'PGS000016_scaled', 'PGS000018_scaled', 'PGS000039_scaled',\n",
-       "       'PGS000057_scaled', 'PGS000058_scaled', 'PGS000059_scaled',\n",
-       "       'PGS000116_scaled', 'PGS000117_scaled', 'PGS000192_scaled',\n",
-       "       'PGS000296_scaled'],\n",
-       "      dtype='object')"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data.var.index[data.var.based_on == \"PGS\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[]"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data.var[data.var.isTarget==\"False\"].index.to_list()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data = pd.read_feather(os.path.join(project_path, dataset_path, project_name, \"baseline.feather\"))\n",
-    "data_description = pd.read_feather(os.path.join(project_path, dataset_path, project_name, \"baseline_description.feather\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>covariate</th>\n",
-       "      <th>dtype</th>\n",
-       "      <th>isTarget</th>\n",
-       "      <th>based_on</th>\n",
-       "      <th>aggr_fn</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>eid</td>\n",
-       "      <td>integer</td>\n",
-       "      <td>False</td>\n",
-       "      <td>admin</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>PGS000011</td>\n",
-       "      <td>numeric</td>\n",
-       "      <td>False</td>\n",
-       "      <td>PGS</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>PGS000013</td>\n",
-       "      <td>numeric</td>\n",
-       "      <td>False</td>\n",
-       "      <td>PGS</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>PGS000016</td>\n",
-       "      <td>numeric</td>\n",
-       "      <td>False</td>\n",
-       "      <td>PGS</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>PGS000018</td>\n",
-       "      <td>numeric</td>\n",
-       "      <td>False</td>\n",
-       "      <td>PGS</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>307</th>\n",
-       "      <td>ASCVD_event_time</td>\n",
-       "      <td>numeric</td>\n",
-       "      <td>True</td>\n",
-       "      <td>score_ASCVD</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>308</th>\n",
-       "      <td>QRISK3_event</td>\n",
-       "      <td>integer</td>\n",
-       "      <td>True</td>\n",
-       "      <td>score_QRISK3</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>309</th>\n",
-       "      <td>QRISK3_event_time</td>\n",
-       "      <td>numeric</td>\n",
-       "      <td>True</td>\n",
-       "      <td>score_QRISK3</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>310</th>\n",
-       "      <td>MACE_event</td>\n",
-       "      <td>integer</td>\n",
-       "      <td>True</td>\n",
-       "      <td>score_MACE</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>311</th>\n",
-       "      <td>MACE_event_time</td>\n",
-       "      <td>numeric</td>\n",
-       "      <td>True</td>\n",
-       "      <td>score_MACE</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>312 rows × 5 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "             covariate    dtype isTarget      based_on aggr_fn\n",
-       "0                  eid  integer    False         admin     NaN\n",
-       "1            PGS000011  numeric    False           PGS     NaN\n",
-       "2            PGS000013  numeric    False           PGS     NaN\n",
-       "3            PGS000016  numeric    False           PGS     NaN\n",
-       "4            PGS000018  numeric    False           PGS     NaN\n",
-       "..                 ...      ...      ...           ...     ...\n",
-       "307   ASCVD_event_time  numeric     True   score_ASCVD     NaN\n",
-       "308       QRISK3_event  integer     True  score_QRISK3     NaN\n",
-       "309  QRISK3_event_time  numeric     True  score_QRISK3     NaN\n",
-       "310         MACE_event  integer     True    score_MACE     NaN\n",
-       "311    MACE_event_time  numeric     True    score_MACE     NaN\n",
-       "\n",
-       "[312 rows x 5 columns]"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "covariates = data_description[data_description.isTarget==\"False\"].covariate.to_list()[1:]\n",
-    "targets = data_description[data_description.isTarget==\"True\"].covariate.to_list()\n",
-    "data_description"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Visualize missing values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import matplotlib as mpl\n",
-    "from matplotlib import gridspec\n",
-    "import matplotlib.pyplot as plt\n",
-    "from scipy.cluster import hierarchy\n",
-    "import seaborn as sns\n",
-    "import pandas as pd\n",
-    "import warnings\n",
-    "\n",
-    "def nullity_sort(df, sort=None, axis='columns'):\n",
-    "    \"\"\"\n",
-    "    Sorts a DataFrame according to its nullity, in either ascending or descending order.\n",
-    "    :param df: The DataFrame object being sorted.\n",
-    "    :param sort: The sorting method: either \"ascending\", \"descending\", or None (default).\n",
-    "    :return: The nullity-sorted DataFrame.\n",
-    "    \"\"\"\n",
-    "    if sort is None:\n",
-    "        return df\n",
-    "    elif sort not in ['ascending', 'descending']:\n",
-    "        raise ValueError('The \"sort\" parameter must be set to \"ascending\" or \"descending\".')\n",
-    "\n",
-    "    if axis not in ['rows', 'columns']:\n",
-    "        raise ValueError('The \"axis\" parameter must be set to \"rows\" or \"columns\".')\n",
-    "\n",
-    "    if axis == 'columns':\n",
-    "        if sort == 'ascending':\n",
-    "            return df.iloc[np.argsort(df.count(axis='columns').values), :]\n",
-    "        elif sort == 'descending':\n",
-    "            return df.iloc[np.flipud(np.argsort(df.count(axis='columns').values)), :]\n",
-    "    elif axis == 'rows':\n",
-    "        if sort == 'ascending':\n",
-    "            return df.iloc[:, np.argsort(df.count(axis='rows').values)]\n",
-    "        elif sort == 'descending':\n",
-    "            return df.iloc[:, np.flipud(np.argsort(df.count(axis='rows').values))]\n",
-    "\n",
-    "\n",
-    "def nullity_filter(df, filter=None, p=0, n=0):\n",
-    "    \"\"\"\n",
-    "    Filters a DataFrame according to its nullity, using some combination of 'top' and 'bottom' numerical and\n",
-    "    percentage values. Percentages and numerical thresholds can be specified simultaneously: for example,\n",
-    "    to get a DataFrame with columns of at least 75% completeness but with no more than 5 columns, use\n",
-    "    `nullity_filter(df, filter='top', p=.75, n=5)`.\n",
-    "    :param df: The DataFrame whose columns are being filtered.\n",
-    "    :param filter: The orientation of the filter being applied to the DataFrame. One of, \"top\", \"bottom\",\n",
-    "    or None (default). The filter will simply return the DataFrame if you leave the filter argument unspecified or\n",
-    "    as None.\n",
-    "    :param p: A completeness ratio cut-off. If non-zero the filter will limit the DataFrame to columns with at least p\n",
-    "    completeness. Input should be in the range [0, 1].\n",
-    "    :param n: A numerical cut-off. If non-zero no more than this number of columns will be returned.\n",
-    "    :return: The nullity-filtered `DataFrame`.\n",
-    "    \"\"\"\n",
-    "    if filter == 'top':\n",
-    "        if p:\n",
-    "            df = df.iloc[:, [c >= p for c in df.count(axis='rows').values / len(df)]]\n",
-    "        if n:\n",
-    "            df = df.iloc[:, np.sort(np.argsort(df.count(axis='rows').values)[-n:])]\n",
-    "    elif filter == 'bottom':\n",
-    "        if p:\n",
-    "            df = df.iloc[:, [c <= p for c in df.count(axis='rows').values / len(df)]]\n",
-    "        if n:\n",
-    "            df = df.iloc[:, np.sort(np.argsort(df.count(axis='rows').values)[:n])]\n",
-    "    return df\n",
-    "\n",
-    "def matrix(df,\n",
-    "           filter=None, n=0, p=0, sort=None,\n",
-    "           figsize=(25, 10), width_ratios=(15, 1), color=(0.25, 0.25, 0.25),\n",
-    "           fontsize=16, labels=None, sparkline=True, inline=False,\n",
-    "           freq=None, ax=None):\n",
-    "    \"\"\"\n",
-    "    A matrix visualization of the nullity of the given DataFrame.\n",
-    "    :param df: The `DataFrame` being mapped.\n",
-    "    :param filter: The filter to apply to the heatmap. Should be one of \"top\", \"bottom\", or None (default).\n",
-    "    :param n: The max number of columns to include in the filtered DataFrame.\n",
-    "    :param p: The max percentage fill of the columns in the filtered DataFrame.\n",
-    "    :param sort: The row sort order to apply. Can be \"ascending\", \"descending\", or None.\n",
-    "    :param figsize: The size of the figure to display.\n",
-    "    :param fontsize: The figure's font size. Default to 16.\n",
-    "    :param labels: Whether or not to display the column names. Defaults to the underlying data labels when there are\n",
-    "    50 columns or less, and no labels when there are more than 50 columns.\n",
-    "    :param sparkline: Whether or not to display the sparkline. Defaults to True.\n",
-    "    :param width_ratios: The ratio of the width of the matrix to the width of the sparkline. Defaults to `(15, 1)`.\n",
-    "    Does nothing if `sparkline=False`.\n",
-    "    :param color: The color of the filled columns. Default is `(0.25, 0.25, 0.25)`.\n",
-    "    :return: If `inline` is False, the underlying `matplotlib.figure` object. Else, nothing.\n",
-    "    \"\"\"\n",
-    "    df = nullity_filter(df, filter=filter, n=n, p=p)\n",
-    "    df = nullity_sort(df, sort=sort, axis='columns')\n",
-    "\n",
-    "    height = df.shape[0]\n",
-    "    width = df.shape[1]\n",
-    "\n",
-    "    # z is the color-mask array, g is a NxNx3 matrix. Apply the z color-mask to set the RGB of each pixel.\n",
-    "    z = df.notnull().values\n",
-    "    g = np.zeros((height, width, 3))\n",
-    "\n",
-    "    g[z < 0.5] = [1, 1, 1]\n",
-    "    g[z > 0.5] = color\n",
-    "\n",
-    "    # Set up the matplotlib grid layout. A unary subplot if no sparkline, a left-right splot if yes sparkline.\n",
-    "    if ax is None:\n",
-    "        plt.figure(figsize=figsize)\n",
-    "        if sparkline:\n",
-    "            gs = gridspec.GridSpec(1, 2, width_ratios=width_ratios)\n",
-    "            gs.update(wspace=0.08)\n",
-    "            ax1 = plt.subplot(gs[1])\n",
-    "        else:\n",
-    "            gs = gridspec.GridSpec(1, 1)\n",
-    "        ax0 = plt.subplot(gs[0])\n",
-    "    else:\n",
-    "        if sparkline is not False:\n",
-    "            warnings.warn(\n",
-    "                \"Plotting a sparkline on an existing axis is not currently supported. \"\n",
-    "                \"To remove this warning, set sparkline=False.\"\n",
-    "            )\n",
-    "            sparkline = False\n",
-    "        ax0 = ax\n",
-    "\n",
-    "    # Create the nullity plot.\n",
-    "    ax0.imshow(g, interpolation='none')\n",
-    "\n",
-    "    # Remove extraneous default visual elements.\n",
-    "    ax0.set_aspect('auto')\n",
-    "    ax0.grid(b=False)\n",
-    "    ax0.xaxis.tick_top()\n",
-    "    ax0.xaxis.set_ticks_position('none')\n",
-    "    ax0.yaxis.set_ticks_position('none')\n",
-    "    ax0.spines['top'].set_visible(False)\n",
-    "    ax0.spines['right'].set_visible(False)\n",
-    "    ax0.spines['bottom'].set_visible(False)\n",
-    "    ax0.spines['left'].set_visible(False)\n",
-    "\n",
-    "    # Set up and rotate the column ticks. The labels argument is set to None by default. If the user specifies it in\n",
-    "    # the argument, respect that specification. Otherwise display for <= 50 columns and do not display for > 50.\n",
-    "    if labels or (labels is None and len(df.columns) <= 50):\n",
-    "        ha = 'left'\n",
-    "        ax0.set_xticks(list(range(0, width)))\n",
-    "        ax0.set_xticklabels(list(df.columns), rotation=45, ha=ha, fontsize=fontsize)\n",
-    "    else:\n",
-    "        ax0.set_xticks([])\n",
-    "\n",
-    "    # Adds Timestamps ticks if freq is not None, else set up the two top-bottom row ticks.\n",
-    "    if freq:\n",
-    "        ts_list = []\n",
-    "\n",
-    "        if type(df.index) == pd.PeriodIndex:\n",
-    "            ts_array = pd.date_range(df.index.to_timestamp().date[0],\n",
-    "                                     df.index.to_timestamp().date[-1],\n",
-    "                                     freq=freq).values\n",
-    "\n",
-    "            ts_ticks = pd.date_range(df.index.to_timestamp().date[0],\n",
-    "                                     df.index.to_timestamp().date[-1],\n",
-    "                                     freq=freq).map(lambda t:\n",
-    "                                                    t.strftime('%Y-%m-%d'))\n",
-    "\n",
-    "        elif type(df.index) == pd.DatetimeIndex:\n",
-    "            ts_array = pd.date_range(df.index[0], df.index[-1],\n",
-    "                                     freq=freq).values\n",
-    "\n",
-    "            ts_ticks = pd.date_range(df.index[0], df.index[-1],\n",
-    "                                     freq=freq).map(lambda t:\n",
-    "                                                    t.strftime('%Y-%m-%d'))\n",
-    "        else:\n",
-    "            raise KeyError('Dataframe index must be PeriodIndex or DatetimeIndex.')\n",
-    "        try:\n",
-    "            for value in ts_array:\n",
-    "                ts_list.append(df.index.get_loc(value))\n",
-    "        except KeyError:\n",
-    "            raise KeyError('Could not divide time index into desired frequency.')\n",
-    "\n",
-    "        ax0.set_yticks(ts_list)\n",
-    "        ax0.set_yticklabels(ts_ticks, fontsize=int(fontsize / 16 * 20), rotation=0)\n",
-    "    else:\n",
-    "        ax0.set_yticks([0, df.shape[0] - 1])\n",
-    "        ax0.set_yticklabels([1, df.shape[0]], fontsize=int(fontsize / 16 * 20), rotation=0)\n",
-    "\n",
-    "    if sparkline:\n",
-    "        # Calculate row-wise completeness for the sparkline.\n",
-    "        completeness_srs = df.notnull().astype(bool).sum(axis=1)\n",
-    "        x_domain = list(range(0, height))\n",
-    "        y_range = list(reversed(completeness_srs.values))\n",
-    "        min_completeness = min(y_range)\n",
-    "        max_completeness = max(y_range)\n",
-    "        min_completeness_index = y_range.index(min_completeness)\n",
-    "        max_completeness_index = y_range.index(max_completeness)\n",
-    "\n",
-    "        # Set up the sparkline, remove the border element.\n",
-    "        ax1.grid(b=False)\n",
-    "        ax1.set_aspect('auto')\n",
-    "        # GH 25\n",
-    "        if int(mpl.__version__[0]) <= 1:\n",
-    "            ax1.set_axis_bgcolor((1, 1, 1))\n",
-    "        else:\n",
-    "            ax1.set_facecolor((1, 1, 1))\n",
-    "        ax1.spines['top'].set_visible(False)\n",
-    "        ax1.spines['right'].set_visible(False)\n",
-    "        ax1.spines['bottom'].set_visible(False)\n",
-    "        ax1.spines['left'].set_visible(False)\n",
-    "        ax1.set_ymargin(0)\n",
-    "\n",
-    "        # Plot sparkline---plot is sideways so the x and y axis are reversed.\n",
-    "        ax1.plot(y_range, x_domain, color=color)\n",
-    "\n",
-    "        if labels:\n",
-    "            # Figure out what case to display the label in: mixed, upper, lower.\n",
-    "            label = 'Data Completeness'\n",
-    "            if str(df.columns[0]).islower():\n",
-    "                label = label.lower()\n",
-    "            if str(df.columns[0]).isupper():\n",
-    "                label = label.upper()\n",
-    "\n",
-    "            # Set up and rotate the sparkline label.\n",
-    "            ha = 'left'\n",
-    "            ax1.set_xticks([min_completeness + (max_completeness - min_completeness) / 2])\n",
-    "            ax1.set_xticklabels([label], rotation=45, ha=ha, fontsize=fontsize)\n",
-    "            ax1.xaxis.tick_top()\n",
-    "            ax1.set_yticks([])\n",
-    "        else:\n",
-    "            ax1.set_xticks([])\n",
-    "            ax1.set_yticks([])\n",
-    "\n",
-    "        # Add maximum and minimum labels, circles.\n",
-    "        ax1.annotate(max_completeness,\n",
-    "                     xy=(max_completeness, max_completeness_index),\n",
-    "                     xytext=(max_completeness + 2, max_completeness_index),\n",
-    "                     fontsize=int(fontsize / 16 * 14),\n",
-    "                     va='center',\n",
-    "                     ha='left')\n",
-    "        ax1.annotate(min_completeness,\n",
-    "                     xy=(min_completeness, min_completeness_index),\n",
-    "                     xytext=(min_completeness - 2, min_completeness_index),\n",
-    "                     fontsize=int(fontsize / 16 * 14),\n",
-    "                     va='center',\n",
-    "                     ha='right')\n",
-    "\n",
-    "        ax1.set_xlim([min_completeness - 2, max_completeness + 2])  # Otherwise the circles are cut off.\n",
-    "        ax1.plot([min_completeness], [min_completeness_index], '.', color=color, markersize=10.0)\n",
-    "        ax1.plot([max_completeness], [max_completeness_index], '.', color=color, markersize=10.0)\n",
-    "\n",
-    "        # Remove tick mark (only works after plotting).\n",
-    "        ax1.xaxis.set_ticks_position('none')\n",
-    "\n",
-    "    if inline:\n",
-    "        warnings.warn(\n",
-    "            \"The 'inline' argument has been deprecated, and will be removed in a future version \"\n",
-    "            \"of missingno.\"\n",
-    "        )\n",
-    "        plt.show()\n",
-    "    else:\n",
-    "        return ax0\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-      "text/plain": [
-       "<Figure size 2880x720 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 2880x720 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import missingno as msno\n",
-    "%matplotlib inline\n",
-    "fig = matrix(data[covariates].sample(1000), sparkline=True, figsize=(40, 10))\n",
-    "\n",
-    "missing = data.columns[data.isnull().any()]\n",
-    "fig = matrix(data[missing].sample(1000), sparkline=True, labels=True, figsize=(40, 10))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Preprocessing"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cat_features = data.select_dtypes(['O', \"category\"]).columns.to_list()\n",
-    "cat_idx = data.columns.get_indexer(cat_features)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[Female, Male]\n",
-      "Categories (2, object): [Female < Male]\n",
-      "['White' 'Black' None 'Asien' 'Mixed' 'Chinese']\n",
-      "['Fair' 'Good' 'Poor' 'Excellent' None]\n",
-      "['Current' 'Previous' 'Never' None]\n",
-      "['Once or twice a week' 'Three or four times a week'\n",
-      " 'One to three times a month' 'Daily or almost daily'\n",
-      " 'Special occasions only' 'Never' None]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Show Categories\n",
-    "df = data\n",
-    "for col in cat_features:\n",
-    "    print(df[col].unique())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[1. 2.]\n",
-      "[ 1.  2. nan  4.  5.  6.]\n",
-      "[ 1.  2.  3.  4. nan]\n",
-      "[ 1.  2.  3. nan]\n",
-      "[ 1.  2.  3.  4.  5.  6. nan]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Encode Categories\n",
-    "from category_encoders import *\n",
-    "enc = OrdinalEncoder(cols=cat_features, handle_missing=\"return_nan\", handle_unknown=\"return_nan\")\n",
-    "enc.fit(data)\n",
-    "data_tf = enc.transform(data)\n",
-    "\n",
-    "df = data_tf\n",
-    "for col in cat_features: print(df[col].unique())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Imputation with Missforest"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.experimental import enable_iterative_imputer\n",
-    "from sklearn.impute import IterativeImputer\n",
-    "from sklearn.ensemble import RandomForestRegressor\n",
-    "from sklearn.ensemble import ExtraTreesRegressor"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Imputation FAST and ALRIGHT"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.ensemble import RandomForestRegressor\n",
-    "estimator = RandomForestRegressor(n_estimators=20, max_features='sqrt', bootstrap=True, max_samples=0.1, n_jobs=20)\n",
-    "imputer = IterativeImputer(estimator=estimator, max_iter=10, verbose=2, skip_complete=True, n_nearest_features=20)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[IterativeImputer] Completing matrix with shape (502504, 312)\n",
-      "[IterativeImputer] Ending imputation round 1/10, elapsed time 96.26\n",
-      "[IterativeImputer] Change: 5025.888209169452, scaled tolerance: 6025.198 \n",
-      "[IterativeImputer] Early stopping criterion reached.\n",
-      "CPU times: user 13min 49s, sys: 28 s, total: 14min 17s\n",
-      "Wall time: 2min 2s\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "IterativeImputer(estimator=RandomForestRegressor(max_features='sqrt',\n",
-       "                                                 max_samples=0.1,\n",
-       "                                                 n_estimators=20, n_jobs=20),\n",
-       "                 n_nearest_features=20, skip_complete=True, verbose=2)"
-      ]
-     },
-     "execution_count": 55,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "# fit to training data\n",
-    "imputer.fit(data_tf)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Imputation PERFECT but SLOW"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.ensemble import RandomForestRegressor\n",
-    "estimator = RandomForestRegressor(n_estimators=50, max_features='sqrt', bootstrap=True, max_samples=0.1, n_jobs=20, oob_score=True)\n",
-    "imputer = IterativeImputer(estimator=estimator, max_iter=10, verbose=2, skip_complete=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[IterativeImputer] Completing matrix with shape (502504, 312)\n",
-      "[IterativeImputer] Ending imputation round 1/10, elapsed time 794.15\n",
-      "[IterativeImputer] Change: 3884.692167206953, scaled tolerance: 6025.198 \n",
-      "[IterativeImputer] Early stopping criterion reached.\n",
-      "CPU times: user 1h 8min 22s, sys: 6min 54s, total: 1h 15min 17s\n",
-      "Wall time: 14min\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "IterativeImputer(estimator=RandomForestRegressor(max_features='sqrt',\n",
-       "                                                 max_samples=0.1,\n",
-       "                                                 n_estimators=50, n_jobs=20),\n",
-       "                 skip_complete=True, verbose=2)"
-      ]
-     },
-     "execution_count": 47,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "# fit to training data\n",
-    "imputer.fit(data_tf)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Apply Imputation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[IterativeImputer] Completing matrix with shape (502504, 312)\n",
-      "[IterativeImputer] Ending imputation round 1/1, elapsed time 16.25\n"
-     ]
-    }
-   ],
-   "source": [
-    "# transform training data\n",
-    "data_imp_tf_array = imputer.transform(data_tf)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# transform back to df\n",
-    "data_imp_tf = pd.DataFrame(data=data_imp_tf_array, columns=data.columns)\n",
-    "for col in cat_features: data_imp_tf[col] = data_imp_tf[col].astype(int)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Inverse Transform labels\n",
-    "data_imp = enc.inverse_transform(data_imp_tf)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Visualize Post Imputation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 2880x720 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 2880x720 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import missingno as msno\n",
-    "%matplotlib inline\n",
-    "fig = matrix(data_imp[covariates].sample(1000), sparkline=True, figsize=(40, 10))\n",
-    "\n",
-    "missing = data.columns[data.isnull().any()]\n",
-    "fig = matrix(data_imp[missing].sample(1000), sparkline=True, labels=True, figsize=(40, 10))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 1.,  2., nan,  4.,  5.,  6.])"
-      ]
-     },
-     "execution_count": 61,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data_tf.ethnic_background.unique()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Write to disk"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "/data/analysis/ag-reils/ag-reils-shared/cardioRS/data/all/cvd_big/baseline_imputed\n"
-     ]
-    }
-   ],
-   "source": [
-    "shared_project_path = os.path.join(shared_path, project_name)\n",
-    "pathlib.Path(shared_project_path).mkdir(parents=True, exist_ok=True)\n",
-    "write_path = os.path.join(shared_project_path, \"baseline_imputed\")\n",
-    "data_imp.to_feather(write_path+\".feather\")\n",
-    "data_imp.to_csv(write_path+\".csv\", index=False, na_rep='NA')\n",
-    "print(write_path)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Plus 1 year "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>myocardial_infarction_event_time</th>\n",
-       "      <th>stroke_event_time</th>\n",
-       "      <th>cancer_breast_event_time</th>\n",
-       "      <th>diabetes_event_time</th>\n",
-       "      <th>atrial_fibrillation_event_time</th>\n",
-       "      <th>copd_event_time</th>\n",
-       "      <th>dementia_event_time</th>\n",
-       "      <th>death_allcause_event_time</th>\n",
-       "      <th>death_cvd_event_time</th>\n",
-       "      <th>SCORE_event_time</th>\n",
-       "      <th>ASCVD_event_time</th>\n",
-       "      <th>QRISK3_event_time</th>\n",
-       "      <th>MACE_event_time</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>10.335387</td>\n",
-       "      <td>10.335387</td>\n",
-       "      <td>10.335387</td>\n",
-       "      <td>10.335387</td>\n",
-       "      <td>10.335387</td>\n",
-       "      <td>10.335387</td>\n",
-       "      <td>10.335387</td>\n",
-       "      <td>10.625599</td>\n",
-       "      <td>10.625599</td>\n",
-       "      <td>10.625599</td>\n",
-       "      <td>10.335387</td>\n",
-       "      <td>10.335387</td>\n",
-       "      <td>10.335387</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>12.065708</td>\n",
-       "      <td>12.065708</td>\n",
-       "      <td>12.065708</td>\n",
-       "      <td>12.065708</td>\n",
-       "      <td>12.065708</td>\n",
-       "      <td>12.065708</td>\n",
-       "      <td>12.065708</td>\n",
-       "      <td>12.355921</td>\n",
-       "      <td>12.355921</td>\n",
-       "      <td>12.355921</td>\n",
-       "      <td>12.065708</td>\n",
-       "      <td>12.065708</td>\n",
-       "      <td>12.065708</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>11.337440</td>\n",
-       "      <td>11.337440</td>\n",
-       "      <td>11.337440</td>\n",
-       "      <td>11.337440</td>\n",
-       "      <td>11.337440</td>\n",
-       "      <td>11.337440</td>\n",
-       "      <td>11.337440</td>\n",
-       "      <td>11.627652</td>\n",
-       "      <td>11.627652</td>\n",
-       "      <td>11.627652</td>\n",
-       "      <td>7.969884</td>\n",
-       "      <td>7.969884</td>\n",
-       "      <td>11.337440</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>5.122519</td>\n",
-       "      <td>10.778919</td>\n",
-       "      <td>10.778919</td>\n",
-       "      <td>10.778919</td>\n",
-       "      <td>10.778919</td>\n",
-       "      <td>0.292950</td>\n",
-       "      <td>10.778919</td>\n",
-       "      <td>11.069131</td>\n",
-       "      <td>11.069131</td>\n",
-       "      <td>11.069131</td>\n",
-       "      <td>5.122519</td>\n",
-       "      <td>5.122519</td>\n",
-       "      <td>5.122519</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>13.760438</td>\n",
-       "      <td>13.760438</td>\n",
-       "      <td>13.760438</td>\n",
-       "      <td>4.722793</td>\n",
-       "      <td>13.760438</td>\n",
-       "      <td>4.840520</td>\n",
-       "      <td>13.760438</td>\n",
-       "      <td>14.050650</td>\n",
-       "      <td>14.050650</td>\n",
-       "      <td>14.050650</td>\n",
-       "      <td>13.760438</td>\n",
-       "      <td>13.760438</td>\n",
-       "      <td>13.760438</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   myocardial_infarction_event_time  stroke_event_time  \\\n",
-       "0                         10.335387          10.335387   \n",
-       "1                         12.065708          12.065708   \n",
-       "2                         11.337440          11.337440   \n",
-       "3                          5.122519          10.778919   \n",
-       "4                         13.760438          13.760438   \n",
-       "\n",
-       "   cancer_breast_event_time  diabetes_event_time  \\\n",
-       "0                 10.335387            10.335387   \n",
-       "1                 12.065708            12.065708   \n",
-       "2                 11.337440            11.337440   \n",
-       "3                 10.778919            10.778919   \n",
-       "4                 13.760438             4.722793   \n",
-       "\n",
-       "   atrial_fibrillation_event_time  copd_event_time  dementia_event_time  \\\n",
-       "0                       10.335387        10.335387            10.335387   \n",
-       "1                       12.065708        12.065708            12.065708   \n",
-       "2                       11.337440        11.337440            11.337440   \n",
-       "3                       10.778919         0.292950            10.778919   \n",
-       "4                       13.760438         4.840520            13.760438   \n",
-       "\n",
-       "   death_allcause_event_time  death_cvd_event_time  SCORE_event_time  \\\n",
-       "0                  10.625599             10.625599         10.625599   \n",
-       "1                  12.355921             12.355921         12.355921   \n",
-       "2                  11.627652             11.627652         11.627652   \n",
-       "3                  11.069131             11.069131         11.069131   \n",
-       "4                  14.050650             14.050650         14.050650   \n",
-       "\n",
-       "   ASCVD_event_time  QRISK3_event_time  MACE_event_time  \n",
-       "0         10.335387          10.335387        10.335387  \n",
-       "1         12.065708          12.065708        12.065708  \n",
-       "2          7.969884           7.969884        11.337440  \n",
-       "3          5.122519           5.122519         5.122519  \n",
-       "4         13.760438          13.760438        13.760438  "
-      ]
-     },
-     "execution_count": 63,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data_imp_yearplus1 = data_imp.copy()\n",
-    "event_time_cols = [s for s in data.columns.to_list() if \"_event_time\" in s] \n",
-    "data_imp[event_time_cols].head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>myocardial_infarction_event_time</th>\n",
-       "      <th>stroke_event_time</th>\n",
-       "      <th>cancer_breast_event_time</th>\n",
-       "      <th>diabetes_event_time</th>\n",
-       "      <th>atrial_fibrillation_event_time</th>\n",
-       "      <th>copd_event_time</th>\n",
-       "      <th>dementia_event_time</th>\n",
-       "      <th>death_allcause_event_time</th>\n",
-       "      <th>death_cvd_event_time</th>\n",
-       "      <th>SCORE_event_time</th>\n",
-       "      <th>ASCVD_event_time</th>\n",
-       "      <th>QRISK3_event_time</th>\n",
-       "      <th>MACE_event_time</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>11.335387</td>\n",
-       "      <td>11.335387</td>\n",
-       "      <td>11.335387</td>\n",
-       "      <td>11.335387</td>\n",
-       "      <td>11.335387</td>\n",
-       "      <td>11.335387</td>\n",
-       "      <td>11.335387</td>\n",
-       "      <td>11.625599</td>\n",
-       "      <td>11.625599</td>\n",
-       "      <td>11.625599</td>\n",
-       "      <td>11.335387</td>\n",
-       "      <td>11.335387</td>\n",
-       "      <td>11.335387</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>13.065708</td>\n",
-       "      <td>13.065708</td>\n",
-       "      <td>13.065708</td>\n",
-       "      <td>13.065708</td>\n",
-       "      <td>13.065708</td>\n",
-       "      <td>13.065708</td>\n",
-       "      <td>13.065708</td>\n",
-       "      <td>13.355921</td>\n",
-       "      <td>13.355921</td>\n",
-       "      <td>13.355921</td>\n",
-       "      <td>13.065708</td>\n",
-       "      <td>13.065708</td>\n",
-       "      <td>13.065708</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>12.337440</td>\n",
-       "      <td>12.337440</td>\n",
-       "      <td>12.337440</td>\n",
-       "      <td>12.337440</td>\n",
-       "      <td>12.337440</td>\n",
-       "      <td>12.337440</td>\n",
-       "      <td>12.337440</td>\n",
-       "      <td>12.627652</td>\n",
-       "      <td>12.627652</td>\n",
-       "      <td>12.627652</td>\n",
-       "      <td>8.969884</td>\n",
-       "      <td>8.969884</td>\n",
-       "      <td>12.337440</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>6.122519</td>\n",
-       "      <td>11.778919</td>\n",
-       "      <td>11.778919</td>\n",
-       "      <td>11.778919</td>\n",
-       "      <td>11.778919</td>\n",
-       "      <td>1.292950</td>\n",
-       "      <td>11.778919</td>\n",
-       "      <td>12.069131</td>\n",
-       "      <td>12.069131</td>\n",
-       "      <td>12.069131</td>\n",
-       "      <td>6.122519</td>\n",
-       "      <td>6.122519</td>\n",
-       "      <td>6.122519</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>14.760438</td>\n",
-       "      <td>14.760438</td>\n",
-       "      <td>14.760438</td>\n",
-       "      <td>5.722793</td>\n",
-       "      <td>14.760438</td>\n",
-       "      <td>5.840520</td>\n",
-       "      <td>14.760438</td>\n",
-       "      <td>15.050650</td>\n",
-       "      <td>15.050650</td>\n",
-       "      <td>15.050650</td>\n",
-       "      <td>14.760438</td>\n",
-       "      <td>14.760438</td>\n",
-       "      <td>14.760438</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   myocardial_infarction_event_time  stroke_event_time  \\\n",
-       "0                         11.335387          11.335387   \n",
-       "1                         13.065708          13.065708   \n",
-       "2                         12.337440          12.337440   \n",
-       "3                          6.122519          11.778919   \n",
-       "4                         14.760438          14.760438   \n",
-       "\n",
-       "   cancer_breast_event_time  diabetes_event_time  \\\n",
-       "0                 11.335387            11.335387   \n",
-       "1                 13.065708            13.065708   \n",
-       "2                 12.337440            12.337440   \n",
-       "3                 11.778919            11.778919   \n",
-       "4                 14.760438             5.722793   \n",
-       "\n",
-       "   atrial_fibrillation_event_time  copd_event_time  dementia_event_time  \\\n",
-       "0                       11.335387        11.335387            11.335387   \n",
-       "1                       13.065708        13.065708            13.065708   \n",
-       "2                       12.337440        12.337440            12.337440   \n",
-       "3                       11.778919         1.292950            11.778919   \n",
-       "4                       14.760438         5.840520            14.760438   \n",
-       "\n",
-       "   death_allcause_event_time  death_cvd_event_time  SCORE_event_time  \\\n",
-       "0                  11.625599             11.625599         11.625599   \n",
-       "1                  13.355921             13.355921         13.355921   \n",
-       "2                  12.627652             12.627652         12.627652   \n",
-       "3                  12.069131             12.069131         12.069131   \n",
-       "4                  15.050650             15.050650         15.050650   \n",
-       "\n",
-       "   ASCVD_event_time  QRISK3_event_time  MACE_event_time  \n",
-       "0         11.335387          11.335387        11.335387  \n",
-       "1         13.065708          13.065708        13.065708  \n",
-       "2          8.969884           8.969884        12.337440  \n",
-       "3          6.122519           6.122519         6.122519  \n",
-       "4         14.760438          14.760438        14.760438  "
-      ]
-     },
-     "execution_count": 64,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "for col in event_time_cols: data_imp_yearplus1[col] = data_imp_yearplus1[col]+1\n",
-    "data_imp_yearplus1[event_time_cols].head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "/data/analysis/ag-reils/ag-reils-shared/cardioRS/data/all/cvd_big/baseline_imputed_years+1\n"
-     ]
-    }
-   ],
-   "source": [
-    "shared_project_path = os.path.join(shared_path, project_name)\n",
-    "pathlib.Path(shared_project_path).mkdir(parents=True, exist_ok=True)\n",
-    "write_path = os.path.join(shared_project_path, \"baseline_imputed_years+1\")\n",
-    "data_imp_yearplus1.to_feather(write_path+\".feather\")\n",
-    "data_imp_yearplus1.to_csv(write_path+\".csv\", index=False, na_rep='NA')\n",
-    "print(write_path)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python [conda env:python]",
-   "language": "python",
-   "name": "conda-env-python-py"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/neuralcvd/preprocessing/ukbb_tabular/ConvertFeather.ipynb b/neuralcvd/preprocessing/ukbb_tabular/ConvertFeather.ipynb
deleted file mode 100644
index 1cbe990..0000000
--- a/neuralcvd/preprocessing/ukbb_tabular/ConvertFeather.ipynb
+++ /dev/null
@@ -1,177 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pathlib\n",
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pyarrow"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'0.17.1'"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pyarrow.__version__"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "project_name = \"cvd_massive_excl_emb_ind\"\n",
-    "data_path = \"/data/analysis/ag-reils/steinfej\"\n",
-    "data_pre = f\"{data_path}/data/2_datasets_pre/{project_name}\"\n",
-    "data_post = f\"{data_path}/data/3_datasets_post/{project_name}\"\n",
-    "\n",
-    "project_label = \"paper1\"\n",
-    "project_path = f\"/data/analysis/ag-reils/ag-reils-shared/cardioRS/results/projects/results/projects/{project_label}\"\n",
-    "figures_path = f\"{project_path}/figures\"\n",
-    "data_results_path = f\"{project_path}/data\"\n",
-    "pathlib.Path(figures_path).mkdir(parents=True, exist_ok=True)\n",
-    "pathlib.Path(data_results_path).mkdir(parents=True, exist_ok=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1_dataset_characterization.pdf\t       endpoint_list.yaml\n",
-      "2_observation_time.pdf\t\t       feature_list.yaml\n",
-      "3_endpoints.pdf\t\t\t       fh_list.yaml\n",
-      "4_endpoint_densities.pdf\t       medication_list.yaml\n",
-      "5_endpoint_km_MACE.pdf\t\t       phenotype_list.yaml\n",
-      "Figure1.pdf\t\t\t       predictions_coxph.csv\n",
-      "Table1.html\t\t\t       predictions_coxph_tall.csv\n",
-      "baseline.feather\t\t       predictions_scores.csv\n",
-      "baseline_clinical.feather\t       scores_list.yaml\n",
-      "baseline_clinical_description.feather  table1_union.html\n",
-      "baseline_clinical_full.feather\t       temp_basics.feather\n",
-      "baseline_description.feather\t       temp_diagnoses.feather\n",
-      "baseline_description_v2.feather        temp_diagnoses_codes.feather\n",
-      "baseline_imputed.feather\t       temp_diagnoses_emb.feather\n",
-      "baseline_imputed_v2.feather\t       temp_family_history.feather\n",
-      "baseline_imputed_years+1.feather       temp_labs.feather\n",
-      "baseline_pgs.feather\t\t       temp_measurements.feather\n",
-      "baseline_pgs_description.feather       temp_medications.feather\n",
-      "death_list.yaml\t\t\t       temp_questionnaire.feather\n"
-     ]
-    }
-   ],
-   "source": [
-    "!ls {\"/data/analysis/ag-reils/steinfej/data/2_datasets_pre/cvd_massive_excl_emb_ind\"}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_description = pd.read_feather(f\"{data_pre}/baseline_pgs_description.feather\")\n",
-    "data = pd.read_feather(f\"{data_pre}/baseline_pgs.feather\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pyarrow"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pyarrow.feather.write_feather(df=data_description, dest=f\"{data_pre}/baseline_pgs_description_v2.feather\",  version=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pyarrow.feather.write_feather(df=data, dest=f\"{data_pre}/baseline_pgs_v2.feather\",  version=1) # v1 necessary!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "TypeError",
-     "evalue": "to_feather() got an unexpected keyword argument 'version'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-12-3fe11a3affb9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdata_description\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_feather\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"{data_pre}/baseline_description_v2.feather\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mversion\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m~/miniconda3/envs/python/lib/python3.7/site-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    212\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    213\u001b[0m                     \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnew_arg_name\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_arg_value\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 214\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    215\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    216\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mF\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mTypeError\u001b[0m: to_feather() got an unexpected keyword argument 'version'"
-     ]
-    }
-   ],
-   "source": [
-    "data_description.to_feather(f\"{data_pre}/baseline_description_v2.feather\", version=2)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python [conda env:python]",
-   "language": "python",
-   "name": "conda-env-python-py"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/neuralcvd/preprocessing/ukbb_tabular/pipeline_centres.py b/neuralcvd/preprocessing/ukbb_tabular/pipeline_centres.py
deleted file mode 100644
index 8769e7f..0000000
--- a/neuralcvd/preprocessing/ukbb_tabular/pipeline_centres.py
+++ /dev/null
@@ -1,392 +0,0 @@
-import os
-import yaml
-import pickle
-import pathlib
-import pandas as pd
-import numpy as np
-import prefect as pf
-import miceforest as mf
-from prefect.engine.results import LocalResult
-from prefect.engine.flow_runner import FlowRunner
-from prefect.engine.serializers import PandasSerializer, JSONSerializer
-from prefect.executors import DaskExecutor, LocalDaskExecutor
-from sklearn.model_selection import KFold, train_test_split
-from sklearn.preprocessing import StandardScaler
-from collections import OrderedDict
-import pickle
-import joblib
-from sklearn.preprocessing import OrdinalEncoder
-
-from copy import deepcopy
-
-
-output_directory = '/data/analysis/ag-reils/ag-reils-shared/cardioRS/data/3_datasets_post'
-output_name = '210616_centres_dask'
-
-# pd_serializer = PandasSerializer(file_type='csv')
-json_serializer = JSONSerializer()
-
-
-class ApplyImputer(pf.Task):
-    """
-    Takes a list of tuples, where the first pos is the eids_dict, the second is the kernel, the third is the split.
-    Then applies imputer and saves to file.
-    """
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-
-    def _update_target(self, cv_partition, split):
-        """
-        Update Target string at runtime.
-        :return:
-        """
-        self.target = f"partition_{cv_partition}/{split}_baseline_imputed.csv"
-
-    def run(self, partition_split_dict):
-        """
-        split tuple is a tuple in the form of
-        (  (partition_idx, eids_dict,  (data_merged, data_merged_description)  ), imputer, split)
-        :param partition_split_dict:
-        :return:
-        """
-        split = partition_split_dict['split']
-        partition = partition_split_dict["cv_partition"]
-        eids = partition_split_dict['eids_dict'][split]
-
-        assert split in ['test', 'train', 'valid']
-        self._update_target(partition_split_dict['cv_partition'], split)
-        data = partition_split_dict['data'].loc[eids]
-
-        # Save partitions
-        data_output_path = f"{output_directory}/{output_name}/partition_{partition}/{split}"
-        pathlib.Path(data_output_path).mkdir(parents=True, exist_ok=True)
-        data.reset_index().to_feather(f"{data_output_path}/data.feather")
-        #data.reset_index().to_csv(f"{data_output_path}/datam.csv")
-
-        # Impute data
-        with open(partition_split_dict['imputer_path'], "rb") as input_file: imputer = pickle.load(input_file)
-        data_imputed = imputer.impute_new_data(new_data=data).complete_data()
-        partition_split_dict['data'] = data_imputed
-
-        data_output_path = f"{output_directory}/{output_name}/partition_{partition}/{split}"
-        pathlib.Path(data_output_path).mkdir(parents=True, exist_ok=True)
-        data_imputed.reset_index().to_feather(f"{data_output_path}/data_imputed.feather")
-        #data_imputed.reset_index().to_csv(f"{data_output_path}/data_imputed.csv")
-
-        return partition_split_dict
-
-
-class ApplyNorm(pf.Task):
-    """
-    Takes a list of tuples, where the first pos is the eid_dict, the second is the kernel, the third is the split.
-    Then applies imputer and saves to file.
-    """
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-
-    def _update_target(self, cv_partition, split):
-        """
-        Update Target string at runtime.
-        :return:
-        """
-        self.target = f"partition_{cv_partition}/{split}_baseline_imputed_normalized.csv"
-
-    def run(self, partition_split_dict):
-        """
-        DICT
-        :param partition_split_dict:
-        :return:
-        """
-        split = partition_split_dict['split']
-        partition = partition_split_dict['cv_partition']
-        self._update_target(partition, split)
-
-        description = partition_split_dict['description']
-
-        noncategorical_covariates = description.reset_index() \
-            .set_index('dtype').loc[['int', "float"]] \
-            .query("(isTarget == False) & (based_on != 'diagnoses_emb') & (based_on != 'eid')")['covariate'].values
-
-
-        noncat_data = partition_split_dict['data'][noncategorical_covariates].copy()
-
-        # log 1p transform!:
-        for c in noncat_data.columns:
-            if c.startswith('NMR'):
-                noncat_data[c] = np.log1p(noncat_data[c].values)
-
-        noncat_data = noncat_data.values
-
-        noncat_data = pd.DataFrame(partition_split_dict['normalizer'].transform(noncat_data),
-                                   columns=noncategorical_covariates)
-
-        #print(noncat_data.head())
-
-        for v in noncategorical_covariates:
-            partition_split_dict['data'][v] = noncat_data[v].values
-
-        # save preprocessed data
-        data_output_path = f"{output_directory}/{output_name}/partition_{partition}/{split}"
-        pathlib.Path(data_output_path).mkdir(parents=True, exist_ok=True)
-        partition_split_dict['data'].reset_index().to_feather(f"{data_output_path}/data_imputed_normalized.feather")
-        #partition_split_dict['data'].reset_index().to_csv(f"{data_output_path}/data_imputed_normalized.csv")
-
-        # return partition_split_dict
-        return partition_split_dict['data']
-
-@pf.task(target="data_merged_dict.p",
-         checkpoint=True,
-         log_stdout=True,
-      result=LocalResult(dir=f"{output_directory}/{output_name}")
-      )
-def read_and_merge_data(covariate_paths, input_data_dir):
-    logger = pf.context.get("logger")
-    logger.info("Data")
-    data_dfs = [pd.read_feather(f"{input_data_dir}/{covariate_paths[covariate][0]}").set_index("eid") for covariate in covariate_paths]
-    data_merged = pd.concat(data_dfs, axis=1)
-    output_path = f"{output_directory}/{output_name}"
-
-    data_merged.reset_index().to_feather(f"{output_path}/data_merged.feather")
-    #data_merged.reset_index().to_csv(f"{output_path}/data.csv")
-
-    logger.info("Descriptions")
-    description_dfs = [pd.read_feather(f"{input_data_dir}/{covariate_paths[covariate][1]}") for covariate in covariate_paths]
-    description_merged = pd.concat([df if i == 0 else df.tail(-1) for i, df in enumerate(description_dfs)], axis=0).reset_index()
-    description_merged.reset_index(drop=True).to_feather(f"{output_path}/description_merged.feather")
-    #description_merged.reset_index().to_csv(f"{output_path}/description.csv")
-
-    return {"data": data_merged, "description": description_merged}
-
-@pf.task(name="encode_categoricals",
-      target="data_encoded.p",
-      checkpoint=True,
-      result=LocalResult(dir=f"{output_directory}/{output_name}")#, serializer=pd_serializer)
-      )
-def encode_categoricals(data_dict):
-    logger = pf.context.get("logger")
-    data = data_dict["data"]
-    description = data_dict["description"]
-
-    from category_encoders.ordinal import OrdinalEncoder
-    cat_cols = [c for c in description.set_index("dtype").loc[["category"]].covariate.to_list() if "date" not in c]
-
-    mapping = [{"col": c, "mapping": {e: i for i, e in enumerate([v for v in data[c].unique().tolist() if v==v])}} for c in cat_cols]
-    for i, c in enumerate(cat_cols): mapping[i]["mapping"].update({np.nan: -2})
-
-    enc = OrdinalEncoder(cols=cat_cols, mapping=mapping, handle_missing="return_nan")
-    data = enc.fit_transform(data)
-
-    description["mapping"] = np.nan
-    for i, c in enumerate(cat_cols):
-        description.loc[description.covariate == c, 'mapping'] = str(enc.mapping[i]["mapping"])
-        if data[c].nunique() > 2:
-            ohe_encoded = pd.get_dummies(data[c], prefix=c)
-            data[ohe_encoded.columns] = ohe_encoded
-            for col in ohe_encoded.columns:
-                description = description.append(
-                    {"covariate": col, "dtype": "bool", "isTarget": False,
-                     "based_on": description.loc[description.covariate == c, "based_on"].iloc[0],
-                     "aggr_fn": np.nan, "mapping": str(enc.mapping[i]["mapping"])}, ignore_index=True)
-    description["based_on"] = description["based_on"].astype(str)
-
-    #data.reset_index().to_feather(f"{output_directory}/{output_name}/data_encoded.feather")
-    description.reset_index(drop=True).to_feather(f"{output_directory}/{output_name}/description.feather")
-
-    logger.info(f"{len(cat_cols)} columns one-hot-encoded")
-    return {"data": data, "description": description}
-
-@pf.task(name="apply_exclusion_criteria",
-      target="data_merged_excluded_dict.p",
-      checkpoint=True,
-      result=LocalResult(dir=f"{output_directory}/{output_name}")#, serializer=pd_serializer)
-      )
-def apply_exclusion_criteria(data_dict, exclusion_criteria):
-    logger = pf.context.get("logger")
-    data = data_dict["data"]
-    data_excl = data.copy().query(exclusion_criteria).reset_index(drop=False).set_index("eid")
-    output_path = f"{output_directory}/{output_name}"
-    data_excl.reset_index().to_feather(f"{output_path}/data_excl.feather")
-    #data_excl.reset_index().to_csv(f"{output_path}/data_excl.csv")
-    logger.info(f"{len(data)-len(data_excl)} eids excluded")
-    return {"data": data, "description": data_dict["description"]}
-
-@pf.task(name="get_eids_for_partitions",
-      target=f"eids.json",
-      checkpoint=True,
-      result=LocalResult(dir=f"{output_directory}/{output_name}", serializer=json_serializer)
-      )
-
-def get_eids_for_partitions(data_dict, partition_column, valid_size=0.1):
-    logger = pf.context.get("logger")
-
-    data_all = data_dict["data"]
-    eids_all = data_all.index.values
-    groups = data_all.reset_index().set_index(partition_column).index.value_counts().index.to_list()
-    splits = {i: data_all.query(f"{partition_column}==@group").index.tolist() for i, group in enumerate(groups)}
-
-    eids_dict = OrderedDict()
-    for partition in range(len(groups)):
-        eids_dict[partition] = {}
-        eids_test = splits[partition]
-        eids_notest = sorted(list(set(eids_all) - set(eids_test)))
-        eids_train, eids_valid = train_test_split(eids_notest, test_size=valid_size, shuffle=False)
-
-        if bool(set(eids_train) & set(eids_valid) & set(eids_test)) == True:
-            logger.warning(f"Overlap of eids in partition {partition}")
-        else:
-            logger.info(f"No overlap of eids in partition {partition}")
-
-        eids_dict[partition]["train"] = eids_train
-        eids_dict[partition]["valid"] = eids_valid
-        eids_dict[partition]["test"] = eids_test
-
-    return eids_dict
-
-@pf.task
-def get_partitions(data_dict, eids_dict):
-    partition_dicts = [{**data_dict, 'cv_partition': partition_idx, 'eids_dict': eids_dict[partition_idx]} for partition_idx in eids_dict.keys()]
-    return partition_dicts
-
-
-@pf.task(name="fit_imputer",
-      target="{task_name}/{task_full_name}_kernel.p",
-      checkpoint=True,
-      result=LocalResult(dir=os.path.join(output_directory, output_name, "pipeline/"))
-      )
-def fit_imputer(partition_dict):
-    """
-    Fit an imputer to train set and pickle it
-    (partition_idx, eids_dict, (data, data_descr) )
-    """
-    eids_train = partition_dict['eids_dict']['train']
-    data = partition_dict['data'].loc[eids_train]#.query('NMR_FLAG==True')
-    partition = partition_dict["cv_partition"]
-
-    missing = data.columns[data.isna().any()].to_list()
-    missing = [col for col in missing if not "NMR_" in col]
-
-    variable_schema = {}
-    for m in missing:
-        variable_schema[m] = [x for x in missing if x != m]+["MACE_event", "MACE_event_time"]
-    kernel = mf.KernelDataSet(data,
-                              variable_schema=variable_schema,
-                              save_all_iterations=True,
-                              random_state=42)
-
-    # Run the MICE algorithm for 3 iterations
-    kernel.mice(3, n_jobs=20, n_estimators=8,
-                max_features="sqrt", bootstrap=True, max_depth=8, verbose=True)
-
-    data_output_path = f"{output_directory}/{output_name}/partition_{partition}"
-    pathlib.Path(data_output_path).mkdir(parents=True, exist_ok=True)
-
-    imputer_path = f"{data_output_path}/imputer.p"
-    with open(imputer_path, "wb") as output_file: pickle.dump(kernel, output_file)
-    del kernel
-    return imputer_path
-
-@pf.task
-def get_splits_per_partition(partition_dict, imputer_path, splits):
-    partition_split_dicts = [{**partition_dict, 'imputer_path': imputer_path, 'split': s} for s in splits]
-    return partition_split_dicts
-
-@pf.task(name="fit_normalization",
-         target="{task_name}/{task_full_name}_norm.p",
-      checkpoint=True,
-      result=LocalResult(dir=os.path.join(output_directory, output_name, "pipeline/"))
-      )
-def fit_normalization(partition_split_dicts):
-    """
-    Fit an imputer to train set and pickle it.
-
-    imputed_tuples should be a list of dicts of the form:
-    data_imputed is the imputed data for a split in the partition for partition idx
-
-    """
-    # first get vars:
-    description = partition_split_dicts[0]['description']
-    noncategorical_covariates = description.reset_index() \
-        .set_index('dtype').loc[['int', "float"]] \
-        .query("(isTarget == False) & (based_on != 'diagnoses_emb') & (based_on != 'eid')")['covariate'].values
-
-    # fit normalizer for each train split:
-    fitted_normalizers = {}
-    for d in partition_split_dicts:
-        if d['split'] == 'train':
-            if 'eid' in d['data'].columns:
-                data = d['data'].set_index('eid')
-            else:
-                data = d['data']
-            noncategorical_data = data[noncategorical_covariates]
-
-            # log 1p transform!:
-            for c in noncategorical_data.columns:
-                if c.startswith('NMR'):
-                    noncategorical_data[c] = np.log1p(noncategorical_data[c].values)
-
-            noncategorical_data = noncategorical_data.values
-
-            norm = StandardScaler(with_mean=True, with_std=True, copy=True).fit(noncategorical_data)
-            fitted_normalizers[d['cv_partition']] = norm
-
-    partition_split_dicts = [{**d, 'normalizer': fitted_normalizers[d['cv_partition']]} for d in partition_split_dicts]
-    return partition_split_dicts
-
-
-Impute = ApplyImputer(name="apply_imputer",
-                      target=f"partition_23/baseline_imputed.csv",
-                      checkpoint=True,
-                      result=LocalResult(dir=f"{output_directory}/{output_name}/cv_partitions/"),
-                                         # serializer=pd_serializer)
-                      )
-
-Normalize = ApplyNorm(name="apply_norm",
-                      target=f"partition_23/baseline_imputed_normalized.csv",
-                      checkpoint=True,
-                      result=LocalResult(dir=f"{output_directory}/{output_name}/cv_partitions/"),
-                                         # serializer=pd_serializer)
-                      )
-
-with pf.Flow("ukb_pipeline") as flow:
-    input_data_dir = pf.Parameter('input_data',
-                                  default='/data/analysis/ag-reils/ag-reils-shared/cardioRS/data/2_datasets_pre/210514_metabolomics/')
-    #exclusion_criteria = pf.Parameter('exclusion_criteria',
-    #                               default="myocardial_infarction == False & stroke == False & statins == False")
-    partition_column = pf.Parameter('partition_column', default="uk_biobank_assessment_centre")
-    valid_size = pf.Parameter('valid_size', default=0.1)
-
-    data_filenames = {
-        "covariates": ("baseline_covariates.feather", "baseline_covariates_description.feather"),
-        "pgs": ("baseline_pgs.feather", "baseline_pgs_description.feather"),
-        "endpoints": ("baseline_endpoints.feather", "baseline_endpoints_description.feather"),
-    }
-    data_dict = read_and_merge_data(data_filenames, input_data_dir)
-    data_dict = encode_categoricals(data_dict)
-    #data_dict = apply_exclusion_criteria(data_dict, exclusion_criteria)
-    eids_dict = get_eids_for_partitions(data_dict, partition_column=partition_column, valid_size=valid_size)
-    partition_dicts = get_partitions(data_dict, eids_dict)
-
-    # fit imputer per partition
-    imputer_paths = fit_imputer.map(partition_dict=partition_dicts)
-
-    partition_split_dicts = get_splits_per_partition.map(partition_dicts,
-                                                     imputer_paths,
-                                                     splits=pf.unmapped(['train', 'test', 'valid'])
-                                                     )
-
-    partition_split_dicts = Impute.map(partition_split_dict=pf.flatten(partition_split_dicts))
-    partition_split_dicts = fit_normalization(partition_split_dicts=partition_split_dicts)
-
-    normalized = Normalize.map(partition_split_dict=partition_split_dicts)
-
-if __name__ == "__main__":
-    flow.executor = LocalDaskExecutor(scheduler="threads", num_workers=80)
-    #flow.executor = DaskExecutor(cluster_kwargs={"n_workers": 10, "threads_per_worker": 20}, debug=True)
-
-    # run locally
-    runner = FlowRunner(flow=flow)
-    flow_state = runner.run(return_tasks=flow.tasks)
-
-    # run in the cloud
-    # flow.register(project_name="ukbb_pipeline")
-    # flow.run()